title: MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models

abstract: The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware mitigation techniques that can operate for long periods of time without the need for modifications or costly re-training. In this paper, we present MaMaDroid, an Android malware detection system that relies on app behavior. MaMaDroid builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app, and uses it to extract features and perform classification. By abstracting calls to their packages or families, MaMaDroid maintains resilience to API changes and keeps the feature set size manageable. We evaluate its accuracy on a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it not only effectively detects malware (with up to 99% F-measure), but also that the model built by the system keeps its detection capabilities for long periods of time (on average, 86% and 75% F-measure, respectively, one and two years after training). Finally, we compare against DroidAPIMiner, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MaMaDroid significantly outperforms it.


title: Stem cell cultivation in bioreactors

abstract: Abstract Cell-based therapies have generated great interest in the scientific and medical communities, and stem cells in particular are very appealing for regenerative medicine, drug screening and other biomedical applications. These unspecialized cells have unlimited self-renewal capacity and the remarkable ability to produce mature cells with specialized functions, such as blood cells, nerve cells or cardiac muscle. However, the actual number of cells that can be obtained from available donors is very low. One possible solution for the generation of relevant numbers of cells for several applications is to scale-up the culture of these cells in vitro. This review describes recent developments in the cultivation of stem cells in bioreactors, particularly considerations regarding critical culture parameters, possible bioreactor configurations, and integration of novel technologies in the bioprocess development stage. We expect that this review will provide updated and detailed information focusing on the systematic production of stem cell products in compliance with regulatory guidelines, while using robust and cost-effective approaches.


title: 3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information

abstract: While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using CNNs. Relative 3D positions between one joint and the other joints are learned via CNNs. The proposed method improves the performance of CNN with two novel ideas. First, we added 2D pose information to estimate a 3D pose from an image by concatenating 2D pose estimation result with the features from an image. Second, we have found that more accurate 3D poses are obtained by combining information on relative positions with respect to multiple joints, instead of just one root joint. Experimental results show that the proposed method achieves comparable performance to the state-of-the-art methods on Human 3.6m dataset.


title: Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

abstract: A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.


Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP.


title: Multi-Sentence Compression: Finding Shortest Paths in Word Graphs

abstract: We consider the task of summarizing a cluster of related sentences with a short sentence which we call multi-sentence compression and present a simple approach based on shortest paths in word graphs. The advantage and the novelty of the proposed method is that it is syntaxlean and requires little more than a tokenizer and a tagger. Despite its simplicity, it is capable of generating grammatical and informative summaries as our experiments with English and Spanish data demonstrate.


CANOPY AND COMMUNITY STRUCTURE OF ROCKWEED BEDS IN NOVA SCOTIA AND NEW BRUNSWICK: REGIONAL VARIATION AND EFFECTS OF COMMERCIAL HARVEST AND PROXIMITY TO AQUACULTURE


title: New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

abstract: Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.


title: The role of the situation in leadership

abstract: Leadership depends on the situation. Few social scientists would dispute the validity of this statement. But the statement can be interpreted in many different ways, depending, at least in part, on what one means by leadership. This article begins with a definition of leadership and a brief description of 3 historically important theories of leadership. The most recent of these, contingency theories, is argued to be most consistent with existing evidence and most relevant to professional practice. The Vroom, Yetton, and Jago contingency models of participation in decision making are described in depth, and their work provides the basis for identifying 3 distinct ways in which situational or contextual variables are relevant to both research on and the practice of leadership.


title: Combining MeSH Thesaurus with UMLS in pseudo relevance feedback to improve biomedical information retrieval

abstract: With the rapid increase in the volume of biomedical publications, developing an efficient search strategy to retrieve relevant biomedical documents that match the user search intention is a tremendous challenge. This paper proposes a novel pseudo relevance feedback technique which combines MeSH terms and UMLS concepts to improve the performance of retrieving biomedical documents from MEDLINE. The MeSH terms that annotate the feedback documents and the UMLS concepts derived from the feedback documents are used to help retrieve publications relevant to the user query. Extensive performance studies using the OHSUMED collection show that the proposed pseudo relevance feedback scheme using both MeSH terms and UMLS concepts improves the retrieval performance by 43.3% over the approach based on unexpanded query in terms of mean average precision. We have integrated the proposed strategy into G-Bean, which is publicly accessible at http://bioinformatics.clemson.edu:8080/G-Bean/index.jsp.


title: BGP security in partial deployment: is the juice worth the squeeze?

abstract: As the rollout of secure route origin authentication with the RPKI slowly gains traction among network operators, there is a push to standardize secure path validation for BGP (i.e., S*BGP: S-BGP, soBGP, BGPSEC, etc.). Origin authentication already does much to improve routing security. Moreover, the transition to S*BGP is expected to be long and slow, with S*BGP coexisting in "partial deployment" alongside BGP for a long time. We therefore use theoretical and experimental approach to study the security benefits provided by partially-deployed S*BGP, vis-a-vis those already provided by origin authentication. Because routing policies have a profound impact on routing security, we use a survey of 100 network operators to find the policies that are likely to be most popular during partial S*BGP deployment. We find that S*BGP provides only meagre benefits over origin authentication when these popular policies are used. We also study the security benefits of other routing policies, provide prescriptive guidelines for partially-deployed S*BGP, and show how interactions between S*BGP and BGP can introduce new vulnerabilities into the routing system.


title: Lost in Translation: Challenges in Automating CUDA-to-OpenCL Translation

abstract: The use of accelerators in high-performance computing is increasing. The most commonly used accelerator is the graphics processing unit (GPU) because of its low cost and massively parallel performance. The two most common programming environments for GPU accelerators are CUDA and OpenCL. While CUDA runs natively only on NVIDIA GPUs, OpenCL is an open standard that can run on a variety of hardware processing platforms, including NVIDIA GPUs, AMD GPUs, and Intel or AMD CPUs. Given the abundance of GPU applications written in CUDA, we seek to leverage this investment in CUDA and enable CUDA programs to "run anywhere" via a CUDA-to-OpenCL source-to-source translator. The resultant OpenCL versions permit the GPU-accelerated codes to run on a wider variety of processors that would not otherwise be possible. However, robust source-to-source translation from CUDA to OpenCL faces a myriad of challenges. As such, this paper identifies those challenges and presents a classification of CUDA language idioms that present practical impediments to automatic translation.


title: Channel Estimation Schemes for IEEE 802.11p Standard

abstract: In vehicle-to-vehicle (V2V) communications, reliable channel estimation is critical for the system performance due to the extremely time-varying characteristic of V2V channels. In this article, we present a survey on the current channel estimation techniques for the IEEE 802.11p standard. According to deficiencies of the current schemes and considering characteristics of V2V channels, we propose a novel channel estimation scheme, which utilizes the data symbols to construct pilots as well as the correlation characteristics between channels within two adjacent symbols. Analysis and simulation results demonstrate that the proposed scheme outperforms currently widely-used schemes especially in high signal-to-noise ratio regime. At the end, some open issues for the future work conclude this article.


title: DALY calculation in practice: a stepwise approach

abstract: disability weight ð1Þ YLL 1⁄4 Number of deaths life expectancy at the age of death ð2Þ DALY 1⁄4 YLD þ YLL ð3Þ The philosophical and methodological aspects of the DALY calculation have been described (and debated) in great detail (Murray 1994; Murray and Acharya 1997; for critiques, see, amongst others, Anand and Hanson 1997), and are summarized in the accompanying Hints and Kinks paper (Devleesschauwer et al. 2014). The steps preceding the actual calculation, however, remain less well documented. This Hints and Kinks paper tries to address this gap by presenting a stepwise approach towards a DALY calculation.


title: The Poor and the Rich: A Look at Economic Stratification and Academic Performance among Undergraduate Students in the United States.

abstract: A number of national studies point to a trend in which highly selective and elite private and public universities are becoming less accessible to lower-income students. At the same time there have been surprisingly few studies of the actual characteristics and academic experiences of low-income students or comparisons of their undergraduate experience with those of more wealthy students. This paper explores the divide between poor and rich students, first comparing a group of selective US institutions and their number and percentage of Pell Grant recipients and then, using institutional data and results from the University of California Undergraduate Experience Survey (UCUES), presenting an analysis of the high percentage of low-income undergraduate students within the University of California system â€” who they are, their academic performance and quality of their undergraduate experience. Among our conclusions: The University of California has a strikingly higher number of low-income students when compared to a sample group of twenty-four other selective public and private universities and colleges, including the Ivy Leagues and a sub-group of other California institutions such as Stanford and the University of Southern California. Indeed, the UC campuses of Berkeley, Davis, and UCLA each have more Pell Grant students than all of the eight Ivy League institutions combined. However, one out of three Pell Grant recipients at UC have at least one parent with a four-year college degree, calling into question the assumption that â€œlow-incomeâ€ and â€œfirst-generationâ€ are interchangeable groups of students. Low-income students, and in particular Pell Grant recipients, at UC have only slightly lower GPAs than their more wealthy counterparts in both math, science and engineering, and in humanities and social science fields. Contrary to some previous research, we find that low-income students have generally the same academic and social satisfaction levels; and are similar in their sense of belonging within their campus communities. However, there are some intriguing results across UC campuses, with low-income students somewhat less satisfied at those campuses where there are more affluent student bodies and where lower-income students have a smaller presence.


Constructing a Lexicon of Relational Nouns.


title: Image stitching with dynamic elements

abstract: This paper presents a new combination of techniques to create pleasing and physically consistent image mosaics despite the presence of moving objects in the scene. The technique uses heuristic seam selection in the intensity and gradient-domains to choose which pixels to use from each image and then blends them smoothly to create the final mosaic. We demonstrate illustrative results obtained by comparing and contrasting our output with that obtained from four representative existing image mosaic systems. One of these is documented in the academic research literature and three are commercially available products. The present algorithm gives comparable or superior results in all examples.


title: Learning to Implement Enterprise Systems: An Exploratory Study of the Dialectics of Change

abstract: This paper reports on a comparative case study of 13 industrial firms that implemented an enterprise resource planning (ERP) system. It compares firms based on their dialectic learning process. All firms had to overcome knowledge barriers of two types: those associated with the configuration of the ERP package, and those associated with the assimilation of new work processes. We found that both strong core teams and carefully managed consulting relationships addressed configuration knowledge barriers. User training that included both technical and business processes, along with a phased implementation approach, helped firms to overcome assimilation knowledge barriers. However, all firms in this study experienced ongoing concerns with assimilation knowledge barriers, and we observed two different approaches to address them. In a piecemeal approach, firms concentrated on the technology first and deferred consideration of process changes. In a concerted approach, both the technology and process changes were undertaken together. Although most respondents clearly stated a preference for either piecemeal or concerted change, all firms engaged in practices that reflected a combination of these approaches.


title: Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory

abstract: The naive Bayes classifier is widely used in interactive applications due to its computational efficiency, direct theoretical base, and competitive accuracy. However, its attribute independence assumption can result in sub-optimal accuracy. A number of techniques have explored simple relaxations of the attribute independence assumption in order to increase accuracy. TAN is a state-of-the-art extension of naive Bayes, that can express limited forms of inter-dependence among attributes. Rough sets theory provides tools for expressing inexact or partial dependencies within dataset. In this paper, we present a variant of TAN using rough sets theory and compare their tree classifier structures, which can be thought of as a selective restricted trees Bayesian classifier. It delivers lower error than both pre-existing TAN-based classifiers, with substantially less computation than is required by the SuperParent approach.


title: Molecular detection of Dirofilaria immitis, Dirofilaria repens and Setaria tundra in mosquitoes from Germany

abstract: BackgroundAs a result of globalization and climate change, Dirofilaria immitis and Dirofilaria repens, the causative agents of dirofilariosis in Europe, continue to spread from endemic areas in the Mediterranean to northern and northeastern regions of Europe where autochthonous cases of dirofilarial infections have increasingly been observed in dogs and humans. Whilst D. repens was recently reported from mosquitoes in putatively non-endemic areas, D. immitis has never been demonstrated in mosquitoes from Europe outside the Mediterranean.MethodsFrom 2011 to 2013, mosquitoes collected within the framework of a German national mosquito monitoring programme were screened for filarial nematodes using a newly designed filarioid-specific real-time PCR assay. Positive samples were further processed by conventional PCR amplification of the cytochrome c oxidase subunit I (COI) gene, amplicons were sequenced and sequences blasted against GenBank.ResultsApproximately 17,000 female mosquitoes were subjected to filarial screening. Out of 955 pools examined, nine tested positive for filariae. Two of the COI sequences indicated D. immitis, one D. repens and four Setaria tundra. Two sequences could not be assigned to a known species due to a lack of similar GenBank entries. Whilst D. immitis and the unknown parasites were detected in Culex pipiens/torrentium, D. repens was found in a single Anopheles daciae and all S. tundra were demonstrated in Aedes vexans. All positive mosquitoes were collected between mid-June and early September.ConclusionThe finding of dirofilariae in German mosquitoes implies the possibility of a local natural transmission cycle. While the routes of introduction to Germany and the origin of the filariae cannot be determined retrospectively, potential culicid vectors and reservoir hosts must prospectively be identified and awareness among physicians, veterinarians and public health personnel be created. The health impact of S. tundra on the indigenous cervid fauna needs further investigation.


title: Devices and Sensors Applicable to 5G System Implementations

abstract: This paper describes some of the device technologies currently in development at Analog Devices, focusing on key areas for future integration within the 5G ecosystem. Three distinct device technologies are examined that will enhance and enable system implementation and are for use in dedicated applications. The first is a state-of-the-art RF MEMs switch, the second, an Energy Harvester for IoT applications and the third, an RF SAW filter.


title: Better Computer Go Player with Neural Network and Long-term Prediction

abstract: Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on leading-edge hardware, and Go's evaluation function could change drastically with one stone change. Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not strictly necessary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural Network (DCNN) that predicts the next move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source Go engines such as Pachi [Baudis & Gailly (2012)] if its search budget is limited. We extend this idea in our bot named darkforest, which relies on a DCNN designed for long-term predictions. Darkforest substantially improves the win rate for pattern-matching approaches against MCTS-based approaches, even with looser search budgets. Against human players, the newest versions, darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015) based on games against other machine players. Adding MCTS to darkfores2 creates a much stronger player named darkfmcts3: with 5000 rollouts, it beats Pachi with 10k rollouts in all 250 games; with 75k rollouts it achieves a stable 5d level in KGS server, on par with state-of-the-art Go AIs (e.g., Zen, DolBaram, CrazyStone) except for AlphaGo [Silver et al. (2016)]; with 110k rollouts, it won the 3rd place in January KGS Go Tournament.


title: Scalable Multi-View Semi-Supervised Classification via Adaptive Regression

abstract: With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named multi-view semi-supervised classification via adaptive regression (MVAR) to address this problem. Specifically, regression-based loss functions with $\ell _{2,1}$ matrix norm are adopted for each view and the final objective function is formulated as the linear weighted combination of all the loss functions. An efficient algorithm with proved convergence is developed to solve the non-smooth $\ell _{2,1}$ -norm minimization problem. Regressing to class labels directly makes the proposed algorithm efficient in calculation and can be applied to large-scale data sets. The adaptively optimized weight coefficients balance the contributions of different views automatically, which makes the performance robust against the existence of low-quality views. With the learned projection matrices and bias vectors, predictions for out-of-sample data can be easily made. To validate the effectiveness of MVAR, comparisons are made with some benchmark methods on real-world data sets and in the scene classification scenario as well. The experimental results demonstrate the effectiveness of our proposed algorithm.


title: Cost evaluations of the protected network layer packet redundancy scheme

abstract: Feature Issue on High Availability in Optical Networks The protected network layer packet redundancy scheme (P-NLPRS), which is a novel approach to providing 1+1 path protection functionality in optical packet switching (OPS) networks, is presented. Traditional 1+1 path protection protects data by transmitting two identical copies of each packet over two node- and link-disjoint paths between an ingress and egress node in a network. With the P-NLPRS, a packet set consisting of k−1 data packets and 1 redundancy packet is transmitted over k node- and link-disjoint paths between an ingress and egress node in the OPS network. The P-NLPRS is especially attractive for OPS, since, in addition to providing 1+1 path protection functionality, it may be utilized to combat packet loss due to contentions. An analytical framework suitable for evaluating the cost of providing 1+1 path protection is presented, along with an analytical model of the P-NLPRS. A quantitative cost study shows that the P-NLPRS can provide 1+1 path protection functionality with 33% to 91% of the cost required by traditional 1+1 path protection, depending on the cost model and the network topology.


title: Design of an eye movement-controlled wheelchair using Kalman filter algorithm

abstract: Nowadays, electric wheelchairs on the market mostly were controlled by joystick which required users to operate by hands flexibly. But this wheelchair cound not be used by patients with hand disability or paraplegics. This paper designs an electric wheelchair controlled by eye movements. An eye tracker of Tobii is connected with a electric wheelchair. The eye tracker of Tobii is used to convert eye movement signals into coordinate of gazing points. But these data of gazing points are too diverging to be used directly. Kalman filter algorithm is used to filter data of coordinate and obtain optimal data. By discriminating eye movement-controlled panel area where gazing points is located in, a computer sends appropriate signal to the wheelchair, so as to achieve the goal of moving wheelchair. Thus patients are no longer using hands to control electric wheelchairs.


title: An ultra-low area and full-swing output 3T XNOR gate using 45nm technology

abstract: In this research paper we are presenting a new idea of a 3 transistor Exclusive NOR (XNOR) gate that produces low power and high performance with more noise immunity. The main aim of this research is to achieve maximum output voltage swing with improved delay, power and power delay product compared to the previous designs. The pre-simulation of the design is performed using Cadence EDA tool of 45nm technology.


title: Twin Gaussian Processes for Structured Prediction

abstract: We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this should hold, on average, between their marginal distributions. TGP captures not only the interdependencies between covariates, as in a typical GP, but also those between responses, so correlations among both inputs and outputs are accounted for. TGP is exemplified, with promising results, for the reconstruction of 3d human poses from monocular and multicamera video sequences in the recently introduced HumanEva benchmark, where we achieve 5 cm error on average per 3d marker for models trained jointly, using data from multiple people and multiple activities. The method is fast and automatic: it requires no hand-crafting of the initial pose, camera calibration parameters, or the availability of a 3d body model associated with human subjects used for training or testing.


title: Security Perspectives For USSD Versus SMS In Conducting Mobile Transactions: A Case Study Of

abstract: Performing transactions using mobile devices is increasing rapidly in developing countries, Tanzania inclusive. USSD and SMS are among the technologies widely used in conducting mobile transactions. These two technologies have their strengths and weaknesses from perspectives of security of systems. They both utilize GSM Services and GSM Security is known to have inherent flaws in its encryption and au thentication algorithms. A description for these platforms is given in this paper of what they are, their modes of operations, and an evaluation of thei r security as related to mobile banking systems. From the evaluations made; this paper suggests a method that is more secure for use in mobile banking systems. As a solution we propose some security features being added to the existing systems in order to improve data confidentiality, message integrit y and user authenticity. The suggestions are based on the capabilities for the technology to accommodate these additional features to protect data that will supplement the protection offered by the GSM.


title: An Assessment of the Accuracy of Automatic Evaluation in Summarization

abstract: Automatic evaluation has greatly facilitated system development in summarization. At the same time, the use of automatic evaluation has been viewed with mistrust by many, as its accuracy and correct application are not well understood. In this paper we provide an assessment of the automatic evaluations used for multi-document summarization of news. We outline our recommendations about how any evaluation, manual or automatic, should be used to find statistically significant differences between summarization systems. We identify the reference automatic evaluation metrics---ROUGE 1 and 2---that appear to best emulate human pyramid and responsiveness scores on four years of NIST evaluations. We then demonstrate the accuracy of these metrics in reproducing human judgements about the relative content quality of pairs of systems and present an empirical assessment of the relationship between statistically significant differences between systems according to manual evaluations, and the difference according to automatic evaluations. Finally, we present a case study of how new metrics should be compared to the reference evaluation, as we search for even more accurate automatic measures.


title: Factors Influencing Student Acceptance and Use of Academic Portals

abstract: Institutions of higher education have increasing turned to web portals as a way to connect with students. These portals are designed to provide students a centralized point of access to information and services. In spite of the efforts put into developing and maintaining these portals, their use by students can be disappointing. The study described in this paper examines factors influencing the acceptance and use of academic intranet portals by university students. The research uses as its theoretical basis the technology acceptance model (TAM), one of the most widely accepted information technology utilization models from information systems literature. Two additional constructs, compatibility and enjoyment were added to the model to determine if an expanded model would better characterize user acceptance and use. A survey administered to 709 university students was analyzed using confirmatory factor analysis and structural equation modeling. The data indicated that ease of use, perceived usefulness, and attitude from the basic TAM model all contributed significantly to explaining intention and usage of the portal. In addition, the integration of enjoyment as a construct was found to improve the fit of the model. Weak measurement properties precluded the analysis of the compatibility construct. The paper includes discussion of practical and academic implications of the research.


title: 5-GHz Band Highly Linear VCO IC with a Novel Resonant Circuit

abstract: This paper presents a 0.35-mum SiGe BiCMOS VCO IC exhibiting a linear VCO gain (Kvco) for 5-GHz band application. To realize a linear Kvco, a novel resonant circuit is proposed. The measured Kvco changes from 224 MHz/V to 341 MHz/V. The ratio of the maximum Kvco to the minimum one is 1.5 which is less than one-half of that of a conventional VCO. The VCO oscillation frequency range is from 5.45 GHz to 5.95 GHz, the tuning range is 8.8 %, and the dc current consumption is 3.4 mA at a supply voltage of 3.0 V. The measured phase noise is -116 dBc/Hz at 1MHz offset, which is similar to the conventional VCO


title: REXEL: computer aided record selection for code-based seismic structural analysis

abstract: In code-based seismic design and assessment it is often allowed the use of real records as an input for nonlinear dynamic analysis. On the other hand, international seismic guidelines, concerning this issue, have been found hardly applicable by practitioners. This is related to both the difficulty in rationally relating the ground motions to the hazard at the site and the required selection criteria, which do not favor the use of real records, but rather various types of spectrum matching signals. To overcome some of these obstacles a software tool for code-based real records selection was developed. REXEL, freely available at the website of the Italian network of earthquake engineering university labs (http://www.reluis.it/index_eng.html), allows to search for suites of waveforms, currently from the European Strong-motion Database, compatible to reference spectra being either user-defined or automatically generated according to Eurocode 8 and the recently released new Italian seismic code. The selection reflects the provisions of the considered codes and others found to be important by recent research on the topic. In the paper, record selection criteria are briefly reviewed first, and then the algorithms implemented in the software are discussed. Finally, via some examples, it is shown how REXEL can effectively be a contribution to code-based real records selection for seismic structural analysis.


title: A Labeling Approach to Incremental Cycle Detection

abstract: In the \emph{incremental cycle detection} problem arcs are added to a directed acyclic graph and the algorithm has to report if the new arc closes a cycle. One seeks to minimize the total time to process the entire sequence of arc insertions, or until a cycle appears. In a recent breakthrough, Bender, Fineman, Gilbert and Tarjan \cite{BeFiGiTa11} presented two different algorithms, with time complexity $O(n^2 \log n)$ and $O(m \cdot \min \{m^{1/2}, n^{2/3} \})$, respectively. In this paper we introduce a new technique for incremental cycle detection that allows us to obtain both bounds (up to a logarithmic factor). Furthermore, our approach seems more amiable for distributed implementation.


The Impact of Customer Satisfaction on Online Purchasing: A Case Study Analysis in Thailand


title: Learning Spatial-Aware Regressions for Visual Tracking

abstract: In this paper, we analyze the spatial information of deep features, and propose two complementary regressions for robust visual tracking. First, we propose a kernelized ridge regression model wherein the kernel value is defined as the weighted sum of similarity scores of all pairs of patches between two samples. We show that this model can be formulated as a neural network and thus can be efficiently solved. Second, we propose a fully convolutional neural network with spatially regularized kernels, through which the filter kernel corresponding to each output channel is forced to focus on a specific region of the target. Distance transform pooling is further exploited to determine the effectiveness of each output channel of the convolution layer. The outputs from the kernelized ridge regression model and the fully convolutional neural network are combined to obtain the ultimate response. Experimental results on two benchmark datasets validate the effectiveness of the proposed method.


title: Matching RGB Images to CAD Models for Object Pose Estimation

abstract: We propose a novel method for 3D object pose estimation in RGB images, which does not require pose annotations of objects in images in the training stage. We tackle the pose estimation problem by learning how to establish correspondences between RGB images and rendered depth images of CAD models. During training, our approach only requires textureless CAD models and aligned RGB-D frames of a subset of object instances, without explicitly requiring pose annotations for the RGB images. We employ a deep quadruplet convolutional neural network for joint learning of suitable keypoints and their associated descriptors in pairs of rendered depth images which can be matched across modalities with aligned RGB-D views. During testing, keypoints are extracted from a query RGB image and matched to keypoints extracted from rendered depth images, followed by establishing 2D-3D correspondences. The object's pose is then estimated using the RANSAC and PnP algorithms. We conduct experiments on the recently introduced Pix3D dataset and demonstrate the efficacy of our proposed approach in object pose estimation as well as generalization to object instances not seen during training.


title: Water and Carbon Footprint of Wine: Methodology Review and Application to a Case Study

abstract: Life cycle assessments (LCAs) play a strategic role in improving the environmental performance of a company and in supporting a successful marketing communication. The high impact of the food industry on natural resources, in terms of water consumption and greenhouse gases emission, has been focusing the attention of consumers and producers towards environmentally sustainable products. This work presents a comprehensive approach for the joint evaluation of carbon (CF) and water (WF) footprint of the wine industry from a cradle to grave perspective. The LCA analysis is carried out following the requirements of international standards (ISO/TS 14067 and ISO 14046). A complete review of the water footprint methodology is presented and guidelines for all the phases of the evaluation procedure are provided, including acquisition and validation of input data, allocation, application of analytic models, and interpretation of the results. The strength of this approach is the implementation of a side-by-side CF vs. WF assessment, based on the same system boundaries, functional unit, and input data, that allows a reliable comparison between the two indicators. In particular, a revised methodology is presented for the evaluation of the grey water component. The methodology was applied to a white and a red wine produced in the same company. A comparison between the two products is presented for each LCA phase along with literature results for similar wines.


title: Color Adjustment for Seamless Cloning Based on Laplacian-Membrane Modulation

abstract: Image compositing aims to combine elements from multiple images producing natural-looking results. The Poisson image editing framework can generate seamless compositions, but tends to introduce color changes in the inserted objects, which may generate unrealistic and unpleasing results. We present an efficient approach for controlling the amount of color preservation in the inserted objects, while ensuring a seamless transition between the foreground and background elements. Our technique modulates a Laplacian membrane according to some automatically computed parameter, which results in more naturally-looking compositions.


title: Nonconscious Behavioral Confirmation Processes: The Self-Fulfilling Consequences of Automatic Stereotype Activation☆☆☆

abstract: It is now well-established that stereotypes can become activated unintentionally and outside of awareness by the presence of the relevant group features. There is also a long tradition of theory and evidence that perceptual and behavioral processes are intimately related (e.g., Berkowitz, 1984; James, 1890; Piaget, 1948). Considering these two phenomena together suggests that stereotype activation can cause the perceiver to act in stereotype-consistent ways, and recent evidence confirms this prediction (Bargh, Chen, & Burrows, 1996). The present study extended these findings by showing that the perceiver's stereotype-consistent behavior causes the target person to reciprocate in kind, thereby confirming the perceiver's stereotypic beliefs. Compared to a control condition, subliminal activation of the African American stereotype in participants resulted in greater hostility in their interaction partners (as rated by outside judges) and more extreme hostility ratings of the targets by their perceiver partner.


title: Predicting army combat outcomes in StarCraft

abstract: Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army. Model parameters are learned from simulated battles, in order to minimize the dependency on player skill. We apply our model to the game of StarCraft, with the end-goal of using the predictor as a module for making high-level combat decisions, and show that the model is capable of making accurate predictions.


title: Robust analysis of feature spaces: color image segmentation

abstract: A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512/spl times/512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.


Implementing UHF RFID Reader on Smartphone Platform for IoT Sensing


title: Analysis and design of controllable leaky-wave antennas inspired by Prof. Arthur Oliner a tribute to Prof. Oliner

abstract: In this paper, we make a summary on the different leaky-wave antenna (LWA) technologies developed by Spanish groups in collaboration with UK and Australia, and which have been inspired by seminal works of Prof. Oliner. First, we will describe the LWAs based on asymmetric waveguides proposed by Prof. Oliner and collaborators in the 80s-90s. Also, the labor of Prof. Oliner to reveal the nature of leaky modes in microstrip lines will be reminded. Based on these pioneering works, the contribution of the authors to the analysis and design of LWAs in three distinct technologies will be described. Namely: hybrid waveguide-printed circuit technology, Fabry-Pérot (FP) technology, and Substrate Integrated Waveguide (SIW) technology. The control of the complex leaky-mode wavenumber is necessary for the synthesis of high-gain frequency scanning patterns. This paper gives an overview of the working mechanism and taper principles used for each technology, in a tribute to Prof. Oliner and collaborators who inspired all these ideas.


title: Big data, bigger dilemmas: A critical review

abstract: The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.


title: T-drive: driving directions based on taxi trajectories

abstract: GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.


title: Ultra-Fine Entity Typing

abstract: We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: this http URL


title: Metabolic Power in Team Sports - Part 1: An Update.

abstract: Team sports are characterised by frequent episodes of accelerated/decelerated running. The corresponding energy cost can be estimated on the basis of the biomechanical equivalence between accelerated/decelerated running on flat terrain and constant speed running uphill/downhill. This approach allows one to: (i) estimate the time course of the instantaneous metabolic power requirement of any given player and (ii) infer therefrom the overall energy expenditure of any given time window of a soccer drill or match. In the original approach, walking and running were aggregated and energetically considered as running, even if in team sports several walking periods are interspersed among running bouts. However, since the transition speed between walking and running is known for any given incline of the terrain, we describe here an approach to identify walking episodes, thus utilising the corresponding energy cost which is smaller than in running. In addition, the new algorithm also takes into account the energy expenditure against the air resistance, for both walking and running. The new approach yields overall energy expenditure values, for a whole match,≈14% smaller than the original algorithm; moreover, it shows that the energy expenditure against the air resistance is≈2% of the total.


title: Fundamentals of Texture Processing for Biomedical Image Analysis: A General Definition and Problem Formulation

abstract: Abstract This chapter aims to provide an overview of the foundations of texture processing for biomedical image analysis. Its purpose is to define precisely what biomedical texture is, how is it different from general texture information considered in computer vision, and what is the general problem formulation to translate 2D and 3D textured patterns from biomedical images to visually and biologically relevant measurements. First, a formal definition of biomedical texture information is proposed from both perceptual and mathematical point of views. Second, a general problem formulation for biomedical texture analysis is introduced, considering that any approach can be characterized as a set of local texture operators and regional aggregation functions. The operators allow locally isolating desired texture information in terms of spatial scales and directions of a texture image. The type of desirable operator invariances are discussed, and are found to be different from photographic image analysis. Scalar-valued texture measurements are obtained by aggregating operator's response maps over regions of interest.


title: Who Will You Share a Ride With: Factors that Influence Trust of Potential Rideshare Partners

abstract: Ridesharing can save energy, reduce pollution, and alleviate congestion. Ridesharing systems allow peer-to-peer ride matching through mobile-phone applications. In many systems people have little information about the driver and co-riders. Trust heavily influences the usage of a ridesharing system, however, work in this area is limited. In this paper, we investigate the role of social media in shaping trust and delineate factors that influence the decisions of trust placed on a driver or co-rider. We design a ridesharing system where a rider can see the Instagram profile and 10 recent pictures of a co-rider and ask them to make judgments on six trust related questions. We discovered three important factors that shape trust forming: \textit{social proof}, \textit{social approval}, and \textit{self-disclosure}. These three factors account for 40\%, 33\%, and 13\% of the variance, and subsequent factors extracted accounted for substantially less variance. Using these three factors, we successfully build a trust prediction model with F-measure of 87.4\%, an improvement from 78.6\% on using all factors collected.


title: Caffeinated FPGAs: FPGA framework For Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works have pushed the performance of GPU implementations of CNNs to significantly improve their classification and training times. With these improvements, many frameworks have become available for implementing CNNs on both CPUs and GPUs, with no support for FPGA implementations. In this work we present a modified version of the popular CNN framework Caffe, with FPGA support. This allows for classification using CNN models and specialized FPGA implementations with the flexibility of reprogramming the device when necessary, seamless memory transactions between host and device, simple-to-use test benches, and the ability to create pipelined layer implementations. To validate the framework, we use the Xilinx SDAccel environment to implement an FPGA-based Winograd convolution engine and show that the FPGA layer can be used alongside other layers running on a host processor to run several popular CNNs (AlexNet, GoogleNet, VGG A, Overfeat). The results show that our framework achieves 50 GFLOPS across 3x3 convolutions in the benchmarks. This is achieved within a practical framework, which will aid in future development of FPGA-based CNNs.


title: Analysis of sentiment on newspaper quotations: A preliminary experiment

abstract: Most Opinion Mining work has been carried out on highly subjective text types where the target is clearly defined and unique across the text like movie or product reviews. However, when applying Opinion Mining to the news domain, it is necessary to clearly define the essence of the task, in a more precise manner than it has been done in the field so far. The main tasks that have been implemented in this paper are: identifying a target, creating three different datasets of quotations (reported speech), pertaining to the identified target, from three different dailies, separation of good and bad news content from the good and bad emotions expressed on the target and comparing the results with manual work.


title: Auxilum Medicine: A Cloud Based Platform for Real-Time Monitoring Medical Devices

abstract: Nowadays, time is a very valuable resource and can make the difference between life and death. Having knowledge about this fact we decided to deal with one of the most important aspects of contemporary medicine, EMS (emergency medical services) response time. Modern systems that encourage intelligent communication methods between medical devices and doctors are a must in ubiquitous health care environments. Auxilum Medicine fosters a triple-win situation regarding the relationship between medical institutions, doctors and patients. Emergency patients should be treated with utmost care because their life is hanging by a thread if nobody is present to take immediate action. We are presenting a platform which enables doctors to simultaneously monitor a large number of patients from different physical locations. By receiving real time notifications, medical history, prevention alarms directly to any network connected devices (mobile phones, tablets, desktops, notebooks, smart watches, etc.), the medical staff can act promptly, exactly when and where it is needed in order to save human lives. Our solution's architecture allows gathering data from any medical signal processing unit and sends it straight to the cloud using encrypted communication protocols. What makes Auxilum Medicine unique refers to the cloud integration with hospital departments' structure, awareness of different medical staff roles and capabilities, privacy data interest, updates sent to patient's relatives as well as a modern responsive adaptive user interface. As a part of our experiment, aimed for testing our platform's capabilities, we have built a biomedical wireless sensor wearable device that provides real-time parameters (temperature and heart rate). Such a system favors medical equipment real time monitoring by using cloud services and permanently keeps alive the link between doctors and their patients, drastically increasing the EMS response time.


title: Time series forecasting using a deep belief network with restricted Boltzmann machines

abstract: Abstract Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely applied to time series forecasting since 1980s. However, for some problems such as initialization and local optima existing in applications, the improvement of ANNs is, and still will be the most interesting study for not only time series forecasting but also other intelligent computing fields. In this study, we propose a method for time series prediction using Hinton and Salakhutdinov׳s deep belief nets (DBN) which are probabilistic generative neural network composed by multiple layers of restricted Boltzmann machine (RBM). We use a 3-layer deep network of RBMs to capture the feature of input space of time series data, and after pretraining of RBMs using their energy functions, gradient descent training, i.e., back-propagation learning algorithm is used for fine-tuning connection weights between “visible layers” and “hidden layers” of RBMs. To decide the sizes of neural networks and the learning rates, Kennedy and Eberhart׳s particle swarm optimization (PSO) is adopted during the training processes. Furthermore, “trend removal”, a preprocessing to the original data, is also approached in the forecasting experiment using CATS benchmark data. Additionally, approximating and short-term prediction of chaotic time series such as Lorenz chaos and logistic map were also applied by the proposed method.


title: A no-regret generalization of hierarchical softmax to extreme multi-label classification

abstract: Extreme multi-label classification (XMLC) is the problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach. In this paper, we investigate Probabilistic Label Trees (PLTs) that have been recently devised for tackling XMLC problems. We show that PLT is a no-regret multi-label generalization of HSM when precision@k is used as a model evaluation metric. Critically, we prove that pick-one-label heuristic---a reduction technique from multi-label to multi-class that is routinely used along with HSM---is not consistent in general. We also show that our implementation of PLTs, referred to as XT, obtains significantly better results than HSM with the pick-one-label heuristic and XML-CNN, a deep network specifically designed for XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches in terms of statistical performance, model size and prediction time which makes it amenable to deploy in an online system.


title: Prevalence and molecular identification of the sinus worm Skrjabingylus petrowi (Nematoda: Metastrongyloidea) from Martes spp. in Germany

abstract: The nematodes of the genus Skrjabingylus (family Metastrongylidae) can parasitise the nasal and frontal sinus cavities of different carnivore species. Until recently, Skrjabingylus petrowi Bageanov & Petrov, 1941, has mainly been described in pine martens (Martes martes Linnaeus, 1758) and sables (Martes zibellina Linnaeus, 1758) sampled in the European part of the former Soviet Union. Newer finds in the stone marten (Martes foina Erxleben, 1777) and from different parts of Europe suggest, however, that the species might have a broader host-species range than previously assumed and be geographically more widespread as well. Since most S. petrowi records have resulted from chance discoveries rather than systematic surveys, very little is known about the prevalence of S. petrowi in marten populations. Here, we report results of a 20-year extensive survey of fresh marten skulls, where we tested 1.059 marten carcasses originating from 248 localities in Germany for the presence of S. petrowi. We identified an infestation in only four M. martes individuals and one M. foina, despite using a reliable identification method. Based on the spicule lengths of the male nematodes, the parasites were identified as S. petrowi and genetic barcoding confirmed the identification of the samples. In a phylogenetic analysis, S. petrowi and Skrjabingylus nasicola (Leuckart, 1842), formed a sister clade to all the other members of the family Metastrongylidae. The low prevalence of S. petrowi is possibly due to its parasitising in the two marten species that are either not very common (M. martes) or predominantly live in urban habitat (M. foina).


title: Asparagine Synthetase deficiency-report of a novel mutation and review of literature

abstract: Asparagine synthetase deficiency is a rare inborn error of metabolism caused by a defect in ASNS, a gene encoding asparagine synthetase. It manifests with a severe neurological phenotype manifesting as severe developmental delay, congenital microcephaly, spasticity and refractory seizures. To date, nineteen patients from twelve unrelated families have been identified. Majority of the mutations are missense and nonsense mutations in homozygous or compound heterozygous state. We add another case from India which harbored a novel homozygous missense variation in exon 11 and compare the current case with previously reported cases.


title: Access Control of Door and Home Security by Raspberry Pi Through Internet

abstract: In the present age Internet of things (IOT) has enterned a golden era of rapid growth. The Internet of things is a concept that aims to extend the benefits of the regular Internet—constant connectivity, remote control ability, data sharing, and so on—to goods in the physical world. Everyday things are getting connected with the internet. This concept can be used tomanage the security concerned issues in a cost effective way. In ths paper work a system is being developed to connect any door with the internet, so that the access control system can be controlled from any where in the world. In a case that one is not at home and a vistor is at his door steps then the authorized person will be notified about the visitor via twitter and the person can see the visitor from the web through the camera from any where and the system will take a picture of the visitor and keep a record by sending an attachment through E-mail or tweet in twitter. If the authorized person wants to give a message the visitor it can be sent easily through the internet and it will appear in a screen on the front face of the door. The door lock can be controlled through the internet. With the help of this system an evidence of the visitor can be kept as a record if any emergency case or situtaion occurs.


title: Closed Ecological Systems, Space Life Support and Biospherics

abstract: This chapter explores the development of a new type of scientific tool – man-made closed ecological systems. These systems have had a number of applications within the past 50 years. They are unique tools for investigating fundamental processes and interactions of ecosystems. They also hold the potentiality for creating life support systems for space exploration and habitation outside of Earth’s biosphere. Finally, they are an experimental method of working with small “biospheric systems” to gain insight into the functioning of Earth’s biosphere. The chapter reviews the terminology of the field, the history and current work on closed ecological systems, bioregenerative space life support and biospherics in Japan, Europe, Russia, and the United States where they have been most developed. These projects include the Bios experiments in Russia, the Closed Ecological Experiment Facility in Japan, the Biosphere 2 project in Arizona, the MELiSSA program of the European Space Agency as well as fundamental work in the field by NASA and other space agencies. The challenges of achieving full closure, and of recycling air and water and producing high-production crops for such systems are discussed, with examples of different approaches being used to solve these problems. The implications for creating sustainable technologies for our Earth’s environment are also illustrated.


title: Data Visualization and Feature Selection: New Algorithms for Nongaussian Data

abstract: Data visualization and feature selection methods are proposed based on the joint mutual information and ICA. The visualization methods can find many good 2-D projections for high dimensional data interpretation, which cannot be easily found by the other existing methods. The new variable selection method is found to be better in eliminating redundancy in the inputs than other methods based on simple mutual information. The efficacy of the methods is illustrated on a radar signal analysis problem to find 2-D viewing coordinates for data visualization and to select inputs for a neural network classifier.


title: A SFCW radar for through wall imaging and motion detection

abstract: Through wall imaging and motion detection is an emerging area of research and development. A stepped frequency continuous wave real aperture radar named RA-TWI has been developed for performing imaging and detecting of static and dynamic targets through non-metallic building materials. A simple and effective method based on differential image formation has been proposed to mitigate the strong reflection of the front wall and detect the human behind the wall. Experimental results demonstrated that the RA-TWI radar can penetrate through common wall buildings and detect motions.


title: Development of a large-scale web crawler and search engine infrastructure

abstract: This paper reports the ongoing development of a large-scale Web crawler and search engine infrastructure at National Institute of Information and Communications Technology. This infrastructure has the following characteristics: (1) It collects one billion Japanese Web pages while keeping them up-to-date. (2) It selects 100 million pages from among the collected pages and converts them into a standard data format to store the results of morphological analysis, dependency parsing, and synonym augmentation. (3) The selected set of pages is searchable and accessible to the users. (4) The scalability of the system is achieved by using a large-scale cluster machine for distributed data processing.


title: Gradual synthesis for static parallelization of single-pass array-processing programs

abstract: Parallelizing of software improves its effectiveness and productivity. To guarantee correctness, the parallel and serial versions of the same code must be formally verified to be equivalent. We present a novel approach, called GRASSP, that automatically synthesizes parallel single-pass array-processing programs by treating the given serial versions as specifications. Given arbitrary segmentation of the input array, GRASSP synthesizes a code to determine a new segmentation of the array that allows computing partial results for each segment and merging them. In contrast to other parallelizers, GRASSP gradually considers several parallelization scenarios and certifies the results using constrained Horn solving. For several classes of programs, we show that such parallelization can be performed efficiently. The C++ translations of the GRASSP solutions sped performance by up to 5X relative to serial code on an 8-thread machine and Hadoop translations by up to 10X on a 10-node Amazon EMR cluster.


title: RNN Decoding of Linear Block Codes

abstract: Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the RNN decoder can be used to improve the performance or alternatively reduce the computational complexity of the mRRD algorithm for low complexity, close to optimal, decoding of short BCH codes.


title: Latent Space Model for Multi-Modal Social Data

abstract: With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.


title: Advance resource reservation and QoS based refunding in cloud federation

abstract: With rapidly increasing digital media, importance of cloud computing is also scaling up. Cloud computing not only provides ubiquitous access, but also, ease of management for hugely increasing data. The next era is going to be of cloud federation, in which services would be brought up to the user through multiple clouds, creating an inter-cloud computing environment. One of the key entities in inter-cloud computing is cloud broker. Cloud broker plays an important role in cloud federation environment. Broker has to reserve resources and perform pricing and billing. At times, promised quality cannot be delivered. When a customer gives up the service without completely consuming it, service provider has to consider quality degradation factor, while refunding the remaining amount. We have proposed a model which addresses the issue of advanced resource allocation and quality of service (QoS) based refund. We have implemented and tested our model on CloudSim 3.0.3 toolkit. The results presented here justify and endorse our model.


title: Catalytic conversion of duckweed to methyl levulinate in the presence of acidic ionic liquids.

abstract: In this study, an efficient strategy is proposed for selective methyl levulinate production from duckweed, a typical fast-growing aquatic microalgae in warm and humid regions, in the presence of acidic ionic liquids (ILs). The results show that IL structure has a significant effect on its acidic strength, which finally determines the process efficiency for levulinate methyl generation. With the optimized catalyst of [C3H6SO3HPy]HSO4, 88.0% duckweed is consumed, resulting in a comparable methyl levulinate yield of 73.7% and a process efficiency of 81.8% at 170 °C for 5 h. Furthermore, this process is substantially influenced by the reaction condition, particularly, it is significantly temperature-dependent. In addition, solvent has a remarkable intensified effect on the process efficiency, which dramatically decreases from 81.8 to 53.7% when methanol is replaced by water.


title: A microgrid protection system with central protection unit and extensive communication

abstract: Microgrids have been proposed in order to handle the impacts of Distributed Generators (DGs) and make conventional grids suitable for large scale deployments of distributed generation. However, the introduction of microgrids brings some challenges. Protection of a microgrid and its entities is one of them. Due to the existence of generators at all levels of the distribution system and two distinct operating modes, i.e. Grid Connected and Islanded modes, the fault currents in a system vary substantially. Consequently, the traditional fixed current relay protection schemes need to be improved. This paper presents a conceptual design of a microgrid protection system which utilizes extensive communication to monitor the microgrid and update relay fault currents according to the variations in the system. The proposed system is designed so that it can respond to dynamic changes in the system such as connection/disconnection of DGs.


title: Study of Lightning Surge Overvoltages at Substations Due to Direct Lightning Strokes to Phase Conductors

abstract: Accurate predictions of lightning surge overvoltages are essential to power equipment insulation design. Recent observations of lightning strokes to ultra-high-voltage designed transmission lines confirmed direct lightning strokes caused by shielding failure and found phenomena unexplainable by conventional shielding theories. However, there are few detailed studies of direct lightning surge overvoltages. This study assumed direct lightning stroke currents based on observational data and performs electromagnetic transient program analysis of the gas-insulated switchgear (GIS) and transformer overvoltages at substations to study the basic characteristics of overvoltages due to direct lightning strokes and evaluate lightning protection design. Consequently, the maximum GIS overvoltages were found during back-flashovers, and the locations of maximum overvoltages from direct lightning strokes and back-flashovers differ. Direct lightning strokes may be more severe than back-flashovers for transformers. This paper also studied the overvoltage generation mechanism and showed the relationship of the maximum voltage to lightning stroke current and transformer capacitance.


title: 802.11mc: Using Packet Collision as an Opportunity in Heterogeneous MIMO-Based Wi-Fi Networks

abstract: Multiple-input–multiple-output (MIMO) technology boosts 802.11 Wi-Fi system capacities by using concurrent transmission of multiple streams from multiple antennas. The MIMO system in 802.11 Wi-Fi, however, typically requires request-to-send/clear-to-send (RTS/CTS)-like control message exchanges to fully realize the advantages of MIMO, although they incur nontrivial overhead. Furthermore, uncontrolled packet collisions severely limit the concurrent transmission gain of the MIMO nodes and the throughput of legacy single-input–single-output (SISO) nodes. In this paper, we propose a new distributed medium access control (MAC) protocol called 802.11 MIMO-based collision resolution (802.11mc). The 802.11mc protocol not only resolves the packet collisions but actually extracts channel information from collided frames as well to use it for concurrent MIMO transmissions. In particular, 802.11mc attaches a postamble after an RTS frame such that the channel information can be obtained, even when RTS frames collide. This information is used for interference alignment (IA) and cancelation for the interpretations of simultaneous frames. To show the feasibility of our proposal, we prototyped the scheme on the Universal Software Radio Peripheral (USRP) N210 testbed. Through both USRP experiments and NS-2-based simulations, we prove that 802.11mc improves the throughput gain of both MIMO and SISO nodes significantly.


title: The design of robust value-creating supply chain networks: A critical review

abstract: This paper discusses Supply Chain Network (SCN) design problem under uncertainty, and presents a critical review of the optimization models proposed in the literature. Some drawbacks and missing aspects in the literature are pointed out, thus motivating the development of a comprehensive SCN design methodology. Through an analysis of supply chains uncertainty sources and risk exposures, the paper reviews key random environmental factors and discusses the nature of major disruptive events threatening SCN. It also discusses relevant strategic SCN design evaluation criteria, and it reviews their use in existing models. We argue for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation. Several definitions of robustness, responsiveness and resilience are reviewed, and the importance of these concepts for SCN design is discussed. This paper contributes to framing the foundations for a robust SCN design methodology.


title: Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

abstract: Dealing with high-dimensional data has always been a major problem in many pattern recognition and machine learning applications. Trace ratio criterion is a criterion that can be applicable to many dimensionality reduction methods as it directly reflects Euclidean distance between data points of within or between classes. In this paper, we analyze the trace ratio problem and propose a new efficient algorithm to find the optimal solution. Based on the proposed algorithm, we are able to derive an orthogonal constrained semi-supervised learning framework. The new algorithm incorporates unlabeled data into training procedure so that it is able to preserve the discriminative structure as well as geometrical structure embedded in the original dataset. Under such a framework, many existing semi-supervised dimensionality reduction methods such as SDA, Lap-LDA, SSDR, SSMMC, can be improved using our proposed framework, which can also be used to formulate a corresponding kernel framework for handling nonlinear problems. Theoretical analysis indicates that there are certain relationships between linear and nonlinear methods. Finally, extensive simulations on synthetic dataset and real world dataset are presented to show the effectiveness of our algorithms. The results demonstrate that our proposed algorithm can achieve great superiority to other state-of-art algorithms.


title: Baseball4D: A tool for baseball game reconstruction & visualization

abstract: While many sports use statistics and video to analyze and improve game play, baseball has led the charge throughout its history. With the advent of new technologies that allow all players and the ball to be tracked across the entire field, it is now possible to bring this understanding to another level. From discrete positions across time, we present techniques to reconstruct entire baseball games and visually explore each play. This provides opportunities to not only derive new metrics for the game, but also allow us to investigate existing measures with targeted visualizations. In addition, our techniques allow users to filter on demand so specific situations can be analyzed both in general and according to those situations. We show that gameplay can be accurately reconstructed from the raw position data and discuss how visualization and statistical methods can combine to better inform baseball analyses.


title: Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method

abstract: An automated method for electrocardiogram (ECG)-artifact detection and elimination is proposed for application to a single-channel electroencephalogram (EEG) without a separate ECG channel for reference. The method is based on three characteristics of ECG artifacts: the spike-like property, the periodicity and the lack of correlation with the EEG. The method involves a two-step process: ECG artifact detection using the energy interval histogram (EIH) method and ECG artifact elimination using a modification of ensemble average subtraction. We applied a smoothed nonlinear energy operator to the contaminated EEG, which significantly emphasized the ECG artifacts compared with the background EEG. The EIH method was initially proposed to estimate the rate of false positives (FPs) and false negatives (FNs) that were necessary to determine the optimal threshold for the detection of the ECG artifact. As a postprocessing step, we used two types of threshold adjusting algorithms that were based on the periodicity of the ECG R-peaks. The technique was applied to four whole-night sleep EEG recordings from four subjects with severe obstructive sleep apnea syndrome, from which a total of 132 878 heartbeats were monitored over 31.8 h. We found that ECG artifacts were successfully detected and eliminated with FP = 0.017 and FN = 0.074 for the epochs where the elimination process is necessarily required.


title: Efficient network coding signatures in the standard model.” Cryptology ePrint Archive, Report 2011/696

abstract: Network Coding is a routing technique where each node may actively modify the received packets before transmitting them.While this departure from passive networks improves throughput and resilience to packet loss it renders transmission susceptible to pollution attacks where nodes can misbehave and change in a malicious way the messages transmitted. Nodes cannot use standard signature schemes to authenticate the modified packets: this would require knowledge of the original sender's signing key. Network coding signature schemes offer a cryptographic solution to this problem. Very roughly, such signatures allow signing vector spaces (or rather bases of such spaces), and these signatures are homomorphic: given signatures on a set of vectors it is possible to create signatures for any linear combination of these vectors. Designing such schemes is a difficult task, and the few existent constructions either rely on random oracles or are rather inefficient. In this paper we introduce two new network coding signature schemes. Both of our schemes are provably secure in the standard model, rely on standard assumptions, and are in the same efficiency class as previous solutions based on random oracles.


title: Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf

abstract: Abstract One of the biggest challenges of the current big data landscape is our inability to pro- cess vast amounts of information in a reasonable time. In this work, we explore and com- pare two distributed computing frameworks implemented on commodity cluster architectures: MPI/OpenMP on Beowulf that is high-performance oriented and exploits multi-machine/multi- core infrastructures, and Apache Spark on Hadoop which targets iterative algorithms through in-memory computing. We use the Google Cloud Platform service to create virtual machine clusters, run the frameworks, and evaluate two supervised machine learning algorithms: KNN and Pegasos SVM. Results obtained from experiments with a particle physics data set show MPI/OpenMP outperforms Spark by more than one order of magnitude in terms of processing speed and provides more consistent performance. However, Spark shows better data manage- ment infrastructure and the possibility of dealing with other aspects such as node failure and data replication.


title: Planar Dual-Band Wide-Scan Phased Array in X-Band

abstract: The design of a planar dual-band wide-scan phased array is presented. The array uses novel dual-band comb-slot-loaded patch elements supporting two separate bands with a frequency ratio of 1.4:1. The antenna maintains consistent radiation patterns and incorporates a feeding configuration providing good bandwidths in both bands. The design has been experimentally validated with an X-band planar 9 × 9 array. The array supports wide-angle scanning up to a maximum of 60 ° ::: and 50 ° ::: at the low and high frequency bands respectively.


title: Budget Optimization for Sponsored Search: Censored Learning in MDPs

abstract: We consider the budget optimization problem faced by an advertiser participating in repeated sponsored search auctions, seeking to maximize the number of clicks attained under that budget. We cast the budget optimization problem as a Markov Decision Process (MDP) with censored observations, and propose a learning algorithm based on the wellknown Kaplan-Meier or product-limit estimator. We validate the performance of this algorithm by comparing it to several others on a large set of search auction data from Microsoft adCenter, demonstrating fast convergence to optimal performance.


title: Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches

abstract: 1.1 Origin The traveling salesman problem (TSP) were studied in the 18th century by a mathematician from Ireland named Sir William Rowam Hamilton and by the British mathematician named Thomas Penyngton Kirkman. Detailed discussion about the work of Hamilton & Kirkman can be seen from the book titled Graph Theory (Biggs et al. 1976). It is believed that the general form of the TSP have been first studied by Kalr Menger in Vienna and Harvard. The problem was later promoted by Hassler, Whitney & Merrill at Princeton. A detailed dscription about the connection between Menger & Whitney, and the development of the TSP can be found in (Schrijver, 1960).


title: Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system.

abstract: In this paper, the interval type-2 fuzzy proportional–integral–derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS). The results of the proposed IT2F-PID controller using a new method of type-reduction are compared with the other proposed IT2F-PID controller using the uncertainty bound method and the type-1 fuzzy PID controller (T1F-PID). The simulation and practical results show that the performance of the proposed controller is significantly improved compared with the T1F-PID controller.


title: Demand driven store site selection via multiple spatial-temporal data

abstract: Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, analytic models based on census data, which are either unable to adapt to the dynamic market or very time consuming. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data- driven prediction models for business store site selection. In this paper, we propose a Demand Driven Store Site Selection (DD3S) framework for business store site selection by mining search query data from Baidu Maps. DD3S first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and supply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also use learning-to-rank model to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiment results demonstrate the effectiveness of our methods. DD3S as the core function for store site selection has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.


title: Multi agent system: concepts, platforms and applications in power systems

abstract: The power system is presently experiencing vital changes: it is advancing from a centralized structure to a decentralized one, primarily because of the enormous advancement of distributed renewable energy sources, so future power system obliges new control strategies. These systems must have the capacity to withstand new requirements, for example, the exceedingly disseminated nature, the irregularity of renewable energy sources and the restricted data transfer capacity for communications. Multi agent systems (MAS) have attributes that meet these prerequisites. A certain degree of distributed or collective intelligence can be accomplished through the connection of these agents with one another, participating or contending to achieve their objectives. This paper introduces outline of the fundamental ideas of MAS and its different platforms. Also, it provides a comprehensive survey on the power system applications in which MAS technique has been applied. For each power system application, technical details are also discussed.


title: Urban mobility study using taxi traces

abstract: In this work, we analyze taxi-GPS traces collected in Lisbon, Portugal. We perform an exploratory analysis to visualize the spatiotemporal variation of taxi services; explore the relationships between pick-up and drop-off locations; and analyze the behavior in downtime (between the previous drop-off and the following pick-up). We also carry out the analysis of predictability of taxi trips for the next pick-up area type given history of taxi flow in time and space.


title: Design-Oriented Modelling of Axial-Flux Variable-Reluctance Resolver Based on Magnetic Equivalent Circuits and Schwarz–Christoffel Mapping

abstract: Axial flux variable reluctance (AFVR) resolvers have substantial benefits that make them suitable for motion control drives. However, they suffer from insufficient accuracy, especially in high-accuracy applications. Hence, optimizing the AFVR resolver structure is necessary for improving its commercial usage. However, its accurate modelling needs three-dimensional (3-D) time stepping finite element analysis (TSFEA) that is computationally expensive and unsuitable for co-usage with optimization algorithms. The aim of this paper is to establish an accurate, yet computationally fast, model suitable for optimal design of AFVR resolvers. The working of the proposed model is based on magnetic equivalent circuit (MEC) and conformal mapping, which are in turn based on Schwarz–Christoffel mapping. The model uses conformal mapping to calculate reluctances that are used in MEC for calculating magnetic fluxes linkages, inductances, and induced voltages. Then, the induced voltages are used for calculating angular position. The results of the proposed model are compared with those of 3-D TSFEA. Finally, the experimental prototype is used to evaluate the developed analytical model.


title: Passive regenerative snubber cell applied to isolated DCM SEPIC converter

abstract: Isolated converter such as SEPIC has a high voltage stress in the main switch due to transformer leakage inductance. To solve this issue an active or passive clamp action is necessary. The common passive solution based on a RCD snubber is simple but impractical when the value of the leakage inductance is significant. In other hands, passive regenerative solutions generally compromise the isolation, making the search for a suitable snubber a challenge. In this paper, an effective passive regenerative snubber cell for isolated DCM SEPIC converters is presented. It is intended to improve the converter efficiency by transferring the energy stored in the transformer leakage inductance to the output. Analysis is presented in detail together with a practical design procedure. To validate the proposal, simulated and experimental results are performed for a 100 W, 100 V input and 50 V output voltage converter.


title: Real-Time RGB-D Camera Relocalization via Randomized Ferns for Keyframe Encoding

abstract: Recovery from tracking failure is essential in any simultaneous localization and tracking system. In this context, we explore an efficient keyframe-based relocalization method based on frame encoding using randomized ferns. The method enables automatic discovery of keyframes through online harvesting in tracking mode, and fast retrieval of pose candidates in the case when tracking is lost. Frame encoding is achieved by applying simple binary feature tests which are stored in the nodes of an ensemble of randomized ferns. The concatenation of small block codes generated by each fern yields a global compact representation of camera frames. Based on those representations we define the frame dissimilarity as the block-wise hamming distance (BlockHD). Dissimilarities between an incoming query frame and a large set of keyframes can be efficiently evaluated by simply traversing the nodes of the ferns and counting image co-occurrences in corresponding code tables. In tracking mode, those dissimilarities decide whether a frame/pose pair is considered as a novel keyframe. For tracking recovery, poses of the most similar keyframes are retrieved and used for reinitialization of the tracking algorithm. The integration of our relocalization method into a hand-held KinectFusion system allows seamless continuation of mapping even when tracking is frequently lost.


title: Narrowing the Gap: Random Forests In Theory and In Practice

abstract: Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also provide an empirical evaluation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in practice. Our experiments provide insight into the relative importance of different simplifications that theoreticians have made to obtain tractable models for analysis.


title: Divided They Tweet: The Network Structure of Political Microbloggers and Discussion Topics

abstract: In the context of a national election, this study explores more than 69,000 Twitter messages containing mentions of political parties and about 2,500 related user profiles to investigate the network structure of political microbloggers with respect to, first, their party preference and, second, the topics they discuss. We find that political microbloggers tend to follow like-minded peers. Microbloggers in a cohesive group tend to have the same political preferences. In addition, we conduct a content analysis of the political debate on Twitter to explore which topics and politicians are discussed and whether this debate reflects an ideological divide among participating users. While there are some discussion topics that are dominated by politically like-minded microbloggers, the majority of topics is discussed by a diverse group of microbloggers with various political preferences.


title: Personalized Course Sequence Recommendations

abstract: Given the variability in student learning, it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multiarmed bandits, an algorithm is developed that can optimally recommend a course sequence that both reduces the time to graduate while also increasing the overall GPA of the student. The algorithm dynamically learns how students with different contextual backgrounds perform for given course sequences and, then, recommends an optimal course sequence for new students. Using real-world student data from the UCLA Mechanical and Aerospace Engineering Department, we illustrate how the proposed algorithms outperform other methods that do not include student contextual information when making course sequence recommendations.


title: Linked data on the web (ldow2008

abstract: The Web is increasingly understood as a global information space consisting not just of linked documents, but also of Linked Data. More than just a vision, the resulting Web of Data has been brought into being by the maturing of the Semantic Web technology stack, and by the publication of an increasing number of datasets according to the principles of Linked Data. The Linked Data on the Web (LDOW2008) workshop brings together researchers and practitioners working on all aspects of Linked Data. The workshop provides a forum to present the state of the art in the field and to discuss ongoing and future research challenges. In this workshop summary we will outline the technical context in which Linked Data is situated, describe developments in the past year through initiatives such as the Linking Open Data community project, and look ahead to the workshop itself.


title: Security Attacks with an Effective Solution for DOS Attacks in VANET

abstract: Vehicular Ad hoc Networks is a special kind of mobile ad hoc network to provide communication among nearby vehicles and between vehicles and nearby fixed equipments. VANETs are mainly used for improving efficiency and safety of (future) transportation. There are chances of a number of possible attacks in VANET due to open nature of wireless medium. In this paper, we have classified these security attacks and logically organized/represented in a more lucid manner based on the level of effect of a particular security attack on intelligent vehicular traffic. Also, an effective solution is proposed for DOS based attacks which use the redundancy elimination mechanism consists of rate decreasing algorithm and state transition mechanism as its components. This solution basically adds a level of security to its already existing solutions of using various alternative options like channel-switching, frequency-hopping, communication technology switching and multiple-radio transceivers to counter affect the DOS attacks. Proposed scheme enhances the security in VANETs without using any cryptographic scheme.