title: Using Gabor filter in 3D convolutional neural networks for human action recognition

abstract: Human action recognition is an important topic in the field of computer vision. We use Gabor filter in 3D CNNs models in recognizing action. Convolutional neural networks (CNNs) are a type of deep learning models, which is an efficient recognition model and has a unique superiority in image processing. Three dimension convolutional neural networks can well analyze action from video data. Gabor filter is a special convolution kernel. Its performance in feature extraction is outstanding. We test out model by KTH dataset and achieve a well result.


title: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention

abstract: This article presents: (i) a multiscale representation of grey-level shape called the scale-space primal sketch, which makes explicit both features in scale-space and the relations between structures at different scales, (ii) a methodology for extracting significant blob-like image structures from this representation, and (iii) applications to edge detection, histogram analysis, and junction classification demonstrating how the proposed method can be used for guiding later-stage visual processes.The representation gives a qualitative description of image structure, which allows for detection of stable scales and associated regions of interest in a solely bottom-up data-driven way. In other words, it generates coarse segmentation cues, and can hence be seen as preceding further processing, which can then be properly tuned. It is argued that once such information is available, many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives intuitive results.


title: Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

abstract: We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent. We develop gradient-based inference methods to learn the unknown function and the non-stationary model parameters, without requiring any model approximations. We propose to infer full parameter posterior with Hamiltonian Monte Carlo (HMC), which conveniently extends the analytical gradient-based GPR learning by guiding the sampling with model gradients. We also learn the MAP solution from the posterior by gradient ascent. In experiments on several synthetic datasets and in modelling of temporal gene expression, the nonstationary GPR is shown to be necessary for modeling realistic input-dependent dynamics, while it performs comparably to conventional stationary or previous non-stationary GPR models otherwise.


title: Endocannabinoids — at the crossroads between the gut microbiota and host metabolism

abstract: Various metabolic disorders are associated with changes in inflammatory tone. Among the latest advances in the metabolism field, the discovery that gut microorganisms have a major role in host metabolism has revealed the possibility of a plethora of associations between gut bacteria and numerous diseases. However, to date, few mechanisms have been clearly established. Accumulating evidence indicates that the endocannabinoid system and related bioactive lipids strongly contribute to several physiological processes and are a characteristic of obesity, type 2 diabetes mellitus and inflammation. In this Review, we briefly define the gut microbiota as well as the endocannabinoid system and associated bioactive lipids. We discuss existing literature regarding interactions between gut microorganisms and the endocannabinoid system, focusing specifically on the triad of adipose tissue, gut bacteria and the endocannabinoid system in the context of obesity and the development of fat mass. We highlight gut-barrier function by discussing the role of specific factors considered to be putative 'gate keepers' or 'gate openers', and their role in the gut microbiota-endocannabinoid system axis. Finally, we briefly discuss data related to the different pharmacological strategies currently used to target the endocannabinoid system, in the context of cardiometabolic disorders and intestinal inflammation.


title: Organic Control of Traffic Lights

abstract: In recent years, Autonomic and Organic Computing have become areas of active research in the computer science community. Both initiatives aim at handling the growing complexity in technical systems by creating systems with adaptation and self-optimisation capabilities. One application scenario for such "life-like" systems is the control of road traffic signals in urban areas. This paper presents an organic approach to traffic light control and analyses its performance by an experimental validation of the proposed architecture which demonstrates its benefits compared to classical traffic control.


Intelligent Smart Home Automation and Security System Using Arduino and Wi-fi


title: Proprioceptive-Inertial Autonomous Locomotion for Articulated Robots

abstract: Inspired by the ability of animals to rely on proprioception and vestibular feedback to adapt their gait, we propose a modular framework for autonomous locomotion that relies on force sensing and inertial information. A first controller exploits anti-compliance, a new application of positive force feedback, to quickly react against obstacles upon impact. We hypothesize that, in situations where a robot experiences occasional impacts with the environment, anti-compliance can help negotiate unknown obstacles, similar to biological systems where positive feedback enables fast responses to external stimuli. A novel parallel controller, based on a bi-stable dynamical system, continuously adjusts the robot's direction of locomotion, and reverts it in reaction to major swerves. We present experimental results, demonstrating how our framework allows a snake robot to autonomously locomote through a row of unevenly-spaced obstacles. Finally, we extend our proprioceptive controller to legged locomotion, showing how a hexaprint robot can adapt its motion to climb over obstacles.


title: The role of tender offer in FDI selection. Study case: Romania

abstract: The present paper creates a tender model specific to FDI. We consider to be necessary to have such a tender since the effects of foreign investments in the national economy are multiple from an economic and social point of view so that it becomes necessary to emphasize a balance of the positive and negative effects. On the other hand, we also suggest two new indicators, the multiplier of foreign direct investments and the accelerator of foreign direct investments that show the way in which this type of investment spreads in the national economy.


Survey: IoT Vehicular Information Network


title: Automatic mitral leaflet tracking in echocardiography by outlier detection in the low-rank representation

abstract: Tracking the mitral valve leaflet in an ultrasound sequence is a challenging task because of the poor image quality and fast and irregular leaflet motion. Previous algorithms usually applied standard segmentation methods based on edges, object intensity and anatomical information to segment the mitral leaflet in static frames. However, they are limited in practical applications due to the requirement of manual input for initialization or large annotated datasets for training. In this paper we present a completely automatic and unsupervised algorithm for mitral leaflet detection and tracking. We demonstrate that the image sequence of a cardiac cycle can be well approximated with a low-rank matrix, except for the mitral leaflet region with fast motion and tissue deformation. Based on this difference, we propose to track the mitral leaflet by detecting contiguous outliers in the low-rank representation. With this formulation, the leaflet is tracked using the motion cue, but the complicated motion computation is avoided. To the best of our knowledge, the proposed algorithm is the first unsupervised method for mitral leaflet tracking. The algorithm was tested on both 2D and 3D echocardiography, which achieved accurate segmentation with an average distance of 0.87 ± 0.42mm compared to the manual tracing.


title: The anatomy of a context-aware application

abstract: We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment.


title: Real time face recognition system (RTFRS)

abstract: Biometrics is a term used to define an individual's DNA, hand geometry, face, etc. or behavioral characteristics, such as hand signature, voice tone, keystrokes and so on. For that reason, these biological characteristics are unique for every individual. In many situations, face recognition related technologies are becoming more popular among biometric-based technologies that measure an individual's natural data. Genetic biometrics has generally used to authenticate and identify individuals by analyzing their physical characteristics, such as fingerprint, eye iris, vein etc. Instead of using a bankcard, a camera installed at the Automated Teller Machines would capture pictures of faces of customers, and compare them with the photos of account holders in the database of banks to verify the customer's identity. The purpose of this paper is to present a Windows based real time application system using face recognition algorithms. This new system can be applied in various different fields such as identity verification and other potential commercial applications. Both Eigen and Local Binary Patterns face algorithms were used to reduce the impact of light exposure that will affect the accuracy of the system.


title: Accelerated Stochastic Subgradient Methods under Local Error Bound Condition

abstract: In this paper, we propose two {\bf accelerated stochastic subgradient} methods for stochastic non-strongly convex optimization problems by leveraging a generic local error bound condition. The novelty of the proposed methods lies at smartly leveraging the recent historical solution to tackle the variance in the stochastic subgradient. The key idea of both methods is to iteratively solve the original problem approximately in a local region around a recent historical solution with size of the local region gradually decreasing as the solution approaches the optimal set. The difference of the two methods lies at how to construct the local region. The first method uses an explicit ball constraint and the second method uses an implicit regularization approach. For both methods, we establish the improved iteration complexity in a high probability for achieving an $\epsilon$-optimal solution. Besides the improved order of iteration complexity with a high probability, the proposed algorithms also enjoy a logarithmic dependence on the distance of the initial solution to the optimal set. We also consider applications in machine learning and demonstrate that the proposed algorithms enjoy faster convergence than the traditional stochastic subgradient method. For example, when applied to the $\ell_1$ regularized polyhedral loss minimization (e.g., hinge loss, absolute loss), the proposed stochastic methods have a logarithmic iteration complexity.


title: GcsDecolor: Gradient Correlation Similarity for Efficient Contrast Preserving Decolorization

abstract: This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.


title: Six-Port Based Interferometry for Precise Radar and Sensing Applications

abstract: Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology.


title: Green apple recognition method based on the combination of texture and shape features

abstract: Automated harvesting requires accurate recognition of fruit in a tree canopy in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Therefore, an accurate recognition algorithm needs to be studied which involves the detection of green apples within scenes of green leaves, shadow patterns, branches and other objects found in natural tree canopies. In this paper, the method is proposed which combines texture features, shape features and color features to solve the problem of segmenting the target and background of apple picking robot in complex background. The gray-scale difference statistical method is utilized to get the texture feature vector of the image. According to the texture feature vector, the support vector machine (SVM) is used to segment the image preliminar, and then the shape and color features are combined to achieve precise segmentation. Experiments show that the algorithm of this paper is superior to other algorithms in recognition rate and running speed. In addition, it has better segmentation effect for fruit with slight background occlusion.


title: Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition

abstract: Semen quality assessed by sperm count and sperm cell characteristics such as morphology and motility, is considered to be the main determinant of men’s reproductive health. Therefore, sperm cell selection is vital in assisted reproductive technology (ART) used for the treatment of infertility. Conventional bright field optical microscopy is widely utilized for the imaging and selection of sperm cells based on the qualitative analysis by experienced clinicians. In this study, we report the development of a highly sensitive quantitative phase microscopy (QPM) using partially spatially coherent light source, which is a label-free, non-invasive and high-resolution technique to quantify various biophysical parameters. The partial spatial coherence nature of light source provides a significant improvement in spatial phase sensitivity and hence reconstruction of the phase of the entire sperm cell is demonstrated, which was otherwise not possible using highly spatially coherent light source. High sensitivity of the system enables quantitative phase imaging of the specimens having very low refractive index contrast with respect to the medium like tail of the sperm cells. Further, it also benefits with accurate quantification of 3D-morphological parameters of sperm cells which might be helpful in the infertility treatment. The quantitative analysis of more than 2500 sperm cells under hydrogen peroxide (H2O2) induced oxidative stress condition is demonstrated. It is further correlated with motility of sperm cell to study the effect of oxidative stress on healthy sperm cells. The results exhibit a decrease in the maximum phase values of the sperm head as well as decrease in the sperm cell’s motility with increasing oxidative stress, i.e., H2O2 concentration. Various morphological and texture parameters were extracted from the phase maps and subsequently support vector machine (SVM) based machine learning algorithm is employed for the classification of the control and the stressed sperms cells. The algorithm achieves an area under the receiver operator characteristic (ROC) curve of 89.93% based on the all morphological and texture parameters with a sensitivity of 91.18%. The proposed approach can be implemented for live sperm cells selection in ART procedure for the treatment of infertility.


title: Personalised book recommendation system based on opinion mining technique

abstract: Recommendation systems are tools in e-commerce websites which helps user to find the most suitable products. From the huge number of books, it is really difficult to choose a particular book. So, the recommendation system technique plays very important role and helps user to get books according to their need and interest. This paper presents online book recommendation system for users who purchase books by considering specific features of a book. These features include language, publisher, author, content, price, edition etc. The main motive of this paper is to develop a technique which recommends the most suitable books to users according to the specific features of the book that are of their own interest. In this paper we have used the features of a product, extracted from the reviews for recommendation. This is based on the combined features of classification and opinion mining technique.


title: Automated Top View Registration of Broadcast Football Videos

abstract: In this paper, we propose a fully automatic method to register football broadcast video frames on the static top view model of the playing surface. Automatic registration has been difficult due to the difficulty of finding sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduces this problem to a minimal per-frame edge map matching problem. We show that the per-frame results can be further improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of the football World Cup 2014 and show significant improvement over the current state of the art.


title: Situation aware UAV mission route planning

abstract: This paper outlines an agent based approach for UAV (Unmanned Air Vehicles) mission route planning problem. In this context, mission route planning can be defined as finding the “best” set of waypoints for the UAV that will enhance its probability of success in its mission. Mission route planning may be carried out either in premission or in mission time. It may be done either by an onboard control system or by a ground mission planning system. Regardless of these considerations, a problem solving agent for UAV mission route planning problem is proposed in this research. Mission Route Planning Agent in UAV mission route planning context needs to perceive the elements in its environment and comprehend the meaning of this situation to compute a route. In other words, it will use target, threat, terrain and air space restrictions to compute the “best” route for that UAV mission. In a dynamic replanning environment, it will compute its new route again and again each time the situation has changed. To compute the “best” route, an A* based graph search algorithm is used. For a node, while real cost is computed using fuel and threat costs with determined weightings, heuristic cost is selected as the geometric distance. Network is constructed in the search time. Consecutive nodes are determined by spanning range and azimuth with determined increments. The values of weightings for fuel and threat costs and the increments used to construct the network have a great effect on the resulting route and the computation time. While common practice is to use default or user defined values for those, this paper presents a situation aware UAV Mission Route Planning Agent that uses the tactical situation to determine these values.


title: The Fast Downward Planning System

abstract: Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downwards approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downwards best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multi-heuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the"classical (i.e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.


title: PPSGen: Learning-Based Presentation Slides Generation for Academic Papers

abstract: In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods.


title: Behavioral Economics Holds Potential To Deliver Better Results For Patients, Insurers, And Employers

abstract: Many programs being implemented by US employers, insurers, and health care providers use incentives to encourage patients to take better care of themselves. We critically review a range of these efforts and show that many programs, although well-meaning, are unlikely to have much impact because they require information, expertise, and self-control that few patients possess. As a result, benefits are likely to accrue disproportionately to patients who already are taking adequate care of their health. We show how these programs could be made more effective through the use of insights from behavioral economics. For example, incentive programs that offer patients small and frequent payments for behavior that would benefit the patients, such as medication adherence, can be more effective than programs with incentives that are far less visible because they are folded into a paycheck or used to reduce a monthly premium. Deploying more-nuanced insights from behavioral economics can lead to policies with the poten...


title: Graph-based semi-supervised learning with multiple labels

abstract: Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. Based on the proposed framework, we further develop two novel graph-based algorithms. We apply the proposed methods to video concept detection over TRECVID 2006 corpus and report superior performance compared to the state-of-the-art graph-based approaches and the representative semi-supervised multi-label learning methods.


Theoretical reaction coordinate for the methyl isocyanide isomerization


title: Reflective and Refractive Objects for Mixed Reality

abstract: In this paper, we present a novel rendering method which integrates reflective or refractive objects into a differential instant radiosity (DIR) framework usable for mixed-reality (MR) applications. This kind of objects are very special from the light interaction point of view, as they reflect and refract incident rays. Therefore they may cause high-frequency lighting effects known as caustics. Using instant-radiosity (IR) methods to approximate these high-frequency lighting effects would require a large amount of virtual point lights (VPLs) and is therefore not desirable due to real-time constraints. Instead, our approach combines differential instant radiosity with three other methods. One method handles more accurate reflections compared to simple cubemaps by using impostors. Another method is able to calculate two refractions in real-time, and the third method uses small quads to create caustic effects. Our proposed method replaces parts in light paths that belong to reflective or refractive objects using these three methods and thus tightly integrates into DIR. In contrast to previous methods which introduce reflective or refractive objects into MR scenarios, our method produces caustics that also emit additional indirect light. The method runs at real-time frame rates, and the results show that reflective and refractive objects with caustics improve the overall impression for MR scenarios.


title: A survey of qualitative spatial representations

abstract: Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work.


title: Digital Watermarking Using MATLAB

abstract: Embedding a hidden stream of bits in a file is called Digital Watermarking. The file could be an image, audio, video or text. Nowadays, digital watermarking has many applications such as broadcast monitoring, owner identification, proof of ownership, transaction tracking, content authentication, copy control, device control, and file reconstruction (Cox et. al., 2008). In literature, the host file is called the “asset”, and the bit stream is called the “message”. The main specifications of a watermarking system are: Robustness (Against intentional attacks or unintentional ones such as compression), Imperceptibility, and Capacity. Importance of each depends on the application. As a matter of fact there is a trade-off between these factors (Barni & Bartolini, 2004). Although watermarking in some literature includes visible imprints, here we only mean the invisible embedding of the data. In this chapter, we will introduce how to use MATLAB to implement image watermarking algorithms. These algorithms include the most famous ones which are widely used in current literature or more complicated approaches are based upon. These are commonly divided into three categories (Barni & Bartolini, 2004) 1. Watermarking in Spatial Domain 2. Watermarking in Spectral Domain 3. Watermarking in Hybrid Domain In section 2 we will go through some basic image processing commands in MATLAB. Section 3 provides information about different fundamental watermarking methods. Evaluating the algorithms is discussed in Section 4, and finally section 5 brings a conclusion.


title: Antitumor Effects of Clinical Dosing Regimens of Bisphosphonates in Experimental Breast Cancer Bone Metastasis

abstract: Background Bisphosphonates exhibit direct antitumor activity in animal models, but only at high doses that are incompatible with the clinical dosing regimens approved for the treatment of cancer patients with skeletal metastases. We compared the antitumor effects of clinical dosing regimens of the bisphosphonates zoledronic acid and clodronate in a mouse model of bone metastasis. Methods Mice (n = 6 – 10 per group) were treated with zoledronic acid, clodronate, or vehicle starting before (preventive protocols) or after (treatment protocols) intravenous injection with human B02/GFP.2 breast cancer cells, which express green fluorescent protein (GFP) and luciferase and metastasize to bone. Zoledronic acid was given as daily, weekly, or single doses at a cumulative dose of 98 – 100 µ g/kg body weight, equivalent to the 4-mg intravenous dose given to patients. Clodronate was given as a daily dose (530 µ g/kg/ day), equivalent to the daily 1600-mg oral clinical dose given to patients. Bone destruction was measured by radiography, x-ray absorptiometry or tomography, and histomorphometry (as the ratio of bone volume to tissue volume). Skeletal tumor burden was measured by histomorphometry (as the ratio of tumor burden to soft tissue volume [TB/STV]) and luciferase activity. All statistical tests were two-sided. Results In treatment protocols, daily clodronate was less effective at decreasing the TB/STV ratio than daily (53% versus 87%, difference = 34%, 95% confidence interval [CI] = 16% to 44%, P <.001) or weekly (53% versus 90%, difference = 37%, 95% CI = 19% to 46%, P <.001) zoledronic acid – dosing regimens. Compared with vehicle, a single dose of zoledronic acid decreased tumor burden by only 16% (95% CI = 9% to 22%, P <.001). In preventive protocols, daily clodronate and daily or weekly zoledronic acid decreased the TB/STV ratio by 49% (95% CI = 40% to 57%, P = .006), 83% (95% CI = 68% to 98%, P <.001), and 66% (95% CI = 47% to 84%, P <.001), respectively, compared with vehicle, whereas a single dose of zoledronic acid decreased tumor burden by only 13% (95% CI = − 2% to 28%, P = .84). Mice treated with a daily preventive regimen of clodronate or with a daily or weekly preventive regimen of zoledronic acid showed a decreased B02/GFP.2 cell tumor burden compared with vehicle, whereas a single preventive dose of zoledronic acid had no effect. Conclusion Daily or repeated intermittent therapy with clinical doses of bisphosphonates inhibits skeletal tumor


title: Sentence Pair Scoring: Towards Unified Framework for Text Comprehension

abstract: We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence model. We set a new state-of-art in performance on the Ubuntu Dialogue dataset.


title: Cloud computing: Technology, security issues and solutions

abstract: The concept of cloud computing can be traced back to almost the middle of last century. Cloud paradigm offers scalable, portable, platform-independent, ubiquitous, shared resilient, sustainable, and near-utility computing. The cloud computing is implemented as a software-only solution or based on advanced hardware supported virtualization technology. This paper presents the fundamental technology behind the cloud computing. Cloud computing posit as a utopia to solve multitudes of challenges of the current time. But in practice security is a major road block to its widespread adoption. This paper discusses the security issues of the cloud computing. Further the paper illustrates upon the security solutions for the virtualization and web services, two major enabling technologies of cloud computing. It also explains the novel concept of integrating the multi-level security in all of the cloud offerings in contrast to the security-as-a-service concept. Finally paper mentions the important guidelines for the development of service level agreements.


title: The filled skutterudite CeOs$_{4}$As$_{12}$: a hybridization gap semiconductor

abstract: X-ray diffraction, electrical resistivity, magnetization, specific heat, and thermoelectric power measurements are presented for single crystals of the new filled skutterudite compound CeOs4As12, which reveal phenomena that are associated with f-electron-conduction electron hybridization. Valence fluctuations or Kondo behavior dominates the physics down to T ∼ 135 K. The correlated electron behavior is manifested at low temperatures as a hybridization gap-insulating state. The small energy gap Δ1/kB ∼ 73 K, taken from fits to electrical resistivity data, correlates with the evolution of a weakly magnetic or nonmagnetic ground state, which is evident in the magnetization data below a coherence temperature Tcoh ∼ 45 K. Additionally, the low-temperature electronic specific heat coefficient is small, γ ∼ 19 mJ/mol K2. Some results for the nonmagnetic analogue compound LaOs4As12 are also presented for comparison purposes.


title: A NEW SPECIES OF CHIRIDOTEA (CRUSTACEA: ISOPODA) FROM NEW ENGLAND WATERS

abstract: The genus Chiridotea. is unique to the eastern coastal region of the United States and southeastern Canada. Members of this genus are small (usually < 1 cm. ) , broad, depressed valviferous forms that inhabit sandy areas and charac teristically burrow just beneath the sediment surface. The species described herein constitutes the fourth known species of this genus. Those previously described are : C. coeca (Say, 1818) ; C. tuftsi ( Stimpson, 1853) ; and C. a/myra Bowman, 1955. Included in Bowman's paper is a revision of the generic characteristics of this group. The description of this new form is based on specimens collected on Georges Bank, a relatively shallow portion of the continental shelf east of Massa chusetts, U. S. A. These specimens were encountered while processing collections of benthic invertebrates taken by the R/V Albatross III and the R/V Delaware for the Woods Hole Laboratory, Bureau of Commercial Fisheries, U. S. Fish and Wildlife Service. Material Exa@nined.â€”Holotype, adult female with oostegites developed, 7.5 mni. in length, deposited in the U. S. National Museum (Catalogue No. 104282); allotype, adult male 6.0 trim. in length (U.S.N.M. Catalogue No. 104280) ; para types, 1 male 5.0 mm. in length and 1 ovigerous female 6.5 mm. in length (U.S.N.M. Catalogue No. 104281) ; all type specimens were collected August 6, 1959, by means of a grab-type bottom sampler on Georges Bank at lat. 41Â°48' N., long. 67Â° 53' W. ; water depth 15 fathoms, sand substrate, bottom water tempera ture 57.0Â°F. (R/V Delaware, cruise number 59-9, station 21). One female, 7.5 mm. in length, collected December 7, 1955, at lat. 40Â° 51' N., long. 68Â° 55' W.; substrate coarse sand, water depth 36 fathoms, bottom water temperature 47.2Â° F. (R/V Albatross III, cruise number 70, collection 3). One male, 6.5 mm. in length, collected December 14, 1955, at lat. 41Â°40' N., long. 67Â°36' W.; substrate gravelly sand, water depth 28 fathoms, bottom water temperature 45.7Â° F. (R/V Albatross III, cruise number 70, collection 38). Five females, body lengths 4.0, 5.0, 6.5, 7.5, 7.5 nim., and two males, body lengths 7.0 and 7.0, collected August 24, 1957, at lat. 41Â°22' N., long. 68Â°20' W.; substrate coarse sand, water depth 24 fathoms (R/V Albatross III, cruise number 101, station 64). Three specimens, 4.0, 4.5, 4.5 mm. in length, collected August 24, 1957, at lat. 41Â° 34' N., long., 67Â° 28' W.; substrate coarse sand, water depth 23 fathoms (R/V AlbatrossIII,cruisenumber 101,station90). Diagnosis.â€”Medium-sized Chiridotea with short antennae; flagellum of antenna 2 is much shorter than the peduncle; antenna 1 usually not reaching beyond the peduncle of antenna 2; outer margin of pereion epinieral plates 5â€”7


title: Industrial data-collector by enabling OPC-UA standard for Industry 4.0

abstract: Industrial Communication Systems have become an increasingly essential part of factory automation, motion control, networked control systems. New and real-time capable solutions are required to support the always more present Industry 4.0 paradigm: for both local and wide area networks. At the same time, standardization has also started moving in this direction: in particular, for future open and dynamic system architectures, promising activities based on OPC-UA are serious attempt to satisfy stricter requirements for interoperability and information modeling with hard real-time needs. For these reasons, in this paper the development of an OPC-UA based architecture is described: the main target is to build a “collector” able to collect data from the shop-floor and convert them in the OPC-UA standard for an agile and effective vertical integration. The subsequent analysis carried out on the testbed mainly focuses on the performance evaluation of the method. To achieve high-performance data transmission in a fair environment, a parallel communication mechanism has been proposed.


title: Palette: A Paper Interface for Giving Presentations

abstract: The Palette is a digital appliance designed for intuitivecontrol of electronic slide shows. Current interfaces demand toomuch of our attention to permit effective computer use insituations where we can not give the technology our fullestconcentration. The Palette uses index cards that are printed withslide content that is easily identified by both humans andcomputers. The presenter controls the presentation by directlymanipulating the cards. The Palette design is based on ourobservation of presentations given in a real work setting. Ourexperiences using the system are described, including new practices(e.g., collaborative presentation, enhanced notetaking) that arisefrom the affordances of this new approach. This system is anexample of a new interaction paradigm called tacitinteraction that supports users who can spare very littleattention to a computer interface.


Ouabain-insensitive86Rb+ transport in peritoneal mast cells: Role in histamine release


title: Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming

abstract: Research has shown that judgments tend to assimilate to irrelevant “anchors.” We extend anchoring eVects to show that anchors can even operate across modalities by, apparently, priming a general sense of magnitude that is not moored to any unit or scale. An initial study showed that participants drawing long “anchor” lines made higher numerical estimates of target lengths than did those drawing shorter lines. We then replicated this Wnding, showing that a similar pattern was obtained even when the target estimates were not in the dimension of length. A third study showed that an anchor’s length relative to its context, and not its absolute length, is the key to predicting the anchor’s impact on judgments. A Wnal study demonstrated that magnitude priming (priming a sense of largeness or smallness) is a plausible mechanism underlying the reported eVects. We conclude that the boundary conditions of anchoring eVects may be much looser than previously thought, with anchors operating across modalities and dimensions to bias judgment.


title: Chaotic bat algorithm

abstract: Abstract Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.


title: Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation

abstract: Low-rank representation (LRR) has been successfully applied in exploring the subspace structures of data. However, in previous LRR-based semi-supervised subspace clustering methods, the label information is not used to guide the affinity matrix construction so that the affinity matrix cannot deliver strong discriminant information. Moreover, these methods cannot guarantee an overall optimum since the affinity matrix construction and subspace clustering are often independent steps. In this paper, we propose a robust semi-supervised subspace clustering method based on non-negative LRR (NNLRR) to address these problems. By combining the LRR framework and the Gaussian fields and harmonic functions method in a single optimization problem, the supervision information is explicitly incorporated to guide the affinity matrix construction and the affinity matrix construction and subspace clustering are accomplished in one step to guarantee the overall optimum. The affinity matrix is obtained by seeking a non-negative low-rank matrix that represents each sample as a linear combination of others. We also explicitly impose the sparse constraint on the affinity matrix such that the affinity matrix obtained by NNLRR is non-negative low-rank and sparse. We introduce an efficient linearized alternating direction method with adaptive penalty to solve the corresponding optimization problem. Extensive experimental results demonstrate that NNLRR is effective in semi-supervised subspace clustering and robust to different types of noise than other state-of-the-art methods.


title: Research Commentary: Information Systems and Conceptual Modeling— A Research Agenda

abstract: Within the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptual modeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question:How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist.


title: Spatial-Temporal Recurrent Neural Network for Emotion Recognition

abstract: In this paper, we propose a novel deep learning framework, called spatial–temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial–temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.


title: Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories

abstract: In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or corrupted entries. In this paper, we develop a robust subspace separation scheme that deals with these practical issues in a unified mathematical framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively on the Hopkins155 motion segmentation database and other motion sequences with outliers and missing data. We compare the performance of our methods to state-of-the-art motion segmentation methods based on expectation-maximization and spectral clustering. For data without outliers or missing information, the results of our methods are on par with the state-of-the-art results and, in many cases, exceed them. In addition, our methods give surprisingly good performance in the presence of the three types of pathological trajectories mentioned above. All code and results are publicly available at http://perception.csl.uiuc.edu/coding/motion/.


title: SwarmSimX: Real-time simulation environment for multi-robot systems

abstract: In this paper we present a novel simulation environment called SwarmSimX with the ability to simulate dozens of robots in a realistic 3D environment. The software architecture of SwarmSimX allows new robots, sensors, and other libraries to be loaded at runtime, extending the functionality of the simulation environment significantly. In addition, SwarmSimX allows an easy exchange of the underlying libraries used for the visual and physical simulation to incorporate different libraries (e.g., improved or future versions). A major feature is also the possibility to perform the whole simulation in real-time allowing for human-in-the-loop or hardware-in-the-loop scenarios. SwarmSimX has been already employed in several works presenting haptic shared control of multiple mobile robots (e.g., quadrotor UAVs). Additionally, we present here two validation tests showing the physical fidelity and the real-time performance of SwarmSimX. For the tests we used NVIDIA® PhysX® and Ogre3D as physics and rendering libraries, respectively.


title: Plasmodium vivax segmentation using modified fuzzy divergence

abstract: This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions.


title: Performance Analysis of Future Multigigabit Wireless Communication Systems at THz Frequencies With Highly Directive Antennas in Realistic Indoor Environments

abstract: In this paper, a simulation environment encompassing realistic propagation conditions and system parameters is employed in order to analyze the performance of future multigigabit indoor communication systems at tetrahertz frequencies. The influence of high-gain antennas on transmission aspects is investigated. Transmitter position for optimal signal coverage is also analyzed. Furthermore, signal coverage maps and achievable data rates are calculated for generic indoor scenarios with and without furniture for a variety of possible propagation conditions.


title: Learning K-way D-dimensional Discrete Code For Compact Embedding Representations

abstract: Embedding methods such as word embedding have become pillars for many applications containing discrete structures. Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on"one-hot"encoding of the discrete symbols. Despite its simplicity, such approach yields number of parameters that grows linearly with the vocabulary size and can lead to overfitting. In this work we propose a much more compact K-way D-dimensional discrete encoding scheme to replace the"one-hot"encoding. In"KD encoding", each symbol is represented by a $D$-dimensional code, and each of its dimension has a cardinality of $K$. The final symbol embedding vector can be generated by composing the code embedding vectors. To learn the semantically meaningful code, we derive a relaxed discrete optimization technique based on stochastic gradient descent. By adopting the new coding system, the efficiency of parameterization can be significantly improved (from linear to logarithmic), and this can also mitigate the over-fitting problem. In our experiments with language modeling, the number of embedding parameters can be reduced by 97\% while achieving similar or better performance.


title: Edge detection using constrained discrete particle swarm optimisation in noisy images

abstract: Edge detection algorithms often produce broken edges, especially in noisy images. We propose an algorithm based on discrete particle swarm optimisation (PSO) to detect continuous edges in noisy images. A constrained PSO-based algorithm with a new objective function is proposed to address noise and reduce broken edges. The localisation accuracy of the new algorithm is compared with that of a modified version of the Canny algorithm as a Gaussian-based edge detector, the robust rank order (RRO)-based algorithm as a statistical based edge detector, and our previously developed PSO-based algorithm. Pratt's figure of merit is used as a measure of localisation accuracy for these edge detection algorithms. Experimental results show that the performance of the new algorithm is higher than the Canny and RRO algorithms in the images corrupted by two different types of noise (impulsive and Gaussian noise). The new algorithm also detects edges more accurately and smoothly than our previously developed algorithm in noisy images.


title: Path Smoothing Using Clothoids for Differential Drive Mobile Robots

abstract: Abstract This paper presents a new online path-smoothing algorithm for smoothing a path that consists of straight line segments and is primarily intended for differential drive mobile robots. Algorithm allows non-zero initial path curvature, which is important for replanning. Further, algorithm uses clothoid curves as primitives for path smoothing, which have inherent property that their curvature changes proportionally with distance traveled along the curve.


title: Machine Learning that Matters

abstract: Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field's energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.


title: An Overview of Hierarchical Task Network Planning

abstract: Hierarchies are the most common structure used to understand the world better. In galaxies, for instance, multiple-star systems are organised in a hierarchical system. Then, governmental and company organisations are structured using a hierarchy, while the Internet, which is used on a daily basis, has a space of domain names arranged hierarchically. Since Artificial Intelligence (AI) planning portrays information about the world and reasons to solve some of world’s problems, Hierarchical Task Network (HTN) planning has been introduced almost 40 years ago to represent and deal with hierarchies. Its requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, but also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and dierences among hierarchical planners, and its current and objective image. On top of these issues, attention attracts the ability of hierarchical planning to truly cope with the requirements of applications from the real world. We propose a framework-based approach to remedy this situation. First, we provide a basis for defining dierent formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps to interpret HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, performance and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work.


title: Sparse and Semi-supervised Visual Mapping with the S^3GP

abstract: This paper is about mapping images to continuous output spaces using powerful Bayesian learning techniques. A sparse, semi-supervised Gaussian process regression model (S3GP) is introduced which learns a mapping using only partially labelled training data. We show that sparsity bestows efficiency on the S3GP which requires minimal CPU utilization for real-time operation; the predictions of uncertainty made by the S3GP are more accurate than those of other models leading to considerable performance improvements when combined with a probabilistic filter; and the ability to learn from semi-supervised data simplifies the process of collecting training data. The S3GP uses a mixture of different image features: this is also shown to improve the accuracy and consistency of the mapping. A major application of this work is its use as a gaze tracking system in which images of a human eye are mapped to screen coordinates: in this capacity our approach is efficient, accurate and versatile.


title: Association between social support and depression in the general population: the HUNT study, a cross-sectional survey

abstract: Aim. The aim was to investigate the associations between perceived social support and depression in a general population in relation to gender and age. ::: ::: Background. Social support is seen as one of the social determinants for overall health in the general population. Studies have found higher probability of experiencing depression among people who have a lack of social support; evidence from the general population has been more limited. Subjective perception that support would be available if needed may reduce and prevent depression and unnecessary suffering. ::: ::: Design. A cross-sectional survey with self-reported health was used. ::: ::: Method. A total of 40,659 men and women aged 20–89 years living in Nord-Trondelag County of Norway with valid ratings of depression subscale of the Hospital Anxiety and Depression Scale in the The Nord-Trondelag Health Study 3 were used. Logistic regression was used to quantify associations between two types of perceived support (emotional and tangible) and depression. Gender, age and interaction effects were controlled for in the final model. ::: ::: Results. The main finding was that self-rated perceived support was significantly associated with Hospital Anxiety and Depression Scale-defined depression, even after controlling for age and gender; emotional support (OR = 3·14) and tangible support (OR = 2·93). The effects of emotional and tangible support differ between genders. Interaction effects were found for age groups and both emotional and tangible support. ::: ::: Conclusion. Self-rated perceived functional social support is associated with Hospital Anxiety and Depression Scale-defined depression. In the group of older people who have a lack of social support, women seem to need more emotional support and men tangible support. ::: ::: Relevance to clinical practice. Health care providers should consider the close association between social support and depression in their continuing care, particularly in the older people.


title: Generating interaction test cases for mobile phone systems from use case specifications

abstract: The mobile phone market has become even more competitive, demanding high quality standards. In this context, applications are built as sets of functionalities, called features. Such features are combined in use scenarios of the application. Due to the fact that the features are usually developed in isolation, the tests of their interactions in such scenarios are compromised. In this paper, we present a proposal of specifying feature interaction requirements with use cases; generating a behavioral model from such specification; and a strategy for generating test cases from the behavioral model that aims to extract feature interaction scenarios in such a way that interactions can be tested.


title: Image Reconstruction is a New Frontier of Machine Learning

abstract: Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of "Machine learning for image reconstruction." This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme "Deep learning in medical imaging" [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.


title: Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

abstract: Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.


title: Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

abstract: End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel cross- entropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.


title: Comparative genomic hybridization analysis for pancreatic cancer specimens obtained by endoscopic ultrasonography-guided fine-needle aspiration

abstract: Background ::: Comparative genomic hybridization (CGH) analysis of pancreatic cancer has been done exclusively for surgical and autopsy specimens, because of the difficulty of tissue sampling without surgery. To overcome this difficulty, we applied CGH technology to cells obtained by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA).


title: RC3E: Reconfigurable Accelerators in Data Centres and Their Provision by Adapted Service Models

abstract: Computing performance and scalability are essential ingredients in modern data centres offering cloud services. Field Programmable Gate Arrays (FPGAs) provide a promising opportunity to improve performance, security and energy efficiency because their hardware architecture can be adapted directly to the application. In this paper we present the development of our FPGA cloud architecture, beginning with realistic use cases and adapted service models for the use of reconfigurable hardware accelerators in a cloud context. Our architecture supports both applications where FPGAs are tightly coupled to host processors and applications using the FPGA for a secured cloud access in an overall homogeneous system. In contrast to other approaches, we model the system as a whole with a more flexible FPGA provision. The application service provider has the opportunity to offer a service with an individual FPGA design or customized secure interfaces to the cloud. For an abstraction from the real hardware and to achieve high device utilization, the FPGAs and the interfaces are fully virtualized. Because of the uncommon reconfigurable hardware integration and our adapted service models, we developed a special resource management system (RC3E) which serves as a hypervisor for the virtualized hardware. The demonstration of an intelligent load balancing as well as the system's performance and flexibility conclude our approach of FPGA services in a cloud.


title: Ensemble methods for spoken emotion recognition in call-centres

abstract: Machine-based emotional intelligence is a requirement for more natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have not been applied in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods.


title: A panoply of errors: polymerase proofreading domain mutations in cancer

abstract: Although it has long been recognized that the exonucleolytic proofreading activity intrinsic to the replicative DNA polymerases Pol δ and Pol e is essential for faithful replication of DNA, evidence that defective DNA polymerase proofreading contributes to human malignancy has been limited. However, recent studies have shown that germline mutations in the proofreading domains of Pol δ and Pol e predispose to cancer, and that somatic Pol e proofreading domain mutations occur in multiple sporadic tumours, where they underlie a phenotype of 'ultramutation' and favourable prognosis. In this Review, we summarize the current understanding of the mechanisms and consequences of polymerase proofreading domain mutations in human malignancies, and highlight the potential utility of these variants as novel cancer biomarkers and therapeutic targets.


title: Vehicle Classification from Low Frequency GPS Data

abstract: Inferring the type of vehicles on a road is a fundamental task within several applications. Some recent works have exploited Global Positioning System (GPS) devices and used classification of GPS traces to tackle the problem. Existing approaches based on GPS data make use of GPS trajectories sampled at high frequency (about 1 sample per second), but GPS trackers currently installed on public and commercial fleets acquire GPS positions at lower frequency (about 1 sample per minute). In this paper, we target the more challenging scenario of low frequency GPS data, which has not been tackled yet in the literature, and explore how this kind of data can be used to effectively categorise vehicles into light-duty and heavy-duty. We define several distance-, speed-, and acceleration-based features, inspired by the literature on related problems like travel mode detection, and add novel features based on road type. Features are aggregated over a GPS track with several aggregation functions. We identify the most effective combinations of features and aggregation functions with a data-driven approach, by applying Recursive Feature Elimination in a cross validation framework. Furthermore, we combine predictions of all tracks of a vehicle to boost classification performance. Experimental results on a large dataset show that the selected features are indeed effective and that the high and low frequency GPS scenarios greatly differ in terms of relevant features.


title: Lightweight memory tracing

abstract: Memory tracing (executing additional code for every memory access of a program) is a powerful technique with many applications, e.g., debugging, taint checking, or tracking dataflow. Current approaches are limited: software-only memory tracing incurs high performance overhead (e.g., for Libdft up to 10×) because every single memory access of the application is checked by additional code that is not part of the original application and hardware is limited to a small set of watched locations. ::: ::: This paper introduces memTrace, a lightweight memory tracing technique that builds on dynamic on-the-fly cross-ISA binary translation of 32-bit code to 64-bit code. Our software-only approach enables memory tracing for unmodified, binary-only ×86 applications using the ×64 extension that is available in current CPUs; no OS extensions or special hardware is required. The additional registers in ×64 and the wider memory addressing enable a low-overhead tracing infrastructure that is protected from the application code (i.e., uses disjunct registers and memory regions). MemTrace handles multi-threaded applications. Two case studies discuss a framework for unlimited read and write watchpoints and an allocation-based memory checker similar in functionality to memgrind. ::: ::: The performance evaluation of memTrace shows that the time overhead is between 1.3× and 3.1× for the SPEC CPU2006 benchmarks, with a geometric mean of 1.97×.


title: WIKI: :SCORE A collaborative environment for music transcription and publishing

abstract: Music sources are most commonly shared in music scores scanned or printed on paper sheets. These artifacts are rich in information, but since they are images it is hard to re-use and share their content in todays' digital world. There are modern languages that can be used to transcribe music sheets, this is still a time consuming task, because of the complexity involved in the process and the typical huge size of the original documents.WIKI::SCORE is a collaborative environment where several people work together to transcribe music sheets to a shared medium, using the notation. This eases the process of transcribing huge documents, and stores the document in a well known notation, that can be used later on to publish the whole content in several formats, such as a PDF document, images or audio files for example.


title: Machine Translation Systems for Indian Languages

abstract: Machine processing of Natural (Human) Languages has a long tradition, benefiting from decades of manual and semiautomatic analysis by linguists, sociologists, psychologists and computer scientists among others. This cumulative effort has seen fruit in recent years in the form of publicly available online resources ranging from dictionaries to complete machine translation systems. Machine translation is the name for computerized methods that automate all or part of the process of translating from one language to another. In a large multilingual society like India, there is great demand for translation of documents from one language to another language. Though work in the area of machine translation has been going on for several decades, efficient methods for machine translation continue to be a challenging task. A fully automatic high quality machine translation system is extremely difficult to build. This paper discusses the various approaches which have been applied in translation systems for Indian languages. Some of the important Indian language translation systems implemented with these techniques along with their capabilities and limitations are also discussed.


title: Development of Eustrongylides ignotus (Nematoda: Dioctophmida) in Domestic Ducks (Anas platyrhynchos domestica (L.))

abstract: Abstract Investigating the development of Eustrongylides ignotus in its definitive host would enable us to trace the complete life cycle of this nematode. Fourth-stage larvae isolated from naturally infected swamp eels (Monopterus albus) were used to infect domestic ducks (Anas platyrhynchos domestica [L.]). We observed that male and female worms exhibited different developmental patterns in host ducks. In males, the fourth molt occurred at day 1–2 post-infection (PI), after which they attained maturity on day 4 PI and died between day 7 and 9 PI. However, females underwent the fourth molt at day 2–4 PI, produced eggs from day 9 to 17 PI, and then degenerated and died. When compared to fourth-stage female larvae, adult females demonstrated a considerable increase in total body size with a 151% increase in average body width and a 17% increase in average body length. However, the increase in size of the male larvae was not as significant as that in females. The average body width in adult males exhibited o...


title: A PREDICTIVE MODEL FOR CUSTOMER PURCHASE BEHAVIOR IN E-COMMERCE CONTEXT

abstract: Predicting customer purchase behaviour is an interesting and challenging task. In e-commerce context, to tackle the challenge will confront a lot of new problems different from those in traditional business. This study investigates three factors that affect purchasing decision-making of customers in online shopping: the needs of customers, the popularity of products and the preference of the customers. Furthermore, exploiting purchase data and ratings of products in the e-commerce website, we propose methods to quantify the strength of these factors: (1) using associations between products to predict the needs of customers; (2) combining collaborative filtering and a hierarchical Bayesian discrete choice model to learn preference of customers; (3) building a support vector regression based model, called Heat model, to calculate the popularity of products; (4) developing a crowdsourcing approach based experimental platform to generate train set for learning Heat model. Combining these factors, a model, called COREL, is proposed to make purchase behaviour prediction for customers. Submitted a purchased product of a customer, the model can return top n the most possible purchased products of the customer in future. Experiments show that these factors play key roles in predictive model and COREL can greatly outperform the baseline methods.


title: The Virtual Mirror: A New Interaction Paradigm for Augmented Reality Environments

abstract: Medical augmented reality (AR) has been widely discussed within the medical imaging as well as computer aided surgery communities. Different systems for exemplary medical applications have been proposed. Some of them produced promising results. One major issue still hindering AR technology to be regularly used in medical applications is the interaction between physician and the superimposed 3-D virtual data. Classical interaction paradigms, for instance with keyboard and mouse, to interact with visualized medical 3-D imaging data are not adequate for an AR environment. This paper introduces the concept of a tangible/controllable virtual mirror for medical AR applications. This concept intuitively augments the direct view of the surgeon with all desired views on volumetric medical imaging data registered with the operation site without moving around the operating table or displacing the patient. We selected two medical procedures to demonstrate and evaluate the potentials of the Virtual Mirror for the surgical workflow. Results confirm the intuitiveness of this new paradigm and its perceptive advantages for AR-based computer aided interventions.


title: 2 Motivation in Software Engineering : A Systematic Literature Review

abstract: Objective: In this paper, we present a systematic literature review of motivation in Software Engineering. The objective of this review is to plot the landscape of current reported knowledge in terms of what motivates developers, what de-motivates them and how existing models address motivation. Methods: We perform a systematic literature review of peer reviewed published studies that focus on motivation in Software Engineering. Systematic reviews are well established in medical research and are used to systematically analyse the literature addressing specific research questions. Results: We found 92 papers related to motivation in Software Engineering. Fifty-six percent of the studies reported that Software Engineers are distinguishable from other occupational groups. Our findings suggest that Software Engineers are likely to be motivated according to three related factors: their 'characteristics' (for example, their need for variety); internal 'controls' (for example, their personality) and external 'moderators' (for example, their career stage). The literature indicates that de-motivated engineers may leave the organisation or take more sick-leave, while motivated engineers will increase their productivity and remain longer in the organisation. Aspects of the job that motivate Software Engineers include problem solving, working to benefit others and technical challenge. Our key finding is that the published models of motivation in Software Engineering are disparate and do not reflect the complex needs of Software Engineers in their career stages, cultural and environmental settings. Conclusions: The literature on motivation in Software Engineering presents a conflicting and partial picture of the area. It is clear that motivation is context dependent and varies from one engineer to another. The most commonly cited motivator is the job itself, yet we found very little work on what it is about that job that Software Engineers find motivating. Furthermore, surveys are often aimed at how Software Engineers feel about 'the organisation', rather than 'the profession'. Although models of motivation in Software Engineering are reported in the literature, they do not account for the changing roles and environment in which Software Engineers operate. Overall, our findings indicate that there is no clear understanding of the Software Engineers' job, what motivates Software Engineers, how they are motivated, or the outcome and benefits of motivating Software Engineers.


title: Efficient codes and balanced networks

abstract: Despite representing a minority of cortical cells, inhibitory neurons deeply shape cortical responses. Inhibitory currents closely track excitatory currents, opening only brief windows of opportunity for a neuron to fire. This explains the variability of cortical spike trains, but may also, paradoxically, render a spiking network maximally efficient and precise.


title: The price of a smile. Hedging and spanning in option markets

abstract: The volatility smile changed drastically around the crash of 1987, and new option pricing models have been proposed to accommodate that change. Deterministic volatility models allow for more flexible volatility surfaces but refrain from introducing additional risk factors. Thus, options are still redundant securities. Alternatively, stochastic models introduce additional risk factors, and options are then needed for spanning of the pricing kernel. We develop a statistical test based on this difference in spanning. Using daily S&P 500 index options data from 1986-95, our tests suggest that both in- and out-of-the-money options are needed for spanning. The findings are inconsistent with deterministic volatility models but are consistent with stochastic models that incorporate additional priced risk factors, such as stochastic volatility, interest rates, or jumps. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.


title: pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up

abstract: Morphological assessment of the degree of differentiation has been shown in numerous studies to provide useful prognostic information in breast cancer, but until recently histological grading has not been accepted as a routine procedure, mainly because of perceived problems with reproducibility and consistency. In the Nottingham/Tenovus Primary Breast Cancer Study the most commonly used method, described by Bloom & Richardson, has been modified in order to make the criteria more objective. The revised technique involves semiquantitative evaluation of three morphological features--the percentage of tubule formation, the degree of nuclear pleomorphism and an accurate mitotic count using a defined field area. A numerical scoring system is used and the overall grade is derived from a summation of individual scores for the three variables: three grades of differentiation are used. Since 1973, over 2200 patients with primary operable breast cancer have been entered into a study of multiple prognostic factors. Histological grade, assessed in 1831 patients, shows a very strong correlation with prognosis; patients with grade I tumours have a significantly better survival than those with grade II and III tumours (P less than 0.0001). These results demonstrate that this method for histological grading provides important prognostic information and, if the grading protocol is followed consistently, reproducible results can be obtained. Histological grade forms part of the multifactorial Nottingham prognostic index, together with tumour size and lymph node stage, which is used to stratify individual patients for appropriate therapy.


title: Improving government services with social media feedback

abstract: Social media is an invaluable source of feedback not just about consumer products and services but also about the effectiveness of government services. Our aim is to help analysts identify how government services can be improved based on citizen-contributed feedback found in publicly available social media. We present ongoing research for a social media monitoring interactive prototype with federated search and text analysis functionality. The prototype, developed to fit the workflow of social media monitors in the government sector, collects, analyses, and provides overviews of social media content. It facilitates relevance judgements on specific social media posts to decide whether or not to engage online. Our user log analysis validates the original design requirements and indicates ongoing utility to our federated search approach.


title: Investigation of Permanent-Magnet Motor Drives Incorporating Damper Bars for Electrified Vehicles

abstract: Understanding the need for steady-state and transient performance improvement in an interior permanent-magnet synchronous machine (IPMSM) drive, this paper exclusively investigates the IPMSM incorporating damper bars in the rotor of electric motor for electrified vehicles (EVs). First, motivation for the employment of damper bars in IPMSM is provided and justified with a case study. Thereafter, a mathematical model of an IPMSM drive with damper bars in the rotor has been developed based on $dq$ -axis theory and validated through experiments performed on a laboratory IPMSM containing damper bars. The validated mathematical model has been then employed to arrive at satisfactory rotor bar parameters for an existing IPMSM on board a commercially available EV. Moreover, a replica of the existing onboard EV motor with and without incorporating dampers have been designed, and finite-element analysis has been performed to investigate various performance characteristics. Comparative performance analyzes of both the machines with and without damper bars under steady-state and transient conditions have been performed wherever necessary, and the results elicited have been discussed.


title: Are graph databases ready for bioinformatics?

abstract: Graphs are ubiquitous in bioinformatics and frequently consist of too many nodes and edges to represent in random access memory. These graphs are thus stored in databases to allow for efficient queries using declarative query languages such as Structured Query Language (SQL). Traditional relational databases (e.g. MySQL and PostgreSQL) have long been used for this purpose and are based on decades of research into query optimization. Recently, NoSQL databases have caught a lot of attention because of their advantages in scalability. The term NoSQL is used to refer to schemaless databases such as key/value stores (e.g. Apache Cassandra), document stores (e.g. MongoDB) and graph databases (e.g. AllegroGraph, Neo4J, OpenLink Virtuoso), which do not fit within the traditional relational paradigm. Most NoSQL databases do not have a declarative query language. The widely used Neo4J graph database is an exception (Webber et al., 2013). Its query language Cypher is designed for expressing graph queries, but is still evolving. ::: ::: Graph databases have so far seen only limited use within bioinformatics (Schriml et al., 2012). To illustrate the pros and cons of using a graph database (exemplified by Neo4J v1.8.1) instead of a relational database (PostgreSQL v9.1), we imported into both the human interaction network from STRING v9.05 (Franceschini et al., 2013), which is an approximately scale-free network with 20 140 proteins and 2.2 million interactions. As all graph databases, Neo4J stores edges as direct pointers between nodes, which can thus be traversed in constant time. Because Neo4j uses the property graph model, nodes and edges can have properties associated with them; we use this for storing the protein names and the confidence scores associated with the interactions (Fig. 1). In PostgreSQL, we stored the graph as an indexed table of node pairs, which can be traversed with either logarithmic or constant look up complexity depending on the type of index used. On these databases we benchmarked the speed of Cypher and SQL queries for solving three bioinformatics graph processing problems: finding immediate neighbors and their interactions, finding the best scoring path between two proteins and finding the shortest path between them. We have selected these three tasks because they illustrate well the strengths and weaknesses of graph databases compared with traditional relational databases. ::: ::: ::: ::: Fig. 1. ::: ::: Relational versus graph database representation of a small protein interaction network. In the relational database, the network is stored as an interactions table (left). By contrast a graph database directly stores interactions as pointers between protein ... ::: ::: ::: ::: A common task in STRING is to retrieve a neighbor network. This involves finding the immediate neighbors of a protein and all interactions between them. To express this as a single SQL query requires the use of query nesting and a UNION set operation. Because Cypher currently supports neither of these features, two queries are needed to solve the task: one to find immediate neighbors and a second to find their interactions, which must be run for each of the immediate neighbors. Although this precludes some query optimizations, running all these Cypher queries is 36× faster than running the single SQL query (Table 1). However, it should be noted that a 49× fold speedup is attainable with PostgreSQL by similarly decomposing the complex query into multiple simple SQL queries. In theory, posing the task as one declarative query maximizes the opportunity for query optimization, but in practice this does not always give good performance. These results also show that even for graph data, using a graph database is not necessarily an advantage. ::: ::: ::: ::: Table 1. ::: ::: Query benchmark of a relational and a graph database ::: ::: ::: ::: Finding the best scoring path in a weighted graph is another frequently occurring task. For example, finding the best scoring path connecting two proteins in the STRING network is a crucial part of the NetworKIN algorithm (Linding et al., 2007). This task can be expressed single query both in (recursive) SQL and in Cypher. However, in practice neither query can be executed unless the maximal path length is severely constrained, in which case the Cypher query was faster by a factor of 981× (Table 1). The poor scalability is because of an exponential explosion in the number of longer paths, which in part is because of the scale-free nature of the network. The task can be efficiently solved using Dijkstra’s algorithm, but neither database is capable of casting queries as dynamic programming problems, although promising results have been achieved with automatic dynamic programming in declarative languages (Zhou et al., 2010). ::: ::: By contrast, the Cypher graph query language has a dedicated function for finding shortest paths, not taking into account edge weights. This leads to a massive speed improvement for this specific task: Neo4j is able to find the shortest path with no length constraint 2441× faster than PostgreSQL can find the shortest path when constraining the maximal path length to two edges. This shows what is possible when tightly integrating efficient algorithms with graph databases. ::: ::: In summary, graph databases themselves are ready for bioinformatics and can offer great speedups over relational databases on selected problems. The fact that a certain dataset is a graph, however, does not necessarily imply that a graph database is the best choice; it depends on the exact types of queries that need to be performed. Graph queries formulated in terms of paths can be concise and intuitive compared with equivalent SQL queries complicated by joins. Nevertheless, declarative graph query languages leave much to be desired, both feature-wise and performance-wise. Relational databases are a better choice when set operations are needed. Such operations are not as natural a fit to graph databases and have yet to make it into declarative graph database query languages. These languages are efficient for basic path traversal problems, but to realize the full benefits of using a graph database, it is presently necessary to tightly integrate the relevant algorithms with the graph database. ::: ::: Conflict of Interest: none declared.


title: A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes

abstract: We investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.


title: Ischaemic forearm testing in a patient with Ca2+-ATPase deficiency

abstract: Sarcoplasmic reticulum Ca 2+ -ATPase deficiency, Brody's disease (McKusick 108740), is a rare inborn error of metabolism. Since its first description by Brody (1969) only very few cases have been described (Karpati et al 1986; Danon et al 1988; Taylor et al 1988). There is no simple diagnostic test available. Proper diagnosis requires muscle biopsy and subsequent measurement of the relevant protein. The clinical signs and symptoms are not very specific. Exercise-related muscle pain (Karpati et al 1986; Taylor et al 1988) or contracture (Brody 1969) and impaired skeletal muscle relaxation (Brody 1969; Karpati et al 1986; Danon et al 1988) have been described


title: Human-assisted motion annotation

abstract: Obtaining ground-truth motion for arbitrary, real-world video sequences is a challenging but important task for both algorithm evaluation and model design. Existing ground-truth databases are either synthetic, such as the Yosemite sequence, or limited to indoor, experimental setups, such as the database developed by Baker et al (2007). We propose a human-in-loop methodology to create a ground-truth motion database for the videos taken with ordinary cameras in both indoor and outdoor scenes, using the fact that human beings are experts at segmenting objects and inspecting the match between two frames. We designed an interactive computer vision system to allow a user to efficiently annotate motion. Our methodology is cross-validated by showing that human annotated motion is repeatable, consistent across annotators, and close to the ground truth obtained by Baker et al (2007). Using our system, we collected and annotated 10 indoor and outdoor real-world videos to form a ground-truth motion database. The source code, annotation tool and database is online for public evaluation and benchmarking.


title: Anterior cruciate ligament injury and ankle dorsiflexion

abstract: PURPOSE ::: The aim was to study whether the degree of ankle dorsiflexion differs between subjects with an anterior cruciate ligament (ACL) injury and uninjured controls. Another aim was to study ankle dorsiflexion between the injured and the uninjured leg and in addition between women and men with an ACL injury. ::: ::: ::: METHOD ::: Sixty subjects (ACL injury, n = 30 and controls, n = 30) were enroled consecutively at two physical therapy settings. Ankle dorsiflexion was measured with a goniometer in a standardized way in a weight-bearing lunge position. ::: ::: ::: RESULTS ::: Repeated-measures ANOVA revealed a significant difference (p < 0.001) in ankle dorsiflexion between subjects with an ACL injury (mean 41.1° SD 5.7) and those without (mean 46.6° SD 5.3). No difference in ankle dorsiflexion was found between the injured leg and the uninjured or between women and men with ACL injury. ::: ::: ::: CONCLUSION ::: The present findings suggest lower degree of ankle dorsiflexion in subjects with an ACL injury than in uninjured controls. A functional test measuring ankle dorsiflexion with a goniometer may be one way of identifying individuals at increased risk of ACL injury. ::: ::: ::: LEVEL OF EVIDENCE ::: Comparative study, Level II.


title: Secure Message Communication Protocol Among Vehicles in Smart City

abstract: Secure messages exchange among different vehicles is one of the most challenging tasks in future smart cities. Any malicious activity has the potential to compromise the confidentiality, integrity, and authenticity of messages exchanged between different vehicles. To ensure secure message communication among the vehicles in a smart city environment, a novel scheme using elliptic curve cryptographic (ECC) technique has been presented in this paper. For this purpose, a two-level authentication key exchange scheme has been designed. In the first level authentication, $\text{CH}$ s are verified by series of messages exchanged between $\text{CH}$ s and the $\text{CA}$ . The verified $\text{CH}$ s are responsible for authentication of vehicles in the second level authentication, followed by exchange of messages between $\text{CH}$ and vehicle. The security analysis using widely accepted Burrows–Abadi–Needham logic, formal security analysis using random oracle model and verification using the widely known automated validation of Internet security protocols and applications (AVISPA) tool, and also the informal security analysis have been done with respect to various types of attacks. Moreover, a comparative analysis of the proposed scheme with existing related schemes reveals that it generates low overhead and latency, and high reliability during messages exchange between vehicles and the $\text{CA}$ .