title: Assistive tagging: A survey of multimedia tagging with human-computer joint exploration

abstract: Along with the explosive growth of multimedia data, automatic multimedia tagging has attracted great interest of various research communities, such as computer vision, multimedia, and information retrieval. However, despite the great progress achieved in the past two decades, automatic tagging technologies still can hardly achieve satisfactory performance on real-world multimedia data that vary widely in genre, quality, and content. Meanwhile, the power of human intelligence has been fully demonstrated in the Web 2.0 era. If well motivated, Internet users are able to tag a large amount of multimedia data. Therefore, a set of new techniques has been developed by combining humans and computers for more accurate and efficient multimedia tagging, such as batch tagging, active tagging, tag recommendation, and tag refinement. These techniques are able to accomplish multimedia tagging by jointly exploring humans and computers in different ways. This article refers to them collectively as assistive tagging and conducts a comprehensive survey of existing research efforts on this theme. We first introduce the status of automatic tagging and manual tagging and then state why assistive tagging can be a good solution. We categorize existing assistive tagging techniques into three paradigms: (1) tagging with data selection & organization; (2) tag recommendation; and (3) tag processing. We introduce the research efforts on each paradigm and summarize the methodologies. We also provide a discussion on several future trends in this research direction.


title: Multi-objective ant colony optimization for requirements selection

abstract: The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.


title: Grid R-CNN

abstract: This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.


title: Learning from PhotoShop Operation Videos: The PSOV Dataset

abstract: In this paper, we present the PhotoShop Operation Video (PSOV) dataset, a large-scale, densely annotated video database designed for the development of software intelligence. The PSOV dataset consists of 564 densely-annotated videos for Photoshop operations, covering more than 500 commonly used commands in the Photoshop software. Videos in this dataset are obtained from YouTube, manually watched and annotated precisely to seconds by experts. There are more than 74 h of videos with 29,204 labeled commands. To the best of our knowledge, the PSOV dataset is the first large-scale software operation video database with high-resolution frames and dense annotations. We believe that this dataset can help advance the development of intelligent software, and has extensive application aspects. In this paper, we describe the dataset construction procedure, data attributes, proposed tasks and their corresponding evaluation metrics. To demonstrate that the PSOV dataset has sufficient data and labeling for data-driven methods, we develop a deep learning based algorithm for the command classification task. We also carry out experiments and analysis with the proposed method to encourage better understanding and usage of the PSOV dataset.


title: TweetMotif: exploratory search and topic summarization for Twitter

abstract: We present TweetMotif, an exploratory search application for Twitter. Unlike traditional approaches to information retrieval, which present a simple list of messages, TweetMotif groups messages by frequent significant terms — a result set’s subtopics — which facilitate navigation and drilldown through a faceted search interface. The topic extraction system is based on syntactic filtering, language modeling, near-duplicate detection, and set cover heuristics. We have used TweetMotif to deflate rumors, uncover scams, summarize sentiment, and track political protests in real-time. A demo of TweetMotif, plus its source code, is available at http://tweetmotif.com.


title: Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approaches and Comprehensive Analysis

abstract: Many-objective optimization problems bring great difficulties to the existing multiobjective evolutionary algorithms, in terms of selection operators, computational cost, visualization of the high-dimensional tradeoff front, and so on. Objective reduction can alleviate such difficulties by removing the redundant objectives in the original objective set, which has become one of the most important techniques in many-objective optimization. In this paper, we suggest to view objective reduction as a multiobjective search problem and introduce three multiobjective formulations of the problem, where the first two formulations are both based on preservation of the dominance structure and the third one utilizes the correlation between objectives. For each multiobjective formulation, a multiobjective objective reduction algorithm is proposed by employing the nondominated sorting genetic algorithm II to generate a Pareto front of nondominated objective subsets that can offer decision support to the user. Moreover, we conduct a comprehensive analysis of two major categories of objective reduction approaches based on several theorems, with the aim of revealing their strengths and limitations. Lastly, the performance of the proposed multiobjective algorithms is studied extensively on various benchmark problems and two real-world problems. Numerical results and comparisons are then shown to highlight the effectiveness and superiority of the proposed multiobjective algorithms over existing state-of-the-art approaches in the related field.


title: The Relative Risk and Timing of Divorce in Families of Children With an Autism Spectrum Disorder

abstract: We compared the occurrence and timing of divorce in 391 parents of children with an autism spectrum disorder (ASD) and a matched representative sample of parents of children without disabilities using a survival analysis. Parents of children with an ASD had a higher rate of divorce than the comparison group (23.5% vs. 13.8%). The rate of divorce remained high throughout the son’s or daughter’s childhood, adolescence, and early adulthood for parents of children with an ASD, whereas it decreased following the son’s or daughter’s childhood (after about age 8 years) in the comparison group. Younger maternal age when the child with ASD was born and having the child born later in the birth order were positively predictive of divorce for parents of children with an ASD. Findings have implications for interventions focused on ameliorating ongoing and long-term marital strains for parents of children with an ASD.


Immediate self-image confrontation and changes in self-concept.


title: Clinical parameter of odontoma with special emphasis on treatment of impacted teeth—a retrospective multicentre study and literature review

abstract: OBJECTIVES ::: Odontoma-separated into complex (CxOD) and compound (CpOD) subtypes-represents the most frequent odontogenic malformation. Retention of permanent teeth is a common symptom. Therefore, in a series of odontomas, an analysis of the management of retained teeth was conducted. ::: ::: ::: MATERIALS AND METHODS ::: In a retrospective multicenter study of two University Medical Centers and one private praxis in Germany, demographic and clinical data regarding odontomas from 01/2000-03/2015 were obtained. In particular, the influence of operative therapy on the dentition and on the treatment of impacted teeth was analyzed. ::: ::: ::: RESULTS ::: Forty-five patients with 15 CxOD and 30 CpOD were included. Initial symptoms were delayed eruption of permanent teeth (n = 11), pain (n = 4), and swellings (n = 2); 28 cases were discovered by incidence, all of them via panoramic radiographs. The mandible/maxilla ratio was about 1:0.55 (29/16). Thirty-five out of 45 odontomas were in close proximity of at least one tooth (n = 16 at molars). A total of 14 teeth were extracted (CxOD: n = 5; CpOD: n = 9). Extractions had to be conducted more often in older patients (mean age 39.8 vs. 25.6 years). Of the non-extracted teeth, 8 teeth were displaced and retained. Of those, 4 teeth were aligned in the dental arch via orthodontic help and 2 teeth erupted spontaneously after operation during the follow-up period. In all cases, no relapse was seen. ::: ::: ::: CONCLUSION ::: Odontomas can cause displacement as well as malformation and resorption of the adjacent teeth. ::: ::: ::: CLINICAL RELEVANCE ::: Mostly, removal of odontomas is conducted. Extirpation of odontoma can allow for normal tooth eruption, often rendering extractions avoidable. Orthodontic alignment, though sometimes challenging, is a reasonable therapeutic option. These findings underline the value of the panoramic radiograph in preventive dentistry in younger patients.


title: Research Topics Map: rtopmap

abstract: In this paper we describe a system for visualizing and analyzing worldwide research topics, {\tt rtopmap}. We gather data from google scholar academic research profiles, putting together a weighted topics graph, consisting of over 35,000 nodes and 646,000 edges. The nodes correspond to self-reported research topics, and edges correspond to co-occurring topics in google scholar profiles. The {\tt rtopmap} system supports zooming/panning/searching and other google-maps-based interactive features. With the help of map overlays, we also visualize the strengths and weaknesses of different academic institutions in terms of human resources (e.g., number of researchers in different areas), as well as scholarly output (e.g., citation counts in different areas). Finally, we also visualize what parts of the map are associated with different academic departments, or with specific documents (such as research papers, or calls for proposals). The system itself is available at \url{this http URL}.


title: Analytical study of security aspects in 6LoWPAN networks

abstract: The Internet of Things (IoT) is a flagship project that aims to connect objects to the Internet to extend their use. For that, it was needed to find a solution to combine between the IEEE 802.15.4 protocol for Low Power Wireless Personal Area Networks (LoWPANs) and IPv6 network protocol that its large address space will allow million devices to integrate the internet. The development of 6LoWPAN technology was an appropriate solution to deal with this challenge and enable the IoT concept becoming a reality. But this was only the beginning of several challenges and problems like the case of how to secure this new type of networks, especially since it includes two major protocols so the combination of their problems too, over and above new problems specific to that network. In this paper, we analyze the security challenges in 6LoWPAN, we studied the various countermeasures to address these needs, their advantages and disadvantages, and we offer some recommendations to achieve a reliable security scheme for a powerful 6LoWPAN networks.


Haplogroup Prediction from Y-STR Values Using an Allele-Frequency Approach


title: Evaluation and Improvement of an Organizational Resource Applying Strategy Patterns

abstract: For any software company that frequently performs quality assurance activities devoted to measurement, evaluation (ME) and change/improvement (MEC) projects, ME and MEC strategies can be valuable organizational assets. In this paper, we analyze the improvement of a ME strategy, which can be considered an organizational resource to be applied to quality assurance activities. This resource is called the GOCAME (Goal-Oriented Context-Aware Measurement and Evaluation) strategy. AME/MEC strategy embraces the next three integrated capabilities: 1) the ME/MEC domain conceptual base and framework; 2) the process perspective specifications; and, 3) the method specifications. The improvement of GOCAME was performed instantiating two strategy patterns. A strategy pattern is a reusable solution to recurrent problems in ME/MEC projects. For an improvement goal, the selected MEC strategy pattern allows instantiating in a project a set of tailored activities and methods for measurement, evaluation, analysis and change. Particularly, we instantiate the GoME_1QV (Goal-oriented Measurement and Evaluation for One Quality View) strategy pattern to understand the GOCAME current quality state and compare it with the so-called GQM+ Strategies. First, this evaluation and analysis allows us to know the GOCAME strengths and weaknesses with regard to the quality of the three capabilities. Second, we instantiate the GoMEC_1QV (Goal-oriented Measurement, Evaluation and Change for One Quality View) strategy pattern to improve the GOCAME current state, producing as result a new version of the GOCAME strategy.


title: A Performance Evaluation of Single and Multi-feature People Detection

abstract: Over the years a number of powerful people detectors have been proposed. While it is standard to test complete detectors on publicly available datasets, it is often unclear how the different components (e.g. features and classifiers) of the respective detectors compare. Therefore, this paper contributes a systematic comparison of the most prominent and successful people detectors. Based on this evaluation we also propose a new detector that outperforms the state-of-art on the INRIA person dataset by combining multiple features.


title: A Novel Substrate Integrated Suspended Line Wideband Leaky-Wave Antenna

abstract: A novel compact wideband leaky-wave antenna based on substrate integrated suspended line is proposed. The proposed antenna is designed to be self-packaged format with five substrate layers and embedded air cavities. Asymmetric feeding is utilized to excite the first higher order mode. In order to achieve the suppression of backlobe and impedance matching, the tapered radiator with an etched slot is introduced. According to the measured results, the maximum antenna gain of 15.1 dBi and 50% fractional bandwidth from 7.5 to 12.5 GHz are obtained. Additionally, the self-packaged antenna has good radiation properties with low backlobe and sidelobe. The scanning angle of the measured main beam is about 44° ranging from 10° to 54°.


title: Selection of a data exchange format for industry 4.0 manufacturing systems

abstract: With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.


title: Unsupervised Learning of Depth and Ego-Motion from Video

abstract: We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous work, our method is completely unsupervised, requiring only monocular video sequences for training. Our method uses single-view depth and multiview pose networks, with a loss based on warping nearby views to the target using the computed depth and pose. The networks are thus coupled by the loss during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performs comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performs favorably compared to established SLAM systems under comparable input settings.


title: New thermoelectric components using microsystem technologies

abstract: This work describes the first thermoelectric devices based on the V-VI-compounds Bi/sub 2/Te/sub 3/ and (Bi,Sb)/sub 2/Te/sub 3/ which can be manufactured by means of regular thin film technology in combination with microsystem technology. Fabrication concept, material deposition for some 10-μm-thick layers and the properties of the deposited thermoelectric materials will be reported. First device properties for Peltier-coolers and thermogenerators will be shown as well as investigations on long term and cycling stability. Data on metal/semiconductor contact resistance were extracted form device data. Device characteristics like response time for a Peltier-cooler and power output for a thermogenerator will be compared to commercial devices.


Attention-Based LSTM with Multi-Task Learning for Distant Speech Recognition.


title: A Graphical Hierarchical CPS Architecture

abstract: Cyber-Physical System (CPS, shortly) is a fusion systems of computing and physical processes. It is a highly integration of computation, communication and control in the framework of networked embedded system. In this paper, we analyze the concept and feature of CPS and propose a graphical hierarchical architecture for CPS. It is a graphic network structure within the same layer and a hierarchical tree structure among the layers. Then, we give several classical cases such as internet of vehicles, space-air-ground integrated networks and wireless sensor nets to address this new CPS architecture available. Finally, we use the interactive model between the vehicle and the smart traffic light as an example of model building, and analyze the spatio-temporal consistence of the interactive units in term with the simulation tool based on the STeC language.


title: Epigenetic Modifications of Major Depressive Disorder

abstract: Major depressive disorder (MDD) is a chronic disease whose neurological basis and pathophysiology remain poorly understood. Initially, it was proposed that genetic variations were responsible for the development of this disease. Nevertheless, several studies within the last decade have provided evidence suggesting that environmental factors play an important role in MDD pathophysiology. Alterations in epigenetics mechanism, such as DNA methylation, histone modification and microRNA expression could favor MDD advance in response to stressful experiences and environmental factors. The aim of this review is to describe genetic alterations, and particularly altered epigenetic mechanisms, that could be determinants for MDD progress, and how these alterations may arise as useful screening, diagnosis and treatment monitoring biomarkers of depressive disorders.


title: A Probabilistic Representation for Dynamic Movement Primitives

abstract: Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforce- ment learning, movement recognition and segmentation and control. Because of this they have become a popular represen- tation for motor primitives. In this work, we showcase how DMPs can be reformulated as a probabilistic linear dynamical system with control inputs. Through this probabilistic repre- sentation of DMPs, algorithms such as Kalman filtering and smoothing are directly applicable to perform inference on pro- prioceptive sensor measurements during execution. We show that inference in this probabilistic model automatically leads to a feedback term to online modulate the execution of a DMP. Furthermore, we show how inference allows us to measure the likelihood that we are successfully executing a given motion primitive. In this context, we show initial results of using the probabilistic model to detect execution failures on a simulated movement primitive dataset.


Service Providers in Electronic Markets - Transformations in the Printing and Publishing Industry


title: Organizational culture and information systems implementation: A Structuration Theory perspective

abstract: The structure and culture of an organization does affect implementation of projects. In this paper we try to identify organizational factors that could affect the implementation efforts of an Integrated Financial Management Information System (IFMIS). The information system in question has taken overtly a long time and it's not complete yet. We set out to And out whether organizational issues are at play in this particular project. The project under study is a large-scale integrated information system which aims at strengthening and further developing Financial Management Information in the wider public service in Kenya. We borrow concepts from Structuration Theory (ST) as applied in sociology to understand the organizational perspective in the project. We use the theory to help explain some of the meanings, norms and issues of power experienced during the implementation of the IFMIS. Without ruling out problems of technological nature, the findings suggest that many of the problems in the IFMIS implementation may be attributed to organizational factors, and that certain issues are related to the existing organization culture within government.


African traditional medicine: a dictionary of plant use and applications. With supplement: search system for diseases.


title: Towards a Philosophy of the Web: Representation, Enaction, Collective intelligence

abstract: We present some initial forays into the questions that underlie the philosophy of the Web around the notions of representation, enactive search, the extended mind, and collective intelligence.


title: The Gift of Confidence: A Vygotskian View of Emotions

abstract: A significant goal for educational reform and an area of focus for this present volume is helping students to become life-long learners. An important component in meeting this objective is teachers building on their students' prior experiences, thereby helping them develop the confidence that engenders competence. In this chapter we focus on highly accomplished adult learners and English as a Second Language (ESL) students to explore affective factors that lead to sustained confidence. We look to Vygotsky not only for the theoretical framework through which to examine these affective factors, but also as a model for teachers who instill confidence in their students by offering caring support. His daughter, Ghita Vygotskaya (1999), quotes Elkonin, one of his students, to illustrate the way that his life powerfully exemplified this building of confidence: Lev Semonovich possessed an extraordinary ability to give support. I have probably never met a single person who was so little interested in proclaiming his own authorship as Lev Semonovich. It was the extraordinary generosity and scope of ideas of the kind of person who gave everything to everyone (p. 37)


title: Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition

abstract: This paper introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian fashion. The hybrid CNN-HMM combines the strong discriminative abilities of CNNs with the sequence modelling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the necessity of dealing with sequence data both for training and evaluation. With our presented end-to-end embedding we are able to improve over the state-of-the-art on three challenging benchmark continuous sign language recognition tasks by between 15% and 38% relative and up to 13.3% absolute.


PoReps: Proofs of Space on Useful Data.


title: Predicting moral sentiment towards physician-assisted suicide: The role of religion, conservatism, authoritarianism, and Big Five personality

abstract: Abstract The issue of physician-assisted suicide is a highly contentious social issue and thus there is importance in understanding the factors that predict attitudes in this domain. In the current study we sought to examine individual differences in moral sentiment towards physician-assisted suicide with a particular focus on religion/religiosity, political ideology, authoritarianism, and Big Five personality traits, all of which were identified in an extensive review of previous studies as potentially relevant predictors. Based on N = 1598 respondents from the Baylor Religion Survey (US) our results indicated an independent role for each of the predictors: being a Protestant or a Catholic (vs. no religion), higher levels of religiosity, higher levels of conservativism (vs. liberalism), and higher levels of authoritarianism uniquely predicted lower levels of support for physician-assisted suicide. Moreover, higher levels of extraversion independently predicted greater support for physician-assisted suicide. These results confirm a set of previously described predictors in an independent data set and extend prior research by showing that they independently predict moral sentiment towards physician-assisted suicide when modelled jointly. In summary, moral sentiment towards physician-assisted suicide reflects individual differences in a broad range of social and psychological factors.


title: Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

abstract: Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the `cavemen graph', and compress them. We show that the block-diagonal mental image of the `cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the Slash Burn method (burn the hubs, and slash the remaining graph into smaller connected components). Our view point has several advantages: (a) it avoids the `no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Experimental results show that our Slash Burn method consistently outperforms other methods on all datasets, giving good compression and faster running time.


Crafting Papers on Machine Learning


title: Beyond object recognition: visual sentiment analysis with deep coupled adjective and noun neural networks

abstract: Visual sentiment analysis aims to automatically recognize positive and negative emotions from images. There are three main challenges, including large intra-class variance, fine-grained image categories, and scalability. Most existing methods predominantly focus on one or two challenges, which has limited their performance. In this paper, we propose a novel visual sentiment analysis approach with deep coupled adjective and noun neural networks. Specifically, to reduce the large intra-class variance, we first learn a shared middle-level sentiment representation by jointly learning an adjective and a noun deep neural network with weak label supervision. Second, based on the learned sentiment representation, a prediction network is further optimized to deal with the subtle differences which often exist in the fine-grained image categories. The three networks are trained in an end-to-end manner, where the middle-level representations learned in previous two networks can guide the sentiment network to achieve high performance and fast convergence. Third, we generalize the training with mutual supervision between the learned adjective and noun networks by a Rectified Kullback-Leibler loss (ReKL), when the adjective and noun labels are not available. Extensive experiments on two widely-used datasets show that our method outperforms the state-of-the-art on SentiBank dataset with 10.2% accuracy gain and surpasses the previous best approach on Twitter dataset with clear margins.


title: A data-driven approach for pedestrian intention estimation

abstract: In the context of future urban automated driving many important problems remain unsolved. A critical one is the analysis and prediction of pedestrian movements around urban roads. Especially the analysis of non-critical situations has not received much attention in the past. This paper focuses on analyzing and predicting movements of pedestrians approaching crosswalks, a very crucial pedestrian-vehicle interaction in urban scenarios. In our previous work, we analyzed the performance of a data-driven Support Vector Machine-based architecture, and the relevance of specific features to infer pedestrian crossing intentions. In this paper, we will use our previous results as baseline to compare against an architecture based on neural networks for time-series classification. In particular we analyze the effectiveness of dense and Long-Short-Term-Memory networks. Furthermore, we will be looking into enhancing our feature vectors by adding LiDAR based images to the classification process. Additionally the evaluation provides an estimate for the temporal prediction horizon. The approaches presented are validated with real world trajectories recorded in Germany. Our results show an average accuracy improvement of 10–20% with respect to our previous Support Vector Machine-based approach.


title: Fast Incremental and Personalized PageRank

abstract: In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, we assume that the social network has $n$ nodes, and $m$ adversarially chosen edges arrive in a random order. We show that with a reset probability of $\epsilon$, the total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is $O(\frac{n\ln m}{\epsilon^{2}})$. This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs $\Omega(\frac{m^2}{\ln(1/(1-\epsilon))})$ total time and the Monte Carlo method needs $O(mn/\epsilon)$ total time; both are prohibitively expensive. Furthermore, we also show that we can handle deletions equally efficiently. We then study the computation of the top $k$ personalized PageRanks starting from a seed node, assuming that personalized PageRanks follow a power-law with exponent $\alpha<1$. We show that if we store $R>q\ln n$ random walks starting from every node for large enough constant $q$ (using the approach outlined for global PageRank), then the expected number of calls made to the distributed social network database is $O(k/(R^{(1-\alpha)/\alpha}))$. We also present experimental results from the social networking site, Twitter, verifying our assumptions and analyses. The overall result is that this algorithm is fast enough for real-time queries over a dynamic social network.


title: Fast bayesian matching pursuit

abstract: A low-complexity recursive procedure is presented for minimum mean squared error (MMSE) estimation in linear regression models. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both an approximate MMSE estimate of the parameter vector and a set of high posterior probability mixing parameters. Emphasis is given to the case of a sparse parameter vector. Numerical simulations demonstrate estimation performance and illustrate the distinctions between MMSE estimation and MAP model selection. The set of high probability mixing parameters not only provides MAP basis selection, but also yields relative probabilities that reveal potential ambiguity in the sparse model.


title: A WL-SPPIM Semantic Model for Document Classification

abstract: In this paper, we explore SPPIM-based text classification method, and the experiment reveals that the SPPIM method is equal to or even superior than SGNS method in text classification task on three international and standard text datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although SPPMI provides a better solution, it is not necessarily better than SGNS in text classification tasks. Based on our analysis, SGNS takes into the consideration of weight calculation during decomposition process, so it has better performance than SPPIM in some standard datasets. Inspired by this, we propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows that WL-SPPIM approach has better classification and higher scalability in the text classification task compared with LDA, SGNS and SPPIM approaches.


title: Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

abstract: Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.


title: ResilientFlow: Deployments of distributed control channel maintenance modules to recover SDN from unexpected failures

abstract: To protect SDN-enabled networks under large-scale, unexpected link failures, we propose ResilientFlow that deploys distributed modules called Control Channel Maintenance Module (CCMM) for every switch and controllers. The CCMMs makes switches able to maintain their own control channels, which are core and fundamental part of SDN. In this paper, we design, implement, and evaluate the ResilientFlow.


title: A Study on Sentiment Computing and Classification of Sina Weibo with Word2vec

abstract: In recent years, Weibo has greatly enriched people's life. More and more people are actively sharing information with others and expressing their opinions and feelings on Weibo. Analyzing emotion hidden in this information can benefit online marketing, branding, customer relationship management and monitoring public opinions. Sentiment analysis is to identify the emotional tendencies of the microblog messages, that is to classify users' emotions into positive, negative and neutral. This paper presents a novel model to build a Sentiment Dictionary using Word2vec tool based on our Semantic Orientation Pointwise Similarity Distance (SO-SD) model. Then we use the Emotional Dictionary to obtain the emotional tendencies of Weibo messages. Through the experiment, we validate the effectiveness of our method, by which we have performed a preliminary exploration of the sentiment analysis of Chinese Weibo in this paper.


PACS: A Guide to the Digital Revolution


Weakly Supervised Summarization of Web Videos ( Supplementary Material )


title: Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery

abstract: This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods.


abstract: We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a holistic manner while ignoring the entanglement of the latent factors. However, a real-world graph is formed by the complex interaction of many latent factors (e.g., the same hobby, education or work in social network). While little effort has been made toward exploring the disentangled representation in GCNs. In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations. In particular, we firstly present disentangled representation learning by neighborhood routing mechanism, and then employ the Hilbert-Schmidt Independence Criterion (HSIC) to enforce independence between the latent representations, which is effectively integrated into a graph convolutional framework as a regularizer at the output layer. Experimental studies on real-world graphs validate our model and demonstrate that our algorithms outperform the state-of-the-arts by a wide margin in different network applications, including semi-supervised graph classification, graph clustering and graph visualization.


title: A Usability Checklist for the Usability Evaluation of Mobile Phone User Interface

abstract: In the last decade, the research of the usability of mobile phones has been a newly evolving area with few established methodologies and realistic practices that ensure capturing usability in evaluation. Thus, there exists growing demand to explore appropriate evaluation methodologies that evaluate the usability of mobile phones quickly as well as comprehensively. This study aims to develop a task-based usability checklist based on heuristic evaluations in views of mobile phone user interface (UI) practitioners. A hierarchical structure of UI design elements and usability principles related to mobile phones were developed and then utilized to develop the checklist. To demonstrate the practical effectiveness of the proposed checklist, comparative experiments were conducted on the usability checklist and usability testing. The majority of usability problems found by usability testing and additional problems were discovered by the proposed checklist. It is expected that the usability checklist proposed in th...


title: Pruning Random Forests for Prediction on a Budget

abstract: We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constraint set to prove that the corresponding LP relaxation solves the original integer program. We then exploit connections to combinatorial optimization and develop an efficient primal-dual algorithm, scalable to large datasets. In contrast to our bottom-up approach, which benefits from good RF initialization, conventional methods are top-down acquiring features based on their utility value and is generally intractable, requiring heuristics. Empirically, our pruning algorithm outperforms existing state-of-the-art resource-constrained algorithms.


title: A graph-coloring secondary resource allocation for D2D communications in LTE networks

abstract: The Long Term Evolution (LTE) system, and especially its current release, LTE-Advanced, is considered as the next step in mobile broadband networks. Two of the main challenges faced by the LTE system are: i) to offer ubiquitous and highly reliable wireless services, and ii) to efficiently utilize the assigned spectrum portion. A promising approach towards this direction is to allow the establishment of direct device-to-device (D2D) communications in the assigned spectrum. In this paper, we study how the traffic load between users located in the same cell (intra-cell communications) can be served by D2D transmissions utilizing uplink spatial spectrum opportunities. Aiming at decongesting the base station (or eNB) and letting more resources for the inter-cell traffic (e.g., web services) extra functionality is added to system users (or UEs) for providing interference information to eNB. The collected information is represented by an enriched node contention graph (eNCG) utilized by graph-coloring algorithms to provide an interference-free secondary allocation. Results show that spatial spectrum opportunities can sufficiently serve the intra-cell traffic, while increased data rates can be offered to the inter-cell traffic.


title: Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology

abstract: This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users' attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole fits the data very well and indicates significant relationships between variables in the model. These results confirm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage an...


title: A Quantitative Evaluation Approach of Subjective Trust for E-Commerce

abstract: For E-commerce, such as online trade and interactions on the Internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known subjective trust holds uncertainty like randomness and fuzziness. However, existing approaches commonly based on probability or fuzzy set theory cannot attach enough importance to uncertainty. To remedy this problem, a new quantificational subjective trust evaluation approach is proposed based on the cloud model. The subjective trust may be modeled with cloud model, and expected value and hyper-entropy of subjective cloud is used to evaluate the reputation of trust objects. Our experimental data shows that the method can effectively support subjective trust decision, which provides a helpful exploitation for the subjective trust evaluation.


title: Applying Deep Learning in Augmented Reality Tracking

abstract: An existing deep learning architecture has been adapted to solve the detection problem in camera-based tracking for augmented reality (AR). A known target, in this case a planar object, is rendered under various viewing conditions including varying orientation, scale, illumination and sensor noise. The resulting corpus is used to train a convolutional neural network to match given patches in an incoming image. The results show comparable or better performance compared to state of art methods. Timing performance of the detector needs improvement but when considered in conjunction with the robust pose estimation process promising results are shown.


title: Exploring Vaccine Hesitancy Through an Artist–Scientist Collaboration

abstract: This project explores vaccine hesitancy through an artist–scientist collaboration. It aims to create better understanding of vaccine hesitant parents’ health beliefs and how these influence their vaccine-critical decisions. The project interviews vaccine-hesitant parents in the Netherlands and Finland and develops experimental visual-narrative means to analyse the interview data. Vaccine-hesitant parents’ health beliefs are, in this study, expressed through stories, and they are paralleled with so-called illness narratives. The study explores the following four main health beliefs originating from the parents’ interviews: (1) perceived benefits of illness, (2) belief in the body’s intelligence and self-healing capacity, (3) beliefs about the “inside–outside” flow of substances in the body, and (4) view of death as a natural part of life. These beliefs are interpreted through arts-based diagrammatic representations. These diagrams, merging multiple aspects of the parents’ narratives, are subsequently used in a collaborative meaning-making dialogue between the artist and the scientist. The resulting dialogue contrasts the health beliefs behind vaccine hesitancy with scientific knowledge, as well as the authors’ personal, and differing, attitudes toward these.


title: Sentence Alignment Methods for Improving Text Simplification Systems

abstract: We provide several methods for sentence alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform ::: the state-of-the-art ATS systems.


title: Towards an Appropriate Receiver Beamforming Scheme for Millimeter Wave Communication: A Power Consumption Based Comparison

abstract: At millimeter wave (mmW) frequencies, beamforming and large antenna arrays are an essential requirement to combat the high path loss for mmW communication. Moreover, at these frequencies, very large bandwidths are available to fulfill the data rate requirements of future wireless networks. However, utilization of these large bandwidths and of large antenna arrays can result in a high power consumption which is an even bigger concern for mmW receiver design. In a mmW receiver, the analog-to-digital converter (ADC) is generally considered as the most power consuming block. In this paper, primarily focusing on the ADC power, we analyze and compare the total power consumption of the complete analog chain for Analog, Digital and Hybrid beamforming (ABF, DBF and HBF) based receiver design. We show how power consumption of these beamforming schemes varies with a change in the number of antennas, the number of ADC bits (b) and the bandwidth (B). Moreover, we compare low power (as in [1]) and high power (as in [2]) ADC models, and show that for a certain range of number of antennas, b and B, DBF may actually have a comparable and lower power consumption than ABF and HBF, respectively. In addition, we also show how the choice of an appropriate beamforming scheme depends on the signal-to-noise ratio regime.


title: Thoughts on Massively Scalable Gaussian Processes

abstract: We introduce a framework and early results for massively scalable Gaussian processes (MSGP), significantly extending the KISS-GP approach of Wilson and Nickisch (2015). The MSGP framework enables the use of Gaussian processes (GPs) on billions of datapoints, without requiring distributed inference, or severe assumptions. In particular, MSGP reduces the standard $O(n^3)$ complexity of GP learning and inference to $O(n)$, and the standard $O(n^2)$ complexity per test point prediction to $O(1)$. MSGP involves 1) decomposing covariance matrices as Kronecker products of Toeplitz matrices approximated by circulant matrices. This multi-level circulant approximation allows one to unify the orthogonal computational benefits of fast Kronecker and Toeplitz approaches, and is significantly faster than either approach in isolation; 2) local kernel interpolation and inducing points to allow for arbitrarily located data inputs, and $O(1)$ test time predictions; 3) exploiting block-Toeplitz Toeplitz-block structure (BTTB), which enables fast inference and learning when multidimensional Kronecker structure is not present; and 4) projections of the input space to flexibly model correlated inputs and high dimensional data. The ability to handle many ($m \approx n$) inducing points allows for near-exact accuracy and large scale kernel learning.


title: A memory based approach to word sense disambiguation in Bengali using k-NN method

abstract: Word Sense Disambiguation (WSD) is an important and challenging task in the area of Natural Language Processing (NLP) where the task is to find the correct sense of an ambiguous word given its context. There have been very few attempts on WSD in Bengali or in Indian languages. The k-Nearest-Neighbor (k-NN) algorithm is a very well known and popular method for text classification. The k-NN algorithm determines the classification of a new sample from its k nearest neighbors. In this paper, we present how k-NN algorithm can be effectively applied to the task of WSD in Bengali. The k-NN algorithm achieved an accuracy of over 71% in a WSD task in Bengali reported in this paper.


AndroTracker : Creator Information based Android Malware Classification System


title: Web-scale web table to knowledge base matching

abstract: Millions of relational HTML tables are found on the World Wide Web. In contrast ::: to unstructured text, relational web tables provide a compact representation of entities ::: described by attributes. The data within these tables covers a broad topical ::: range. Web table data is used for question answering, augmentation of search results, ::: and knowledge base completion. Until a few years ago, only search engines ::: companies like Google and Microsoft owned large web crawls from which web ::: tables are extracted. Thus, researches outside the companies have not been able to ::: work with web tables. ::: In this thesis, the first publicly available web table corpus containing millions of ::: web tables is introduced. The corpus enables interested researchers to experiment ::: with web tables. A profile of the corpus is created to give insights to the characteristics ::: and topics. Further, the potential of web tables for augmenting cross-domain ::: knowledge bases is investigated. For the use case of knowledge base augmentation, ::: it is necessary to understand the web table content. For this reason, web ::: tables are matched to a knowledge base. The matching comprises three matching ::: tasks: instance, property, and class matching. Existing web table to knowledge ::: base matching systems either focus on a subset of these matching tasks or are evaluated ::: using gold standards which also only cover a subset of the challenges that ::: arise when matching web tables to knowledge bases. ::: This thesis systematically evaluates the utility of a wide range of different features ::: for the web table to knowledge base matching task using a single gold standard. ::: The results of the evaluation are used afterwards to design a holistic matching ::: method which covers all matching tasks and outperforms state-of-the-art web table ::: to knowledge base matching systems. In order to achieve these goals, we first ::: propose the T2K Match algorithm which addresses all three matching tasks in an ::: integrated fashion. In addition, we introduce the T2D gold standard which covers ::: a wide variety of challenges. By evaluating T2K Match against the T2D gold standard, ::: we identify that only considering the table content is insufficient. Hence, we ::: include features of three categories: features found in the table, in the table context ::: like the page title, and features that base on external resources like a synonym dictionary. ::: We analyze the utility of the features for each matching task. The analysis ::: shows that certain problems cannot be overcome by matching each table in isolation ::: to the knowledge base. In addition, relying on the features is not enough for the ::: property matching task. Based on these findings, we extend T2K Match into T2K ::: Match++ which exploits indirect matches to web tables about the same topic and ::: uses knowledge derived from the knowledge base. We show that T2K Match++ ::: outperforms all state-of-the-art web table to knowledge base matching approaches ::: on the T2D and Limaye gold standard. Most systems show good results on one ::: matching task but T2K Match++ is the only system that achieves F-measure scores ::: above 0:8 for all tasks. Compared to results of the best performing system TableMiner+, ::: the F-measure for the difficult property matching task is increased by 0.08, ::: for the class and instance matching task by 0.05 and 0.03, respectively.


title: Sustainable business strategies and PESTEL framework

abstract: This paper aims to provide a brief review of cloud computing, followed by an analysis of cloud computing environment using the PESTEL framework. The future implications and limitations of adopting cloud computing as an effective eco-friendly strategy to reduce carbon footprint are also discussed in the paper. This paper concludes with a recommendation to guide researchers to further examine this phenomenon.Organizations today face tough economic times, especially following the recent global financial crisis and the evidence of catastrophic climate change. International and local businesses find themselves compelled to review their strategies. They need to consider their organizational expenses and priorities and to strategically consider how best to save. Traditionally, Information Technology (IT) department is one area that would be affected negatively in the review. Continuing to fund these strategic technologies during an economic downturn is vital to organizations. It is predicted that in coming years IT resources will only be available online. More and more organizations are looking at operating smarter businesses by investigating technologies such as cloud computing, virtualization and green IT to find ways to cut costs and increase efficiencies.


title: Breast cancer diagnosis using GA feature selection and Rotation Forest

abstract: Breast cancer is one of the primary causes of death among the women worldwide, and the accurate diagnosis is one of the most significant steps in breast cancer treatment. Data mining techniques can support doctors in diagnosis decision-making process. In this paper, we present different data mining techniques for diagnosis of breast cancer. Two different Wisconsin Breast Cancer datasets have been used to evaluate the system proposed in this study. The proposed system has two stages. In the first stage, in order to eliminate insignificant features, genetic algorithms are used for extraction of informative and significant features. This process reduces the computational complexity and speed up the data mining process. In the second stage, several data mining techniques are employed to make a decision for two different categories of subjects with or without breast cancer. Different individual and multiple classifier systems were used in the second stage in order to construct accurate system for breast cancer classification. The performance of the methods is evaluated using classification accuracy, area under receiver operating characteristic curves and F-measure. Results obtained with the Rotation Forest model with GA-based 14 features show the highest classification accuracy (99.48 %), and when compared with the previous works, the proposed approach reveals the enhancement in performances. Results obtained in this study have potential to open new opportunities in diagnosis of breast cancer.


title: Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures

abstract: We define the double hierarchy linguistic term set (DHLTS).We propose the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS).We establish some operational laws and properties of the DHHFLTS.We develop a MULTIMOORA method on the basis of DHHFLTS.We apply this MULTIMOORA method to deal with a practical decision making problem. In recent years, hesitant fuzzy linguistic term sets (HFLTSs) have been studied by many scholars and are becoming gradually mature. However, some shortcomings of HFLTS also emerged. To describe the complex linguistic terms or linguistic term sets more accurately and reasonably, in this paper, we introduce the novel concepts named double hierarchy linguistic term set (DHLTS) and double hierarchy hesitant fuzzy linguistic term set (DHHFLTS). The operational laws and properties of the DHHFLTSs are developed as well.Afterwards, we investigate the multiple criteria decision making model with double hierarchy hesitant fuzzy linguistic information. We develop a double hierarchy hesitant fuzzy linguistic MULTIMOORA (DHHFL-MULTIMOORA) method to solve it. Furthermore, we apply the DHHFL-MULTIMOORA method to deal with a practical case about selecting the optimal city in China by evaluating the implementation status of haze controlling measures. Some comparisons between the DHHFL-MULTIMOORA method and the hesitant fuzzy linguistic TOPSIS method are provided to show the advantages of the proposed method.


title: Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles

abstract: We consider the problem of recommending comment-worthy articles such as news and blog-posts. An article is defined to be comment-worthy for a particular user if that user is interested to leave a comment on it. We note that recommending comment-worthy articles calls for elicitation of commenting-interests of the user from the content of both the articles and the past comments made by users. We thus propose to develop content-driven user profiles to elicit these latent interests of users in commenting and use them to recommend articles for future commenting. The difficulty of modeling comment content and the varied nature of users' commenting interests make the problem technically challenging. The problem of recommending comment-worthy articles is resolved by leveraging article and comment content through topic modeling and the co-commenting pattern of users through collaborative filtering, combined within a novel hierarchical Bayesian modeling approach. Our solution, Collaborative Correspondence Topic Models (CCTM), generates user profiles which are leveraged to provide a personalized ranking of comment-worthy articles for each user. Through these content-driven user profiles, CCTM effectively handle the ubiquitous problem of cold-start without relying on additional meta-data. The inference problem for the model is intractable with no off-the-shelf solution and we develop an efficient Monte Carlo EM algorithm. CCTM is evaluated on three real world data-sets, crawled from two blogs, ArsTechnica (AT) Gadgets (102,087 comments) and AT-Science (71,640 comments), and a news site, DailyMail (33,500 comments). We show average improvement of 14% (warm-start) and 18% (cold-start) in AUC, and 80% (warm-start) and 250% (cold-start) in Hit-Rank@5, over state of the art.


title: Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

abstract: Deep learning models have achieved high performance on many tasks, and thus have been applied to many security-critical scenarios. For example, deep learning-based face recognition systems have been used to authenticate users to access many security-sensitive applications like payment apps. Such usages of deep learning systems provide the adversaries with sufficient incentives to perform attacks against these systems for their adversarial purposes. In this work, we consider a new type of attacks, called backdoor attacks, where the attacker's goal is to create a backdoor into a learning-based authentication system, so that he can easily circumvent the system by leveraging the backdoor. Specifically, the adversary aims at creating backdoor instances, so that the victim learning system will be misled to classify the backdoor instances as a target label specified by the adversary. In particular, we study backdoor poisoning attacks, which achieve backdoor attacks using poisoning strategies. Different from all existing work, our studied poisoning strategies can apply under a very weak threat model: (1) the adversary has no knowledge of the model and the training set used by the victim system; (2) the attacker is allowed to inject only a small amount of poisoning samples; (3) the backdoor key is hard to notice even by human beings to achieve stealthiness. We conduct evaluation to demonstrate that a backdoor adversary can inject only around 50 poisoning samples, while achieving an attack success rate of above 90%. We are also the first work to show that a data poisoning attack can create physically implementable backdoors without touching the training process. Our work demonstrates that backdoor poisoning attacks pose real threats to a learning system, and thus highlights the importance of further investigation and proposing defense strategies against them.


9 Chapter Nine Mobile Augmented Reality


title: Model-driven artificial intelligence for online network optimization

abstract: Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question "what is the right ONO technique?" remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.


title: When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach

abstract: Query authentication has been extensively studied to ensure the integrity of query results for outsourced databases, which are often not fully trusted. However, access control, another important security concern, is largely ignored by existing works. Notably, recent breakthroughs in cryptography have enabled fine-grained access control over outsourced data. In this paper, we take the first step toward studying the problem of authenticating relational queries with fine-grained access control. The key challenge is how to protect information confidentiality during query authentication, which is essential to many critical applications. To address this challenge, we propose a novel access-policy-preserving (APP) signature as the primitive authenticated data structure. A useful property of the APP signature is that it can be used to derive customized signatures for unauthorized users to prove the inaccessibility while achieving the zero-knowledge confidentiality. We also propose a grid-index-based tree structure that can aggregate APP signatures for efficient range and join query authentication. In addition to this, a number of optimization techniques are proposed to further improve the authentication performance. Security analysis and performance evaluation show that the proposed solutions and techniques are robust and efficient under various system settings.


title: Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

abstract: With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).


title: ArnetMiner: An Expertise Oriented Search System for Web Community

abstract: Expertise Oriented Search aims at providing comprehensive analysis and mining for people from distributed sources. In this paper, we give an overview of the expertise oriented search system (ArnetMiner). The system addresses several key research issues in extraction and mining of a researcher social network. The system is in operation on the internet for more than one year and receives accesses from about 1,500 users per month. Feedbacks from users and system logs indicate that users consider the system can really help people to find and share information in the web community.


title: Enhanced Immersion in Augmented Reality Applications

abstract: This thesis will examine and evaluate different mechanics that could be used in games using augmented reality. Augmented reality, the technology used to integrate computer-generated images with the ...


title: DNA methylation and cancer.

abstract: DNA methylation is one of the most intensely studied epigenetic modifications in mammals. In normal cells, it assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture. The covalent addition of a methyl group occurs generally in cytosine within CpG dinucleotides which are concentrated in large clusters called CpG islands. DNA methyltransferases are responsible for establishing and maintenance of methylation pattern. It is commonly known that inactivation of certain tumor-suppressor genes occurs as a consequence of hypermethylation within the promoter regions and a numerous studies have demonstrated a broad range of genes silenced by DNA methylation in different cancer types. On the other hand, global hypomethylation, inducing genomic instability, also contributes to cell transformation. Apart from DNA methylation alterations in promoter regions and repetitive DNA sequences, this phenomenon is associated also with regulation of expression of noncoding RNAs such as microRNAs that may play role in tumor suppression. DNA methylation seems to be promising in putative translational use in patients and hypermethylated promoters may serve as biomarkers. Moreover, unlike genetic alterations, DNA methylation is reversible what makes it extremely interesting for therapy approaches. The importance of DNA methylation alterations in tumorigenesis encourages us to decode the human epigenome. Different DNA methylome mapping techniques are indispensable to realize this project in the future.


title: Composite electrospun nanofibers of reduced graphene oxide grafted with poly(3-dodecylthiophene) and poly(3-thiophene ethanol) and blended with polycaprolactone

abstract: AbstractIn this paper, an effective method was employed for preparation of nanofibers using conducting polymer-functionalized reduced graphene oxide (rGO). First, graphene oxide (GO) was obtained from graphite by Hommer method. GO was reduced to rGO by NaBH4 and covalently functionalized with a 3-thiophene acetic acid (TAA) by an esterification reaction to reach 3-thiophene acetic acid-functionalized reduced graphene oxide macromonomer (rGO-f-TAAM). Afterward, rGO-f-TAAM was copolymerized with 3-dodecylthiophene (3DDT) and 3-thiophene ethanol (3TEt) to yield rGO-f-TAA-co-PDDT (rGO-g-PDDT) and rGO-f-TAA-co-P3TEt (rGO-g-PTEt), which were confirmed by Fourier transform infrared spectra. The grafted materials depicted better electrochemical properties and superior solubilities in organic solvents compared to GO and rGO. The soluble rGO-g-PDDT and rGO-g-PTEt composites blended with polycaprolactone were fabricated by electrospinning, and then cytotoxicity, hydrophilicity, biodegradability and mechanical proper...


A Primer on Data-Driven Gamification Design


title: What do we perceive in a glance of a real-world scene

abstract: What do we see when we glance at a natural scene and how does it change as the glance becomes longer? We asked naive subjects to report in a free-form format what they saw when looking at briefly presented real-life photographs. Our subjects received no specific information as to the content of each stimulus. Thus, our paradigm differs from previous studies where subjects were cued before a picture was presented and/or were probed with multiple-choice questions. In the first stage, 90 novel grayscale photographs were foveally shown to a group of 22 native-English-speaking subjects. The presentation time was chosen at random from a set of seven possible times (from 27 to 500 ms). A perceptual mask followed each photograph immediately. After each presentation, subjects reported what they had just seen as completely and truthfully as possible. In the second stage, another group of naive individuals was instructed to score each of the descriptions produced by the subjects in the first stage. Individual scores were assigned to more than a hundred different attributes. We show that within a single glance, much object- and scene-level information is perceived by human subjects. The richness of our perception, though, seems asymmetrical. Subjects tend to have a propensity toward perceiving natural scenes as being outdoor rather than indoor. The reporting of sensory- or feature-level information of a scene (such as shading and shape) consistently precedes the reporting of the semantic-level information. But once subjects recognize more semantic-level components of a scene, there is little evidence suggesting any bias toward either scene-level or object-level recognition.


title: Evaluation of different turbine concepts for wind power

abstract: Every year the number of installed wind power plants in the world increases. The horizontal axis wind turbine is the most common type of turbine but there exist other types. Here, three different wind turbines are considered; the horizontal axis wind turbine and two different concepts of vertical axis wind turbines; the Darrieus turbine and the H-rotor. This paper aims at making a comparative study of these three different wind turbines from the most important aspects including structural dynamics, control systems, maintenance, manufacturing and electrical equipment. A case study is presented where three different turbines are compared to each other. Furthermore, a study of blade areas for different turbines is presented. The vertical axis wind turbine appears to be advantageous to the horizontal axis wind turbine in several aspects.


title: Graphcut Texture Synthesis for Single-Image Superresolution

abstract: Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture synthesis methods for texture superresolution.


title: Design of an Underactuated Compliant Gripper for Surgery Using Nitinol

abstract: This paper presents the development of an underactuated compliant gripper using a biocompatible superelastic alloy, namely, nitinol. This gripper has two fingers with five phalanges each and can be used as the end-effector of an endoscopic instrument. Optimization procedures are required to obtain the geometry of the transmission mechanism because of its underactuated nature and its underlying complexity. A driving mechanism further incorporated in the gripper to distribute actuation to both fingers and accomplish the grasping of asymmetrical objects without requiring supplementary inputs is also discussed. Finally, the results of numerical simulations with different materials and different grasped objects are presented and discussed.


Isolation and Characterization of Plant Growth Promoting Rhizobacteria Enterobacter hormaechei and their Suppression Efficacy against Colletotrichum falcatum in Combination with Chitosan


title: Evaluation of Native Femoral Neck Version and Final Stem Version Variability in Patients With Osteoarthritis Undergoing Robotically Implanted Total Hip Arthroplasty

abstract: Abstract Background Combined anteversion in total hip arthroplasty influences both dislocation risk and range of motion. One of its components, stem version (SV), could be dictated by many factors, from native femoral anatomy to stem geometry and surgeon’s choice. In the present multicenter study, robotic technology was used to assess the influence of native femoral version on final SV and combined anteversion using a straight, uncemented stem. Methods Three hundred sixty-two patients undergoing total hip arthroplasty were enrolled from 3 different orthopedic centers from 2012 and 2016. All patients underwent computed tomography planning with measurement of femoral neck version (FNV) and intraoperative measurement of stem version (SV), acetabular component version (AV), and combined version (CV) with robotic instrumentation. Results Mean FNV was 5.0° ± 9.6°, and SV was 6.4° ± 9.7°. The average difference between FNV and SV was 1.6° ± 9.8°. A moderate correlation was found between FNV and SV (R = 0.48, P Conclusion With the use of an uncemented, single-wedge, straight stem, SV is highly variable. Despite being moderately correlated with native FNV, SV can be partially influenced by the surgeon. A low SV could be hardly corrected, bringing high risk of low CV.


title: Query Term Suggestion in Academic Search

abstract: In this paper, we evaluate query term suggestion in the context of academic professional search. Our overall goal is to support scientists in their information seeking tasks. We set up an interactive search system in which terms are extracted from clicked documents and suggested to the user before every query specification step. We evaluated our method with the iSearch collection of academic information seeking behaviour and crowdsourced term relevance judgements. We found that query term suggestion can significantly improve recall in academic search.


title: A Benefit Congruency Framework of Sales Promotion Effectiveness

abstract: Are monetary savings the only explanation for consumer response to a sales promotion? If not, how do the different consumer benefits of a sales promotion influence its effectiveness? To address the first question, this research builds a framework of the multiple consumer benefits of a sales promotion. Through a series of measurement studies, the authors find that monetary and nonmonetary promotions provide consumers with different levels of three hedonic benefits (opportunities for value expression, entertainment, and exploration) and three utilitarian benefits (savings, higher product quality, and improved shopping convenience). To address the second question, the authors develop a benefit congruency framework, which argues that a sales promotion's effectiveness is determined by the utilitarian or hedonic nature of the benefits it delivers and the congruence these benefits have with the promoted product. Among other results, two choice experiments show that, as predicted for high-equity brands, monetary promotions are more effective for utilitarian products than for hedonic products. The authors then discuss the implications of the multibenefit and the benefit congruency frameworks for understanding consumer responses to sales promotions, reexamining the value of everyday-low-price policies, and designing more effective sales promotions.


title: Mining Individual Life Pattern Based on Location History

abstract: The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework.


title: An ergonomic evaluation of manual Cleco plier designs: effects of rubber grip, spring recoil, and worksurface angle.

abstract: The present study evaluated two design modifications (rubber grip and torsion spring) to the conventional manual Cleco pliers by electromyography (EMG), hand discomfort, and design satisfaction. This study also surveyed workers' satisfaction with selected design features of the pliers for ergonomic improvement. A two-way (plier design x worksurface angle) within-subject (nested within gender and hand size) design was employed. Eleven workers simulated the plier task in an adjustable workstation for different plier designs and worksurface angles (0 degrees , 60 degrees , and 90 degrees ). Lower EMG values were obtained for the pliers with rubber grip and at 60 degrees of worksurface angle. EMG values varied significantly between the participants, but showed low correlations (Spearman's rank correlation = -0.27 approximately -0.58) with their work experience with the pliers. The hand discomfort and design satisfaction evaluations identified that the grip span (max = 14.0 cm) and grip force requirement (peak = 220.5 N) of the current pliers need ergonomic modification. The present study shows the needs of both the ergonomic design of a hand tool and the training of a proper work method to control work-related musculoskeletal disorders at the workplace.


title: Spatial and behavioral correlates of hippocampal neuronal activity

abstract: The firing rate of hippocampal neurons in rats was related both to spatial location and to multiple behavioral variables as rats performed 2 kinds of tasks that rely on hippocampal function: a spatial navigation task similar in performance demands to the radial-arm maze task and a simultaneous cue odor-discrimination task. In the place task, most cells had distinct single or multiple place fields, that is, neurons increased firing when the rat was in a particular location or locations. However, in most of these cells, firing rate also varied systematically in relation to behavioral variables, including the speed, direction, and turning angle of the rat as it moved through the place field. In addition, the activity of most cells was time-locked to task-relevant approach movements. In the odor task, most cells fired as the rat sampled discriminative cues or when it executed specific, task-relevant approach movements. Some cells fired selectively in relation to which odors were presented, the configuration of odor cues, the locus of the response, or a combination of these variables. Many cells with spatial correlates in the place task also had striking behavioral correlates when rats performed the odor task in the same environment, and the locus of the increased firing associated with behavior in the odor task was not the same as the place field in the place task. Thus, while the complex stimuli that compose spatial cues are reflected in hippocampal neuronal activity, hippocampal processing is not limited to the representation of spatial location. Rather, the domain of hippocampal representation includes both spatial and nonspatial relations among multiple cues and the actions directed in relation to these cues, across cue modalities, and across behavioral paradigms.


title: Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy

abstract: A comprehensive supervisor control for a hybrid system that comprises wind and photovoltaic generation subsystems, a battery bank, and an ac load is developed in this paper. The objectives of the supervisor control are, primarily, to satisfy the load power demand and, second, to maintain the state of charge of the battery bank to prevent blackout and to extend the life of the batteries. For these purposes, the supervisor controller determines online the operation mode of both generation subsystems, switching from power regulation to maximum power conversion. Decision criteria for the supervisor based on measurable system variables are presented. Finally, the performance of the supervisor controller is extensively assessed through computer simulation using a comprehensive nonlinear model of the plant.


title: Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions

abstract: A good and robust sensor data fusion in diverse weather conditions is a quite challenging task. There are several fusion architectures in the literature, e.g. the sensor data can be fused right at the beginning (Early Fusion), or they can be first processed separately and then concatenated later (Late Fusion). In this work, different fusion architectures are compared and evaluated by means of object detection tasks, in which the goal is to recognize and localize predefined objects in a stream of data. Usually, state-of-the-art object detectors based on neural networks are highly optimized for good weather conditions, since the well-known benchmarks only consist of sensor data recorded in optimal weather conditions. Therefore, the performance of these approaches decreases enormously or even fails in adverse weather conditions. In this work, different sensor fusion architectures are compared for good and adverse weather conditions for finding the optimal fusion architecture for diverse weather situations. A new training strategy is also introduced such that the performance of the object detector is greatly enhanced in adverse weather scenarios or if a sensor fails. Furthermore, the paper responds to the question if the detection accuracy can be increased further by providing the neural network with a-priori knowledge such as the spatial calibration of the sensors.


title: Design of the planar transformer in llc resonant converters for micro-grid applications

abstract: LLC resonant converters may be applied as an intermediate converter in low power level micro-grid systems. The field of planar magnetics is continuously pushing its utilization in resonant converters as higher power densities and lower profiles are achieved. The design process for the planar transformer is significant due to the gap being placed on the magnetic core for the proper magnetizing inductance requirement. The optimum design methodology for the conventional transformer has been extended to the planar transformer. Design considerations and equations are presented. The planar transformer for a 240 W, 105 kHz LLC resonant converter was fabricated. The experiments were carried out and the results verify the design methodology and the implementation method.


title: Wide phenotypic variability in families with holoprosencephaly and a sonic hedgehog mutation

abstract: Mutations in the human sonic hedgehog gene ( SHH) are the most frequent cause of autosomal dominant inherited holoprosencephaly (HPE), a complex brain malformation resulting from incomplete cleavage of the developing forebrain into two separate hemispheres and ventricles. Here we report the clinical and molecular findings in five unrelated patients with HPE and their relatives with an identified SHH mutation. Three new and one previously reported SHH mutations were identified, a fifth proband was found to carry a reciprocal subtelomeric rearrangement involving the SHH locus in 7q36. An extremely wide intrafamilial phenotypic variability was observed, ranging from the classical phenotype with alobar HPE accompanied by typical severe craniofacial abnormalities to very mild clinical signs of choanal stenosis or solitary median maxillary central incisor (SMMCI) only. Two families were initially ascertained because of microcephaly in combination with developmental delay and/or mental retardation and SMMCI, the latter being a frequent finding in patients with an identified SHH mutation. In other affected family members a delay in speech acquisition and learning disabilities were the leading clinical signs. Conclusion: mutational analysis of the sonic hedgehog gene should not only be considered in patients presenting with the classical holoprosencephaly phenotype but also in those with two or more clinical signs of the wide phenotypic spectrum of associated abnormalities, especially in combination with a positive family history.


title: Deep learning for virtual metrology: Modeling with optical emission spectroscopy data

abstract: Virtual Metrology is one of the most prominent Advanced Process Control applications in Semiconductor Manufacturing. The goal of Virtual Metrology is to provide estimations of quantities that are important for production and to assess process quality, but are costly or impossible to be measured. Virtual Metrology solutions are based on Machine Learning approaches. The bottleneck of developing Virtual Metrology solutions is generally the feature extraction phase that can be time-consuming, and can deeply affect the estimation performance. In particular, in presence of data with additional dimensions, such as time, feature extraction is typically performed by means of heuristic approaches that may pick features with poor predictive capabilities. In this work, we propose the usage of modern Deep Learning approaches to bypass manual feature extraction and to provide high-performance automatic Virtual Metrology modules. The proposed methodology is tested on a real industrial dataset related to Etching. The dataset at hand contains Optical Emission Spectroscopy data and it is paradigmatic of the feature extraction problem under examination.


title: Rapid free-space mapping from a single omnidirectional camera

abstract: Low-cost robots such as floor cleaners generally rely on limited perception and simple algorithms, but some new models now have enough sensing capability and computation power to enable Simultaneous Localisation And Mapping (SLAM) and intelligent guided navigation. In particular, computer vision is now a serious option in low cost robotics, though its use to date has been limited to feature-based mapping for localisation. Dense environment perception such as free space finding has required additional specialised sensors, adding expense and complexity.


title: Robust statistics on Riemannian manifolds via the geometric median

abstract: The geometric median is a classic robust estimator of centrality for data in Euclidean spaces. In this paper we formulate the geometric median of data on a Riemannian manifold as the minimizer of the sum of geodesic distances to the data points. We prove existence and uniqueness of the geometric median on manifolds with non-positive sectional curvature and give sufficient conditions for uniqueness on positively curved manifolds. Generalizing the Weiszfeld procedure for finding the geometric median of Euclidean data, we present an algorithm for computing the geometric median on an arbitrary manifold. We show that this algorithm converges to the unique solution when it exists. This method produces a robust central point for data lying on a manifold, and should have use in a variety of vision applications involving manifolds. We give examples of the geometric median computation and demonstrate its robustness for three types of manifold data: the 3D rotation group, tensor manifolds, and shape spaces.


title: Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation

abstract: Understanding where traffic accidents occur is crucial for improving road safety and proper traffic enforcement allocation. One of the most common methods of analyzing traffic accidents is spatial hotspot detection. Existing hotspot detection methods, e.g., spatial scan statistics, spatial and spatiotemporal kernel density estimation, mostly focus on Euclidean space. These methods ignore an important aspect of traffic accident hotspots, i.e., traffic accident locations are constrained to road networks. Several techniques have been proposed to detect spatial hotspot on the network space, including network kernel density-estimation, and significant linear route detection, but the time dimension and temporal dynamics of hotspots are not incorporated. To address the limitations of existing methods, we demonstrated a new method called Spatial-Temporal Network Kernel Density Estimation (STNKDE) that integrates both of these features. We also developed a prototype system and visualized the dynamics of traffic accident hotspots in New York City 2017.


title: 77 GHz radar-based altimeter for unmanned aerial vehicles

abstract: For multi-copter unmanned aerial vehicles (UAVs) sensing of the actual altitude is an important task. Many functions providing increased flight safety and easy maneuverability rely on altitude data. Commonly used sensors provide the altitude only relative to the starting position, or are limited in range and/or resolution. With the 77 GHz FMCW radar-based altimeter presented in this paper not only the actual altitude over ground but also obstacles such as trees and bushes can be detected. The capability of this solution is verified by measurements over different terrain and vegetation.


title: Systematic mapping study of big data mining tools and techniques

abstract: With the inception of new era data has grown expeditiously not only in volume but also in variety. There is difficulty in analyzing such a big data. Big data mining has the ability of extracting useful information and hidden patterns from the large datasets or streams of data. The big data mining challenges are becoming stimulating opportunities for the coming years. Since the literature of big data mining has increased briskly in recent years, it becomes mandatory to develop an overview of the state-of-the-art. This systematic mapping study focuses on big data mining tools, techniques and application domains used in previous studies. The existing work is classified into different categories and is presented using visualizations.


Graph Drawing And Applications For Software And Knowledge Engineers


title: A study of high-frequency photovoltaic pulse charger for lead-acid battery guided by PI-INC MPPT

abstract: A high-frequency photovoltaic pulse charger (PV-PC) for lead-acid battery (LAB) guided by a power-increment-aided incremental-conductance maximum power point tracking (PI-INC MPPT) is proposed. The PV-PC implemented by a boost current converter (BCC) is to eliminate sulfating crystallization on the electrode plates of the LAB and to prolong the battery life. The BCC associated with the PV module is modeled to maximize the energy charging to battery under maximum power transfer. A duty-control guided by the PI-INC MPPT is designed to drive the BCC operating at MPP against the random insolation. A design example of a PV-PC system for a four-in-series LAB battery (48 V dc ) is examined. The charging behavior of the PV-PC system in comparison with that of CC-CV charger is studied. Four scenarios of solar insolation changes to describe tracking behavior of PI-INC MPPT in PV-BC system are investigated, which is also compared with that of INC MPPT.


title: Nearly Optimal Private LASSO

abstract: We present a nearly optimal differentially private version of the well known LASSO estimator. Our algorithm provides privacy protection with respect to each training example. The excess risk of our algorithm, compared to the non-private version, is O(1/n2/3), assuming all the input data has bounded l∞ norm. This is the first differentially private algorithm that achieves such a bound without the polynomial dependence on p under no additional assumptions on the design matrix. In addition, we show that this error bound is nearly optimal amongst all differentially private algorithms.


title: A first look at vehicle data collection via smartphone sensors

abstract: Smartphones serve as a technical interface to the outside world. These devices have embedded, on-board sensors (such as accelerometers, WiFi, and GPSes) that can provide valuable information for investigating users' needs and behavioral patterns. Similarly, computers that are embedded in vehicles are capable of collecting valuable sensor data that can be accessed by smartphones through the use of On-Board Diagnostics (OBD) sensors. This paper describes a prototype of a mobile computing platform that provides access to vehicles' sensors by using smartphones and tablets, without compromising these devices' security. Data such as speed, engine RPM, fuel consumption, GPS locations, etc. are collected from moving vehicles by using a WiFi On-Board Diagnostics (OBD) sensor, and then backhauled to a remote server for both real-time and offline analysis. We describe the design and implementation details of our platform, for which we developed a library for in-vehicle sensor access and created a non-relational database for scalable backend data storage. We propose that our data collection and visualization tools are useful for analyzing driving behaviors; we also discuss future applications, security, and privacy concerns specific to vehicular networks.