title: Cemento-ossifying fibroma of jaws—correlation of clinical and pathological findings

abstract: Cemento-ossifying fibroma (COF) belongs to the group of bone-related lesions of the oral cavity. The aim of this study was refine its histologic features and to correlate histopathological picture and clinical behavior of the tumor. The quantity of bone spherules, their cellularity, the existence of hemorrhage, inflammation, and endochondral ossification in the stroma of the tumor were analyzed and correlated with patients main symptoms of ten patients with COF. All patients had swelling that lasted between 3 and 20 months (mean 10.4 months). Other clinical symptoms were facial asymmetry, eye bulb protrusion, and teeth displacement. The evolution of the tumor was longer in the mandible (12.3 months) than in maxilla (9.5 months). The main histomorphological diagnostic criterion for COF was psammoma-like structures. In lesions with longer case history, the number of “psammomatoid” bodies was greater. In lesions with shorter course of the disease, their number was lower and stromal hemorrhage and inflammation were presented. One patient had secondary superposition of aneurysmal bone cyst in the prime COF of mandible. Histological appearance of COF may be influenced by the tumor evolution. A siginificant correlation between the clinical course of the disease and histological parameters has been certified.


title: Leader Emergence: The Case of the Narcissistic Leader:

abstract: These studies investigate whether individuals with high narcissism scores would be more likely to emerge as leaders during leaderless group discussions. The authors hypothesized that narcissists would emerge as group leaders. In three studies, participants completed personality questionnaires and engaged in four-person leaderless group discussions. Results from all three studies reveal a link between narcissism and leader emergence. Studies 1 and 2 further reveal that the power dimension of narcissism predicted reported leader emergence while controlling for sex, self-esteem, and the Big Five personality traits. Study 3 demonstrates an association between narcissism and expert ratings of leader emergence in a group of executives. The implications of the propensity of narcissists to emerge as leaders are discussed.


title: NUMERICAL ANALYSIS OF A COUPLED FINITE-INFINITE ELEMENT METHOD FOR EXTERIOR HELMHOLTZ PROBLEMS

abstract: Coupled finite-infinite element computations are very efficient for modeling large scale acoustics problems. Parallel algorithms, like sub-structuring and domain decomposition methods, have shown to be very efficient for solving huge linear systems arising from acoustics. In this paper, a coupled finite-infinite element method is described, formulated and analyzed for parallel computations purpose. New numerical results illustrate the efficiency of this method for academic test cases and industrial problems alike.


title: A feasible method for optimization with orthogonality constraints

abstract: Minimization with orthogonality constraints (e.g., $$X^\top X = I$$) and/or spherical constraints (e.g., $$\Vert x\Vert _2 = 1$$) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform--a Crank-Nicolson-like update scheme--to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem "tai256c" in QAPLIB can be reached in 5 min on a typical laptop.


title: Estimation of Total Energy Consumption in Jordan 1980‐1999: Evidence From Cointegration and Stock

abstract: The purpose of this study is to estimate the long‐run elasticities of the demand (consumption) for total energy in Jordan for the 1980‐1999 period. Assuming a simple linear relationship, in order to estimates the elasticities of a simple long‐run demand equation, we then employ a procedure just recently prescribed by Stock‐Watson (1993) known as Dynamic OLS (DOLS). The DOLS procedure allows for co‐integrated variables as well as tackling the problem of simultaneity amongst the regressors. Furthermore, Stock and Watson showed that DOLS is more favorable, particularly in small size samples, compared to a number of alternative estimators of longrun parameters, including those proposed by Engle‐Granger (1987), Johansen (1988) and error correction model. The analysis provided in this paper showed that the income elasticity of final energy consumption is 1.15, which indicated that the economic growth is accompanied by proportional increase in energy consumption. The responsiveness of energy consumption to price...


title: Consideration of Future Consequences and Unprotected Anal Intercourse Among Men Who Have Sex with Men

abstract: Abstract This study of men who have sex with men (MSM) examined whether tendencies to consider the future consequences of one's actions were associated with sexual behaviors that place oneself at risk for HIV infection. A total of 339 HIV-negative MSM responded to the Consideration of Future Consequences Scale (CFC; Strathman et al, 1994) and to questions about their anal intercourse practices in the past year. In bivariate analyses, men with a stronger future orientation were less likely to engage in anal intercourse unprotected by a condom (p < .05). Multivariate analyses revealed that CFC accounted for significant variance in three of four measures of unprotected anal sex after statistically controlling for demographic covariates (education, income, ethnicity, age). CFC was a better predictor of sexual behavior and accounted for more unique variance than any of the demographic factors. Additional research is needed to confirm that CFC is an antecedent of behavior and to examine the feasibility and effi...


title: Faster Discovery of Neural Architectures by Searching for Paths in a Large Model

abstract: We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss. On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank. On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017). Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.


title: Dynamic attack detection and mitigation in IoT using SDN

abstract: With the advent of smart devices and lowering prices of sensing devices, adoption of Internet of Things (IoT) is gaining momentum. These IoT devices come with greater threat of being attacked or compromised that could lead to Denial of Service (DoS) and Distributed Denial of Service (DDoS). The high volume of IoT devices with high level of heterogeneity, magnify the possibility of security threats. So far, there is no protocol to guarantee the security of IoT devices. But to enable resilience, continuous monitoring is required along with adaptive decision making. These challenges can be addressed with the help of Software Defined Networking (SDN) which can effectively handle the security threats to the IoT devices in dynamic and adaptive manner without any burden on the IoT devices. In this paper, we propose an SDN-based secure IoT framework called SoftThings to detect abnormal behaviors and attacks as early as possible and mitigate as appropriate. Machine Learning is used at the SDN controller to monitor and learn the behavior of IoT devices over time. We have conducted experiments on Mininet emulator. Initial results show that this framework is capable to detect attacks on IoT with around 98% precision.


title: Recurrent Marked Temporal Point Processes: Embedding Event History to Vector

abstract: Large volumes of event data are becoming increasingly available in a wide variety of applications, such as healthcare analytics, smart cities and social network analysis. The precise time interval or the exact distance between two events carries a great deal of information about the dynamics of the underlying systems. These characteristics make such data fundamentally different from independently and identically distributed data and time-series data where time and space are treated as indexes rather than random variables. Marked temporal point processes are the mathematical framework for modeling event data with covariates. However, typical point process models often make strong assumptions about the generative processes of the event data, which may or may not reflect the reality, and the specifically fixed parametric assumptions also have restricted the expressive power of the respective processes. Can we obtain a more expressive model of marked temporal point processes? How can we learn such a model from massive data? In this paper, we propose the Recurrent Marked Temporal Point Process (RMTPP) to simultaneously model the event timings and the markers. The key idea of our approach is to view the intensity function of a temporal point process as a nonlinear function of the history, and use a recurrent neural network to automatically learn a representation of influences from the event history. We develop an efficient stochastic gradient algorithm for learning the model parameters which can readily scale up to millions of events. Using both synthetic and real world datasets, we show that, in the case where the true models have parametric specifications, RMTPP can learn the dynamics of such models without the need to know the actual parametric forms; and in the case where the true models are unknown, RMTPP can also learn the dynamics and achieve better predictive performance than other parametric alternatives based on particular prior assumptions.


title: The classification and labeling of nonhomosexual gender dysphorias.

abstract: This report suggests systematic strategies for the descriptive classification of nonhomosexual gender identity disorders, based on clinical observations and research findings. The classification of biological males is considered first. A review of cross-gender taxonomies shows that previous observers have identified and labeled a homosexual type far more consistently than any other category of male gender dysphoric. It is suggested that the apparent difficulty in differentiating reliably among the nonhomosexual types results from the sharing of many overlapping characteristics by the various groups. This is supported by a review of informal, mostly clinical, observations and by the findings of three studies designed to test the hypothesis that the nonhomosexual gender dysphorias, together with transvestism, constitute a family of related disorders in men. It is concluded that the main varieties of nonhomosexual gender dysphoria are more similar to each other than any of them is to the homosexual type. Two recommendations, based on the foregoing review, are offered for the classification of male gender dysphorics in research studies. When the number of subjects is small, they may be classified simply as homosexual or nonhomosexual. When the number is larger, the nonhomosexual cases may be classified as heterosexual, bisexual, or analloerotic (unattracted to male or female partners, but not necessarily devoid of sexual drive or activities).


title: A Review of Cloud Business Models and Sustainability

abstract: This paper reviews current cloud computing business models and presents proposals on how organisations can achieve sustainability by adopting appropriate models. Using the Jericho Forum’s Cloud Cube Model (CCM), we classify cloud computing business models into eight types: (1) Service Provider and Service Orientation; (2) Support and Services Contracts; (3) In-House Private Clouds; (4) All-In-One Enterprise Cloud; (5) One-Stop Resources and Services; (6) Government Funding; (7) Venture Capitals; and (8) Entertainment and Social Networking. We newly propose the Hexagon Model that includes six key elements for sustainability based on Sun Tzu’s Art of War and literature review, and the sixth factor is rated based on case studies and peer reviews. Areas occupied in the Hexagon can represent strengths and weaknesses of a cloud business, and several cases are presented with rationale explained. Apart from the qualitative approach, the quantitative approach we use is the Capital Asset Pricing Model and Modern Portfolio Theory, both of which aim computing organisational sustainability and predict how well an organisation can perform. The OMII-UK data is used to demonstrate sustainability and study the impact on cloud businesses, and is presented by statistical computation, 3D visualisation and the Hexagon Model. We believe that adopting an appropriate cloud computing business model will help organisations investing in this technology to stand firm at all times.


title: Fast computation of graph kernels

abstract: Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n3) worst-case time. This includes kernels whose previous worst-case time complexity was O(n6), such as the geometric kernels of Gartner et al. [1] and the marginal graph kernels of Kashima et al. [2]. Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.


title: Visual statistical learning in infancy : evidence for a domain general learning mechanism

abstract: The rapidity with which infants come to understand language and events in their surroundings has prompted speculation concerning innate knowledge structures that guide language acquisition and object knowledge. Recently, however, evidence has emerged that by 8 months, infants can extract statistical patterns in auditory input that are based on transitional probabilities defining the sequencing of the input's components (Science 274 (1996) 1926). This finding suggests powerful learning mechanisms that are functional in infancy, and raises questions about the domain generality of such mechanisms. We habituated 2-, 5-, and 8-month-old infants to sequences of discrete visual stimuli whose ordering followed a statistically predictable pattern. The infants subsequently viewed the familiar pattern alternating with a novel sequence of identical stimulus components, and exhibited significantly greater interest in the novel sequence at all ages. These results provide support for the likelihood of domain general statistical learning in infancy, and imply that mechanisms designed to detect structure inherent in the environment may play an important role in cognitive development.


Combining active and semi-supervised learning for spoken language understanding


title: Topic-based Evaluation for Conversational Bots

abstract: Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.


title: Evaluation of a Smart Algorithm for Commercial Vehicle Driver Drowsiness Detection

abstract: Drowsiness is a safety hazard in commercial vehicle driving. The conditions to which truck drivers are exposed put them at higher risk as compared to passenger car drivers. Unobtrusive drowsiness detection methods can avoid catastrophic crashes by warning or assisting the drivers. This paper describes an experimental analysis of commercially licensed drivers who were subjected to drowsiness conditions in a truck driving simulator and evaluates the performance of a neural network based algorithm which monitors only the drivers' steering input. Correlations are found between the change in steering and the state of drowsiness. The results show steering signals differences can be used effectively for detection.


title: Bidirectional wireless power transfer topology using current-fed half bridge CLC transmitter LC receiver configuration for medium power applications

abstract: This paper presents a new bidirectional wireless power transfer (WPT) topology using current fed half bridge converter. Generally, WPT topology with current fed converter uses parallel LC resonant tank network in the transmitter side to compensate the reactive power. However, in medium power application this topology suffers a major drawback that the voltage stress in the inverter switches are considerably high due to high reactive power consumed by the loosely coupled coil. In the proposed topology this is mitigated by adding a suitably designed capacitor in series with the transmitter coil. Both during grid to vehicle and vehicle to grid operations the power flow is controlled through variable switching frequency to achieve extended ZVS of the inverter switches. Detail analysis and converter design procedure is presented for both grid to vehicle and vehicle to grid operations. A 1.2kW lab-prototype is developed and experimental results are presented to verify the analysis.


title: Micro Controller Based Automatic Plant Irrigation System

abstract: The main aim of this paper is to provide automatic irrigation to the plants which helps in saving money and water. The entire system is controlled using 8051 micro controller which is programmed as giving the interrupt signal to the sprinkler.Temperature sensor and humidity sensor are connected to internal ports of micro controller via comparator,When ever there is a change in temperature and humidity of the surroundings these sensors senses the change in temperature and humidity and gives an interrupt signal to the micro-controller and thus the sprinkler is activated.


title: The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment

abstract: Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. The author gives 4 reasons for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it deemphasizes the private reasoning done by individuals and emphasizes instead the importance of social and cultural influences. The model is an intuitionist model in that it states that moral judgment is generally the result of quick, automatic evaluations (intuitions). The model is more consistent than rationalist models with recent findings in social, cultural, evolutionary, and biological psychology, as well as in anthropology and primatology.


title: A taxonomy of DDoS attack and DDoS defense mechanisms

abstract: Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space. The attack classification criteria was selected to highlight commonalities and important features of attack strategies, that define challenges and dictate the design of countermeasures. The defense taxonomy classifies the body of existing DDoS defenses based on their design decisions; it then shows how these decisions dictate the advantages and deficiencies of proposed solutions.


title: Electrically Steerable Single-Layer Microstrip Traveling Wave Antenna With Varactor Diode Based Phase Shifters

abstract: The design of electrically steerable traveling wave microstrip antenna arrays is presented. Varactor diode based phase shifters implemented on the same metallic layer as the patch elements are used to provide a variable progressive phase shift in the array. Two antennas for the 5.8 GHz ISM band, manufactured as single layer printed designs on a standard PTFE soft substrate, are demonstrated. A ten-element beam-tilting vertical array using transmission type phase shifters is realized, yielding between 11.8 to 13.9 dBi gain for the 0deg to 11deg beam tilt tuning range. Using wide phase tuning range reflection type phase shifters a five-element horizontally scanning array with -32deg to 32deg steering range and 10.9-11.3 dBi gain has been realized.


title: SA-REST: Semantically Interoperable and Easier-to-Use Services and Mashups

abstract: Services based on the representational state transfer (REST) paradigm, a lightweight implementation of a service-oriented architecture, have found even greater success than their heavyweight siblings, which are based on the Web Services Description Language (WSDL.) and SOAP. By using XML-based messaging, RESTful services can bring together discrete data from different services to create meaningful data sets; mashups such as these are extremely popular today.


title: Position control for a class of vehicles in SE(3)

abstract: A hierarchical design framework is presented to control the position of a class of vehicles in SE(3) that are propelled by a thrust vector along a single body axis and incorporate some mechanism to induce torques about all body axes. A position control outer loop provides reference signals for an attitude control inner loop. The main result of this paper is a set of conditions under which a position controller designed for a point-mass system and an attitude controller can be combined to form a position controller that almost globally asymptotically stabilizes the vehicle to a desired position with desired heading. As opposed to the classical backstepping framework, the proposed approach is modular, in that position control and attitude control designs are completely separate. Thus, for instance, with the proposed technique one can employ any attitude controller from the vast literature on attitude stabilization, provided it enjoys a basic almost global stability property.


title: Pupil Dilation Degrades Iris Biometric Performance

abstract: Iris biometrics research has largely ignored the problems associated with variations in pupil dilation between the enrollment image and the image to be recognized or verified. Indeed, in most current systems, information about pupil dilation is discarded when the iris region is normalized to a dimensionless polar coordinate system from which the iris code is obtained. This work studies the effect of pupil dilation on the accuracy of iris biometrics. We found that when the degree of dilation is similar at enrollment and recognition, comparisons involving highly dilated pupils result in worse recognition performance than comparisons involving constricted pupils. We also found that when the matched images have similarly highly dilated pupils, the mean Hamming distance of the match distribution increases and the mean Hamming distance of the non-match distribution decreases, bringing the distributions closer together from both directions. We further found that when matching enrollment and recognition images of the same person, larger differences in pupil dilation yield higher template dissimilarities, and so a greater chance of a false non-match. We recommend that a measure of pupil dilation be kept as meta-data for every iris code. Also, the absolute dilation of the two images, and the dilation difference between them, should factor into a confidence measure for an iris match.


title: Safe cities. A participatory sensing approach

abstract: Smart cities combine technology and human resources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing smart, safe cities, by combining the use of personal mobile devices and social networks to make users aware of the safety of their surroundings. We propose novel metrics to define location and user based safety values. We evaluate the ability of forecasting techniques including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) to predict future safety values. We devise iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile device users and integrate our approach into an Android application for visualizing safety levels. We further investigate relationships between location dependent social network activity and crime levels. We evaluate our contributions using data we collected from Yelp as well as crime and census data.


title: Annotation of paintings with high-level semantic concepts using transductive inference and ontology-based concept disambiguation

abstract: Domain-specific knowledge of paintings defines a wide range of concepts for annotation and flexible retrieval of paintings. In this work, we employ the ontology of artistic concepts that includes visual (or atomic) concepts at the intermediate level and high-level concepts at the application level. Visual-level color and brushwork concepts are widely used by art historians to analyze paintings and serve as cues for annotating high-level concepts such as the artist names, painting styles and art periods for paintings. In this research we combine the color and brushwork concepts with low-level features and utilize the transductive inference framework to annotate high-level concepts to the image blocks. In order to resolve conflicting assignments of high-level concepts, we further employ the ontology-based concept disambiguation method and generate image-level annotations. This method performs global optimization of the block-level annotations using the linear constraints extracted from domain knowledge. Our experiments on annotating high-level concepts demonstrate that: a) the use of visual-level concepts significantly improves the accuracy as compared to using low-level features only; and b) the proposed transductive inference framework out-performs the conventional baseline methods and c) the proposed ontology-based disambiguation method generates superior results for several annotation scenarios.


title: Investigation and improvement of multi-layer perceptron neural networks for credit scoring

abstract: We present an Average Random Choosing method which increases 0.04 classification accuracy.Investigate different MLP models and get the best model with accuracy of 87%.Accuracy increases when the model has more hidden neurons. Multi-Layer Perceptron (MLP) neural networks are widely used in automatic credit scoring systems with high accuracy and efficiency. This paper presents a higher accuracy credit scoring model based on MLP neural networks that have been trained with the back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: (i) to optimise the data distribution in datasets using a new method called Average Random Choosing; (ii) to compare effects of training-validation-test instance numbers; and (iii) to find the most suitable number of hidden units. We trained 34 models 20 times with different initial weights and training instances. Each model has 6 to 39 hidden units with one hidden layer. Using the well-known German credit dataset we provide test results and a comparison between models, and we get a model with a classification accuracy of 87%, which is higher by 5% than the best result reported in the relevant literature of recent years. We have also proved that our optimisation of dataset structure can increase a model's accuracy significantly in comparison with traditional methods. Finally, we summarise the tendency of scoring accuracy of models when the number of hidden units increases. The results of this work can be applied not only to credit scoring, but also to other MLP neural network applications, especially when the distribution of instances in a dataset is imbalanced.


title: Benign intestinal glandular lesions in the vagina: a possible correlation with implantation

abstract: Enteric-type glandular lesions are extremely rare in the vagina. Their histological origin remains a matter of speculation at present. We review two rectal mucosal prolapse-like polyps and one intestinal-type adenosis in the vagina. Case 1, a 64-year-old woman, presented with a vaginal polypoid lesion with a size of 4 × 3 × 3 cm. Case 2, an 8-year-old girl, had a 1.5 × 1.5 × 0.8-cm pedunculated polyp in the vaginal navicular fossa and a clinically suspected rectovaginal fistula. Case 1 and 3 had an obsolete severe perineal laceration. On histopathological examination, cases 1 and 2 resembled rectal mucosal prolapse or inflammatory cloacogenic polyp (rectal mucosal prolapse-like polyp). Case 3 had an incidental intestinal-type adenosis in the removed vaginal wall. Immunohistochemistry confirmed the intestinal differentiation in all 3 lesions by showing diffuse CDX2-positive, CK20-positive, and scattered chromogranin A-positive neuroendocrinal cells in the lower compartment of the crypt. In summary, we report herein three unusual cases of benign intestinal-type glandular lesions in the vagina including two rectal mucosal prolapse-like polyps and one case of intestinal-type adenosis, and discuss possibilities for their histogenetic basis.


title: Mining social media with social theories: a survey

abstract: The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social media data is big, linked, noisy, highly unstructured and in- complete, and differs from data in traditional data mining, which cultivates a new research field - social media mining. Social theories from social sciences are helpful to explain social phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question - can we apply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale social media data. Social theories have been applied to mining social media. In this article, we review some key social theories in mining social media, their verification approaches, interesting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories.


title: Image Features Detection, Description and Matching

abstract: Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.


title: Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)

abstract: The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.


title: An empirical study on real bug fixes

abstract: Software bugs can cause significant financial loss and even the loss of human lives. To reduce such loss, developers devote substantial efforts to fixing bugs, which generally requires much expertise and experience. Various approaches have been proposed to aid debugging. An interesting recent research direction is automatic program repair, which achieves promising results, and attracts much academic and industrial attention. However, people also cast doubt on the effectiveness and promise of this direction. A key criticism is to what extent such approaches can fix real bugs. As only research prototypes for these approaches are available, it is infeasible to address the criticism by evaluating them directly on real bugs. Instead, in this paper, we design and develop B ug S tat , a tool that extracts and analyzes bug fixes. With B ug S tat 's support, we conduct an empirical study on more than 9,000 real-world bug fixes from six popular Java projects. Comparing the nature of manual fixes with automatic program repair, we distill 15 findings, which are further summarized into four insights on the two key ingredients of automatic program repair: fault localization and faulty code fix. In addition, we provide indirect evidence on the size of the search space to fix real bugs and find that bugs may also reside in non-source files. Our results provide useful guidance and insights for improving the state-of-the-art of automatic program repair.


title: Agent-based modeling of China’s rural-urban migration and social network structure

abstract: We analyze China's rural-urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core-periphery method developed from the k-core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period.


title: Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

abstract: R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification. However, the proposal generation phase in this paradigm is usually time consuming, which would slow down the whole detection time in testing. This paper suggests that the value discrepancies among features in deep convolutional feature maps contain plenty of useful spatial information, and proposes a simple approach to extract the information for fast region proposal generation in testing. The proposed method, namely Relief R-CNN (\(R^2\)-CNN), adopts a novel region proposal generator in a trained R-CNN style model. The new generator directly generates proposals from convolutional features by some simple rules, thus resulting in a much faster proposal generation speed and a lower demand of computation resources. Empirical studies show that \(R^2\)-CNN could achieve the fastest detection speed with comparable accuracy among all the compared algorithms in testing.


title: Big Data Capabilities Applied to Semiconductor Manufacturing Advanced Process Control

abstract: As requirements on data volumes, rates, quality, merging, and analytics increase exponentially in the digital universe, semiconductor manufacturers are faced with a need for new approaches to data management and use across the Fab. These are often termed “big data” challenges. In our industry big data solutions will be key to scaling advanced process control (APC) solutions to finer levels of control and diagnostics. However the main impact will be to better enable more effective predictive technologies such as predictive maintenance (PdM), virtual metrology and yield prediction, all of which utilize data from traditional APC capabilities that include fault detection and classification and run-to-run control. PdM represents one area where big data solutions are generating significant benefits across a variety of process types. Moving to big data solutions involves addressing the aforementioned requirements either with enhancements of existing systems or moving to more big data friendly platforms. Big data friendly platforms applied to APC systems provide quantifiable cost-of-ownership and speed improvements, thereby better enabling high quality prediction solutions. Initially, big data solutions will largely be delegated to off-line and on-time critical tasks; over the longer term these big data solutions will increasingly be leveraged for time critical and real-time capabilities.


title: Identifying potential breakthrough publications using refined citation analyses: Three related explorative approaches

abstract: The article presents three advanced citation-based methods used to detect potential breakthrough papers among very highly cited papers. We approach the detection of such papers from three different perspectives in order to provide different typologies of breakthrough papers. In all three cases we use the classification of scientific publications developed at CWTS based on direct citation relationships. This classification establishes clusters of papers at three levels of aggregation. Papers are clustered based on their similar citation orientations and it is assumed that they are focused on similar research interests. We use the clustering as the context for detecting potential breakthrough papers. We utilize the Characteristics Scores and Scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited followers, papers not considered breakthroughs in themselves. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited paper in a micro-cluster is considered as a potential breakthrough paper (M1); as well as two conceptually different methods, one that detects potential breakthrough papers among the two percent highest cited papers according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS two percent filter, knowledge diffusion is also taken in as an extra parameter (M2b). The advance citation-based methods are explored and evaluated using specifically validated publication sets linked to different Danish funding instruments including centres of excellence.


title: EScala: modular event-driven object interactions in scala

abstract: Event-driven programming style in OO languages based on imperatively triggered events does not support separate and more declarative event definitions by composition or transformation of other events. AO language mechanisms for defining events as declarative queries over implicitly available low-level events seem good candidates to approach these problems. However, being designed for modularizing mostly globally scoped, crosscutting concerns, AO mechanisms deliberately break with the OO design and modular reasoning style and are thus inappropriate for addressing modularity concerns related to event-based interactions in OO designs. The contribution of this paper is a language design that combines imperatively triggered events with AO-like mechanisms that are specifically designed to address modularity issues in event-driven object-oriented designs. In particular, they seamlessly integrate with OO-style encapsulation, late binding, and modular reasoning. We present an efficient and type-safe implementation of the proposed design as an extension to Scala.


title: CIDD: A Cloud Intrusion Detection Dataset for Cloud Computing and Masquerade Attacks

abstract: Masquerade attacks pose a serious threat for cloud system due to the massive amount of resource of these systems. Lack of datasets for cloud computing hinders the building of efficient intrusion detection of these attacks. Current dataset cannot be used due to the heterogeneity of user requirements, the distinct operating systems installed in the VMs, and the data size of Cloud systems. This paper presents a Cloud Intrusion Detection Dataset (CIDD) that is the first one for cloud systems and that consists of both knowledge and behavior based audit data collected from both UNIX and Windows users. With respect to current datasets, CIDD has real instances of host and network based attacks and masquerades, and provides complete diverse audit parameters to build efficient detection techniques. The final statistic tables for each user are built by Log Analyzer and Correlator System (LACS) that parses and analyzes user's binary log files, and correlates audits data according to user IP address(es) and audit time. We describe in details the components and the architecture of LACS and CIDD, and the attacks distribution in CIDD.


title: 500 ps/1 kV pulse generator based on avalanche transistor Marx circuit

abstract: Ultra-short pulse generator based on Marx circuit, whose switch is avalanche transistor, could generate very short pulse with high peak voltage. The ultra-fast rising-edge of the pulse is mainly determined by transistor's avalanche effect. On the other hand, the avalanche effect have negative influence on the falling-edge, which lead to the waveform unacceptable for signal processing. In this paper, we analyze the oscillation of the falling edge and proposed a method to optimize the waveform of the pulse. Experimental investigation showed that, by optimizing the charging capacitors, pulse width can be effectively reduced and the waveform of the pulse is more close to zero order gaussian pulse.


title: Auctioning over Probabilistic Options for Temporal Logic-Based Multi-Robot Cooperation Under Uncertainty

abstract: Coordinating a team of robots to fulfill a common task is still a demanding problem. This is even more the case when considering uncertainty in the environment, as well as temporal dependencies within the task specification. A multi-robot cooperation from a single goal specification requires mechanisms for decomposing the goal as well as an efficient planning for the team. However, planning action sequences offline is insufficient in real world applications. Rather, due to uncertainties, the robots also need to closely coordinate during execution and adjust their policies when additional observations are made. The framework presented in this paper enables the robot team to cooperatively fulfill tasks given as temporal logic specifications while explicitly considering uncertainty and incorporating observations during execution. We present the effectiveness of our ROS implementation of this approach in a case study scenario.


title: Heart valve tissue donation does not preclude the diagnosis of clinically significant pediatric cardiac abnormalities.

abstract: Medical examiners/coroners often have the responsibility of deciding whether to allow organ and tissue donation to proceed in cases under their jurisdiction. A 10-year retrospective study was conducted of autopsy cases in children younger than 6 years to determine whether the donation of cardiac valve tissue would preclude the diagnosis of clinically significant pediatric cardiac abnormalities. Only 1 case (0.1%) was found in the entire 10-year period in which valve tissue donation might have prevented the correct diagnosis. According to the results of the study, (1) most cardiac findings in forensic pediatric autopsies are not clinically significant; (2) clinically significant findings will be diagnosed in properly examined hearts, even those processed for valve tissue donation, and special studies will remain possible in nearly all hearts except those with suspicion of conduction defects; and (3) as long as there is good communication between forensic pathologists and organ and tissue procurement organizations, the concern of failing to diagnose significant cardiac findings should not inhibit medical examiners/coroners from allowing the donation of pediatric cardiac valve tissue.


title: FEATURE EXTRACTION BASED ON DECISION BOUNDARIES

abstract: A novel approach to feature extraction for classification based directly on the decision boundaries is proposed. It is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms. >


title: Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions

abstract: Complex systems are pervasive in the world around us. Making sense of a complex system should require that a person construct a network of concepts and principles about some domain that represents key (often dynamic) phenomena and their interrelationships. This raises the question of how expert understanding of complex systems differs from novice understanding. In this study we examined individuals’ representations of an aquatic system from the perspective of structural (elements of a system), behavioral (mechanisms), and functional aspects of a system. Structure–Behavior–Function (SBF) theory was used as a framework for analysis. The study included participants from middle school children to preservice teachers to aquarium experts. Individual interviews were conducted to elicit participants’ mental models of aquaria. Their verbal responses and pictorial representations were analyzed using an SBF-based coding scheme. The results indicated that representations ranged from focusing on structures with minimal understanding of behaviors and functions to representations that included behaviors and functions. Novices’ representations focused on perceptually available, static components of the system, whereas experts integrated structural, functional, and behavioral elements. This study suggests that the SBF framework can be one useful formalism for understanding complex systems.


title: Planogram Compliance Checking Using Recurring Patterns

abstract: This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training. Product layout information is extracted from one single input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram. To improve the efficiency, a divide-conquer strategy is employed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region respectively and then merged together to estimate product layout information. Experimental results on real data from a supermarket chain have verified the effectiveness and efficiency of the proposed method.


title: Local positioning for wireless sensors based on Bluetooth/spl trade/

abstract: Self-organizing sensor networks are one of the systems that would benefit from the new local positioning features offered by the new generation of wireless technologies. Location dependent sensor data transfers could be optimized by means of local positioning services. Many start-up companies have available proprietary positioning systems meeting the unique requirements of each application. Therefore, the use of a standard like Bluetooth/spl trade/ is a step towards a universal solution. Our system provides location services for mobile industrial and biomedical Bluetooth/spl trade/ -enabled sensors. The RSSI distance estimation, together with a GPS-like triangulation algorithm, lead to a 3 m error positioning system with remote and self positioning topologies. A real time positioning tracking system for one mobile sensor is provided in this article.


title: Engineering Time-Expanded Graphs for Faster Timetable Information

abstract: We present an extension of the well-known time-expanded approach for timetable information. By remodeling unimportant stations, we are able to obtain faster query times with less space consumption than the original model. Moreover, we show that our extensions harmonize well with speed-up techniques whose adaption to timetable networks is more challenging than one might expect.


title: Dual Poisson-Disk Tiling: An Efficient Method for Distributing Features on Arbitrary Surfaces

abstract: This paper introduces a novel surface-modeling method to stochastically distribute features on arbitrary topological surfaces. The generated distribution of features follows the Poisson disk distribution, so we can have a minimum separation guarantee between features and avoid feature overlap. With the proposed method, we not only can interactively adjust and edit features with the help of the proposed Poisson disk map, but can also efficiently re-distribute features on object surfaces. The underlying mechanism is our dual tiling scheme, known as the dual Poisson-disk tiling. First, we compute the dual of a given surface parameterization, and tile the dual surface by our specially-designed dual tiles; during the pre-processing, the Poisson disk distribution has been pre-generated on these tiles. By dual tiling, we can nicely avoid the problem of corner heterogeneity when tiling arbitrary parameterized surfaces, and can also reduce the tile set complexity. Furthermore, the dual tiling scheme is non-periodic, and we can also maintain a manageable tile set. To demonstrate the applicability of this technique, we explore a number of surface-modeling applications: pattern and shape distribution, bump-mapping, illustrative rendering, mold simulation, the modeling of separable features in texture and BTF, and the distribution of geometric textures in shell space.


title: Planar broadband annular-ring antenna with circular polarization for RFID system

abstract: Novel circular polarization (CP) design of planar annual-ring antenna with broadband operation for UHF RFID system is proposed and experimentally studied. By introducing the Wilkinson divider into the feeding network and dual L-shaped strips, the proposed CP design can easily be achieved with the impedance bandwidth (RL ≧10 dB) of about 246 MHz (25.0% @ 985 MHz) and the 3 dB axial-ratio (AR) bandwidth of about 180 MHz (19.5 % @ 925 MHz) for UHF RFID applications. The measured peak gain and radiation efficiency are about 7.7 dBic and 60% across the operating band, respectively, with unidirectional pattern in the XZ- and YZ-plane.


title: Streaming and Exploration of Dynamically Changing Dense 3D Reconstructions in Immersive Virtual Reality

abstract: We introduce a novel framework that enables large-scale dense 3D scene reconstruction, data streaming over the network and immersive exploration of the reconstructed environment using virtual reality. The system is operated by two remote entities, where one entity – for instance an autonomous aerial vehicle – captures and reconstructs the environment as well as transmits the data to another entity – such as human observer – that can immersivly explore the 3D scene, decoupled from the view of the capturing entity. The performance evaluation revealed the framework’s capabilities to perform RGB-D data capturing, dense 3D reconstruction, streaming and dynamic scene updating in real time for indoor environments up to a size of 100m2, using either a state-of-the-art mobile computer or a workstation. Thereby, our work provides a foundation for enabling immersive exploration of remotely captured and incrementally reconstructed dense 3D scenes, which has not shown before and opens up new research aspects in future.


title: A Bayesian-node fusion approach for optimization of cyber attack modeling at the virtual layer of the cloud

abstract: Networks, the cloud included, are susceptible to cyber-attacks owing to the myriad of vulnerabilities exhibited in networked components. Attackers chain together these vulnerabilities to generate optimized attack paths with varying degrees of success. The challenge in cloud networks has been to capture the dependencies amongst the vulnerabilities in the attack paths of the exploited components. We in this paper partition the cloud into three discrete layers with concentration on the virtual layer where, via applied node fusion in the resultant Bayesian attack network, endeavor to capture the aforementioned relationships by grouping like nodes together for conjunction probabilities of intersection and disjunction probabilities of union of two or more events. We further explore these dependencies through the connectivity matrix and employ CVEs and edge weighting for effective path determination. We likewise demonstrate how failure nodes can be induced and utilized for attack mitigation and prioritization in security plan formulation.


title: Algorithmic Self-Assembly of DNA Sierpinski Triangles

abstract: Algorithms and information, fundamental to technological and biological organization, are also an essential aspect of many elementary physical phenomena, such as molecular self-assembly. Here we report the molecular realization, using two-dimensional self-assembly of DNA tiles, of a cellular automaton whose update rule computes the binary function XOR and thus fabricates a fractal pattern—a Sierpinski triangle—as it grows. To achieve this, abstract tiles were translated into DNA tiles based on double-crossover motifs. Serving as input for the computation, long single-stranded DNA molecules were used to nucleate growth of tiles into algorithmic crystals. For both of two independent molecular realizations, atomic force microscopy revealed recognizable Sierpinski triangles containing 100–200 correct tiles. Error rates during assembly appear to range from 1% to 10%. Although imperfect, the growth of Sierpinski triangles demonstrates all the necessary mechanisms for the molecular implementation of arbitrary cellular automata. This shows that engineered DNA self-assembly can be treated as a Turing-universal biomolecular system, capable of implementing any desired algorithm for computation or construction tasks.


title: Selective visual attention enables learning and recognition of multiple objects in cluttered scenes

abstract: A key problem in learning representations of multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter. Distinguishing individual objects in a scene would allow unsupervised learning of multiple objects from unlabeled images. There is psychophysical and neurophysiological evidence that the brain employs visual attention to select relevant parts of the image and to serialize the perception of individual objects. We propose a method for the selection of salient regions likely to contain objects, based on bottom-up visual attention. By comparing the performance of David Lowe's recognition algorithm with and without attention, we demonstrate in our experiments that the proposed approach can enable one-shot learning of multiple objects from complex scenes, and that it can strongly improve learning and recognition performance in the presence of large amounts of clutter.


title: Microwave Power Transmission: Historical Milestones and System Components

abstract: Microwave power transmission (MPT) is the wireless transfer of large amounts of power at microwave frequencies from one location to another. MPT research has been driven primarily by the desire to remotely power unmanned aerial vehicles (UAVs) and by the concept of space solar power (SSP) first conceived by Dr. Peter Glaser of the Arthur D. Little Company in 1968. This paper attempts to reveal, in adequate chronological detail, many of the MPT milestones reached over the past 50 years, including those related to SSP. Key components to various MPT systems are presented as well as design schemes for achieving efficient MPT. Special focus is given to rectenna design since this particular MPT component has received the most attention from researchers over the last couple of decades.


title: The Ecosystem of Software Platform: A Study of Asymmetric Cross-Side Network Effects and Platform Governance

abstract: While past research on software platform has recognized the existence of cross-side network effects (CNEs) between the application side and the user side, little is known about the asymmetry between the CNEs of the two sides on each other. Informed by a perspective of complex adaptive systems, this study theorizes how the user-to-application CNE is temporally different from the application-to-user CNE, and how these CNEs may be influenced by the governance mechanisms of the platform. We empirically test our theoretical arguments using a longitudinal data about a leading software browser. Our first main finding is the temporal asymmetry between the user-to-application CNE and the application-to-user CNE. Specifically, while the increased installed base of end users has a primarily long-term impact on the growth of application number and variety, the increased number and variety of applications have primarily short-term impacts on the growth of end users. Our second finding is that the length of application review time weakens the long-term user-to-application effect, but not the short-term application-to-user effect. Third, we also find that frequent platform updating can significantly weaken both the long-term user-to-application CNE and the short-term application-to-user CNE. Our study generates important theoretical and practical implications.


SCP: A Computationally-Scalable Byzantine Consensus Protocol For Blockchains.


title: TextRunner: Open Information Extraction on the Web

abstract: Traditional information extraction systems have focused on satisfying precise, narrow, pre-specified requests from small, homogeneous corpora. In contrast, the TextRunner system demonstrates a new kind of information extraction, called Open Information Extraction (OIE), in which the system makes a single, data-driven pass over the entire corpus and extracts a large set of relational tuples, without requiring any human input. (Banko et al., 2007) TextRunner is a fully-implemented, highly scalable example of OIE. TextRunner's extractions are indexed, allowing a fast query mechanism.


title: Linear Precoder Design with Imperfect CSI in Underlay Device-to-Device Communication for a Vehicular Platooning Scenario

abstract: This paper proposes a novel design for linear precoder in underlay Device-to-Device (D2D) communication using cellular network, speciﬁcally for a use case with vehicle-to-vehicle communication and platooning. To increase the communication reliability, we develop an optimization algorithm for precoder that takes into account the outage probability when channel state information (CSI) is only partially available at devices and transmission could be in outage. The algorithm aims at maximizing the sum throughput over each transmission link received at one device, while constraining the interference caused by the transmit power from each device to the cellular network. Due to the intractable form of outage probability, an extended Markov Inequality is used to transform the problem into an upper-bound expression. A two-step alternating algorithm is then adapted to solve the multi- variable optimization. The proposed algorithm is compared with other state-of-the-art technologies in the ﬁeld of vehicular communication for achieving high throughput. Simulation results show that our proposed algorithm outperforms the current techniques and achieves higher average throughput with extremely low outage probability, thus enables reliable vehicle- to-vehicle communication for platooning in a D2D underlay setting.


title: CloudCmp: comparing public cloud providers

abstract: While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a systematic comparator of the performance and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance of these measurements while limiting measurement cost. Applying CloudCmp to four cloud providers that together account for most of the cloud customers today, we find that their offered services vary widely in performance and costs, underscoring the need for thoughtful provider selection. From case studies on three representative cloud applications, we show that CloudCmp can guide customers in selecting the best-performing provider for their applications.


title: DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data

abstract: The open nature of Android allows application developers to take full advantage of the system. While the flexibility is brought to developers and users, it may raise significant issues related to malicious applications. Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware. To solve the problem, machine learning algorithms are used to learn the distinctions between malware and benign apps automatically. Deep learning, as a new area of machine learning, is developing rapidly as its better characterization of samples. We thus propose DeepFlow, a novel deep learning-based approach for identifying malware directly from the data flows in the Android application. We test DeepFlow on thousands of benignware and malware. The results show that DeepFlow can achieve a high detection F1 score of 95.05%, outperforming traditional machine learning-based approaches, which reveals the advantage of deep learning technique in malware detection.


title: Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition

abstract: This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.


title: Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry

abstract: Over recent years, interest has been growing in Bitcoin, an innovation which has the potential to play an important role in e-commerce and beyond. The aim of our paper is to provide a comprehensive empirical study of the payment and investment features of Bitcoin and their implications for the conduct of e-commerce. Since network externality theory suggests that the value of a network and its take-up are interlinked, we investigate both adoption and price formation. We discover that Bitcoin returns are driven primarily by its popularity, the sentiment expressed in newspaper reports on the cryptocurrency, and total number of transactions. The paper also reports on the first global survey of merchants who have adopted this technology and model the share of sales paid for with this alternative currency, using both ordinary and Tobit regressions. Our analysis examines how country, customer and company-specific characteristics interact with the proportion of sales attributed to Bitcoin. We find that company features, use of other payment methods, customers’ knowledge about Bitcoin, as well as the size of both the official and unofficial economy are significant determinants. The results presented allow a better understanding of the practical and theoretical ramifications of this innovation.


CORRELATIONS BETWEEN ELEMENTS AND SEQUENCES IN A NUMERICAL PRISM


title: Coverage and Connectivity Analysis of Millimeter Wave Vehicular Networks

abstract: The next generations of vehicles will require data transmission rates in the order of terabytes per driving hour, to support advanced automotive services. This unprecedented amount of data to be exchanged goes beyond the capabilities of existing communication technologies for vehicular communication and calls for new solutions. A possible answer to this growing demand for ultra-high transmission speeds can be found in the millimeter-wave (mmWave) bands which, however, are subject to high signal attenuation and challenging propagation characteristics. In particular, mmWave links are typically directional, to benefit from the resulting beamforming gain, and require precise alignment of the transmitter and the receiver beams, an operation which may increase the latency of the communication and lead to deafness due to beam misalignment. In this paper, we propose a stochastic model for characterizing the beam coverage and connectivity probability in mmWave automotive networks. The purpose is to exemplify some of the complex and interesting tradeoffs that have to be considered when designing solutions for vehicular scenarios based on mmWave links. The results show that the performance of the automotive nodes in highly mobile mmWave systems strictly depends on the specific environment in which the vehicles are deployed, and must account for several automotive-specific features such as the nodes speed, the beam alignment periodicity, the base stations density and the antenna geometry.


title: From Monolith to Microservices: A Dataflow-Driven Approach

abstract: Emerging from the agile practitioner communities, the microservice-oriented architecture emphasizes implementing and employing multiple small-scale and independently deployable microservices, rather than encapsulating all function capabilities into one monolithic application. Correspondingly, microservice-oriented decomposition, which has been identified to be an extremely challenging and complex task, plays a crucial and prerequisite role in developing microservice-based software systems. To address this challenge and reduce the complexity, we proposed a top-down analysis approach and developed a dataflow-driven decomposition algorithm. In brief, a three-step process is defined: first, engineers together with users conduct business requirement analysis and construct a purified while detailed dataflow diagram of the business logic; then, our algorithm combines the same operations with the same type of output data into a virtual abstract dataflow; finally, the algorithm extracts individual modules of "operation and its output data" from the virtual abstract dataflow to represent the identified microservice candidates. We have employed two use cases to demonstrate our microservice identification mechanism, as well as making comparisons with an existing microservice identification tool. The comparison and evaluation show that, our dataflow-driven identification mechanism is able to deliver more rational, objective, understandable and consistent microservice candidates, through a more rigorous and practical implementation procedure.


title: Predictive modeling for monocular vision based rail track extraction

abstract: Camera based approaches are the most widely analyzed applications of track extraction for railway systems. Finalizing the extraction with an analytic representation of the rail tracks is very useful both for representing the smooth characteristics of the railways well and for compensating the faulty image processing results. Moreover, polynomial or geometric definition of the rails creates parametric representations, which can be transformed to generate a dynamic ROI around the rails and can be used for applications like obstacle detection. However, the existing studies only use presumed knowledge and assumptions about the geometry of railway tracks. This paper provides a statistical analysis of railway track turns on various video records to quantify the margins of variations. Moreover, based on the results of those statistical analyses, this study introduces a method to predict the railway tracks by means of polynomial approximation followed by multilayer perceptron networks.


title: Legolizer: A Real-Time System for Modeling and Rendering LEGO Representations of Boundary Models

abstract: In this work we propose a method for converting triangular meshes into LEGO bricks through a voxel representation of boundary meshes. We present a novel voxelization approach that uses points sampled from a surface model to define which cubes (voxels) and their associated colors will compose the model. All steps of the algorithm were implemented on the GPU and real-time performance was achieved with satisfactory volumetric resolutions. Rendering results are illustrated using realistic graphics techniques such as screen space ambient occlusion and irradiance maps.


title: Generation Alpha at the Intersection of Technology, Play and Motivation

abstract: This paper considers the intersection of technology and play through the novel approach of gamification and its application to early years education. The intrinsic connection between play and technology is becoming increasingly significant in early years education. By creating an awareness of the early years adoption of technology into guiding frameworks, and then exploring the makeup of gaming elements, this paper draws connections for guiding principles in adopting more technology-focused play opportunities for Generation Alpha.


title: TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters

abstract: TetriSched is a scheduler that works in tandem with a calendaring reservation system to continuously re-evaluate the immediate-term scheduling plan for all pending jobs (including those with reservations and best-effort jobs) on each scheduling cycle. TetriSched leverages information supplied by the reservation system about jobs' deadlines and estimated runtimes to plan ahead in deciding whether to wait for a busy preferred resource type (e.g., machine with a GPU) or fall back to less preferred placement options. Plan-ahead affords significant flexibility in handling mis-estimates in job runtimes specified at reservation time. Integrated with the main reservation system in Hadoop YARN, TetriSched is experimentally shown to achieve significantly higher SLO attainment and cluster utilization than the best-configured YARN reservation and CapacityScheduler stack deployed on a real 256 node cluster.


title: A Cognitive System for Understanding Human Manipulation Actions

abstract: This paper describes the architecture of a cognitive system that interprets human manipulation actions from perceptual information (image and depth data) and that includes interacting modules for perception and reasoning. Our work contributes to two core problems at the heart of action understanding: (a) the grounding of relevant information about actions in perception (the perception-action integration problem), and (b) the organization of perceptual and high-level symbolic information for interpreting the actions (the sequencing problem). At the high level, actions are represented with the Manipulation Action Grammar, a context-free grammar that organizes actions as a sequence of sub events. Each sub event is described by the hand, movements, objects and tools involved, and the relevant information about these factors is obtained from biologicallyinspired perception modules. These modules track the hands and objects, and they recognize the hand grasp, objects and actions using attention, segmentation, and feature description. Experiments on a new data set of manipulation actions show that our system extracts the relevant visual information and semantic representation. This representation could further be used by the cognitive agent for reasoning, prediction, and planning.


title: Biometric System Security

abstract: Security is “freedom from risk or danger”, while computer and data security is “the ability of a system to protect information and system resources with respect to confidentiality and integrity”. Defining biometrics system security is difficult, because of the ways biometric systems differ from traditional computer and cryptographic security [40]. Implicit in all definitions is the concept of an attacker; however, biometrics should always be assumed to operate in an (at least somewhat) hostile environment – after all, why should one test identity if all can be trusted? The ability of a biometric system to stand up to “zero-effort” attackers is measured by the false accept rate (FAR). Attackers may then change makeup, facial hair and glasses, or abrade and cut fingerprints in order to avoid being recognized; attackers prepared to try harder may use spoofing . This chapter deals with attacks which are not spoofing, but those that target processing within the biometric system. We define biometric system security by its absence. Since biometrics is “automated recognition of individuals based on their behavioral and biological characteristics”, a vulnerability in biometric security results in incorrect recognition or failure to correctly recognize individuals. This definition includes methods to falsely accept an individual (template regeneration), impact overall system performance (denial of service), or to attack another system via leaked data (identity theft). Vulnerabilities are measured against explicit or implicit design claims.


title: Exploring the Security Vulnerabilities of LoRa

abstract: Internet-of-Things (IoT) deployments increasingly incorporate long range communication technologies. To support this transition, wide area IoT deployments are employing LoRa as their communication technology of choice due to its low power consumption and long range. The security of LoRa networks and devices is currently being put to the test in the wild, and has already become a major challenge. New features and characteristics of LoRa technology also intorduce new vulnerabilities against security attacks. In this paper, we investigate potential security vulnerabilities in LoRa. In particular, we analyze the LoRa network stack and discuss the possible susceptibility of LoRa devices to different types of attacks using commercial-off-the-shelf hardware. Our analysis shows that the long range transmissions of LoRa are vulnerable to multiple security attacks.


title: Influence of Rotor Geometry of an IPM Motor on Sensorless Control Feasibility

abstract: The sensorless control technique based on the superimposition of a high-frequency rotating voltage vector is commonly used to detect the rotor position of a synchronous interior permanent magnet (IPM) motor. This technique is effective at zero and low motor speed, where back E.M.F. is null or extremely low. Then the accuracy of the rotor position detection depends strictly on the rotor saliency, that is, on the geometry of the IPM rotor. The aim of this paper is to determine some IPM rotor design criteria so as to improve the sensorless rotor position detection. Since the design has to be effective in different operating conditions, both saturation and cross-coupling effects have to be taken into account. It is found that the effectiveness of the sensorless rotor position detection can be improved by means of a proper design of the IPM motor geometry, even under heavy operating conditions.


title: A Proposed Approach with Analysis of Speech Signals for Sentiment Detection

abstract: In last few decades Sentiment Analysis is one of the most researched area in the field of Speech Recognition. Different methods like use of textual data, face expressions, voice signals, body movements and physiological signals were adopted for sentiment detection. Presented paper discuss about emotions/Sentiment and proposed an approach for analyzing these sentiment based on speech signals of an individual. Paper proposed an approach for extracting speech signals produced by an individual and also provides some results which show variation in pitch and time of an individual's voice during different emotions. Signals were produced by different individual in different sentiment/moods. Signals are being analyzed using different tools which helps in generating graphs for different signals. This paper discuss about three basic sentiment/emotions were observed in this paper i.e. Angry, sad and neutral (relaxed). The data collected from every individual i.e. From both male and female with different age range.


title: Security and privacy for storage and computation in cloud computing

abstract: Cloud computing emerges as a new computing paradigm that aims to provide reliable, customized and quality of service guaranteed computation environments for cloud users. Applications and databases are moved to the large centralized data centers, called cloud. Due to resource virtualization, global replication and migration, the physical absence of data and machine in the cloud, the stored data in the cloud and the computation results may not be well managed and fully trusted by the cloud users. Most of the previous work on the cloud security focuses on the storage security rather than taking the computation security into consideration together. In this paper, we propose a privacy cheating discouragement and secure computation auditing protocol, or SecCloud, which is a first protocol bridging secure storage and secure computation auditing in cloud and achieving privacy cheating discouragement by designated verifier signature, batch verification and probabilistic sampling techniques. The detailed analysis is given to obtain an optimal sampling size to minimize the cost. Another major contribution of this paper is that we build a practical secure-aware cloud computing experimental environment, or SecHDFS, as a test bed to implement SecCloud. Further experimental results have demonstrated the effectiveness and efficiency of the proposed SecCloud.


title: G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering

abstract: Abstract With the advent of Web 2.0, we see a new and differentiated scenario: there is more data than that can be effectively analyzed. Organizing this data has become one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to the Data Mining area, specifically the clustering algorithms. However, these algo- rithms are still a computational challenge because of the volume of data that needs to be processed. We found in the literature some proposals to make these algorithms feasible, and, recently, those related to parallelization on graphics processing units (GPUs) have presented good results. In this work we present the G-DBSCAN, a GPU parallel version of one of the most widely used clustering algorithms, the DBSCAN. Although there are other parallel versions of this algorithm, our technique distinguishes itself by the simplicity with which the data are indexed, using graphs, allowing various parallelization opportu- nities to be explored. In our evaluation we show that the G-DBSCAN using GPU, can be over 100 x faster than its sequential version using CPU.


title: IOT based smart garbage alert system using Arduino UNO

abstract: Waste management is one of the primary problem that the world faces irrespective of the case of developed or developing country. The key issue in the waste management is that the garbage bin at public places gets overflowed well in advance before the commencement of the next cleaning process. It in turn leads to various hazards such as bad odor & ugliness to that place which may be the root cause for spread of various diseases. To avoid all such hazardous scenario and maintain public cleanliness and health this work is mounted on a smart garbage system. The main theme of the work is to develop a smart intelligent garbage alert system for a proper garbage management. This paper proposes a smart alert system for garbage clearance by giving an alert signal to the municipal web server for instant cleaning of dustbin with proper verification based on level of garbage filling. This process is aided by the ultrasonic sensor which is interfaced with Arduino UNO to check the level of garbage filled in the dustbin and sends the alert to the municipal web server once if garbage is filled. After cleaning the dustbin, the driver confirms the task of emptying the garbage with the aid of RFID Tag. RFID is a computing technology that is used for verification process and in addition, it also enhances the smart garbage alert system by providing automatic identification of garbage filled in the dustbin and sends the status of clean-up to the server affirming that the work is done. The whole process is upheld by an embedded module integrated with RF ID and IOT Facilitation. The real time status of how waste collection is being done could be monitored and followed up by the municipality authority with the aid of this system. In addition to this the necessary remedial / alternate measures could be adapted. An Android application is developed and linked to a web server to intimate the alerts from the microcontroller to the urban office and to perform the remote monitoring of the cleaning process, done by the workers, thereby reducing the manual process of monitoring and verification. The notifications are sent to the Android application using Wi-Fi module.


Chronic lichen sclerosus successfully treated with intralesional adalimumab


title: What makes you click?—Mate preferences in online dating

abstract: We estimate mate preferences using a novel data set from an online dating service. The data set contains detailed information on user attributes and the decision to contact a potential mate after viewing his or her profile. This decision provides the basis for our preference estimation approach. A potential problem arises if the site users strategically shade their true preferences. We provide a simple test and a bias correction method for strategic behavior. The main findings are (i) There is no evidence for strategic behavior. (ii) Men and women have a strong preference for similarity along many (but not all) attributes. (iii) In particular, the site users display strong same-race preferences. Race preferences do not differ across users with different age, income, or education levels in the case of women, and differ only slightly in the case of men. For men, but not for women, the revealed same-race preferences correspond to the same-race preference stated in the users’ profile. (iv) There are gender differences in mate preferences; in particular, women have a stronger preference than men for income over physical attributes.


title: Bistatic and Multistatic Radar Systems

abstract: Radar systems, based on bistatic radar concept attracted a substantial attention in the recent years. Pas- sive coherent location systems using "transmitters of op- portunity" like radio or TV broadcasters, GSM base sta- tions, satellite communication and GNSS signals proved their potential in detection and tracking moving targets over a significant area. In this paper the multistatic loca- tion system with non-cooperative transmitters is described and various aspects of signal processing and signal pa- rameters are discussed.


title: Link Prediction Based on Whale Optimization Algorithm

abstract: Link prediction problem has a wide variety of useful applications in diverse fields. For example, in bio-informatics, such as protein-protein interaction metabolic and diseases-gene networks, there are links between nodes, which indicate they have an interaction relationship. This paper presents a possible solution to the well known link prediction problem using a Whale Optimization algorithm (WOA), which is considered as one of the recent optimization algorithms. The link prediction problem is formulated as an optimization problem to predict the links in any type of the networks. Experimental results of the proposed algorithm (WOA-LP) on a number of real networks are good evidence that the proposed approach can enhance the link prediction accuracy.


title: Data structures for traveling salesmen

abstract: The choice of data structure for tour representation plays a critical role in the efficiency of local improvement heuristics for the traveling salesman problem. The tour data structure must permit queries about the relative order of cities in the current tour and must allow sections of the tour to be reversed. The traditional array-based representation of a tour permits the relative order of cities to be determined in small constant time, but requires worst-case ?(N) time (where N is the number of cities) to implement a reversal, which renders it impractical for large instances. This paper considers alternative tour data structures, examining them from both a theoretical and experimental point of view. The first alternative we consider is a data structure based on splay trees, where all queries and updates take amortized time O(log N). We show that this is close to the best possible, because in the cell probe model of computation any data structure must take worst-case amortized time ?(log N/log log N) per operation. Empirically (for random Euclidean instances), splay trees overcome their large constant-factor overhead and catch up to arrays by N = 10,000, pulling ahead by a factor of 4-10 (depending on machine) when N = 100,000. Two alternative tree-based data structures do even better in this range, however. Although both are asymptotically inferior to the splay tree representations, the latter does not appear to pull even with them until N ~ to 1,000,000.


title: Fast Active Set Methods for Online Deconvolution of Calcium Imaging Data

abstract: Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of $O(10^5)$ traces of whole-brain larval zebrafish imaging data on a laptop.


title: Fault diagnosis of in-wheel BLDC motor drive for electric vehicle application

abstract: Permanent magnet Brushless DC (BLDC) motors have been attracted by electric vehicle (EV) manufacturers in the last decade. The paper presents a simple fault diagnosis technique to detect switch faults of three phases Voltage Source Inverter (VSI) drive of BLDC motor in a closed-loop control scheme. The proposed fault diagnosis system is capable to detect the fault occurrence, identify fault type and the faulty switch of inverter based on Discrete Fourier Transform (DFT) analysis of the measured line voltages of BLDC motor. BLDC motor drive and the proposed fault diagnosis system are simulated. Simulation results were validated first by experimental data for BLDC motor operation under healthy condition. A knowledge based table is developed to identify switch faults of VSI by analyzing the simulation results under various fault conditions. The proposed fault diagnosis algorithm does not need massive computational effort and can be implemented as a subroutine with a closed-loop control algorithm of the BLDC motor on a single chip microcontroller. The obtained results show correct detection and identification of inverter switch faults in BLDC motor.


title: Person Re-identification with Hierarchical Deep Learning Feature and efficient XQDA Metric

abstract: Feature learning and metric learning are two important components in person re-identification (re-id). In this paper, we utilize both aspects to refresh the current State-Of-The-Arts (SOTA). Our solution is based on a classification network with label smoothing regularization (LSR) and multi-branch tree structure. The insight is that some middle network layers are found surprisingly better than the last layers on the re-id task. A Hierarchical Deep Learning Feature (HDLF) is thus proposed by combining such useful middle layers. To learn the best metric for the high-dimensional HDLF, an efficient eXQDA metric is proposed to deal with the large-scale big-data scenarios. The proposed HDLF and eXQDA are evaluated with current SOTA methods on five benchmark datasets. Our methods achieve very high re-id results, which are far beyond state-of-the-art solutions. For example, our approach reaches 81.6%, 96.1% and 95.6% Rank-1 accuracies on the ILIDS-VID, PRID2011 and Market-1501 datasets. Besides, the code and related materials (lists of over 1800 re-id papers and 170 top conference re-id papers) are released for research purposes.


title: Extractive Video Summarizer with Memory Augmented Neural Networks

abstract: Online videos have been growing explosively in recent years. How to help human users efficiently browse videos becomes more and more important. Video summarization can automatically shorten a video through extracting key-shots from the raw video, which is helpful for digesting video data. State-of-the-art supervised video summarization algorithms directly learn from manually-created summaries to mimic the key-frame/key-shot selection criterion of humans. Humans usually create a summary after viewing and understanding the whole video, and the global attention mechanism capturing information from all video frames plays a key role in the summarization process. However, previous supervised approaches ignored the temporal relations or simply modeled local inter-dependency across frames. Motivated by this observation, we proposed a memory augmented extractive video summarizer, which utilizes an external memory to record visual information of the whole video with high capacity. With the external memory, the video summarizer simply predicts the importance score of a video shot based on the global understanding of the video frames. The proposed method outperforms previous state-of-the-art algorithms on the public SumMe and TVSum datasets. More importantly, we demonstrate that the global attention modeling has two advantages: good transferring ability across datasets and high robustness to noisy videos.


title: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

abstract: In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly.


title: Image description with features that summarize

abstract: We present a new method for describing images for the purposes of matching and registration. We take the point of view that large, coherent regions in the image provide a concise and stable basis for image description. We develop a new algorithm for feature detection that operates on several projections (feature spaces) of the image using kernel-based optimization techniques to locate local extrema of a continuous scale-space of image regions. Descriptors of these image regions and their relative geometry then form the basis of an image description. The emphasis of the work is on features that summarize image content and are highly robust to viewpoint changes and occlusion yet remain discriminative for matching and registration.We present experimental results of these methods applied to the problem of image retrieval. We find that our method performs comparably to two published techniques: Blobworld and SIFT features. However, compared to these techniques two significant advantages of our method are its (1) stability under large changes in the images and (2) its representational efficiency.