title: DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks

abstract: We propose an action recognition framework using Gen- erative Adversarial Networks. Our model involves train- ing a deep convolutional generative adversarial network (DCGAN) using a large video activity dataset without la- bel information. Then we use the trained discriminator from the GAN model as an unsupervised pre-training step and fine-tune the trained discriminator model on a labeled dataset to recognize human activities. We determine good network architectural and hyperparameter settings for us- ing the discriminator from DCGAN as a trained model to learn useful representations for action recognition. Our semi-supervised framework using only appearance infor- mation achieves superior or comparable performance to the current state-of-the-art semi-supervised action recog- nition methods on two challenging video activity datasets: UCF101 and HMDB51.


title: Leveraging Citation Networks to Visualize Scholarly Influence Over Time

abstract: Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars.


title: Vehicular mobility simulation with VanetMobiSim

abstract: During the last few years, continuous progresses in wireless communications have opened new research fields in computer networking, aimed at extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular communication is attracting growing attention from both academia and industry, owing to the amount and importance of the related applications, ranging from road safety to traffic control and up to mobile entertainment. Vehicular Ad-hoc Networks (VANETs) are self-organized networks built up from moving vehicles, and are part of the broader class of Mobile Ad-hoc Networks (MANETs). Owing to their peculiar characteristics, VANETs require the definition of specific networking techniques, whose feasibility and performance are usually tested by means of simulation. One of the main challenges posed by VANETs simulations is the faithful characterization of vehicular mobility at both the macroscopic and microscopic levels, leading to realistic non-uniform distributions of cars and velocity, and unique connectivity dynamics. However, freely distributed tools which are commonly used for academic studies only consider limited vehicular mobility issues, while they pay little or no attention to vehicular traffic generation and its interaction with its motion constraints counterpart. Such a simplistic approach can easily raise doubts on the confidence of derived VANETs simulation results. In this paper we present VanetMobiSim, a freely available generator of realistic vehicular movement traces for networks simulators. The traces generated by VanetMobiSim are validated first by illustrating how the interaction between featured motion constraints and traffic generator models is able to reproduce typical phenomena of vehicular traffic. Then, the traces are formally validated against those obtained by TSIS-CORSIM, a benchmark traffic simulator in transportation research. This makes VanetMobiSim one of the few vehicular mobility simulator fully validated and freely available to the vehicular networks research community.


title: A testbed for secure and robust SCADA systems

abstract: The Supervisory Control and Data Acquisition System (SCADA) monitor and control real-time systems. SCADA systems are the backbone of the critical infrastructure, and any compromise in their security can have grave consequences. Therefore, there is a need to have a SCADA testbed for checking vulnerabilities and validating security solutions. In this paper we develop such a SCADA testbed.


title: Value and Growth Investing: Review and Update

abstract: A great deal of academic empirical research has been published on value and growth investing. We review and update this literature, discuss the various explanations for the performance of value versus growth stocks, review the empirical research on the alternative explanations, and provide some new results based on an updated and expanded sample. The evidence suggests that, even after taking into account the experience of the late 1990s, value investing generates superior returns. Common measures of risk do not support the argument that the return differential is a result of the higher riskiness of value stocks. Instead, behavioral considerations and the agency costs of delegated investment management lie at the root of the value–growth spread.


Challenges to Technological Advancement in Economically Weak Countries: An Assessment of the Nigerian Educational Situation


title: Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)

abstract: A novel method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects with fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method is validated by experiments on both synthetic and real images. The experimental results show that the proposed method outperformed two popular methods.


title: Google news personalization: scalable online collaborative filtering

abstract: Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.


title: A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

abstract: We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov's scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov's scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.


title: A Cognitive Policy Framework for Next-Generation Distributed Federated Systems: Concepts and Research Directions

abstract: Next-generation collaborative activities and missions will be carried out by autonomous groups of devices with a large variety of cognitive capabilities. These devices will have to operate in environments characterized by uncertainty, insecurity (both physical and cyber), and instability. In such environments, communications may be fragmented. Proper policy-based management of such autonomous device groups is thus critical. However current policy management systems have many limitations, including lack of flexibility. In this paper, we articulate novel architectural approaches addressing the requirements for the effective management of autonomous groups of devices and discuss the notion of generative policies - a novel paradigm that enhances the flexibility of policy-based approaches to management. In this paper, we also survey types of policy that are essential for managing device groups. Even though many such policy types exist in conventional settings, their use in our context poses novel challenges that we articulate in the paper. We also introduce a research roadmap discussing several research directions towards the development of a cognitive and flexible policy-based approach to the management of autonomous groups of devices for collaborative missions. Finally, as our proposed policy paradigm is data-intensive, we discuss the problem of supplying the data required for policy decisions in environments characterized by mobility, uncertainly, and fragmented communications.


Real-time camera based lane detection for autonomous driving


title: The struggling student: a thematic analysis from the self-regulated learning perspective

abstract: Context ::: ::: Students who engage in self-regulated learning (SRL) are more likely to achieve academic success compared with students who have deficits in SRL and tend to struggle with academic performance. Understanding how poor SRL affects the response to failure at assessment will inform the development of better remediation. ::: ::: ::: ::: Methods ::: ::: Semi-structured interviews were conducted with 55 students who had failed the final re-sit assessment at two medical schools in the UK to explore their use of SRL processes. A thematic analysis approach was used to identify the factors, from an SRL perspective, that prevented students from appropriately and adaptively overcoming failure, and confined them to a cycle of recurrent failure. ::: ::: ::: ::: Results ::: ::: Struggling students did not utilise key SRL processes, which caused them to make inappropriate choices of learning strategies for written and clinical formats of assessment, and to use maladaptive strategies for coping with failure. Their normalisation of the experience and external attribution of failure represented barriers to their taking up of formal support and seeking informal help from peers. ::: ::: ::: ::: Conclusions ::: ::: This study identified that struggling students had problems with SRL, which caused them to enter a cycle of failure as a result of their limited attempts to access formal and informal support. Implications for how medical schools can create a culture that supports the seeking of help and the development of SRL, and improves remediation for struggling students, are discussed.


title: Smart home energy management systems based on non-intrusive load monitoring

abstract: The concept of smart home has attracted considerable attention in recent years, and energy management is one of its key component. This attributes to the growing concern in environmental protection and energy conservation, as well as the demands for big data collection from utility companies and policy makers. Current solutions often approach this problem by either the centralized non-intrusive load monitoring (NILM) or the decentralized smart controls but seldom both, rendering them impractical to some extent. Therefore, in this paper, we propose a novel framework of smart home energy management systems incorporating both approaches, so that accurate power consumption monitoring and intuitive interaction with the home appliances are simultaneously achieved. The smart components directly control the appliances, while the central controller coordinates the data collection and communication. The key feature is the capability of automatically mapping the appliances to their corresponding sockets, reducing the necessity for manual initial setup. Numerical simulations prove the accuracy and efficiency of the framework. We believe that our systems, if widely deployed, can benefit not only individual households by saving energy bills and simplifying life but also society by the big data generated.


title: Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

abstract: Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. However, the cooperation of small and mini drones in a network is capable of further improving the performance and the coverage area of UAVs. There are numerous new challenges to be solved before the widespread introduction of multi-UAV-based heterogeneous flying ad hoc networks (FANET), including the formulation of a stable network structure. Meanwhile, an efficient gateway-selection algorithm and management mechanism is required. However, the stability control of the hierarchical UAV network guarantees the efficient collaboration of the drones. In this article, we begin by surveying the FANET structure and its protocol architecture. Then, a variety of distributed gateway-selection algorithms and cloud-based stability-control mechanisms are addressed, complemented by a range of open challenges.


title: A Novel Binary Neural Network with Enhanced Dense Connection

abstract: Binary Neural Networks (BNNs), whose weights and activations can be represented by a single bit, are gaining more and more attention. Because their inference speed and memory consumption are far superior to full-precision neural networks. However, the previous work on BNNs often leads to severe accuracy degradation. To address this issue, we propose a novel BNN model named Binary Densely Connected Network (BDCN), which connects each mlpconv (a multilayer perceptron to extract better features) layer to all preceding mlpconv layers. Such enhanced dense connection can strengthen the representation capabilities of BNN, which is beneficial to improve the accuracy of the network. This work further narrows the accuracy gap between BNNs and their full-precision counterpart. We evaluate our BDCN on a range of visual datasets (MNIST, CIFAR-10 and ImageNet) and find that our method outperforms other state-of-art binary networks whilst keeping the model size acceptable.


title: Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life

abstract: Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile)-based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.


title: Preventing the 51%-Attack: a Stochastic Analysis of Two Phase Proof of Work in Bitcoin

abstract: The security of Bitcoin (a relatively new form of a distributed ledger) is threatened by the formation of large public pools, which form naturally in order to reduce reward variance for individual miners. By introducing a second cryptographic challenge (two phase proof-of-work or 2P-PoW for short), pool operators are forced to either give up their private keys or provide a substantial part of their pool’s mining hashrate which potentially forces pools to become smaller. This document provides a stochastic analysis of the Bitcoin mining protocol extended with 2PPoW, modelled using CTMCs (continuous-time Markov chains). 2P-PoW indeed holds its promises, according to these models. A plot is provided for dierent "strengths" of the second cryptographic challenge, which can be used to select proper values for future implementers.


title: Disciplinary differences in the use of academic social networking sites

abstract: Purpose – The purpose of this paper is to detect and describe disciplinary differences in the users and use of several social networking sites by scientists. Design/methodology/approach – Consejo Superior de Investigaciones Cientificas (CSIC) (Spanish National Research Council) researchers registered in the most currently relevant academic social network sites (Google Scholar Citations, Academia.edu, ResearchGate (RG) and Mendeley) were analysed. In total, 6,132 profiles were classified according the eight research areas of the CSIC. Findings – Results show that Academia.edu is massively populated by humanists and social scientists, while RG is popular among biologists. Disciplinary differences are observed across every platform. Thus, scientists from the humanities and social sciences and natural resources show a significant activity contacting other members. On the contrary, biologists are more passive using social tools. Originality/value – This is the first study that analyses the disciplinary perform...


title: Entrepreneurial finance, credit cards, and race $

abstract: This paper examines the impact of financial deregulation on entrepreneurship. We assess the impact of credit card deregulation on transitions into self-employment using state-level removal of credit card interest rate ceilings following the US Supreme Court's 1978 Marquette decision as a quasi-natural experiment. We find that credit card deregulation increases the probability of entrepreneurial entry, with a particularly strong effect for black entrepreneurs. We demonstrate that these effects are magnified in states with a history of racial discrimination and link the results to discrimination-based barriers to entry.


The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes


title: CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

abstract: Stock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short-term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide. CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using artificial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two scenarios. In the first, it will predict a market - in our case, the Spanish IBEX 35 stock market. In the second, it will predict single-company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.


UNDERSTANDING THE ROLE OF SOCIAL PRESENCE IN CROWDFUNDING: EVIDENCE FROM LEADING U.S. AND GERMAN PLATFORMS


title: Comparison of data mining techniques for Money Laundering Detection System

abstract: The work of a police analyst, who inspects money laundering cases, is strongly based on ability to find patterns in a large amounts of financial data, consisting of entries describing bank accounts and money transfers. Out of the need for tools simplifying process of analyzing such data, came the Money Laundering Detection System (MLDS). The mechanisms of money laundering and the system itself are shortly presented in this paper. The main part focuses on implemented algorithms that help finding suspicious patterns in the money flow. Two independent sets of experiments were performed focusing on different algorithms implemented in MLDS, in which their performance was measured, compared and summarized.


title: Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets

abstract: Mobile crowdsourcing markets (e.g., Gigwalk and TaskRabbit) offer crowdworkers tasks situated in the physical world (e.g., checking street signs, running household errands). The geographic nature of these tasks distinguishes these markets from online crowdsourcing markets and raises new, fundamental questions. We carried out a controlled study in the Chicago metropolitan area aimed at addressing two key questions: (1) What geographic factors influence whether a crowdworker will be willing to do a task? (2) What geographic factors influence how much compensation a crowdworker will demand in order to do a task? Quantitative modeling shows that travel distance to the location of the task and the socioeconomic status (SES) of the task area are important factors. Qualitative analysis enriches our modeling, with workers mentioning safety and difficulties getting to a location as key considerations. Our results suggest that low-SES areas are currently less able to take advantage of the benefits of mobile crowdsourcing markets. We discuss the implications of our study for these markets, as well as for "sharing economy" phenomena like UberX, which have many properties in common with mobile crowdsourcing markets.


title: CITIESData: a smart city data management framework

abstract: Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.


title: Real-time Video Summarization on Commodity Hardware

abstract: We present a method for creating video summaries in real-time on commodity hardware. Real-time here refers to the fact that the time required for video summarization is less than the duration of the input video. First, low-level features are use to discard undesirable frames. Next, video is divided into segments, and segment-level features are extracted for each segment. Tree-based models trained on widely available video summarization and computational aesthetics datasets are then used to rank individual segments, and top-ranked segments are selected to generate the final video summary. We evaluate the proposed method on The SumMe Video Summarization (SumMe) dataset and show that our method is able to achieve summarization accuracy that is comparable to that of a current state-of-the-art deep learning method, while posting significantly faster run-times. Our method on average is able to generate a video summary in time that is shorter than the duration of the video.


'Fitness that Fits': A Prototype Model for Workout Video Recommendation


title: Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

abstract: We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the layers is not the optimal way to incorporate randomness. Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way. Second, we formulate the mini-max problem in BNN to learn the best model distribution under adversarial attacks, leading to an adversarial-trained Bayesian neural net. Experiment results demonstrate that the proposed algorithm achieves state-of-the-art performance under strong attacks. On CIFAR-10 with VGG network, our model leads to 14\% accuracy improvement compared with adversarial training (Madry 2017) and random self-ensemble (Liu 2017) under PGD attack with $0.035$ distortion, and the gap becomes even larger on a subset of ImageNet.


Solving the Trolley Problem


title: An interferometric coherence optimization method in radar polarimetry for high-resolution imagery

abstract: This paper investigates to what extent a new interferometric coherence optimization in radar polarimetry allows the separation of point scatterers located in the same resolution cell according to their interferometric phases. An interferometric coherence definition called the single-mechanism coherence is introduced, and the corresponding optimization method is briefly discussed. This method was first validated theoretically when no volume decorrelation occurs. Then, it has been applied to simple target measurements acquired in an anechoic chamber, and to an X-band polarimetric and interferometric synthetic aperture radar image containing man-made targets. In both cases, the single-mechanism coherence optimization enables to resolve the interferometric phases of several scattering centers inside the same resolution cell.


title: Urban pattern: layout design by hierarchical domain splitting

abstract: We present a framework for generating street networks and parcel layouts. Our goal is the generation of high-quality layouts that can be used for urban planning and virtual environments. We propose a solution based on hierarchical domain splitting using two splitting types: streamline-based splitting, which splits a region along one or multiple streamlines of a cross field, and template-based splitting, which warps pre-designed templates to a region and uses the interior geometry of the template as the splitting lines. We combine these two splitting approaches into a hierarchical framework, providing automatic and interactive tools to explore the design space.


title: VM consolidation: A real case based on OpenStack Cloud

abstract: In recent years, Cloud computing has been emerging as the next big revolution in both computer networks and Web provisioning. Because of raised expectations, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers, and some open-source solutions are also underway, such as Eucalyptus and OpenStack. Cloud architectures exploit virtualization techniques to provision multiple Virtual Machines (VMs) on the same physical host, so as to efficiently use available resources, for instance, to consolidate VMs in the minimal number of physical servers to reduce the runtime power consumption. VM consolidation has to carefully consider the aggregated resource consumption of co-located VMs, in order to avoid performance reductions and Service Level Agreement (SLA) violations. While various works have already treated the VM consolidation problem from a theoretical perspective, this paper focuses on it from a more practical viewpoint, with specific attention on the consolidation aspects related to power, CPU, and networking resource sharing. Moreover, the paper proposes a Cloud management platform to optimize VM consolidation along three main dimensions, namely power consumption, host resources, and networking. Reported experimental results point out that interferences between co-located VMs have to be carefully considered to avoid placement solutions that, although being feasible from a more theoretical viewpoint, cannot ensure VM provisioning with SLA guarantees. ? We discuss VM consolidation issues in Cloud Infrastructure as a Service (IaaS). ? We survey related works to clarify current state-of-the-art and ongoing research. ? We propose a management infrastructure for the open-source OpenStack Cloud. ? We highlight interferences due to network virtualization between co-located VMs.


title: Patent Citation Recommendation for Examiners

abstract: There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.


title: Android Anti-forensics: Modifying CyanogenMod

abstract: Mobile devices implementing Android operating systems inherently create opportunities to present environments that are conducive to anti-forensic activities. Previous mobile forensics research focused on applications and data hiding anti-forensics solutions. In this work, a set of modifications were developed and implemented on a CyanogenMod community distribution of the Android operating system. The execution of these solutions successfully prevented data extractions, blocked the installation of forensic tools, created extraction delays and presented false data to industry accepted forensic analysis tools without impacting normal use of the device. The research contribution is an initial empirical analysis of the viability of operating system modifications in an anti-forensics context along with providing the foundation for future research.


title: Fucoxanthin from edible seaweed , Undaria pinnatifida , shows antiobesity effect through UCP 1 expression in white adipose tissues q

abstract: Mitochondrial uncoupling protein 1 (UCP1) is usually expressed only in brown adipose tissue (BAT) and a key molecule for metabolic thermogenesis to avoid an excess of fat accumulation. However, there is little BAT in adult humans. Therefore, UCP1 expression in tissues other than BAT is expected to reduce abdominal fat. Here, we show reduction of abdominal white adipose tissue (WAT) weights in rats and mice by feeding lipids from edible seaweed, Undaria pinnatifida. Clear signals of UCP1 protein and mRNA were detected in WAT of mice fed the Undaria lipids, although there is little expression of UCP1 in WAT of mice fed control diet. The Undaria lipids mainly consisted of glycolipids and seaweed carotenoid, fucoxanthin. In the fucoxanthin-fed mice, WAT weight significantly decreased and UCP1 was clearly expressed in the WAT, while there was no difference in WAT weight and little expression of UCP1 in the glycolipids-fed mice. This result indicates that fucoxanthin upregulates the expression of UCP1 in WAT, which may contribute to reducing WAT weight.


title: Using constraint programming and local search methods to solve vehicle routing problems

abstract: We use a local search method we term Large Neighbourhood Search (LNS) to solve vehicle routing problems. LNS is analogous to the shuffling technique of job-shop scheduling, and so meshes well with constraint programming technology. LNS explores a large neighbourhood of the current solution by selecting a number of related customer visits to remove from the set of planned routes, and re-inserting these visits using a constraint-based tree search. Unlike similar methods, we use Limited Discrepancy Search during the tree search to re-insert visits. We analyse the performance of our method on benchmark problems. We demonstrate that results produced are competitive with Operations Research meta-heuristic methods, indicating that constraint-based technology is directly applicable to vehicle routing problems.


title: Automating function point analysis with model driven development

abstract: This paper describes a technique for automating the size estimation of software projects conducted using Model Driven Development methods. Specifically, an algorithm has been implemented for performing the function point count of a software project starting from its conceptual model. Differently from previous approaches, the proposed technique works on the same conceptual model that is used for producing the implementation, eliminating any unnecessary ad-hoc specification task. We evaluate the precision of the FP computation algorithm on a set of real-world projects and describe its implementation within a commercial Model Driven Development tool suite.


title: Finding and reminding: file organization from the desktop

abstract: This paper summarizes and synthesizes two independent studies of the ways users organize and find files on their computers. The first study (Barreau 1995) investigated information organization practices among users of DOS, Windows and OS/2. The second study (Nardi, Anderson and Erickson 1995), examined the finding and filing practices of Macintosh users. There were more similarities in the two studies than differences. Users in both studies (1) preferred location-based finding because of its crucial reminding function; (2) avoided elaborate filing schemes; (3) archived relatively little information; and (4) worked with three types of information: ephemeral, working and archived. A main difference between the study populations was that the Macintosh users used subdirectories to organize information and the DOS users did not.


title: Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

abstract: Abstract Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.


title: Cloud Free Line of Sight Prediction Modeling for Optical Satellite Communication Networks

abstract: Analytical models for the prediction of cloud-free line-of-sight (CFLOS) probability for a single optical satellite link and for multiple spatially separated optical satellite links are presented. The presence of clouds along the slant paths causes their blockage. As a result, an ON/OFF channel with the presence of clouds is assumed. The proposed model for the calculation of CFLOS considers the elevation angle of the optical satellite links, the altitude of ground stations, and the spatial variability of clouds. The methodology is based on the assumption that integrated liquid water content (ILWC) follows lognormal distribution and a well-defined relationship between ILWC and liquid water density. Moreover, joint CFLOS statistics are evaluated and CFLOS probability for simultaneously available optical links for the application of spatial multiplexing transmission techniques is estimated. The models are validated through simulated results and finally, the numerical results are devoted to optical satellite network examples with a view to achieving specific availability percentage.


title: Facing a psychopath: Detecting the dark triad from emotionally-neutral faces, using prototypes from the Personality Faceaurus

abstract: Abstract Is facial structure a valid cue of the dark triad of personality (Machiavellianism, narcissism, and psychopathy)? I obtained self-reports and peer reports of personality as well as expression-neutral photographs of targets, and then I created prototypes of people high and low on each of the three dimensions by digitally combining select photographs of Caucasian targets. The results indicated that unacquainted observers reliably detected the dark triad composite, especially in female prototypes. Thus, not only is the dark triad a set of psycho-social characteristics—it may also be a set of physical–morphological characteristics. In the Discussion, I introduce a website that stores these personality prototypes and many others ( http://www.nickholtzman.com/faceaurus.htm ).


title: Regional differences in synaptogenesis in human cerebral cortex

abstract: The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical regions begins in the fetus, before conceptual age 27 weeks. Synaptic density increases more rapidly in auditory cortex, where the maximum is reached near postnatal age 3 months. Maximum synaptic density in middle frontal gyrus is not reached until after age 15 months. Synaptogenesis occurs concurrently with dendritic and axonal growth and with myelination of the subcortical white matter. A phase of net synapse elimination occurs late in childhood, earlier in auditory cortex, where it has ended by age 12 years, than in prefrontal cortex, where it extends to midadolescence. Synaptogenesis and synapse elimination in humans appear to be heterochronous in different cortical regions and, in that respect, appears to differ from the rhesus monkey, where they are concurrent. In other respects, including overproduction of synaptic contacts in infancy, persistence of high levels of synaptic density to late childhood or adolescence, the absolute values of maximum and adult synaptic density, and layer specific differences, findings in the human resemble those in rhesus monkeys.


title: Device mismatch and tradeoffs in the design of analog circuits

abstract: Random device mismatch plays an important role in the design of accurate analog circuits. Models for the matching of MOS and bipolar devices from open literature show that matching improves with increasing device area. As a result, accuracy requirements impose a minimal device area and this paper explores the impact of this constraint on the performance of general analog circuits. It results in a fixed bandwidth-accuracy-power tradeoff which is set by technology constants. This tradeoff is independent of bias point for bipolar circuits whereas for MOS circuits some bias point optimizations are possible. The performance limitations imposed by matching are compared to the limits imposed by thermal noise. For MOS circuits the power constraints due to matching are several orders of magnitude higher than for thermal noise. For the bipolar case the constraints due to noise and matching are of comparable order of magnitude. The impact of technology scaling on the conclusions of this work are briefly explored.


title: A study of retrospective and on-line event detection

abstract: This paper investigates the use and extension ::: of text retrieval and clustering techniques for event ::: detection. The task is to automatically detect novel ::: events from a temporally-ordered stream of news stories, ::: either retrospectively or as the stories arrive. We applied ::: hierarchical and non-hierarchical document clustering algorithms ::: to a corpus of 15,836 stories, focusing on the ::: exploitation of both content and temporal information. ::: We found the resulting cluster hierarchies highly informative ::: for retrospective detection of previously unidentified ::: events, effectively supporting both query-free and ::: query-driven retrieval. We also found that temporal distribution ::: patterns of document clusters provide useful ::: information for improvement in both retrospective detection ::: and on-line detection of novel events. In an ::: evaluation using manually labelled events to judge the ::: system-detected events, we obtained a result of 82% in ::: the Fl measure for retrospective detection, and a Fl ::: value of 42% for on-line detection.


title: The Five-Factor Model of Personality and Managerial Performance: Validity Gains Through the Use of 360 Degree Performance Ratings

abstract: This study investigated the usefulness of the five-factor model (FFM) of personality in predicting two aspects of managerial performance (task vs. contextual) assessed by utilizing the 360 degree performance rating system. The authors speculated that one reason for the low validity of the FFM might be the failure of single-source (e.g., supervisor) ratings to comprehensively capture the construct of managerial performance. The operational validity of personality was found to increase substantially (50%-74%) across all of the FFM personality traits when both peer and subordinate ratings were added to supervisor ratings according to the multitrait-multimethod approach. Furthermore, the authors responded to the recent calls to validate tests via a multivariate (e.g., multitrait-multimethod) approach by decomposing overall managerial performance into task and contextual performance criteria and by using multiple rating perspectives (sources). Overall, this study contributes to the evidence that personality may be even more useful in predicting managerial performance if the performance criteria are less deficient.


title: New axial-flux switched reluctance motor for e-scooter

abstract: The world market of e-scooter is expected to experiment an increase of 15% in Western Europe between 2015 and 2025. In order to push this growth it is needed to develop new low-cost more efficient and reliable drives with high torque to weight ratio. In this paper a new axial-flux switched reluctance motor is proposed in order to accomplish this goal. The motor is constituted by a stator sandwiched by two rotors in which the ferromagnetic parts are made of soft magnetic composites. It has a new disposition of the stator and the rotor poles and shorter flux paths Simulations have demonstrated that the proposed axial-flux switched reluctance motor drive is able to meet the requirements of an e-scooter.


title: Autonomous car driving by a humanoid robot

abstract: Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor-based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP-2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.


title: Influence of music and its genres on respiratory rate and pupil diameter variations in cats under general anaesthesia: contribution to promoting patient safety

abstract: OBJECTIVES ::: The aims of the study were to recognise if there is any auditory sensory stimuli processing in cats under general anaesthesia, and to evaluate changes in respiratory rate (RR) and pupillary diameter (PD) in anaesthetised patients exposed to different music genres, while relating this to the depth of anaesthesia. ::: ::: ::: METHODS ::: A sample of 12 cats submitted for elective ovariohysterectomy was exposed to 2 min excerpts of three different music genres (classical [CM], pop [PM] and heavy metal [HM]) at three points during surgery (T1 = coeliotomy; T2 = ligature placement and transection of the ovarian pedicle; T3 = ligature placement and transection of the uterine body). A multiparametric medical monitor was used to measure the RR, and a digital calliper was used for PD measurement. Music was delivered through headphones, which fully covered the patient's ears. P values <0.05 were considered to be statistically significant. ::: ::: ::: RESULTS ::: Statistically significant differences between stimuli conditions for all surgical points were obtained for RR (T1, P = 0.03; T2, P = 0.00; T3, P = 0.00) and for PD (T1, P = 0.03; T2, P = 0.04; T3, P = 0.00). Most individuals exhibited lower values for RR and PD when exposed to CM, intermediate values to PM and higher values to HM. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: The results suggest that cats under general anaesthesia are likely to perform auditory sensory stimuli processing. The exposure to music induces RR and PD variations modulated by the genre of music and is associated with autonomic nervous system activity. The use of music in the surgical theatre may contribute to allowing a reduced anaesthetic dose, minimising undesirable side effects and thus promoting patient safety.


title: Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

abstract: The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the"Droplet"strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.


title: Real-time detection and prevention of android SMS permission abuses

abstract: The Android permission system informs users about the privileges demanded by applications (apps), and in principle allows users to assess potential risks of apps. Unfortunately, recent studies showed that the installation-time permission verification procedure is often ignored, due to users' lack of attention or insufficient understanding of the privileges or the Android permission system. As a consequence, malicious apps are likely granted with security- and privacy-critical permissions, and launch various kinds of attacks without being noticed by the users. In this paper, we present the design, analysis, and implementation of DroidPAD, a novel solution that aims to leverage system-wide state information to detect and block in real-time possible abuses of Android permissions. Especially, with a focus on SMS-related permissions, we have implemented a proof-of-concept prototype. Our evaluation based on 48 representative benign and malicious apps shows that DroidPAD successfully detected SMS permissions-abusing activities with low false-positive rates, and zero false-negative rates.


title: Genetically modified food versus knowledge and fear: A Noumenic approach for consumer behaviour

abstract: The theme of genetically modified organisms is very important for modern consumers, especially when they approach novel foods. In this paper, we have attempted to assess the impact of genetically modified foods on the consumers' preferences, considering a new vision of ours: however, the conclusions also form a topic for further discussion. We conducted an investigation on a sample size survey. The analysis was carried out in a representative sample of more than 900 Italian families, selected based on a functional relationship to the objectives of the work. The aims of the present study were: firstly, investigating consumers' preferences regarding genetically modified food consumption and developing a quantitative model to formalize the origins of behaviours regarding consumers' preferences toward genetically modified food consumption; secondly, detecting the drivers of their purchase, underlining that only by reasoning it is possible to ensure that specific variables do not condition purchasing behaviour.


title: Articial Intelligence and Intelligent Design

abstract: Interlocutors in the debate over Intelligent Design frequently talk past each other, failing to understand why their arguments are not compelling to the other side. We bring a few insights and analogies from the eld of computer science to the playing eld, in hopes of making the discussion more clear and constructive. In particular, we introduce the imagery of local optima as a means of quickly visualizing the limits of evolution and the problem of irreducible complexity. Emphasizing a method of constructive, respectful dialogue between world views, we take inspiration from Bayesian statistics and use graphical models to diagram the hermeneutic cycle of the dierent camps { a poor understanding of which frequently serves to aggravate an already palpable aversion to dialogue. We then consider the role that observations and computer simulations of emergent complexity can play in confounding attempts to infer design from apparent irreducible complexity, and nally discuss the possibility that computer simulations of evolutionary processes can serve as a proxy for measuring our understanding of evolutionary mechanisms. We conclude that, while Intelligent Design can form a valid part of a religious world view founded on personal experience or some other evidence, the current state of its observations are sucient neither to prove the existence of a designer nor to disprove evolution.


title: Chaos in Charged Gauss-Bonnet AdS Black Holes in Extended Phase Space

abstract: We study the onset of chaos due to temporal and spatially periodic perturbations in charged Gauss-Bonnet AdS black holes in extended thermodynamic phase space, by analyzing the zeros of the appropriate Melnikov functions. Temporal perturbations coming from a thermal quench in the unstable spinodal region of P-V diagram, may lead to chaos, when a certain perturbation parameter $\gamma$ saturates a critical value, involving the Gauss-Bonnet coupling $\alpha$ and the black hole charge $Q$. A general condition following from the equation of state is found, which can rule out the existence of chaos in any black hole. Using this condition, we find that the presence of charge is necessary for chaos under temporal perturbations. In particular, chaos is absent in neutral Gauss-Bonnet and Lovelock black holes in general dimensions. Chaotic behavior continues to exist under spatial perturbations, irrespective of whether the black hole carries charge or not.


title: Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines

abstract: Metabolomics is a rapidly advancing field, and much of our understanding of the subject has come from research on cell lines. However, the results and interpretation of such studies depend on appropriate normalization of the data; ineffective or poorly chosen normalization methods can lead to frankly erroneous conclusions. That is a recurrent challenge because robust, reliable methods for normalization of data from cells have not been established. In this study, we have compared several methods for normalization of metabolomic data from cell extracts. Total protein concentration, cell count, and DNA concentration exhibited strong linear correlations with seeded cell number, but DNA concentration was found to be the most generally useful method for the following reasons: (1) DNA concentration showed the greatest consistency across a range of cell numbers; (2) DNA concentration was the closest to proportional with cell number; (3) DNA samples could be collected from the same dish as the metabolites; and (4)...


title: On the dynamic model of a two-wheeled inverted pendulum robot

abstract: Currently existing dynamic models for a two-wheeled inverted pendulum mobile robot have some common mistakes. In order to find where the errors of the dynamic model are induced, Lagrangian method and Kane's method are compared in deriving the equation of motion. Numerical examples are given to illustrate the effect of the incorrect terms. Finally, a complete dynamic model is proposed without any error and missing terms.


title: Human pose estimation via Convolutional Part Heatmap Regression

abstract: This paper is on human pose estimation using Convolutional Neural Networks. Our main contribution is a CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions. To this end, we propose a detection-followed-by-regression CNN cascade. The first part of our cascade outputs part detection heatmaps and the second part performs regression on these heatmaps. The benefits of the proposed architecture are multi-fold: It guides the network where to focus in the image and effectively encodes part constraints and context. More importantly, it can effectively cope with occlusions because part detection heatmaps for occluded parts provide low confidence scores which subsequently guide the regression part of our network to rely on contextual information in order to predict the location of these parts. Additionally, we show that the proposed cascade is flexible enough to readily allow the integration of various CNN architectures for both detection and regression, including recent ones based on residual learning. Finally, we illustrate that our cascade achieves top performance on the MPII and LSP data sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/.


title: Social Capital: Prospects for a New Concept

abstract: A growing number of sociologists, political scientists, economists, and organizational theorists have invoked the concept of social capital in the search for answers to a broadening range of questions being confronted in their own fields. Seeking to clarify the concept and help assess its utility for organizational theory, we synthesize the theoretical research undertaken in these various disciplines and develop a common conceptual framework that identifies the sources, benefits, risks, and contingencies of social capital.


title: Predicting Big Five Personality Traits of Microblog Users

abstract: Personality can be defined as a set of characteristics which makes a person unique. The study of personality is of central importance in psychology. Conventional personality assessment is performed by self-report inventory, which costs much manual efforts and cannot be done in real time. To solve these problems, this research aims to measure the Big-Five personality from the usages of Sina Microblog objectively. By conducting a user study with 444 users, this paper proposes multi-task regression and incremental regression algorithms to predict the Big-Five personality from online behaviors. The results indicate that personality can be predicted with a high accuracy through online Microblog usage.


title: A Cost-Based Security Analysis of Symmetric and Asymmetric Key Lengths

abstract: Recently, there has been considerable debate concerning key sizes for publ i c key based cry p t o graphic methods. Included in the debate have been considerations about equivalent key sizes for diffe rent methods and considerations about the minimum re q u i red key size for diffe rent methods. In this paper we propose a method of a n a lyzing key sizes based upon the value of the data being protected and the cost of b reaking ke y s . I . I n t ro d u c t i o n A . W H Y I S K E Y S I Z E I M P O R T A N T ? In order to keep transactions based upon public key cryptography secure, one must ensure that the underlying keys are sufficiently large as to render the best possible attack infeasible. However, this really just begs the question as one is now left with the task of defining ‘infeasible’. Does this mean infeasible given access to (say) most of the Internet to do the computations? Does it mean infeasible to a large adversary with a large (but unspecified) budget to buy the hardware for an attack? Does it mean infeasible with what hardware might be obtained in practice by utilizing the Internet? Is it reasonable to assume that if utilizing the entire Internet in a key breaking effort makes a key vulnerable that such an attack might actually be conducted? If a public effort involving a substantial fraction of the Internet breaks a single key, does this mean that similar sized keys are unsafe? Does one need to be concerned about such public efforts or does one only need to be concerned about possible private, sur reptitious efforts? After all, if a public attack is known on a particular key, it is easy to change that key. We shall attempt to address these issues within this paper. number 13 Apr i l 2000 B u l l e t i n News and A dv i c e f rom RSA La bo rat o r i e s I . I n t ro d u c t i o n I I . M et ho ds o f At tac k I I I . H i s tor i ca l R es u l t s and t he R S A Ch a l le nge I V. Se cu r i t y E st i m ate s


GEOMETRICAL INTERPRETATION OF QUANTUM FORMALISM


title: Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support

abstract: We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks , a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support.


title: Marker-less Deformable Mesh Tracking for Human Shape and Motion Capture

abstract: We present a novel algorithm to jointly capture the motion and the dynamic shape of humans from multiple video streams without using optical markers. Instead of relying on kinematic skeletons, as traditional motion capture methods, our approach uses a deformable high-quality mesh of a human as scene representation. It jointly uses an image-based 3D correspondence estimation algorithm and a fast Laplacian mesh deformation scheme to capture both motion and surface deformation of the actor from the input video footage. As opposed to many related methods, our algorithm can track people wearing wide apparel, it can straightforwardly be applied to any type of subject, e.g. animals, and it preserves the connectivity of the mesh over time. We demonstrate the performance of our approach using synthetic and captured real-world video sequences and validate its accuracy by comparison to the ground truth.


title: Social Engineering 2.0: A Foundational Work: Invited Paper

abstract: During the past few years, social engineering has rapidly evolved and has become a mainstream technique in cybercrime and terrorism. It is used especially in targeted attacks involving complex human and technological exploits, aimed at deceiving humans and IT systems. Building on the work carried out in the DOGANA project, funded by the European Union, this paper provides an overview of the evolution and of the current landscape of social engineering, and introduces as its main contribution a theoretical model of how human exploits are built, named the Victim Communication Stack.


title: How to Succeed in Crowdfunding: a Long-Term Study in Kickstarter

abstract: Crowdfunding platforms have become important sites where people can create projects to seek funds toward turning their ideas into products, and back someone else's projects. As news media have reported successfully funded projects (e.g., Pebble Time, Coolest Cooler), more people have joined crowdfunding platforms and launched projects. But in spite of rapid growth of the number of users and projects, a project success rate at large has been decreasing because of launching projects without enough preparation and experience. Little is known about what reactions project creators made (e.g., giving up or making the failed projects better) when projects failed, and what types of successful projects we can find. To solve these problems, in this manuscript we (i) collect the largest datasets from Kickstarter, consisting of all project profiles, corresponding user profiles, projects' temporal data and users' social media information; (ii) analyze characteristics of successful projects, behaviors of users and understand dynamics of the crowdfunding platform; (iii) propose novel statistical approaches to predict whether a project will be successful and a range of expected pledged money of the project; (iv) develop predictive models and evaluate performance of the models; (v) analyze what reactions project creators had when project failed, and if they did not give up, how they made the failed projects successful; and (vi) cluster successful projects by their evolutional patterns of pledged money toward understanding what efforts project creators should make in order to get more pledged money. Our experimental results show that the predictive models can effectively predict project success and a range of expected pledged money.


title: Ground-based image analysis: A tutorial on machine-learning techniques and applications

abstract: Ground-based whole-sky cameras have opened up new opportunities for monitoring the earth?s atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole-sky imagers (WSI) can have high spatial and temporal resolution, which is an important prerequisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, and more. Extracting the valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine-learning techniques have become available to aid with the image analysis. This article provides a detailed explanation of recent developments in these techniques and their applications in ground-based imaging, aiming to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine-learning techniques in ground-based image analysis via three primary applications: segmentation, classification, and denoising.


IT Consumerization: When Gadgets Turn Into Enterprise IT Tools


title: An Alternative Approach for Calculation of Braking Force of an Eddy-Current Brake

abstract: We have analyzed an eddy-current brake (ECB), using calculations based on the Fourier transform method. Here, we compare our results with test results and numerical results of the finite-element code ANSYS 11.0. We use our model to study the influence of certain design parameters on the torque-speed characteristics.


title: The Origin of Concepts

abstract: Only human beings have a rich conceptual repertoire with concepts like tort, entropy, Abelian group, mannerism, icon and deconstruction. How have humans constructed these concepts? And once they have been constructed by adults, how do children acquire them? While primarily focusing on the second question, in The Origin of Concepts, Susan Carey shows that the answers to both overlap substantially. Carey begins by characterizing the innate starting point for conceptual development, namely systems of core cognition. Representations of core cognition are the output of dedicated input analyzers, as with perceptual representations, but these core representations differ from perceptual representations in having more abstract contents and richer functional roles. Carey argues that the key to understanding cognitive development lies in recognizing conceptual discontinuities in which new representational systems emerge that have more expressive power than core cognition and are also incommensurate with core cognition and other earlier representational systems. Finally, Carey fleshes out Quinian bootstrapping, a learning mechanism that has been repeatedly sketched in the literature on the history and philosophy of science. She demonstrates that Quinian bootstrapping is a major mechanism in the construction of new representational resources over the course of childrens cognitive development. Carey shows how developmental cognitive science resolves aspects of long-standing philosophical debates about the existence, nature, content, and format of innate knowledge. She also shows that understanding the processes of conceptual development in children illuminates the historical process by which concepts are constructed, and transforms the way we think about philosophical problems about the nature of concepts and the relations between language and thought.


Deep reinforcement learning for de novo drug design


title: Scene classification based on single-layer SAE and SVM

abstract: The SAE and SVM based scene classification method achieves excellent performance.A single-layer SAE is proposed to extract sufficient and appropriate features.A mean-pooling operation is proposed to reduce the dimension of feature vectors.A PSO-based algorithm is employed to optimize the parameters for the SVM classifier. Scene classification aims to group images into semantic categories. It is a challenging problem in computer vision due to the difficulties of intra-class variability and inter-class similarity. In this paper, a scene classification approach based on single-layer sparse autoencoder (SAE) and support vector machine (SVM) is proposed. This approach consists of two steps: SAE-based feature learning step and SVM-based classification step. In the first step, a single-layer SAE network is constructed and trained by the patches which are sampled randomly from the source images. The feature representation of images is learned by the trained single-layer SAE network. Meanwhile, a pooling operation is used to reduce the dimension of the learned feature vectors. In the second step, in order to improve the classification accuracy, the parameters of SVM are optimized by a particle swarm optimization (PSO) based algorithm. The one-versus-one strategy is employed for the multiple scene classification problem. To show the efficiency of the proposed approach, several public data sets are employed. The results reveal that the proposed approach achieves better classification accuracy than the existing state-of-the-art methods.


title: Behind the Article: Recognizing Dialog Acts in Wikipedia Talk Pages

abstract: In this paper, we propose an annotation schema for the discourse analysis of Wikipedia Talk pages aimed at the coordination efforts for article improvement. We apply the annotation schema to a corpus of 100 Talk pages from the Simple English Wikipedia and make the resulting dataset freely available for download. Furthermore, we perform automatic dialog act classification on Wikipedia discussions and achieve an average F1-score of 0.82 with our classification pipeline.


title: Internet of Things (IoT): A Literature Review

abstract: One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently.


title: A hybrid ARIMA and support vector machines model in stock price forecasting

abstract: Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.


title: Survival in Patients with Degenerative Mitral Stenosis: Results from a Large Retrospective Cohort Study.

abstract: BACKGROUND ::: Severe mitral annular calcification causing degenerative mitral stenosis (DMS) is increasingly encountered in patients undergoing mitral and aortic valve interventions. However, its clinical profile and natural history and the factors affecting survival remain poorly characterized. The goal of this study was to characterize the factors affecting survival in patients with DMS. ::: ::: ::: METHODS ::: An institutional echocardiographic database was searched for patients with DMS, defined as severe mitral annular calcification without commissural fusion and a mean transmitral diastolic gradient of ≥2 mm Hg. This resulted in a cohort of 1,004 patients. Survival was analyzed as a function of clinical, pharmacologic, and echocardiographic variables. ::: ::: ::: RESULTS ::: The patient characteristics were as follows: mean age, 73 ± 14 years; 73% women; coronary artery disease in 49%; and diabetes mellitus in 50%. The 1- and 5-year survival rates were 78% and 47%, respectively, and were slightly worse with higher DMS grades (P = .02). Risk factors for higher mortality included greater age (P < .0001), atrial fibrillation (P = .0009), renal insufficiency (P = .004), mitral regurgitation (P < .0001), tricuspid regurgitation (P < .0001), elevated right atrial pressure (P < .0001), concomitant aortic stenosis (P = .02), and low serum albumin level (P < .0001). Adjusted for propensity scores, use of renin-angiotensin system blockers (P = .02) or statins (P = .04) was associated with better survival, and use of digoxin was associated with higher mortality (P = .007). ::: ::: ::: CONCLUSIONS ::: Prognosis in patients with DMS is poor, being worse in the aged and those with renal insufficiency, atrial fibrillation, and other concomitant valvular lesions. Renin-angiotensin system blockers and statins may confer a survival benefit, and digoxin use may be associated with higher mortality in these patients.


title: GraphGAN: Graph Representation Learning with Generative Adversarial Nets

abstract: The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.


title: Analysis of Stock Price Flow Based on Social Media Sentiments

abstract: Prediction of mood uses the sentiment word lists obtained in various sources where general state of mood can be found using such word list or emotion tokens. With the number of messages posted on Stock Twits, it is believed that the general state of mood can be predicted with certain statistical significance. This paper explores the relationship between Stock Twits messages relationship with stock market movement, and how well, sentiment extracted from these feeds can be related to the shifts in stock prices. For this case we chose Apple Inc to perform the analysis, and estimate its accuracy.


title: Quantum versus Classical Pushdown Automata in Exact Computation

abstract: Even though quantum computation is useful for solving certain problems, classical computation is more powerful in some cases. Thus, it is significant to compare the abilities of quantum computation and its classical counterpart, based on such a simple computation model as automata. In this paper we focus on the quantum pushdown automata which were defined by Golovkins in 2000, who showed that the class of languages recognized by quantum pushdown automata properly contains the class of languages recognized by finite automata. However, no one knows the entire relationship between the recognitive abilities of quantum and classical pushdown automata. As a part, we show a proposition that quantum pushdown automata can deterministically solve a certain problem that cannot be solved by any deterministic pushdown automata.


title: The emergence of network media logic in political communication: A theoretical approach

abstract: In this article we propose a concept of network media logic in order to discuss how online social media platforms change political communication without resorting to technological determinism or normalization. We argue that social media platforms operate with a distinctly different logic from that of traditional mass media, though overlapping with it. This is leading to different ways of producing content, distributing information and using media. By discussing the differences between traditional mass media and social media platforms in terms of production, consumption and use, we carve out the central elements of network media logic – that is, the rules/format of communication on social media platforms – and some consequences for political communication.


title: An effect analysis of industry 4.0 to higher education

abstract: From history to the present day, especially in the field of technology is provided a wide range of developments. The most important of these developments has achieved the realization of the industrial revolution, has led to play a leading role in the production is done by human beings. The effects on production of industry 4.0 emerging with this mind is very important. The qualified employees should be trained to make the necessary preparations for the company in this period, it is experiencing the fourth industrial revolution. Therefore, the impacts on higher education of industry 4.0 are examined in this paper. The importance in education of industry 4.0 has revealed with statistical data presented in this study.


title: Security Threats to Automotive CAN Networks --- Practical Examples and Selected Short-Term Countermeasures

abstract: The IT security of automotive systems is an evolving area of research. To analyse the current situation we performed several practical tests on recent automotive technology, focusing on automotive systems based on CAN bus technology. With respect to the results of these tests, in this paper we discuss selected countermeasures to address the basic weaknesses exploited in our tests and also give a short outlook to requirements, potential and restrictions of future, holistic approaches.


title: Degree of Modularity in Engineering Systems and Products with Technical and Business Constraints

abstract: There is consensus that modularity has many benefits from cost savings due to increased commonality to enabling a higher variety of products. Full modularity is, however, not always achievable. How engineering systems and products whose design is heavily influenced by technical constraints, such as weight or size limitations, tend to exhibit rather integral architectures is shown in this study. For this, two metrics are defined on the basis of a binary design structure matrix (DSM) representation of a system or product. The non-zero fraction (NZF) captures the sparsity of the interrelationships between components between zero and one, while the singular value modularity index (SMI) captures the degree of internal coupling, also between zero and one. These metrics are first developed using idealized canonical architectures and are then applied to two different product pairs that are functionally equivalent, but different in terms of technical constraints. Empirical evidence is presented that the lightweight variant of the same product tends to be more integral, presumably to achieve higher mass efficiency. These observations are strengthened by comparing the results to another, previously published, modularity metric as well as by comparing sparsity and modularity of a set of 15 products against a control population of randomly generated architectures of equivalent size and density. The results suggest that, indeed, some products are inherently less modular than others due to technological factors. The main advantage of SMI is that it enables analysis of the degree of modularity of any architecture independent of subjective module choices.


title: Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows

abstract: BACKGROUND ::: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. ::: ::: ::: RESULTS ::: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. ::: ::: ::: CONCLUSIONS ::: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://alan.cs.gsu.edu/vira.html (AmpMCF) and http://alan.cs.gsu.edu/NGS/?q=content/shotmcf (ShotMCF).


title: Synthesizing Implementations of Abstract Data Types from Axiomatic Specifications

abstract: This paper describes a system for automatically generating an implementation of an abstract data type from its axiomatic specifications. Such a system can be useful for rapid prototyping and for detecting inconsistencies in the specifications by testing the generated implementation. In the generated Implementation, an instance of the data type is represented by its state. An operation on the data type is implemented by a collection of functions — a function for each of the axioms specified for the operation, and a function for the operation that determines, depending on the state of the instance(s) on which the operation is being performed, which of the axioms of the operation is applicable. The system is developed on a Sun-3 workstation running Unix. It is written in C and generates the implementation of the abstract data type in C.


title: A Survey on Resource Management in IoT Operating Systems

abstract: Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices’ diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted.


title: Adaptive configuration of lora networks for dense IoT deployments

abstract: Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.


title: Compressive sensing applied to radar systems: an overview

abstract: Modern radar systems tend to utilize high bandwidth, which requires high sampling rate, and in many cases, these systems involve phased array configurations with a large number of transmit–receive elements. In contrast, the ultimate goal of a radar system is often to estimate only a limited number of target parameters. Thus, there is a pursuit to find better means to perform the radar signal acquisition as well as processing with much reduced amount of data and power requirement. Recently, there has been a great interest to consider compressive sensing (CS) for radar system design; CS is a novel technique which offers the framework for sparse signal detection and estimation for optimized data handling. In radars, CS enables the achievement of better range-Doppler resolution in comparison with the traditional techniques. However, CS requires the selection of suitable (sparse) signal model, the design of measurement system as well as the implementation of appropriate signal recovery method. This work attempts to present an overview of these CS aspects, particularly when CS is applied in monostatic pulse-Doppler and MIMO type of radars. Some of the associated challenges, e.g., grid mismatch and detector design issues, are also discussed.


title: A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

abstract: We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.


title: Everyday magical powers : The role of apparent mental causation in the overestimation of personal influence

abstract: These studies examined whether having thoughts related to an event before it occurs leads people to infer that they caused the event—even when such causation might otherwise seem magical. In Study 1, people perceived that they had harmed another person via a voodoo hex. These perceptions were more likely among those who had first been induced to harbor evil thoughts about their victim. In Study 2, spectators of a peer’s basketball-shooting performance were more likely to perceive that they had influenced his success if they had first generated positive visualizations consistent with that success. Observers privy to those spectators’ visualizations made similar attributions about the spectators’ influence. Finally, additional studies suggested that these results occur even when the thought-about outcome is viewed as unwanted by the thinker and even in field settings where the relevant outcome is occurring as part of a live athletic competition.


title: Fault Tolerance for Stream Processing Engines

abstract: Distributed Stream Processing Engines (DSPEs) target applications related to continuous computation, online machine learning and real-time query processing. DSPEs operate on high volume of data by applying lightweight operations on real-time and continuous streams. Such systems require clusters of hundreds of machine for their deployment. Streaming applications come with various requirements, i.e., low-latency, high throughput, scalability and high availability. In this survey, we study the fault tolerance problem for DSPEs. We discuss fault tolerance techniques that are used in modern stream processing engines that are Storm, S4, Samza, SparkStreaming and MillWheel. Further, we give insight on fault tolerance approaches that we categorize as active replication, passive replication and upstream backup. Finally, we discuss implications of the fault tolerance techniques for different streaming application requirements.


title: Neural Programmer-Interpreters

abstract: We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.


title: Online social spammer detection

abstract: The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.


title: Inference of gene regulatory networks via multiple data sources and a recommendation method

abstract: Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to map the GRNs inspiring problem into a recommendation problem and then used a known recommendation method to predict gene relationships based on multiple data sources, e.g., which molecules regulate others. The method we used is based on Pareto dominance and collaborative filtering. For the experiments, we used a combination of two datasets, namely microarray data and transcription factor (TF) binding data. The reported results show that using information from multiple sources improves the performance. Also, we observed that employing an approach from the recommendation systems domain revealed interesting results and good performance.


title: A brief evaluation of Intel®MPX

abstract: MPX implements hardware accelerated support for detection and prevention of memory corruption. This paper will examine the effectiveness of MPX. Herein we attempt to find false positives and false negatives, and to determine what attacks may still be feasible. In particular we wish to see if a system protected by MPX is still exploitable. Intel MPX appears to provide a solid mitigation technique, but may be vulnerable in special circumstances related to how it depends on the surrounding framework to function.