title: Cybercrime Victimization: An Examination of Individual and Situational Level Factors

abstract: IntroductionOver the past two decades, cybercrime has emerged as a salient area of inquiry for criminologists and a growing concern for public policy. Although there are many definitions of cybercrime, the term generally refers to crimes committed through the use of computers and computer networks, but it also includes crimes that do not rely heavily on computers (Britz, 2008). Extant research has explored the nature and extent of cybercrime (Cukier & Levin, 2009; Finley, 2009; Finn, 2004; Geis et al., 2009; Huang et al., 2009; Jaishankar, Halder, & Ramdoss, 2009; Ponte, 2009; Stroik & Huang, 2009), correlates of offending and victimization (Berg, 2009; Bossler & Holt, 2010; Buzzell et al., 2006; Choi, 2008; Higgins, 2005; 2006; Higgins, Fell & Wilson, 2007; Higgins & Makin, 2004; Higgins, Wolfe & Marcum, 2008; Holt & Bossler, 2009; Marcum, 2008; Skinner & Fream, 1997; Turgeman-Goldschmidt, 2009) and issues relating to investigating and prosecuting this type of crime (Roberson, 2009; Hinduja, 2009; Shoemaker & Kennedy, 2009). In spite of the considerable and growing scholarship on cybercrime, however, few studies have examined the theoretical causes and correlates of cybercrime victimization.To date, there have been five studies that applied the lifestyles/routine activities perspective (Cohen & Felson, 1979; Hindelang, Gottfredson & Garafalo; 1978) and the general theory of crime (Gottfredson & Hirschi, 1990) to account for cybercrime victimization (Ashalan, 2006; Bossler & Holt, 2010; Choi, 2008; Holt & Bossler, 2009; Marcum, 2008). Overall, the findings generated from these studies underscore the importance of both situational and individual factors in understanding online victimization. However, whether both individual and situational factors predict all types of cybercrime victimization equally remains elusive. For instance, there is evidence that low self-control is a significant predictor of person-based cybercrime victimization (i.e., offenses where a specific person was the target) but not computer-based cybercrime victimization (i.e., offenses where computers and not the individuals were the targets; see Bossler & Holt, 2010). While the expectation would be that criminological theories such as the general theory of crime, routine activities, rational choice, and different versions of social control theory should be able to explain different types of crimes and different subtypes of cybercrimes equally because they claim to be general theories, our empirical knowledge on this matter is rather meager. Hence, assessing the role that individual and situational factors play in certain forms of cybercrime victimization will be pertinent not only to the development of a theoretical framework on cybercrime victimization but also on the advancement of the victimology scholarship and providing information for specific public policies.To that end, the current study explores the effects of individual and situational factors on seven forms of cybercrime: computer virus, unwanted exposure to pornographic materials, sex solicitation, online harassment by a stranger, online harassment by a nonstranger, phishing and online defamation.3 In particular, this study applies the general theory of crime (Gottfredson & Hirschi, 1990) and the lifestyles/routine activities perspective (Cohen & Felson, 1979; Hindelang, Gottfredson & Garofalo, 1978) to determine if low levels of self-control, and exposure to motivated offenders, online risky behaviors and activities, and capable guardianship affect the above seven forms of cybercrime crime similarly. In the following sections, we first review the lifestyles/routine activities perspective and the general theory of crime. We also discuss the empirical evidence among exposure to motivated offenders, online risky behaviors and activities, capable guardianship, low levels of self-control and cybercrime victimization. …


title: Measuring discrimination in algorithmic decision making

abstract: Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational data, such models may systematically disadvantage people belonging to certain categories or groups, instead of relying solely on individual merits. This may happen even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies of how to make predictive models free from discrimination, when the historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination-aware data mining is an emerging research discipline, and there is no firm consensus yet of how to measure the performance of algorithms. The goal of this survey is to review various discrimination measures that have been used, analytically and computationally analyze their performance, and highlight implications of using one or another measure. We also describe measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. This survey is primarily intended for researchers in data mining and machine learning as a step towards producing a unifying view of performance criteria when developing new algorithms for non-discriminatory predictive modeling. In addition, practitioners and policy makers could use this study when diagnosing potential discrimination by predictive models.


title: Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

abstract: Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.


Game , Set , Match-LSTM : Question Answering on SQUaD


title: Bayesian Optimisation for informative continuous path planning

abstract: Environmental monitoring with mobile robots requires solving the informative path planning problem. A key challenge is how to compute a continuous path over space and time that will allow a robot to best sample the environment for an initially unknown phenomenon. To address this problem we devise a layered Bayesian Optimisation approach that uses two Gaussian Processes, one to model the phenomenon and the other to model the quality of selected paths. By using different acquisition functions over both models we tackle the exploration-exploitation trade off in a principled manner. Our method optimises sampling over continuous paths and allows us to find trajectories that maximise the reward over the path. We test our method on a large scale experiment for modelling ozone concentration in the US, and on a mobile robot modelling the changes in luminosity. Comparisons are presented against information based criteria and point-based strategies demonstrating the benefits of our method.


title: Automated traffic classification and application identification using machine learning

abstract: The dynamic classification and identification of network applications responsible for network traffic flows offers substantial benefits to a number of key areas in IP network engineering, management and surveillance. Currently such classifications rely on selected packet header fields (e.g. port numbers) or application layer protocol decoding. These methods have a number of shortfalls e.g. many applications can use unpredictable port numbers and protocol decoding requires a high amount of computing resources or is simply infeasible in case protocols are unknown or encrypted. We propose a novel method for traffic classification and application identification using an unsupervised machine learning technique. Flows are automatically classified based on statistical flow characteristics. We evaluate the efficiency of our approach using data from several traffic traces collected at different locations of the Internet. We use feature selection to find an optimal feature set and determine the influence of different features


title: Directive Minds: How Dynamics Shapes Cognition

abstract: The future progress of cognitive science looks set to involve ever-increasing efforts to anchor research to the real world poles of sensing and acting. Thus anchored, time, world and body emerge as significant players in the cognitive arena. How could we ever have forgotten them? —A. Clark 1995, 101 In the cognitive sciences, we currently witness a “pragmatic turn” 1 away from the traditional representation-centered framework toward a paradigm that focuses on understanding the intimate relation between cognition and action. Such an “action-oriented” paradigm has earliest and most explicitly been developed in robotics, and has only recently begun to have a notable impact on cognitive psychology and neurobiology. The basic concept is that cognition should not be understood as a capacity of deriving world-models, which then might provide a “database” for thinking, planning, and problem solving. Rather, it is emphasized that cognitive processes are not only closely intertwined with action but that cognition can actually best be understood as “enactive,” as a form of practice itself. Cognition, on this account, is grounded in a prerational understanding of the world that is based on sensorimotor acquisition of real-life situations. The goal of this chapter is to explore possible implications of such a “pragmatic turn” for cognitive neuroscience. In addition to reviewing major conceptual components of this new framework, I will discuss neurobiological evidence supporting this notion. Specifically, I will relate this new view to recent findings on the dynamics of signal flow in the nervous system and on encoding dimensions of neural activity patterns. As I will argue, new vistas on the “meaning,” the functional roles, and the presumed “representational” nature of neural processes are likely to emerge from this confrontation. 8


title: Affective Computing for HCI

abstract: Not all computers need to pay attention to emotions, or to have emotional abilities. Some machines are useful as rigid tools, and it is fine to keep them that way. However, there are situations where the human-machine interaction could be improved by having machines naturally adapt to their users, and where communication about when, where, how, and how important it is to adapt involves emotional information, possibly including expressions of frustration, confusion, disliking, interest, and more. Affective computing expands humancomputer interaction by including emotional communication together with appropriate means of handling affective information.


title: A statistics-guided progressive RAST algorithm for peak template matching in GCxGC

abstract: Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation. Chemical identification is one of the critical tasks in GCxGC analysis. Peak template matching is a technique for automatic chemical identification. Peak template matching can be formulated as a point pattern matching problem. This paper proposes a progressive RAST algorithm to solve the problem. Search space pruning techniques based on peak location distributions and transformation distributions are also investigated for guided search. Experiments on seven real data sets indicate that the new techniques are effective.


Critical Thinking: Why Is It So Hard to Teach?


title: Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis

abstract: In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.


title: Learning language through pictures

abstract: We propose Imaginet, a model of learning visually grounded representations of language from coupled textual and visual input. The model consists of two Gated Recurrent Unit networks with shared word embeddings, and uses a multi-task objective by receiving a textual description of a scene and trying to concurrently predict its visual representation and the next word in the sentence. Like humans, it acquires meaning representations for individual words from descriptions of visual scenes. Moreover, it learns to effectively use sequential structure in semantic interpretation of multi-word phrases.


title: Data transmission via GSM voice channel for end to end security

abstract: Global System for Mobile Communications (GSM) technology still plays a key role because of its availability, reliability and robustness. Recently, additional consumer applications are proposed in which GSM is used as a backup or data transmission service. Unfortunately sending data via GSM channel is a challenging task since it is speech sensitive and suppresses other forms of signals. In this paper, a systematic method is proposed to develop a modem that transmits data over GSM voice channel (DoGSMV) using speech like (SL) symbols. Unlike the previous approaches an artificial search space is produced to find best SL symbols and analyses by synthesis (AbyS) method is introduced for parameter decoding. As a result 1.6 kbps simulation data rate is achieved when wireless communication errors are ignored.


title: Evaluating the robustness of repeated measures analyses: The case of small sample sizes and nonnormal data

abstract: Repeated measures analyses of variance are the method of choice in many studies from experimental psychology and the neurosciences. Data from these fields are often characterized by small sample sizes, high numbers of factor levels of the within-subjects factor(s), and nonnormally distributed response variables such as response times. For a design with a single within-subjects factor, we investigated Type I error control in univariate tests with corrected degrees of freedom, the multivariate approach, and a mixed-model (multilevel) approach (SAS PROC MIXED) with Kenward–Roger’s adjusted degrees of freedom. We simulated multivariate normal and nonnormal distributions with varied population variance–covariance structures (spherical and nonspherical), sample sizes (N), and numbers of factor levels (K). For normally distributed data, as expected, the univariate approach with Huynh–Feldt correction controlled the Type I error rate with only very few exceptions, even if samples sizes as low as three were combined with high numbers of factor levels. The multivariate approach also controlled the Type I error rate, but it requires N ≥ K. PROC MIXED often showed acceptable control of the Type I error rate for normal data, but it also produced several liberal or conservative results. For nonnormal data, all of the procedures showed clear deviations from the nominal Type I error rate in many conditions, even for sample sizes greater than 50. Thus, none of these approaches can be considered robust if the response variable is nonnormally distributed. The results indicate that both the variance heterogeneity and covariance heterogeneity of the population covariance matrices affect the error rates.


title: Combining Radiotherapy and Cancer Immunotherapy: A Paradigm Shift

abstract: The therapeutic application of ionizing radiation has been largely based on its cytocidal power combined with the ability to selectively target tumors. Radiotherapy effects on survival of cancer patients are generally interpreted as the consequence of improved local control of the tumor, directly decreasing systemic spread. Experimental data from multiple cancer models have provided sufficient evidence to propose a paradigm shift, whereby some of the effects of ionizing radiation are recognized as contributing to systemic antitumor immunity. Recent examples of objective responses achieved by adding radiotherapy to immunotherapy in metastatic cancer patients support this view. Therefore, the traditional palliative role of radiotherapy in metastatic disease is evolving into that of a powerful adjuvant for immunotherapy. This combination strategy adds to the current anticancer arsenal and offers opportunities to harness the immune system to extend survival, even among metastatic and heavily pretreated cancer patients. We briefly summarize key evidence supporting the role of radiotherapy as an immune adjuvant. A critical appraisal of the current status of knowledge must include potential immunosuppressive effects of radiation that can hamper its capacity to convert the irradiated tumor into an in situ, individualized vaccine. Moreover, we discuss some of the current challenges to translate this knowledge to the clinic as more trials testing radiation with different immunotherapies are proposed.


title: Dropout Training as Adaptive Regularization

abstract: Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an L2 regularizer applied after scaling the features by an estimate of the inverse diagonal Fisher information matrix. We also establish a connection to AdaGrad, an online learning algorithm, and find that a close relative of AdaGrad operates by repeatedly solving linear dropout-regularized problems. By casting dropout as regularization, we develop a natural semi-supervised algorithm that uses unlabeled data to create a better adaptive regularizer. We apply this idea to document classification tasks, and show that it consistently boosts the performance of dropout training, improving on state-of-the-art results on the IMDB reviews dataset.


title: ICT Integration in an M&A Process

abstract: As deals are becoming more complex, and as technology, and the people supporting it, are becoming key drivers of merger and acquisition processes, planning of information and communication technologies in early stages of the integration process is vital to the realization of benefits of an Merger & Acquisition process. This statement is substantiated through review of literature from academics as well as practitioners, and case exemplifications of the financial service organization, the Nordea Group.


title: Accreditation software for higher education programs

abstract: In a higher educational institute, enhancing the educational process and achieving a high quality of education will help decision makers in a better management of resources of the educational institutes. Jordan universities applied for national and international higher education accreditation, whose one of evaluation criteria depends on students' performance and course delivery process. Quality of course delivery process is considered one of the main objectives in the educational field. Currently, universities collect, process, and store huge amount of educational data. The collected data in educational systems include students' performance such as grades of exams, assignments, projects, and quizzes. The absence of knowledge for using the collected data in improving the courses delivery process, has currently made a developing system to face the requirement of accreditation in Jordan is a mandatory. Also, the process of coordinating and assessing the quality of extracting knowledge is a time consuming. Assistance of every decision-maker in higher educational institute is required to enhance the educational process and achieve high quality in the educational field. In addition, the data collected from exams, assignments, and quizzes will take time to be analyzed and evaluated without an automated system. In this work, an automated system was coded and developed in order to face the previous requirements. The system evaluates and enhances the educational process in Jordanian universities. The system converts the collected data in a valuable knowledge that would help in the accreditation process. Once a department in a university enters program learning outcomes or recently called Student Outcomes (SO), Program Objectives (PO), the system requires all courses information including Course Learning Outcomes (CLO), Course Objectives (CO), and assessment policy. Then, the collected data is automatically coordinated and analyzed. Therefore, the system provides different functions including computing the percent of attained course learning outcomes and achieved student outcomes. The system tries to integrate different educational assessments into a unified evaluation report that provides a plan for continues improvements. Also, the report aids the instructors in determining the weakness and strength of course delivery process. In order to make the education procedures easier, the system builds an automated survey and a syllabus for each course. The survey and syllabus are shared among all instructors who teach the same course. At the end, we have improved tools, interfaces, and database to improve assessment process and students' performance. The system currently is adapted by different departments in Jordan University of Science and Technology in Jordan. The system helps decision makers to assess the educational process, aid any institute to accredit the performance of the university as a whole and make a decision where all data is up to date. The system shows a powerful capability in producing different reports that provide a plan to improve the quality of education for present and ready to be used in continuous improvement.


title: Database Engines: Evolution of Greenness

abstract: Context: Information Technology consumes up to 10\% of the world's electricity generation, contributing to CO2 emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an energy-efficient (green) database engine could reduce energy consumption and CO2 emissions. Goal: To understand the factors driving databases' energy consumption and execution time throughout their evolution. Method: We conducted an empirical case study of energy consumption by two MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the relationships of four software metrics to energy consumption and execution time to determine which metrics reflect the greenness and performance of a database. Results: Our analysis shows that database engines' energy consumption and execution time increase as databases evolve. Moreover, the Lines of Code metric is correlated moderately to strongly with energy consumption and execution time in 88% of cases. Conclusions: Our findings provide insights to both practitioners and researchers. Database administrators may use them to select a fast, green release of the MySQL database engine. MySQL database-engine developers may use the software metric to assess products' greenness and performance. Researchers may use our findings to further develop new hypotheses or build models to predict greenness and performance of databases.


title: Transformational leadership in nursing practice

abstract: Traditionally, nurses have been over-managed and led inadequately, yet today they face unprecedented challenges and opportunities. Organisations constantly face changes that require an increasingly adaptive and flexible leadership. This type of adaptive leadership is referred to as 'transformational'; under it, environments of shared responsibilities that influence new ways of knowing are created. Transformational leadership motivates followers by appealing to higher ideas and moral values, where the leader has a deep set of internal values and ideas. This leads to followers acting to sustain the greater good, rather than their own interests, and supportive environments where responsibility is shared. This article focuses on transformational leadership and its application to nursing through the four components of transformational leadership. These are: idealised influence; inspirational motivation; intellectual stimulation; and individual consideration.


title: Algorithms for Cooperative Multisensor Surveillance

abstract: The Video Surveillance and Monitoring (VSAM) team at Carnegie Mellon University (CMU) has developed an end-to-end, multicamera surveillance system that allows a single human operator to monitor activities in a cluttered environment using a distributed network of active video sensors. Video understanding algorithms have been developed to automatically detect people and vehicles, seamlessly track them using a network of cooperating active sensors, determine their three-dimensional locations with respect to a geospatial site model, and present this information to a human operator who controls the system through a graphical user interface. The goal is to automatically collect and disseminate real-time information to improve the situational awareness of security providers and decision makers. The feasibility of real-time video surveillance has been demonstrated within a multicamera testbed system developed on the campus of CMU. This paper presents an overview of the issues and algorithms involved in creating this semiautonomous, multicamera surveillance system.


title: Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation

abstract: The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer. However, design of accurate automated methods remains challenging due to high noise in CT scans, low contrast between liver and lesions, as well as large lesion variability. We propose a 3D automatic, unsupervised method for liver lesions segmentation using a phase separation approach. It is assumed that liver is a mixture of two phases: healthy liver and lesions, represented by different image intensities polluted by noise. The Cahn-Hilliard equation is used to remove the noise and separate the mixture into two distinct phases with well-defined interfaces. This simplifies the lesion detection and segmentation task drastically and enables to segment liver lesions by thresholding the Cahn-Hilliard solution. The method was tested on 3Dircadb and LITS dataset.


7 Simulations with Particle Method


title: The effect of badges on the engagement of students with special educational needs: A case study

abstract: This paper addresses the perceived benefits from gamification in the context of special education. It presents the findings of a study evaluating the effects of a specific gamification element (badges) on the engagement of five students with special learning needs, through online courses developed on the Moodle Learning Management System (LMS). The results indicate that this particular gamification element yielded positive effects on students’ engagement and on their overall attitude towards the educational process in general.


title: Energy-efficient hybrid wakeup logic

abstract: The instruction window is a critical component and a major energy consumer in out-of-order superscalar processors. An important source of energy consumption in the instruction window is the instruction wakeup: a completing instruction broadcasts its result register tag and an associative comparison is performed with all the entries in the window. This paper shows that a very large fraction of the completing instructions have to wake up no more than a single instruction currently in the window. Consequently, we propose to save energy by using indexing to only enable the comparator at the single instruction to wake up. Only in the rare case when more than one instruction needs to wake up, our scheme reverts to enabling all the comparators or a subset of them. For this reason, we call our scheme Hybrid. Overall, our scheme is very effective: for a processor with a 96-entry window, the number of comparisons performed by the average completing instruction with a destination register is reduced to 0.8. The exact magnitude of the energy savings will depend on the specific instruction window implementation. Furthermore, the application suffers no performance penalty.


title: Fisher Score Based Naive Bayesian Classifier

abstract: The naive Bayesian classifier (NBC) is a simple yet very efficient classification technique in machine learning. But the unpractical condition independence assumption of NBC greatly degrades its performance. There are two primary ways to improve NBC's performance. One is to relax the condition independence assumption in NBC. This method improves NBC's accuracy by searching additional condition dependencies among attributes of the samples in a scope. It usually involves in very complex search algorithms. Another is to change the representation of the samples by creating new attributes from the original attributes, and construct NBC from these new attributes while keeping the condition independence assumption. Key problem of this method is to guarantee strong condition independencies among the new attributes. In the paper, a new means of making attribute set, which maps the original attributes to new attributes according to the information geometry and Fisher score, is presented, and then the FS-NBC on the new attributes is constructed. The condition dependence relation among the new attributes theoretically is discussed. We prove that these new attributes are condition independent of each other under certain conditions. The experimental results show that our method improves performance of NBC excellently


Managing Organizational Knowledge By Diagnosing Intellectual Capital: Framing and Advancing the State of the Field


title: Architectural implications of smart city business models: an evolutionary perspective

abstract: Smart cities have rapidly become a hot topic within technology communities, and promise both improved delivery of services to end users and reduced environmental impact in an era of unprecedented urbanization. Both large hightech companies and grassroots citizen-led initiatives have begun exploring the potential of these technologies. Significant barriers remain to the successful rollout and deployment of business models outlined for smart city applications and services, however. Most of these barriers pertain to an ongoing battle between two main schools of thought for system architecture, ICT and telecommunications, proposed for data management and service creation. Both of these system architectures represent a certain type of value chain and the legacy perspective of the respective players that wish to enter the smart city arena. Smart cities services, however, utilize components of both the ICT industry and mobile telecommunications industries, and do not benefit from the current binary perspective of system architecture. The business models suggested for the development of smart cities require a longterm strategic view of system architecture evolution. This article discusses the architectural evolution required to ensure that the rollout and deployment of smart city technologies is smooth through acknowledging and integrating the strengths of both the system architectures proposed.


title: Public-Key Cryptosystems from Lattice Reduction Problems

abstract: We present a new proposal for a trapdoor one-way function, from which we derive public-key encryption and digital signatures. The security of the new construction is based on the conjectured computational difficulty of lattice-reduction problems, providing a possible alternative to existing public-key encryption algorithms and digital signatures such as RSA and DSS.


title: A survey-based study of the mapping of system properties to ISO/IEC 9126 maintainability characteristics

abstract: The ISO/IEC 9126 international standard for software product quality is a widely accepted reference for terminology regarding the multi-faceted concept of software product quality. Based on this standard, the Software Improvement Group has developed a pragmatic approach for measuring technical quality of software products. This quality model introduces another level below the hierarchy defined by ISO/IEC 9126, which consists of system properties such as volume, duplication, unit complexity and others. A mapping between system properties and ISO/IEC 9126 characteristics is defined in a binary fashion: a property either influences a characteristic or not. This mapping embodies consensus among three experts based, in an informal way, on their experience in software quality assessment. We have conducted a survey-based experiment to study the mapping between system properties and quality characteristics. We used the Analytic Hierarchy Process as a formally structured method to elicit the relative importance of system properties and quality characteristics from a group of 22 software quality experts. We analyzed the results of the experiment with two objectives: (i) to validate the original binary mapping and (ii) to refine the mapping using the elicited relative weights.


title: Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum

abstract: Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages.


title: GPU-enabled back-propagation artificial neural network for digit recognition in parallel

abstract: In this paper, we show that the GPU (graphics processing unit) can be used not only for processing graphics, but also for high speed computing. We provide a comparison between the times taken on the CPU and GPU to perform the training and testing of a back-propagation artificial neural network. We implemented two neural networks for recognizing handwritten digits; one consists of serial code executed on the CPU, while the other is a GPU-based version of the same system which executes in parallel. As an experiment for performance evaluation, a system for neural network training on the GPU is developed to reduce training time. The programming environment that the system is based on is CUDA which stands for compute unified device architecture, which allows a programmer to write code that will run on an NVIDIA GPU card. Our results over an experiment of digital image recognition using neural network confirm the speed-up advantages by tapping on the resources of GPU. Our proposed model has an advantage of simplicity, while it shows on par performance with the state-of-the-arts algorithms.


title: Growth mindset tempers the effects of poverty on academic achievement

abstract: Two largely separate bodies of empirical research have shown that academic achievement is influenced by structural factors, such as socioeconomic background, and psychological factors, such as students’ beliefs about their abilities. In this research, we use a nationwide sample of high school students from Chile to investigate how these factors interact on a systemic level. Confirming prior research, we find that family income is a strong predictor of achievement. Extending prior research, we find that a growth mindset (the belief that intelligence is not fixed and can be developed) is a comparably strong predictor of achievement and that it exhibits a positive relationship with achievement across all of the socioeconomic strata in the country. Furthermore, we find that students from lower-income families were less likely to hold a growth mindset than their wealthier peers, but those who did hold a growth mindset were appreciably buffered against the deleterious effects of poverty on achievement: students in the lowest 10th percentile of family income who exhibited a growth mindset showed academic performance as high as that of fixed mindset students from the 80th income percentile. These results suggest that students’ mindsets may temper or exacerbate the effects of economic disadvantage on a systemic level.


Using Advice in Model-Based Reinforcement Learning


Overview of the BioCreative VI chemical-protein interaction Track


title: Evolving Multi-level Graph Partitioning Algorithms

abstract: Optimal graph partitioning is a foundational problem in computer science, and appears in many different applications. Multi-level graph partitioning is a state-of-the-art method of efficiently approximating high quality graph partitions. In this work, genetic programming techniques are used to evolve new multi-level graph partitioning heuristics that are tailored to specific applications. Results are presented using these evolved partitioners on traditional random graph models as well as a real-world computer network data set. These results demonstrate an improvement in the quality of the partitions produced over current state-of-the-art methods.


title: The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

abstract: Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.


title: A Non-Foster VHF Monopole Antenna

abstract: Non-Foster matching promises to improve the performance of electrically small antennas by tens of decibels over a decade of bandwidth, but only a few successful demonstrations have been reported in the literature. A 15-cm monopole antenna has been matched using a variable negative capacitance based on Linvill's balanced negative impedance converter. The realized gain is improved by >;10 dB relative to the unmatched case over 30-200 MHz. This letter reports the design, simulation, and measurement data and gives an equivalent circuit that can be used to synthesize future designs.


title: Designing gaze-supported multimodal interactions for the exploration of large image collections

abstract: While eye tracking is becoming more and more relevant as a promising input channel, diverse applications using gaze control in a more natural way are still rather limited. Though several researchers have indicated the particular high potential of gaze-based interaction for pointing tasks, often gaze-only approaches are investigated. However, time-consuming dwell-time activations limit this potential. To overcome this, we present a gaze-supported fisheye lens in combination with (1) a keyboard and (2) and a tilt-sensitive mobile multi-touch device. In a user-centered design approach, we elicited how users would use the aforementioned input combinations. Based on the received feedback we designed a prototype system for the interaction with a remote display using gaze and a touch-and-tilt device. This eliminates gaze dwell-time activations and the well-known Midas Touch problem (unintentionally issuing an action via gaze). A formative user study testing our prototype provided further insights into how well the elaborated gaze-supported interaction techniques were experienced by users.


title: Ultrasonographic assessment of splenic volume at presentation and after anti-malarial therapy in children with malarial anaemia

abstract: BackgroundSplenic enlargement is a component of the host response to malaria and may also influence the genesis and progression of malarial anaemia. Few cross-sectional and no longitudinal studies have assessed the relationship between splenic volume measured ultrasonographically and haemoglobin concentrations in children with malaria.MethodsFifteen Papua New Guinean children with severe malarial anaemia (SMA; haemoglobin <50 g/L) and ten with moderate malarial anaemia (MMA; 51–99 g/L) were recruited. The SMA patients were given intramuscular artemether followed by oral artemisinin combination therapy (ACT), and were transfused one unit of packed cells 0.3-4.0 days post-admission. The MMA patients were treated with ACT. Splenic enlargement (Hackett’s grade, subcostal distance and ultrasonographically determined volume) and haemoglobin concentrations were measured on days 0, 1, 2, 3, 7, 14, 28, and 42.ResultsAssociations between Hackett’s grade, subcostal distance and splenic volume were modest (rs ≤ 0.62, P <0.001). Baseline splenic volume was not associated with age or haemoglobin (P ≥0.90). Mean splenic volume had fallen by approximately 50 % at day 14 in children with MMA (P ≤0.011 vs days 0, 1 and 2), but there was no change in the SMA group (P ≥0.30). There was no change in haemoglobin in the MMA group during follow-up but a rise in the SMA group to day 7 (P ≤0.05 vs days 0, 1, 2, and 3) which paralleled the packed cell volume transfused.ConclusionsClinical assessment of splenomegaly is imprecise compared with ultrasonography. Serial splenic volumes and haemoglobin concentrations suggest that the spleen does not influence post-treatment haemoglobin, including after transfusion.


title: Origami Theory and Its Applications: A Literature Review

abstract: This paper presents the fundamentals of Origami engineering and its application in nowadays as well as future industry. Several main cores of mathematical approaches such as Huzita- Hatori axioms, Maekawa and Kawasaki's theorems are introduced briefly. Meanwhile flaps and circle packing by Robert Lang is explained to make understood the underlying principles in designing crease pattern. Rigid origami and its corrugation patterns which are potentially applicable for creating transformable or temporary spaces is discussed to show the transition of origami from paper to thick material. Moreover, some innovative applications of origami such as eyeglass, origami stent and high tech origami based on mentioned theories and principles are showcased in section III; while some updated origami technology such as Vacuumatics, self-folding of polymer sheets and programmable matter folding which could greatlyenhance origami structureare demonstrated in Section IV to offer more insight in future origami. Keywords—Origami, origami application, origami engineering, origami technology, rigid origami.


title: Building a Generic SOAP Framework over Binary XML

abstract: The prevailing binding of SOAP to HTTP specifies that SOAP messages be encoded as an XML 1.0 document which is then sent between client and server. XML processing however can be slow and memory intensive, especially for scientific data, and consequently SOAP has been regarded as an inappropriate protocol for scientific data. Efficiency considerations thus lead to the prevailing practice of separating data from the SOAP control channel. Instead, it is stored in specialized binary formats and transmitted either via attachments or indirectly via a file sharing mechanism, such as GridFTP or HTTP. This separation invariably complicates development due to the multiple libraries and type systems to be handled; furthermore it suffers from performance issues, especially when handling small binary data. As an alternative solution, binary XML provides a highly efficient encoding scheme for binary data in the XML and SOAP messages, and with it we can gain high performance as well as unifying the development environment without unduly impacting the Web service protocol stack. In this paper we present our implementation of a generic SOAP engine that supports both textual XML and binary XML as the encoding scheme of the message. We also present our binary XML data model and encoding scheme. Our experiments show that for scientific applications binary XML together with the generic SOAP implementation not only ease development, but also provide better performance and are more widely applicable than the commonly used separated schemes


title: Efficient memory disaggregation with INFINISWAP

abstract: Memory-intensive applications suffer large performance loss when their working sets do not fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging out to disks even in the presence of large imbalance in memory utilizations across a cluster. Existing proposals for memory disaggregation call for new architectures, new hardware designs, and/or new programming models, making them infeasible. ::: ::: This paper describes the design and implementation of INFINISWAP, a remote memory paging system designed specifically for an RDMA network. INFINISWAP opportunistically harvests and transparently exposes unused memory to unmodified applications by dividing the swap space of each machine into many slabs and distributing them across many machines' remote memory. Because one-sided RDMA operations bypass remote CPUs, INFINISWAP leverages the power of many choices to perform decentralized slab placements and evictions. ::: ::: We have implemented and deployed INFINISWAP on an RDMA cluster without any modifications to user applications or the OS and evaluated its effectiveness using multiple workloads running on unmodified VoltDB, Memcached, PowerGraph, GraphX, and Apache Spark. Using INFINISWAP, throughputs of these applications improve between 4× (0:94×) to 15:4× (7:8×) over disk (Mellanox nbdX), and median and tail latencies between 5:4× (2×) and 61× (2:3×). INFINISWAP achieves these with negligible remote CPU usage, whereas nbdX becomes CPU-bound. INFINISWAP increases the overall memory utilization of a cluster and works well at scale.


title: A Dataset for Breast Cancer Histopathological Image Classification

abstract: Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database . The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.


title: ARTIFICIAL INTELLIGENCE 249 AM: A Case Study in AI Methodology

abstract: Abstract Much artificial intelligence research is based on the construction of large impressive-looking programs, the theoretical content of which may not always be clearly stated. This is unproductive from the point of view of building a stable base for further research. We illustrate this problem by referring to Lenat's am program, in which the techniques employed are somewhat obscure in spite of the impressive performance.


title: Developing therapies for Alzheimer's disease.

abstract: With the greying of the population, many more people will suffer from senile dementia, placing an enormous burden on families and on health provision. Alzheimer's disease, by far the most common form of dementia, is a complex disease, whose causes are still poorly understood despite a recent mushrooming in research. Many drugs are being developed that may provide symptomatic relief or slow down deterioration, but preventive therapies still seem a long way off.


Mobile Robot Localisation and Mapping in Extensive Outdoor Environments


title: Conducting realistic experiments in software engineering

abstract: An important goal of most empirical software engineering research is the transfer of research results to industrial applications. Two important obstacles for this transfer are the lack of control of variables of case studies, i.e., the lack of explanatory power, and the lack of realism of controlled experiments. While it may be difficult to increase the explanatory power of case studies, there is a large potential for increasing the realism of controlled software engineering experiments. To convince industry about the validity and applicability of the experimental results, the tasks, subjects and the environments of the experiments should be as realistic as practically possible. Such experiments are, however, more expensive than experiments involving students, small tasks and pen-and-paper environments. Consequently, a change towards more realistic experiments requires a change in the amount of resources spent on software engineering experiments. This paper argues that software engineering researchers should apply for resources enabling expensive and realistic software engineering experiments similar to how other researchers apply for resources for expensive software and hardware that are necessary for their research. The paper describes experiences from recent experiments that varied in size from involving one software professional for 5 days to 130 software professionals, from 9 consultancy companies, for one day each.


title: Visual Navigation for Mobile Robots: A Survey

abstract: Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.


title: Procedural facade variations from a single layout

abstract: We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality.


title: Lifelong Machine Learning Systems: Beyond Learning Algorithms

abstract: Lifelong Machine Learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a review of prior work on LML, we propose that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the nature of systems that are capable of learning over a lifetime. Reasons for our position are presented and potential counter-arguments are discussed. The remainder of the paper contributes by defining LML, presenting a reference framework that considers all forms of machine learning, and listing several key challenges for and benefits from LML research. We conclude with ideas for next steps to advance the field.


Participant Notification Financial Relationships / Resolution Keys Speaker


title: INDIVIDUALITY DYNAMIC ANALYSIS OF THE PROTAGONIST’S CHARACTER IN THOMAS HARDY’S NOVEL TESS OF THE D’URBERVILLES

abstract: Tess of the D’Urbervilles is a novel written by Hardy that describes Tess as the victim of men who fall in love with her. The protagonist’s character in this novel will be analysed based on psychoanalysis theory especially about the dynamic of individuality explaining about the power urging behind the human’s action. The result is expected to give the reader a description and understanding Tess character based on psychoanalysis theory especially based on individuality dynamic part. Keywords: drive, sexuality, aggression, anxiety


title: Ranking Entities for Web Queries Through Text and Knowledge

abstract: When humans explain complex topics, they naturally talk about involved entities, such as people, locations, or events. In this paper, we aim at automating this process by retrieving and ranking entities that are relevant to understand free-text web-style queries like Argentine British relations, which typically demand a set of heterogeneous entities with no specific target type like, for instance, Falklands_-War} or Margaret-_Thatcher, as answer. Standard approaches to entity retrieval rely purely on features from the knowledge base. We approach the problem from the opposite direction, namely by analyzing web documents that are found to be query-relevant. Our approach hinges on entity linking technology that identifies entity mentions and links them to a knowledge base like Wikipedia. We use a learning-to-rank approach and study different features that use documents, entity mentions, and knowledge base entities -- thus bridging document and entity retrieval. Since established benchmarks for this problem do not exist, we use TREC test collections for document ranking and collect custom relevance judgments for entities. Experiments on TREC Robust04 and TREC Web13/14 data show that: i) single entity features, like the frequency of occurrence within the top-ranke documents, or the query retrieval score against a knowledge base, perform generally well; ii) the best overall performance is achieved when combining different features that relate an entity to the query, its document mentions, and its knowledge base representation.


title: Linked Data Architecture for Plan Execution in Distributed CPS

abstract: Future cyber-physical systems (CPS) require their components to perform autonomously. To do that safely and efficiently, CPS components will need access to the global state of the whole CPS. These components will require near real-time updates to a subset of the global state to react to changes in the environment. A particular challenge is to monitor state updates from the distributed CPS components: one needs to ensure that only states consistent with the PDDL plan execution semantics can be observed within the system. In order to guarantee that, a component to monitor plan execution is proposed. Microservices based on Linked Data technologies are used to provide a uniform way to access component states, represented as Resource Description Framework (RDF) resources. To ensure the correct ordering of state updates, we present an extension of the OASIS OSLC TRS protocol. Specifically, we strengthen the ordering guarantees of state change events and introduce inlining of the state with the events to prevent state mismatch at the dereferencing stage.


title: A Cyber Attack Modeling and Impact Assessment framework

abstract: The paper suggests a framework for cyber attack modeling and impact assessment. It is supposed that the common approach to attack modeling and impact assessment is based on representing malefactors' behavior, generating attack graphs, calculating security metrics and providing risk analysis procedures. The main aspects outlined are achieving near-real time mode, event analysis and prognosis mechanisms, security and impact assessment. To optimize the attack graph generation and security evaluation we apply an anytime approach to have the result at any time by applying a set of algorithms with different timelines and precision. The architecture of the Cyber Attack Modeling and Impact Assessment Component (CAMIAC) is proposed. We present the prototype of the component, the results of experiments carried out, and comparative analysis of the techniques used.


title: An information filter for voice prompt suppression

abstract: Modern speech enabled applications provide for dialog between a machine and one or more human users. The machine prompts the user with queries that are either prerecorded or synthesized on the fly. The human users respond with their own voices, and their speech is then recognized and understood by a human language understanding module. In order to achieve as natural an interaction as possible, the human user(s) must be allowed to interrupt the machine during a voice prompt. In this work, we compare two techniques for such voice prompt suppression. The first is a straightforward adaptation of a conventional Kalman filter, which has certain advantages over the normalized least squares algrithm in terms of robustness and speed of convergence. The second algorithm, which is novel in this work, is also based on a Kalman filter, but differs from the first in that the update or correction step is performed in information space and hence allows for the use of diagonal loading in order to control the growth of the subband filter coefficients, and thereby add robustness to the VPS.


title: Design of a Privacy-Preserving Decentralized File Storage with Financial Incentives

abstract: Surveys indicate that users are often afraid to entrust data to cloud storage providers, because these do not offer sufficient privacy. On the other hand, peer-2-peer–based privacy-preserving storage systems like Freenet suffer from a lack of contribution and storage capacity, since there is basically no incentive to contribute own storage capacity to other participants in the network. We address these contradicting requirements by a design which combines a distributed storage with a privacy-preserving blockchain-based payment system to create incentives for participation while maintaining user privacy. By following a Privacy-by-Design strategy integrating privacy throughout the whole system life cycle, we show that it is possible to achieve levels of privacy comparable to state-of-the-art distributed storage technologies, despite integrating a payment mechanism. Our results show that it is possible to combine storage contracts and payments in a privacy-preserving way. Further, our system design may serve as an inspiration for future similar architectures.


title: Technical evaluation of the Carolo-Cup 2014 - A competition for self-driving miniature cars

abstract: The Carolo-Cup competition conducted for the eighth time this year, is an international student competition focusing on autonomous driving scenarios implemented on 1:10 scale car models. Three practical sub-competitions have to be realized in this context and represent a complex, interdisciplinary challenge. Hence, students have to cope with all core topics like mechanical development, electronic design, and programming as addressed usually by robotic applications. In this paper we introduce the competition challenges in detail and evaluate the results of all 13 participating teams from the 2014 competition. For this purpose, we analyze technical as well as non-technical configurations of each student group and derive best practices, lessons learned, and criteria as a precondition for a successful participation. Due to the comprehensive orientation of the Carolo-Cup, this knowledge can be applied on comparable projects and related competitions as well.


title: Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care

abstract: Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users’ forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users’ forum posts. We then introduced a novel network-based approach for modeling users’ forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.


title: How Applicable Is ISO/IEC 29110 in Game Software Development?

abstract: Software development in a small development team is a challenge, as people have to fulfill several roles, which in larger groups would have separate, dedicated people for the work. To help small development teams to organize their activities, ISO/IEC standardization body has developed the standard ISO/IEC 29110, Lifecycle profiles for Very Small Entities. Our study focuses on the application of this model in the game industry, an industry that develops software. However, the game industry has its own set of unusual features in software development, such as the demand for artistic vision, need for novelty and demand for creative designs. In this study we analyze how the standard would work in seven different game industry companies and identify a set of additions that would help these organizations to apply the standard in practice. Based on our results, the ISO/IEC 29110 should incorporate more support for iterative development to allow easier adaptation to real-life organizations.


title: A cascade model for externalities in sponsored search

abstract: One of the most important yet insufficiently studied issues in online advertising is the externality effect among ads: the value of an ad impression on a page is affected not just by the location that the ad is placed in, but also by the set of other ads displayed on the page. For instance, a high quality competing ad can detract users from another ad, while a low quality ad could cause the viewer to abandon the page altogether. ::: ::: In this paper, we propose and analyze a model for externalities in sponsored search ads . Our model is based on the assumption that users will visually scan the list of ads from the top to the bottom. After each ad, they make independent random decisions with ad-specific probabilities on whether to continue scanning. We then generalize the model in two ways: allowing for multiple separate blocks of ads, and allowing click probabilities to explicitly depend on ad positions as well. For the most basic model, we present a polynomial-time incentive-compatible auction mechanism for allocating and pricing ad slots. For the generalizations, we give approximation algorithms for the allocation of ads.


title: Gaussian Message Passing Iterative Detection for MIMO-NOMA Systems with Massive Access

abstract: This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) algorithm for Multiple-Input Multiple-Output systems with Non-Orthogonal Multiple Access (MIMO-NOMA), in which a base station with $N_r$ antennas serves $N_u$ sources simultaneously. Both $N_u$ and $N_r$ are very large numbers and we consider the cases that $N_u>N_r$. The GMPID is based on a fully connected loopy graph, which is well understood to be not convergent in some cases. The large-scale property of the MIMO-NOMA is used to simplify the convergence analysis. Firstly, we prove that the variances of the GMPID definitely converge to that of Minimum Mean Square Error (MMSE) detection. Secondly, two sufficient conditions that the means of the GMPID converge to a higher MSE than that of the MMSE detection are proposed. However, the means of the GMPID may still not converge when $ N_u/N_r<(\sqrt{2}-1)^{-2}$. Therefore, a new convergent SA-GMPID is proposed, which converges to the MMSE detection for any $N_u>N_r$ with a faster convergence speed. Finally, numerical results are provided to verify the validity of the proposed theoretical results.


title: Emotion Recognition from Text Based on Automatically Generated Rules

abstract: With the growth of the Internet community, textual data has proven to be the main tool of communication in human-machine and human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. For example, by providing insights to users for personal preferences and automated recommendations based on their emotional state. In this work, we propose a framework for emotion classification in English sentences where emotions are treated as generalized concepts extracted from the sentences. We start by generating an intermediate emotional data representation of a given input sentence based on its syntactic and semantic structure. We then generalize this representation using various ontologies such as Word Net and Concept Net, which results in an emotion seed that we call an emotion recognition rule (ERR). Finally, we use a suite of classifiers to compare the generated ERR with a set of reference ERRs extracted from a training set in a similar fashion. The used classifiers are k-nearest neighbors (KNN) with handcrafted similarity measure, Point Mutual Information (PMI), and PMI with Information Retrieval (PMI-IR). When applied on different datasets, the proposed approach significantly outperformed the existing state-of-the art machine learning and rule-based classifiers with an average F-Score of 84%.


title: A new methodology for collision risk assessment of autonomous vehicles

abstract: Risk assessment methods of autonomous vehicles (AVs) have recently begun to treat the motion of the vehicles as dependent on the context of the traffic scene that the vehicle resides in. In most of the cases, Dynamic Bayesian Network (DBN) models are employed for interaction aware motion models (i.e. models that take inter-vehicle dependencies into account). However, communications between vehicles are assumed and the developed models require a lot of parameters to be tuned. Even with these requirements, current approaches cannot cope with traffic scenarios of high complexity. To overcome these limitations, the current study proposes a new methodology that integrates real-time collision prediction as studied by traffic engineers with an interaction-aware motion model for autonomous vehicles real-time risk assessment. Results from a random forest classifier for real-time collision prediction are used as an example for the estimation of probabilities required for the DBN model. It is shown that a well-calibrated collision prediction classifier can provide a supplementary hint to already developed interaction-aware motion models and enhance real-time risk assessment for autonomous vehicles.


title: The 10-ps Multitime Measurements Averaging TDC Implemented in an FPGA

abstract: We present a theoretical analysis of the characteristics of a multitime averaging TDC and an improved implementation scheme that is adapted to our previous version of FPGA TDCs from Xilinx Virtex 4 family without any modifications to the hardware. Using MATLAB we simulate the timing performance of the multitime averaging TDC on a variety of averaging times. The simulation results are verified with bench-top tests. Based on the agreement between the theoretical analysis and bench-top tests, we offer guidelines for optimizing timing performance. With a 4 times averaging TDC we achieve a typical timing performance of 9 ps RMS and 12 ps effective bin size. Without the averaging scheme the timing performance is 25 ps RMS and 50 ps per bin size.


title: Device adapter concept towards enabling plug&produce production environments

abstract: Modern manufacturing systems require a transformation from mass production towards mass customization. This results in a trend towards more agile production lines. It also demands a reduction of configuration times when building the production line as well as faster reconfiguration when adding new hardware and product variants to the manufacturing line. This paper introduces the concept of a device adapter that allows the device to be seamlessly plugged into the agile production systems. The device adapter wraps the device functionality and offers it as a service, hiding away the low-level process capability (skill) implementation and allowing to formally represent the production steps. Preliminary tests have been performed on an industrial demonstrator that simulates a real manufacturing process.


title: RMPflow: A Computational Graph for Automatic Motion Policy Generation

abstract: We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion policies designed to parameterize non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task spaces. Given a set of RMPs designed for individual tasks, RMPflow can consistently combine these local policies to generate an expressive global policy, while simultaneously exploiting sparse structure for computational efficiency. We study the geometric properties of RMPflow and provide sufficient conditions for stability. Finally, we experimentally demonstrate that accounting for the geometry of task policies can simplify classically difficult problems, such as planning through clutter on high-DOF manipulation systems.


title: Guiding Policies with Language via Meta-Learning

abstract: Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require a human expert to be able to actually perform the task in order to generate the demonstration. Instruction following from natural language instructions provides an appealing alternative: in the same way that we can specify goals to other humans simply by speaking or writing, we would like to be able to specify tasks for our machines. However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task. In this work, we propose an interactive formulation of the task specification problem, where iterative language corrections are provided to an autonomous agent, guiding it in acquiring the desired skill. Our proposed language-guided policy learning algorithm can integrate an instruction and a sequence of corrections to acquire new skills very quickly. In our experiments, we show that this method can enable a policy to follow instructions and corrections for simulated navigation and manipulation tasks, substantially outperforming direct, non-interactive instruction following.


title: Fast Linear Model for Knowledge Graph Embeddings

abstract: This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText.


title: E-Commerce Adoption:Perceptions of Managers/Owners of Small and Medium Sized Firms in Chile

abstract: Although the adoption of e-commerce is widely studied in the industrialized world, only a small number of these studies focus on developing countries. An even smaller fraction of these studies focus on small and medium sized enterprises (SMEs), which account for a high percent of the economy generated in developing countries. In this study, managers/owners of SMEs in Chile identified variables that differentiate between adopters and non-adopters of e-commerce. The analysis suggests that Chilean managers/owners most receptive to adopting e-commerce possess the financial and technological resources to implement it, see e-commerce as increasing managerial productivity and supporting strategic decisions, feel external pressure to put ecommerce into operation, perceive e-commerce as compatible with preferred work practices and existing technology infrastructure, and perceive e-commerce as useful for their firms.


title: Recommender system based on customer segmentation (RSCS)

abstract: Purpose – Recommender systems are techniques that allow companies to develop sales and marketing and as a result, attract more customers. There are several different types of recommender systems which collaborative filtering (CF) method is more popular and is used in various fields. However, similar to other recommender systems, this system has its own limitations. Nowadays, recommender systems are combined with other systems to enhance the quality and precision. The purpose of this paper is to present a new method to increase the accuracy and quality of recommendations associated with filtering systems. Design/methodology/approach – First, the recency, frequency, and monetary (RFM) variables of the clients are extracted and variables’ weights are calculated. Then, using weighted RFM and expectation maximization clustering algorithms and their combination with the closest K-neighbors, recommendations for each cluster is independently extracted. Finally, the results are compared with the outcome of convent...


title: Convolutional neural networks on irregular domains based on approximate vertex-domain translations

abstract: We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such as images, convolutional layers are designed by translating a convolutional kernel over all pixels, thus enforcing translation equivariance. In the case of general graphs however, translation is not a well-defined operation, which makes shifting a convolutional kernel not straightforward. In this article, we introduce a methodology to allow the design of convolutional layers that are adapted to signals evolving on irregular topologies, even in the absence of a natural translation. Using the designed layers, we build a CNN that we train using the initial set of signals. Contrary to other approaches that aim at extending CNNs to irregular domains, we incorporate the classical settings of CNNs for 2D signals as a particular case of our approach. Designing convolutional layers in the vertex domain directly implies weight sharing, which in other approaches is generally estimated a posteriori using heuristics.


Umbrella sampling


title: A review on text mining

abstract: Because of large amounts of unstructured text data generated on the Internet, text mining is believed to have high commercial value. Text mining is the process of extracting previously unknown, understandable, potential and practical patterns or knowledge from the collection of text data. This paper introduces the research status of text mining. Then several general models are described to know text mining in the overall perspective. At last we classify text mining work as text categorization, text clustering, association rule extraction and trend analysis according to applications.


Real-time Simultaneous Localization and Mapping for UAV : A Survey


title: BUILDING INTERACTIVE MODELING FOR CONSTRUCTION EDUCATION IN VIRTUAL WORLDS

abstract: The number of design and construction professionals who are using Building Information Modeling (BIM) is rapidly growing and simultaneously the architecture, engineering, and construction (AEC) community is faced with the challenge of remote collaboration as offshore outsourcing continues to grow. While BIM facilitates information sharing between AEC professionals via purpose-built three-dimensional (3D), 4D design, analysis, evaluation, and documentation models, the communication of such 3D data-rich models if often fragmented and typically limits real-time communication and interaction of users who may be geographically dispersed and/or lack the modeling and analyses skills to interact with these models. AEC education needs to expose students to these emerging practice changes while finding new ways to more effectively address the fundamentals of design and construction. Virtual worlds – in this research the Second Life platform – have the potential to address the communication issues and effectively complement traditional teaching approaches and furthermore integrate with BIM to enhance construction education. Nevertheless, despite the potential benefits of virtual worlds, various obstacles exist. This paper presents the concept of Building interactive Modeling (BiM) which complements the capabilities of BIM with social interaction to enhance collaborative information and knowledge sharing. Role-playing scenarios developed in Second Life demonstrate specific opportunities of BiM.


title: One shot learning gesture recognition from RGBD images

abstract: We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches [1].


title: All-pairs: An abstraction for data-intensive cloud computing

abstract: Although modern parallel and distributed computing systems provide easy access to large amounts of computing power, it is not always easy for non-expert users to harness these large systems effectively. A large workload composed in what seems to be the obvious way by a naive user may accidentally abuse shared resources and achieve very poor performance. To address this problem, we propose that production systems should provide end users with high-level abstractions that allow for the easy expression and efficient execution of data intensive workloads. We present one example of an abstraction - all-pairs - that fits the needs of several data-intensive scientific applications. We demonstrate that an optimized all-pairs abstraction is both easier to use than the underlying system, and achieves performance orders of magnitude better than the obvious but naive approach, and twice as fast as a hand-optimized conventional approach.


title: Image Super-Resolution via Deep Recursive Residual Network

abstract: Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks, recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark evaluation shows that DRRN significantly outperforms state of the art in SISR, while utilizing far fewer parameters. Code is available at https://github.com/tyshiwo/DRRN_CVPR17.


title: Western diet induces dysbiosis with increased E coli in CEABAC10 mice, alters host barrier function favouring AIEC colonisation

abstract: Objective Western diet is a risk factor for Crohn9s disease (CD). Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) is abnormally expressed in CD patients. This allows adherent-invasive Escherichia coli (AIEC) to colonise the gut mucosa and leads to inflammation. We assessed the effects of a high fat/high sugar (HF/HS) Western diet on gut microbiota composition, barrier integrity and susceptibility to infection in transgenic CEABAC10 mice expressing human CEACAMs. Design Colonic microbiota composition and susceptibility of CEABAC10 mice to AIEC LF82 bacteria infection were determined in mice fed a conventional or HF/HS diet. Barrier function and inflammatory response were assessed by studying intestinal permeability, tight junction protein and mucin expression and localisation, and by determining histological score and levels of cytokine release. Results HF/HS diet led to dysbiosis in WT and transgenic CEABAC10 mice, with a particular increase in E coli population in HF/HS-fed CEABAC10 mice. These mice showed decreased mucus layer thickness, increased intestinal permeability, induction of Nod2 and Tlr5 gene transcription, and increased TNFα secretion. These modifications led to a higher ability of AIEC bacteria to colonise the gut mucosa and to induce inflammation. Conclusions Western diet induces changes in gut microbiota composition, alters host homeostasis and promotes AIEC gut colonisation in genetically susceptible mice. These results support the multifactorial aetiology of CD and highlight the importance of diet in CD pathogenesis.


title: Single image super-resolution from transformed self-exemplars

abstract: Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing results without extensive training on external databases. Such algorithms exploit the statistical prior that patches in a natural image tend to recur within and across scales of the same image. However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene. In this paper, we extend self-similarity based SR to overcome this drawback. We expand the internal patch search space by allowing geometric variations. We do so by explicitly localizing planes in the scene and using the detected perspective geometry to guide the patch search process. We also incorporate additional affine transformations to accommodate local shape variations. We propose a compositional model to simultaneously handle both types of transformations. We extensively evaluate the performance in both urban and natural scenes. Even without using any external training databases, we achieve significantly superior results on urban scenes, while maintaining comparable performance on natural scenes as other state-of-the-art SR algorithms.


title: A content-constrained spatial (CCS) model for layout analysis of mathematical expressions

abstract: This paper proposes a content-constrained spatial (CCS) model to recover the mathematical layout (M-layout, or ML me ) of an mathematical expression (ME) from its font setting layout (F-layout, or FLme). The M-layout can be used for content analysis applications such as ME based indexing and retrieval of documents. The first of the two-step process is to divide a compounded ME into blocks based on explicit mathematical structure primitives such as fraction lines, radical signs, fence, etc. Subscripts and superscripts within a block are resolved by probabilistic inference of their likelihood based on a global optimization model. The dual peak distributions of the features to capture the relative position between sibling blocks as super/subscript call for a sampling based non-parametric probability distribution estimation method to resolve their ambiguity. The notion of spatial constraint indicators is proposed to reduce the search space while improving the prediction performance. The proposed scheme is tested using the InftyCDB data set to achieve the F1 score of 0.98.


Assessing the effect of simulation models on systems learning in an introductory environmental science course


title: Control of a humanoid robot by a noninvasive brain – computer interface in humans

abstract: We describe a brain–computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot’s cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain–computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.


title: Smart soldier assistance using WSN

abstract: The paper presents the development of a system which will help wirelessly monitor the health parameters of a soldier and also send and receive messages wirelessly from the base station computer. The system must also detect reduced vitals and alert the base station along with the GPS location so that a rescue team can be sent. The system comprises of a biosensor signal conditioning module, a GPS module, a keypad and a LCD. These are interfaced with the Stellaris LM4F120XL Launchpad, which in turn broadcasts messages wirelessly via the EZ430-RF2500 wireless module. These messages are sent to all the EZ430-RF2500 modules in the locality. A LabVIEW program has been developed to act as the central commanding station. This will process all the data from the individual soldiers, display the data and perform the functions mentioned. This will help in strategic planning of military missions and also to reduce casualties.


title: Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

abstract: With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.


title: What do online behavioral advertising privacy disclosures communicate to users?

abstract: Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices", the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We dicuss challenges in crafting disclosures and provide suggestions for improvement.


title: Semantic Edge Detection with Diverse Deep Supervision

abstract: Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. We shed light on how such distracted supervision targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. In this paper, we propose a novel fully convolutional neural network using diverse deep supervision (DDS) within a multi-task framework where lower layers aim at generating category-agnostic edges, while higher layers are responsible for the detection of category-aware semantic edges. To overcome the distracted supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated in several popular benchmark datasets, including SBD, Cityscapes, and PASCAL VOC2012. Source code will be released upon paper acceptance.


A tutorial of diverse genome analysis tools found in the CoGe web-platform using Plasmodium spp. as a model


title: Lawn tennis balls, Rolling friction experiment and Trouton viscosity

abstract: Three lawn tennis balls were arbitrarily taken. One ball was new, one was moderately old and another was old. We have fabricated a conveyor belt set-up and measured rolling friction coefficients,µr, of the three balls as a function of their angular velocities, !. For the new ball, quadratic fit is perfect. For the moderately old ball, linear fit is accurate. In all the three cases, from linear fits, we obtain krol, where, µr = krol! +intercept. We deduce krol theoretically also, assuming Trouton ratio as three. The experimental results and theoretical estimates are of the same order of magnitude.


title: A Survey on Cascaded Multilevel Inverters

abstract: Cascaded multilevel inverters synthesize a medium-voltage output based on a series connection of power cells which use standard low-voltage component configurations. This characteristic allows one to achieve high-quality output voltages and input currents and also outstanding availability due to their intrinsic component redundancy. Due to these features, the cascaded multilevel inverter has been recognized as an important alternative in the medium-voltage inverter market. This paper presents a survey of different topologies, control strategies and modulation techniques used by these inverters. Regenerative and advanced topologies are also discussed. Applications where the mentioned features play a key role are shown. Finally, future developments are addressed.


title: A Survey of Game Theory as Applied to Network Security

abstract: Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.


title: Travel Information Search - The Presence of Social Media

abstract: In this paper we will study the intersection of conceptual frameworks that deal with travel, information search and social media, in order to investigate the presence of social media in travel information search. Social media could become a communication channel for the hospitality industry. This may offer some benefits for the tourists and travellers - as well as for the hospitality industry. Extensive research has over the years been carried out on methods and technology for travel information search and there has been a growing research on social media and its influence on the lives of its users. There has not been much research on social media as a channel for the search for travel information. Here we will build on a study by Xiang and Gretzel (2009) but shift the focus to specific hotel brand search in order to gain an understanding of how social media influences the search for travel information.