Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Optimization of Transmit Pulse Shaping , Guard Interval length , and receiver side narrow-band interference mitigation in the HomePlugAV OFDM system

The primary subject of this paper is the sekction of a pulse-shaping wavefom for in-home power line communications. As system performance is also determined by other parameters affecting the length and shaping of the OFDMsymbol, the problem formulation is expanded to also include the simultaneous selection of guard in t end length and Hunning window length, creating a joint optimization problem. Given the constraints of allowing no transmit notch Flters, and adequate receive side mitigation of narrow band jammers, we jointly optimize the selection of guurd intend length, transmit pulse-shuping and receive side windowing for throughput petfiormance on the average power line channel. Throughput perfotmance is inferred from SNR duta and associated p a r d interval overhead. We also present resulfs of pe gormance assessment and parameter selection, for the average power line channel, based on U collection of 120 measured power line channel impulse responses.
Factors Influencing Mode Choice for Intercity Travel from Northern New England to Major Northeastern Cities

Long-distance and intercity travel generally make up a small portion of the total number of trips taken by an individual, while representing a large portion of aggregate distance traveled on the transportation system. While some research exists on intercity travel behavior between large metropolitan centers, this report addresses a need for more research on travel behavior between non-metropolitan areas and large metropolitan centers. This research specifically considers travel from home locations in northern New England, going to Boston, New York City, Philadelphia, and Washington, DC. These trips are important for quality of life, multimodal planning, and rural economies. This research identifies and quantifies factors that influence people’s mode choice (automobile, intercity bus, passenger rail, or commercial air travel) for these trips. The research uses survey questionnaire data, latent factor analysis, and discrete choice modeling methods. Factors include sociodemographic, built environment, latent attitudes, and trip characteristics. The survey, designed by the University of Vermont Transportation Research Center and the New England Transportation Institute, was conducted by Resource Systems Group, Inc. in 2014, with an initial sample size of 2560. Factor analysis was used to prepare 6 latent attitudinal factors, based on 70 attitudinal responses from the survey statements. The survey data were augmented with built environment variables using geographic information systems (GIS) analysis. A set of multinomial logit models, and a set of nested logit models, were estimated for business and non-business trip mode choice. Results indicate that for this type of travel, factors influencing mode choice for both business and non-business trips include trip distance; land use; personal use of technology; and latent attitudes about auto dependence, preference for automobile, and comfort with personal space and safety on public transportation. Gender is a less significant factor. Age is only significant for non-business trips. View the NCST Project Webpage
Reward Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning 1

Direct policy search is a promising reinforcement learning framework in particular for controlling continuous, high-dimensional systems. Policy search often requires a large number of samples for obtaining a stable policy update estimator. However, this is prohibitive when the sampling cost is expensive. In this paper, we extend an expectation-maximization based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, called Reward-weighted Regression with sample Reuse (R 3), is demonstrated through robot learning experiments.
Proceedings of the Joint Workshop on Interfaces and Human Decision Making in Recommender Systems

The talk will give an overview of some of the existing approaches for visualizing recommendation mechanisms and eventually allowing users to control them. Starting with work from the area of open/scrutable learner models in the area of intelligent tutoring systems, through approaches for explaining recommendations to approaches visualizing aspects of collaborative, hybrid and social recommenders, as well as the filter bubble, the talk will be based both on the speakers' own work in this area and on a review of other work and will touch on some philosophical issues about how to evaluate
Improved Weighting in Particle Filters Applied to Precise State Estimation

This letter presents a methodology to reduce the computational burden of Particle Filters (PF) used to achieve a target state estimation accuracy in Bayesian tracking problems. A strategy, named Multiple Weighting (MW), that exploits information diversity of the input measurements for an efficient weighting of the particles is introduced. This study shows that, by relying on the prior knowledge of the state-observations relationships, it is possible to achieve a significant reduction in the number of samples required to obtain any target accuracy. An experimental assessment is provided with an application to precise positioning estimation in Global Navigation Satellite System (GNSS) receivers.
On Log-Loss Scores and (No) Privacy

A common metric for assessing the performance of binary classifiers is the Log-Loss score, which is a real number indicating the cross entropy distance between the predicted distribution over the labels and the true distribution (a point distribution defined by the ground truth labels). In this paper, we show that a malicious modeler, upon obtaining access to the Log-Loss scores on its predictions, can exploit this information to infer all the ground truth labels of arbitrary test datasets with full accuracy. We provide an efficient algorithm to perform this inference. A particularly interesting application where this attack can be exploited is to breach privacy in the setting of Membership Inference Attacks. These attacks exploit the vulnerabilities of exposing models trained on customer data to queries made by an adversary. Privacy auditing tools for measuring leakage from sensitive datasets assess the total privacy leakage based on the adversary’s predictions for datapoint membership. An instance of the proposed attack can hence, cause complete membership privacy breach, obviating any attack model training or access to side knowledge with the adversary. Moreover, our algorithm is agnostic to the model under attack and hence, enables perfect membership inference even for models that do not memorize or overfit. In particular, our observations provide insight into the extent of information leakage from statistical aggregates and how they can be exploited.
A pluridisciplinary methodology for integrated management of a coastal aquifer : Geological, hydrogeological and economic studies of the Roussillon aquifer (Pyrenees -Orientales, France)

In order to study Mediterranean coastal water management, a pluridisciplinary approach is developed. Reservoir geology and some of its tools, used in oil prospecting, are applied to build a detailed sedimentary model. The analysis of depositional environments and sedimentary process allows the correlation of pre-existing data (outcrop, borehole, and seismic profile) using Genetic stratigraphy (onshore domain) and seismic stratigraphy (offshore domain). The interpretation results in a better knowledge of the sedimentary geometries following correlations between onshore and offshore domains. It is thus possible to differentiate the coastal groundwater aquifers precisely and to establish their relative connections. At the same time, hydrogeological investigations such as hydrochemistry and geophysical prospecting allow us to elaborate the hydrogeological conceptual model of the case studies. Variable-density flow and solute transport simulations constitute the hydrogeological work. Experimental economy constitutes the third part of this integrated methodology. It assesses the effectiveness of institutional arrangements to cope with aquifer overexploitation. Feed-back from these three fields of research will also authenticate our methodology. This approach applied on Roussillon basin (south-west of the French Mediterranean coastline) could be exported to many other coastal areas.
Query languages for graph databases

Query languages for graph databases started to be investigated some 25 years ago. With much current data, such as linked data on the Web and social network data, being graph-structured, there has been a recent resurgence in interest in graph query languages. We provide a brief survey of many of the graph query languages that have been proposed, focussing on the core functionality provided in these languages. We also consider issues such as expressive power and the computational complexity of query evaluation.
Mantzaris, Alexander

In the social sciences, the hypothesis of triadic closure contends that new links in a social contact network arise preferentially between those who currently share neighbours. Here, in a proof-of-principle study, we show how to calibrate a recently proposed evolving network model to time-dependent connectivity data. The probabilistic edge birth rate in the model contains a triadic closure term, so we are also able to assess statistically the evidence for this effect. The approach is shown to work on data generated synthetically from the model. We then apply this methodology to some real, large-scale data that records the build up of connections in a business-related social networking site, and ﬁnd evidence for triadic closure.
Waiting for the real dragon

Southeast Asia is the world’s most diverse and rapidly modernizing region, yet one where modernization theory is challenged (e.g. Bell et al., 1995) and where allegiance to authoritarian regimes appears trenchant (Chang et al., 2013; Dalton and Shin, 2014). Using the AsiaBarometer, I consider the influence of modernization and allegiance on potential democratization by analyzing an established indicator of assertive change: tolerance of homosexuality (Inglehart and Welzel, 2005: Ch. 5, forthcoming). The findings reveal differences between Southeast Asians and the West and between citizens in the Sinic and Indic subregions. Exposure to foreign cultures produces a significant increase in tolerance among Sinicized citizens, but has no meaningful effect on those in Indic states. Patriotism and trust in civil society also boost tolerance among Sinicized citizens, but reduce the probability of tolerance among citizens in Indic states. The strong linkage between sexual tolerance and gender equality driven by social factors and secularism in the West does not manifest in Southeast Asia. While the findings suggest potential for change in the Sinicized region, they underscore the different effects that globalization and allegiance may have on citizens living under Southeast Asia’s variety of regimes.
Statistical Issues in the Analysis of Neuronal Data

[PDF] [Full Text] [Abstract] , June 1, 2007; 19 (6): 1503-1527. Neural Comput. H. Shimazaki and S. Shinomoto A Method for Selecting the Bin Size of a Time Histogram [PDF] [Full Text] [Abstract] , June 13, 2007; 27 (24): 6461-6472. J. Neurosci. M. Avissar, A. C. Furman, J. C. Saunders and T. D. Parsons Auditory Nerve Responses Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of [PDF] [Full Text] [Abstract] , October 1, 2007; 98 (4): 2285-2296. J Neurophysiol A. Thiel, M. Greschner, C. W. Eurich, J. Ammermuller and J. Kretzberg Encoding Contribution of Individual Retinal Ganglion Cell Responses to Velocity and Acceleration [PDF] [Full Text] [Abstract] , October 31, 2007; 27 (44): 11807-11819. J. Neurosci. R. Silver, K. Boahen, S. Grillner, N. Kopell and K. L. Olsen Tools Neurotech for Neuroscience: Unifying Concepts, Organizing Principles, and Emerging [PDF] [Full Text] [Abstract] , December 1, 2007; 98 (6): 3696-3707. J Neurophysiol E. F. Chang, R. S. Turner, J. L. Ostrem, V. R. Davis and P. A. Starr Dystonia Neuronal Responses to Passive Movement in the Globus Pallidus Internus in Primary
Learning Fast Optimizers for Contextual Stochastic Integer Programs

We present a novel reinforcement learning (RL) approach to learning a fast and highly scalable solver for a two-stage stochastic integer program in the large-scale data setting. Mixed integer programming solvers do not scale to large datasets for this problem class. Additionally, they solve each instance independently, without any knowledge transfer across instances. We address these limitations with a learnable local search solver that jointly learns two policies, one to generate an initial solution and another to iteratively improve it with local moves. The policies use contextual features for a problem instance as input, which enables learning across instances and generalization to new ones. We also propose learning a policy to compute a bound on the objective using dual decomposition. Benchmark results show that on test instances our approach rapidly achieves approximately 30% to 2000% better objective value, which a state of the art integer programming solver (SCIP) requires more than an order of magnitude more running time to match. Our approach also achieves better solution quality on seven out of eight benchmark problems than standard baselines such as Tabu Search and Progressive Hedging.
Issues in Information Systems

The notion of outcome-based contracting originated from the manufacturing discipline and has evolved as one of the most desirable business models in service science. Although previous Information Systems (IS) studies have glimpsed the role of outcome-based contracting as a driver for system thinking and value co-creation, there are not many studies that examine fundamental issues arising from an outcome-based contract for IT outsourcing. This research examines the mechanism of IT outsourcing contracting processes at two levels, namely, environmental factors of contracting strategy and solution value. It proposes a comprehensive framework to derive value cocreation using a system dynamics approach.
Towards classification of head movements in audiovisual recordings of read news

In this paper we develop a system for detection of word-related head movements in audiovisual recordings of read news. Our materials consist of Swedish television news broadcasts and comprise audiovisual recordings of five news readers (two female, three male). The corpus was manually labelled for head movement, applying a simplistic annotation scheme consisting of a binary decision about absence/presence of a movement in relation to a word. We use OpenCV for frontal face detection and based on this we calculate velocity and acceleration features. Then we train a machine learning system to predict absence or presence of head movement and achieve an accuracy of 0.892, which is better than the baseline. The system may thus be helpful for head movement labelling.
Selecting Profitable Custom Instructions for Area–Time-Efficient Realization on Reconfigurable Architectures

Profitable custom instructions provide higher performance for a given reconfigurable area. Hence, choosing profitable custom instructions that are also area–time efficient is essential if design constraints must be met by field-programmable-gate-array (FPGA)-based reconfigurable processors. In this paper, we propose a framework for FPGA-based reconfigurable processors in order to rapidly identify a reduced set of profitable custom instructions without the need for actual hardware synthesis. The proposed framework is capable of estimating the area utilization and latencies of custom instructions on lookup-table-based commercial FPGAs. Simulations based on 15 applications from benchmark suites show that the proposed method provides, on average, an area reduction of over 29% for loss of mere 1.3% in compute performance. Our evaluations also confirm that the proposed framework is superior to an existing area-optimization approach that relies on exploiting the regularity of custom instruction data paths. In particular, an average area–time product gain of over 59% was achieved by deploying a reduced set of custom instructions obtained using the proposed framework.
Holes, Pits, and Valleys: Guiding Large-Display Touchless Interactions with Data-Morphed Topographies

Large, high-resolution displays enable efficient visualization of large datasets. To interact with these large datasets, touchless interfaces can support fluid interaction at different distances from the display. Touchless gestures, however, lack haptic feedback. Hence, users’ gestures may unintentionally move off the interface elements and require additional physical effort to perform intended actions. To address this problem, we propose data-morphed topographies for touchless interactions : constraints on users’ cursor movements that guide touchless interaction along the structure of the visualized data. To exemplify the potential of our concept, we envision applying three data-morphed topographies—holes, pits, and valleys— to common problem-solving tasks in visual analytics.
Machine Learning Techniques for Identifying the Defective Patterns in Semiconductor Wafer Maps: A Survey, Empirical, and Experimental Evaluations

This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the effectiveness of ML in wafer defect identification, there is a noticeable absence of comprehensive reviews on this subject. This survey attempts to fill this void by amalgamating available literature and providing an in-depth analysis of the advantages, limitations, and potential applications of various ML algorithms in the realm of wafer defect detection. An innovative taxonomy of methodologies that we present provides a detailed classification of algorithms into more refined categories and techniques. This taxonomy follows a four-tier structure, starting from broad methodology categories and ending with specific sub-techniques. It aids researchers in comprehending the complex relationships between different algorithms and their techniques. We employ a rigorous empirical and experimental evaluation to rank these varying techniques. For the empirical evaluation, we assess techniques based on a set of four criteria. The experimental evaluation ranks the algorithms employing the same sub-techniques, techniques, sub-categories, and categories. This integration of a multi-layered taxonomy, empirical evaluations, and comparative experiments provides a detailed and holistic understanding of ML techniques and algorithms for identifying wafer defects. This approach guides researchers towards making more informed decisions in their work. Additionally, the paper illuminates the future prospects of ML techniques for wafer defect identification, underscoring potential advancements and opportunities for further research in this field.
AIS Electronic Library (AISeL)

In context aware applications such as location based, m-health care, and m-business applications, it is expected that a huge amount of users context information will be collected, which threatens their privacy concerns. Users consent is a mandatory requirement of users privacy support. Increasingly, it seems clear that user consent decision implies the consideration of a number of factors. These factors vary in their impact on the user consent from one situation to another and from a user to another. In this paper, we propose a consent provider model that considers a number of factors influencing users consent, modeling their impact and evaluating their roles in the consent decision process based on fuzzy logic reasoning. Increasingly, we define a new data set called “contextual privacy attributes” which is associated with each user contextual data, and corresponds to influencing factors. We have prototyped the proposed consent provider model and integrated it into the real time UMTS mobile information and entertainment services (MIES) platform hosted at the university campus.
Research on Influencing Factors of Information Dissemination of WeChat Public Accounts Based on FSIPS Two-stage Model

The popularity and application of social media have a profound impact on user information dissemination in the Internet era. In China, WeChat's operation will face greater challenges as the number of Official Accounts climbs. This study focuses on the key factors of information dissemination effect in the two stage of "Sympathy under Feedback-Identify" and "Participate-Share & Spread" through constructing FSIPS two stage model, and analyzes the influencing factors of reading rate, comment rate and share rate from two dimensions in order to improve the distribution rate and information transmission for public operators.
A Theoretical Framework for Serious Game Design : Exploring Pedagogy , Play and Fidelity and their Implications for the Design Process

It is widely acknowledged that digital games can provide an engaging, motivating and “fun” experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design (Kiili, 2005; Seeney & Routledge, 2009). Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore a triadic theoretical framework for serious game design comprising play, pedagogy and fidelity. It will outline underpinning theories, review key literatures and identify challenges and issues involved in balancing these elements in the process of serious game design. DOI: 10.4018/ijgbl.2012100103 42 International Journal of Game-Based Learning, 2(4), 41-60, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. game design (Kiili, 2005; Seeney & Routledge, 2009). While a sound pedagogical framework is considered essential to their effectiveness as learning tools, equally important is the integration of game play elements which harness and sustain player engagement. Additionally, with the advent of sophisticated and immersive technologies, as exemplified in the virtual worlds of contemporary games, and increasing interest in the opportunities for constructivist learning offered by these worlds, concepts of fidelity, and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore this triadic theoretical framework for serious game design, outlining underpinning theories, reviewing key literatures and identifying associated challenges and issues (Figure 1). The paper begins by reflecting on pedagogical theories commonly utilised to conceptualise game-based learning, focusing on three constructivist theories. Following this, attention switches to theories used to conceptualise players’ engagement with digital games, and thus inform effective, engaging and “fun” game design. As a key component of engaging and pedagogically effective game design, the concept of fidelity, and how it relates to game design and game-based learning, is discussed. The paper will conclude by reflecting on issues and challenges involved in balancing these components when designing