Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bespoke 3DUI XNA Framework: a low-cost platform for prototyping 3D spatial interfaces in video games

This paper presents the Bespoke 3DUI XNA Framework, an open-source software platform for research in 3D user interaction. The Bespoke 3DUI XNA Framework distinguishes itself from other platforms, in that it provides 3D user interface machinery in a game development framework. This combination leverages low-cost, widely available game technologies, enabling researchers to investigate 3DUI techniques, and providing game developers a foundation for prototyping 3DUIs in commercial video games. The paper explores the functionality and utility of the software library and describes how researchers and game makers can leverage the platform to investigate 3D user interfaces in the context of prototypical interactive experiences.
Comfortably Numb

Two studies tested the hypothesis that exposure to violent media reduces aid offered to people in pain. In Study 1, participants played a violent or nonviolent video game for 20 min. After game play, while completing a lengthy questionnaire, they heard a loud fight, in which one person was injured, outside the lab. Participants who played violent games took longer to help the injured victim, rated the fight as less serious, and were less likely to “hear” the fight in comparison to participants who played nonviolent games. In Study 2, violent- and nonviolent-movie attendees witnessed a young woman with an injured ankle struggle to pick up her crutches outside the theater either before or after the movie. Participants who had just watched a violent movie took longer to help than participants in the other three conditions. The findings from both studies suggest that violent media make people numb to the pain and suffering of others.
A Constraint Programming Approach for Workflow Applications

This paper proposes a constraint programming approach for workflow applications in which a slip is regarded as a tuple of the RDB model. A workflow is considered to be a series of database updating and constraint maintenance processes in this case. The proposed method utilizes pre-conditions and postconditions in a constraint programming style for an agent that interprets and executes these constraints. The method has been experimentally applied to a real application and the following advantages have been confirmed: (1) No programming for opposite directional flow to correct the value of an erroneous item when an error is detected in the workflow downstream stage, and (2) Small semantic gap between the domain knowledge and the declarative programming description. Topics : Knowledge-based methods and tools for software engineering
VISTA: A View of Effective Sampling for Frequent Itemset Mining

Sampling is a well established technique to speed up the process of discovering frequent itemsets. While the early literature focused on heuristic techniques, mathematical bounds on the sample size required to probabilistically achieve approximately correct results were recently presented in [6], [12]. A particularly appealing feature of these bounds is that they are independent of the database row-cardinality. In this report, we demonstrate through an extensive empirical evaluation that the bounds, although theoretically elegant, are loose by as much as one to two orders of magnitude in practice. We therefore investigate the possibility of obtaining better bounds through prior knowledge of statistics on the datasets. In particular, we assume that the number of maximal frequent itemsets in the data mining result is known in advance. However, even with such a strong assumption, the revised bound turns out to be several multiples of the required sample size. This motivates us to consider the question of algorithmically identifying a reduced sample size that is sufficient to obtain accurate results. To address this issue, we present VISTA, a voting-based iterative sampling algorithm for accurately discovering frequent itemsets, whose sampling overheads are comparable to the ideal sample size for one-shot frequent itemset mining. VISTA incrementally mines samples in small batches and uses the presence or absence of a frequent itemset in each batch to determine its voting characteristics. The stopping condition is the reaching of a fix point in the identities of frequent itemsets that receive a clear majority of the votes across the batches. All results presented here are validated through extensive experimental evaluation on massive synthetic and real datasets.
A Time Petri Net Based Approach for Embedded Hard Real-Time Software Synthesis with Multiple Operational Modes

In general, complex embedded systems rely on specialized operating system kernels. However, the operating system usage may introduce significant time overhead, which may affect the tasks' deadline in embedded hard real-time systems. In addition, hardware requirements (e.g. memory) are usually increased. As an alternative, this paper presents a software synthesis approach for eliminating such overheads, meeting timing constraints, and providing multiple operational modes. In order to satisfy timing requirements, a pre-runtime scheduling is used to find a feasible schedule by adopting a formalism based on time Petri net. Pre-runtime scheduling is considered inflexible, but using multiple operational modes, significant flexibility may be achieved
Optimal conditions for camera calibration using a planar template

Different techniques exist for camera calibration based on photogrammetry or self-calibration. Much work has been done to obtain intrinsic and extrinsic camera parameters and also in data pre - and post-processing techniques. From a practical viewpoint, it is quite difficult to decide which calibration method produces the best results and even whether any data processing is necessary. This paper proposes a set of optimal performance conditions to resolve the calibration process accurately. The calibration method based on a 2D pattern has been chosen since camera is calibrated using several images of a planar template easily. The proposed optimal conditions define the number of points to design the calibration pattern, the number of images to resolve the calibration accurately, as well as positions and orientations from where images should be taken. The recommend rules will help to obtain a well-conditioned calibration process.
A CUSTOMIZABLE SoPC ARCHITECTURE FOR GA BASED ON COMPLETE HARDWARE

A Genetic Algorithm (GA) is a computer based search optimization technique that uses the Darwinian “Theory of Evolution” as a model for finding exact and approximate solutions. GAs belong to a large family of heuristic algorithms called Evolutionary algorithms (EA) which are being increasingly utiliz ed for solving complex optimization and search problem s. The large computation time consumed by a GA implemented in software makes it unsuitable for rea l time applications. This hurdle is overcome by shi fting the implementation to hardware, which drastically s peeds up the time factor, thus presenting a scope f r real time applications. Major issues to be addresse d in hardware implementation are silicon utilizatio n, scalability, providing flexibility and reduced comp utational delays. This work presents a customizable Complete Hardware Evolution (CHE) based GA architec ture. Along with the generic modules for the genetic operators of the GA, modules named Flush an d Replace Memory (FRM), Memory Module (MM), and a sorting module form the main components of th is architecture. These modules can facilitate Syste m on Programmable Chip (SoPC) implementation for diff erent applications of GA. The coding is done using Verilog Hardware Description Language (HDL) and sim ulated using Xilinx ISE 9.1i simulator. Each module is separately simulated and synthesized for a Commercial Off The Shelf (COTS) Field Programmable Gate Array (FPGA). The resource utiliz a on and the critical path delay of the modules ar e evaluated and presented for Xilinx Virtex – 4 FPGA.
SmartCampusAAU -- An Open Platform Enabling Indoor Positioning and Navigation

This paper describes SmartCampusAAU - an open, extendable platform that supports the easy creation of indoor location based systems. SmartCampusAAU offers an app and backend that can be used to enable indoor positioning and navigation in any building. The SmartCampusAAU app is available on all major mobile platforms (Android, iPhone and Windows Phone) and supports both device- and infrastructure-based positioning. SmartCampusAAU also offers a publicly available OData backend that allows researchers to share radio map and location tracking data.
Fast group discovery and non-repudiation in D2D communications using IBE

Proximity services discovery using LTE-based Device-to-Device (D2D) communications has been recently in the heart of the discussions about the advent of 5G networks. Meanwhile, their related security aspects are of a major concern especially when dealing with direct radio communications and large-scale deployment of D2D. Yet, existing security algorithms and solutions are not adapted to these emerging new types of communications. This paper is focused on D2D communications' security issues in both discovery and communication phases. First, we address the global security stakes and challenges in D2D. Then, a solution based on the Identity-Based Encryption (IBE) mechanism is proposed to secure the exchanged D2D messages during the discovery and communication phases. The proposed solution is discussed under two D2D use cases and is further used to introduce an efficient key management system for group communication. Finally, a security requirements analysis is presented in order to evaluate the scalability and efficiency levels of the proposed solution.
Facial expression recognition in still pictures and videos using active appearance models: a comparison approach

The paper highlights the performance of video sequence-oriented facial expression recognition using Active Appearance Model -- AAM, in a comparison with the analysis based on still pictures. The AAM is used to extract relevant information regarding the shapes of the faces to be analyzed. Specific key points from a Facial Characteristic Point - FCP model are used to derive the set of features. These features are used for the classification of the expressions of a new face sample into the prototypic emotions. The classification method uses Support Vector Machines.
Data-Driven Relevance Judgments for Ranking Evaluation

Ranking evaluation metrics are a fundamental element of design and improvement efforts in information retrieval. We observe that most popular metrics disregard information portrayed in the scores used to derive rankings, when available. This may pose a numerical scaling problem, causing an under- or over-estimation of the evaluation depending on the degree of divergence between the scores of ranked items. The purpose of this work is to propose a principled way of quantifying multi-graded relevance judgments of items and enable a more accurate penalization of ordering errors in rankings. We propose a data-driven generation of relevance functions based on the degree of the divergence amongst a set of items' scores and its application in the evaluation metric Normalized Discounted Cumulative Gain (nDCG). We use synthetic data to demonstrate the interest of our proposal and a combination of data on news items from Google News and their respective popularity in Twitter to show its performance in comparison to the standard nDCG. Results show that our proposal is capable of providing a more fine-grained evaluation of rankings when compared to the standard nDCG, and that the latter frequently under- or over-estimates its evaluation scores in light of the divergence of items' scores.
Using a graphic data-processing system to design artwork for manufacturing hybrid integrated circuits

This paper will describe a computer program that utilizes a graphic data processing system to aid in the design of mask artwork for hybrid integrated circuit modules of the type used in IBM System/360 Data Processing Systems. The system includes a small digital computer connected to a large-screen buffered display equipped with a light pen. A draftsman uses the light pen to assemble a circuit schematic on the display screen; simultaneously, a description of the schematic is entered into the computer memory. Thereafter, the draftsman can use the light pen to layout detailed artwork for fabrication of the circuit mask, subject to automatic checking against the stored schematic. When the layout on the display screen is complete, the corresponding mask artwork will be drawn by the computer via its digitally-controlled plotter. The graphical manipulations on the display screen, the automatic checking operations, and the control of the digital plotter are all part of a FORTRAN program that employs graphical subroutines to communicate with the light pen, display, and plotter.
Angular Clustering of Galaxies at 3.6 Microns from the Spitzer Wide-area Infrared Extragalactic (SWIRE) Survey

We present the first analysis of large-scale clustering from the Spitzer Wide-area Infrared Extragalactic legacy survey (SWIRE). We compute the angular correlation function of galaxies selected to have 3.6 μm fluxes brighter than 32 μJy in three fields totaling 2 deg2 in area. In each field we detect clustering with a high level of significance. The amplitude and slope of the correlation function is consistent between the three fields and is modeled as w(θ) = Aθ1-γ with A = (0.6 ± 0.3) × 10-3,γ = 2.03 ± 0.10. With a fixed slope of γ = 1.8, we obtain an amplitude of A = (1.7 ± 0.1) × 10-3. Assuming an equivalent depth of K ≈ 18.7 mag we find that our errors are smaller but our results are consistent with existing clustering measurements in K-band surveys and with stable clustering models. We estimate our median redshift z ≃ 0.75, and this allows us to obtain an estimate of the three-dimensional correlation function ξ(r), for which we find r0 = 4.4 ± 0.1 h-1 Mpc.
Development and Validation of a Mediterranean Oriented Culture-Specific Semi-Quantitative Food Frequency Questionnaire

The objectives were to develop a Mediterranean oriented semi-quantitative food frequency questionnaire (FFQ) and evaluate its validity in measuring energy and nutrient intakes. For FFQ development, the main challenge was to merge food items and practices reflecting cultural Mediterranean preferences with other food choices ensuing from diet transition to more westernized dietary patterns. FFQ validity was evaluated by comparing nutrient intakes against the average of two 24-h dietary recalls for 179 pregnant women. Although the mean intake values for most nutrients and energy tended to be higher when determined by the FFQ, the Cohen’s d was below 0.3. Bland-Altman plots confirmed the agreement between the two methods. Positive significant correlations ranged from 0.35 to 0.77. The proportion of women classified correctly was between 73.2% and 92.2%, whereas gross misclassification was low. Weighted kappa values were between 0.31 and 0.78, while intraclass correlation coefficients were between 0.49 and 0.89. Our methodological approach for the development and validation of this FFQ provides reliable measurements of energy, macro- and micronutrient intakes. Overall, our culture-specific FFQ could serve as a useful assessment tool in studies aiming at monitoring dietary intakes, especially in the Mediterranean region, where countries share common cultural dietary habits.
From Robots to Reinforcement Learning

In this paper, we review recent advances in Reinforcement Learning (RL) in light of potential applications to robotics, introduce the basic concepts of RL and Markov Decision Process (MDP), and compare different RL algorithms such as Q-learning, Temporal Difference learning, the Actor Critic, and the Natural Actor Critic. We conclude that policy gradient methods are more suitable for solving continuous state/action MDP problems than RL with lookup tables or general function approximators. Further, natural policy gradient methods can efficiently converge to locally optimal solutions. Some simulation results are given to support our arguments. We also present a brief overview of our approach to developing an autonomous robot agent that can perceive, learn from and interact with the environment, and reason about and handle unexpected problems using its knowledge base.
Predictive Power of a Body Shape Index for Development of Diabetes, Hypertension, and Dyslipidemia in Japanese Adults: A Retrospective Cohort Study

Background/Objectives Recently, a body shape index (ABSI) was reported to predict all-cause mortality independently of body mass index (BMI) in Americans. This study aimed to evaluate whether ABSI is applicable to Japanese adults as a predictor for development of diabetes, hypertension, and dyslipidemia. Subjects/Methods We evaluated the predictive power of ABSI in a retrospective cohort study using annual health examination data from Chiba City Hall in Japan, for the period 2008 to 2012. Subjects included 37,581 without diabetes, 23,090 without hypertension, and 20,776 without dyslipidemia at baseline who were monitored for disease incidence for 4 years. We examined the associations of standardized ABSI, BMI, and waist circumference (WC) at baseline with disease incidence by logistic regression analyses. Furthermore, we conducted a case-matched study using the propensity score matching method. Results Elevated BMI, WC, and ABSI increased the risks of diabetes and dyslipidemia [BMI-diabetes: odds ratio (OR) = 1.26, 95% confidence interval (95%CI) = 1.20−1.32; BMI-dyslipidemia: OR = 1.15, 95%CI = 1.12−1.19; WC-diabetes: OR = 1.24, 95%CI = 1.18−1.31; WC-dyslipidemia: OR = 1.15, 95%CI = 1.11−1.19; ABSI-diabetes: OR = 1.06, 95%CI = 1.01−1.11; ABSI-dyslipidemia: OR = 1.04, 95%CI = 1.01−1.07]. Elevated BMI and WC, but not higher ABSI, also increased the risk of hypertension [BMI: OR = 1.32, 95%CI = 1.27−1.37; WC: OR = 1.22, 95%CI = 1.18−1.26; ABSI: OR = 1.00, 95%CI = 0.97−1.02]. Areas under the curve (AUCs) in regression models with ABSI were significantly smaller than in models with BMI or WC for all three diseases. In case-matched subgroups, the power of ABSI was weaker than that of BMI and WC for predicting the incidence of diabetes, hypertension, and dyslipidemia. Conclusions Compared with BMI or WC, ABSI was not a better predictor of diabetes, hypertension, and dyslipidemia in Japanese adults.
Inclined ergometer to enhance fes-assisted indoor rowing exercise performance

Improving the FES-assisted indoor rowing exercise (FES-rowing) performance enables the spinal cord injury (SCI) people to perform hybrid FES-exercise in a higher level of intensity. High level of exercise volume and intensity can play a big role in prevention of cardiovascular disease, type 2 diabetes and obesity which is a significant threat to the health of people with chronic SCI. FES-rowing can be enhanced to achieved the high level exercise through the arrangement of the rowing ergometer. In this paper, the performance of FES-rowing using an adjustable inclined rowing ergometer is investigated. Two different methods to enhance the FES-rowing performance using inclined ergometer are implemented. A model of the adjustable inclined ergometer and humanoid are developed using the Visual Nastran (vN4D) software environment and validated by the experimental work. Fuzzy logic control is implemented to control the knee and elbow trajectories for smooth rowing manoeuvre. The generated level of electrical stimulations for activation of quadriceps and hamstrings muscles are recorded and analysed. The FES-rowing efficiency for both methods have been defined and illustrated. The results show the inclined ergometer with upper body effort is the best performance in enhancing the FES-rowing.
Exercise Intensity Modulates Glucose-Stimulated Insulin Secretion when Adjusted for Adipose, Liver and Skeletal Muscle Insulin Resistance

Little is known about the effects of exercise intensity on compensatory changes in glucose-stimulated insulin secretion (GSIS) when adjusted for adipose, liver and skeletal muscle insulin resistance (IR). Fifteen participants (8F, Age: 49.9±3.6yr; BMI: 31.0±1.5kg/m2; VO2peak: 23.2±1.2mg/kg/min) with prediabetes (ADA criteria, 75g OGTT and/or HbA1c) underwent a time-course matched Control, and isocaloric (200kcal) exercise at moderate (MIE; at lactate threshold (LT)), and high-intensity (HIE; 75% of difference between LT and VO2peak). A 75g OGTT was conducted 1 hour post-exercise/Control, and plasma glucose, insulin, C-peptide and free fatty acids were determined for calculations of skeletal muscle (1/Oral Minimal Model; SMIR), hepatic (HOMAIR), and adipose (ADIPOSEIR) IR. Insulin secretion rates were determined by deconvolution modeling for GSIS, and disposition index (DI; GSIS/IR; DISMIR, DIHOMAIR, DIADIPOSEIR) calculations. Compared to Control, exercise lowered SMIR independent of intensity (P<0.05), with HIE raising HOMAIR and ADIPOSEIR compared with Control (P<0.05). GSIS was not reduced following exercise, but DIHOMAIR and DIADIPOSEIR were lowered more following HIE compared with Control (P<0.05). However, DISMIR increased in an intensity based manner relative to Control (P<0.05), which corresponded with lower post-prandial blood glucose levels. Taken together, pancreatic insulin secretion adjusts in an exercise intensity dependent manner to match the level of insulin resistance in skeletal muscle, liver and adipose tissue. Further work is warranted to understand the mechanism by which exercise influences the cross-talk between tissues that regulate blood glucose in people with prediabetes.
Fair welfare maximization

We consider the general problem of finding fair constrained resource allocations. As a criterion for fairness we propose an inequality index, termed “fairness ratio,” the maximization of which produces Lorenz-undominated, Pareto-optimal allocations. The fairness ratio does not depend on the choice of any particular social welfare function, and hence it can be used for an a priori evaluation of any given feasible resource allocation. The fairness ratio for an allocation provides a bound on the discrepancy between this allocation and any other feasible allocation with respect to a large class of social welfare functions. We provide a simple representation of the fairness ratio as well as a general method that can be used to directly determine optimal fair allocations. For general convex environments, we provide a fundamental lower bound for the optimal fairness ratio and show that as the population size increases, the optimal fairness ratio decreases at most logarithmically in what we call the “inhomogeneity” of the problem. Our method yields a unique and “balanced” fair optimum for an important class of problems with linear budget constraints.
Testing-Based Conceptual Schema Validation in a Model-Driven Environment

In Model-Driven Development, the main artefacts are conceptual schemas, and efforts are focused on their creation, testing and evolution at different levels of abstraction through transformations. If a conceptual schema has defects, these are passed on to the following stages, including coding. Therefore, techniques for improving the quality of conceptual schemas must be implemented to assure the correct generation of final software products. One of the challenges in Model-Driven Development is being able to identify defects early on, at the level of conceptual schemas, as this would help reduce development costs and improve software quality. In this research proposal we suggest an approach for testing-based conceptual schema validation in order to improve quality.