Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating pre-purchase affect in product concept development

The Pre-purchase Affect Model (PAM) was developed. It predicts how product criteria for purchase may engender affect. A field survey was conducted to determine what criteria consumers use to purchase electronic products and items of clothing. A total of 23 and 21 criteria were identified for buying clothing and electronic products, respectively. There were 11 types of criteria for clothes and nine for electronic products. The criteria were compared with the product attributes. If there was a match pre-purchase affect resulted. The use of the model will help define design attributes that are capable of eliciting affect.
Personalizing Persuasive Strategies in Gameful Systems to Gamification User Types

Persuasive gameful systems are effective tools for motivating behaviour change. Research has shown that tailoring these systems to individuals can increase their efficacy; however, there is little knowledge on how to personalize them. We conducted a large-scale study of 543 participants to investigate how different gamification user types responded to ten persuasive strategies depicted in storyboards representing persuasive gameful health systems. Our results reveal that people's gamification user types play significant roles in the perceived persuasiveness of different strategies. People scoring high in the 'player' user type tend to be motivated by competition, comparison, cooperation, and reward while 'disruptors' are likely to be demotivated by punishment, goal-setting, simulation, and self-monitoring. 'Socialisers' could be motivated using any of the strategies; they are the most responsive to persuasion overall. Finally, we contribute to CHI research and practice by offering design guidelines for tailoring persuasive gameful systems to each gamification user type.
Exploring Data in Virtual Reality: Comparisons with 2D Data Visualizations

Virtual Reality (VR) has often been discussed as a promising medium for immersive data visualization and exploration. However, few studies have evaluated users' open-ended exploration of multi-dimensional datasets using VR and compared the results with that of traditional (2D) visualizations. Using a workload- and insight-based evaluation methodology, we conducted a user study to perform such a comparison. We find that there is no overall task-workload difference between traditional visualizations and visualizations in VR, but there are differences in the accuracy and depth of insights that users gain. Our results also suggest that users feel more satisfied and successful when using VR data exploration tools, thus demonstrating the potential of VR as an engaging medium for visual data analytics.
COMPOzE-intention-based music composition through constraint programming

We goal of the work is to derive four-voice music pieces from given musical plans, which describe the harmonic flow and the intentions of a desired composition. We developed the experimentation platform COMPOzE for intention based composition. COMPOzE is based on constraint programming over finite domains of integers. We argue that constraint programming provides a suitable technology for this task and that the libraries and tools available for the constraint programming system Oz effectively support the implementation of COMPOzE. This work links the research areas of automatic music composition on one hand and finite domain constraint programming on the other, and contributes the tool COMPOzE, which practically demonstrates the potential of constraint programming to open up new areas of application for automatic music composition.
Statistical estimators for aggregate relational algebra queries

This paper discusses the estimation of COUNT(E) queries by sampling, where E is an arbitrary relational algebra expression. Consistent and unbiased statistical estimators for COUNT(E) are proposed without any assumptions on the distributions of attribute values or the orderings of tuples in the operand relatlons of E. We present a set of COUNT’(E) estimator evaluation algorithms, all based on simple random sampling, and each suitable for a different type of relational algebra expression E. To improve the efficiency, we propose three enhancements, and revise the estimator evaluation algorithms to incorporate the enhancements. One of the enhancements m the use of cluster sampling. Estimator evaluation algorithms with the enhancements have been incorporated into a prototype DBMS. We present the performance evaluation of the estimators using the implemented prototype DBMS.
Three-dimensional distance field metamorphosis

Given two or more objects of general topology, intermediate objects are constructed by a distance field metamorphosis. In the presented method the interpolation of the distance field is guided by a warp function controlled by a set of corresponding anchor points. Some rules for defining a smooth least-distorting warp function are given. To reduce the distortion of the intermediate shapes, the warp function is decomposed into a rigid rotational part and an elastic part. The distance field interpolation method is modified so that the interpolation is done in correlation with the warp function. The method provides the animator with a technique that can be used to create a set of models forming a smooth transition between pairs of a given sequence of keyframe models. The advantage of the new approach is that it is capable of morphing between objects having a different topological genus where no correspondence between the geometric primitives of the models needs to be established. The desired correspondence is defined by an animator in terms of a relatively small number of anchor points
Tracking with multi-level features

We present a novel formulation of the multiple object tracking problem which integrates low and mid-level features. In particular, we formulate the tracking problem as a quadratic program coupling detections and dense point trajectories. Due to the computational complexity of the initial QP, we propose an approximation by two auxiliary problems, a temporal and spatial association, where the temporal subproblem can be efficiently solved by a linear program and the spatial association by a clustering algorithm. The objective function of the QP is used in order to find the optimal number of clusters, where each cluster ideally represents one person. Evaluation is provided for multiple scenarios, showing the superiority of our method with respect to classic tracking-by-detection methods and also other methods that greedily integrate low-level features.
Photovoltaic power data analysis using hierarchical clustering

Photovoltaic is attracting attention as one of the renewable energy sources that replace existing fossil fuel-based power generation. Photovoltaic power generation data clustering is becoming increasingly popular because it allows us to identify PV systems without extensive analysis and simulation. Generally, clustering method of PV power data analyzes only the meaning of each cluster, so it is difficult to grasp the similarity between clusters. In this paper, clustering of PV power data through hierarchical clustering algorithm is performed to calculate the relationship between clusters. The hierarchical clustering method was used to analyze the similarity of PV power data between clusters. As a result, PV power data were classified into seasonal clusters and defective clusters.
Deprivation and faecal haemoglobin: implications for bowel cancer screening

Objective To investigate the relationship between deprivation and faecal haemoglobin concentration (f-Hb). Setting Scottish Bowel Screening Programme. Methods A total of 66725 men and women, aged 50 to 74, were invited to provide a single sample for a faecal immunochemical test. Deprivation was estimated using the Scottish Index of Multiple Deprivation quintiles: f-Hb was measured (OC-Sensor, Eiken, Japan) on 38439 participants. The relationship between deprivation quintiles and f-Hb was examined. Results Median age was 60 years, 53.6% women, with 14.1%, 19.7%, 17.7%, 25.9% and 22.6% in the lowest to the highest deprivation quintiles respectively. No detectable f-Hb was found in 51.9%, ranging from 45.5% in the most deprived up to 56.5% in the least deprived. As deprivation increased, f-Hb increased (p < 0.0001). This trend remained controlling for sex and age (p < 0.001). Participants in the most deprived quintile were more likely to have a f-Hb above a cut-off of 80 µg Hb/g faeces compared with the least deprived, independent of sex and age (adjusted odds ratio 1.70, 95% confidence interval: 1.37 to 2.11). Conclusions Deprivation and f-Hb are related. This has important implications for selection of cut-off f-Hb for screening programmes, and supports the inclusion of deprivation in risk-scoring systems.
Improvements to boolean resynthesis

In electronic design automation Boolean resynthesis techniques are increasingly used to improve the quality of results where algebraic methods hit local minima. Boolean methods rely on complete functional properties of a logic circuit, preferably including don't care information. Computationally expensive engines such as truth tables, SAT and binary decision diagrams are required to gather such properties. The choice of the engine determines the scalability of Boolean resynthesis. In this paper, we present improvements to Boolean resynthesis, enabling more optimization opportunities to be found at the same or smaller runtime cost as compared to state-of-the-art methods. Our contributions include (i) a theory of Boolean filtering to drastically reduce the number of gates processed and still retain all possible optimization opportunities, (ii) a weaker notion of maximum set of permissible functions, which can be computed efficiently via truth tables, (iii) a generalized refactoring engine that supports multiple representation forms, and (iv) a practical Boolean resynthesis flow, which combines the techniques proposed so far. Using our Boolean resynthesis on the EPFL benchmarks, we improve 10 of the best known area results in the synthesis competition. Embedded in a commercial EDA flow for ASICs, the Boolean resynthesis flow reduces the area by −2.67% and total negative slack by −5.48%, after physical implementation, at negligible runtime cost.
An insight into tree based machine learning techniques for big data analytics using Apache Spark

Data Analysis, Classification and Regression on Big Data using machine learning algorithms in a novel approach used in various science and medical streams. Map Reduce is widely used frame work to parallelize machine learning algorithms. These algorithms are tuned to attain best outcomes. This techniques uses lots of time for processing map reduce model multiple times by tuning the parameters as per the requirement. To achieve the shortest time consumption for tuning the jobs, Apache Spark based model is proposed for optimizing the assignments. This model is to predict the temperature from existing data by training using tree based machine learning techniques. This model replaces the map reduce by spark for implementing the best prediction result. The prediction outcomes computed are compared on tree structured ML methods with respect to time and space utilization.
Crowd Intelligence for the Classification of Fractures and Beyond

Background Medical diagnosis, like all products of human cognition, is subject to error. We tested the hypothesis that errors of diagnosis in the realm of fracture classification can be reduced by a consensus (group) diagnosis; and that digital imaging and Internet access makes feasible the compilation of a diagnostic consensus in real time. Methods Twelve orthopaedic surgeons were asked to evaluate 20 hip radiographs demonstrating a femoral neck fracture. The surgeons were asked to determine if the fractures were displaced or not. Because no reference standard is available, the maximal accuracy of the diagnosis of displacement can be inferred from inter-observer reliability: if two readers disagree about displacement, one of them must be wrong. That method was employed here. Additionally, virtual reader groups of 3 and 5 individual members were amalgamated, with the response of those groups defined by majority vote. The purpose of this step was to see if increasing the number of readers would improve accuracy. In a second experiment, to study the feasibility of amassing a reader group on the Internet in real time, 40 volunteers were sent 10 periodic email requests to answer questions and their response times were assessed. Results The mean kappa coefficient for individual inter-observer reliability for the diagnosis of displacement was 0.69, comparable to prior published values. For 3-member virtual reader groups, inter-observer reliability was 0.77; and for 5-member groups, it was 0.80. In the experiment studying the feasibility of amassing a reader group in real time, the mean response time was 594 minutes. For all cases, a 9-member group (theoretically 99% accurate) was amassed in 135.8 minutes or less. Conclusions Consensus may improve diagnosis. Amassing a group for this purpose on the Internet is feasible.
in Advances in Neural Information Processing

A signi(cid:12)cant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Our algo-rithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that Trepan is able to produce decision trees that maintain a high level of (cid:12)delity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large networks and problems with high-dimensional input spaces.
Performance analysis of improved q-ZS AC-AC converter with high frequency isolation

This paper extends the concept of q-ZS and introduces a new single-phase improved quasi-ZS ac-ac converter, which is developed by different arrangement of components in conventional Z-source ac-ac converter. In addition to all the merits of the conventional ZS and q-ZS ac-ac power convertors, this proposed improved q-ZS topology have high frequency isolation for safety, low voltage stress, high power density and improved pf. This paper presents the performance analysis of improved q-ZS ac-ac converter with HFTI, which can manage the phase angle of the load voltage as compared to supply voltage even in buck mode, that is very difficult in other ZS ac-ac converters. Simulation results demonstrate the new features of improved quasi-ZS ac-ac converter.
Medical Cyber-Physical Systems Development: A Forensics-Driven Approach

The synthesis of technology and the medical industry has partly contributed to the increasing interest in Medical Cyber-Physical Systems (MCPS). While these systems provide benefits to patients and professionals, they also introduce new attack vectors for malicious actors (e.g. financially-and/or criminally-motivated actors). A successful breach involving a MCPS can impact patient data and system availability. The complexity and operating requirements of a MCPS complicates digital investigations. Coupling this information with the potentially vast amounts of information that a MCPS produces and/or has access to is generating discussions on, not only, how to compromise these systems but, more importantly, how to investigate these systems. The paper proposes the integration of forensics principles and concepts into the design and development of a MCPS to strengthen an organization's investigative posture. The framework sets the foundation for future research in the refinement of specific solutions for MCPS investigations.
Analysis of volumetric images of filamentous bacteria in industrial sludge

This article describes the application of image analysis in evaluation of filamentous bacteria in industrial sludge bulking which may be useful in environmental pollution control. The volumetric images are obtained from confocal microscope. Many conventional and heuristic methods are conveniently used to reduce the noise and enhance the object features. Moreover, it has been shown that the segmentation based on the combination of different image analysis tools is very useful in extracting the filamentous bacterial structure, qualitative and quantitative evaluation of the industrial sludge images and to study the spatial distribution of the filamentous bacteria.
Mechanical properties of pristine and Fe, V and Ti doped arsenene: density functional theory calculation

In this paper, the density functional theory is used to study the mechanical properties of pristine and doped arsenene. Fe, Ti and V doped arsenenes with the doping percentages of 25% and 50% are considered for investigation. Applying uniaxial and biaxial strains on the pristine and doped arsenene nanosheets, their Young’s and bulk moduli are evaluated. It is observed that the elastic modulus of doped arsenene is smaller than that of pristine one. The most and least reductions in the elastic modulus of the arsenene nanosheet are for doping with Ti and V atoms, respectively. Also, the inharmonic region of the pristine arsenene is larger than that of doped ones. Finally, by increasing the applied strain, the plastic properties of arsenene are investigated.
Optimal Histograms with Quality Guarantees

Histograms are commonly used to capture attribute value distribution statistics for query optimizers. More recently, histograms have also been considered as a way to produce quick approximate answers to decision support queries. This widespread interest in histograms motivates the problem of computing histograms that are good under a given error metric. In particular, we are interested in an e(cid:14)cient algo-rithm for choosing the bucket boundaries in a way that either minimizes the estimation error for a given amount of space (number of buckets) or, conversely, minimizes the space needed for a given upper bound on the error. Under the assumption that (cid:12)nding optimal bucket boundaries is computationally ine(cid:14)cient, previous research has focused on heuristics with no provable bounds on the quality of the solutions. In this paper, we present algorithms for computing optimal bucket boundaries in time proportional to the square of the number of distinct data values, for a broad class of optimality metrics. This class includes the V-Optimality constraint, which has been shown to result in the most accurate histograms for several selectivity estimation problems. Through experiments, we show that optimal histograms can achieve substantially lower estimation errors than histograms produced by popular heuristics. We also present new heuristics with provably good space-accuracy trade-o(cid:11)s that are signi(cid:12)cantly faster than the optimal al-gorithm. Finally, we present an enhancement to traditional histograms that allows us to provide quality guarantees on individual selectivity estimates. In our experiments, these quality guarantees were highly effective in isolating outliers in selectivity estimates.
A D2D-Multicast Based Computation Offloading Framework for Interactive Applications

Computation offloading manages resource-intensive and interactive applications on mobile devices where much processing is replicated with multiple users in the same environment. In this paper, we consider the scenario where duplicated computation tasks are processed on specific mobile users and computation results are shared through Device-to-Device (D2D) multicast channel. Our goal is to find an optimal network partition for D2D multicast offloading, in order to minimize the overall energy consumption at the mobile terminal side. To this end, we first propose a D2D multicast-based computation offloading framework where the problem is modelled as a combinatorial optimization problem, and then solved using the concepts of from maximum weighted bipartite matching and coalitional game. Note that our proposal considers the delay constraint for each mobile user as well as the battery level to guarantee fairness. To gauge the effectiveness of our proposal, we simulate three typical interactive components. Simulation results show that our algorithm can significantly reduce the energy consumption, and guarantee the battery fairness among multiple users at the same time.
Dimension in complexity classes

A theory of resource-bounded dimension is developed using gales, which are natural generalizations of martin-gales. When the resource bound /spl Delta/(a parameter of the theory) is unrestricted, the resulting dimension is precisely the classical Haludolff dimension (sometimes called "fractal dimension"). Other choices of the parameter /spl Delta/ yield internal dimension theories in E, E/sub 2/, ESPACE, and other complexity classes, and in the class of all decidable problems. In general, if C is such a class, then every set X of languages has a dimension in C, which is a real number dim(X|C)/spl isin/[0, 1]. Along with the elements of this theory two preliminary applications are presented: 1. For every real number 0/spl les//spl alpha//spl les/ 1/2 , the set FREQ(/spl ap//spl alpha/), consisting of all languages that asymptotically contain at most /spl alpha/ of all strings, has dimension /spl Hscr/(/spl alpha/)-the binary entropy of /spl alpha/-in E and in E/sub 2/. 2. For every real number 0/spl les//spl alpha//spl les/1, the set SIZE(/spl alpha/2/sup n//n), consisting of all languages decidable by Boolean circuits of at most /spl alpha/2/sup n//n gates, has dimension /spl alpha/ in ESPACE.
A replication study on proposed candidate genes in Ménière's disease, and a review of the current status of genetic studies

Abstract Objective: Multiple candidate genes have been presented for Ménière's disease (MD), but to date no positive replications have been reported. We review here all the previously proposed candidate genes for MD and report our results on the analysis of six such genes, AQP2, KCNE1, KCNE3, HCFC1, COCH, and ADD1. Study sample: A well-defined sample set of 38 sporadic and 21 familial Finnish MD patients. Design: Mutation analysis, case-control study, and review of literature. Results: A polymorphism rs1805127 in the potassium channel gene, KCNE1, was associated with MD in sporadic (p = 0.011), but not familial patients (p = 0.62). In addition, we identified four novel unique variations in the KCNE1 gene. PolyPhen and Mutation Taster analyses indicated that at least one of the variations c.259T > C; p.Trp87Arg is probably damaging to the coded protein. Conclusions: Our review of the reported candidate genes shows that the current understanding of the genetic factors contributing to the development of MD is limited, and that the study of its etiology would benefit greatly from more comprehensive genetic knowledge.