Reviewerly/exHarmony huggingface dataset:
Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer

Innovation performance of regional innovation systems can serve as an important tool for policymaking to identify best practices and provide aid to regions in need. Accurate forecasting of regional innovation performance plays a critical role in the implementation of policies intended to support innovation because it can be used to simulate the effects of actions and strategies. However, innovation is a complex and dynamic socio-economic phenomenon. Moreover, patterns in regional innovation structures are becoming increasingly diverse and non-linear. Therefore, to develop an accurate forecasting tool for this problem represents a challenge for optimization methods. The main aim of the paper is to develop a model based on a variant of genetic programming to address the regional innovation performance forecasting problem. Using the historical data related to regional knowledge base and competitiveness, the model should accurately and effectively predict a variety of innovation outputs, including patent counts, technological and non-technological innovation activity and economic effects of innovations. We show that the proposed model outperforms state-of-the-art machine learning methods.
Data mining approach for accelerating the classification accuracy of cardiotocography

<h2>Abstract</h2><h3>Objective</h3> The objective of current study is to increase the classification accuracy of learning algorithms over cardiotocography data by applying preprocessing technique. Due to the diversity of sources, large amount of data is being generated and also has various problems including mislabeled data, missing values, noise, high dimensionality and imbalanced class labels. <h3>Method</h3> In this study, we suggested a technique to handle imbalanced data to increase the classification performance of various lazy learners, rule based induction models and tree based models. We used Symmetric Minority Over Sampling Technique (SMOTE) on real dataset to accelerate the performance of various classifiers. We identified that primary dataset is suffering with imbalanced problem, which means the most of the records belong to same class label. Prediction of imbalanced data is biased towards the class with majority instances. To overcome this problem, dataset has to be balanced. <h3>Results</h3> As a result of the suggested method the performance of classification algorithms are increased. The obtained result show that majority of classification techniques performed better over balanced dataset when compared with imbalanced dataset. <h3>Conclusion</h3> Classification performance over balanced dataset has recorded improved performance than imbalanced dataset after applying the SMOTE.
Toward Hand-Dominated Activity Recognition Systems With Wristband-Interaction Behavior Analysis

The increasing usage of wearable devices for ambulatory monitoring and pervasive computing systems has given rise to the need of convenient and efficient activity recognition techniques. Hand-dominated activity recognition has great potential in understanding users' gesture and providing context-aware computing services. This paper investigates the feasibility and applicability on the usage of wristband-interaction behavior for recognizing hand-dominated activities, with the advantage of great compliance and long wearing time. For each action, sensor data from wristband are analyzed to obtain kinematic sequences. The sequences are then depicted by statistics-, frequency-, and wavelet-domain features for providing accurate and fine-grained characterization of hand-dominated actions, and the correlation between the wristband-sensor features and the actions is analyzed. Classification techniques (Naive Bayes, nearest neighbor, neural network, support vector machine, and Random Forest) are applied to the feature space for performing hand-dominated activity recognition. Analyses are conducted using the data from 51 participants with a diversity in gender, age, weight, and height. Extensive experiments demonstrate the efficacy of the proposed approach, achieving a recognition rate of 97.29% and an F-score above 0.94. Additional experiments on the effect of feature selection and wristband sampling rate are provided to further examine the effectiveness of our approach. Our data are publicly available.
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics model) is a landscape evolution model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that needs estimation with appropriate uncertainty quantification; given the observed present-day ground truth such as surface topography and the stratigraphy of sediment deposition through time. The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model. In this paper, we take a Bayesian approach to provide inference using Markov chain Monte Carlo sampling (MCMC). We present \textit{Bayeslands}; a Bayesian framework for Badlands that fuses information obtained from complex forward models with observational data and prior knowledge. As a proof-of-concept, we consider a synthetic and real-world topography with two parameters for Bayeslands inference, namely precipitation and erodibility. The results of the experiments show that Bayeslands yields a promising distribution of the parameters. Moreover, we demonstrate the challenge in sampling irregular and multi-modal posterior distributions using a likelihood surface that has a range of sub-optimal modes.
External barriers affecting the successful implementation of mobile educational interventions

This article presents lessons learnt about the implementation of an innovative 1:1 tablet initiative in three Portuguese lower secondary schools. We analysed 99 questionnaires, 7 focus groups, 3 interviews and 11 field notes. Findings reveal 15 external barriers that need to be addressed if such initiatives are to become sustainable and fully embedded in educational practice: ICT infrastructure, hardware, software, physical learning spaces, digital textbooks, curriculum, assessment, provision of professional development, training programs, time, timetabling and technical support. Implications for current and future practice include important lessons for educational stakeholders who are working with or considering the integration of technology into their schools or education systems.
Iterative Relaxed Collaborative Representation With Adaptive Weights Learning for Noise Robust Face Hallucination

In recent years, the collaborative representation (CR)-based techniques have been widely employed for face hallucination. However, the conventional CR model becomes less efficient in handling noisy low-resolution face images. In this paper, an iterative relaxed CR (iRCR) model with adaptive weights learning is presented to enhance the resolution of face images corrupted by noise. The core idea of iRCR is that a diagonal weight matrix is incorporated into the objective function, which helps to debase the influence of noise in representation. Different from existing collaborative methods with reweighting strategy where the weights require manually tuning, the weights in iRCR are adaptively learned to stay more consistent with the model error. Moreover, considering the local manifold structure property and nonlocal prior of small patches, the locality regularization and collaborative regularization are incorporated into a unified framework. This enables the proposed iRCR not only to capture the true topology structure of patch manifold but also to exploit the meaningful patterns among the whole training samples for reconstruction. Experimental results on both face dataset and real-world images demonstrate the superiority of our proposed method over several state-of-the-art face hallucination methods.
A Multi-Study Investigation into Dead Code

Dead code is a bad smell and it appears to be widespread in open-source and commercial software systems. Surprisingly, dead code has received very little empirical attention from the software engineering research community. In this paper, we present a multi-study investigation with an overarching goal to study, from the perspective of researchers and developers, when and why developers introduce dead code, howthey perceive and cope with it, and whether dead code is harmful. To this end, we conducted semi-structured interviews with software professionals and four experiments at the University of Basilicata and the College of William & Mary. The results suggest that it is worth studying dead code not only in the maintenance and evolution phases, where our results suggest that dead code is harmful, but also in the design and implementation phases. Our results motivate future work to develop techniques for detecting and removing dead code and suggest that developers should avoid this smell.
Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects

In tasks such as abandoned luggage detection and stopped car detection, stationary foreground objects (SFOs) need to be detected and properly classified in real time. Different methods have been proposed to detect SFOs, but they are mainly focused on certain types of objects. In this paper, an incremental singular value decomposition-based method is presented to detect all types of SFOs such as abandoned objects and removed objects. The proposed method decomposes the video tensor spatiotemporally and divides it into background and foreground components. An appropriate analysis is applied to the foreground tensor to define a pixel time series of each stationary foreground category. Such analysis leads to the fact that SFOs can be detected easily owing to their continuous persistence in the decomposed foreground tensor. Furthermore, the unique structure of the pixel time series of each category allows identifying the category of the detected objects, whether they are abandoned or removed, and detecting the exact time of the start and end of each event. The results demonstrate that the proposed method achieves a superior performance in detecting SFOs at both object and pixel levels. In addition, the proposed method is computationally simple, and its complexity is lower compared to other approaches; hence, it can adequately satisfy real-time requirements.
Lower bounds on the run time of the Univariate Marginal Distribution Algorithm on OneMax

The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-distribution algorithm – is studied from a run time perspective. On the classical OneMax benchmark function on bit strings of length n, a lower bound of Ω(λ+μn+nlog⁡n), where μ and λ are algorithm-specific parameters, on its expected run time is proved. This is the first direct lower bound on the run time of UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation-of-distribution algorithms in general.
Low-Dimensional Euclidean Embedding for Visualization of Search Spaces in Combinatorial Optimization

This paper proposes a method for visualizing combinatorial search spaces named low-dimensional Euclidean embedding (LDEE). The proposed method transforms the original search space, such as a set of permutations or binary vectors, to R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> (with k = 2 or 3 in practice) while aiming to preserve spatial relationships existing in the original space. The LDEE method uses the t-distributed stochastic neighbor embedding (t-SNE) to transform solutions from the original search space to the Euclidean one. In this paper, it is mathematically shown that the assumptions underlying the t-SNE method are valid in the case of permutation spaces with the Mallows distribution. The same is true for other metric spaces provided that the distribution of points is assumed to be normal with respect to the adopted metric. The embedding obtained using t-SNE is further refined to ensure visual separation of individual solutions. The visualization obtained using the LDEE method can be used for analyzing the behavior of the population in a population-based metaheuristic, the working of the genetic operators, etc. Examples of visualizations obtained using this method for the four peaks problem, the firefighter problem, the knapsack problem, the quadratic assignment problem, and the traveling salesman problem are presented in this paper.
Fine-grained access control based on Trusted Execution Environment

With the wide adoption of mobile devices, it becomes increasingly a reality that mobile users use a variety of apps from various sources. Since the enforcement of strict privacy is difficult, the inappropriate access by malicious apps is a major concern for mobile users, and access control becomes a challenge. In order to prevent the leakage of sensitive information (such as the contact lists, or private pictures) by inappropriate or illegal access, we propose a fine-grained access-control scheme based on Ciphertext-Policy Attribute-Based Encryption (CPABE) and Trusted Execution Environment (TEE), which can effectively protect data. In the scheme, CPABE is adopted in a novel way to solve the important security problems by supporting fine-grained access control during the access period and by supporting the critical operations running in the trusted execution environment. The scheme can be used to mitigate the sensitive information attacks and enhance confidentiality. Moreover, it can reduce the risk in the case of one single authority. Compared to the traditional access-control mechanisms, our experimental results indicate that the proposed scheme satisfies the security requirements, and is superior to other existing schemes.
Normative and conceptual ELSI research: what it is, and why it’s important

<h2>Abstract</h2> The Ethical, Legal, and Social Implications (ELSI) Research Program of the National Human Genome Research Institute sponsors research examining ethical, legal, and social issues arising in the context of genetics/genomics. The ELSI Program endorses an understanding of research not as the sole province of empirical study, but instead as systematic study or inquiry, of which there are many types and methods. ELSI research employs both empirical and nonempirical methods. Because the latter remain relatively unfamiliar to biomedical and translational scientists, this paper seeks to elucidate the relationship between empirical and nonempirical methods in ELSI research. It pays particular attention to the research questions and methods of normative and conceptual research, which examine questions of value and meaning, respectively. To illustrate the distinct but interrelated roles of empirical and nonempirical methods in ELSI research, including normative and conceptual research, the paper demonstrates how a range of methods may be employed both to examine the evolution of the concept of incidental findings (including the recent step toward terming them 'secondary findings'), and to address the normative question of how genomic researchers and clinicians should manage incidental such findings.
Robust Localization of Interpolated Frames by Motion-Compensated Frame Interpolation Based on an Artifact Indicated Map and Tchebichef Moments

Motion-compensated frame interpolation (MCFI), a frame-interpolation technique to increase the motion continuity of low frame-rate video, can be utilized by counterfeiters for faking high bitrate video or splicing videos with different frame rates. For existing MCFI detectors, their performances are degraded under real-world scenarios such as H.264/AVC compression, noise, or blur. To address this issue, a robust MCFI detector is proposed to locate interpolated frames. By analyzing the distribution of residual energies within interpolated frames, we observe that there exist strong correlations between artifact regions and high residual energies. Thus, an artifact indicated map is introduced to select candidate artifact regions. Then, Tchebichef moments (TMs) are exploited to characterize the blurring effects or deformed structures among these regions. Specifically, the mean value of absolute high-order TMs of selected regions is used to model these temporal inconsistencies. Finally, a sliding window is adopted to locate interpolated frames, which are further refined by three post-processing operations. Chrominance information is also integrated with luminance information for robust identification of interpolated frames. Extensive experimental results show that compared with the state-of-the-art MCFI detectors, the proposed approach is more robust for compressed videos under various real-world scenarios.
The Text-Based Adventure AI Competition

In 2016-2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games. This competition fills a gap in existing game artificial intelligence (AI) competitions that have typically focused on traditional card/board games or modern video games with graphical interfaces. By providing a platform for evaluating agents in textbased adventures, the competition provides a novel benchmark for game AI with unique challenges for natural language understanding and generation. This paper summarizes the three competitions ran in 2016-2018 (including details of open-source implementations of both the competition framework and our competitors) and presents the results of an improved evaluation of these competitors across 20 games.
Survey of tools for Linked Data consumption

There is a large number of datasets published as Linked (Open) Data (LOD/LD). At the same time, there is also a multitude of tools for publication of LD. However, potential LD consumers still have difficulty discovering, accessing and exploiting LD. This is because compared to consumption of tradit ional data formats such as XML and CSV files, there is a distinct lack of tools for consumption of LD. The promoters of LD use the well-known 5-star Open Data deployment scheme to suggest that consumption of LD is a better experience once the consumer knows RDF and related technologies. This suggestion, however, falls short when the consumers search for an appropriate tooling support for LD consumption. In this paper we define a LD consumption process. Based on this process and current literature, we define a set of 34 requirements a hypothetical Linked Data Consumption Platform (LDCP) should ideally fulfill. We cover those requirements with a set of 94 evaluation criteria. We survey 110 tools identified as potential candidates for an LDCP, eliminating them in 3 rounds until 16 candidates for remain. We evaluate the 16 candidates using our 94 criteria. Based on this evaluation we show which parts of the LD consumption process are covered by the 16 candidates. Finally, we identify 8 tools which satisfy our requirements on being a LDCP. We also show that there are important LD consumption steps which are not sufficiently covered by existing tools. The authors of LDCP implementations may use this survey to decide about directions of future development of their tools. LD experts may use it to see the level of support of the state of the art technologies in existing tools. Non-LD experts may use it to choose a tool which supports their LD processing needs without requiring them to have expert knowledge of the technologies. The paper can also be used as an introductory text to LD consumption.
Numerical solution for ruin probability of continuous time model based on neural network algorithm

In the classical risk model, the ruin probability satisfies the renewal Integro-differential equation, which only has an analytic solution when the claim distribution obeys the exponential distribution. In this paper, due to the characteristics of the existence of initial conditions for the equations, by using the modern artificial intelligence and machine learning theory, we construct a neural network model, in which trigonometric function serves as the activation function. Then, based on the thinking of ELM algorithm, at the same time, different from the classic ELM algorithm, the initial condition of the equation are added to the solver model, and the improved ELM algorithm (IELM) are designed. Finally, through some numerical experiments by using Matlab programming, the numerical solutions of the integral Integro-differential equation under arbitrary claims distribution at any time have been obtained. Through the comparison of numerical solutions with the analytical solutions and traditional numerical solutions, the feasibility and superiority of the proposed IELM algorithm are clearly proved.
3-D Human Pose Estimation Using Cascade of Multiple Neural Networks

Estimating three-dimensional (3-D) human poses from a given two-dimensional (2-D) shape is still an inherently ill-posed problem in computer vision. This paper proposes a method called cascade of multiple neural networks (CMNN) to solve this problem in following two steps: 1) create the initial estimated 3-D shape using the Zhou et al. method with a small number of basis shapes and 2) make this initial shape more alike to the original shape by using the CMNN. In comparing to existing works, the proposed method shows a significant outperformance in both accuracy and processing time. This paper also introduces a new system called Human3D that can estimate the 3-D pose of all people in a single RGB image. This system comprises two part: convolution pose machine (CPM) for estimating 2-D poses of all people in an RGB image and CMNN for reconstructing 3-D poses of them from outputs of the CPM.
Integrated MOPSO algorithms for task scheduling in cloud computing

Task Scheduling is one of the most challenging problems in cloud computing. It is an NP-Hard and plays an important role in optimizing the use of available resources. Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks scheduling. However, the execution time of the GA is higher than Particle Swarm Optimization (PSO), and the convergence is slower. PSO converges fast because it can be implemented without too many parameters and operators. In this paper, Multi-Objectives PSO (MOPSO) and MOPSO with Importance Strategy (IS) (MOPSO_IS) algorithms are proposed. MOPSO algorithm is integrated with the IS to select the global best leader. Furthermore, incorporating a mutation operator in MOPSO_IS resolved the problem of premature convergence to the local Pareto-optimal front. The performance of the proposed algorithms was compared with MOGA and produced better results. The results of the experiments showed that the proposed MOPSO and MOPSO_IS significantly minimized the total task time and average task time and obtained better distribution for tasks on the available resources in a minimal time.