Reviewerly/exHarmony huggingface dataset:
An ensemble meta-estimator to predict source code testability

Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. The effectiveness of the test suite and the budget assigned to the test highly impact the testability of the code under test. The size of a test suite determines the test effort and cost, while the coverage measure indicates the test effectiveness. Therefore, testability can be measured based on the coverage and number of test cases provided by a test suite, considering the test budget. This paper offers a new equation to estimate testability regarding the size and coverage of a given test suite. The equation has been used to label 23,000 classes belonging to 110 Java projects with their testability measure. The labeled classes were vectorized using 262 metrics. The labeled vectors were fed into a family of supervised machine learning algorithms, regression, to predict testability in terms of the source code metrics. Regression models predicted testability with an R2 of 0.68 and a mean squared error of 0.03, suitable in practice. Fifteen software metrics highly affecting testability prediction were identified using a feature importance analysis technique on the learned model. The proposed models have improved mean absolute error by 38% due to utilizing new criteria, metrics, and data compared with the relevant study on predicting branch coverage as a test criterion. As an application of testability prediction, it is demonstrated that automated refactoring of 42 smelly Java classes targeted at improving the 15 influential software metrics could elevate their testability by an average of 86.87%.
Spatial-Temporal Pyramid Graph Reasoning for Action Recognition

Spatial-temporal relation reasoning is a significant yet challenging problem for video action recognition. Previous works typically apply local operations like 2D or 3D CNNs to conduct space-time interactions in video sequences, or simply capture space-time long-range relations of a single fixed scale. However, this is inadequate for obtaining a comprehensive action representation. Besides, most models treat all input frames equally for the final classification, without selecting key frames and motion-sensitive regions. This introduces irrelevant video content and hurts the performance of models. In this paper, we propose a generic Spatial-Temporal Pyramid Graph Network (STPG-Net) to adaptively capture long-range spatial-temporal relations in video sequences at multiple scales. Specifically, we design a temporal attention (TA) module and a spatial-temporal attention (STA) module to learn the contribution of each frame and each space-time region to an action at a feature level, respectively. We then apply the selected key information to build spatial-temporal pyramid graphs for long-range relation reasoning and more comprehensive action representation learning. STPG-Net can be flexibly integrated into 2D and 3D backbone networks in a plug-and-play manner. Extensive experiments show that it brings consistent improvements over many challenging baselines on several standard action recognition benchmarks (<i>i.e</i>., Something-Something V1 &#x0026; V2, and FineGym), demonstrating the effectiveness of our approach.
Systematic Review of Augmented Reality Training Systems

Recent augmented reality (AR) advancements have enabled the development of effective training systems, especially in the medical, rehabilitation, and industrial fields. However, it is unclear from the literature what the intrinsic value of AR to training is and how it differs across multiple application fields. In this work, we gathered and reviewed the prototypes and applications geared towards training the intended user&#x0027;s knowledge, skills, and abilities. Specifically, from IEEE Xplore plus other digital libraries, we collected 64 research papers present in high-impact publications about augmented reality training systems (ARTS). All 64 papers were then categorized according to the training method used, and each paper&#x0027;s evaluations were identified by validity. The summary of the results shows trends in the training methods and evaluations that incorporate ARTS in each field. The narrative synthesis illustrates the different implementations of AR for each of the training methods. In addition, examples of the different evaluation types of the current ARTS are described for each of the aforementioned training methods. We also investigated the different training strategies used by the prevailing ARTS. The insights gleaned from this review can suggest standards for designing ARTS regarding training strategy, and recommendations are provided for the implementation and evaluation of future ARTS.
In situ manipulation of perpendicular magnetic anisotropy in half-metallic NiCo<sub>2</sub>O<sub>4</sub> thin film by proton insertion

Abstract Herein we report the manipulation of perpendicular magnetic anisotropy (PMA), a very important technique for spintronics devices, which is achieved in an all-solid-state redox (reduction/oxidation) device. The device consisted of NiCo 2 O 4 (NCO) thin film with a c -axis orientation and a proton-conducting polymer (Nafion). The PMA of NCO can be manipulated in situ with low voltage ( V ≤ 0.7 V) due to the utilization of a proton-conducting solid electrolyte. The magnetic anisotropy variation was quantitatively evaluated by an anomalous Hall measurement. The magnetic anisotropy field of the NCO thin film was successfully manipulated in the range of 5%, although the rotation of the easy axis of magnetization was not observed. This modulation is attributed to the strain and electronic configuration variation by proton insertion. This manipulation method for PMA with the solid electrolyte enables a variety of reservoir states and contributes to lower power consumption and high-precision machine learning.
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.
Enabling Blockchain Services for IoE with Zk-Rollups

The Internet of Things includes all connected objects from small embedded systems with low computational power and storage capacities to efficient ones, as well as moving objects like drones and autonomous vehicles. The concept of Internet of Everything expands upon this idea by adding people, data and processing. The adoption of such systems is exploding and becoming ever more significant, bringing with it questions related to the security and the privacy of these objects. A natural solution to data integrity, confidentiality and single point of failure vulnerability is the use of blockchains. Blockchains can be used as an immutable data layer for storing information, avoiding single point of failure vulnerability via decentralization and providing strong security and cryptographic tools for IoE. However, the adoption of blockchain technology in such heterogeneous systems containing light devices presents several challenges and practical issues that need to be overcome. Indeed, most of the solutions proposed to adapt blockchains to devices with low resources confront difficulty in maintaining decentralization or security. The most interesting are probably the Layer 2 solutions, which build offchain systems strongly connected to the blockchain. Among these, zk-rollup is a promising new generation of Layer 2/off-chain schemes that can remove the last obstacles to blockchain adoption in IoT, or more generally, in IoE. By increasing the scalability and enabling rule customization while preserving the same security as the Layer 1 blockchain, zk-rollups overcome restrictions on the use of blockchains for IoE. Despite their promises illustrated by recent systems proposed by startups and private companies, very few scientific publications explaining or applying this barely-known technology have been published, especially for non-financial systems. In this context, the objective of our paper is to fill this gap for IoE systems in two steps. We first propose a synthetic review of recent proposals to improve scalability including onchain (consensus, blockchain organization, …) and offchain (sidechain, rollups) solutions and we demonstrate that zk-rollups are the most promising ones. In a second step, we focus on IoE by describing several interesting features (scalability, dynamicity, data management, …) that are illustrated with various general IoE use cases.
A new improved maximal relevance and minimal redundancy method based on feature subset

Feature selection plays a very significant role for the success of pattern recognition and data mining. Based on the maximal relevance and minimal redundancy (mRMR) method, combined with feature subset, this paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset. In ImRMR, the Pearson correlation coefficient and mutual information are first used to measure the relevance of a single feature to the sample category, and a factor is introduced to adjust the weights of the two measurement criteria. And an equal grouping method is exploited to generate candidate feature subsets according to the ranking features. Then, the relevance and redundancy of candidate feature subsets are calculated and the ordered sequence of these feature subsets is gained by incremental search method. Finally, the final optimal feature subset is obtained from these feature subsets by combining the sequence forward search method and the classification learning algorithm. Experiments are conducted on seven datasets. The results show that ImRMR can effectively remove irrelevant and redundant features, which can not only reduce the dimension of sample features and time of model training and prediction, but also improve the classification performance.
Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.
Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios

Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements' specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person's face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to 5.6∘ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches 14.1∘ and 11.0∘ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise

Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs). Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum Algorithms, which may allow for near-term quantum advantage. Such applications require good optimizers to train PQCs. Recent works have focused on quantum-aware optimizers specifically tailored for PQCs. However, ignorance of the cost landscape could hinder progress towards such optimizers. In this work, we analytically prove two results for PQCs: (1) We find an exponentially large symmetry in PQCs, yielding an exponentially large degeneracy of the minima in the cost landscape. Alternatively, this can be cast as an exponential reduction in the volume of relevant hyperparameter space. (2) We study the resilience of the symmetries under noise, and show that while it is conserved under unital noise, non-unital channels can break these symmetries and lift the degeneracy of minima, leading to multiple new local minima. Based on these results, we introduce an optimization method called Symmetry-based Minima Hopping (SYMH), which exploits the underlying symmetries in PQCs. Our numerical simulations show that SYMH improves the overall optimizer performance in the presence of non-unital noise at a level comparable to current hardware. Overall, this work derives large-scale circuit symmetries from local gate transformations, and uses them to construct a noise-aware optimization method.
A simplified model to enhance SMEs’ investment in renewable energy sources in Ghana

Although the Renewable Energy Act 2011 (Act, 832) was enacted to facilitate development and investment in the Renewable Energy sources in Ghana as part of the efforts to achieve Sustainable Development Goal 7, the actual impacts of the policy are yet to be felt as manifested in the amendment Act 2020 (Act, 1045). There is a need to develop an alternative model to enhance investment in the renewable energy sector. Drawing from the Resources Based View (RBV) and Porter’s Five Forces this paper is aimed to develop a simplified model to explain Small and Medium Enterprises (SMEs)investment determinants in the renewable energy sources in Ghana. We argued that the simplified integrated model provides robust predictability and wider generalization. Our paper is anchored on the positivists’ epistemology and quantitative methodology. Our hypotheses have been tested using cross-sectional data from the Ghanaian SMEs. Variance Based Partial Least Squares (PLS) method has been used to analyze the survey data. Our results indicate that entrepreneurial competency, financial resource, marketing capability, and technological usage significantly relate to investment in renewable energy. Moreover, the results have shown that competitive rivalry, the threat of entry, and bargaining power of customers significantly relate to investment in renewable energy. These results substantiate the well-known argument that industry forces and resource competitive strategies are significant determinants of a firm’s competitiveness and behavioral intentions. Overall, these results have theoretical and practical implications to facilitate the capacity of SMEs and create enabling renewable energy local content policy to enhance SMEs ’ participation in the sector. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
An Empirical Study on Retinex Methods for Low-Light Image Enhancement

A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods.
Conscious or Unconscious: The Intention of Hate Speech in Cyberworld—A Conceptual Paper

Exponential growth in social media has led to the increasing popularity of hate speech and hate-based propaganda. Hate speech or malicious expression refers to the use of offensive, violent, or offensive language and its religious conduct with a specific group of people who share a common property, such as gender, ethnicity, race, or beliefs. Online hate diffusion has now become a serious problem as it creates a series of international initiatives aimed at defining problems and developing effective countermeasures; this study delves into the exploration of the intention of hate speech posting on social media, especially on Twitter. Both dramaturgical models of social interaction and cultivation theory were used to explain the hate speech culture phenomenon. A qualitative method is proposed for this study as part of the exploration. Results revealed that most of the previous studies on hate speech focused on the field of computer science but rarely on the communication field. The paper presents the results of past studies and shows the new proposed framework. The investigation suggests future directions for the problem and possible solutions; it starts with the background of the research, the explanation of the problem, the meaning of the research, and pursuing the research questions and goals of the research before finally explaining the limits.
High-order robust control and Stackelberg game-based optimization for uncertain fuzzy PMSM system with inequality constraints

There exist the uncertainties and the inequality constraints in permanent magnet synchronous motor (PMSM) system. In order to meet the safety control requirements in industrial applications, the state transformation is used to meet the inequality constraints for limiting the outputs within desired bounds. Then, fuzzy set theory, which is different from fuzzy logic, is used to describe uncertainty, and the fuzzy PMSM dynamical model is established. Based on that, a robust control with high-order term is proposed to compensate for the time-varying uncertainty. Furthermore, for improving the system performance and decreasing the control cost, the Stackelberg game is introduced into the optimization scheme design, in which the leader plays a more important role than follower. These characteristic corresponds to the influence of the two tunable control parameters on the system. Thus, the optimal parameters are obtained by the rules of Stackelberg game. Finally, experimental results show the effectiveness of the above theories.
The impact of economic-related freedoms on the national entrepreneurial activity

Abstract This article aims to analyze the explanatory power of the constituent components of the Heritage Foundation’s Index of Economic Freedom over the Global Entrepreneurship and Development Institute’s Global Entrepreneurship Index and its sub-indexes. We analyze a sample of 118 countries with available historic data from 2014–2019. We evaluate the impact of the business, labor, monetary, trade, investment, and financial freedom indexes over the Global Entrepreneurship Index and its sub-indexes. We performed a cross-sectional analysis using generalized linear models and weighted least squares models. We also analyzed our panel data using panel-corrected standard error models. We find a significant and positive relationship between the Global Entrepreneurship Index and the business and financial freedom indexes. We also find a significant and positive relationship between the business and financial freedom indexes and the entrepreneurial attitudes, abilities, and aspirations sub-indexes. The limitations of our study include the diversity of national entrepreneurial ecosystems and the difficulty of measuring them with a few quantitative variables. Additionally, our results are heavily influenced by the selection of countries in our sample. To the best of the authors’ knowledge, no previous research article has studied the explanatory power of the Index of Economic Freedom’s constituent components over the Global Entrepreneurship Index and its sub-indexes. Similarly, our sample of 118 countries with data from 2014–2019 makes our study the most comprehensive global analysis about the relationship between economic freedom and entrepreneurship so far.