Reviewerly/exHarmony huggingface dataset:
Cross-Project Defect Prediction Method Based on Manifold Feature Transformation

Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.
The Construction of Immersive Learning System Based on Virtual Testing Technology of Virtual Reality

Objective. To explore the construction of immersive learning system based on virtual reality (VR) and test its learning effect. Methods. 20 qualified subjects were divided into two groups, each tending to agree, each with 5 boys and 5 girls. Experimental group 1 is in the real operating environment, and experimental group 2 is in the VR virtual disassembly experimental environment. The task process errors, knowledge questionnaire scores and user subjective satisfaction were analyzed statistically. Results. The significance probability <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> of knowledge questionnaire in the Levene test was 0.777, greater than 0.05, and the variance homogeneous, so the final <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> </math> of independent sample <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>t</mi> </math> -test was subject to “assumed variance equal”; the significance probability was 0.613, greater than 0.05; the questionnaire scores of two groups showed no significant difference, so VR virtual environment can achieve the learning effect of the real environment. The number of errors in a VR virtual situation is significantly lower than the number of errors in the real environment, the VR virtual environment can achieve the learning effect of the real environment, and the VR virtual environment can achieve more interaction, with good interaction. Conclusion. The immersive learning system based on VR detection technology can realize the cognition of three-dimensional model structure and has a certain learning effect.
Tight upper bound on the quantum value of Svetlichny operators under local filtering and hidden genuine nonlocality

Nonlocal quantum correlations among the quantum subsystems play essential roles in quantum science. The violation of the Svetlichny inequality provides sufficient conditions of genuine tripartite nonlocality. We provide tight upper bounds on the maximal quantum value of the Svetlichny operators under local filtering operations, and present a qualitative analytical analysis on the hidden genuine nonlocality for three-qubit systems. We investigate in detail two classes of three-qubit states whose hidden genuine nonlocalities can be revealed by local filtering.
Volunteering as a Form of Students’ Educational Activities in the Context of the University’s Third Mission

The article considers the service learning program and volunteering as relevant forms for the university’s “third mission” implementation. Volunteering in the educational process is understood as students’ gratuitous activities aimed at solving urgent social problems and designed to increase students’ social and civic activity. The article states that the volunteering has an educational resource for the development of students’ skills, which determines their professional development. As part of theoretical research, volunteering is presented as a basis and mechanism for implementing the service learning program in the context of university’s third mission. Within the framework of the service learning program, student social activity is viewed just as important as educational and research activities. The results of the research (student focus groups with experience in volunteering) aimed at identifying empirical signs of metacompetencies that are formed under volunteering conditions are presented. The study analyzes metacompetences as cognitive, behavioral and emotional aspects leading to more effective behavior in various situations. They are characterized by student’s self-awareness and self-government. The students’ metacompetencies as an educational result of their participation in volunteer activities are considered on the example of the National Research Tomsk State University.
Digital exposure tools: Design for privacy, efficacy, and equity

Apps can cut transmission of SARS-CoV-2-but how do we ensure that they don’t exacerbate public health inequities?.
Ensuring the success of data sharing in Canada

The Canadian federal Tri-Agency Research Data Management Policy has recently been released. This will require Canadian universities and other research institutes to create and share strategic plans regarding data management and to equip their researchers with skills to complete data deposits. To help maximize the success of data sharing we outline five domains for research institutions to consider during implementation: training and education, paying for data sharing, audit and feedback, meta-science, and career advancement.
Incremental classifier learning based on PEDCC-loss and cosine distance

Traditionally, the performance of deep convolutional neural networks relies on a large number of labeled datasets in advance. However, in real-world applications, training data are not collected at once, so an algorithm which can deal with continuous incoming data is needed. This learning method is called incremental learning, whose main problem is the catastrophic forgetting. Neural network will perform badly on the old classes after training for the new classes. To solve this problem, this paper proposes an integrated incremental network approach based on the PEDCC-loss, and the cosine distance between the sample’s output feature and the PEDCC (predefined evenly distributed class centroids). Old and new knowledge learned by neural networks are stored separately. During the training of each network, PEDCC-Loss is used to constrain the cosine distance between the output feature and their corresponding pre-defined class center. Meanwhile, the old knowledge is retained by different learning rates across the network, and the retention mode of the old samples is discussed. In test phase, the final prediction is determined by the cosine distances between PEDCC and output features across all networks. Our experiments on EMNIST, CIFAR100 and TinyImageNet datasets show that our approach can learn classes incrementally without quickly failing of performance. Compared with some existing algorithms, such as Hou, iCaRL and finetune, our approach has better performance.
Impact of Foreign Direct Investment on Economic Growth in Ethiopia

This article analyses the impact of foreign direct investment (FDI) on Ethiopia’s economic growth.&#x0D; For this purpose, it uses Vector Autoregressions (VARs) model for the period comprised by years&#x0D; 1981-2017. It finds that FDI had a significant positive impact on Ethiopia’s economic growth for&#x0D; both the short and long-run periods. Adequate human capital and stable macroeconomic&#x0D; envirornment have catalysed the contribution of FDI to economic growth. Gross fixed capital&#x0D; formation and government consumption exerted a negative and significant effects on economic&#x0D; growth during the period of interest. Moreover, the study reveals that there is no causal relationship&#x0D; between FDI and economic development. Ethiopia needs to open up the economy and restructure&#x0D; the financial sector to attract foreign multinational companies (MNC), especially in the manufacturing&#x0D; and agro-industry sectors. Human capital investment should be strength to absorb more foreign&#x0D; direct investment and transform the agricultural-based economy to a modern one. Effective&#x0D; budgeting system and prioritisation of government consumption will support a more rapidly growing&#x0D; economy.
Multi Agent Architecture for Automated Health Coaching

For software applications in health coaching domains to be effective, it is vital that they address issues of privacy, modularity, scalability, individualization, data integration, transferability, coordination and flexibility. In this paper, we propose a novel generic multi-agent architecture which serves as a template for health coaching applications involving wearable sensors. Analyzer and communication modules allow different functionalities like goal formation, planning, scheduling, event detection, learning, inter-agent + human communication and long-term data collection, based on the capabilities of the underlying sensor platforms. To show the flexibility of our proposed architecture, we have successfully built two different health coaching systems with the proposed architecture: (1) a static system based on the Fitbit platform where the coaching is done at specific preset times to encourage increased physical activity, and (2) a dynamic system based on the Apple Watch platform where the smart coach adapts and learns when to intervene to encourage physical activity and reduce sedentary behavior.
Refining the Efficiency of R-CNN in Pedestrian Detection

The use of pre-trained deep convolutional neural networks for the purpose of enhancing the performance of detectors such as the region-based convolutional neural networks has demonstrated an exceptional role in the field of pedestrian detection. There have been various methods that have been investigated with the intent of improving the detection results of pedestrian detectors. In particular, most deep convolutional neural network techniques have shown advanced results in recent experimentation and studies. The primary reason for this improvement in performance based on deep neural networks is the ability of deep networks to learn substantial mid-level and high-level image features and also generalize well in complex images. In this study, an advanced deep learning technique based on R-CNN is refined and investigated for better performance in pedestrian detection on four specific pedestrian detection databases. The experiments involve the application of a deep learning feature extraction model in conjunction with the R-CNN detector. The deep learning feature extraction models employed are the AlexNet, VGG16 and the VGG19. The architecture of the R-CNN is re-modelled for enhanced performance by incorporating deep stacking networks and altering the activation function from sigmoid to Rectified Liner Unit (ReLU). The results of the experiments across the four investigated datasets provide significant findings about the performance of R-CNN detector when trained on different pre-trained deep convolutional neural networks with the application of deep stacking networks and ReLU activation function.
Accelerating reinforcement learning with a Directional-Gaussian-Smoothing evolution strategy

&lt;p style='text-indent:20px;'&gt;The objective of reinforcement learning (RL) is to find an optimal strategy for solving a dynamical control problem. Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, where the underlying dynamical system is only accessible as a black box such that adjoint methods cannot be used. However, existing ES methods have two limitations that hinder its applicability in RL. First, most existing methods rely on Monte Carlo based gradient estimators to generate search directions. Due to low accuracy of Monte Carlo estimators, the RL training suffers from slow convergence and requires more iterations to reach the optimal solution. Second, the landscape of the reward function can be deceptive and may contain many local maxima, causing ES algorithms to prematurely converge and be unable to explore other parts of the parameter space with potentially greater rewards. In this work, we employ a Directional Gaussian Smoothing Evolutionary Strategy (DGS-ES) to accelerate RL training, which is well-suited to address these two challenges with its ability to (i) provide gradient estimates with high accuracy, and (ii) find nonlocal search direction which lays stress on large-scale variation of the reward function and disregards local fluctuation. Through several benchmark RL tasks demonstrated herein, we show that the DGS-ES method is highly scalable, possesses superior wall-clock time, and achieves competitive reward scores to other popular policy gradient and ES approaches.&lt;/p&gt;
Necessary and Sufficient Explanations for Argumentation-Based Conclusions

In this paper, we discuss necessary and sufficient explanations – the question whether and why a certain argument or claim can be accepted (or not) – for abstract and structured argumentation. Given a framework with which explanations for argumentation-based conclusions can be derived, we study necessity and sufficiency: what (sets of) arguments are necessary or sufficient for the (non-)acceptance of an argument or claim? We will show that necessary and sufficient explanations can be strictly smaller than minimal explanations, while still providing all the reasons for a conclusion and we discuss their usefulness in a real-life application.
Automatic Digitization of Engineering Diagrams using Intelligent Algorithms

At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.
Phase transition of an open quantum walk

It has been discovered that open quantum walks diffusively distribute in space, since they were introduced in 2012. Indeed, some limit distributions have been demonstrated and most of them are described by Gaussian distributions. We operate an open quantum walk on $\mathbb{Z}=\left\{0, \pm 1, \pm 2,\ldots\right\}$ with parameterized operations in this paper, and study its 1st and 2nd moments so that we find its standard deviation. The standard deviation tells us whether the open quantum walker shows diffusive or ballistic behavior, which results in a phase transition of the walker.
Improving Prediction Confidence in Learning-Enabled Autonomous Systems

Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the predictions. We design a classifier using Inductive Conformal Prediction (ICP) based on a triplet network architecture in order to learn representations that can be used to quantify the similarity between test and training examples. The method allows computing confident set predictions with an error rate predefined using a selected significance level. A feedback loop that queries the sensors for a new input is used to further refine the predictions and increase the classification accuracy. The method is computationally efficient, scalable to high-dimensional inputs, and can be executed in a feedback loop with the system in real-time. The approach is evaluated using a traffic sign recognition dataset and the results show that the error rate is reduced.
An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

Aiming at the shortcoming that the basic beetle antennae search algorithm fails to consider the differences between individuals and dynamic information in the searching process, this paper proposed a new search algorithm based on the elite selection mechanism and the neighbor mobility strategy.The elitist selection mechanism is used to weaken beetles having bad performance and generate new beetles to ensure diversity and adaptability in the whole population.The neighbor mobility strategy will guide the algorithm to open up a wider searching area to ensure that individuals having good positions owns a chance to infect individuals with poor performances.To verify the searching ability and the optimization speed of the proposed algorithm in this paper, different testing functions were selected for numerical testing experiments, and the iteration figures, box plots, and searching path figures were given to conduct a comprehensive statistical test and analysis.The experimental results show that the proposed algorithm in this paper is superior to the original algorithm and other comparative algorithms in solving accuracy, convergence speed, and stability.
Estimating coherence with respect to general quantum measurements

The conventional coherence is defined with respect to a fixed orthonormal basis, i.e., to a von Neumann measurement. Recently, generalized quantum coherence with respect to general positive operator-valued measurements has been presented. Several well-defined coherence measures, such as the relative entropy of coherence $$C_{r}$$ , the $$l_{1}$$ norm of coherence $$C_{l_{1}}$$ and the coherence $$C_{T,\alpha }$$ based on Tsallis relative entropy with respect to general POVMs have been obtained. In this work, we investigate the properties of $$C_{r}$$ , $$C_{l_{1}}$$ and $$C_{T,\alpha }$$ . We estimate the upper bounds of $$C_{l_{1}}$$ ; we show that the minimal error probability of the least square measurement state discrimination is given by $$C_{T,1/2}$$ ; we derive the uncertainty relations given by $$C_{r}$$ , and calculate the average values of $$C_{r}$$ , $$C_{T,\alpha }$$ and $$C_{l_{1}}$$ over random pure quantum states. All these results include the corresponding results of the conventional coherence as special cases.
Adaptive Gaussian-Like Response Correlation Filter for UAV Tracking

Existing Discriminative Correlation Filters (DCF) based methods usually use a heatmap with a two-dimensional Gaussian distribution to represent the foreground probability map on the search image plane and regularize tracker’s response map in every frame. However, in many real-world scenarios, there often exist non-zero correlation responses in background regions due to the existence of distractors that have similar appearance as the target. In such cases, forcing the output response map to be ideally Gaussian-distributed will lead to contradictory constrains on representation learning, thus hurting performance. To alleviate this, we propose a novel tracker, named Gaussian-like response Correlation Filter (GLCF), which constructs expected response maps by assigning non-zero values to the locations of distractors adaptively depending on their similarity with the target, and thus maintains inter-object consistency and improves robustness. Extensive experiments on four benchmarks well demonstrate the superiority of the proposed method over both DCF and deep based trackers. Specifically, our method achieves new state-of-the-art performance on UAVDT dataset while running at a speed of 28.9 FPS on a single CPU.
Learning Hierarchical Document Graphs From Multilevel Sentence Relations

Organizing the implicit topology of a document as a graph, and further performing feature extraction via the graph convolutional network (GCN), has proven effective in document analysis. However, existing document graphs are often restricted to expressing single-level relations, which are predefined and independent of downstream learning. A set of learnable hierarchical graphs are built to explore multilevel sentence relations, assisted by a hierarchical probabilistic topic model. Based on these graphs, multiple parallel GCNs are used to extract multilevel semantic features, which are aggregated by an attention mechanism for different document-comprehension tasks. Equipped with variational inference, the graph construction and GCN are learned jointly, allowing the graphs to evolve dynamically to better match the downstream task. The effectiveness and efficiency of the proposed multilevel sentence relation graph convolutional network (MuserGCN) is demonstrated via experiments on document classification, abstractive summarization, and matching.