Reviewerly/exHarmony huggingface dataset:
Overview of multiobjective particle swarm optimization algorithm

In the real world, the development model of optimization problems tends to be diversified and large scale. Therefore, optimization technologies are facing severe challenges in terms of nonlinearity, multi-dimensionality, intelligence, and dynamic programming. Multiobjective optimization problems have multiple conflicting objective functions, so the unique optimal solution is impossible to obtain when optimizing, and multiple objective values must be considered to obtain a compromise optimal solution set. When traditional optimization methods treat complex multiobjective problems, such as those with nonlinearity and high dimensionality, good optimization results are difficult to ensure or even infeasible. The evolutionary algorithm is a method that simulates the natural evolution process and is optimized via group search technology. It has the characteristics of strong robustness and high search efficiency. Inspired by the foraging behavior of bird flocks in nature, the particle swarm optimization algorithm has a simple implementation, fast convergence, and unique updating mechanism. With its outstanding performance in the single-objective optimization process, particle swarm optimization has been successfully extended to multiobjective optimization, and many breakthrough research achievements have been made in combinatorial optimization and numerical optimization. Consequently, the multiobjective particle swarm algorithm has far-reaching research value in theoretical research and engineering practice. As a meta-heuristic optimization algorithm, particle swarm optimization is widely used to solve multiobjective optimization problems. This paper summarized the advanced strategies of the multiobjective particle swarm optimization algorithm. First, the basic theories of multiobjective optimization and particle swarm optimization were reviewed. Second, the difficult problems involving multiobjective optimization were analyzed. Third, the achievements in recent years were summarized from five aspects: optimal particle selection strategies, diversity maintenance mechanisms, convergence improvement measures, coordination methods between diversity and convergence, and improvement schemes of iterative formulas, parametric and topological structure. Finally, the problems to be solved and the future research direction of the multiobjective particle swarm optimization algorithm were presented.
A new method on motion planning for mobile robots using jump point search and Bezier curves

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.
Composing Photos Like a Photographer

We show that explicit modeling of composition rules benefits image cropping. Image cropping is considered a promising way to automate aesthetic composition in professional photography. Existing efforts, however, only model such professional knowledge implicitly, e.g., by ranking from comparative candidates. Inspired by the observation that natural composition traits always follow a specific rule, we propose to learn such rules in a discriminative manner, and more importantly, to incorporate learned composition clues explicitly in the model. To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules. The KCM can reveal the common laws hidden behind different composition rules and can inform the cropping model of what is important in composition. With the KCM, we present a novel cropping-by-composition paradigm and instantiate a network to implement composition-aware image cropping. Extensive experiments on two benchmarks justify that our approach enables effective, interpretable, and fast image cropping.
VR-Technology in Teaching: Opportunities and Challenges

Use of virtual reality (VR) to teach in upper-secondary schools has become more common during recent years. This article discusses the implementation and testing of VR to teach Swedish in upper-secondary school, a pilot study carried out during the 2020/2021. The purpose of this study is to investigate how VR can be used to teach Swedish, what possibilities and challenges arise from using VR as a learning resource. The method used was inspired by action-based research, where teachers and researchers together, in a symmetrical and complementary approach, explore and evaluate an action. Central theoretical perspectives were TPACK-competences and design principles for gamified learning. The results indicate that students&amp;rsquo; motivation increases by possibilities to co-create, co-design and customize their own learning, where the students solve problems and consider and reflect on their own learning. Both students and teachers point out didactical potentials and explain that VR technology offers many opportunities, but cannot exist on its own. It must function in accordance with the curriculum and regulatory documents of the educational institution.
A Multi-Channel Convolutional Neural Network approach to automate the citation screening process

The systematic literature review (SLR) process is separated into several steps to increase rigor and reproducibility. The selection of primary studies (i.e., citation screening) is an important step in the SLR process. The citation screening process aims to identify the relevant primary studies fairly and with high rigor using selection criteria. Through the study selection criteria, reviewers determine whether an article should be included or excluded from the SLR. However, the screening process is highly time-consuming and error-prone as the researchers must read each title and possibly hundreds to thousands of abstracts and full-text documents. This study aims to automate the citation screening process using Deep Learning algorithms. With this, it is aimed to reduce the time and costs of the citation screening process and increase the precision and recall of the relevant primary studies. A Multi-Channel Convolutional Neural Network (CNN) is proposed, which can automatically classify a given set of citations. As the architecture uses the title and abstract as features, our end-to-end pipeline is domain-independent. We have performed six experiments to assess the performance of Multi-Channel CNNs across 20 publicly available systematic literature review datasets. It was shown that for 18 out of 20 review datasets, the proposed method achieved significant workload savings of at least 10%, while in several cases, our model yielded a statistically significantly better performance over two benchmark review datasets. We conclude that Multi-Channel CNNs are effective for the citation screening process in SLRs. Multi-Channel CNNs perform best on large datasets of over 2500 samples with few abstracts missing.
Analysis of the factors affecting the adoption and compliance of the NHS COVID-19 mobile application: a national cross-sectional survey in England

Objectives To conduct an independent study investigating how adults perceive the usability and functionality of the ‘National Health Service (NHS) COVID-19’ application (app). This study aims to highlight strengths and provide recommendations to improve adoption of future contact tracing developments. Design A 60-item, anonymous online questionnaire, disseminated through social media outlets and email lists by a team from Imperial College London. Setting England. Participants Convenience sample of 1036 responses, from participants aged 18 years and above, between December 2020 and January 2021. Primary outcome measures Evaluate the compliance and public attitude towards the ‘NHS COVID-19’ app regarding its functionality and features. This included whether participants’ expectations were met, and their thoughts on the app privacy and security. Furthermore, to distinguish how usability, perception, and adoption differed with varying demographics and user values. Results Fair compliance with the app features was identified, meeting expectations of the 62.1% of participants who stated they downloaded it after weighted analysis. However, participants finding the interface challenging were less likely to read information in the app and had a lesser understanding of its functionality. Furthermore, little understanding regarding the app’s functionality and privacy concerns was a possible reason why users did not download it. A readability analysis of the text revealed information within the app was conveyed at a level that may be too complex for up to 43% of the UK population. The study highlighted issues related to the potential of false positives caused by the design choices in the ‘Check-In’ feature. Conclusion This study showed that while the ‘NHS COVID-19’ app was viewed positively, there remained issues regarding participants’ perceived knowledge of app functionality, potentially affecting compliance. Therefore, we recommended improvements regarding the delivery and presentation of the app’s information, and highlighted the potential need for the ability to check out of venues to reduce the number of false positive contacts.
The impact of public governance on the economic growth: Evidence from gulf cooperation council countries

This study aims to examine how public governance impacts and affects economic growth in the Gulf Cooperation Council (GCC) which was established on 25th May 1981 and is active till to date, and whether public governance indicators significantly influence the economic growth of these countries.The study's sample includes the six GCC countries in the period of 1996-2019.The study model tests the effects of the independent variables of public governance which are the worldwide governance indicators on the dependent variables of economic growth which are the Gross Domestic Product (annual GDP growth in %), GDP per capita growth (annual in %) and GDP current US$) using a multiple regression model (the fixed effect approach).In this study, only four worldwide governance indicators were selected: the control of corruption, government effectiveness, regulatory quality, and the rule of law.Furthermore, this impact was examined using several control variables, including labor force, working capital, oil price, inflation rate, population, and Human Development Index.The data on dependent and independent variables was collected from the World Bank official website.It is found that the control of corruption and the rule of law have a positive, statistically insignificant impact on economic growth, moreover, government effectiveness and regulatory quality have positive, statistically significant impact on economic growth.The control variable's results concern the labor force, working capital, oil price, inflation rate, population and Human Development Index explained further in the study.
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the DNN training process itself implicitly implements a form of Self-Regularization. The empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of explicit regularization. Building on relatively recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. These phases can be observed during the training process as well as in the final learned DNNs. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a scale separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This results from correlations arising at all size scales, which arises implicitly due to the training process itself. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size. This demonstrates that---all else being equal---DNN optimization with larger batch sizes leads to less-well implicitly-regularized models, and it provides an explanation for the generalization gap phenomena.
Towards a New Generation of Cognitive Diagnosis

Cognitive diagnosis is a type of assessment for automatically measuring individuals' proficiency profiles from their observed behaviors, e.g. quantifying the mastery level of examinees on specific knowledge concepts/skills. As one of the fundamental research tasks in domains like intelligent education, a number of Cognitive Diagnosis Models (CDMs) have been developed in the past decades. Though these solutions are usually well designed based on psychometric theories, they still suffer from the limited ability of the handcrafted diagnosis functions, especially when dealing with heterogeneous data. In this paper, I will share my personal understanding of cognitive diagnosis and review our recent developments of CDMs mostly from a machine learning perspective. Meanwhile, I will show the wide applications of cognitive diagnosis.
Arabic light-based stemmer using new rules

Superior stemming algorithms aid significantly in many natural language processing (NLP) applications such as information retrieval. Arabic light-based stemmer is one of the most important stemming algorithms. However, partially due to the highly inflected and complexity of Arabic language morphological structure, most of the existing Arabic light-based stemmer algorithms eliminate a few numbers of suffixes and prefixes or both in the process of recognising the infix patterns to determine roots. The elimination of suffixes and prefixes leads to many inefficient results. Hence, this study aims to develop an improved light-based algorithm of the Arabic stemmer by proposing an appropriate suffixes and prefixes list, which is supported by rules according to word length (without using a morpheme or patterns on a stem). Our improved Dlight Arabic stemmer focuses on determining and removing the infix patterns under many rules on length-words and according to a specific order of the stages of the stemming to extract the double, triple and quadruple roots from long and short Arabic words. To evaluate our proposed light-based Arabic stemmer, we compared our stemmer against existing Arabic stemmers, namely Light10, Condlight and ARLST. The experimental results showed the proposed Develop Arabic Light-Based Stemmer (Dlight) obtained the best performance with 68% of F-measure, while the other three Arabic stemmers yield slightly lower F-measure. Finally, establishing an appropriate list of suffixes and prefixes with word length rules to stem Arabic words can improve the performance of a light-based Arabic stemmer.
Metallogenic characteristics and tectonic setting of the Jiaodong gold deposit, China

Jiaodong is the largest gold deposit concentration area in China. In recent years, great breakthroughs have been made in deep prospecting, and it has become the third largest concentration area of gold deposits in the world. A series of prospecting discoveries in the Jiaodong area provided the basic conditions for summarizing the regional metallogenic law, constructing the deposit model, carrying out research on the genesis of the deposit and innovating metallogenic theory. The Jiaodong gold deposits are mainly distributed in Northwest Jiaodong, Qi-Peng-Fu and Mu-Ru metallogenic regions, and mainly occur in Precambrian metamorphic rock series, Jurassic Linglong-type granite, Early Cretaceous Guojialing-type granite and Laiyang group. The orebodies are controlled by Sanshandao, Jiaojia, Zhaoping, Xilin-Douya, Jinniushan and other major ore-controlling fault zones. The main mineralization types include altered rock-type, quartz vein-type, stockwork type, sulfide quartz vein-type, interlayer detachment zone-type, altered conglomerate-type, basin margin fault breccia-type and pyrite carbonate vein-type and so on. The important progress of deep prospecting is summarized: two super giant deposits have been evaluated in the Sanshandao and Jiaojia areas; the amount of altered rock-type gold resources identified by deep prospecting has exceeded that of quartz vein-type gold deposits in the Linglong gold deposit field; the Hushan large scale gold deposit has been discovered in Qixia gold deposit field; and the Liaoshang large-size gold deposit is a new discovery of deep prospecting in Pengjiakuang gold deposit field. The ore-controlling faults of gold deposits have several changing steps with dip angle varying from steep to gentle along the dip angle. The gold ore bodies are mainly enriched in sections along the steep, gentle turning points and relatively gentle parts of the fault dip angle, forming a stepped distribution pattern. In the Early Cretaceous, the subduction and rollback of the Paleo-Pacific plate induced crust mantle interaction, resulting in large-scale magmatic and fluid activities. The crust tension and magma uplift form the dome extension structure of granite, which provides a channel for the migration of ore-forming fluid and also provides favorable space for the deposition of ore-forming fluid.
Research on knowledge graph alignment model based on deep learning

• We propose a novel knowledge graph alignment model based on deep learning. • The experiments reveal that our model has effectively improved the performance. • We propose a novel negative sampling method, i.e., transformation negative sampling. • We examine the key influencing factors of knowledge graph alignment. • Our research has practical implications for improving the alignment performance. The construction of large-scale knowledge graphs from heterogeneous sources is fundamental to knowledge-driven applications. To solve the problem of redundancy and inconsistency in the process of domain knowledge fusion, this paper reports studies of domain knowledge alignment from the perspective of a knowledge graph. A novel knowledge graph alignment (KGA) model is proposed, based on knowledge graph deep representation learning. To assess the validity of the model, comparative experiments are conducted on the datasets of heterogeneous, cross-lingual, and domain-specific knowledge graphs. Our results of experiments suggest significant improvement on all of these datasets. We discuss the implications for improving the alignment effect of knowledge graph entities, enhancing the coverage and correctness of knowledge graphs, and promoting the performance of knowledge graphs in knowledge-driven applications.
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit(PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
Practical Medical Files Sharing Scheme Based on Blockchain and Decentralized Attribute-Based Encryption

Medical files can help people prevent diseases, increase cure rates, promote medical development and help solve major public health crises. However, medical files are strongly private. It is an urgent problem needed to be solved that how to share medical files with privacy and data security. The existing models based on the centralized certificate authority is a feasible method, but it is possible to experience a single point failure. Besides there is a mismatch between the models and real-life scenario since they are only suitable for single patient. Therefore, this paper proposes a practical medical file sharing scheme based on blockchain and decentralized attribute-based encryption. The blockchain is used to record application and grant of authorizations. Smart contracts provide an interactive platform for all users in the system. By utilizing decentralized attribute-based encryption, fine-grained access control of medical files is carried out to ensure privacy and security as well as avoiding single point failure. Attribute-based algorithm that support multi-person democratic decision making and dynamic personnel changes are designed to make the model much closer to the real scene. Finally, through security, performance and comparative analysis with other solutions, the scheme in this paper can meet the needs of real-life scenarios in terms of security and practicability, and provides a new practical model for medical file sharing.
Tumor Segmentation in Breast Ultrasound Image by Means of Res Path Combined with Dense Connection Neural Network

Over the past few years, researchers have demonstrated the possibilities to use the Computer-Aided Diagnosis (CAD) to provide a preliminary diagnosis. Recently, it is also becoming increasingly common for doctors and computer practitioners to collaborate on developing CAD. Since the early diagnosis of breast cancer is the most critical step, a precise segmentation of breast tumor with accurate edge and shape is vital for accurate diagnoses and reduction in the patients’ pain. In view of the deficient accuracy of existing method, we proposed a novel method based on U-Net to improve the tumor segmentation accuracy in breast ultrasound images. First, Res Path was introduced into the U-Net to reduce the difference between the feature maps of the encoder and decoder. Then, a new connection, dense block from the input of the feature maps in the encoding-to-decoding section, was added to reduce the feature information loss and alleviate the vanishing gradient problem. A breast ultrasound database, which contains 538 tumor images, from Xinhua Hospital in Shanghai and marked by two professional doctors was used to train and test models. We, using ten-fold cross-validation method, compared the U-Net, U-Net with Res Path, and the proposed method to verify the improvements. The results demonstrated an overall improvement by the proposed approach when compared with the other in terms of true-positive rate, false-positive rate, Hausdorff distance indices, Jaccard similarity, and Dice coefficients.