Reviewerly/exHarmony huggingface dataset:
Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance

Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness.
Development of Anatomy Web-Based Assessment based Augmented Reality (AR)

The main objectives of this study are to describe the development design of the Augmented Reality (AR) based Anatomy web-based assessment, to describe the implementation of the Augmented Reality (AR) based Anatomy web-based assessment development, and to describe the teacher's response to the use of Augmented Reality (AR) based Anatomy web-based assessment. The research was conducted at the State University of Medan. This web-based assessment development design is based on the Borg &amp; Gall research and development (R&amp;D) stage pattern as adopted by Sugiyono. As for the product design model, this web-based assessment follows the ASSURE model. The results of this study indicate that the product developed, namely web-based assessment, has gone through design and testing and revision, so the product is declared suitable for use. Students also gave a good response, namely 92% of the use of the web as an assessment tool equipped with Augmented Reality (AR)-based animation.
Image Classification Learning Method Incorporating Zero-Sample Learning and Small-Sample Learning

At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems.
A Systematic Approach for Developing a Robust Artwork Recognition Framework Using Smartphone Cameras

The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.
Early Career Researcher: From Managerial Construct to Socio-Epistemic Reality

The article presents the results of the study of young scientists (early career researchers) and their role in the functioning of research teams and the academic system. It shows why this topic has not only applied relevance connected with the theoretical justification of science policy but also concerns fundamental issues of philosophy of science. The nature of the structural organization of scientific teams and the scientific community as a whole is discussed. It is argued that science shares with other social institutions a socio-epistemic hierarchy, involving the division of participants into more and less experienced ones, performing certain functions in accordance with the available amount of knowledge and skills. It is shown that this hierarchy is supported by the system of division of labor in science, but does not lead to the formation of a rigid structure, which is reflected in the mismatch of social and cognitive hierarchies of research teams. It is also shown that the contribution of young scientists to the overall scientific result can not only be great due to the appearance of young geniuses. Scientific youth performs a number of cognitive and social functions that are system-forming and are not duplicated at other levels of the scientific hierarchy. These functions may undergo changes depending on the general state of both a separate research area and the scientific system as a whole. This makes the research of scientific youth promising for studying the transformations of science as a social institution and a cultural and historical phenomenon, in particular, for analyzing scientific communications that constitute the scientific community as a collective subject of scientific knowledge, and changes in scientific ethos.
Generative Adversarial Network for Joint Headline and Summary Generation

With the ever-increasing amount of electronic documents being generated, it is imperative to provide an intuitive headline and a concise summary of the document to help readers quickly get the gist without going through the all details.While humans have strong abilities to create headlines and generate summaries, the automatic text generation of this research field is still challenging due to the difficult language understanding and complex text synthesis.Moreover, human annotation for machine learning is another matter needed to be addressed.In this paper, we propose a joint model to resolve aforementioned issues simultaneously.To perform unsupervised training to reduce labor cost, we employ a generative adversarial network (GAN) without parallel training data.Considering that the headline and summary have strong relationship, we propose a joint model to learn better representations by including an additional type representation into the GAN.Experiments are conducted on the public dataset, NEWS-ROOM.The experimental results demonstrate that our approach is able to effectively create a reasonable headline as well as a concise summary.
Object Detection Using Various Camera System

Multiple cameras use to simultaneously view an object from multiple angles and at high resolutions detect using real-time tracking for surveillance and security management. The component key of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. An object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video.
Quantifying body ownership information processing and perceptual bias in the rubber hand illusion

Bodily illusions have fascinated humankind for centuries, and researchers have studied them to learn about the perceptual and neural processes that underpin multisensory channels of bodily awareness. The influential rubber hand illusion (RHI) has been used to study changes in the sense of body ownership: how a limb is perceived to belong to one’s body, which is a fundamental building block in many theories of bodily awareness, self-consciousness, embodiment, and self-representation. However, the methods used to quantify perceptual changes in bodily illusions — including the RHI — have mainly relied on subjective questionnaires and rating scales, and the degree to which such illusory sensations depend on sensory information processing has been difficult to test directly. Here we introduce a signal detection theory (SDT) framework to study the sense of body ownership in the RHI. We provide evidence that the illusion is associated with changes in body ownership sensitivity that depend on the information carried in the degree of asynchrony of correlated visual and tactile signals, as well as perceptual bias and sensitivity that reflect the distance between the rubber hand and the participant’s real body. We found that the illusion’s sensitivity to asynchrony is remarkably precise; even a 50 ms visuo-tactile delay significantly affected body ownership sensitivity. Our findings conclusively link changes in a complex bodily experience such as body ownership to basic sensory information processing and provide a proof of concept that SDT can be used to study bodily illusions.
A Study on the Application of a Multisubject Collaborative Model Based on Intelligent Sensor Networks in Sports Training

In today’s rapidly developing information technology, computers are becoming faster and more accurate with more and more sensors, and the birth of new technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence has turned the whole world upside down. At the same time, there is a growing interest in more natural and harmonious ways of human-machine interaction, and many people are devoting themselves to research in this area. Its nodes can be distributed arbitrarily in the target environment and can obtain relevant information about the surrounding environment. In a multinode wireless sensor network, the nodes organize themselves with each other through special protocols to realize their respective functions. In this paper, the application of a multisubject collaboration model to sports training is analyzed, and its superiority is verified by means of examples.
Goals and Directions of Updating the Federal State Educational Standard of Primary General Education

The article discusses the problem of implementing the priority goals of the updated version of the Federal State Educational Standard for Primary General Education: providing a unified educational space, developing in primary school children systemic knowledge about Russia's place in the world, preserving and developing cultural diversity and the linguistic heritage of the peoples of the Russian Federation, solving the most important task of personal student development. The conditions under which the successful education of younger schoolchildren is ensured are characterized: the use of an individually differentiated approach, the integration of educational and extracurricular activities.
Cadmium geochemistry and groundwater pollution status evaluation using indexing and spatial analysis for Keffe community and Environs Sokoto Basin, North Western Nigeria

Representative groundwater samples were collected from the Kaffe community and environs to evaluate the concentrations and geochemical constraints for mobilizing cadmium (Cd) and selected heavy metals. Field-based in-situ measurements of physicochemical parameters were combined with Atomic Absorption Spectrophotometer analysis of dissolved elemental concentrations. Pollution indices (i.e. heavy metal pollution index; HPI, heavy metal evaluation index; HEI, contamination degree; Cd, metal index; MI, synthetic pollution index; SPI, ecological risk index; ERI and Nemerow index; NI) evaluations highlighted the levels of heavy metals in the groundwater. Cadmium and iron (Fe) concentrations exceeded the recommended limits in 97% of the analysed samples, with an average pH of 6.3. Strong positive correlations were observed between cadmium and the computed pollution indices (p 0.774 to p 0.100), suggesting significant Cd pollution of the groundwater. Components analysis grouped Cd, Fe, and the pollution indices in the first PC. This was favourably compared to the correlation analysis result. Cluster analysis categorized Cd, Zn and pH in the first cluster consistent with the suggested dissolution and enrichment of Cd and Zn in the groundwater under similar geochemical conditions. The study area is medium and moderately polluted based on HPI, HEI, Cd, and NI.
A multivariate binary decision tree classifier based on shallow neural network

In this paper, a novel decision tree classifier based on shallow neural networks with piecewise and nonlinear transformation activation functions are presented.A shallow neural network is recursively employed into linear and non-linear multivariate binary decision tree methods which generates splitting nodes and classifier nodes.Firstly, a linear multivariate binary decision tree with a shallow neural network is proposed which employs a rectified linear unit function.Secondly, there is presented a new activation function with non-linear property which has good generalization ability in learning process of neural networks.The presented method shows high generalization ability for linear and non-linear multivariate binary decision tree models which are called a Neural Network Decision Tree (NNDT).The proposed models with high generalization ability ensure the classification accuracy and performance.A novel split criterion of generating the nodes which focuses more on majority objects of classes on the current node is presented and employed in the new NNDT models.Furthermore, a shallow neural network based NNDT models are converted into a hyperplane based linear and non-linear multivariate decision trees which has high speed in the processing classification decisions.Numerical experiments on publicly available datasets have showed that the presented NNDT methods outperform the existing decision tree algorithms and other classifier methods.
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain

Magnetic resonance imaging plays an important role in computer-aided diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, fine perceptive generative adversarial networks (FP-GANs) is proposed to produce HR MR images from low-resolution counterparts. It can cope with the detail insensitive problem of the existing super-resolution model in a divide-and-conquer manner. Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain. Then each sub-band generative adversarial network (sub-band GAN) conquers the super-resolution procedure of each single sub-band image. Meanwhile, sub-band attention is deployed to tune focus between global and texture information. It can focus on sub-band images instead of feature maps to further enhance the anatomical reconstruction ability of FP-GANs. In addition, inverse discrete wavelet transformation (IDWT) is integrated into model for taking the reconstruction of whole image into account. Experiments on MultiRes_7T dataset demonstrate that FP-GANs outperforms the competing methods quantitatively and qualitatively.
Bisimulations Respecting Duration and Causality for the Non-interleaving Applied π-Calculus

This paper shows how we can make use of an asynchronous transition system, whose transitions are labelled with events and which is equipped with a notion of independence of events, to define non-interleaving semantics for the applied π-calculus.The most important notions we define are: Start-Termination or ST-bisimilarity, preserving duration of events; and History-Preserving or HPbisimilarity, preserving causality.We point out that corresponding similarity preorders expose clearly distinctions between these semantics.We draw particular attention to the distinguishing power of HP failure similarity, and discuss how it affects the attacker threat model against which we verify security and privacy properties.We also compare existing notions of located bisimilarity to the definitions we introduce.
Fuzzy Information Enrichment for Self-healing Recommendation Systems of COVID-19 Patient

The global emergency caused by the COVID-19 pandemic does not yet have a registered drug. Many studies suggest strengthening the immune system in the human body as an alternative solution to treating COVID-19 before the discovery of drugs. This study reports on various types of potential treatments and factors associated with the immune response to the virus. The analysis shows that the effectiveness of the treatment depends on the current preferences of the COVID-19 patient. Therefore, this study aims to use crowdsourced fuzzy information enrichment through Self-healing Recommender Systems (ShRS) to provide recommendations for the best treatment therapy. It is hoped that the proper treatment therapy will cure the healing of COVID-19 patients who are self-isolating. To demonstrate the ShRS, an illustrative example was conducted. We used a crowdsourcing approach to generate treatment therapy recommendations in Bojonegoro, an area with a high number of COVID-19 cases in Indonesia. Most contextual input parameters such as age category, physical condition, and nutritional status are fuzzy. Therefore, we perform ShRS in proposing fuzzy inference to compute a new score/rank with each treatment pooled in it. The purpose of this study is to build a more practical recommendation system because the use of website applications and gadgets can open up opportunities for the public to contribute to human care. This study proposes a system to uncover the best options for healing people infected with COVID-19. It can help health practitioners and the general public cope with self-healing during a pandemic as an alternative lifesaver.
A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficiency of knowledge graph-based recommendation methods in the face of relational sparse graphs, a pretraining-based implicit characterization algorithm strategy is proposed for the recall stage, which can solve the problems of difficulty in going online and high delay in the recall stage of the recommendation system while improving the accuracy, and not only this can be applied in the recall stage, but also the sorting and postsorting modules can be used as features. To study the relationship between signaling activity and energy metabolism of pyramidal neurons, an empirical model of the synaptic vesicle cycle is proposed to simulate the synaptic transmission process, the role played by energy metabolism in synaptic transmission is studied from the perspective of feedback control, and the quantitative relationship between neuronal pulse discharge frequency, energy consumption, and information quantity in dendritic integration is analyzed using the cable theory and atrial chamber model. It was found that, when 0 ≤ ε ≤ 0.6, the chaotic region shrinks and eventually disappears with the increase of the memory factor ε; however, when 0.6 ≤ ε ≤ 1 is used, chaos is recreated and the chaotic area gradually increases with the increase of the memory factor ε. This paper conducts comparative experiments on data sets in the recommendation domain and verifies that the proposed model and the feature intersection module can effectively perform feature interaction between items and entities, thus enhancing the recommendation effect.
Study on spatial planning and design of learning commons in university libraries based on fuzzy matrix model

Abstract Campus university library as a spiritual force, in the development of social economy and science and technology innovation, according to the requirements of education management research scholars and students growth changes proposed learning Shared space, the effective integration of space, resources, service as one of the new construction idea, not only makes the university library become more perfect effective, It can also enhance the cohesion and innovation of internal operation. Nowadays, with the increase of the number of university libraries around the country, the scale of internal construction is getting bigger and bigger, and the planning and design of learning shared space based on university libraries has become the focus of scientific research. In essence, learning shared space, as a seamless learning environment, begins to innovate toward intellectualization and modernization while transforming the functional role of university libraries. In this article, therefore, learn about the current university library, on the basis of Shared space planning and design content, according to the fuzzy matrix model, analysis of the application of image retrieval algorithm is verified, the final results show that in the university library to promote learning sharing space, can not only meet the diverse needs of the readers, you can also change the traditional mode of the library service.