Reviewerly/exHarmony huggingface dataset:
Social Distance Violation Detection

This paper presents a strategy for social distancing location utilising deep learning to gauge the distance between individuals to decrease the effect of the Covid pandemic. This app was created to make individuals aware of keeping a protected distance with one another by assessing a video feed. The video outline from the camera is utilised as information, and the item identification model in view of the YOLOv3 calculation was utilised for pedestrian location. The distance between individuals can be estimated and any disregarding sets of individuals in the showcase will be shown with a red casing and red line. The proposed strategy was approved on a pre-recorded video of people on foot strolling in the city. The outcome shows that the proposed technique can decide the social separating measures between various individuals in the video. The created strategy can be additionally evolved as a recognition based application.
A metaheuristic with a neural surrogate function for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many tasks such as machine translation, information extraction, and semantic parsing. In this work, a new approach for WSD is proposed which uses a neural network as a surrogate fitness function in a metaheuristic algorithm. Also, a new method for simultaneous training of word and sense embeddings is proposed in this work. Accordingly, the node2vec algorithm is employed on the WordNet graph to generate sequences containing both words and senses. These sequences are then used along with paragraphs from Wikipedia in the word2vec algorithm to generate embeddings for words and senses at the same time. In order to address data imbalance in this task, sense probability distribution data extracted from the training corpus is used in the search process of the proposed simulated annealing algorithm. Furthermore, we introduce a new approach for clustering and mapping senses in the WordNet graph, which considerably improves the accuracy of the proposed method. In this approach, nodes in the WordNet graph are clustered on the condition that no two senses of the same word be present in one cluster. Then, repeatedly, all nodes in each cluster are mapped to a randomly selected node from that cluster, meaning that the representative node can take advantage of the training instances of all the other nodes in the cluster. Training the proposed method in this work is done using the SemCor dataset and the SemEval-2015 dataset has been used as the validation set. The final evaluation of the system is performed on SensEval-2, SensEval-3, SemEval-2007, SemEval-2013, SemEval-2015, and the concatenation of all five mentioned datasets. The performance of the system is also evaluated on the four content word categories, namely, nouns, verbs, adjectives, and adverbs. Experimental results show that the proposed method achieves accuracies in the range of 74.8 to 84.6 percent in the ten aforementioned evaluation categories which are close to and in some cases better than the state of the art in this task.
Application of Game Analysis and Mathematical Optimization Model in Soil Fertility Evaluation Intelligent Monitoring System

Based on the study of various game analysis and mathematical optimization, this paper establishes a hierarchical structure model, uses weighted C4.5 and weighted DBSCAN algorithms for soil analysis and evaluation, and develops and designs a soil fertility evaluation system based on J2EE technology. Based on game theory, by analyzing the KMRW reputation game between environmental supervision departments and enterprises, the game of illegal reporting by intermediaries and the game of due diligence by government supervision departments, the soil pH value has dropped from 1981 to 2006 for 26 years. 23 units, the soil organic matter content decreased from 21.5 gk in 1981 to 14.1 gk in 2006, a decrease of 35.3%; the contents of soil total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus and available potassium were significantly rise.
Overlaying a hypergraph with a graph with bounded maximum degree

Let G and H be respectively a graph and a hypergraph defined on a same set of vertices, and let F be a fixed graph. We say that G F-overlays a hyperedge S of H if F is a spanning subgraph of the subgraph of G induced by S, and that G F-overlays H if it F-overlays every hyperedge of H. Motivated by structural biology, we study the computational complexity of two problems. The first problem, (Δ≤k) F-Overlay, consists in deciding whether there is a graph with maximum degree at most k that F-overlays a given hypergraph H. It is a particular case of the second problem Max (Δ≤k) F-Overlay, which takes a hypergraph H and an integer s as input, and consists in deciding whether there is a graph with maximum degree at most k that F-overlays at least s hyperedges of H. We give a complete polynomial/NP-complete dichotomy for the Max (Δ≤k)−F-Overlay problems depending on the pairs (F,k), and establish the complexity of (Δ≤k) F-Overlay for many pairs (F,k).
Improve Quality of Life Client with Visual Impairment

Introduction:Blindness is considered one of the most serious diseases.Blindness causes mental and emotional problems in a person and is one of the most complex health problems in the world.Rehabilitation of blind clients focuses on independent performance of daily activities.The Kawa model in occupational therapy is a model that promotes better interaction between the client and the therapist and makes it possible to guide the client taking into account cultural factors.The model is used in different areas of occupational therapy and brings positive results.The purpose of this research is to learn about the Kawa model and apply it to blind clients.Methods: a qualitative research, a singular diachronic single case study was conducted.One case, which lasted 4 months, included a client who has been blind since birth.The occupational therapy process was conducted using Kawa's step-by-step models.A tactile picture and a tactile magnetic board were made to represent the models, which are an innovative occupational therapy medium.Results: The Kawa model is interesting due to a different way of handling and at the same time very easy for the client to understand.The tactile aids made also contributed to this.The use of the Kawa model is also interesting from the point of view of how the client applies the theoretical knowledge in practice.The client expressed satisfaction with the ease of use of the model itself.Conclusion: The used modified media magnetic board and tactile picture represent an important contribution to the treatment.The occupational therapist in the rehabilitation of blind clients contributes with her knowledge to the independence and quality of life of these clients.It would be useful to conduct further studies on a larger sample and to include more other occupational therapy assessments in the occupational therapy process.
Special Issue on Creative Robot Contest for Decommissioning

The decommissioning of the Fukushima Daiichi Nuclear Power Plant, the plant affected by the Great East Japan Earthquake, will continue for a long time to come, but few young people are willing to take on the challenge because they tend to have the impression that decommissioning work is a post-accident cleanup. However, decommissioning is the most important issue for Fukushima and is of global importance. Therefore, the National Institute of Technology, Fukushima College (Fukushima KOSEN), which is located closest to the Fukushima Daiichi Nuclear Power Plant, established a council of cooperation and started the Creative Robot Contest for Decommissioning in cooperation with other National Institute of Technologies (KOSEN) to increase the awareness and interest in decommissioning among the young. The robot contest, themed on issues related to the decommissioning of the Fukushima Daiichi Nuclear Power Plant, has been held annually since 2016 for students of KOSEN across Japan, and the 7th Creative Robot Contest for Decommissioning is scheduled to be held in December 2022. Although the ideas and robots developed by KOSEN students from all over Japan to solve the problems involved in the decommissioning will not directly contribute to future decommissioning work, the ideas and technologies learned can be used in other fields, and the participating students are expected to be active in a wide range of fields as well. This special issue contains review papers of the Creative Robot Contest for Decommissioning, development reports, and research papers on the robots developed. A total of eight papers are included: two review papers, two research papers, and four development reports on decommissioning robots designed by students. At the same time, we hope that young people in Japan and abroad will also become interested in the robots introduced in this special issue, and that the decommissioning work of Fukushima Daiichi Nuclear Power Plant will proceed safely and securely by combining the wisdom of the whole world.
A Study on the Acceptance Intention of Robotic Process Automation Using Integrated Technology Acceptance Model

This study related to the acceptance intention of the robotic process automation. A research model using variables such as innovative technology acceptance, perceived values and acceptance intention is developed based on UTAUT. We verified the effects of performance expectancy, effort expectancy and facilitating conditions as factors of innovative technology acceptance and functional values and Monetary values as factors of perceived values on the acceptance intention using a structural equation model. Reliability of the model was verified by Cronbach’s α coefficient and discriminant validity and model’s goodness of fit were tested by confirmatory factor analysis. As a result of this study, performance expectancy, effort expectancy and facilitating conditions of innovative technology significantly and positively acceptance affects functional and Monetary values of perceived values. Functional and Monetary values have a positive and significant effect on the acceptance intention. Effort expectancy and facilitating conditions have a positive and significant effect on the acceptance intention, but performance expectancy does not have a significant effect. This study is different from the previous studies by verifying the relationship between user acceptance of innovative technology, perceived values, and the acceptance intention in the implementation and use of robot process automation. It is expected that the results of this study will be used to establish operational strategies for the robotic process automation.
Quantum Networking and Communications at Oak Ridge National Laboratory

In this article we review recent and ongoing research in quantum communications and networking within the Quantum Information Science Section at Oak Ridge National Laboratory. Our research spans applications such as quantum key distribution as well as more fundamental aspects needed to develop scalable quantum networks including quantum repeaters and space-based quantum communications platforms.
Adaptive Decision-Making Framework for Federated Learning Tasks in Multi-Tier Computing

Employing federated learning (FL) in multi-tier computing to achieve various intelligent services is widely in demand. However, adaptive decision-making of FL tasks to improve latency performance is still mostly limited to theoretical studies of local computational optimality, and is challenging to carry out in practical systems. This paper proposes an adaptive decision-making framework (ADMF) for FL tasks with multilayer computational participation to attain lower latency with a global optimization perspective. In this demo, a prototype framework of ADMF in multi-tier computing is demonstrated. First, the feasibility of implementing the proposed framework is provided. Then, we show the latency performance through the experimental results that validate the practicality and effectiveness of the proposed framework.
LightFIG: simplifying and powering feature interactions via graph for recommendation

The attributes of users and items contain key information for recommendation. The latest advances demonstrate that better representations can be learned by performing graph convolutions on attribute graph of the user-item pair. Recently proposed models construct graphs that not only connect edges between user attributes and item attributes, but also within user (item) attributes. However, to determine whether a user is interested in an item, the relationships between user attributes and item attributes are what we really need to mine. In many cases, due to the low correlation between relationships within user (item) attributes and preference of the user, the artificially connected edges within user (item) attributes contribute little to the recommendation. Even worse, including them will not only drastically increase the training time, but may also introduce noise and lead to degraded performance. In addition, the use of the optimizer is also relatively simple. One single optimizer is the default configuration for recommendation models. This may not be the best way to exploit it in many cases however. To solve these problems, we propose an enhanced model named LightFIG in this work. The key idea of LightFIG is twofold: First, we simplify the construction of attribute graph which focuses on mining relationships cross user attributes and item attributes, not between user (item) attributes. Second, we propose the idea of relay optimization, which employs two different optimizers to continuously optimize model parameters. Comprehensive experiments on three public datasets demonstrate the effectiveness of our proposed model.
Design and Research of College English Reading, Writing, and Translation Teaching Classroom Based on 5G Technology

Traditional college English lectures, writing, and translation over wireless networks have several problems. As a consequence, research and development for fifth-generation (5G) technologies are already beginning. High data speeds, decreased latency, energy savings, low cost, increased system capability, and large-scale device connectivity are all characteristics of 5G, the fifth generation of mobile communication technology. The 5G network is a wireless network interaction approach for the digital cellular network that allows for massive volumes of data traffic and a large number of wireless connections. The newest 5G technologies must be incorporated into the curriculum to attain higher-order educational capacities. Educators must fully use network resources, especially 5G network interaction tools, to improve English lecture, writing, and translation, as the fast expansion of 5G network interaction methods provides a fresh viewpoint for English lecture, writing, and translation. In this paper, we design a 5G-enabled Genetic Algorithm- (GA-) based approach for college English lectures, writing, and translation. The proposed method can promote resource sharing, empower teacher-student connections, and improve students’ overall English writing performance by researching this novel kind of network. The proposed method is further improved by incorporating 5G network-dependent English writing and interaction tools. Experimental results reveal that the proposed method outperforms state-of-the-art methods.
Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim

Fraud on health insurance impacts cost overruns and a quality decline in health services in the long term. The use of machine learning to detect fraud on health insurance is increasingly popular. However, one challenge in predicting health insurance fraud is the data imbalance. The data imbalance can cause a bias towards the majority class in many machine learning methods. Oversampling is a solution for data imbalance by augmenting new data based on the existing minority class data. Recently, there has been growing interest in employing deep learning for data augmentation. One of them is using Generative Adversarial Networks (GAN). This paper proposes using GAN as an oversampling method to generate additional data for minority classes. Since data for detecting health insurance fraud are tabular, we adopt Conditional Tabular GAN (CTGAN) architecture where the generator is conditioned to adjust the tabular data input and receive additional information to produce samples according to the specified class conditions. The new balanced data are used to train 17 classification algorithms. Our experiments showed that the proposed method performs better than other oversampling methods on several evaluation metrics, i.e., accuracy, precision score, F1-score, and ROC.
Towards the Identification and Classification of Solar Granulation Structures Using Semantic Segmentation

Solar granulation is the visible signature of convective cells at the solar surface. The granulation cellular pattern observed in the continuum intensity images is characterised by diverse structures e.g., bright individual granules of hot rising gas or dark intergranular lanes. Recently, the access to new instrumentation capabilities has given us the possibility to obtain high-resolution images, which have revealed the overwhelming complexity of granulation (e.g., exploding granules and granular lanes). In that sense, any research focused on understanding solar small-scale phenomena on the solar surface is sustained on the effective identification and localization of the different resolved structures. In this work, we present the initial results of a proposed classification model of solar granulation structures based on neural semantic segmentation. We inspect the ability of the U-net architecture, a convolutional neural network initially proposed for biomedical image segmentation, to be applied to the dense segmentation of solar granulation. We use continuum intensity maps of the IMaX instrument onboard the Sunrise I balloon-borne solar observatory and their corresponding segmented maps as a training set. The training data have been labeled using the multiple-level technique (MLT) and also by hand. We performed several tests of the performance and precision of this approach in order to evaluate the versatility of the U-net architecture. We found an appealing potential of the U-net architecture to identify cellular patterns in solar granulation images reaching an average accuracy above 80% in the initial training experiments.
Discussion on Education Big Data Analytics using Deep Learning Models

Big data is being collected across all sectors of society, and the education sector is no exception. The generalization of big data can lead to educational policy decisions based on information derived from big data and enhance the effectiveness of education. Big data in the education field can be roughly divided into three types according to its depth and utilization: macro-level data that includes student (including parents) level, teacher level, and school level data generated and collected by educational institutions; meso-level data for text such as students’ forum discussions, postings, and essay; and small-scale data (such as clickstream) for recording learning activities. In this study, a part of the data from the Korea Education Employment Panel Wave II was fitted to the deep learning model as an example of the spread of the use of big data in education. Using a neural network model including two hidden layers with 140 variables of various student levels such as learning, leisure, and family life as the input layer, the level of student career maturity was predicted and the prediction performance was compared with the multinomial logistic regression model. Discussions related to education big data analysis using deep learning models are presented along with the analysis results.
Intelligent Basketball Dribble Trajectory Monitoring Based on Data-Driven Technology

Data-driven technology is a problem-solving method, starting from the initial data or observations, using heuristic rules to find and establish the relationship between internal features, to discover some theorems or laws. With the sharp increase in the number of basketball players, there are increasingly studies on the dribbling action of its trajectory and placement. Data-driven technology usually also refers to natural language processing methods based on large-scale statistical data. This paper proposes to use this technology to monitor the trajectory of basketball dribbling, which can more effectively reduce the pain caused by improper dribbling when teenagers play basketball, and can also more effectively improve a shooting percentage. The invention also relates to the field of Internet of Things technology, moving target detection, and the algorithm of target movement trajectory. The Internet of Things technology originated in the media field and is the third revolution in the information technology industry. The Internet of Things refers to the connection of any object with the network through the information-sensing equipment and according to the agreed protocol, and the objects exchange and communicate information through the information dissemination medium to realize intelligent identification, positioning, tracking, supervision, and other functions. After that, a lot of experiments were carried out, and the results showed that the number of breakthroughs after using this technology accounted for 30%, and the hit rate after the breakthrough was also above 70%. The TFHBase system and its optimization strategy are efficient and can effectively solve the problem of basketball trajectory monitoring, and we also do some exploration on trajectory visualization, to further control the landing point of the basketball trajectory.
Augmented Reality Interactive Guide System and Method for Tourist Attractions Based on Geographic Location

With the increasing improvement of people’s living standards, more and more tourists choose to travel independently, which puts forward higher requirements for the existing tourist guide system of scenic spots. Augmented reality (AR) is a technology that integrates computer-generated virtual information into real scenes. It provides more interactive modes, allowing visitors to have a stronger sense of immersion in the real world. The interactive navigation system is an analysis and processing system that recommends algorithms for tourists through the back-end server by collecting the user’s registration information data. It provides tourists with reasonable travel itineraries. This can not only allow users to avoid the peak flow of people and save tourists’ time, but also reduce the hidden dangers of scenic spots and improve the turnover rate of passenger flow. This paper aims to study an interactive guide system combined with augmented reality technology to provide tourism services for tourists in tourist attractions based on geographic locations. This paper proposes an improved probabilistic algorithm model to accurately locate tourists. (Probabilistic algorithms are also called randomization algorithms. Probabilistic algorithms allow the random choice of the next computational step during execution. In many cases, when the algorithm is faced with a choice during the execution process, the random choice is time-saving compared to the optimal one. Therefore, the probabilistic algorithm can greatly reduce the complexity of the algorithm.) At the same time, the paper designs an AR scenic spot tour guide system. It tests the performance of the system. The results show that the initial response speed of the improved probability algorithm is 0.69 s, the average response time is 4 s when the number of concurrent users is finally increased to 600, and the growth rate is about 0.006 s. The improved algorithm can obviously enhance the response speed.