Reviewerly/exHarmony huggingface dataset:
FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments

In this paper we describe the methods we used for our submissions to the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. For all three subtasks we fine-tuned freely available transformer-based models from the Huggingface model hub. We evaluated the performance of various pre-trained models after fine-tuning on 80% of the training data with different hyperparameters and submitted predictions of the two best performing resulting models. We found that this approach worked best for subtask 3, for which we achieved an F1-score of 0.736.
PlayerNetVis: A Visual Analytics System for Evaluating NBA Player Performance Based on Network Topology

With traditional statistical method such as point, rebound and assist, the self-worth of basketball player cannot be truly reflected in the original evaluation system. In this paper, combined with the structure characteristics of network topology established by passing path, passing quality and technical statistics, a novel visual evaluation model is proposed to redefine the indicator system of player performance and analyze the player’s tactical role in the team. A visual analytics system called PlayerNetVis is developed to analyze the real position and role of single player in team tactics, focus on the key moments of the whole game, compare and analyze the influence of players’ performance on game trend. Finally, the validity of the system in this paper is verified through the analysis of multiple set of cases.
Geographical Information System Model for Covid-19 Patient

The increasing number of Covid-19 patients causes an increase in the need for care for Covid-19 patients which also increases. The need for drugs, treatment rooms, medical personnel available at the hospital is important information that must be updated because an increase in Covid-19 patients hospitalized will reduce the availability of the number of health facilities available so that the government can take strategic actions to manage all resources necessary health facilities. The problem found was that information on health facilities resources such as treatment rooms, medicine, medical equipment, Covid-19 patient information and regional government data were not well integrated, making it difficult to update information based on changes in data in the field. The purpose of this research is to build a geographical model of the COVID-19 patient information system that integrates information resources on health facilities and regional areas. The GIS concept is used in research outputs to facilitate management in obtaining spatial information for policy making and strategic decisions related to handling Covid-19 patients. The result of this research is a GIS model for Covid-19 patients.
An Empirical Evaluation of Machine Learning Methods for the Insurance Industry

We identified two use cases for machine learning (ML) in the process of handling insurance claims related to water damage. The first use case is a classification task and concerns the decision, whether or not to send an expert to review the water damage. The second use case is a regression task and involves estimating the initial reserve to declare for the claim. We compared the performance of different ML models for both use cases. Concerning both the classification and regression task, a neural network (NN) with four layers and about 9,000 parameters performed the best. On the classification task, the NN has a precision of 0.8093. On the regression task, the NN has a mean absolute error (MAE) of 1,799 EUR and outperforms human estimations, which have an MAE of 2,258 EUR.
A Comparative Simulation Study of Multi-aircraft Cooperative Task Planning Based on Artificial Intelligence Optimization Algorithm

Considering the inherent characteristics of the return module in manned space missions, this paper proposes a multi-aircraft collaborative target search strategy and establishes a multi-aircraft collaborative target search model. Based on the analysis of collaborative search solutions, this paper proposes a multi-aircraft collaborative search task solution based on an artificial intelligence optimization algorithm. However, the general artificial intelligence optimization algorithm suffers from slow convergence rate and excessive resource consumption. In order to effectively address this problem, this paper proposes GA-Dstar hybrid UAV trajectory planning algorithm to reduce the computation time by using the Dstar algorithm for planning the local path for searching within the local search range. The simulation results show that the proposed hybrid algorithm is more effective than the traditional GA algorithm for performing a global search. The proposed algorithm significantly reduces resource consumption while maintaining the optimality of the trajectory planning route. The improved algorithm proposed in this work is useful for multi-aircraft collaborative mission planning and related research.
Research on analysis and design of cloud ERP based on blockchain technology

Cloud ERP systems are the ultimate aspiration of the business community as they provide collaboration with partners, external applications and information systems. However, Cloud ERP solutions still have issue on the data security, data transparency, and trust. Due to these problems, a radical change is required within Cloud ERP framework to enhance Cloud ERP infrastructural and functionality. The goal of this research is to merge cloud ERP and Blockchain benefits together to improve the Data Security, Transaction Traceability, Transparency, Data Ownership, Encryption and Trust through a proposed framework. For this purpose, we firstly analyze the prominent issues in current ERP systems through a comprehensive comparison between cloud ERP before and after moving to blockchain environment. Secondly, we propose a generic framework for Cloud ERP systems based on Blockchain. The proposed framework follows the Distributed Ledger Techniques in blockchain. Finally, the feasibility and efficiency of the proposed framework is tested through the study of the Supply Chain Management Module.
Prediction of emergency supplies based on improved Case-based reasoning

In order to overcome the problems of old cases on prediction accuracy in traditional case-based reasoning (CBR), an improved case-based reasoning (CBR) method for emergency materials forecasting is proposed. The method adapts to the dependence of emergency events on social development by weakening the weight of old cases. Firstly, the coefficient of variation method is used to calculate the weight coefficient between case attributes. Secondly, the consumption strategy is used to reduce the weight of the old cases, and then the similarity between the cases is calculated to retrieve the best source case matching the target case. Finally, the simulation results show that the prediction accuracy of the improved method is proved to be better than that of the traditional method. Therefore, this method has a certain reference value for the prediction of emergency rescue materials.
Behavior Recognition Based on Two-Stream Temporal Relation-Time Pyramid Pooling Network (TTR-TPPN)

Nowadays, intelligent surveillance has received extensive attention from academia, business, and industry. Deep learning algorithms are widely used in the field of intelligent surveillance. Recently, most deep learning models are limited to a short-term behavior recognition in the entire video. In order to better identify human behavior in the video, we combined a Two-stream network and a Temporal Relation network (TRN) and added a time pyramid pooling operation. In this way, the Two-Stream Temporal Relation-Time Pyramid Pooling Network (TTR-TPPN) can be constructed. The relational pyramid pool network integrated the frame-level features in the video into video-level features. We applied the TTR-TPPN to the Internet public standard data set UCF101 and the self-made DW20 data set. It is found through experiments that this network has a higher recognition rate than other behavior recognition methods on both data sets, and it has better performance in long-term behavior recognition. Therefore, the TTR-TPPN enables it to recognize long-time sequence behavior and improves the accuracy of human behavior recognition.
Real-Time Image and Video Artistic Style Rendering System Based on GPU

Aiming at the practical engineering application of video stylization, in this paper, a GPU-based video art stylization algorithm is proposed, and a real-time video art stylization rendering system is implemented. The four most common artistic styles including cartoon, oil painting, pencil painting and watercolor painting are realized in this system rapidly. Moreover, the system makes good use of the GPU's parallel computing characteristics, transforms the video stylized rendering algorithm into the texture image rendering process, accelerates the time-consuming pixel traversal processing in parallel and avoids the loop processing of the traditional CPU. Experiments show that the four art styles achieved good results, and the system has a good interactive experience.
Introducing Motherboard Components Using Virtual Reality

The study aims to introduce the main component of computers, the main component of the computer, and the sockets where these components need to be installed, firstly to secondary school, high school, and university students and then to every user who installs the application by using virtual reality technology.With the online publication and use of the application, the motherboard that needs to be learned, which will be shown to the application, but not available and only photographed, is recognized and all components on the motherboard will be learned.In addition, by showing the sockets where the components need to be installed, anyone with the application will be able to easily remove and reattach all components in their computer.The application was carried out using the 3DQR program because it allows the visuals to be displayed and processed in 3 dimensions.In the written article, virtual reality technology will be mentioned, then 3DQR Studio software will be introduced in the application used in the application and will be finalized by explaining it with the visuals presented.
Book Collections of European Military Libraries of the 18th — 19th Centuries in the Library for Natural Sciences of the Russian Academy of Sciences

It is known that the history of book collections of past centuries’ libraries, including military ones, is tragic: for many reasons, they are often either scattered or preserved in small fragments. Today, when libraries intensively work to digitize their holdings and search out historical book collections, it is very important to identify and attribute books from foreign, as well as from Russian military libraries. For the first time, the article describes the work carried out to identify publications of foreign (and partly Russian) military libraries from the rare books collection of the Library for Natural Sciences of the Russian Academy of Sciences. The article considers the publications as a source of information about scientific knowledge (military libraries were aimed to acquaint their readers with the basics of military art and the latest achievements in military science). There is shown that the content of such libraries’ collections did not exclude thematic diversity, targeted to comprehensively form the officer’s personality: professional, moral and cultural. The article examines the books of the 18th—19th centuries from military libraries of Europe, currently stored in the Library for Natural Sciences of the Russian Academy of Sciences, and the book marks in them proving the publications’ belonging to specific military libraries. There is highlighted the importance of book marks for the attribution of collections (and libraries) that existed in the past, but turned out to be scattered. The results obtained show that the publications and book marks of military book collections are a strong and reliable source in studying the history of military affairs, military science, and the history of military libraries, and confirm the need for further serious work with the library’s rare book collection.
Explanation with the Winter Value: Efficient Computation for Hierarchical Choquet Integrals

Multi-Criteria Decision Aiding arises in many industrial applications where the user needs an explanation of the recommendation. We consider in particular an explanation taking the form of a contribution level assigned to each variable. Decision models are often hierarchical, and the influence is computed by the Winter value, which is an extension of the Shapley value on trees. The contribution of the paper is to propose an exact algorithm to compute efficiently the Winter values for a very general class of decision models known as the Choquet integral. The main idea of our algorithm is to prune the combinatorial structure on which the Winter value is computed, based on upper and lower bounds of the utility on subtrees. Extensive simulations show that this new algorithm provides very significant computation gains compared to the state of the art.
Secured Blind Image Watermarking Using Entropy Technique in DCT Domain

In the current situation, we can communicate sound, video and pictures with the utmost ease. However, security and copyright of the media turned into a significant issue, so a rising method, known as digital watermarking, is utilized for shielding digital media from counterfeiting and unapproved use. In this paper, a blind image watermarking technique is presented, which uses the entropy method and watermark encryption with the predefined mathematical function to make the process more robust, secure and imperceptible. A watermark is inserted in the DCT domain, which facilitates robustness in bandpass filtering actualized channels. To provide objective evidence of the performance, the performance measures peak signal to noise ratio (PSNR) and normalized correlation (NC) are used. The method can achieve greater than 40 dB PSNR value, and the NC values are in the range of 0.9–1. The proposed technique has been actualized in MATLAB 2020a, and on testing, under various attacks, the technique achieved a great balance between imperceptibility and robustness.
Assisting the Adversary to Improve GAN Training

Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely overlooked regularization technique which we refer to as the Adversary's Assistant (AdvAs). We motivate this using a different perspective to that of prior work. Specifically, we consider a common mismatch between theoretical analysis and practice: analysis often assumes that the discriminator reaches its optimum on each iteration. In practice, this is essentially never true, often leading to poor gradient estimates for the generator. To address this, AdvAs is a penalty imposed on the generator based on the norm of the gradients used to train the discriminator. This encourages the generator to move towards points where the discriminator is optimal. We demonstrate the effect of applying AdvAs to several GAN objectives, datasets and network architectures. The results indicate a reduction in the mismatch between theory and practice and that AdvAs can lead to improvement of GAN training, as measured by FID scores.
The Syllable Structure of Tiv

Linguistic studies reveal that every language has a particular way of combining its sounds to form words or parts of words called syllables. The paper looks at the syllable structure of the Tiv language, one of the Bantoid languages spoken mostly in the Middle Belt area of Nigeria, especially in Benue, Plateau, Taraba, Nasarawa and Cross River states of Nigeria. The objective of the study is to investigate the internal structure of syllables in the Tiv language in order to establish the regularities and restrictions inherent in the language. The study, therefore, aims at ascertaining the syllable patterns that are found in Tiv. This study adopts qualitative and analytical research design using the C V tier model of the phonological theory of syllable analysis as proposed by McCarthy (1979) and adopted by Clements and Keyser (1983), to explicate the permissible patterns of syllable structures in Tiv. Data for the study were gathered from native speakers of Tiv, whose language has not been corrupted by urbanisation and the researchers' intuitive knowledge of the Tiv language. It was found out in the study that, some of the permissible syllable structures in Tiv language include vowels and consonants like v, cv, ccv, cccv, cvc. It was also discovered that all the five vowels of the English alphabets may begin or end a syllable in the Tiv language. As found in English and other languages where the sequential occurrence of two or more consonants is termed consonant cluster, the Tiv syllable structure permits two or more consonants at the initial or final positions of the syllable which could occur as onset or coda, but they are not regarded as consonant clusters. They are regarded as co-articulations. The study concludes that Tiv language has a wide range of phonotactic constraints which if studied can contribute to the development of Tiv language.
Analysis of changes in the layout of the educational standard of secondary vocational education

Today, changes are being made to the main regulatory documents in the field of education, which require a revision of the structure and content of federal state standards. In addition, the labor market requires synchronization of content with the requirements of specific industries. All this required a revision of the layout of the standard for secondary vocational education. Today, 100 educational standards are already in development. For the study, an analysis of the current federal state educational standards and their changes since their introduction in the system of secondary vocational education was carried out. And also considered the latest changes in the regulatory framework of secondary vocational education. Based on the method of analysis, the main changes in the structure and content of federal educational standards were identified. On the basis of the modeling method, the directions of changes in the approximate basic educational programs were determined. The changes identified in the course of the study will allow educational organizations to more successfully apply these standards in the development of basic educational programs. Changes in the vocational education system associated with the approbation of a new layout will allow the education system to respond to the dynamically changing demands of the labor market and provide training for workers who meet the modern requirements of the economy.
A Comprehensive Drug Management System by Segregating Spurious and Substandard Drugs Using Blockchain Technology

Nowadays, one of the most serious problem is spurious drugs in pharmacology. Some prominent research funding organizations stated that nearly ten to 30 percent of the medicines are fake in developing countries. This have now become a major worldwide problem. One of every ten medicines are either fake otherwise don’t follow standard of drugs in developing countries and making the situation worse there. It is very tough to standardize safety regulation of medicines because of the rise of internet pharmacies. Counterfeited drug passes through various distributed complex networks. So, it is hard to detect the counterfeited medicines. For distributed complex networks, the fake drugs get chance to get into the genuine drug supply chain. The security of the drug supply chain has become the utmost concern of public health sector. To get drug supply chain records which are secure, a drug management system is developed using Hyperledger Fabric Blockchain platform in this work. This developed system solves the problem by performing transactions of drug records on a blockchain platform to create a digital, smart drug supply chain. To limit the access to electronic drug records, a smart contract is launched. A flexible android application for manufacturers, distributors, pharmacies and consumers have been made to increase the traceability of the drug.
Research on Visual Recognition Method of Unmanned Driving Based on Deep Learning

Abstract Visual recognition is an important part of unmanned driving technology. With the development of computer technology, the application of deep learning in the field of unmanned driving has been promoted. This article uses Raspberry Pi to build a car with a camera, uses deep learning technology to recognize the acquired image information, and optimizes the structure and parameters of the convolutional neural networks, and finally makes the efficiency of car recognition as high as possible.
Transformer-based Relation Detect Model for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to detect the sentiment polarities of a sentence with a given aspect. Aspect-term sentiment analysis (ATSA) is a subtask of ABSA, in ATSA, the aspect is given by aspect term: a word or a phrase in sentence. In previous work, most models apply attention mechanism or gating mechanism to capture the key part of the sentence and detect the sentiment polarity by classifying the weighted sum vector, which are regardless of the aspect information during the classification. However, same contexts may show different sentiment polarity with differnet aspects. For example, in sentence “The scene hunky waiters dub diner darling and it sounds like they mean it.” the context “dub diner dearling” shows negative polarity towards the aspect term “waiters”. But in sentence “The diner's husband dub diner darling.”, the same context would show neural polarity towards “husband”. The absent of aspect information would make model get a wrong result in some sentence. To solve this problem, we propose a model called Trasfomer-based context-aspect Relation Detect Model (TRDM), and add two special tokens “[CLSA]” and “[SEPA]” to specify the aspect term in the sentence. TRDM uses the embedding of two tyes of tokens (i.e. “[CLS]” and “[CLSA]”) for classification where “[CLS]” is used to represent the entire sentence in BERT. This scheme enable TRDM to combine the sentence information and aspect information in the step of sentiment polarity classification. We evaluate the performance of our model on two datasets: Restaurant dataset from SemEval2014 and MAMS from NLPCC 2020. Experiment results show that our model obtain noticeable improvement compared with state-of-art transfromer-based models.
Detection and Monitoring of High Germ Spreading Activities Using Wearable Sensors

This paper discusses our preliminary study and results on data collection, data processing, feature extraction and classification in developing a machine learning model for detecting daily activities of living, especially the germ spreading activities during the COVID-19 pandemic. In this research, a Mbient Lab MetaWear wearable sensor system is used to collect arm and hand motion data from subjects performing various activities. After data was collected from these different activities, the data was processed. Important statistical time-domain features and frequency domain features, such as the total energy in different frequency bands, were extracted with respect to these different activities to differentiate between them. Various features were collected to create a feature matrix and were used to train different Machine Learning algorithms to determine the germ spreading activity classification accuracy. Using the ensemble bagged tree model, a classification accuracy of 97.0% was obtained.