Reviewerly/exHarmony huggingface dataset:
Knowledge mining and graph visualization of ancient Chinese scientific and technological documents bibliographic summaries based on digital humanities

Purpose This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents. Design/methodology/approach This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs. Findings The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist. Originality/value This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.
ABNORMAL TRAFFIC ANALYSIS USING WIRESHARK (CASE STUDY: SISTER.UMMETRO.AC.ID)

Information technology that is increasingly developing requires a security mechanism that can guarantee confidentiality, integrity, and availability. one of the existing information technologies within Muhammadiyah Metro University, namely SISTER (Integrated Resource Information System) SISTER is an application that is used to fill in the portfolios of educators in Indonesia. SISTER is installed in each tertiary institution, the SISTER problem for university administrators is that this system does not give root access to university administrators so if there is an abnormality in the application it will make it difficult for administrators to block access from attackers, for this reason, this research will analyze the traffic that is on SISTER and see abnormal activity using the Wireshark tool so that if there is abnormal activity on the SISTER network it can be blocked on the internet network side through the router, this research will also test penetration testing on SISTER using the HPING3 tool to find out whether Wireshark can detect attacks which are conducted. The results of this study are an analysis of abnormal activity at SISTER Muhammadiyah University Metro and anticipating using a firewall, the result is that Wireshark can detect HPING3 attacks and the attacker's IP address can be blocked with a firewall.
EXPERT SYSTEM APPLICATION FOR DIAGNOSIS OF DENTAL DISEASES BASED ON WEBSITE USING BAYESIAN THEOREM METHOD

Teeth are small parts located in the upper or lower jaw. Each tooth is embedded in the gums, with its roots penetrating the gum line and entering the jawbone. The total number of teeth in the upper and lower jaw is 32. Teeth are divided into five types, each with a different function. Maintaining dental health is very important, as there are various dental diseases caused by leftover food consumed. Therefore, researchers have developed a system for diagnosing dental diseases by collecting symptoms that have been gathered through interviews with experts and patients, as well as collecting journals related to teeth. Then, all the data is entered into the application to determine the probability of the patient having a certain type of dental disease using the Bayes Theorem method.
A Reinforcement Learning Based Online Coverage Path Planning Algorithm

Coverage Path Planning (CPP) is a common task in robotics that consists in computing collision-free paths that pass through all the specified points from an area of interest. This task is known to be NP-Hard, and increasingly complex when the agent relies exclusively on sensor information. Reinforcement Learning methods appear as an interesting solution to deal with the complexity of this problem and obtain efficient solutions. This paper presents an online CPP algorithm based on Tabular Temporal Difference Learning methods, for a generic robotic platform with a ranging sensor. The problem is formulated as a Partially Observed Markov Decision Process and an RL scheme that includes a modified policy with a heuristic method is proposed. The presented approach provides a way to mix the concepts of classical algorithms with RL, enabling the tabular algorithm to overcome the shortcomings of the inherent large state space of CPP, and accelerated the training process by optimizing and reducing the policy space. The proposed algorithm is tested and its performance is compared in simulation using different Temporal Difference Learning methods, showing that it can efficiently complete the task with no prior information, with different map sizes, starting positions, and a random number of obstacles.
An Evaluation of Image Texture Descriptors and their Invariant Properties

Image processing applications include image classification, image segmentation, image synthesis and many others. Each such task depends on extracting an effective set of features to characterize the images, and texture analysis has proven to output some of the most valuable features. For this reason, image texture analysis has been an actively researched topic and numerous methods have been proposed, each of them having its advantages and limitations. In practical applications, it is impossible to ensure that all images have the same scale, rotation, viewpoint, etc., so texture analysis methods should ideally be invariant. This study inspects the most commonly used operators for extracting textural features, tests their accuracy in classifying the Kylberg texture dataset, and evaluates their invariant properties by the means of various synthetically transformed images. By conducting this analysis, we identified the shortcomings of the existing approaches, and will be able to address them in our future work by formulating some improvements to existing operators to increase their accuracy and to make them invariant to a larger set of transformations.
Cerebral Metastases Segmentation using Transfer Gliomas Learning and GrabCut

Segmentation of medical images is an important area of research that can be used in prognosis prediction and patient treatment. Due to the high variability of data, the task to develop an accurate segmentation method remains challenging. In this paper we address the problem of cerebral metastases segmentation and focus our analysis on the BrainMetShare dataset. In order to enhance the metastases segmentation performance we propose a two stage method. In the first stage we employ a transfer learning procedure where we train an Unet model on the similar task of low and high grade gliomas segmentation provided by the BraTS dataset and then fine-tune the model for solving our problem of cerebral metastases segmentation. In the second stage we use GrabCut to refine the metastases segmentation masks obtained from the first stage. In the experimental evaluation we show that our two stage method based on transfer learning and GrabCut progressively outperforms the baseline model trained only on cerebral metastases data from BrainMetShare.
Using Deep Reinforcement Learning for Navigation in Simulated Hallways

Reinforcement Learning (RL) is a well-suited paradigm to train robots since it does not require any previous information or database to train an agent. This paper explores using Deep Reinforcement Learning (DRL) to train a robot to navigate in maps containing different sorts of obstacles and which emulate hallways. Training and testing were performed using the Flatland 2D simulator and a Deep Q-Network (DQN) provided by OpenAI gym. Different sets of maps were used for training and testing. The experiments illustrate how well the robot is able to navigate in maps distinct from the ones used for training by learning new behaviours (namely following walls) and highlight the key challenges when solving this task using DRL, including the appropriate definition of the state space and reward function, as well as of the stopping criteria during training.
The Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms

Breast cancer is the most commonly diagnosed type of cancer. It is essential to classify patients as quickly as possible into groups with a high or low risk of cancer, to provide adequate treatment. This paper aims to address the impact of the parameters of convolutional neural networks in the binary classification of mammograms. In this paper, we treat two types of binary classification, namely: classification between normal and abnormal tissues, respectively classification between benign and malignant tumors. In the analysis, we investigate the correlation and impact of batch size and learning rate in increasing the performance of the proposed model. Following the experiments on the MIAS dataset, we concluded that for the treated problems, it is appropriate to choose a learning rate lower than 0.001. For the classification of tissues (normal/abnormal), we obtained the fact that training the model on a batch size of 32 brings the best results, namely an accuracy of 0.67, and for the classification of tumors (benign/malignant), it is more appropriate to use a batch size of 8, for which we obtained an accuracy of 0.63. For the best results configurations, we continued the experiments by investigating the impact of data augmentation. We have increased the number of training data by applying horizontal flip and rotation operations. Following these attempts, we noticed an improvement only for the tissue classification, for which we obtained an accuracy of 0.70.
Research on Gaze Estimation Method Combined with Head Motion Changes

Abstract The line of sight reflects the focus of human attention. Gaze estimation technology has a wide range of application prospects in human-computer interaction, human emotion analysis, commercial advertising, and so on. Gaze estimation needs to be jointly determined by eye movement and head movement because the human gaze is often with the head movement. In this paper, a gaze estimation system with head movement is implemented by a monocular camera, using eye movement features and head movement posture changes to estimate the gaze point. A camera is used as the information acquisition device to extract the eye movement feature information, and the offset of head movement is estimated at the same time. The final fixation point position coordinates are obtained by compensating the fixation point position in the case of head movement, and then the fixation point position coordinates are estimated. For the compensated gaze drop point, the average pixel error on the X-axis is estimated to be 118 pixels, and the average pixel error on the Y-axis is estimated to be 136 pixels.
Eye Blink Oriented Chat

Abstract: Digitalization has become an essential aspect of modern-day long-distance communication and is used extensively in daily life. As a result, it is crucial that this technology is accessible to everyone, including differently-abled individuals. The objective of this study is to create an eye-controlled virtual keyboard that enables differently-abled individuals to use text features effectively. To achieve this, the study utilizes Dlib, OpenCV, and Convolutional Neural Networks (CNNs) to monitor eye movement and blinks, allowing the user to select desired keys on the virtual keyboard. By accurately predicting the eye's state using neural networks, the system operates the virtual keyboard with precision. Through this study, a highly efficient method for differently-abled individuals to communicate through text has been established, paving the way for future advancements and research in this field
The Belitung Shipwreck in Virtual Reality: Exploring the Narrative Framework of Digital Cultural Heritage

Abstract Building on the work of Belitung VR, this article seeks to explore a narrative framework for digital cultural heritage storytelling. Belitung VR is a serious game intended to disseminate cultural knowledge about its subject via VR, wherein users can freely explore the virtual reconstruction of a shipwrecked Arab dhow to learn about historical shipping between China and West Asia while collecting clues to the cause(s) of the wreck. The framework consists of narrative goals (physical and nonphysical), narrative elements (character, world, and plot), and narrative grammar (immersion, interaction, rhetoric, and factuality). This work can serve as a practice guide and evaluative framework for designers/artists with an aspiration to digitally disseminate cultural heritage.
Notes from the “Yellow House”. The Case of A. A. Zinoviev

The outstanding thinker Alexander Zinoviev encountered in his life the unique experience of fi rst losing all his academic degrees and ranks and then their reinstatement. In both cases it was he initiative of the Institute of Philosophy at the Soviet Academy of Sciences (in 1976 and 1990), where Alexander Zinoviev also worked until 1976. The documentary history of those events re-constructed on the basis of the archival materials, allows to add substantial facts to the scientifi c biography of the thinker. This also demonstrates the moral and psychological atmosphere and specifi cs of the scientifi c discussions and decisions in the institute of philosophy in various historical periods. The institute is of interest as a representational organization of academic science, as well as colorful social and cultural phenomenon of the Soviet and post-Soviet society.
UV-C Based Disinfectant Robot

Abstract: UVC (ultraviolet-C) light has been proven to kill various bacteria and viruses, including the COVID-19-producing virus, which has been discovered to be a powerful disinfectant. Manual UVC disinfection, on the other hand, can be tiresome, time-consuming, and hazardous for human operators. As our senior project, we developed a UVC-based disinfection robot to address these problems. Our robot has been designed to move and clean on its own in a variety of applications including hospitals, schools, and public places. Ultrasonic sensors for navigation and obstacle avoidance, a mobile platform, a UVC light source, and an Arduino microcontroller for communication and control comprise the robot.We conducted a microbiological test to gauge the robot's performance by comparing the number of microbe cells on a surface before and after UVC disinfection. Our findings demonstrated the robot's effectiveness as a disinfection tool by showing that bacterial colonies were reduced by 93.9%. By measuring UVC radiation levels at various angles and at various distances from the light source, we were also able to assess the safety of the robot. Our findings demonstrated that the robot complied with safety requirements for UVC radiation exposure to humans.
Data Mining for Fraud Detection: An Overview of Techniques and Applications

The process of data mining involves extracting knowledge and insights from vast amounts of data. It can be done through the use of various computational and statistical techniques to identify anomalous, correlational, and pattern patterns. On the other hand, fraud detection is a process that involves identifying and preventing activities that are fraudulent. This process can be carried out through the use of various technologies and techniques, such as artificial intelligence and data mining. It aims to minimize the financial losses caused by these types of activities and ensure that the company follows proper regulatory and legal requirements. The ability to identify fraud prior to it happening is very important for organizations to prevent it from happening. This paper looks into the various aspects of data mining and how it can be utilized for fraud detection. This paper discusses the various aspects of fraud detection and how it can be utilized for organizations to prevent it from happening. We then talk about the various techniques that are used for this process, such as clustering, unsupervised learning, and neural networks. In addition, we talk about the various data preprocessing techniques that are used in the detection of fraud. These include data normalization, feature selection, and extraction. Data visualization is also important in interpreting and understanding the results of mining analyses. The paper then covers the various fraud detection applications of data mining. These include healthcare fraud, credit card fraud, financial statement fraud, and insurance fraud. We provide examples of how these techniques have been utilized to identify fraudulent activities. The paper then discusses the limitations of mining data for fraud detection, as well as the need for an integrated approach that combines various techniques, such as human intervention and audit trails. This paper provides an extensive overview of the various aspects of this field and highlights the significance of this technology in the fight against financial crime.
Blockchain-based IoT Systems: Techniques, Applications, and Challenges

The potential for blockchain-based IoT systems to enable secure and effective data sharing in a variety of industries has attracted a lot of attention in recent years. The fusion of blockchain technology and IoT devices can open up new avenues for growth and innovation, but it also comes with a number of serious drawbacks, including issues with scalability, security, interoperability, data privacy, cost, energy use, and governance. This study offers a thorough analysis of the various methods for putting into practise Blockchain-based IoT systems, along with their benefits and drawbacks. In order to address the issues with Blockchain-based IoT systems, we also suggest a hybrid technique that combines the benefits of several techniques. We also cover the important issues that need to be solved as well as the potential uses of Blockchain-based IoT devices. In order to help researchers and practitioners better grasp the current state of the art and the possibilities for further study and development, this paper seeks to provide a thorough review of Blockchain-based IoT systems.
Secure and Efficient Association Rule Mining over Encrypted Cloud Data

Association rule mining and frequent itemset mining are two approaches to data analysis that have garnered a lot of interest and have been subjected to a lot of research. These approaches can be used for a range of applications. In this effort, our primary focus is on mining encrypted cloud databases while maintaining strict adherence to privacy standards. The owners of the data in this scenario are interested in discovering the association rules or frequent item sets derived from a collective data collection, but they want to do so while limiting the amount of their sensitive raw data that is made available to other owners of the data and to third parties as much as is practically possible. This study looks at the topic of outsourcing jobs related to association rule mining and doing it in a cloud environment. The study does so within the context of various frameworks that are supposed to preserve corporate privacy. According to the findings of the study, there is a novel approach that can be used to guarantee that every item that is updated and sent from the data owner to the server can be interchanged without the attackers needing any further prior knowledge. Our methods are efficient, scalable, and safeguard users' privacy when applied to a transaction database that is both large and authentic. This database is a representation of our complete algorithm. In this technique, the final protocol for mining association rules is proposed, in addition to mining that protects users' privacy by using a cloud infrastructure that is both safe and resistant to data breaches.
A Comparative Study of AI-based Recommender Systems

The purpose of this research is to analyze and compare the various AI-based recommender systems that are currently available. The purpose of the research is to offer a complete review of the many methods and approaches that are utilized in recommender systems, as well as to analyze the strengths and limitations of each of these techniques and approaches. A wide variety of subjects, such as collaborative filtering, content-based filtering, matrix factorization, deep learning, and hybrid recommender systems are discussed in the literature review. In addition to this, a case study is provided to show the use of AI-based recommender systems in a real-world setting. In last, a comparison table is offered to briefly outline the most important aspects shared by each of the various recommender systems. The findings of this research can provide researchers and practitioners working in the field of recommendation systems with a better understanding of the various methods that are currently available, as well as assistance in selecting the strategy that is best suited for the particular application they are working on.
A financial derivatives related multi-label text classification algorithm based on financial knowledge graph

Chinese financial derivatives market is both complicated and highly leveraged. Investors and regulators of derivatives market are required to aggregate information efficiently for detecting potential risks and keeping track of investment opportunities. Gathering information from financial news reports could be modeled as a multi-label text classification task. Many studies have been conducted in this area. However, most previous studies are designed to address common issues rather than specializing in financial area. These studies focus on the texts themselves while omitting supportive financial knowledge. Meanwhile, many previous studies employ a hierarchical label system, which may not be suitable for financial derivatives market because relations among financial labels are web-like rather than tree-like. To draw these limitations when aggregating news report information of Chinese financial derivatives market, this paper models texts to be classified and label structures simultaneously. A financial knowledge graph about Chinese financial derivatives market is firstly built. Then representative labels describing Chinese financial derivatives market are chosen. Necessary financial knowledge for classification task is finally extracted from the knowledge graph and merged into a multi-label text classification model. Experimental results on actual financial derivatives related data show that our method brings improvements compared to methods without label networks and methods without financial knowledge.
Three technical aspects and security issues of Ethereum

Ethereum is a blockchian-based technology that enables a public ledger to be created and maintained without a trusted third party. Ethereum uses elliptic curve cryptography to create accounts and signature, the consensus of PoW as well as PoS to verify transactions and create blocks, and stores data in the modified Merkle Patricia tree. This paper introduced the main three technical aspects of Ethereum and updated some concepts as its technology is updating over time. At the time of Ethereum's transition from PoW to PoS, this paper compared the two mechanism and concluded that PoS is more vulnerable to security issues and more analysis is needed to maintain its long-time security. The paper also discussed uncle block attack and proposed two possible strategies to prevent.