Reviewerly/exHarmony huggingface dataset:
Introduction to the Cyber-Security Landscape

The importance of cybersecurity in the contemporary digital age is profound. In this chapter, the authors will traverse through the complex and evolving landscape of cybersecurity, exploring its progression, the driving forces behind it, the key challenges it faces, and its future trajectory. With an in-depth analysis of various threat actors and types of cyber threats, the authors will delve into the tools and technologies developed to combat these threats. The authors also explore and compare different cybersecurity approaches, studying their effectiveness and their implications. Through real-world case studies of major cyber-attacks, the chapter will provide insightful lessons learned and the impact they had on the cybersecurity landscape. We will also discuss the often-overlooked human factor in cybersecurity, focusing on the significance of cybersecurity training and the psychology of social engineering attacks. By providing a comprehensive overview of the field, this chapter aims to equip the reader with a well-rounded understanding of cybersecurity.
Blockchain in the Food Industry: Integrating Machine Learning in a Systematic Literature Review

This study provides a systematic literature review of blockchain technology in the food industry with a supply chain lens.We identify primary and sub-themes, discuss implications and applications in terms of delivering value and propose literatureinformed future research directions.This review uncovered three primary themes: a) Value Creation; b) Supply Chain Digitization; and c) Transformation.It demonstrates that digitalizing food networks with blockchain technology may benefit from increased stakeholder involvement and data visibility to gain competitive advantage.Beyond the theoretical contributions, this research provides a methodological contribution by demonstrating the uses of machine learning techniques to perform systematic literature reviews.
Animal Encroachment Detection in Farms: A Survey

Wildlife such as elephants, monkeys intruding farms that are meant for harvesting is extremely damaging and can result in injury, loss of life of humans and wildlife, damage to human property and crops. Farmers not only lose the harvest that they worked on for months, but it is also dangerous for other livestock and people living in the same vicinity. Farmers resort electrical fencing or barbed wires to avoid this which violate the Prevention of Cruelty to Animals Act. Manually guarding the farm is also not optimal as it takes a lot of effort and there is only limited land a person can cover. This increased the need for an application that will detect animal movement or any unusual activity and set out alarms that can avoid loss of money and effort. Using deep learning and computer vision will help process unstructured data such as images collected at different timestamps. Computer vision using Artificial Intelligence will compare colours and contours in images and indicate an encroachment, giving the owner a much more elaborate recognition of the trespasser. Bearing in mind how most of the farmers are relatively alien to a system of this sort, it requires least amount of intervention from the user as input retrieval and processing is automated. This application could also be incorporated in other areas like to detect wildfires in forests or fields that are often detected late.
Enhancing Reading Comprehension By Using Aldiko Book Through Paraphrase Method

This research was use a quantitative and qualitative study, the research applied mixed method in the form of nonequivalent control group design because of there was possibility for the research to randomize the groups as the sample of This research. The reseacher devided the research object into two groups, experimental group who was given treatment, and the control group without treatment. This division was intended to test the hypotesis to know the condition after treatment. The pre-test will administratered students prior knowledge and the post-test will given to find out provement of the students reading comprehension using Aldiko book through paraphrase method. After giving the treatment, the researcher stated that the students in experimental group which were given treatment by using Aldiko Book as a media and paraphrase as method was effective in teaching reading than the students in control group by PDF Reader. The showed by the Mean score and standard Deviation of Students’ in Post-test score which is experimental with 20 sample had a mean score 65.25 and 11.64 higher then the control group 58.00 and 8.013. It means that there was a significance different between the experiental and control group
B-146 Discriminative Index: A Novel Indicator for Evaluating Machine Learning Algorithms in Laboratory Medicine

Abstract Background In clinical scenarios, incorrect predictions by machine learning (ML) models are inevitable. One way to reduce misleading predictions is to avoid reporting predictions that fall within a predefined “gray zone”. This method improves predictive performance by reporting only the less uncertain cases. However, the cost-effectiveness of applying the “gray zone” rule in an ML model is unclear without massive computation. Thus, this study aims to propose a novel metric to evaluate the effectiveness of using gray zones and validate the metric in real-world ML models. Methods This study defined a statistical metric called the “discriminative index” (D-index) for evaluating the effectiveness of gray zones. To calculate the D-index, the predictive outcomes of the ML model are first transformed into two probability distributions based on the truth labels (e.g., positive or negative). The D-index is then derived from the kurtosis of these two distributions. To validate the metric, we applied the D-index to three different antibiotic susceptibility-predicting ML models (namely, convolutional neural network (CNN), random forest (RF), and XGBoost (XGB)) based on mass spectrometry data. We assessed the performance and unpredicted case numbers of each model with different gray zones and correlated the results with the proposed D-index. Results The D-index values for the CNN, XGB, and RF models were 4.36, 0.38, and −1.66, respectively. When applying the “gray zone” rule to achieve 90% area under the receiver operating characteristic, the CNN, XGB and RF models retained up to 90%, 68%, and 62% of total cases, respectively. A higher D-index value indicates a more effective application of the gray zone rule. Conclusion The D-index is a simple and statistically insightful metric for evaluating the cost-effectiveness of applying gray zone rule in an ML model. This metric has been validated in three mass spectrometry-based predictive models and has shown promising results. The D-index can be a useful tool for comparing and applying different ML algorithms.
Research on College English Translation Teaching Based on Parallel Corpus

Corpus linguistics is an important branch of Computational linguistics, and various forms of corpora have become indispensable first-hand materials for linguistic theory and applied research. Bilingual parallel corpora, as a basic research theory, can organically combine learning knowledge with theoretical practice, help students construct knowledge and form meaning, and have strong application value in language comparison, dictionary compilation, and translation teaching. In order to effectively promote the development of translation teaching, this article designs a system function consisting of a corpus management subsystem and a query statistics subsystem based on relevant theories and methods. It analyzes the English translation process based on corpus examples, systematically studies parallel corpus alignment tools, and proposes application strategies for parallel corpora in college English translation teaching: fully utilizing the multiple functions of parallel corpora, improving the openness between teaching and learning ,strengthening the cultivation of students' cross-cultural awareness and constructing a translation teaching model based on parallel corpora.
An Edge-Fog-Cloud-based Hierarchical Adaptive Model for Human-Robot Interaction*

With the rapid advancement of intelligent devices and the Internet of Things (IoT), a vast amount of detailed information can now be captured and analyzed from the human body. This progress has led to the development of more comprehensive and versatile Body Area Networks (BANs). Despite this promising landscape, the field lacks mature global applications, and numerous challenges await resolution, particularly in Human-Robot Interaction (HRI).To address the limitations of HRI, this paper focuses on recognizing complex human activities and achieving rapid, stable transmission of high-load data. An Edge-Fog-Cloud-based hierarchical adaptive system is proposed, utilizing multiple IMU sensors for human activity recognition (HAR). The system introduces an adaptive strategy to enhance the accuracy of identifying complex human behavior. By incorporating hierarchical communication and classification methods, the system improves the communication network’s real-time performance and the algorithms’ architectural structure, ensuring real-time capabilities for the HRI system.
Research on Earthquake Early Warning System Based on Seismic Wave Depth Learning

Earthquakes have a very devastating impact on human life, and scholars have been working to discover methods and equipment that can provide early warning of earthquakes. This study found that by converting the data recorded by seismometers into corresponding audio signals to detect earthquakes, better perception can be obtained, and speech recognition techniques such as MEL frequency cepstrum coefficients (MFCC) can be used to extract features from audio signals. The collected data features will be used for offline training by the established Convolutional neural network (CNN) and Short-term memory (LSTM) networks. Using this modeling method, the waveform can be directly used as input data, and the occurrence and level of earthquakes can be used as output. It is a very suitable real-time earthquake monitoring model. Therefore, it can be well used as an earthquake early warning system. Our research results prove that temporal networks have the best generalization ability. Our models also shows that when the p-wave reaches two seconds, the Earthquake warning system will sound a warning.
UAV Path Planning Based on Ant Colony Algorithm

This paper proposes a novel approach for unmanned aerial vehicle (UAV) flight path planning using ant colony optimization (ACO). The method compares the target location to a food source pursued by ants, dividing the enemy defense zone into equidistant grids. Ants traverse different grid points to reach the target location. The path selection of ants is determined using the visibility function of the ACO algorithm, considering enemy threats and distance to the target node. Additionally, a scoring function is introduced, considering flight path length, threat cost, and maximum deviation angle constraints. Pheromone concentration on the path is updated based on the scoring function. The optimal UAV flight path is represented as a set of node numbers received by the ants during the search. The ACO-based UAV flight path planning method is simple, easy to implement, and exhibits significant optimization effects. Through simulation experiments, we demonstrate the effectiveness and superiority of the method.
Intelligent Detection of Remote Sensing Image Objects with Attention Mechanism

This work aims to overcome the limitations of existing remote sensing images with a wide variety of target objects, which makes it difficult to directly apply salient target detection methods for natural scene images. First, a complete salient target detection scheme for remote sensing images is proposed, which can achieve efficient and accurate detection and provide technical support for the practical application of salient target detection for remote sensing images. In order to address the challenge of diverse target object types in remote sensing images, an Attention Pyramid Decoder Network (APDNet) model is proposed. This model embeds a global attention mechanism to capture feature correlations among different target object types, effectively alleviating the interference of target object type variations on detection performance. Next, the model adopts a pyramid decoder structure to integrate multi-level encoding information at different scales, achieving multi-level feature information aggregation and better feature decoding. Experimental results show that the APDNet model can effectively detect salient objects with diverse types, verifying the stability of the proposed method. The relevant research on intelligent detection of remote sensing image targets can promote the development of computer vision technology in the field of remote sensing and further promote the application of remote sensing data in various scenes. This work has crucial research significance and practical value.
Audio-Driven Gaze Estimation for MOOC Video

Gaze estimation, which refers to predicting the gaze direction of human eyes, is essential in users state awareness and human attention estimation. Most existing methods only use the images of eyes or faces to obtain a good result, which is not enough in some teaching scenarios. In order to be more in line with the teaching scenarios, some additional information could have been used to correct the gaze model predictions, including the video and audio content viewed by the user. Therefore, we propose a new dataset, denoted as MOOCGaze dataset, which includes time-synchronized screen recordings, the corresponding audio, user-facing camera views, and eye gaze data. We also select a state-of-the-art model to obtain the initial Point-of-Gaze and introduce the RefineNet with an audio-video modality fusion scheme to correct the prediction results. Our final method yields significant improvements on our MOOCGaze dataset, with 26.1% improvement in the prediction of eye direction (resulting in 1.947 degrees in angular error).
Research on Attack Simulation Training System of Basketball Players Under Computer Artificial Intelligence Assisted Technology

In this paper, combining virtual reality technology and computer technology, a basketball technology simulation system based on virtual reality is established. Firstly, the exact solution of the best shot Angle and speed is obtained by derivation of the basketball trajectory equation. Then gray contour marks are used to implement 3D fusion of the collected image information to obtain a visual imaging model. Judging the effectiveness of basketball training posture according to visual imaging results and pixel difference feature quantity. The intelligent monitoring output optimization model is constructed to realize the software development and design of basketball training posture intelligent monitoring system in intelligent embedded environment. The simulation results show that athletes can use this visual environment to calculate the best shot Angle and speed.
FPGA Theoretical Analysis and Its Advantage Comparison in Artificial Intelligence

With the continuous development of The Times and technology, artificial intelligence algorithm and AI technology based on Field Programmable Gate Array (FPGA) technology have been widely used in people's life. This paper introduces the extensive application of artificial intelligence technology in production and life in recent years and its challenges. In view of the complexity of AI algorithm operation, real-time requirements and fast update and iteration characteristics, this paper proposes that FPGA chip can better solve these problems. This paper introduces the working principle of FPGA chip to realize digital processing and other hardware resources in detail, and analyzes the application advantages of FPGA in AI industry, life, and engineering. Compared with other chips, FPGA has the characteristics of low cost and flexible implementation of new algorithms, to achieve the balance between performance and cost. Finally, this paper summarizes the advantages of FPGA compared with other chips, and puts forward the possible development way and development platform in the future.
Security Culture as an Expression of Values

As digital technology rapidly evolves, researchers in the Digital Humanities (DH) face the challenge of securing their work and protecting stakeholders across the research life cycle. Security practices, encompassing data integrity, software assurance, and authenticated access, must align with the values of openness, transparency, and collaboration that underpin scholarly research. The increasing sophistication of cyber threats, coupled with the politicization of research and the rise of autocratic governments, necessitates a holistic approach to security that considers both cyber and physical dimensions. This paper explores the intersection of security culture and DH research, emphasizing the importance of trust, informed consent, and ethical collaboration. It argues that security practices should be user-centric, participatory, and transparent, reflecting the values and goals of researchers and participants. By integrating security best practices into research methods, scholars can mitigate risks, protect research assets, and ensure the continued availability and impact of scholarship in a world where threats are constantly evolving.
Describing the HSS Commons: The View from Metadata

In the first year since the launch of the HSS Commons, members have added over 100 publications and the features of the repository’s design are beginning to manifest. Building from Kim Martin’s talk on the possibilities for serendipity in browsing the Commons (Martin, 2022), I explore the descriptive metadata for this first cohort of publications, with particular focus on author-supplied keywords. Information studies predicts that author-supplied metadata enacts diverse sense of the intent and operation of fields, a tendency toward very high-level or general terminology, and the relevance of interaction design choices. This paper presents the metadata practices already visible in the HSS Commons and suggests interaction and maintenance design that can facilitate serendipitous discovery without compromising on the usability and creativity of the platform.
Embracing Open Science at DARIAH-EU: How Openness Became a Bridge Between Research Infrastructure Strategy and Research Realities in the Arts and Humanities

When it was founded in 2014, DARIAH-EU, the European digital research infrastructure for arts and humanities, recognized the importance of open science but did not place particular emphasis on it. In the time since, however, openness has come to rest at the heart of everything the infrastructure does. This piece will look at the process by which a research infrastructure comes to view itself as both a resource and champion for open science in their community, and how the contributions of such organizations can uniquely enhance openness in the arts and humanities.
The role of formal and non-formal education in the development of technological literacy

ducation for technological literacy is a necessity of the 21st century if we want to educate individuals capable of planning, problem-solving, making informed decisions that will contribute to the proper use and advance of technology. Participation in free-time activities as a form of lifelong non-formal learning makes an important contribution to the child's overall development and serves to support formal education. The aim of the case study carried out among 20 participants was to explore their attitudes towards technical education; the challenges and tasks that it imposes on children, teachers and free-time technology–oriented activity instructors; and to evaluate the knowledge and skills it brings to children's lives.
Historical econometric analysis of the fiscal discipline determinants in the African countries

The signing of the Bretton Woods Agreement led the world towards a global economic liberalization system. This study aims to investigate the historical economic determinants of the fiscal discipline especially the interaction effect between the depreciation movements and the trade openness polices in the African countries. Surprisingly, the results demonstrated that the depreciation movements do not always success to achieve the fiscal discipline when the trade openness policies moderating this relationship.
Dark Web: A Playground for Cyber Criminals

So far as we know, DarkWeb Crimes is one of the more sophisticated organised cybercrimes that not many people are aware of. DarkWeb is a haven for international criminals. It may not be simple to define what DarkWeb is. The primary entry point to the dark web (TOR) is the Tor network, commonly referred to as The Onion Router. The word comes from the idea that the numerous layers of an onion symbolise the various levels of anonymity and secrecy available on the dark web. The technology found on the Darknet is not limited to a particular platform or location. The Darknet saw a surge in several crimes, including as the sale of illegal substances, financial fraud, counterfeit money, data breaches, the sale of forged passports, viral forums, child pornography, and other unlawful activities. The author of this essay has covered the many types of cybercrimes that occur on the dark web. The writers have provided instructions on how to enter the dark web and take advantage of many individuals.
Research on bilingual text similarity detection and analysis based on improved fragment merging algorithm

To achieve cross language text similarity analysis, an improved fragment merging algorithm based on dynamic programming is proposed. Dynamic programming is introduced into the fragment merging algorithm to improve the merging algorithm, so as to improve the cross language detection, gradually merge fragments from keyword detection, and verify the performance of the algorithm, such as recall, accuracy and detection time, through comparative analysis experiments. The results show that the recall and accuracy of the merging algorithm based on dynamic programming are more than 80% in the performance test. In addition, it can be found that the fragment merging algorithm has faster fragment merging speed and plagiarism detection speed in the comparison of algorithms. The performance of the improved fragment merging algorithm in plagiarism detection has great advantages, but also has great application value, which provides a new solution for the field of text similarity calculation.