Reviewerly/exHarmony huggingface dataset:
Model Heterogeneous Federated Learning for Intrusion Detection based on knowledge distillation

To solve the problem of model heterogeneity in federated learning intrusion detection, a model heterogeneous federated intrusion detection framework based on knowledge distillation (MHFLID-KD) is proposed in this paper. This framework consists of two parts: grouping and knowledge distillation and aggregation in the dual teacher-student paradigm. First, the server groups nodes according to the model type, and the models of the same type are assigned to the same group, while models of different types are assigned to different groups. Secondly, a knowledge distillation polymerization method based on the dual teacher-student paradigm is proposed. The top two nodes are selected as teachers according to their accuracy in the knowledge distillation of the two-teacher-student paradigm. We use two public datasets, NSL-KDD and UNSW-NB15, in different heterogeneous scenarios to verify the superiority of the MHFLID-KD framework. The experimental results show that this framework not only solves the problem of model heterogeneity in federated intrusion detection but also improves the detection accuracy of nodes.
Athletes' throwing action recognition method based on PCA-LBP algorithm

In order to improve the average recognition rate and accuracy of motion recognition, and improve the recognition effect, a method of athlete throwing motion recognition based on PCA-LBP algorithm is proposed. Kinect device is used to collect motion images, and filter the collected images to reduce the noise contained in the images; LBP method is used to collect the features of the pre processed moving images, obtain the feature codes, and PCA method is used to reduce the dimensions of the extracted motion features. The motion image is segmented to obtain the throwing action area of the athlete, and the action recognition is conducted based on the segmentation results to obtain the throwing action recognition results of the athlete. The analysis of experimental results shows that the method in this paper effectively improves the average recognition rate and recognition accuracy of actions, and the recognition effect is good.
Science and technology researchers' experiences with goal-directed motivational reasoning and strategies for overcoming it

Objectives The purpose of this study is to qualitatively explore the phenomenon of goal-directed motivated reasoning experienced by Korean S&amp;T researchers during the research process and their own strategies for overcoming it. Based on the findings, we hope to draw implications for science education.&#x0D; Methods 8 Korean science and technology researchers participated in this study. In-depth interviews were conducted with each researcher to collect research data, and the collected research data was analyzed through grounded theory analysis. For this purpose, open and axial coding were conducted. Based on the result of coding, We attempted to construct paradigme model.&#x0D; Results As a result of open and axial coding, three broad categories were revealed: First, triggers of motivated reasoning, patterns of motivated reasoning, and strategies for overcoming motivated reasoning. Specifically, in terms of triggers of motivated reasoning, the four themes emerged: “culture of intolerance for failure,” “race to the priority,” “pressure for new results,” and “superficial understanding of theory”. In terms of patterns of motivated reasoning, the three themes emerged: “Avoiding reversal of position,” “Getting wanted data,” and “Between manipulation and control”. Finally, the nine themes were identified as strategies for preventing motivated reasoning: “Pause”, “Doubt”, “Self-objectification”, “Letting go of obsession for the significance”, “Enjoying research”, “learning from failure”, “Sharing”, “Organized skepticism”, and “Listening to others”. Based on those results, a paradigm model of S&amp;T researchers' experiences with motivated reasoning was identified.&#x0D; Conclusions S&amp;T researchers experienced motivated reasoning under social pressure and expectations for research performance. Nevertheless, they have tried to prevent motivated reasoning based on a collaborative research culture that is wary of biased interpretations and the culture with mutual criticism. This led to the objectivity of the research. Based on these findings, this study suggested that it is necessary to make a science education culture that is tolerant of failure and encourages the process of actively overcoming failure. It also suggested that students need to experienced the “organized skepticism” during collaborative inquiry.
The Effect of the Question Generation based Reading Instruction on the Middle School Students' Metacognition and Reading Comprehension

Objectives The purpose of this study was to analyze the effects of the question generation based reading instruction on the middle school students' metacognition and reading comprehension.&#x0D; Methods A total of 10 middle school students who attend a private reading education institution were selected as subjects, and a 10-session reading program was conducted. One of the non-parametric tests Mann-Whitney U-test was conducted to examine the effects of the question generation based reading instruction.&#x0D; Results It was found out that the question generation based reading instruction had not any a statistically significant impact on the middle school students’ metacognition enhancement. However, the post-test scores of metacognition in the experimental group were higher than those of the control group in all sub-factors, except for ‘Inspection’. it was confirmed that the question generation based reading instruction had a statistically positive impact on the middle school students’ reading comprehension.&#x0D; Conclusions This study suggested that the question generation strategies be the important instructional tools of reading teachers. And through the scaffolding of teachers about questioning and reading texts, students will be equipped with the powerful learning strategies to be independent learners and readers.
Effects of Pre-education Program Supporting Teaching Practicum on Teaching Performance Ability of Pre-service Elementary Teachers

Objectives The purpose of this study is to examine whether a pre-education program supporting teaching practicum is effective in improving the teaching performance ability of pre-service elementary teachers.&#x0D; Methods To this end, a pre-education program supporting teaching practicum was conducted for a total of 55 students (experimental group: 34, control group: 21) at G University of Education, and then the difference between the pre- and post-test of teaching performance ability was verified.&#x0D; Results As a result of the dependent sample t-test, the experimental group showed a statistically significant difference in the mean difference between pre- and post-teaching performance ability, but the control group did not. Next, as a result of conducting ANCOVA and MANCOVA, it was found that the level of teaching performance ability was significantly improved in the experimental group than in the control group.&#x0D; Conclusions The pre-education program supporting teaching practicum in this study is a program that helps pre-service teachers improve their teaching performance ability and ultimately increases the effectiveness of teaching practicum.
A case study on the development of a framework of implementing customer centered management: based on the case of Jeju Development Corporation

As the existence value of customers increases in corporate management activities, the need for customer-centered management is on the rise. The purpose of this study is to propose a framework that companies can use in practice to develop a customer centered management system. To this end, Jeju Special Self-Governing Province Development Corporation(JPDC)'s customer centered management cases were defined as 79 detailed activities and the types were classified. Although JPDC is a public enterprise, it sells general consumer good, bottled water called “Jeju Samdasoo” and has been certified for Consumer Centered Management in 2022, so it was judged to be an appropriate company to use in developing a framework of implementing customer centered management. As a result, the framework consisting of strategy establishment, system establishment, process operation, and product/service improvement was derived. In addition, 13 employees of the JPDC were used as subjects and, as a result of receiving and analyzing surveys belonging to which types of 79 activities, the reliability of the framework presented in this study was secured. We expect the suggested framework of implementing customer centered management will improve understanding of customer centered management and present a direction of business strategy.
Virtual Reality Application for Height Fear Treatment

In the article we propose a software-based implementation of a virtual reality exposure therapy application for the treatment of a particular phobia, such as a fear of heights. One of the aims of the research is to implement a method that would enable users of application to gradually face their phobias inside within the boundaries of a safe environment in the hopes of assisting them in overcoming it.
Autonomous Mobile Robot Equipped with a Monocular Camera and Cross-line Laser that Can Measure Obstacle Distance in Real Time Independent of Brightness

In this paper, we propose an algorithm that can measure obstacle distances in real time independent of brightness. The proposed method acquires camera information from a robot equipped with a cross-line laser and a monocular camera, binarizes only the cross-line laser light data using YUV values as threshold values, and calculates the ratio of color and luminance, thereby improving the method to measure the linear distance to obstacles in real time even in bright environments. Experimental results confirm that the proposed method can accurately detect the distance to obstacles. It was also able to accurately calculate the distance to obstacles even when the measurement environment was bright (717 Lux).
SMS Connectivity and Information Display in 4IR Projects for Small-Scale Farmers

Smaller-scale communal or individual farming is underdeveloped in South Africa compared to commercial farming, which is well developed. Despite the increase of commercial agriculture, the agricultural expansion of smallholders is essential for long-term socioeconomic stability and food security. A smart farming culture is described in the fourth industrial revolution (4IR) for the agricultural site to boost productivity competitively. The 4IR concept combines artificial intelligence and big data, which have been very popular in precision farming and have been used to monitor, identify insect pests, detect soil moisture, determine when to harvest, and eliminate labour-intensive monitoring by people. A company that develops precision agriculture and its application is integrated into intelligent observation, which acquires real-time information from field-level data with minimum human participation. A sensor box will be constructed to receive and send SMSs using the GSM module, enabling small-scale farmers to connect with it and acquire time stamped data readings using basic cell phones that do not support cutting-edge technology and sophisticated software which can be used for forecasting. For the small-scale farmer to make educated agricultural decisions, the data containing the soil moisture conditions are supplied to a basic mobile phone through SMS upon request by sending a text to the wireless sensor node. As most small-scale farmers are people with limited to no knowledge and skills to use a computer and other related technology, and struggle with the idea of the internet of things, this eliminates the complicity of smart phone applications and advanced technology.
Drowning Recognition for Ocean Surveillance using Computer Vision and Drone Control

According to WHO's report from 2021, Drowning is the 3rd leading cause of unintentional death worldwide. The use of autonomous drones for drowning recognition can increase the survival rate and help lifeguards and rescuers with their life saving mission. This paper presents a real-time drowning recognition model and algorithm for ocean surveillance that can be implemented on a drone. The presented model has been trained using two different approaches and has 88% accuracy. Compared to the contemporary models of drowning recognition designed for swimming pools, the model presented is better suited for outdoor applications in the ocean.
Block Transforms

In this chapter we explain the workings of an optimal transform coding system. Its components are forward and inverse transformations of blocks of source samples and encoders and decoders of the transformed blocks. We derive the optimal allocation of rate and distortion for encoding a Gaussian source using the mean squared error distortion criterion. We derive a formula for Transform Coding Gain for an arbitrary transform and the optimal Karhunen-Loève Transform.
Two-Dimensional Transforms for Images

The development so far has treated only transformations of one-dimensional sources. Images and two-dimensional data also need compression and two-dimensional (2-D) transformations. The one-dimensional transformations generalize in a logical way to two dimensions. We show how to implement a general and separable two-dimensional transform and specifically the important 2-D Discrete Cosine Transform (DCT).
Cybersecurity Training Acceptance: A Literature Review

User behavior is widely acknowledged as a crucial part of cybersecurity, and training is the most commonly suggested way of ensuring secure behavior. However, an open challenge is to get users to engage with such training to a high enough extent. Consequently, this paper provides research into user acceptance of cybersecurity training. User acceptance can be understood from a socio-technical perspective and depends on the training itself, the organization where it is deployed, and the user expected to engage with it. A structured literature review is conducted to review previous research on cybersecurity training acceptance using a social-technical approach. The paper contributes with an overview of how user acceptance has been researched in the three social-technical dimensions and with what results. The review shows that previous research mostly focused on how the training method itself affects user acceptance, while research focusing on organizational or user-related dimensions is more scarce. Consequently, the paper calls for further research on the organizational aspects of user acceptance of cybersecurity training and how user acceptance can differ between user groups.
Revenue Recognition

A key accounting principle called revenue recognition controls how revenue is recognized, measured, and disclosed in financial statements. Assessing a company's financial performance, profitability, and ability to generate cash flow all depend on accurate revenue recognition. The concepts of revenue recognition are examined in this essay along with the difficulties in applying them and the effects of revenue recognition on enterprises and stakeholders. For fair financial reporting and well-informed decision-making, it is essential to comprehend the nuances of revenue recognition.
Suboptimal Transforms

The discrete Karhunen-Loève transform (KLT) requires knowledge of the covariance function of the source and a solution for the eigenvectors of the covariance matrix. In general, especially for large N, the solution and the transform are computationally burdensome procedures with no fast algorithms for their execution. Instead, one almost always uses a source-independent transform with a fast execution algorithm. We present several useful sub-optimal transforms and calculate coding gains of two of them.
Secure E-commerce Trading Using Blockchain with Smart Contract Based on Proof of Work

Progressing proficiency and performance are a necessary topic in today's world and security in the transaction system is not out of it. There are several Blockchain and Smart contract methods have been created and put to use in health care applications, banking industry, real property transactions, smart home appliances, cloud-based internet of things (IoT), medical transcript, data access and permission, E-Voting Recording, Online Education, virtual power plant etc. E-commerce trading is one of the basic needs in the present world, but there are few Blockchain and Smart Contract findings in this area and a lack of properly implemented models. Therefore, in this work, there is an implementation of Blockchain and Smart contract model to secure E-commerce trading. There is a proposed algorithm to secure the trading. Furthermore, the proof of work concept is used to demonstrate Blockchain transaction implementation. The features of the protocol include a proof of work consensus algorithm, a verification of the Blockchain to prevent tampering, the generation of wallets and the signing of transactions. Smart contract implementation is shown by Ethereum smart contract with solidity. The methods are tested by using JavaScript VM virtual Blockchain. In E-commerce, this proposed model shows a better performance in terms of fast and secure transactions.
Blockchain Solution for Electronic Health Records Using Hyperledger Fabric

Electronic health records (EHRs) are vital digital documents that contain patient medical information. The use of blockchain technology in healthcare has the potential to transform the industry’s data management practices while also addressing security, privacy, and data-sharing concerns. In this paper, we propose an architecture for a blockchain-based system for storing and sharing EHRs that prioritizes privacy and improves healthcare data security. The paper introduces various Hyperledger Fabric concepts and proposes a permissioned blockchain system solution for storing and sharing healthcare records based on the Hyperledger Fabric framework and some cryptography techniques. Patients have complete control over their medical information and can authorize doctors to view it by using grant and revoke access mechanisms.
Six-Year Study of Emails Sent to Unverified Addresses

In addition to a username and password, more and more websites are requiring an email address during account creation. But often, users are granted full access before email verification, allowing them to create accounts using others’ email addresses. As a result, emails sent to such users will instead end up in the true owners’ inboxes. These emails can contain private data ranging from personally identifiable information to banking details and social security numbers. Additionally, the true owner can reset the password and access the account directly. But regardless of potential malicious action, both the user and the true owner face negative consequences. In this paper, we enumerate the prevalence of this shortcoming by manually analyzing account accessibility before email verification on leading United States websites—72% of them failed to restrict potentially harmful actions. Then, we used our rare opportunity to use a short, dictionaryword Gmail address to analyze unverified emails over the course of six years. We categorize the exposed private data and reveal user risks. We aim to bring attention to the dangers of this security flaw and call for the development of new privacy policies.
ICON: A Linguistically-Motivated Large-Scale Benchmark Indonesian Constituency Treebank

Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly available manually-annotated benchmark Indonesian constituency treebank with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. As part of the process of building the treebank, we review and revamp the constituent and POS tagsets in use in existing treebanks to ensure that the labels are relevant and suitable for the grammatical features of Indonesian. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in Indonesian that pose challenges for constituency parsing.
Physical Computing in Rust

Up until now, all the programs you have written have only existed in the virtual world. However, a big part of the physical world we live in is controlled by software. Traffic lights, self-driving cars, airplanes, and even rockets and satellites are just a few examples. Much of this software has to be built and executed in a drastically different environment than the usual desktop environment. It usually has to run on relatively weaker CPUs with less available memory. It might sometimes need to run without an operating system, or on specialized embedded operating systems. This chapter explores how Rust can be used to write such software.
A Research Paper on Negation Handling: Sentiment Analysis Using Super Ensemble Method in Deep Learning

Sentiment analysis, a vital technique for comprehending the ideas and attitudes represented in natural language text includes negation handling as a key component. The technique of automatically identifying and categorising the polarity of the sentiment expressed in a text, which might be positive, negative, or neutral, is known as sentiment analysis. Negation handling is tricky too because words like "not", "no", and "never" can flip the polarity of a mood, making it challenging to recognise the sentiment being represented. Therefore, in this research paper, a model is proposed using the super ensemble technique which can correctly comprehend the sentiments expressed in the reviews and give approximately 96% accuracy.
Robust Keystroke Behavior Features for Continuous User Authentication for Online Fraud Detection

Recently, behavioral biometric-based user authentication methods, such as keystroke dynamics, have become a popular alternative to improve security of online platforms, due to their non-invasive nature. However, currently there are very few behavioral biometric authentication methods that provide non-invasive continuous user authentication for online education platforms, resulting in frequent network intrusion and online assessment fraud. Existing approaches mostly analyze the typing behavior of users using a fixed sequence of characters. Furthermore, a better set of features are required to reduce false positive rate for satisfactory performance to prevent online fraud. Existing behavioral analysis methods also mostly rely on conventional machine learning approaches despite recent advancement in deep learning approaches. We identify a set of keystroke behavioral biometric features that yield satisfactory performance by identifying most frequently used features. We also collect new free-form keystroke behavior data during online assessment activities and develop non-invasive continuous authentication methods for free-form text behavior analysis using deep learning approaches. We also compare performance between deep learning and conventional machine learning approaches and evaluate the robustness of the most frequently used features. Result analysis shows that deep learning approaches outperform machine learning approaches on most frequently used feature set. Furthermore, it is found that the identified feature set is robust and results in satisfactory performance in deep learning approaches.