Reviewerly/exHarmony huggingface dataset:
Predicting user engagement with anti-gender, homophobic and sexist social media posts – a choice-based conjoint study in Hungary and Germany

Social media are used by populist radical right parties for anti-gender campaigns to mobilize against the ‘gender ideology’. Anti-gender hate speech targets women, sexual minorities, and feminist activists and therefore poses a threat to their participation in society. In this study, we examine the engagement with anti-gender hate speech on social media in Germany and Hungary, using anti-gender, homophobic, and sexist Facebook posts as examples. These countries were chosen because of the cultural differences in the way gender issues and discrimination are dealt with, as well as the position of populist radical right parties. Employing a choice-based conjoint design, we analyze which content and source characteristics as well as cultural and individual factors influence users’ engagement with such postings. Findings show country differences in the acceptance (likes, shares) and disapproval (flagging behavior) of anti-gender hate speech but also with respect to relevance of the three main topics of hate speech (anti-gender, homophobia, sexism).
Communication Challenges of AEC English Teachers in Australia (A case study of the culture shock experienced by AEC teachers in Australia)

AEC teachers are those who have taught English in a school called Armidale English College. They also have been exposed enough with English language and its culture. AEC has a program of sending its teachers to Australia with a mission to improve their English skills and to gain cultural studies for three months. These English teachers were expected to have a high ability to communicate well in English, they were also expected to be prepared in cultural differences, in fact, what they experienced was far from the expectations for most people. They actually experienced culture shock in Australia. The purpose of this study is to acknowledge the communication challanges and how they experienced the culture shock in Australia. This study was conducted through a qualitative research using case study approach. Data collection techniques consisted of in-depth interviews and documentation studies. The results of the research on the communication challenges by AEC teachers mostly came from themselves and the language which consisted of 1) the accent 2) slang words, 3) vocabularies, and 4) conversation topics. The culture shock they experienced consisted of 1) different mindset 2) openness 3) use of profanity words and 4) life habits. The results also emphasized that overcoming cultural shock does not only focus on having language skills, but also having cultural competence and intelligence.
Searching for an Efficient System of Equations Defining the AES Sbox for the QUBO Problem

The time complexity of solving the QUBO problem depends mainly on the number of logical variables in the problem. This paper focuses mainly on finding a system of equations that uniquely defines the Sbox of the AES cipher and simultaneously allows us to obtain the smallest known optimization problem in the QUBO form for the algebraic attack on the AES cipher. A novel method of searching for an efficient system of equations using linear-feedback shift registers has been presented in order to perform that task efficiently. Transformation of the AES cipher to the QUBO problem, using the identified efficient system, is presented in this paper as well. This method allows us to reduce the target QUBO problem for AES-128 by almost 500 logical variables, compared to our previous results, and allows us to perform the algebraic attack using quantum annealing four times faster.
SCALAR INTERACTIONS IN THE MODIFIED LEFT-RIGHT SYMMETRY MODEL

The Standard Model is a model of particle physics in which one Higgs particle has been confirmed with a mass of 126 GeV. In 2016 some discoveries made it possible to have other scalar particles similar to the Higgs. The modified left-right symmetric model extends the standard model with an expanded scalar sector. There are ϕ_L and Δ_L left sector scalar particles, ϕ_L and Δ_L right sector scalar particles and two singlet η and ξ scalar particles. Therefore, this research objective is to analyze of the possibility of a Higgs interaction with other scalar particles. The method of this research is using a Feynman diagram to describe the interaction terms at the Higgs Potential. The interaction probability is sought using the Feynman rule for Toy Theory. The decay rate uses the Golden Rule. When the universe's temperature reaches the mass of η, the scalar becomes non-relativistic and decays into ϕ_L and ϕ_R. The scalar ξ is scattered into ϕ_L through the η scalar propagator and into ϕ_R. The scalars Δ_L and Δ_R do not decay, they only scatter into ϕ_L and ϕ_R. The η and ξ scalars have transformed into ϕ_L in the left sector and ϕ_R in the right sector, and only ϕ_L in the sectors are likely to be detected as the Higgs Standard Model.
Deep Classifiers and Wavelet Transformation for Fake Image Detection

The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.
Improving Students’ Reading Comprehension by Using Story Pyramid and Plus Minus Interesting Strategy

This research shows that there were several problems faced by students at SMA Negeri 2 Alasa in learning English, especially learning reading comprehension. Therefore, researcher was interested in conducting research entitled " Improving Students’ Reading Comprehension by Using Story Pyramid and Plus Minus Interesting Strategy at the Tenth Grade of SMA Negeri 2 Alasa". This research aims to observe the learning difficulties of these students, whether using the Story Pyramid and Plus Minus Interesting strategies could improve reading comprehension skills of the tenth grade students at SMA Negeri 2 Alasa and find out which strategy was more effective in improving reading comprehension skills. In this research, the researcher used classroom action research (CAR). CAR is an examination of learning activities in the form of an action, which is carried out to improve the teaching and learning process so as to improve learning outcomes that are better than before. Based on the results of the analysis, the pre-test results in class X-IPS which used the Story Pyramid strategy obtained an average score of 61.05%, while in class X-IPA got 56,84%. After the students learned by using these two strategies, the average post-test score for class X-IPS was 73.68% and class X-IPA was 74.21%. From these calculations it was concluded that there was a significant improvement in students' scores in reading comprehension and the use of Plus Minus Interesting strategy was more effective in improving students' reading comprehension with an average score increase of 17.37% compared to Story Pyramid's 12.63%.
Apps in LD: gifts or gimmicks?

In ‘one-shot’ Learning Development (LD) teaching, how much space is there for using apps (and similar technologies) creatively? Can they further students’ understanding and enable active learning within workshops; is there even potential for using them in tutorials? What barriers are there to their use, how could we remove these, and indeed should we – is there enough to be gained or are they just a gimmick? This Wild Card session was an opportunity to collaboratively explore the potential of a selection of apps and technologies which are used in other areas of education, and consider whether they can augment provision in LD. The apps and programmes chosen for this session were used in an international Erasmus+ research project, which worked with lecturers, teachers, students, and school children who observed their strengths and limitations in use (Digital Learning Across Boundaries: Developing Changemakers (DLAB), 2017-2022). This Wild Card session looked to evaluate how these apps might be applied in the LD world by reviewing benefits and barriers to their use in an LD context, and considering how we might maximise any usefulness and contend with limitations or concerns. I wanted to explore how they might support transition through familiarity and links with previous education experiences, and whether the activities which use them might impact on belonging. Participants were encouraged to evaluate and share their thoughts on the apps, and collectively generate a resource of ideas as to their use in LD. This resource could then be shared and drawn on by others.
Rancang Bangun Sistem Analisa Biaya Manfaat Ternak Sapi Berdasarkan Berat Badan Sapi

Cattle fattening proccess is very important to increase the weight of cattle before being sold. However, there are many traditional breeders looking for cows that have reached puberty, but the cow's body is still thin. This could be due to the absence of records in monitoring the weight growth of cows so that feeding becomes less precise. Based on these problems, a system was designed that helps farmers in managing their livestock management by recording the progress of each beef cattle that are kept, regulating cattle feeding so that it is more optimal, and knowing the cost benefits of cattle that are kept based on the growth of cow body weight. This system uses two load cells to weigh cows. The weight of the weighed cow can be stored in the web application database. The main function of this system is to be able to calculate and display the cost of benefits of cattle based on the growth of the cow's body weight. Based on the tests carried out, the system can weight cows using two 500kg load cells with an error value of 3.61%. The system can check cow data using keypad input with an average time of 3.99 seconds and store cattle data in the database with an average time of 4.99 seconds. The system can calculate the cost of cow feed needs in one month and income due to changes in cow weight with a 100% success rate.
Application of Digital Forensic Evidence in Hit and Run: A Comparative Study with Special Reference to § 304 Part II of IPC

The developments of the new era in technology have reflected change in the existing system of investigating and judicial system with regard to evidence admissibility and reliance by the judiciary across jurisdictions. The criminal-jurisprudence has witnessed notable transformation with the use of scientific tools, procedures, and methodologies, which has become increasingly pivotal in delivering justice to the aggrieved. India accounts for 11% of all fatal traffic accident deaths worldwide despite owning only 1% of all vehicles and even fewer than 1% conviction rate, due to lack of proper evidence. How the digital forensic evidence in helping the legal system to encase allegations made under § 304A of the IPC to under § 304 Part II of the IPC is conveyed in this paper with the help of hit and run case instances. In addition to it, this paper also aims to establish the veracity and admissibility of the digital forensic evidence in the court of law and how the examination of its role has a different impact on two mostly identical cases with different investigation outcomes. In one instance, the accused was convicted due to the evaluation of digital evidence while in other he was acquitted due to lack of any proper forensic evidence. The authors have highlighted the challenges before the judges in deciding a criminal case or settle a legal dispute. Relying solely on witness testimony, who are seldom tutors or could not be relied upon might jeopardize the entire case proceedings. Thus, the authors argue for using forensics evidences to reconstruct the crime screen and corroborate the witness/victims testimony.
Research of Agricultural Supply Chain Finance Based on Blockchain Technology: Findings from Chinese Industry

Supply chain finance has brought new choices to agricultural enterprises facing funding shortages. However, due to the different credit ratings and frequent default events of participating enterprises in the chain, the promotion of agricultural supply chain finance business is hindered. This can be solved by the technical characteristics of blockchain. This study discusses the rationality and feasibility of applying blockchain technology to agricultural supply chain finance through literature review and mechanism analysis, using data from Chinese industries. The application of blockchain technology helps to build a good supply chain financial ecosystem, helping Chinese agricultural enterprises explore new financing channels and overcome financial difficulties.
Design Mapping of Wastewater Treatment Installation (IPAL) in the Coal Special Port Area of PT Telen Orbit Prima Montallat District North Barito Regency Central Kalimantan Province

This research aims to design an effective Waste Water Treatment Plant (IPAL) system in the special port area of Persia (PT) in order to ensure the sustainability of mining operations while complying with environmental regulations. The IPAL design is based on assessing the topography, hydrological characteristics, and water quality of the catchment area. Water quality analysis is carried out both during the dry season and after rain, taking important parameters such as pH, Total Suspended Solids (TSS), iron (Fe), and manganese (Mn) into consideration. The results of this research provide important information for designing IPALs that comply with environmental conditions and applicable regulations. Based on the data that has been analyzed, it can be concluded that the proposed WWTP design is an effective solution in managing coal mining wastewater in this port area.
Cyber Security Issues and Challenges on Non-fungible Tokens

Blockchain Technology helps buy and sell digital assets known as Non-Fungible Tokens (NFT). It is an asset that is not fungible. It is a different type of asset that exists in the virtual world and uses cryptocurrencies. Unique individual cartoons, like the ‘Bored Ape Yacht Club’, are made available through the NFT collection with unlimited, choices. The owners of such NFTs exhibit them with pride as avatars on social media. These tokens are not merely images. They include assets in the form of art, music, video game items, collectibles, sports memorabilia, domain names, and tweets, on various websites such as ‘Decentraland’ and the ‘Sandbox’. It is interesting to note that even land in the virtual world using NFT. This process of recording the ownership details of a digital asset is sure to revolutionize the thinking in the minds of people. This paper aims to study the overview of NFTs in detail. It further focuses on the advantages and limitations of NFTs. An attempt has been made to compare NFTs with Blockchain and cryptocurrencies. This paper also deals with the standards and protocols used for NFTs. Further, the issues and challenges with cyber security and privacy in NFTs are discussed. This paper focuses on the future scope of NFTs in the context of the ‘Metaverse’.
Multi-Level Optimization for Data-Driven Camera–LiDAR Calibration in Data Collection Vehicles

Accurately calibrating camera-LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera-LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera-LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision.
CTDNet: cartoon-texture decomposition-based gray image super-resolution network with multiple degradations

In the case of multiple degradations, current deep-learning-based gray image super-resolution (SR) methods equally process all components in an image, resulting in missing subtle details. To address this issue, we elaborate a cartoon-texture decomposition-based (CTD) module that can automatically decompose an image into a smooth cartoon component and an oscillatory texture component. The CTD module is a plug-and-play prior module that can be applied in solving imaging inverse problems. Specifically, for the SR task under multiple degradations, we apply CTD as a prior module to build an unfolding SR network termed CTDNet. For the SR task of real terahertz images, the boundary (i.e., the boundary between the object of interest and the carrier table) recovered by CTDNet has artifacts, which limits its realistic applications. To reduce these boundary artifacts, we post-process the SR terahertz images by using a boundary artifact reduction method. Experimental results on the synthetic dataset and real terahertz images demonstrate that the proposed algorithms can maintain subtle details and achieve comparable visual results. The code can be found at https://github.com/shibaoshun/CTDNet.
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The article examines the essence of the psychology of interaction in the process of innovative professional activity.The authors define the essence of the "management team" concept, the stages of its formation, and the features of the management team.In addition, the causes of conflicts in management teams, their characteristics, and the consequences for effective teamwork on the project are considered.The study describes the mechanisms of interaction in management teams and the ways to solve their problems.The authors describe the essence of disagreements in management teams, where the group members are both managers with a
Inheritance and Revitalization: Exploring the Synergy Between AIGC Technologies and Chinese Traditional Culture

Diffusion models like Stable Diffusion have made impressive progress in T2I (text-to-image) generation. However, when applied to image generation tasks concerned with Chinese cultural subjects, Stable Diffusion needs to improve the quality of its results. This paper proposes a practical approach to address the challenges of utilizing popular AIGC (AI-Generated Content) technologies and integrating them into a cohesive system, which makes it easier to use Stable Diffusion to create high-quality generated images related to Chinese cultural subjects with direct Chinese prompts. Specifically, with the capabilities of Large Language Models (LLMs), the approach can weave expressive visual descriptions based on initial inputs (scattered words in Chinese, Chinese poems…) and align them in suitable English text prompts for subsequent image generation with Stable Diffusion. Through the parameter-efficient finetuning method called LoRA, Stable Diffusion can effectively learn complex and nuanced concepts of Chinese culture. Additionally, Prompt Engineering plays a role in optimizing inputs, assuring quality and stability, and setting the detailed behavior patterns of LLMs throughout the workflow. This success is attributed to overcoming the constraints of accepting only English prompts and significantly improving the understanding of certain concepts in Chinese culture. The experiments show that our method can produce high-quality images associated with complex and nuanced concepts in Chinese culture by leveraging the fusion of all independent components.
Factors influencing customer satisfaction with AR shopping assistant applications in e-commerce: an empirical analysis utilizing text-mining techniques

Purpose This study investigates the key factors that influence customer satisfaction when interacting with augmented reality shopping assistance applications (ARSAPs). ARSAPs grant consumers the capability to experience products in a virtually simulated user environment before product acquisition. With the development of mobile e-commerce due to breakthroughs in smartphone and augmented reality (AR) technologies, there is an increasing potential for these emergent AR mobile services, yet there is a need for further improvement. Design/methodology/approach This study initially explored the key satisfaction factors for ARSAPs by utilizing topic modeling of a collection of actual user reviews. These factors are subsequently revisited and complemented by existing literature, and finally verified through logistic regression analysis supported by sentiment analysis. Findings This study identified the key factors that influence customer satisfaction with ARSAPs, including visuality, sense of reality, credibility, format, completeness, understandability, relevance, flexibility, response time, reliability, availability, ease of use and privacy. In particular, two additional factors (i.e. visuality and sense of reality) were newly identified as important in the context of AR, despite their previous omissions in existing literature. Originality/value This study is the first to investigate the key factors that influence customer satisfaction with ARSAPs from users' perspectives, utilizing topic modeling of a large amount of real-world data on actual user feedback. By identifying new factors (i.e. visuality and sense of reality) that were not identified in previous literature, this study provides important academic implications for a broader understanding of AR and related technologies that are essential elements of the metaverse. This study also provides valuable insights for developers and companies in the e-commerce industry on how to optimize AR applications and develop more targeted and effective marketing strategies in this field.
Reservoir computing-based digital signal equalizer for equivalent-time sampling

A sampling oscilloscope is an important instrument for evaluating the quality of optical communication signals. Since its working principle is equivalent-time sampling, the data obtained for digital signals with random characteristics do not have continuity, which makes it impossible to use methods such as filtering and averaging to equalize the signal. For this reason, this paper proposes a signal equalization method based on reservoir computing. Through training of the reservoir model, an equivalent-time equalizer is established to solve the problem that the sampling oscilloscope cannot equalize random digital signals. Compared with the continuous-time equalizer, the coincidence degree exceeds 95%. The eye height and eye width are increased by 7 and 1.6, respectively, while the jitter in the eye diagram is reduced by 2.3 times, which solves the problem that the sampling oscilloscope cannot equalize random digital signals.