Reviewerly/exHarmony huggingface dataset:
The Role of Awareness and Experience in Designing Social Inclusion

A disability exists when people with health conditions experience difficulties engaging in certain activities due to a lack of access. According to the World Health Organization (WHO), disabilities are a global public health issue affecting over 1 billion people, 15% of the global population. However, despite this widespread impact, the lack of awareness and understanding of disabilities means the issue is still not properly addressed in many countries. Instead of focusing on correcting the lack of access, people see those with disabilities as different from most people and see the problem as a personal health issue. This perception has led to social exclusion, as evidenced by stigmatization and the lack of inclusive public spaces. To address this problem and design a socially inclusive society, it is important to know how the concept of disability is understood and to raise public awareness. This paper examines various ways of thinking about disabilities by reviewing published journals that have focused on how people understand the issue, while also exploring the role of awareness and experience from the viewpoint of cognitive science. A review of the journals reveals that people have various ways of perceiving this issue; we need to understand these from a social point of view to reduce social exclusion. It was found that awareness is the first step needed to persuade people to care more about the issue. Letting people directly experience the benefits of inclusivity leads them to understand the importance of providing access. The authors have concluded that awareness gained by social experience plays a significant role in shaping the way people think, resulting in a greater willingness to provide social inclusion.
“This Has Been Written by a Bot”: A Bot Detection Study of the SubsimulatorGPT2 Subreddit

The r/subsimulatorGPT2 is an online forum in which all posts and comments are generated through automation using a fine-tuned version of the GPT-2 language model developed using OpenAI. This subreddit has been created and is moderated by the Reddit user u/disumbrationist (Disumbrationist in What is R/subsimulatorgpt2? 2020). The intricate language model employed here results in very coherent, highly realistic, simulated content. Approximately, 1,00,000 users subscribe to the forum; however, the posting of content and comments is done only by bots trained by the moderator. The process of training a bot includes the machine learning of 5 lac comments from a particular subreddit, which then sorts those comments through their popularity on those respective subreddits. The casual employment of language model machine learning on a popular website like Reddit is unique and important. This study examines the comments and a title posted by the bots and tests them on a sample of 126 participants, where the survey places posts generated by human users versus posts created by bots. The study tries to gauge whether popular and public, social media websites like Reddit have the potential to host content that can be entirely generated through artificial intelligence and whether this content can be distinguished by the layperson. The study opens up vast avenues which indicate that automation during industry 4.0 would include huge sections of online information in casual settings that will purely be generated through machine learning. It also questions the authenticity of online found text and ponders upon its implications in the rapidly approaching technological change.
The Evolving Landscape of Data and Databases: Implications for Database Law

Data has become the fuel for many digitalized sectors from finance to healthcare. Without data, many systems would either not operate or be less efficient when they operate. Until recently, emphasis was on the quantity of data and market players were trying to gather as much as data possible for their own economic benefit. However, slowly but steadily, quality of data is becoming more important than quantity of data as numerous constraints and problems appear that is related to data acquisition in large numbers. To overcome this, various techniques such as synthetic data generation, data distillation and so on have been developed. Also, new database technologies such as vector database systems will create a shift from traditional database structures. All of these developments will have serious effects on content and structures of databases. As the content and structure of databases change, legal protection for databases will also be effected as they protect content and structure of databases under different legal regimes.
BullySpotter: Online Bullying Detection In Bengali Text Using Transform Learning

Prevalence of virtual entertainment has expanded quickly and presently it is very simple to cooperate with various people across web-based entertainment. Subsequently, online bullying towards individuals across web-based entertainment has additionally expanded. As online bullying can cause critical mental trouble, it ought to be prevented. There's been a lot of work done by experts to identify online bullying in Bengali language but they have many limitations. In this paper, we developed a detection system utilizing Deep Learning and Transform Learning algorithms that can detect bully word from Bengali text, which we called BullySpotter. We created a dataset with over 10,000 Bengali texts from the YouTube comment section. Bengali texts are first completely preprocessed, then manually classified into multiple classes (6) preparing them for transformer-based neural network (i.e., multilingual Bidirectional Encoder Representations (cased), bangla Bidirectional Encoder Representations (base)). At long last, we figure thoroughness and adequacy scores to gauge the nature of clarifications with respect to dependability. The banglaBERT model achieved the best accuracy of 89 percent in comparison to deep learning (BLSTM and CNN with word embeddings) and transform-based models (i.e., banglaBERT, mBERT).
A Study on the Semantic Roles and Participant Roles of ‘wa/gwa’ Noun Phrases

Korean Semantics, 80. Semantics represent the semantic function of an argument, which is associated with the meaning of the predicates that make up a sentence and corresponds to the argument of semantic structure. Humans recognize an incident, compose it in language, and give it a semantic function to an object that plays an important role in the incident. The meaning area must know the role in the case and how the role appears in the text. There has been much debate about integrating or excluding companion main meanings from other meanings, compared to the meanings commonly presented in some studies. This is because the “wa/gua” noun phrase in Korean has different characteristics from general sentence components. “wa/gua” is used to connect words to words, to connect sentences to sentences, and to represent the function of companionship and comparison with an actor or subject in a certain case. Therefore, various discussions have been held in research dealing with “wa/gua” noun phrases and meaning areas. In this paper, we consider that the noun phrase of “wa/gua” was discussed as an essential component and that the joint role was discussed in the semantic relation theory. The Korean “wa/gua” noun phrase may be necessary to realize the meaning of the predicate. If you set this noun phrase to discussion, discuss the corresponding semantics and see if you can set other noun phrases to the common station besides the “wa/gua” noun phrase. In this paper, from the viewpoint that the same noun phrase can be given multiple semantic areas, the function of discussion in semantic structure and verb structure is explained without setting semantic areas such as “comparison” and “criteria”. In this paper, we explain that the interaction between the meaning area and the participants and the knowledge of the world are reflected in the text, and the “wa/gua” noun phrase is introduced.
Structuring the Space of Chronicles

This section is devoted to the definition of subclasses of chronicles with potentially interesting properties for algorithmic usages. More especially, we would like to identify semi-lattice structure in subclasses of chronicles. This section successively introduces the notion of slim, simple and pairwise flush chronicles. The slim chronicle is briefly introduced as a technical construction. Then, we introduce the class of simple chronicles to discard this construction. The class of simple chronicle corresponds to the notion of chronicle commonly encountered in pattern mining literature. We illustrate that we fail to derive a usable construction from this class of chronicles. Then, we propose the notion of flush, which can be used to derive subclasses of chronicles being semi-lattices.
Multi-View Clustering via Triplex Information Maximization

In this paper, we address the problem of multi-view clustering (MVC), integrating the close relationships among views to learn a consistent clustering result, via triplex information maximization (TIM). TIM works by proposing three essential principles, each of which is realized by a formulation of maximization of mutual information. 1) Principle 1: Contained. The first and foremost thing for MVC is to fully employ the self-contained information in each view. 2) Principle 2: Complementary. The feature-level complementary information across pairwise views should be first quantified and then integrated for improving clustering. 3) Principle 3: Compatible. The rich cluster-level shared compatible information among individual clustering of each view is significant for ensuring a better final consistent result. Following these principles, TIM can enjoy the best of view-specific, cross-view feature-level, and cross-view cluster-level information within/among views. For principle 2, we design an automatic view correlation learning (AVCL) mechanism to quantify how much complementary information across views by learning the cross-view weights between pairwise views automatically, instead of view-specific weights as most existing MVCs do. Specifically, we propose two different strategies for AVCL, i.e., feature-based and cluster-based strategy, for effective cross-view weight learning, thus leading to two versions of our method, TIM-F and TIM-C, respectively. We further present a two-stage method for optimization of the proposed methods, followed by the theoretical convergence and complexity analysis. Extensive experimental results suggest the effectiveness and superiority of our methods over many state-of-the-art methods.
Are People with Cyber Security Training Worse at Checking Phishing Email Addresses? Testing the Automaticity of Verifying the Sender’s Address

Cyber security training emphasises checking the sender’s email address to identify phishing emails. Dual process theories of cognition suggest that with practice such tactics can transition from effortful, analytic processes to involuntary heuristics and become ‘automatic’. We tested the automaticity of this email habit by developing a scale for cyber security experience and then deployed an interference task where participants (n = 61) had to make a decision about text colour and ignore sender’s addresses from either legitimate or phishing emails. A surprising result emerged: the more cyber security training participants had, the less interference they exhibited in the colour selection task and the more they were able to ignore the content of the sender’s addresses. This suggests that evaluating sender’s addresses does not fulfill the criterion for ‘automatic’ processes when practiced and that more experienced people seem to be able to ignore this important cue when extraneous task goals are present.
WEBSITE REKOMENDASI DAN KLASIFIKASI LAGU MENGGUNAKAN METODE WEIGHTED K-NEAREST NEIGHBOR

As the years went by, music has become one of the most evolving aspects of human history. There is a load of musical development around the globe, especially in music genres. Due to these differences and developments, a design was created to be able to make song recommendations according to the genre types and classifications of music or song. The data that is processed as training data is in the form of song metadata with various music features sourced from Spotify. Song recommendations are performed using the Euclidean Distance calculation between musical features or songs, while song classification is carried out using the Weighted K-Nearest Neighbor (WKNN) method calculation through audio wave type file analysis which then takes the musical features and calculates them based on the existing song or music data. The end result of this process is the genre class label. There is also a classification evaluation calculation using a confusion matrix. With the design of this system, it is hoped that the user will be able to search for song recommendations that have similarities to the song chosen by the user and classify genres according to the user's input song.
Challenges and Opportunities of Spatial Sound Design in Cinematic Virtual Reality: A Scoping Review

In screen-based cinema, the relationship between audiences and the cinema screens remains fixed. The visual frame on-screen serves as a reference for all filmmaking conventions, including sound design. In virtual reality films, this visual reference no longer exists. The audience as a user can freely choose the viewing direction and interacts with the space. This presents new challenges for spatial sound design in terms of on-location sound recording, situating sound elements in 360-degree space, perspective, and guiding viewers’ attention. Still, the research on spatial sound design in cinematic virtual reality remains limited. A scoping review of the literature was undertaken to explore the breadth of literature available on spatial sound design for cinematic virtual reality focusing on live-action filmmaking. Searches were conducted in Web of Science, SCOPUS, IEEE Explore, ACM Digital Library, and Google scholar, followed by backward and forward-searching of relevant references. Searches were restricted to publications in the English language from 2015 onwards. After eliminating the duplicate entries, the search results were screened for the title and abstract first and later for full text. Ten studies were included in the final review to explore different aspects of sound design, challenges, and opportunities. The current literature review aims to contribute to the extant body of knowledge on sound design in cinematic virtual reality and identify gaps for further studies.
Discussing blockchain applications in TED Talks: A fashion wave approach to understanding the blockchain phenomenon

Since its first appearance as the infrastructure supporting Bitcoin, blockchain has received different waves of attention from practitioners and academics. Besides the degree of interest, the attention to blockchain has often focused not merely on its own potential and characteristics but on its applications field. However, looking at its application and deployment in domains other than cryptocurrency or the nascent non-fungible tokens (NFTs), blockchain technology does not seem to be widely adopted or is still in its early stages. Given this, we are interested in how managers and the general public are informed about new technology other than through academic papers or the specialized press. In this paper, we analyze how blockchain has been presented by speakers invited to give TED and TEDx Talks to identify the most common terms used to present blockchain in these talks using a multi-methods approach (qualitative analysis and big data analysis) and see if the discourse surrounding blockchain has shifted over time. The results of this study show how the different perspectives brought by broadcasters like TED often overshadow a technological innovation like the blockchain in its evolution and application by the fact that the focus is instead shifted to products and services built on it. Also, this study shows how different degrees of attention and expertise are associated with each fashion wave of new or emergent technology innovations.
Performance Optimization Blockchain-Enabled Fog Computing with Deep Reinforcement Learning

With the rapid development of Internet of Things technology and cloud computing technology, Fog Computing emerges with the demand of distributed and decentralized. Blockchain is considered to be an advanced technology, which enables Fog Computing data to be stored and shared safely, greatly improving the security and privacy of Fog Computing. However, the Blockchain technology currently applied in Fog Computing still has some significant disadvantages, one of which is the low throughput performance. In order to meet the demand of improving throughput, this paper proposes a Blockchain-Enabled Fog Computing performance optimization framework based on Deep Reinforcement Learning (DRL), which can dynamically adjust according to the system state to improve throughput. Dueling Deep Reinforcement Learning is adopted to obtain the optimal configuration, which can be dynamically adjusted according to Fog node status, network bandwidth and user's quality of service (QoS) requirements, including dynamic selection of block producers, block size and network bandwidth allocation. Simulation results show that the proposed scheme can effectively improve throughput performance.
Identity-based Linkable Blind Ring Signature Scheme on Lattice

Identity-based link-ring signature schemes are widely used in e-commerce, e-voting, blockchain security and other fields.The link-ability of signature scheme ensures that the same user can only sign once with the same private key. Aiming at the requirements of question confidentiality, anonymity of the signature user identity, and one-time signature in the process of question signing in online exams, this paper proposes an identity-based linkable blind ring signature scheme. In this paper, the lattice delegation algorithm is used to generate the user's private key, a one-time random matrix is introduced, and the one-time signature key is generated by running the preimage sampling algorithm (SamplePre) based on the signed user's private key. Finally, this paper proves the unconditional anonymity, unforgeability and link-ability of the scheme under the random oracle model based on the small integer solution(SIS) difficulty assumption.
A Qualitative Content Analysis of Actionable Advice in Swedish Public Agencies’ Information Security Policies

Information security policies (ISPs) are an essential type of formal control that must be designed in a manner that is easily understandable for employees. Prior studies have recommended the inclusion of actionable advice; however, it is unclear how such advice should be worded to minimize the scope for interpretation. Therefore, this study investigates existing ISPs to assess how clear the pieces of actionable advice are and provide suggestions on how actionable advice should be worded in order to be clear. A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. First, the findings revealed an unbalance between the ISPs, where one-third of the ISPs provide over 50% of the analyzed actionable advice. Second, around two-thirds offer advice that is ambiguous and does not provide advice that employees can act upon. We, therefore, recommended that ISP designers exercise caution when using words in the ISP and maintain consistency in their word choices throughout.
Decreasing Physical Access Bottlenecks through Context-Driven Authentication

Despite advancements made to improve the effectiveness and efficiency of access control mechanisms deployed in cyber-physical environments, their controlling logic-driven systems may remain heavily dependent on user interaction during the authentication process. In instances where elevated security requirements are warranted, the authentication methods may be multi-factored in nature, leading to bottlenecks that become more pronounced during peak traffic hours. Logical access control has considered location and time for authentication and re-authentication for some time now and context-driven authentication is fast becoming a standard feature of enterprise-class publicly available products and is used in financial technology (fintech), and eCommerce solutions. This paper focuses on the prototype implementation of context-driven authentication in a cyber-physical access control environment, deployed in a large organization that makes use of information typically collected in the course of the organization’s business operations to uphold security requirements while maintaining efficient access control.
The Effectiveness of Slow Sand Filtration on Reduction of Iron (Fe) and Calcium (CaCO3) Levels in Water

Ground water or well water is the largest source of clean water used. The most common obstacle in using groundwater is the problem of iron (Fe) and calcium (CaC03) content in raw water. This research aims to determine the effectiveness of slow sand filtration to reduce Fe and CaCO3 levels in water. This type of research is a pure experimental study in a re-treatment design (One Group Pretest Posttest). The samples of raw water were taken from water pump wells owned by residents on JI. Datu Adam, Lere Village. Sample testing after slow sand filtering was carried out at the UPTD Laboratory of the Office of Environment, Donggala. The filter was carried out with a medium thickness of 45 cm sand, 5 cm gravel sand, 7 cm gravel and 5 cm high puddle water. Before filtering the test results Fe 0.4mg/L and CaCO3 546.37mg/L. Based on the effectiveness test of slow sand filtration with 3 treatments, it was found that the levels of substances after filtering were iron (Fe) 0.29, 0.21 and 0.I2 mg/L respectively, while calcium (CaCO3) was 273.41, 241.35 and 210.11 mg/L respectively. From these results, slow sand filtration is effective in reducing Fe and CaCO3 levels in water. The community is expected to be able to use simple filtering media such as slow sand filters to reduce iron (Fe) and calcium (CaCO3) levels in water. It is also expected to provide additional knowledge studies in the field of public health, especially environmental health.
BLOCKCHAIN IMPLEMENTATION AND PRINCIPAL-AGENT THEORY

One of the biggest issues in organization in centuries is problem raised by the relationship between actor inside. This problem called principal-agent problem which explained by principal agent theory (PAT). The emerged new technology disruption called blockchain technology (BCT) receive the challenge to offers the solution for principal agent problem, it claims empirically could reduce or even eliminate the problem, thus lead to lower cost to solved the problem, called agency cost. This technology application wide spread in several sectors, the example is the implementation of Decentralized Autonomous Organization (DAO). DAO is the blockchain based new form of organization, who run based on a smart contract or algorithm run in the computer network. This paper is conceptual paper, we explained about the basic of blockchain, and we analyzed the correlation between blockchain and principal agent theory.