Reviewerly/exHarmony huggingface dataset:
Enhancing Chemistry Learning with ChatGPT, Bing Chat, Bard, and Claude as Agents-to-Think-With: A Comparative Case Study

This study explores the potential of Generative AI chatbots (GenAIbots), such as ChatGPT and Bing Chat, in Chemistry education, within a constructionist theoretical framework. A single-case study methodology was used to analyse extensive interaction logs between between AI systems and a simulated student persona in simulated Chemistry learning experiences. The results highlight the ability of ChatGPT and Bing Chat to act as 'agents-to-think-with', fostering critical thinking, problem-solving, concept comprehension, creativity, and personalised learning experiences. GenAIbots nurture students' curiosity and promote active learning using Socratic questioning. The study emphasises the significance of prompt crafting, a technique to elicit desired responses from GenAIbots, fostering iterative reflections and interactions. It underlines the need for comprehensive educator training to integrate these tools into classrooms effectively. The study concludes that while ChatGPT and Bing Chat as agents-to-think-with offer promising avenues to revolutionise STEM education through a constructionist lens, fostering a more interactive, inclusive learning environment and promoting deeper comprehension and critical thinking in students across diverse Chemistry topics, ChatGPT consistently outperformed Bing Chat, providing more comprehensive, detailed, and accurate responses and skilfully addressing nuances and context.
Epistemology Artificialized

This paper will discuss how Quine’s proposal for a naturalised epistemology to replace traditional epistemologies is now being advanced with artificial intelligence (AI) as the pivot for mutual interaction between numerous disciplines, such as, neuroscience, cognitive science and psychology.This paper proceeds on the basis that AI is, at its core, a philosophical programme, although advanced by the methods of modern techno-science with an implicit philosophy, and instrumentalised by engineers.This implicit philosophy entails that mind and brain research now proceeds in the direction of the artificialisation of the mind rather than its naturalisation, hence, Quine’s famous question of “Why not psychology?” is becoming re-phrased as “Why not AI?” as more research programmes seek to “reverse engineer” intelligence, often recovering models that are re-deployed across a wide range of disciplines and domains of knowledge. However, the nature of intelligence and cognition remains opaque and what is really gained is knowledge about the generalisability of an AI model, not knowledge corresponding to the real nature of the systems under investigation.The notion that machine intelligence can become the instrument from which knowledge about the world can be drawn necessitates an understanding of machine epistemology, to properly characterise the status of such knowledge. Hence, this paper emphasises the need for epistemological perspectives in AI research to better understand what computers can know and how they may know.
Investing in Information Systems Security: Operational Insights on Resource Allocation

Information security is an inseparable operational component for any business that utilizes information systems. Given this significance, firms are increasingly concerned about the cost-effectiveness of their investments in information security. In this paper, we seek to address two key decisions by a firm related to such investments: how much the firm should invest in information systems security, and how this investment should be allocated over different categories of security countermeasures. As part of our findings, we derive a simple functional relationship between the potential total losses of a firm and the optimal amount that the firm should invest in information systems security. Related to this, we find that firms in finance, energy, and technology sectors should invest twice more in trying to detect information security breaches, than in trying to prevent them. In other industries, information security investments should be split evenly between preventive and detective measures.Moreover, the overall information security budgets for certain types of firms in the former set of industries should be on average 15% higher than other industries, even when the potential total losses under a security breach are the same. As some additional conclusions, we find that the value of these optimal policies is higher for small to medium sized firms, while a gradual investment strategy over a budget period is better than early utilization of the budget at the beginning of this period.
AI in Legal Education: Drafting Policies for Balancing Innovation and Integrity

Artificial Intelligence (AI) stands as a testament to human ingenuity, representing thepinnacle of our drive to create machines that can think. At its essence, AI is a branch of computerscience dedicated to designing systems capable of mimicking human intelligence, decipheringpatterns, understanding language, and making complex decisions. By exploring the nuances,challenges, and potential solutions, we can ensure that AI is effectively integrated into legaleducation while preserving the legal profession's core values.A significant advancement within AI is the emergence of Generative Artificial Intelligence(GAI). Unlike traditional models that process and analyze data, GAI can draft texts,design images, and even compose music. For law students, the applications of GAI are profound.This is a tool that not only aids in researching vast databases of texts but can also generate draftarguments or synthesize law summaries. Such capabilities can provide invaluable assistance,especially when grappling with complex legal principles or contemplating intricate arguments.The introduction of GAI also sparks a debate within the venerable confines of law schools.While its potential to revolutionize legal research and drafting is undeniable, how do schoolsensure that students maintain the rigorous analytical skills that are the cornerstone of legaleducation? As we stand at the start of this new era, institutions face the challenge of integratingthis potent tool while preserving the sanctity and rigor of the legal profession.
From Smart Legal Contracts to Contracts on Blockchain: An Empirical Investigation

The issue surrounding the nature and function of smart contracts in the context of legal relationships has garnered significant attention from the European and national legislators, regulatory bodies and legal scholarship. Sections I and II of this essay give an account of the results of the ongoing doctrinal debate, which is not univocal. The objective is to provide an assessment of both the advantages and limitations associated with smart legal contracts. In Section III, the authors introduce a novel negotiation process termed "contracts on chain". This process enables parties to engage in negotiations, formalize agreements and execute contracts directly on the blockchain. Consequently, this negotiation approach serves as a potential bridge between the realms of Web 2 and Web 3. Further, it offers a user experience akin to online contracts but benefits from the inherent capabilities of third-generation blockchains. Albeit on-chain contracts can be deployed on both private and public blockchains, the authors express a preference for their use on the public blockchain within a "logical platform". This choice allows to enhance regulatory compliance and mitigate the effects of decentralization on liability regimes, while simultaneously optimizing the efficiency gains of public blockchains. Notably, this approach ensures a level of protection commensurate with that offered by private blockchains.The ultimate goal of this innovative process is to streamline the ongoing technological transition and cultivate greater trust within the market for emerging technologies.
Issues on Particular Market Situation to Calculate Korean Steel Products Dumping Margin by the USA

Purpose - The U.S. Trade Preference Expansion Act(TPEA) of 2015 enables the US Department of Commerce (DOC) to inflate dumping margin when the particular market situation(PMS) exists in the exporter’s home market. DOC applied PMS provisions to the steel products from Korea. This paper analyzes whether DOC's calculation by using the regression analysis is consistent with WTO rules. Design/Methodology – This paper analyzes the PMS application in law and regression analysis that extends the data period from 10years to 18years using the same economic model with DOC, and reclarify the country group according to the quantities of steelmaking capacity.Findings - Results show that DOC's argument conflating the sales-based with cost-based PMS designed to inflate dumping margins might not be consistent with WTO Antidumping Agreement Article 2.2 and 2.2.1.1 in which costs shall normally be calculated on the basis of records kept by the exporter, providing generally accepted accounting principles and reasonably reflection of the costs and PMS that exists in the Korean steel product markets. Even if it will be consistent, DOC’s calculated margin by the regression analysis using a 10-year data is a big gap(5 times) compared with 18-year data projection and different countries' data through the same methodology, which is a huge gap of regression coefficient. It means that dumping margin would be very wide range from 7.8% to 38.54% and unstable to calculate. Inflating dumping margin by DOC using regression analysis would not only be inconsistent with WTO rules, but also projection result is unreliable.Originality/value - Literature papers have mainly analyzed WTO law itself . This paper however, would be the first attempt to analyze the DOC’s new way of dumping margin calculation in both manners of law and an empirical methodology perspective at the same time.
Laughing to Keep From [User Input Undefined]: ChatGPT, Jewish Humor, and Cultural Erasure

This article uses the embodied, Jewish identities of its three authors, and the experimentalmethodology of kibbitzing as a form of collective inquiry and self-reflexive praxis in order todemonstrate the limitations of chatbots to produce humorous narratives from an explicitly Jewishepistemology. By contrasting the affordances of large language models (LLMs) and theirassociated chatbots with the context-based logics of Jewish joke craft and storytelling, thearticle goes on to demonstrate the risk of cultural erasure that is posed by the positivist,denotative meanings associated with ChatGPT’s attempts at producing jokes for, or about,Jews.
Individual oath-swearing and lying under peer pressure

Previous studies have shown that an oath can reduce lying at an individual level. Can oaths reduce lying in groups, a context where the prevalence of lying is typically higher? Results from a lab experiment reveal that the impact of an oath on lying in a group context depends on the incentive structure. Oath reduces lying only when payoffs are independent. Evidence supports the notion that payoff interdependence creates pressure on individuals to conform to the group, crowding out the impact of oath on honest reporting. An implication is that to be effective in an organizational context, an oath intervention must be designed in strict connection with the incentive structures.
Hispanic Serving Artificial Intelligence: Do Hispanic Serving Institutions Use Chatbots and Can They Speak Spanish?

Although many scholars have evaluated how Hispanic-Serving Institutions (HSIs) serve and can better serve Latinx students and their communities, scant research has integrated artificial intelligence (AI) technology within this evaluation of diversity and servingness. As a result, this study analyzes all 558 Hispanic-Serving Institution (.edu) websites and interacts with embedded AI chatbots to evaluate whether HSIs utilize chatbots and if these chatbots are engineered or staffed to communicate with Spanish speaking audiences. Findings suggest roughly 20% of HSIs employ AI chatbots, but far fewer use bilingual English/Spanish chatbots (12%). However, HSIs are equally as likely to staff English (20%) live agents as Spanish (21%) live agents when users are elevated from AI. Implications for research, policy, and practice are addressed, as well as this study’s suggestions regarding the role AI and language plays in diversity, equity, and inclusion initiatives within HSIs and servingness of Latinx students and communities.
Towards an enhanced semantic map for imperatives

Abstract This article is a contribution to the semantic map of imperatives, prompted by an implicational map proposed earlier in the literature. The tentative semantic map proposed here abstracts away from differences in use that can be attributed to pragmatics, like distinctions between commands, requests, suggestions etc. The functional differentiation resulting from person and number value, which determines a different character of the directive speech act, is adopted as the basis for the characterization of polysemy in imperatives, and taken as the nucleus of the semantic map, which is then expanded by taking into account non-directive meanings, which are also differentiated with regard to the person value from which they develop.
Responsibly Buying Artificial Intelligence: A ‘Regulatory Hallucination’

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A study of text-theoretical approach to S-box construction with image encryption applications

Data protection is regarded as one of the biggest issues facing companies that have been using public data for a long time. Numerous encryption techniques have been used to address these issues and safeguard data from any malicious attempts and assaults. A substitution box (S-box) is the basic component for modern block ciphers, which helps to ensure robust security of plain data while encryption and permits its lawful decipherment. The goal of this paper is to recommend an effective, original, and straightforward technique for the creation of robust S-boxes. A sample S-box is generated in the proposed work using the word "UNITY" but other words can also be used to generate many powerful S-boxes. The ASCII code is used to translate the word "UNITY" into binary form, after which a distinct matrix is constructed for each character of the word. In the next phase, a linear fractional transformation is constructed using these matrices, which is then utilized to generate the S-box. The constructed S-box was then evaluated against typical security standards to support its high cryptographic authority. The generated S-box's statistical and algebraic resilience is demonstrated by its very low linear probability and differential probability scores of 0.125 and 0.039, respectively, and a high non-linearity score of 111.5. To evaluate the effectiveness of the image encryption scheme, digital images are encoded using the created S-box. The performance and comparative research demonstrate that the suggested S-box is a real candidate for applications in the field of image encryption and has a stronger performance base.
A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints

This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For intra-regional path planning, we present an enhanced method for acquiring reciprocating flight paths to ensure complete coverage of convex polygonal regions with shorter flight paths when a UAV is equipped with sensors featuring circular sampling ranges. An additional comparison was made for spiral flight paths, and second-order Bezier curves were employed to optimize both sets of paths. This optimization not only reduced the path length but also enhanced the ability to counteract inherent drone jitter. Additionally, we propose a variable neighborhood descent algorithm based on K-nearest neighbors to solve the inter-regional access order path-planning problem with priority. We establish parameters for measuring distance and evaluating the priority order of UAV flight paths. Simulation and experiment results demonstrate that the proposed algorithm can effectively assist UAVs in performing path-planning tasks with priority constraints, enabling faster information collection in important areas and facilitating quick exploration of three-dimensional characteristics in unknown disaster areas by rescue workers. This algorithm significantly enhances the safety of rescue workers and optimizes crucial rescue times in key areas.
Pemanfaatan Google Form Sebagai Media Pembelajaran Bagi Guru SD Di Masa Pandemi Covid-19

Google Form was one of Google's services primarily used in educational settings. One of its functions is to conduct surveys. Google Form can make learning neater. Many schools studied and used Google Forms as an alternative learning media during the Covid-19 pandemic. Unfortunately, it was assumed that Google Form was only operated in the survey or research activity even though it had much potential to be explored. Therefore, this community service activity focuses on assisting teachers in using the Google Form. The method in this activity is to provide an introduction and socialization about using Google Forms for teachers. This activity's target was teachers at State Elementary School of Panancangan 2. Through this activity, it is hoped that teachers at State Elementary School of Panancangan 2 can find out the benefits and use of Google forms. This community service aims to get an idea of the extent to which teachers in schools use Google Forms and help them develop the use of Google Forms in learning. The results of this activity are that teachers could use Google Forms, and students have the facilities and infrastructure to carry out online education. After a good understanding of the potential of Google Form, the teacher was expected to keep progressing delivery to other platforms to help them conduct online learning.
Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees

Several structure-learning algorithms for staged trees, asymmetric extensions of Bayesian networks, have been proposed. However, these do not scale efficiently as the number of variables considered increases, a priori restricting the set of models, or they do not find comparable models to existing methods. Here, we define an alternative algorithm based on a totally-ordered hyperstage. We demonstrate how it can be used to obtain a quadratically-scaling structural learning algorithm for staged trees that restricts the model space a posteriori. Through comparative analysis, we show that through the ordering provided by the mean posterior distributions, we can outperform existing methods in computational time whilst providing comparable model scores. This method also enables us to learn more complex relationships than existing model selection techniques by expanding the model space. We illustrate how this can embellish inferences in a real study.
PiP-X: Online feedback motion planning/replanning in dynamic environments using invariant funnels

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as “ funnels”. We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory; and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility; helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.
Community-Enhanced Contrastive Learning for Graph Collaborative Filtering

Graph collaborative filtering can efficiently find the hidden interests of users for recommender systems in recent years. This method can learn complex interactions between nodes in the graph, identify user preferences, and provide satisfactory recommendations. However, recommender systems face the challenge of data sparsity. To address this, recent studies have utilized contrastive learning to make use of unlabeled data structures. However, the existing positive and negative example sampling methods are not reasonable. Random-based or data augmentation-based sampling cannot make use of useful latent information. Clustering-based sampling methods ignore the semantics of node features and the relationship between global and local information. To utilize the latent structures in the data, we introduce a novel Community-Enhanced Contrastive Learning method to help the recommendation main task called CECL which uses a community detection algorithm to sample examples with semantic and global information, using both known and hidden community connections in the bipartite interaction graph. Extensive experiments are conducted on two well-known datasets, the results of which show a 12% and 8% performance improvement compared to that of the existing baseline methods.
Advancements in Encryption Techniques for Secure Data Communication

This paper explores the evolving landscape of encryption techniques for secure data communication in the modern digital age. With the proliferation of digital information and the increasing threats to data privacy and security, encryption has become an essential component of information protection. This paper reviews the fundamentals of encryption, discusses recent advancements in encryption algorithms, and examines their applications in various domains. We also discuss the challenges and prospects of encryption technologies