Rapid advancement in generative AI and large language models (LLMs) has enabled the creation of highly realistic and context-aware digital content. Multimodal systems such as ChatGPT with DALL-E integration and diffusion models like Stable Diffusion can now generate images that are often indistinguishable from human-created media, posing significant challenges for content authenticity and provenance. Ensuring the integrity and origin of digital data is critical for preserving trust, accountability, and legal compliance in digital ecosystems. This paper presents an embedding-based framework for detecting AI-generated images using vector similarity analysis. The proposed approach is based on the hypothesis that AI-generated images exhibit closer embedding proximity to other synthetic content, whereas human-created images cluster within distinct semantic regions. We validate this hypothesis by developing a prototype system that processes a diverse dataset of AI- and human-generated images using five benchmark embedding models. Furthermore we investigated whether perturbation with image, such as blurring or putting patches, would allow AI-generated images to confuse the system. Experimental results confirm the robustness of the method, showing that image perturbations have minimal impact on the embedding space, with altered images retaining high similarity to their originals.

Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.

Peer review is an essential component of the evaluation and dissemination of new scientific knowledge. The peer review process can be viewed as a decision support framework relying on scholarly review systems, where decision-makers (editors) solicit input from experts (reviewers) to make editorial decisions on submitted manuscripts. Unfortunately, the challenges editors face in securing sufficient reviewers are well-documented, leading to prolonged review times and potentially diminished review quality. We explore and validate this trend through a literature review and interviews with scholars. We then employ a design science research methodology to design, develop, and evaluate potential incentive mechanisms to reverse that trend. In addition to proposing formal design principles that such mechanisms should follow, we suggest a concrete blockchain-based token system that enables editors to offer review incentives while enabling reviewers to flexibly utilize these incentives to meet their needs. We also explain how different types of tokens can be connected to practical submission and reward policies that journals may adopt. Our cost analysis, along with a survey-based field study and qualitative interviews with academics, highlight the effectiveness of our solution. Finally, we propose a formal design theory framework that designers of peer review systems can follow to create meaningful incentives to attract reviewers.

Peer review is an integral part of academic publication and is necessary to maintain high standards and novelty of published research. Despite its importance, peer reviewers are rarely provided incentives, leading to journals having difficulty finding reviewers inclined to accept invitations and submit reviews on time. This paper proposes a Blockchain-based Anonymous Reviewer Incentive Token (BARIT) to incentivize peer reviewers. BARIT introduces flexible incentive schemes to provide both recognition and tangible benefits for the reviewers' contribution while preserving the anonymity of reviewers. Using blockchain technology to record reward tokens ensures their permanence and acceptance across different publishers. Incentive models are designed to encourage the involvement of researchers as reviewers, reduce reviewer refusal rates, and prompt the timely submission of review reports.

Recent regulatory changes have enabled NCAA student-athletes to profit from their name, image, and likeness (NIL), departing from previous policies requiring those athletes to maintain their amateur status. However, despite the changes, it is unlikely that all the approximately 500,000 NCAA student-athletes will profit from NIL contracts. Within this context, we study how to design a fair and inclusive solution that may help all student-athletes secure NIL financial resources. Following a design science approach, we define design requirements after interviewing student-athletes. Subsequently, we derive three design principles: inclusiveness, fairness, and transparency. Thereafter, we suggest a blockchain-based artifact that satisfies all design principles. Our idea lies in designing collectibles as non-fungible tokens (NFTs) that pay different royalties whenever a transaction (purchase or exchange) happens in different markets (primary or secondary). Finally, we evaluate our solution by discussing its features with current student-athletes.

In this paper, we analyze the value of smart contracts and blockchains as an alternative to traditional contractual obligations. In particular, we start by exploring some of the advantages of these technologies, specifically the immutability of blockchains and automated contract remittance. We also discuss two critical shortcomings of decentralized smart contracts, namely regulatory uncertainty and a lack of confidential execution. With these issues in mind, we next explore how American legislators have begun to address smart contracts and blockchains. Though quite limited, there have been a few provisions clarifying the status of these technologies. We break down some of the language expressed in these bills so as to understand the current legal status of smart contracts and blockchains. Given this foundation, we consider the next steps that should be taken as smart contracts mature. This pertains to both the continued improvement of the underlying technology as well as the progress taken by regulators. Finally, assuming a futuristic scenario where there are no technological or regulatory barriers to smart contract adoption, we discuss how the process of contract remittance can be expedited in a world fully committed to the use of smart contracts.

We propose a cooperative coevolutionary genetic algorithm for learning Bayesian network structures from fully observable data sets. Since this problem can be decomposed into two dependent subproblems, that is to find an ordering of the nodes and an optimal connectivity matrix, our algorithm uses two subpopulations, each one representing a subtask. We describe the empirical results obtained with simulations of the Alarm and Insurance networks. We show that our algorithm outperforms the deterministic algorithm K2.

In soccer, scoring goals is a fundamental objective which depends on many conditions and constraints. Considering the RoboCup soccer 2D-simulator, this paper presents a data mining-based decision system to identify the best time and direction to kick the ball towards the goal to maximize the overall chances of scoring during a simulated soccer match. Following the CRISP-DM methodology, data for modeling were extracted from matches of major international tournaments (10691 kicks), knowledge about soccer was embedded via transformation of variables and a Multilayer Perceptron was used to estimate the scoring chance. Experimental performance assessment to compare this approach against previous LDA-based approach was conducted from 100 matches. Several statistical metrics were used to analyze the performance of the system and the results showed an increase of 7.7% in the number of kicks, producing an overall increase of 78% in the number of goals scored.