Reviewerly/exHarmony huggingface dataset:
Fantastic MASs and Where to Find Them: First Results and Lesson Learned

Nowadays, the Multiagent Systems research community is facing new challenges related to engineering the overall process of software development, tailoring it to the specific needs of the community, and integrating SE techniques into many studies in the MAS area. More and more frequently, researchers need already developed MASs for validating their new proposals. Often, they spend time in looking for the code of existing tools to compare with the state of the art. Unfortunately, accessing this kind of resources, which are the starting point for many SE activities, is not always easy. In this paper, we present the first outcome of the initiative “Fantastic MAS and where to find them”, launched in June 2022, where we asked the agent community to contribute in the creation of a repository to facilitate the sharing of the already existing tools (agent development frameworks, libraries, add ons of already existing platforms) and MASs with their code. The “Fantastic MAS” goals are to i) improve the sharing and reusing of research results ii) support the SE activities in our research community, iii) help making a step towards the Open Science movement, which has been already widely adopted in other research communities. Besides providing an overview of the submissions we got, we discuss the open problems that emerged in these eight months of the initiative, so that to stimulate the discussion in the community.
Dynamics of Causal Dependencies in Multi-agent Settings

In this paper we discuss how causal models can be used for modeling multi-agent interaction in complex organizational settings, where agents’ decisions may depend on other agents’ decisions as well as the environment. We demonstrate how to reason about the dynamics of such models using concurrent game structures where agents can change the organisational setting and thereby their decision dependencies. In such concurrent game structure, agents can choose to modify their reactions on other agents’ decisions and on the environment by intervening on their part of a causal model. We propose a generalized notion of interventions in causal models that allow us to model and reason about the dynamics of agents’ dependencies in a multi-agent system. Finally, we discuss how to model uncertainty and reason about agents’ responsibility concerning their dependencies and thereby their choices.
COVID-19 Contact Tracing Mobile Applications in New York State (NYS): an Empirical Study

In this paper, we surveyed to examine individual use of mobile apps for COVID-19 contact tracing in New York State (NYS). Additionally, we investigated the impact of privacy and security concerns on adopting these apps and whether there are differences in adoption rates among different racial and age groups. We adopted the Antecedent-Privacy Concerns-Outcomes (APCO) framework to identify factors affecting the individual-level adoption of these apps. Results indicate no significant correlation between race and the perceived usefulness of the NYS COVID-Alert app. Only certain demographic variables affected privacy concerns, trust, or behavioral intentions related to contact tracing apps. Specifically, the more influential factors were pandemic experiences, political affiliation, education, and income. Political affiliation and education were more influential demographic factors regarding the adoption and perceived usefulness of contact tracing applications. Interestingly, respondents with the highest and lowest income were less likely to be concerned about the privacy and security concerns of the COVID-19 mobile apps. However, middle-income respondents were more likely to be concerned about privacy and security. Our findings shed light on the challenges and opportunities associated with contact-tracing mobile apps in the context of the COVID-19 pandemic and provide insights into how these apps can be optimized to improve their effectiveness and reach.
The Entity-Operation Model for Practical Multi-entity Deployment

In the world of multi-agent system (MAS) frameworks, developers are many times forced into a fixed and reduced array of abstractions, with limited options in expressive modeling of all the components of a MAS. For instance, in JADE, the most popular agent framework, developers are limited to using agents as sole abstraction for all elements of the MAS. These limitations hinder interoperability, the deployment of open, heterogeneous systems, and the use of agents in complex scenarios involving a great variety of elements such as physical devices, context managers, services, and communication infrastructures. We introduce the entity-operation model for multi-agent systems, as an approach to integrate all elements in the MAS deployment as first-class entities in the MAS model, to support heterogeneity and flexibility in the implementation, and to achieve context-aware access control to the functionalities offered by entities. We present a formalization of the model, together with mechanisms for authorizing operations and for routing operation calls in the MAS. We discuss the entity-operation model in relation to other existing MAS frameworks, and we give insight into implementation challenges which arose when integrating the model with the Flash-mas framework.
An Analysis of Philosophy and Morality in Wearable Human-Computer Interaction

This article aims to delve into the philosophical and ethical issues of intelligent wearable human-computer interaction. Specifically, this article will explore four issues: privacy protection, data security, personality rights, and social and cultural issues. Based on these four issues, the main research content of this article is as follows: Firstly, the development status and application scenarios of intelligent wearable technology will be introduced, including the evolution and innovation of wearable technology. Secondly, this article will explore the potential ethical and moral issues of intelligent wearable technology from the perspectives of personal privacy and security, social relationships and interactions, and human cognition and emotions. Next, this article will propose ethical design principles for intelligent wearable human-computer interaction based on the design and application practices of wearable devices. These principles include design based on human dignity, transparent and controllable design, design balancing personal interests and public interests, and design respecting cultural differences. Finally, this article will look forward to the future development and prospects of wearable devices from the perspectives of technological development trends and ethical issues and propose directions for further exploration and research. Intelligent wearable human-computer interaction technology is an emerging form of interaction with broad application prospects, but it also faces a series of philosophical and ethical issues. This article systematically analyzes the philosophical and ethical issues in intelligent wearable human-computer interaction and proposes corresponding ethical design principles, providing some beneficial thinking and suggestions for the future development of wearable devices.
The search for plausible economic mineral deposits in the central parts of Tanzania; insight from stream sediment geochemistry, multivariate statistics and geostatistics

Exploration success relies heavily on the data obtained, but, significantly on the type of analytical methods deployed and the interpretation reached. A poorly analyzed data may obscure the true reflectivity of the data, and thus, compromised the decision made. A combined data processing approach of descriptive statistics, enrichment-depletion data normalization, geospatial elemental distribution, and stacked overlayed comparison of elements have been used in this study. The prime purpose was to demonstrate potential elemental anomalies, and predict areas of higher degree of confidence for subsequent exploration and mineral resource evaluation. One-hundred and sixty-six stream sediment samples from the Dodoma Region of the Tanzania Craton have been examined; to reveal potential elements or mineral commodity that warrant further exploration. Forty-three elements of target were examined, as this craton is globally known for its rich earth mineral commodity. Our result indicates an enrichment of transition metals (TMs) (Cu, Ni, Co, Cr, Mn and Zn), High Field Strength Elements (Y, Th, U, Zr, Nb, Hf, Ta and Pb), Large Ion Lithophile Elements (Ba and Rb) and Rare-Earth Elements (La and Ce), Platinum Group Element (Pd and Pt) and other metals (Au, As, Bi, W, Mo and Li).Obtained result point to a likely poly-metallic sources and processes; as the underlain geology is marked largely by pegmatite and migmatites, and moderate proportion of fine clastic sedimentary rocks, and minor volcanic rocks mostly to the northern domain. Theoretically, the Large Ion Lithophile Elements (LILEs), Rare-Earth Elements (REEs) and Platinum Group Elements (PGEs) are associated with felsic rocks or variable stages of granitization. Although, the TMs are often associated with mafic-ultramafic rocks, the linkage of such metals with organic-rich shales been reported elsewhere. These rocks may equally contribute to the occurrence of other metals as stated in this paper. Its intriguing to note a strong positive correlation of Li with TMs, possibility of Li control by mafic minerals in pegmatite bodies. This work proposes a polymetallic enrichment controlled by the area geology. To suggest an alluvial mining potential of the above elements in the area, resource evaluation is a requirement. The geospatial maps reveal areas worth focusing for subsequent exploration. The adopted geostatistical methods and other approach utilized in this research are effective, indicative of handling bulk exploration data for decision and subsequent exploration.
Applications of Large Language Models (LLMs) in Business Analytics – Exemplary Use Cases in Data Preparation Tasks

The application of data analytics in management has become a crucial success factor for the modern enterprise. To apply analytical models, appropriately prepared data must be available. Preparing this data can be cumbersome, time-consuming, and error prone. In the current era of Artificial Intelligence (AI), Large Language Models (LLMs) like OpenAI’s ChatGPT offer a promising pathway to support these tasks. However, their potential in enhancing the efficiency and effectiveness of data preparation remains largely unexplored. In this paper, we apply and evaluate the performance of OpenAI’s ChatGPT for data preparation. Based on four real-life use cases we show, that ChatGPT demonstrates high performance in the context of translating text, assigning products to given categories, classifying sentiments of customer reviews, and extracting information from textual requests. The results of our paper indicate that ChatGPT can be a valuable tool for many companies, helping with daily data preparation tasks. We demonstrated that ChatGPT can handle different languages and formats of data and have shown that LLMs can perform multiple tasks with minimal or no fine-tuning, leveraging their pre-trained knowledge and generalization abilities. However, we have also observed that ChatGPT may sometimes produce incorrect outputs, especially when input data is noisy or ambiguous. We have also noticed that ChatGPT may struggle with tasks that require more complex reasoning or domain-specific knowledge. Future research should focus on improving the robustness and reliability of LLMs for data preparation tasks, as well as on developing more efficient and user-friendly ways to deploy and interact with them.
Explainable AI-Based Interface System for Weather Forecasting Model

Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.
How Can Natural Language Processing and Generative AI Address Grand Challenges of Quantitative User Personas?

Human-computer interaction (HCI) and natural language processing (NLP) can engage in mutually beneficial collaboration. This article summarizes previous literature to identify grand challenges for the application of NLP in quantitative user personas (QUPs), which exemplifies such collaboration. Grand challenges provide a collaborative starting point for researchers working at the intersection of NLP and QUPs, towards improved user experiences. NLP research could also benefit from focusing on generating user personas by introducing new solutions to specific NLP tasks, such as classification and generation. We also discuss the novel opportunities introduced by Generative AI to address the grand challenges, offering illustrative examples.
Load Balancing in Distributed Multi-Agent Path Finder (DMAPF)

The Multi-Agent Path Finding (MAPF) is a problem of finding a plan for agents to reach their desired locations without collisions. Distributed Multi-Agent Path Finder (DMAPF) solves the MAPF problem by decomposing a given MAPF problem instance into smaller subproblems and solve them in parallel. DMAPF works in rounds. Between two consecutive rounds, agents may migrate between two adjacent subproblems following their abstract plans, which are pre-computed, until all of them reach the areas that contain their desired locations. Previous works on DMAPF compute an abstract plan for each agent without the knowledge of other agents’ abstract plans, resulting in high congestion in some areas, especially those that act as corridors. The congestion negatively impacts the runtime of DMAPF and prevents it from being able to solve dense MAPF problems. In this paper, we (i) investigate the use of Uniform-Cost Search to mitigate the congestion. Additionally, we explore the use of several other techniques including (ii) using timeout estimation to preemptively stop solving and relax a subproblem when it is likely to get stuck; (iii) allowing a solving process to manage multiple subproblems – aimed to increase concurrency; and (iv) integrating with MAPF solvers from the Conflict-Based Search family. Experimental results show that our new system is several times faster than the previous ones; can solve larger and denser problems that were unsolvable before; and has better runtime than PBS and EECBS, which are state-of-the-art centralized suboptimal MAPF solvers, in problems with a large number of agents.
User Perception and Evaluation of a Deep Learning Framework for Audience Engagement Analysis in Mass Events

As video volume on the web grows exponentially over time, video streaming platforms have been enhanced with Artificial Intelligence (AI) to analyze their content. This paper proposes a novel evaluation methodology for video events streaming platforms that use AI for content analysis and helps them measure user experience, customer satisfaction, and AI acceptance. Models like System Usability Scale (SUS), Technology Acceptance Model (TAM), European Customer Satisfaction Index (ECSI), and Net Promoter Score (NPS) have been fused, creating a novel methodology for the evaluation. To this end, correlations between items, model scores, and statistic metrics were utilized. Experimental results in a real AI-enabled video streaming platform verified the potential of this evaluation methodology, with insightful conclusions drawn from it. The study helps in similar evaluation tasks and provides crucial information to software and system designers who want to know where to emphasize and how to evaluate similar systems. Results provide rich details on the platform’s user experience, demonstrating how important it is to enhance online video streaming platforms with AI analysis features.
Aristotle's Phronesis as a Philosophical Foundation in Designing the Algorithmic Motivator-Driven Insulating Model (AMOI)

Technology advancements have led to an increase in the use of algorithmic management (AM) to oversee workforces in big businesses. A number of AM tactics, such as real-time monitoring of employee output and performance, allocating employees to activities that are appropriate for them, and anticipating staff turnover, all strive to improve work processes. The program has drawn criticism for its lack of accountability between employers and employees, as well as for its prejudices and prejudice. The Algorithmic Motivation-driven Insulation Model (AMOI) has been developed to close this gap. In order to improve workplace relationships, give employees’ mental health and wellness a high priority, and create fair and equitable work environments, this model uses a chatbot-based emotion-aware deep learning framework. In order to achieve eudaimonia, our proposed AI architecture was built on employing a scientific methodology and phronesis-inspired concepts. This strategy intends to develop fair and responsible digital technology for workers to be employed in future AM solutions by elevating human values and improving employee engagement and performance.
Link Prediction with Simple Path-Aware Graph Neural Networks

Graph Neural Networks (GNNs) are expert in node classification and graph classification, but are relatively weak on link prediction due to their limited expressiveness. Recently, two popular GNN variants, namely higher-order GNNs and labeling trick are proposed to address the limitations of GNNs. Compared with plain GNNs, these variants provably capture inter-node patterns such as common neighbors which facilitates link prediction. However, we notice that these methods actually suffer from two critical problems. First, their algorithm complexities are impractical for large graphs. Second, we prove that although these methods can identify paths between target nodes, they cannot identify simple paths, which are very fundamental in the field of graph theory. To overcome these deficiencies, we systematically study the common advantages of previous link prediction GNNs and propose a novel GNN framework that summarizes these advantages while remaining simple and efficient. Various experiments show the effectiveness of our method.
Modeling Both Collaborative and Temporal Information for Sequential Recommendation

Sequential recommendation has drawn a lot of attention due to its good performance in recent years. The temporal order of user interactions cannot be ignored in sequential recommendation, and user preferences are constantly changing over time. The application of deep neural network in sequential recommendation has achieved many remarkable results, especially self-attention based methods. However, previous works mainly focused on item-item temporal information of the sequence while ignoring the latent collaborative relations in user-item interactions. Therefore, we propose a new method named Collaborative-Temporal modeling for Sequential Recommendation (CTSR) to learn both collaborative relations and temporal information. The proposed CTSR method introduces a graph convolutional network to learn the user-item collaborative relations while using self-attention to capture item-item temporal information. We apply focal loss to reduce the loss contribution of the easy samples and increase the contribution of the hard samples, to train the model more effectively. We extract a portion of item-item pairs that are most valuable, and then feed these pairs as augmented information into adjacency matrix of the graph neural network. More importantly, it is the first work to encode the relative positions of items into their embeddings in sequential recommendation. The experimental results show that CTSR surpasses previous works on three real-world datasets.
Joint Entity and Relation Extraction for Legal Documents Based on Table Filling

Joint entity and relation extraction for legal documents is an important research task of judicial intelligence informatization, aiming at extracting structured triplets from rich unstructured legal texts. However, the existing methods for joint entity relation extraction in legal judgment documents often lack domain-specific knowledge, and are difficult to effectively solve the problem of entity overlap in legal texts. To address these issues, we propose a joint entity and relation extraction for legal documents method based on table filling. Firstly, we construct a legal dictionary with knowledge characteristics of the judicial domain based on the characteristics of judicial document data and incorporate it into a text encoding representation using a multi-head attention mechanism; Secondly, we transform the joint extraction task into a table-filling problem by constructing a two-dimensional table that can express the relation between word pairs for each relation separately and designing three table-filling strategies to decode the triples under the corresponding relations. The experimental results on the information extraction dataset in "CAIL2021" show that the proposed method has a significant improvement over the existing baseline model and achieves significant results in addressing the complex entity overlap problem in legal texts.
Feasibility Analysis of Solar Tracking Technologies Connected to Renewable Energy Systems

The environmental benefits of photovoltaic (PV) panels have attracted attention worldwide although they still lack the necessary levels of performance and financial viability. To meet these standards, PV tracking technologies are utilized to maximize the efficiency of PV systems. The principal goal of this research work is to perform a techno-economic feasibility analysis and optimal sizing of renewable energy systems integrated into solar tracking technologies for a rural residential user in South Africa. The vertical axis (VC), dual-axis tracker (DA), horizontal axis with monthly (HM), and continuous (HC) adjustments are the tracking options implemented to determine a profitable energy system. The preferred optimal cases based on these scenarios are introduced, and then sensitivity analysis is carried out to estimate the effect of variation of technical, economic, and climatic parameters. The VC-based system offers a financially desirable alternative having net present cost (NPC), levelized energy cost (LCOE), and CO2 emissions of $13.7k, $0.258/kWh, and 2.81.1 kg/year, respectively. Sensitivity analysis indicates that an increase in SOCmin would raise NPC and CO2 emissions. The DA and VC trackers, respectively, show the greatest and lowest increases in NPC due to diesel prices increase. In VC and HM-based cases, PV generation and the renewable fraction are more sensitive to albedo (ground reflectance) variation. Comparing the current optimal results with the previous research in this location demonstrates that NPC and LCOE can be reduced by implementing a proper controlling strategy by $58k and $0.005/kWh, respectively, to meet the same electrical load.