Reviewerly/exHarmony huggingface dataset:
Enhancing Cloud Communication Security: A Blockchain-Powered Framework with Attribute-Aware Encryption

The global production of information continuously increases in quantity and variety. However, the tools and technologies developed to handle such large volumes of data have not adequately met the security and privacy requirements. Existing cloud security systems, often managed by a trusted third party, are susceptible to various security risks. To address these challenges and ensure the protection of personal information, blockchain technology emerges as a crucial solution with substantial potential. This research uses the blockchain-powered attribute-aware encryption method to establish a real-time secure communication approach over the cloud. By employing attribute-based encryption technology, data owners can implement fine-grained search permissions for data users. The proposed solution incorporates accessible encryption technology to enable secure access to encrypted data and facilitate keyword searches on the blockchain. This study provides a functional comparison of recently developed attribute-based encryption algorithms. The access control strategy comprises two access tree types and a linear secret-sharing system, serving as the main components. The elliptic curve’s base field was set to 512b, and the bilinear pairing parameter type used was Type-A. This approach involves storing keywords on a remote server and encrypting them using attribute-based encryption. Furthermore, the encrypted data blockchain and the corresponding ciphertext are stored in the blockchain. Numerical experiments were conducted to evaluate the system’s key generation, trapdoor building, and keyword retrieval capabilities.
Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.
Estimating the master stability function from the time series of one oscillator via reservoir computing

The master stability function (MSF) yields the stability of the globally synchronized state of a network of identical oscillators in terms of the eigenvalues of the adjacency matrix. In order to compute the MSF, one must have an accurate model of an uncoupled oscillator, but often such a model does not exist. We present a reservoir computing technique for estimating the MSF given only the time series of a single, uncoupled oscillator. We demonstrate the generality of our technique by considering a variety of coupling configurations of networks consisting of Lorenz oscillators or H\'enon maps.
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization

The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have discussed the influence of the temporal redundancy in the patches with stationary objects and background and usually use all the information in the adjacent frames without any discrimination. In this paper, we observe that the temporal redundancy will bring adverse effect to the information propagation, which limits the performance of the most existing VSR methods and causes the severe generalization problem. Motivated by this observation, we aim to improve existing VSR algorithms by handling the temporal redundancy patches in an optimized manner. We develop two simple yet effective plug-and-play methods to improve the performance and the generalization ability of existing local and non-local propagation-based VSR algorithms on widely-used public videos. For more comprehensive evaluating the robustness and performance of existing VSR algorithms, we also collect a new dataset which contains a variety of public videos as testing set. Extensive evaluations show that the proposed methods can significantly improve the performance and the generalization ability of existing VSR methods on the collected videos from wild scenarios while maintain their performance on existing commonly used datasets.
Quantized minimum error entropy with fiducial points for robust regression

Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data.
SUR-Driven Video Coding Rate Control for Jointly Optimizing Perceptual Quality and Buffer Control

Rate control plays an important role in video coding and has attracted lots of attention from researchers. However, the problems of human visual experience and buffer stability still remain. For scenes with drastic motions, parts of distortions can be masked due to the limitation of the Human Visual System (HVS), while buffers tend to suffer more overflow and underflow cases from the fluctuating bits. In this paper, we propose a novel joint rate control scheme, which is composed of the proposed SUR-based perception modeling and the proposed SUR-based Perception-Buffer Rate Control (PBRC), for HEVC to maximize human visual perception quality while preventing the underflow and overflow of buffers. First of all, to effectively model human visual quality, we introduce the perception-related Satisfied-User-Ratio (SUR) metric into the rate control process. Secondly, a time-efficient video quality prediction method called Fast Visual Multimethod Assessment Fusion (VMAF) Quality Prediction (FVQP) is designed for the generation of SUR curves within an affordable computational complexity. Thirdly, a dual-objective optimization framework is established. By jointly conducting perception modeling and PBRC, we can flexibly adjust the optimization priority between human visual quality and buffer stability, and thus the quality of achieved reconstructed videos can be effectively improved because of the decrease in frame skipping. Experimental results demonstrate that the proposed joint rate control scheme improves the human visual experience when considering frame skipping and more effectively stabilizes buffer stability than existing methods.
Explainability and transparency in the realm of digital humanities: toward a historian XAI

Abstract The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.
Shaping corporate character via chatbot social conversation: Impact on organization-public relational outcomes

As an empirical effort to explore the public relations potential of AI-enabled technologies such as chatbots, this study examined how chatbot social conversation—a communication strategy that encompasses both social presence and conversational human voice and is conceptualized in consideration of chatbots’ mediated communication environment—can contribute to organization-public relational outcomes (i.e., OPRs). To understand how this process works, we incorporate the personification approach to organizations, and also investigate the impact of chatbot social conversation in shaping public perceptions of corporate character (i.e., agreeableness, enterprise, competence, and ruthlessness). A survey of 778 Facebook users in the United States was conducted, where participants were randomly assigned to have a guided conversation with an AI-enabled social chatbot utilized by real companies on Facebook. Results confirm that chatbot social conversation can serve as a strong antecedent of the corporate character of the organization that deployed it and OPRs. Perceived corporate character also directly influences the quality of OPRs, demonstrating the potential of using AI-enabled social chatbots for public relations purposes.
CALYPSO: LLMs as Dungeon Master's Assistants

The role of a Dungeon Master, or DM, in the game Dungeons &amp; Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of these tasks while maintaining consistency within the narrative and story world is no small feat of human cognition, making the task tiring and unapproachable to new players. Large language models (LLMs) like GPT-3 and ChatGPT have shown remarkable abilities to generate coherent natural language text. In this paper, we conduct a formative evaluation with DMs to establish the use cases of LLMs in D&amp;D and tabletop gaming generally. We introduce CALYPSO, a system of LLM-powered interfaces that support DMs with information and inspiration specific to their own scenario. CALYPSO distills game context into bite-sized prose and helps brainstorm ideas without distracting the DM from the game. When given access to CALYPSO, DMs reported that it generated high-fidelity text suitable for direct presentation to players, and low-fidelity ideas that the DM could develop further while maintaining their creative agency. We see CALYPSO as exemplifying a paradigm of AI-augmented tools that provide synchronous creative assistance within established game worlds, and tabletop gaming more broadly.
Case studies about smart contracts in healthcare

The Internet of Things (IoT) such as devices and sensors are a fast growth reality which our bureaucratical and archaic institutional system is not yet ready to embrace its functionalities. In the health system, many developments are made, and smart devices are the key to preventing, studying, investigating, and solving a lot of diseases and improving our health system. But along with this, innovation is necessary for the hospitals, for example, to have a proper system that provides storage of health data information and respects the General Data Protection Regulation (GDPR) with the use of smart contracts that secure the integrity and disclosure of the patient's data, since the majority of hospitals still use paper, physical records to store data. In this study, we will briefly analyse and explain three different suggested methods to deal with the challenges that Internet of Medical Things (IoMT) encounters. We will not choose which one is the best because of the different features and the countries they are proposed but will emphasize the benefits and challenges which one has.
A Systematic Review of “The Performance of Knowledge Organizations and Modelling Human Action”

The purpose of this systematic review is to examine the performance of knowledge organizations and the role of modelling human action in improving organizational performance. Using the PRISMA scheme, a comprehensive search of relevant databases and sources was conducted to identify relevant articles. A total of 15 articles were selected based on predefined criteria, including relevance to the research question and quality assessment. The articles were analyzed and synthesized to identify key findings and trends. The findings suggest that knowledge organizations that focus on knowledge management capabilities and human resource configurations tend to perform better than those that do not. Additionally, modelling human action, such as through simulation and decision-making tools, can help organizations improve their performance by providing insights into the effects of different actions and strategies. However, the effectiveness of such models depends on their accuracy and the ability of organizations to use them effectively. Overall, this systematic review provides insights into the factors that contribute to the performance of knowledge organizations and the potential benefits of modelling human action. Future research can build on these findings by exploring additional factors that may influence organizational performance and by developing more advanced models for predicting and improving organizational outcomes.
YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.
Learnable Graph Convolutional Network With Semisupervised Graph Information Bottleneck

Graph convolutional network (GCN) has gained widespread attention in semisupervised classification tasks. Recent studies show that GCN-based methods have achieved decent performance in numerous fields. However, most of the existing methods generally adopted a fixed graph that cannot dynamically capture both local and global relationships. This is because the hidden and important relationships may not be directed exhibited in the fixed structure, causing the degraded performance of semisupervised classification tasks. Moreover, the missing and noisy data yielded by the fixed graph may result in wrong connections, thereby disturbing the representation learning process. To cope with these issues, this article proposes a learnable GCN-based framework, aiming to obtain the optimal graph structures by jointly integrating graph learning and feature propagation in a unified network. Besides, to capture the optimal graph representations, this article designs dual-GCN-based meta-channels to simultaneously explore local and global relations during the training process. To minimize the interference of the noisy data, a semisupervised graph information bottleneck (SGIB) is introduced to conduct the graph structural learning (GSL) for acquiring the minimal sufficient representations. Concretely, SGIB aims to maximize the mutual information of both the same and different meta-channels by designing the constraints between them, thereby improving the node classification performance in the downstream tasks. Extensive experimental results on real-world datasets demonstrate the robustness of the proposed model, which outperforms state-of-the-art methods with fixed-structure graphs.
An Investigation of the Relationship between Organizational Justice, Job Autonomy, Workplace Trust and Psychological Ownership: The Role of Ethical Leadership

This study investigates the effect of â€œorganizational justice, job autonomy, organizational trust, and ethical leadershipâ€ on psychological ownership. It also examines the moderating role of ethical leadership on psychological ownership. The study used a survey method to obtain the data from the respondents of the banking sector. Our results support six direct and one moderating relationship. The study suggests that organizational, distributive, interpersonal, and informal justice do not affect psychological ownership. At the same time, we found that job autonomy, workplace trust, trust in co-workers, trust in organizations, trust in immediate manager, and ethical leadership affects psychological ownership. The study also found that ethical leadership moderates organizational justice and psychological ownership. But psychological ownership does not moderate (1) job autonomy and psychological leadership, and (2) workplace trust and psychological ownership.
Language models and psychological sciences

Large language models (LLMs) are demonstrating impressive performance on many reasoning and problem-solving tasks from cognitive psychology. When tested, their accuracy is often on par with average neurotypical adults, challenging long-standing critiques of associative models. Here we analyse recent findings at the intersection of LLMs and cognitive science. Here we discuss how modern LLMs resurrect associationist principles, with abilities like long-distance associations enabling complex reasoning. While limitations remain in areas like causal cognition and planning, phenomena like emergence suggest room for growth. Providing examples and increasing the dimensions of the network are methods that further improve LLM abilities, mirroring facilitation effects in human cognition. Analysis of LLMs errors provides insight into human cognitive biases. Overall, we argue LLMs represent a promising development for cognitive modelling, enabling new explorations of the mechanisms underlying intelligence and reasoning from an associationist point of view. Carefully evaluating LLMs with the tools of cognitive psychology will further understand the building blocks of the human mind.
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML libraries. In this demo paper, we present ExeKGLib, a Python library that allows users with coding skills and minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs to improve the transparency and reusability of the built ML workflows, and to ensure that they are executable. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show ExeKGLib ’s benefits.
A Note on the Smallest Eigenvalue of the Empirical Covariance of Causal Gaussian Processes

We present a simple proof for bounding the smallest eigenvalue of the empirical covariance in a causal Gaussian process. Along the way, we establish a one-sided tail inequality for Gaussian quadratic forms using a causal decomposition. Our proof only uses elementary facts about the Gaussian distribution and the union bound. We conclude with an example in which we provide a performance guarantee for least squares identification of a vector autoregression.
What is the Trade-Off Between CO2 Emission and Video-Conferencing QoE?

It is a natural thing that users of multimedia services want to have the highest possible Quality of Experience (QoE), when using said services. This is especially so in contexts such as video-conferencing and video streaming services, which are nowadays a large part of many users' daily life, be it work-related Zoom calls, or relaxing while watching Netflix. This has implications in terms of the energy consumed for the provision of those services (think of the cloud services involved, the networks, and the users' own devices), and therefore it also has an impact on the resulting CO 2 emissions. In this column, we look at the potential trade-offs involved between varying levels of QoE (which for video services is strongly correlated with the bit rates used), and the resulting CO 2 emissions. We also look at other factors that should be taken into account when making decisions based on these calculations, in order to provide a more holistic view of the environmental impact of these types of services, and whether they do have a significant impact.