Reviewerly/exHarmony huggingface dataset:
Explainable data stream mining: Why the new models are better

Explainable Machine Learning brings expandability, interpretability, and accountability to Data Mining Algorithms. Existing explanation frameworks focus on explaining the decision process of a single model in a static dataset. However, in data stream mining changes in data distribution over time, called concept drift, may require updating the learning models to reflect the current data environment. It is therefore important to go beyond static models and understand what has changed among the learning models before and after a concept drift. We propose a Data Stream Explanability framework (DSE) that works together with a typical data stream mining framework where support vector machine models are used. DSE aims to help non-expert users understand model dynamics in a concept drifting data stream. DSE visualizes differences between SVM models before and after concept drift, to produce explanations on why the new model fits the data better. A survey was carried out between expert and non-expert users on the effectiveness of the framework. Although results showed non-expert users on average responded with less understanding of the issue compared to expert users, the difference is not statistically significant. This indicates that DSE successfully brings the explanability of model change to non-expert users.
A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.
Application of Perceptual Model-Driven Multimedia Information Retrieval Technology in English Teaching Management Systems

The application of multimedia technology in college English classroom teaching is suitable for sports and mute with illustrations, excellent audio, and video. The application of multimedia in the field of education has become the focus of the current English education reform, and education has entered the industrial age and the information age. This research aims at teaching practice in order to obtain high-quality educational results. And gradually turn multimedia, a new way, into an effective teaching tool in English education. Based on the research on the demand of information perception for English teaching, this article studies the relationship between constant information perception and English teaching demand from the perspective of multimedia information perception. An evolving model of English teaching needs caused by the change of multimedia information perception due to the input of external resources is established. The study found that when the average consumer perception is low, the revenue of enterprises can be rapidly improved by investing external resources.
Braiding drive data processing through rote learning

It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.
Early Perceptions of Teaching and Learning Using Generative AI in Higher Education

This project explored perceptions of ChatGPT in higher education among students and faculty to assess teaching and learning implications of this Generative Artificial Intelligence (Generative AI)–based novel tool. Two theoretical frameworks inspired the project, including Diffusion of Innovation theory (Rogers, 1962) and Technology Acceptance Model (Davis, 1989). An online survey was completed by 380 participants ( N = 380). Participants indicated that they would not use ChatGPT to plagiarize but believed others would. When asked to rate the accuracy of ChatGPT's output, more than half took the incorrect output as correct/somewhat correct or could not tell whether it was correct or incorrect. Results varied based on participant demographics, including age, gender, and occupation. These findings support the need for data literacy. If Generative AI is to be used in higher education to aid in the learning process, it is imperative to continue teaching critical thinking.
Multi-Modal Enhancement Transformer Network for Skeleton-Based Human Interaction Recognition

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods.
Securing DILITHIUM Against Physical Attacks: Performances Analysis

Post-quantum cryptography will be the future standard of cryptography resistant to quantum and classical computers. The migration to quantum-safe products is not without a significant impact on hardware designs and capacities. In addition to mathematical security, the security of implementations regarding physical attacks: side-channel and faults, has to be taken into account. In this paper, we present a performances analysis of a full masked implementation in software of DILITHIUM. We provide a quantitative analysis of the bottlenecks introduced by the countermeasures in different configurations: from full software to implementation using assembly code for Keccak core, and, AES in hardware for seed extension. This work contributes to define the specifications of future hardware capabilities of components providing quantum-safe security services.
Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data

Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in-depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time-consuming and costly but also require systematic and cost-effective approaches.The main objective of this study was to investigate the feasibility of training an end-to-end deep learning (DL) model by directly inputting time-series activity tracking and sleep data obtained from consumer-grade wrist-worn activity trackers to identify comorbid depression and anxiety.To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non-time-series data. The basic structure of the DL model was designed to process mixed-input data and perform multi-label-based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short-term memory (LSTM), were applied to process the time-series data, and model selection was conducted by comparing the performances of the hyperparameters.This study achieved significant results in the multi-label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed-input DL model based on activity tracking data. The comparison of hyper-parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results.This study can be considered as the first to develop a mixed-input DL model based on activity tracking data for the multi-label identification of late-life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer-grade wrist-worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi-label classification framework for identifying the complex symptoms of depression and anxiety.
Digital Transformation of Media: Challenges and Opportunities

This paper is the result of the analysis of the evolution of media landscape in the context of the development and spread of digital technologies. The article exam­ines the qualitative impact of digitalization on the business and operational models of traditional print media in the context of the obvious need for organizations in this field to adapt to the requirements of the digital age. The research emphasizes the importance of trust as a cultural phenomenon and its key role in decision-making under conditions of information overload. Using analytical and systemic approaches, the authors of the study explore the scale and multidimensionality of the evolution of the media industry in a multifaceted way. The article also analyzes the current state and prospects of AI integration in media activities, in particular, such technology products as predictive analytics and content personalization, which are significantly changing the processes of information content creation and distribution. The authors also con­sider the shift from hierarchical content distribution to a model of co-production and co-consumption, and highlight the importance of content personalization and the impact of the platform economy on the business models of modern media. The authors conclude that, despite the challenges, print media can remain relevant and successful if they can pay sufficient attention to the quality and uniqueness of their content to maintain the trust of an audience that needs to effectively navigate the competitive digital environment.
To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other's working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality- derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.
Use of eye tracking in medical education

Eye tracking has become increasingly applied in medical education research for studying the cognitive processes that occur during the performance of a task, such as image interpretation and surgical skills development. However, analysis and interpretation of the large amount of data obtained by eye tracking can be confusing. In this article, our intention is to clarify the analysis and interpretation of the data obtained from eye tracking. Understanding the relationship between eye tracking metrics (such as gaze, pupil and blink rate) and cognitive processes (such as visual attention, perception, memory and cognitive workload) is essential. The importance of calibration and how the limitations of eye tracking can be overcome is also highlighted.
Documental interactivo y visualización de datos

Software development has influenced cultural products for years, and documentaries have not been an exception in this revolution. From non-linear and interactive webdocs to participatory, co-created, and transmedia projects, the documen-tary genre has given rise to diverse narrative structures. We analyze how webdocs have devel-oped different strategies when integrating data visualization into their narratives and interfaces. Multimedia content and data visualizations have been gaining prominence over film, and projects that don't use video have been considered docu-mentaries. This has given rise to what we consider the germ of a new genre and a new set of narrative structures.
Science 4.0 as a Model of Scientific Activity in an Innovative Environment of Industry 4.0

Technological innovations in the generation, storage, transmission, and processing of data have transformed traditional scientific activities and contributed to the accelerated development of the concept of “data-driven science.” The article analyzes the image of Science 4.0 in a new format since organizing and managing scientific activities with the widespread use of the Industry 4.0 technological platform has become relevant here. A four-level classification of stages in the development of science and the use of e-Science as a technological platform for Science 4.0 is proposed. The essence of Science 4.0 is revealed in the analysis of works devoted to scientific research with broad involvement of the internet of things, cyber-physical systems, artificial intelligence, cloud computing, big data analytics, and other intelligent solutions. Implementation of the proposed concept contributes to an increase in the efficiency of scientific research and supports solutions for the operational management of science.
On the Application of Digitized Virtual Reality Technology in the Teaching of Landscape Architecture Design

Landscape garden design is an important design foundation course for undergraduate students of landscape architecture in China, but the current classroom teaching has the problem of limited spatial dimension of the course. This paper proposes a virtual scene model construction method through the computer software with the creation of the need to display the scene. This virtual reality technology has immersive, interactive, and risqué characteristics. This paper focuses on the key points of virtual reality technology, discusses the specific implementation method of virtual reality technology in the “landscape garden design” course, and introduces the interactive control principle. The results of the research show that virtual reality technology to assist teaching can cultivate students' spatial thinking and enhance students' spatial simulation experience, greatly reducing the cost of traditional teaching.
Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions

Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations from a project featuring a CA for financial advisory services. Advisors initially envisioned a CA that ''blends with the background'' and acts on context information. However, when engaging with a CA, they used conversational interactions in one part of the encounter and command-based interactions in another. We discuss this observation and contrast it against previous literature. This insight has implications for design and research.
Adaptive Shrink and Shard Architecture Design for Blockchain Storage Efficiency

One of the problems in the blockchain is the formation of increasingly large data (big data) because each block must store all the transactions it makes. With the problem of the appearance of extensive data (big data), many studies aim to maintain the data in small amounts. This research combines a sorting data technique and a proper compression technique to obtain efficient data storage on the blockchain. The result of this research is a blockchain platform called Adaptive Shrink and Shard Blockchain (AS2BC), which conceptually and computationally can minimize the use of storage space in the blockchain up to 22 times smaller.
Semi-supervised medical image classification via distance correlation minimization and graph attention regularization

We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled deep neural networks architectures. In addition, it incorporates a data-driven graph-attention based regularization strategy to model affinities among images within the unlabeled data by exploiting their inherent relational information in the feature space. We conduct extensive experiments on four medical imaging benchmark data sets involving X-ray, dermoscopic, magnetic resonance, and computer tomography imaging on single and multi-label medical imaging classification scenarios. Our experiments demonstrate the effectiveness of our method in achieving very competitive performance and outperforming several state-of-the-art semi-supervised learning methods. Furthermore, they confirm the suitability of distance correlation as a versatile dependence measure and the benefits of the proposed graph-attention based regularization for semi-supervised learning in medical imaging analysis.