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

Authors:
raw_author_name: Hanqing Hu
raw_author_name: Mehmed Kantardzic

Abstract: 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

Authors:
raw_author_name: Yao Xu
raw_affiliation_string: Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
raw_author_name: Yunxiao Liu
raw_affiliation_string: Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
raw_author_name: Han Li
raw_affiliation_string: Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
raw_author_name: Jianliang Ai
raw_affiliation_string: Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China

Abstract: 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

Authors:
raw_author_name: Jingfang Sun
raw_affiliation_string: Shandong Foreign Trade Vocational College, China

Abstract: 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.
Early Perceptions of Teaching and Learning Using Generative AI in Higher Education

Authors:
raw_author_name: Amanda D. Damiano
raw_affiliation_string: School of Communication & the Arts, Marist College, Poughkeepsie, NY, USA
raw_author_name: Eitel J.M. Lauría
raw_affiliation_string: School of Computer Science & Mathematics, Marist College, Poughkeepsie, NY, USA
raw_author_name: Ningjing Zhao
raw_affiliation_string: Department of Communication, Johns Hopkins University, Baltimore, MD, USA

Abstract: 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

Authors:
raw_author_name: Qianshuo Hu
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
raw_author_name: Haijun Liu
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Abstract: 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

Authors:
raw_author_name: Antoine Loiseau
raw_affiliation_string: Univ. Grenoble Alpes, CEA, LETI MINATEC Campus, F-38054 Grenoble, France

Abstract: 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.
To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Authors:
raw_author_name: Geoff Musick
raw_affiliation_string: Clemson University, Clemson, SC, USA
raw_author_name: Wen Duan
raw_affiliation_string: Clemson University, Clemson, SC, USA
raw_author_name: Subhasree Sengupta
raw_affiliation_string: Clemson University, Clemson, SC, USA
raw_author_name: Christopher Flathmann
raw_affiliation_string: Clemson University, Clemson, SC, USA
raw_author_name: Bart Knijnenburg
raw_affiliation_string: Clemson University, Clemson, SC, USA
raw_author_name: Nathan McNeese
raw_affiliation_string: Clemson University, Clemson, SC, USA

Abstract: 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

Authors:
raw_author_name: John Sandars
raw_affiliation_string: Health Research Institute, Edge Hill University, Ormskirk, UK

Abstract: 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

Authors:
raw_author_name: Víctor Herrero-Solana
raw_affiliation_string: Universidad de Granada (UGR)

Abstract: 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

Authors:
raw_author_name: Shakir A. Mehdiyev
raw_affiliation_string: Insitute of Information Technology, Baku, Azerbaijan
raw_author_name: Tahmasib Kh. Fataliyev
raw_affiliation_string: Institute of Information Technology, Baku, Azerbaijan

Abstract: 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

Authors:
raw_author_name: Zheng Xu
raw_affiliation_string: Zhejiang Institute of Mechanical and Electrical Engineering, China

Abstract: 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

Authors:
raw_author_name: Andreas Bucher
raw_affiliation_string: University of Zurich, Zurich, Switzerland
raw_author_name: Mateusz Dolata
raw_affiliation_string: University of Zurich, Zurich, Switzerland
raw_author_name: Sven Eckhardt
raw_affiliation_string: University of Zurich, Zurich, Switzerland
raw_author_name: Gerhard Schwabe
raw_affiliation_string: University of Zurich, Zurich, Switzerland

Abstract: 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

Authors:
raw_author_name: Daniel Soesanto
raw_affiliation_string: Department of Electrical and Information Engineering, Gadjah Mada University, Jogjakarta, Indonesia; Department of Informatics Engineering, University of Surabaya, Surabaya, Indonesia
raw_author_name: Igi Ardiyanto
raw_affiliation_string: Department of Electrical and Information Engineering, Gadjah Mada University, Jogjakarta, Indonesia
raw_author_name: Teguh Bharata Adji
raw_affiliation_string: Department of Electrical and Information Engineering, Gadjah Mada University, Jogjakarta, Indonesia

Abstract: 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

Authors:
raw_author_name: Abel Díaz Berenguer
raw_affiliation_string: Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
raw_author_name: Matías Nicolás Bossa
raw_affiliation_string: Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
raw_author_name: Tanmoy Mukherjee
raw_affiliation_string: Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
raw_author_name: Nikos Deligiannis
raw_affiliation_string: Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Heverlee, Belgium; Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium
raw_author_name: Hichem Sahli
raw_affiliation_string: Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Heverlee, Belgium; Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), Pleinlaan 2, 1050 Brussels, Belgium

Abstract: 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.