Reviewerly/exHarmony huggingface dataset:
A Conflict Early Warning Platform Based on Multidimensional Personnel Relationships and Event Association Model

Based on multidimensional personnel information, non-police incidents, online public opinion, petition, 12345 hotline, and conflict mediation data, natural language technology and big data analysis technology are used to extract and structure the multi-source data. The structured data is subjected to event relationship analysis, and an intelligent classification model is created for conflict classification to form a conflict and dispute subject library. The personnel theme information of conflict events is input into the personnel relationship model for high-frequency and high-risk personnel analysis and early warning. The event information of conflict events is input into the high-frequency and high-risk conflict model for high-frequency and high-risk conflict analysis and early warning. In the prevention and resolution of social governance conflicts, this platform provides a problem-oriented approach to strengthen the conflict resolution capabilities at the grassroots level, providing a practical solution for resolving conflicts at the initial and nascent stages.
A Blockchain-Based Decentralized IoT Sensing as a Service Framework for Smart Supply Chain

Smart supply chain services rely on the utilization of massive amount of data collected by sensor networks deployed in different enterprises. Sensing as a Service (S2aaS) is a promising Internet of Things (IoT) business model pattern for data exchange. The current centralized IoT S2aaS models are not suitable for IoT big data exchange due to the issues on privacy disclosure, single point of failure, data security, performance, etc. In this paper, we propose a blockchain-based decentralized framework for IoT S2aaS for smart supply chain, which can ensure the IoT solution owners have full control of their data and securely exchange data with data consumers without intermediaries. We introduce the system model and the layered architecture of our proposed framework, based on which we give a concrete scheme, smart contract is used to perform the whole process of IoT S2aaS. We implement a software prototype on Ethereum. Experiment results show the validity and effectiveness of our proposed solution.
PSO-BP neural network based on physical health perception of the elderly in retirement home

The recreational community retirement home staff can keep abreast of the elderly's health conditions and provide personalised services. However, how health status is collected and judged is more traditional, and there is a lack of prediction of the healthy development of the elderly. The information and intelligent upgrading of the elderly recreation system is imperative, and neural network modelling based on the Internet of Things and its intelligent optimisation methods is one of the means to do this. Based on 5G IoT technology, this paper adopts BP neural network and particle swarm optimization modelling methods to use the physical health and long-term living environment health indicators collected by IoT terminals as model input information. Through selecting evaluation indicators, the construction of evaluation models, the establishment of a primary database and the determination of evaluation criteria, intelligent analysis and optimisation processes are carried out to form a health posture awareness system for the elderly in recreational community retirement home. A comparison of the simulation results shows that the proposed geriatric health situational awareness system has improved the efficiency and accuracy of its geriatric health awareness capabilities.
Understanding Latency and QoE in Social XR

The Social eXtended Reality (Social XR) is conceived as an immersive shared space where users can interact with others. Thus, Social XR requires an immersive interactive communication. However, the technical aspects of communication that can affect the Quality of Experience (QoE) have not been fully explored, particularly in relation with latency. Although latency has been extensively studied in classical communication systems, its effect on the user experience in Social XR communications requires further study. In this work we propose a shared communication framework to identify the sources of the delay in various use cases in Social XR and their different implications on the QoE. Additionally, we present a QoE model based on ITU recommendations, enabling the prediction of quality level from the measured perception and acceptance delay values.
Immerse Yourself in a Fight Against your Fears: A Vision for using Virtual Reality Serious Games and Physiology Assessment in Phobia Treatment

The evolution and miniaturization of technology allows the development of portable solutions for physiology assessment, and the chance to mimic real world settings using Virtual Reality (VR). This work proposes an end-to-end framework for assisting Exposure Therapy (ET) with VR serious games and physiology monitoring. It relies on a generic architecture changing the VR stimuli, i.e., the phobia being considered, while the remaining modules can be re-used for data collection and analysis by a therapist team. It also integrates customizable bio-feedback, triggering specific events or adjusting the amount of stimuli based on the physiological response. The VR serious games were assessed with 16 participants before being integrated into the architecture. The framework's capacity to acquire bio-signals and other metrics in a synchronous manner was evaluated in two user studies with a total of 23 participants.
Automatic Extractive Summarization using GAN Boosted by DistilBERT Word Embedding and Transductive Learning

Text summarization is crucial in diverse fields such as engineering and healthcare, greatly enhancing time and cost efficiency. This study introduces an innovative extractive text summarization approach utilizing a Generative Adversarial Network (GAN), Transductive Long Short-Term Memory (TLSTM), and DistilBERT word embedding. DistilBERT, a streamlined BERT variant, offers significant size reduction (approximately 40%), while maintaining 97% of language comprehension capabilities and achieving a 60% speed increase. These benefits are realized through knowledge distillation during pre-training. Our methodology uses GANs, consisting of the generator and discriminator networks, built primarily using TLSTM - an expert at decoding temporal nuances in timeseries prediction. For more effective model fitting, transductive learning is employed, assigning higher weights to samples nearer to the test point. The generator evaluates the probability of each sentence for inclusion in the summary, and the discriminator critically examines the generated summary. This reciprocal relationship fosters a dynamic iterative process, generating top-tier summaries. To train the discriminator efficiently, a unique loss function is proposed, incorporating multiple factors such as the generator’s output, actual document summaries, and artificially created summaries. This strategy motivates the generator to experiment with diverse sentence combinations, generating summaries that meet high-quality and coherence standards. Our model’s effectiveness was tested on the widely accepted CNN/Daily Mail dataset, a benchmark for summarization tasks. According to the ROUGE metric, our experiments demonstrate that our model outperforms existing models in terms of summarization quality and efficiency.
Securing Patient Medical Records with Blockchain Technology in Cloud-based Healthcare Systems

Blockchain technology presents a promising solution to myriad challenges pervasive in the healthcare domain, particularly concerning the secure and efficient management of burgeoning health information technology (HIT) data. This paper delineates a novel blockchain-based approach to enhance various aspects of healthcare management, including data accuracy, drug prescriptions, pregnancy data, supply chain management, electronic health record (EHR) management, and risk data management, with a special emphasis on ensuring secure access, immutable record-keeping, and robust data sharing. We propose a solution focusing on leveraging blockchain technology, particularly utilizing a Hyperledger network within Amazon Web Services (AWS), to securely manage patients' medical records in the cloud. The implemented framework, housed within a Virtual Private Cloud (Amazon VPC) to ensure restricted access and cost-effective resource utilization, underscores advancements in data availability, security, traceability, and sharing, addressing key challenges within healthcare data management, and presenting a scalable, efficient, and secure approach to EHR management in contemporary healthcare contexts.
Integrated SVM-FFNN for Fraud Detection in Banking Financial Transactions

Detecting fraud in financial transactions is crucial for guaranteeing the integrity and security of financial systems. This paper presents an integrated approach for detecting fraudulent activities that incorporates Support Vector Machines (SVM) and Feedforward Neural Networks (FFNN). The proposed methodology utilizes the strengths of SVM and FFNN to distinguish between classes and capture complex patterns and relationships, respectively. The SVM model functions as a feature extractor, supplying the FFNN with high-level representations as inputs. Through an exhaustive evaluation utilizing labeled financial transaction data, the integrated SVM-FFNN model shows promise in detecting fraud with increased accuracy and precision. This research contributes to the development of innovative techniques for enhancing financial fraud detection systems.
Intelligent Design of Ethnic Patterns in Clothing using Improved DCGAN for Real-Time Style Transfer

In view of the problems that traditional real-time style transmission technology requires a large number of sample map training, low image quality, lack of realism and detail, this study combines the improved generative adversarial network (GANs) with real-time style transfer technology, and enhances the real-time style transfer calculation with adaptive instance normalization. As a result, a novel intelligent clothing ethnic pattern design model is developed. Experimental results show that the model reduces physical memory usage by 45.7%, with only 453MB, and utilizes only 26% of CPU resources in terms of CPU usage. The training time is approximately 20 minutes and 48 seconds. This model performance is obviously higher than other models. The designed intelligent clothing ethnic pattern design model in this study demonstrates higher clarity and shorter processing time, and has potential applications in the field of image generation.
Mitigating Membership Inference Attacks via Weighted Smoothing

Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to membership inference attack (MIA), where adversaries can determine if specific data was present in a training set, thus potentially revealing sensitive information. In this paper, we introduce a technique, weighted smoothing (WS), to mitigate MIA risks. Our approach is anchored on the observation that training samples differ in their vulnerability to MIA, primarily based on their distance to clusters of similar samples. The intuition is clusters will make model predictions more confident and increase MIA risks. Thus WS strategically introduces noise to training samples, depending on whether they are near a cluster or isolated. We evaluate WS against MIAs on multiple benchmark datasets and model architectures, demonstrating its effectiveness. We publish code at https://github.com/BennyTMT/weighted-smoothing.
2MO Multimodal data fusion enhanced precision neoadjuvant chemotherapy in breast cancer with a multi-task transformer-CNN-mixed learning

In medical practice, clinicians merge diverse information sources. While AI has the potential to aid healthcare professionals, its current ability to smoothly integrate various algorithms and diverse multimodal data is limited, posing a constraint on its practical utilization in clinical settings. The objective of this study is to leverage AI techniques that combine histopathology and clinical data to enhance the precision of neoadjuvant chemotherapy (NAC) in breast cancer management. We retrospectively recruited 756 patients in the training cohort from the campus 1 of Sun Yat-sen Memorial Hospital (SYSMH), 560 in the validation cohort from the campus 2 of SYSMH. Additionally, 227 patients were included in the prospective test cohort for a blinded prospective validation. We developed a AI-pathology model comprising both CNN-based and Transformer-based feature extraction channels. Building upon the AI-pathology model, we devised an AI-multimodal model that fused pathological and clinical information. The study's endpoints were pathological complete response (pCR) and disease-free survival (DFS). A total of 1,598 patients were enrolled in this study. The AI-pathology model demonstrated favorable accuracy for pCR prediction in the training cohort (AUC 0.999), as well as in the validation and the prospective test cohorts (0.995, 0.981). Notably, the AI-multimodal model (AUC 0.999, 0.994 in the validation and prospective test cohorts) surpassed the AI-pathology model. Besides, Kaplan-Meier analysis showed that patients predicted by the AI-multimodal model to achieve a pCR had a favourable DFS compared with non-pcr patients (p<0.05). The combined visualization heatmap and single-cell analysis provided insights into decision-making processes, linking model predictions with the tumor microenvironment, particularly the infiltration and functional status of T cells and B cells. The AI-multimodal model, integrating both pathological and clinical information, effectively predicted pCR and DFS in the context of NAC. Its high accuracy and robustness present a novel tool for guiding personalized breast cancer management based on pre-treatment pathological images.
Why is my Agent so Slow? Deploying Human-Like Conversational Turn-Taking

The emphasis on one-to-one speak/wait spoken conversational interaction with intelligent agents leads to long pauses between conversational turns, undermines the flow and naturalness of the interaction, and undermines the user experience. Despite ground breaking advances in the area of generating and understanding natural language with techniques such as LLMs, conversational interaction has remained relatively overlooked. In this workshop we will discuss and review the challenges, recent work and potential impact of improving conversational interaction with artificial systems. We hope to share experiences of poor human/system interaction, best practices with third party tools, and generate design guidance for the community.
The Importance of Human Factors for Trusted Human-Robot Collaborations

The next generation of robots is expected to work collaboratively with humans in natural (dynamic) settings. For this, it is important to properly study and model human factors, so that the AI and Robotic models can include them to enable robust Human-Robot Collaborations. This will enable safe and trustworthy hybrid decision-making approaches – Responsible AI – thereby streamlining robust collaborations (as per human-centred expectations). This interdisciplinary workshop will focus on the intersection of Cognitive Human Factors, Interpretable & Explainable AI methods, Social Interaction, and Human-Centred Robotics to stimulate novel long-range avenues for innovative human-centred collaborative methods in real-world contexts.
Cloud Storage Data Security and Integrity Protection Using Localized and Customized Key Manipulator (LCKM) Algorithm

In leveraging cloud storage services for efficient data storage while addressing data integrity concerns. Traditional data integrity auditing schemes necessitate private key usage, relying on hardware tokens and password memorization, which can be problematic if lost or forgotten. In prevailing methods data integrity checking without private key storage and key generation through third-party key distributors used, biometric data like iris scans and fingerprints, linear sketching and signatures are used to verify user data. These methods do not serve the purpose as biometrics has default drawbacks and third-party key generators can also not be trusted. To overcome these challenges, a pioneering approach called the proposed Localized key generator and storage is implemented in our proposed system. This LCKM algorithm generates customized keys for each file in a localized key generator and cloud details. Localized Key Manipulator will be provided to each user which is a password protector and the key customization will done according to the confidentiality, priority, and importance of the file. Customization of keys will be done with various combinations of digits, alphabets, symbols, and different sizes along with details of cloud storage chosen by the user. This localized key manipulator is mobile and carried as a protected file anywhere but only authenticated users can access it. Security and localized data integrity confirm the method’s effectiveness, ensuring robust security and operational efficiency.
Openid Authentication Model for Cloud Security Using Trusted Platform

Online services on the Internet have expanded incredibly quickly in recent years. To use any online service, Internet users must first register for a new account. The issue is evident when one user often requires many services and as a result maintains various accounts. To prevent identity theft, these various accounts must be managed in a safe and convenient manner. SSO (single sign-on) and OpenID have been used to make maintaining the many accounts needed in the Internet identity context less difficult. Two excellent trusted computing-based technologies to address security issues in the Internet identity context are Trusted Platform Module (TPM) and Trust Multitenancy.
TEACHERS’ TALK IN TEACHING ENGLISH: ROLES AND CHALLENGES

This study aimed to identify the most common teachers' talk in EFL classrooms as well as the reasons that influenced their selection. This research focused on the teacher's perception of how the teacher talks with students in class and some of the challenges that teachers might face while interacting with students to encourage students in the learning process to take place. The participants of this research were 2 English teachers who received professional training in English teaching as part of the undergraduate program. They were English teachers at an integrated Islamic school in Medan. The observations were made to describe the teacher interview process data and also the learning carried out by the teacher to find insights about how the teacher talks and what challenges the teacher faced when interacting with students in class. This study used a qualitative descriptive method. The findings in this study were that the teacher applied more dominant English communication as input. Teachers felt that increasing the use of the target language will be more challenging because it is influenced by several aspects, such as the motivation, attitude, and skills of the teacher and students. In addition, when the teacher spoke it varies greatly on several levels based on the factors that influence the teacher and student. The results of this study also showed that most teacher conversations are used in the form of questions, then explaining the lesson, giving directions to students, and praising or encouraging students. Apart from that, because students are considered as learners or beginners, class interaction must be adjusted to the right and appropriate choice of language so that communication becomes easier and more effective. In addition, the analysis of teachers’ talk in the classroom would be beneficial for teachers to plan and conduct enhanced learning situations.
Learning in an Immersive VR Environment: Role of Learner Characteristics and Relations Between Learning and Psychological Outcomes

In an immersive virtual reality (IVR) environment, this study investigated: (1) the role of learner characteristics in various learning and psychological outcomes, including knowledge retention, perceived learning, cognitive load, self-efficacy, enjoyment, presence, and usefulness; and (2) the relationship among these intended outcomes. Forty adult participants experienced an IVR nature trail tour involving science learning topics, followed by a knowledge test and multiple surveys. Multivariate analysis of covariance results indicated age by gender interaction effects on perceived presence and usefulness as well as an age by IVR prior experience interaction effect on perceived presence. Additionally, multiple correlational relationships were detected among the intended outcomes. This study contributes to the limited research on the role of learner characteristics in STEM learning and psychological outcomes in IVR environments.
A Java Application for Teaching Graphs in Undergraduate Courses

Graph theory is a common topic that is introduced as part of the curricula of computing courses such as Computer Science, Computer Engineering, Data Science, Information Technology and Software Engineering. Understanding graphs is fundamental for solving many real-world problems, such as network routing, social network analysis, and circuit design; however, many students struggle to grasp the concepts of graph theory, as they often have difficulties in visualising and manipulating graphs. To overcome these difficulties, educational software can be used to aid in the teaching and learning of graph theory. This work focuses on the development of a Java system for graph visualisation and computation, called MaGraDa (Graphs for Discrete Mathematics), that can help both students and teachers of undergraduate or high school courses that include concepts and algorithms related to graphs. A survey on the use of this tool was conducted to explore the satisfaction level of students on a Discrete Mathematics course taken as part of a Computer Engineering degree at the University of Alicante (Spain). An analysis of the results showed that this educational software had the potential to enhance students’ understanding of graph theory and could enable them to apply these concepts to solve practical problems in the field of computer science. In addition, it was shown to facilitate self-learning and to have a significant impact on their academic performance.
Efficient Video Transformers via Spatial-Temporal Token Merging for Action Recognition

Transformer has exhibited promising performance in various video recognition tasks, but brings a huge computational cost in modeling spatial-temporal cues. This work aims to boost the efficiency of existing video transformers for action recognition through eliminating redundancies in their tokens and efficiently learning motion cues of moving objects. We propose a light-weight and plug-and-play module, namely Spatial-Temporal Token Merger (STTM), to merge the tokens belonging to the same object into a more compact representation. STTM first adaptively identifies crucial object clues underlying the video as meta-tokens. Similarity scores between input tokens and meta-tokens are hence computed, and used to guide the fusion of similar tokens in both spatial and temporal domains, respectively. To compensate motion cues lost in the merging procedure, we compute the linear aggregation of spatial-temporal positions of tokens as motion features. STTM hence outputs a compact set of tokens fusing both appearance and motion features of moving objects. This procedure substantially decreases the number of tokens need to be processed by each Transformer block, and boosts the efficiency. As a general module, STTM can be applied to different layers of various video Transformers. Extensive experiments on action recognition datasets Kinectics-400 and SSv2 demonstrate its promising performance. For example, it reduces the computation complexity of ViT by 38% while maintaining a similar performance on Kinectics-400. It also brings 1.7% gains of top-1 accuracy on SSv2 under the same computational cost.