Reviewerly/exHarmony huggingface dataset:
JoVE Video Dataset

The COVID-19 pandemic has changed many industries, empowering some sectors and causing many others to disappear. The education sector is not exempt from major changes; in some countries or cities, classes were taught 100% online for at least 1 year. However, some university careers need laboratory practices to complement learning, especially in engineering areas, and having only theoretical lessons online could affect their knowledge. For this reason, in this work, a mixed reality system called mixed reality for education (MRE) was developed to help students develop laboratory practices to complement online classes. An experiment was carried out with 30 students; 10 students did not use MRE, 10 used MRE, and 10 more used MRE with teacher feedback. With this, one can see the advantages of mixed reality in the education sector. The results show that using MRE helps to improve knowledge in engineering subjects; the students obtained qualifications with grades 10% to 20% better than those who did not use it. Above all, the results show the importance of feedback when using virtual reality systems.
Towards Robots That Meet Users’ Need for Explanation

While humans are used to reason about other humans’ behavior, they are not readily able to understand the decision processes of artificial agents. This can be harmful in human-robot interaction (HRI) settings where a user may suspect erroneous or, even worse, intentionally non-cooperative behavior, resulting in reduced acceptance of the robot. In order to mitigate such negative effects, autonomous robots may be equipped with the ability to adequately explain their behavior. To that end, a robot is required to have the ability to (1) robustly detect a user’s need for explanation and (2) identify the situation-specific nature of the explanation need. Further it needs to be endowed with (3) communicative capabilities in order to deliver suitable explanations and ensure sufficient understanding. This extended abstract presents recent work towards endowing a social robot with such qualities and discusses how robots can meet users’ explanation needs more adequately.
How Displaying AI Confidence Affects Reliance and Hybrid Human-AI Performance

Confidence signals are often used in human interactions to communicate the likelihood of a decision being correct. Similarly, confidence may also be used to indicate the reliability of advice given by an AI. While previous work on explainable AI (XAI) has explored the effect of AI confidence on AI-advice adoption and joint accuracy of the human-AI team, most studies use AI-assistants that exceed human performance. It is unclear how displaying the confidence interacts with the accuracy of the AI. We conduct a comprehensive investigation of the effect of displaying AI confidence on two factors: 1) the accuracy of AI-assisted decision making, and 2) reliance on the AI’s assistance. We conduct two behavioral experiments, one where participants were shown AI confidence, and another where no confidence ratings were shown. Our work goes beyond the typical focus on high accuracy AI assistants. In both experiments, participants were assisted by one of three AI classifiers of varying accuracy. Our results demonstrate that displaying AI confidence increases joint accuracy when people are assisted by a classifier that is better than humans on average. Conversely, when assisted by a classifier with performance worse than an average human, joint accuracy was better when no AI confidence was displayed. However, for the adoption of AI advice we observed the opposite pattern: people rely more on a higher accuracy classifier that does not display confidence compared to one that does, and people rely more on a lower accuracy classifier that does display AI confidence compared to one that does not.
Self-adaptive and Efficient Training Node Selection for Federated Learning in B5G/6G Edge Network

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.
Federated Learning Aided Deep Convolutional Neural Network Solution for Smart Traffic Management

Machine learning models, especially neural network (NN) classifiers, have shown tremendous potential of being used in complex tasks such as image classification, object detection and video analytics. However, to be adopted in the real-world applications, there are still problems to be answered. One of these problems is that training machine learning models, especially NN models, requires a certain level of computation and data processing. Other problems are the limited bandwidth of the network and the possibility of exposing the privacy of the users to attacks if the training data (specially video) is going to be transferred through the network. To mitigate these problems, researchers recently proposed the concept of federated learning.In this paper, we build a video analytic application for traffic management and train it using federated learning. More specifically, each traffic surveillance camera combined with its co-located small PC are seen as the worker node in federated learning. In this way, the NN model in each node can be trained on data collected from all nodes without transmitting and sharing with a central server, which resolves all of the above mentioned problems. The performance of the trained NN model is evaluated via experiments under different open sourced datasets to demonstrate that the proposed work has the potential to enhance the detection accuracy (mAP) over 40%.
Beyond Individual Active Customers: Citizen and Renewable Energy Communities in the European Union

The engagement of energy customers has been considered an important pillar of the European Union (EU) strategy to reach net-zero greenhouse gas emissions by 2050, while preserving a competitive and secure energy system. The legislative package Clean Energy for all Europeans (CEP), adopted by the EU in 2019, confirms the relevance of removing the existing barriers for end users not only to choose their energy supplier but also to invest in distributed generation and storage and participate in all energy markets. Interestingly, the new European legal framework goes beyond the recognition of the rights and duties of individual active customers, the so-called prosumers, and introduces for the first time an explicit reference to energy communities, which take two specific definitions: citizen energy communities (CECs) and renewable energy communities (RECs).
Ex-situ deformation measurement system based on dual-reflector

We propose an ex-situ reposition error compensation method to solve the reposition error in the ex-situ state of 2D digital image correlation (2D-DIC) in the long-term deformation of the measurement specimen or under special load deformation. Using the dual-reflector auxiliary measurement system with an included angle of 90 deg, the two sides of the repositioned specimen are mapped into left and right mirrors. The in- and out-of-plane rigid body displacements are compensated via the error compensation method, and the real in-plane displacement of the tested specimen is obtained. The accuracy and practicability of this method are verified by conducting an in-plane rigid body displacement experiment, unloaded specimen reposition experiment, thermal deformation experiment, and finite element analysis. We provide a simple, effective, and high-precision deformation measurement method for 2D-DIC to realize ex-situ measurement.
Open Science in the Wild

This talk uses real-world case studies to explore where and how open research can address global challenges and make a difference in the lives of everyday people worldwide. We explore these challenges through the work of UCL’s Office for Open Science and Scholarship and discuss how it is approaching support for similar projects and the expansion of the institution’s achievements in Open Science across the board.
EXPERT SYSTEM FOR DENTAL DISEASES

The objective of an expert system in the field of medicine is to support doctors during the diagnosis process. Any software that is capable of drawing conclusions and making decisions based on the data stored in its database can be called an "expert system." Expert systems are widely employed in many industries, including the health sector. There are numerous types of dental ailments in the field of dentistry. Few symptoms were employed in the existing methods for dental diagnostics. A diagnosis in dentistry requires more than a few symptoms. The aim of this chapter is to analyse a medical expert system based on fuzzy rules that is used for the diagnosis of dental illnesses. Fuzzy inference based on a possibility metric and information extraction based on fuzzy clustering are both used in the modeling. The relevant parameters for dentistry were determined in the initial modeling phase of the system using clinical data. The evaluation of the dental variables based on soft computing will be carried out in the next stage. The third step introduces applied fuzzy inference based on possibility measures and provides examples to support it. The case data gathered from 100 patients makes up the knowledge foundation of the modeled system. Keywords: dental disease, big data, expert system, fuzzy clustering
A INFLUÊNCIA DAS EMOÇÕES NA SOBRECARGA DO CUIDADOR INFORMAL

The present work is associated with Sigmund Freud's perspective on libido, which is a fundamental concept in psychoanalysis.For Freud, the libido was a psychological force that drove the sexual life and the entire psyche of a person.His theory of the libido was developed over several decades and he incorporated it into his general theory of psychoanalysis.Freud analyzed human development as a psychosexual development from an energy, being defined as libido.This original affective energy that develops during life begins in childhood.Thus, at each stage of this libido progression, it is defined in the stages of development from a child to adulthood.In this way, energy is related to the proposal of a realization, and this realization, in turn, with the attainment of pleasure, through an energetic discharge.And as the child develops, this sexual energy acquires affective connections that mark the subject's structure throughout his or her life.Libido will happen in two waves, one autoerotic and one towards otherness.The first of these waves is divided into three stages of child development: the oral stage, the anal stage and the phallic stage.Soon after, there is a period defined as the latency period, to provide structure for the second wave, which will be organized in the adult phase, by the organization of a genital phase.
Optimal few-weight codes and their subfield codes

Linear codes with a few weights are an important class of codes in coding theory and have attracted a lot of attention. Recently, Wu et al., constructed few-weight quaternary linear codes and their subfield codes by using simplicial complexes. In this paper, we will extend the results from binary case to arbitrarily odd prime number [Formula: see text]. More specifically, we have constructed [Formula: see text] classes of distance optimal linear codes, all of them meet the Griesmer bound.
THE DEVELOPMENT OF E-COMICS MEDIA BASED ON THE VARK MODEL TO MEASURE THE UNDERSTANDING OF ELEMENTARY SCHOOL STUDENTS

The purpose of this research is to develop digital-based thematic learning media, namely e-comics media for elementary school students. E-comics will be developed with the VARK (Visual, Auditory, Reading, Kinesthetic) learning model which displays illustrated stories with sound reflections and is related to students' reading and kinesthetic activities in using the media. Develop digital-based thematic learning media, namely e-comics media for elementary school students. E-comics will be developed with the VARK (Visual, Auditory, Reading, Kinesthetic) learning model which displays illustrated stories with sound reflections and is related to students' reading and kinesthetic activities in using the media. The e-comics media will discuss ethnic diversity material for fourth grade elementary school students. The results of the validity test above, conclude that the average e-comics media based on the vark model is 96%, which means that the media is valid. The application of media at SD Muhamammadiyah Lamongan resulted in an increase in cycles 1 and 2, so e-comic learning media could increase students' understanding. The response results of using e-comics media based on the vark model were easy to apply by teachers and students at partner schools of PGSD UM Lamongan.
On Reducing User Interaction Data for Personalization

Most recommender systems rely on user interaction data for personalization. Usually, the recommendation quality improves with more data. In this work, we study the quality implications when limiting user interaction data for personalization purposes. We formalize this problem and provide algorithms for selecting a smaller subset of user interaction data. We propose a selection method that picks the subset of a user’s history items that maximizes the expected recommendation quality. We show on well studied benchmarks that it is possible to achieve high quality results with small subsets of less than ten items per user.
Generalized Image Embedding for Multi-Domain Image Retrieval

Image embedding, being a fundamental task in computer vision, plays a crucial role in various downstream tasks such as image retrieval. Widely adopted in e-commerce and social media collaboration, image retrieval benefits greatly from representations learned by the embedding model. However, conventional embedding models are often trained on a single domain, leading to inadequate performance in the multi-domain scenario. To address this challenge, we introduce a generalized image embedding model designed for multi-domain image retrieval. The proposed method employs a contrastively learned Vision Transformer and a carefully crafted training scheme to enhance domain generalization capability. Our theoretical analysis and experimental results, conducted on a large-scale, real-world multi-domain image retrieval dataset, demonstrate the superiority of the proposed method over existing embedding models in terms of both accuracy and domain generalization capability.
NewsCollab: Fostering Data-driven Journalism with Crowdsourcing

The amount of data produced over the last decade has increased at a fast pace. Many domains of knowledge were completely transformed by the availability of massive datasets. Journalism is one of these domains. Commonly, news articles are produced by analyzing massive datasets containing raw data and previously published articles. This new type of journalism is called data-driven journalism. However, data-driven journalism requires that previously published articles are accessible, categorized, and organized. This can be a laborious task to be manually performed. Starting from the assumption that news articles should be able to be imported from multiple sources, and collaboratively analyzed and categorized by other journalists (to avoid bias), we propose the use of a new collaborative system named NewsCollab. NewsCollab allows a multitude of journalists to download news articles from different portals and provide feedback on these articles, giving to the journalistic community a better understanding of the published articles that can be used to produce new material.
Temporal Weighted Heterogeneous Multigraph Embedding for Ethereum Phishing Scams Detection

Ethereum phishing scams have proven to be highly profitable in recent years, and pose a serious risk to the security of the blockchain ecosystem. Existing techniques for detecting phishing scams mostly model the transaction network at a very coarse-grained level. These methods rarely take into account the heterogeneity of the network, and do not consider multiple transactions over time between pairs of accounts. To this end, we model the Ethereum transaction network as a heterogeneous muiltidigraph and propose a novel graph embedding technique. Specifically, we use a temporal-weighted biased walking method based on the Jump-Stay strategy, which not only captures the properties of the dynamic transaction network more comprehensively, but also elegantly balances the distribution of different types of nodes. The superior performance of our model is shown through classification experiments on a real-world dataset of Ethereum transactions.
A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.
Multi-View Heterogeneous Graph Attention Network

Heterogeneous Graph Neural Networks (HGNNs) have achieved significant success in various real-world applications. However, existing methods often employ meta-paths, the random walk or shallow homogeneous convolutional graph neural networks such as GCN and GAT to aggregate neigh-borhood information. The meta-path requires prior knowledge of domain experts, while the random walk is not conducive to the link prediction task compared with the direct neighbor method. And the shallow models may have limited capability to capture the information of high-order neighbors. To address these challenges, we propose Multi-View Heterogeneous Graph Attention Network (MHAN), a novel approach that captures the neighbor information from both the local and the global view to effectively learn node representations. In MHAN, we first map the attributes of nodes of different types into the same feature space. Secondly, we utilize the attention mechanism to aggregate information from the first-order and high-order neighbors, respectively. Finally, we generate the final node representations by aggregating the obtained node embeddings from different views. Extensive experiments on three real-world datasets of heterogeneous graphs show that our model outperforms state-of-the-art baselines on the link prediction task.
Image Mining: Improving Image Retrieval Technique Using Variational Models and Relevance Feedback

Recently, multimedia data are growing rapidly in our life. Images have been playing a vital role in many fields and carry valuable information. This information needs to be processed. In addition, image mining field has seemed to present a valuable help to systems and persons who need to extract knowledge from images dataset. Content-Based Image Retrieval (CBIR), a type of image mining, aims to retrieve similar images from large image databases according to users' interests. However, the big problem with this technique is how to recover pertinent images. In this work, we focus on improving the performance of Content-Based Image Retrieval systems by reducing the semantic gap between query images and the similar retrieved images. The proposed approach gives an effective solution based on TV-L1 model and relevance feedback in order to ensure a relevant searching. Finally, the evaluation is made using the precision and recall metrics to demonstrate the results accuracy.
Independent Split Model Inference at Operator Network for Network Performance Estimation

In telecommunications, information delivery is per-formed over inherently distributed elements such as application servers, packet core network equipment, radio base stations, and mobile user equipment (UE). Predicting key performance indicators (KPIs) for services is important for mobile operators in preventing customer churn. In addition, it is important to achieve accurate estimations, to troubleshoot and localize faults in the end-to-end transmission by collecting data from potentially causal distributed measurement points located on the distributed delivery elements. Due to the regulations for protecting data privacy, there is an increasing interest in decentralized machine learning models including Split Learning (SL). A distributed learning technique such as parallel SL enables accurate model training jointly, but it necessitates all distributed elements to synchronize and correlate on a unique identifier such as sample id, to align the vertically distributed data samples, which causes dependency and high signaling overhead between the collaborating nodes. In this paper, we demonstrate a Translator Model that imputes the remote model parameters based on the local ones in a SL setting. This way, sample alignment overhead during model inference is addressed, and significant reduction in communication cost is achieved.