Reviewerly/exHarmony huggingface dataset:
Federated Continuous Learning With Broad Network Architecture

Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of the existing FL models focus on one-time learning without consideration for continuous learning. Continuous learning supports learning from streaming data continuously, so it can adapt to environmental changes and provide better real-time performance. In this article, we present a federated continuous learning scheme based on broad learning (FCL-BL) to support efficient and accurate federated continuous learning (FCL). In FCL-BL, we propose a weighted processing strategy to solve the catastrophic forgetting problem, so FCL-BL can handle continuous learning. Then, we develop a local-independent training solution to support fast and accurate training in FCL-BL. The proposed solution enables us to avoid using a time-consuming synchronous approach while addressing the inaccurate-training issue rooted in the previous asynchronous approach. Moreover, we introduce a batch-asynchronous approach and broad learning (BL) technique to guarantee the high efficiency of FCL-BL. Specifically, the batch-asynchronous approach reduces the number of client-server interaction rounds, and the BL technique supports incremental learning without retraining when learning newly produced data. Finally, theoretical analysis and experimental results further illustrate that FCL-BL is superior to the existing FL schemes in terms of efficiency and accuracy in FCL.
Playable Video Generation

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website.
Perception of Artificial Intelligence in Spain

This paper analyses the perception of Artificial Intelligence (AI) by individuals in Spain and the factors associated with it. It uses data from 6308 individuals from a 2018 Spanish survey. A binary logit regression model is formulated and estimated for the attitude towards robots and AI and its possible determinants. As main results are that a gender gap is detected, and that people have a negative attitude if they are not interested in scientific discoveries and technological developments and if AI and robots are not useful at work.
IIoT based Augmented Reality for Factory Data Collection and Visualization

In Industry 4.0, machine data acquisition plays an important role in improving overall performance on the factory floor by monitoring processes, providing feedback, and integrating the information with other machines. Leveraging these hidden data instantaneously to reduce machine downtime could also minimize efforts in determining the causes of maintenance issues. This paper explores a novel method of bringing the factory floor data into an Industrial Internet of Things (IIoT) environment and visualizing real-time machine analytics through Augmented Reality (AR) and Mixed Reality (MR) based smart devices. The factory floor data from selective machines at a Volvo Group plant was integrated with IIoT and visualized in real-time through the use of AR-enabled devices such as an iPad and a HoloLens headset. The configuration process for the server and IIoT environment was automated through a software solution, and the performance and feasibility of spatial anchors were evaluated by testing accuracy, triggering times, and latency of data. The results displayed promising applications of AR/MR on factory floors for real-time machine data visualization and associated troubleshooting.
Tutorial on Fairness of Machine Learning in Recommender Systems

Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendation, which may hurt users' or providers' satisfaction in recommender systems as well as the interests of the platforms. The tutorial focuses on the foundations and algorithms for fairness in recommendation. It also presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking. The tutorial will introduce the taxonomies of current fairness definitions and evaluation metrics for fairness concerns. We will introduce previous works about fairness in recommendation and also put forward future fairness research directions. The tutorial aims at introducing and communicating fairness in recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video

Recently, many deep learning based researches are conducted to explore the potential quality improvement of compressed videos. These methods mostly utilize either the spatial or temporal information to perform frame-level video enhancement. However, they fail in combining different spatial-temporal information to adaptively utilize adjacent patches to enhance the current patch and achieve limited enhancement performance especially on scene-changing and strong-motion videos. To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features. Specifically, we design a temporal and spatial-wise attention-based feature distillation structure to adaptively utilize the adjacent patches for distilling patch-wise temporal features. For adaptively enhancing different patch with spatial and temporal information, a channel and spatial-wise attention fusion block is proposed to achieve patch-wise recalibration and fusion of spatial and temporal features. Experimental results demonstrate our network achieves peak signal-to-noise ratio improvement, 0.55 - 0.69 dB compared with the compressed videos at different quantization parameters, outperforming state-of-the-art approach.
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning

In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods employ a query-image matching branch to select the highest-score point in the image feature map as the target box center, with its width and height predicted by another branch. Such methods, however, fail to utilize the contextual relation between the target and reference objects, and lack interpretability on its reasoning procedure. To solve these problems, we propose an iterative shrinking mechanism to localize the target, where the shrinking direction is decided by a reinforcement learning agent, with all contents within the current image patch comprehensively considered. Besides, the sequential shrinking processes enable to demonstrate the reasoning about how to iteratively find the target. Experiments show that the proposed method boosts the accuracy by 4.32% against the previous state-of-the- art (SOTA) method on the RefCOCOg dataset, where query sentences are long and complex with many targets referred by other reference objects.
Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the “black-box” nature of DL towards model transparency.
MFeature: Towards high performance evolutionary tools for feature selection

• Eight feature selection approaches using enhanced moth-flame algorithm are proposed. • The ensemble strategy, SA strategy and crossover scheme are employed to boost binary MFO. • The performance is assessed on 30 datasets, and compared with seven methods. Feature selection commonly refers to a process of using the candidate algorithm to detect the optimal feature sets during the preprocessing steps in machine learning and data mining. This procedure can optimize the dataset's features to be analyzed and maximize the classification performance based on the selected optimal feature combination. In this work, a hybridization model is developed and utilized to select optimal feature subset based on an innovative binary version of moth-flame optimizer and the K-Nearest Neighbor Classifier (KNN) for classification tasks. The proposed new technique, abbreviated as MFeature or ESAMFO, applies several strategies, including two types of transfer functions, ensemble strategy, simulated annealing (SA) disturbance mechanism, and crossover scheme to improve the equilibrium between the global exploration and local exploitation capabilities of the basic MFO. Each individual in the proposed algorithm is evaluated by the size of selected features and the error rate of the KNN classifier. The proposed model's efficacy is assessed on 30 benchmark datasets with different dimensions from the UCI repository and compared with other KNN based feature selection algorithms from the literature. The comprehensive results via various comparisons reveal the efficiency of the proposed technique in decreasing the classification error rate compared with other feature selection algorithms, ensuring the capability of ESAMFO in exploring the feature space and selecting the most informative features for classification purposes. For post publications that support this research, readers can refer to https://aliasgharheidari.com .
Performance Analysis of Body Tracking with the Microsoft Azure Kinect

Real-time control of cooperative robots, or cobots, in industrial environments is a mandatory task to reduce the risk for workers by improving their safety. The task of cobot control always requires input data about the surroundings to enable planning procedures and proper reactions to unpredictable events, such as human actions. In this case, the exact position of the humans can be easily inferred from RGB-D cameras, whose output can be processed by body tracking modules to produce exact pose estimations in real-time. This paper experimentally explores the performance of the affordable Microsoft Azure Kinect RGB-D camera and its body-tracking library. A parametric analysis of the uncertainty of the estimation of the skeleton joints is performed by changing the ambient light conditions, the presence of occlusions, the infrared camera resolution, and the human-camera distance. The output of this investigation proves the need for uncertainty management in the control of cobots working with humans.
Ultra-short-term prediction of photovoltaic output based on an LSTM-ARMA combined model driven by EEMD

A new method is proposed for ultra-short-term prediction of photovoltaic (PV) output, based on an LSTM (long short-term memory)-ARMA (autoregressive moving average) combined model driven by ensemble empirical mode decomposition (EEMD) and aiming to reduce the intermittency and uncertainty of PV power generation. Considering the superposition of the overall trend and local fluctuations contained in the PV output data, an EEMD adaptive decomposition criterion based on continuous mean square error is proposed to extract the various scale components of the PV output data in the time–frequency domain; an ARMA (autoregressive moving average) model suitable for short correlation analysis is constructed for the intrinsic mode function components that characterize local fluctuations of PV output. Environmental parameters such as solar radiation, temperature, and humidity are introduced to construct a LSTM prediction model with autocorrelation capability and environmental characteristics for the EEMD residual that characterizes the overall trend of PV output. Finally, the overall trend and the local fluctuation forecast results are fused to realize an ultra-short-term forecast of PV output. The training set and test set were randomly selected from the PV microgrid system of Hangzhou Dianzi University and used for PV output prediction according to different seasons and weather types. The maximum MAPE on sunny, cloudy, and rainy days was 23.43%, 32.34%, and 33.10%, respectively. The minimum MAPE on sunny, cloudy, and rainy days was 5.53%, 6.47%, and 19.19%, respectively. The results show that the prediction performance of this method is better than traditional models. The ultra-short-term forecasting method for PV output proposed in this paper can help us to improve the safety, flexibility, and robustness of PV power systems.
Framework for ethical and acceptable use of social distancing tools and smart devices during COVID-19 pandemic in Zimbabwe

Despite the successful development of vaccines, coronavirus disease (COVID-19) continues to present unprecedented challenges. Besides the ongoing vaccination activities, many countries still rely on measures including social distancing, contact tracing, mandatory face masking among others. Several digital technologies such as smart devices, social distancing tools, smart applications have been adopted to enhance public adherence to reduce secondary transmission. Such technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation and quarantine compliance. The use of digital technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which subsequently impact their efficacy and adoption. Also, the aggressive and mandatory use of large-scale digital technologies is not easy to implement, adhere to and subsequently difficult to practice which ultimately lead to imperfect public compliance. To alleviate these impediments, we analysed the available literature and propose an ethical framework for the use of digital technologies centred on ethical practices. The proposed framework highlights the trade-offs, potential roles and coordination of different stakeholders involved in the development and implementation of digital technologies, from various social and political contexts in Zimbabwe. We suggest that transparency, regular engagement and participation of potential users are likely to boost public trust. However, the potential violation of ethical values, poor communication, hasty implementation of digital technologies will likely undermine public trust, and as such, risk their adoption and efficacy.
An Overview of Correlation-Filter-Based Object Tracking

In the last two decades, object tracking has been one of the prevalent fields in social media. Object tracking uses a dynamic model to track the same target, aiming to analyze the same social object and its behavior in a set of consecutive video frames. In this article, we provide a comprehensive survey on some representative and latest correlation-filter-based object tracking methods and compare their respective strengths and weaknesses under the theoretical and experimental analyses. First of all, we divide these correlation-filter-based object tracking methods into four categories according to their characteristics, including categorized features, space weight factors, scale factors, and expert strategies. Second, extensive experiments on benchmark datasets with 11 tracking challenges are performed and discussed. Finally, this article also attempts to provide some insights for the readers to comprehend and improve the involved tracking algorithms. It is expected that the overview would present a detailed framework and some enlightenments of object tracking to readers who are new in this area.
How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits. Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized. Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise. Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.
Don’t Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams

As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19.COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic.However, these newly emerging scams are poorly understood by our community.In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams.We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources.Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc).We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams.We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims.For each type of scams, we further investigated the tricks and social engineering techniques they used.To facilitate future research, we have released all the well-labelled scams to the research community.TABLE I A TAXONOMY OF 6 KINDS OF COVID-19 CRYPTOCURRENCY SCAMS.Type of Scams Tricks Scam Entities Example COVID-19 token scam Issuing new tokens that claim to be used for charity; "Pump-and-dump" schemes Token CoronaCoin (0x10Ef64cb79Fd4d75d4Aa7e8502d95C42124e434b) COVID-19 giveaway scam Promising to reward users based on the money they sent.Social Network Domain https://gatesbtc.liveCOVID-19 themed Blackmail Threatening to spread coronavirus if not receive cryptos, or selling products/cures for COVID-19.Email abtconsultnl@oceanenergy.ch Crypto Malware Locking victims' phones or computers and asking for cryptos Software https://coronavirusapp.site Ponzi Schemes Claiming to return high interest payback if someone invest with cryptos Domain, Social Network https://coronainvest.io
Knowledge graph embedding by relational and entity rotation

Knowledge graphs are typical large-scale multi-relational structures and useful for many artificial intelligence tasks. However, knowledge graphs often have missing facts, which limits the development of downstream tasks. To refine the knowledge graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models aim to learn distributed representations for entities and relations and predict unknown triplets by scoring candidate triplets. Nevertheless, state-of-the-art works either aim to capture different relation patterns, or to model the multi-fold relations, and yet fail to consider these two aspects simultaneously. To fill this gap, in this paper, we propose a novel knowledge graph embedding model, MRotatE. It exploits triplet features from the perspective of relational and entity rotations, which can model and infer various relation patterns and handle with multi-fold relations at the same time. The experimental results demonstrate that MRotatE outperforms existing approaches and attains the state-of-the-art performance.