Reviewerly/exHarmony huggingface dataset:
Mobile Robot Navigation Using Deep Reinforcement Learning

Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm. The simulation results show that the mobile robot can autonomously navigate, recognize, and reach the target object location in an unknown environment without colliding with static and dynamic obstacles. Similar results are obtained in real-world experiments, but only with static obstacles. The DDQN agent outperforms the DQN agent in reaching the target object location in the test simulation by 5.06%.
F<sup>2</sup>Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection

In recent years, face forgery detectors have aroused great interest and achieved impressive performance, but they are still struggling with generalization and robustness. In this work, we explore taking full advantage of the fine-grained forgery traces in both spatial and frequency domains to alleviate this issue. Specifically, we propose a novel High-Frequency Fine-Grained Transformer (F2Trans) network which contains two important components, namely Central Difference Attention (CDA) and High-frequency Wavelet Sampler (HWS). The premier CDA module is capable of capturing invariant fine-grained manipulation patterns by aggregating both pixel-level intensity and gradient information of the query to generate key and value pairs. Subsequently, the proposed HWS discards the low-frequency components of wavelet transformation and hierarchically explores high-frequency forgery cues of feature maps, which prevents model confusion caused by low-frequency components and pays attention to local frequency information. In addition, HWS can be employed as a special pooling layer for the F2Trans architecture to produce hierarchical feature representations in the spatial-frequency domain. Extensive experiments on multiple popular benchmarks demonstrate the generalization and robustness of the specially designed F2Trans framework is well-tailored for face forgery detection when confronting the cross-dataset, cross-manipulation, and unseen perturbations.
Toward Deeper Understanding of Camouflaged Object Detection

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localize, segment, and rank</i> camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JingZhang617/COD-Rank-Localize-and-Segment</uri> .
Digitally Deprived Children in Europe

The COVID-19 pandemic has completely changed the need for internet connectivity and technological devices across the population, but especially among school-aged children. For a large proportion of pupils, access to a connected computer nowadays makes the difference between being able to keep up with their educational development and falling badly behind. This paper provides a detailed account of the digitally deprived children in Europe, according to the latest available wave of the European Union - Statistics on Income and Living Conditions (EU-SILC). We find that 5.4% of school-aged children in Europe are digitally deprived and that differences are large across countries. Children that cohabit with low-educated parents, in poverty or in severe material deprivation are those most affected.
A Multitask learning model for multimodal sarcasm, sentiment and emotion recognition in conversations

Sarcasm, sentiment and emotion are tightly coupled with each other in that one helps the understanding of another, which makes the joint recognition of sarcasm, sentiment and emotion in conversation a focus in the research in artificial intelligence (AI) and affective computing. Three main challenges exist: Context dependency, multimodal fusion and multitask interaction. However, most of the existing works fail to explicitly leverage and model the relationships among related tasks. In this paper, we aim to generically address the three problems with a multimodal joint framework. We thus propose a multimodal multitask learning model based on the encoder–decoder architecture, termed M2Seq2Seq. At the heart of the encoder module are two attention mechanisms, i.e., intramodal (Ia) attention and intermodal (Ie) attention. Ia attention is designed to capture the contextual dependency between adjacent utterances, while Ie attention is designed to model multimodal interactions. In contrast, we design two kinds of multitask learning (MTL) decoders, i.e., single-level and multilevel decoders, to explore their potential. More specifically, the core of a single-level decoder is a masked outer-modal (Or) self-attention mechanism. The main motivation of Or attention is to explicitly model the interdependence among the tasks of sarcasm, sentiment and emotion recognition. The core of the multilevel decoder contains the shared gating and task-specific gating networks. Comprehensive experiments on four bench datasets, MUStARD, Memotion, CMU-MOSEI and MELD, prove the effectiveness of M2Seq2Seq over state-of-the-art baselines (e.g., CM-GCN, A-MTL) with significant improvements of 1.9%, 2.0%, 5.0%, 0.8%, 4.3%, 3.1%, 2.8%, 1.0%, 1.7% and 2.8% in terms of Micro F1.
A state of art review on estimation of solar radiation with various models

<h2>Abstract</h2> Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radiation with low errors. Accordingly, it is noticed that various types of models have been continuously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accordingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.
Extrapolation over temporal knowledge graph via hyperbolic embedding

Abstract Predicting potential facts in the future, Temporal Knowledge Graph (TKG) extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts. Intuitively, facts (events) that happened at different timestamps have different influences on future events, which can be attributed to a hierarchy among not only facts but also relevant entities. Therefore, it is crucial to pay more attention to important entities and events when forecasting the future. However, most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts, which may be affected by the diversity and variability of the evolution, and they might fail to attach importance to facts that matter. Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data, which is considered to be a solution for modelling hierarchical relations among facts. To this end, we propose ReTIN, a novel model integrating real‐time influence of historical facts for TKG reasoning based on hyperbolic geometry, which provides low‐dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG. Considering both real‐time and global features of TKG boosts the adaptation of ReTIN to the ever‐changing dynamics and inherent constraints. Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines. The ablation study further supports the value of exploiting temporal information.
Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education

Purpose The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables. Design/methodology/approach A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Findings The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots. Practical implications The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education. Originality/value T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.
Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation

Energy consumption data are crucial for various smart energy management applications, such as demand forecasting, customer segmentation, and energy efficiency analysis. However, collecting sufficient training data can be a challenge due to data privacy concerns, technical barriers, and cost issues. This paper presents a novel data generation model called Federated-WDCGAN, which combines federated learning with an enhanced generative adversarial network to generate scalable and realistic energy consumption data while preserving privacy. The proposed model is trained in a federated manner, where the generator is trained to generate realistic energy consumption data, and the discriminators are trained to distinguish the generated data from real data. The proposed model is evaluated through analysis of the generated data and its use in a machine learning task for the classification of household characteristics, demonstrating its ability to produce high-quality data comparable to real data in terms of statistics, patterns, and classification performance. The privacy-preserving capabilities of the model are also assessed using non-independently identically distributed and independently identically distributed data, achieving satisfactory results.
Face Detection and Recognition Using Face Mesh and Deep Neural Network

Face detection and recognition are emerging and active research areas in computer vision and deep learning. Face detection and recognition have a wide range of applications such as recognizing the people in particular areas namely stores, and banks. Identifying people in a particular database (police database). To control people's entry into restricted areas or grant access to ATMs or computers. In this paper, the proposed model can detect and recognize the face using Face mesh. Due to Face mesh, the model operates in a variety of conditions such as varying illumination and background. The model can also handle non-frontal images of males and females of all ages and races. The Labeled wild face (LWF) dataset images and images captured in real-time are used to train the deep neural network of the model. During testing, if the face landmarks of the test image match with the face landmarks of any of the training images the model gives the name of the person else model outputs as “unknown”.94.23% accuracy are achieved for face recognition by the proposed model.
An Empirical Study of A Smart Education Model Enabled by the Edu-Metaverse to Enhance Better Learning Outcomes for Students

The Edu-Metaverse, a vast ensemble of different technologies, has initiated a great and unprecedented change in the field of education. This change has been effected through the following Edu-Metaverse characteristics: embodied and multimodal interaction; immersive teaching scenarios, which can accelerate learning and skill acquisition; and the emergence of AI-enabled agents. In comparison to traditional classroom teaching models, smart education is a collaborative and visual model that adopts the latest AI technologies to reach a learning outcome. However, a problem that should be considered is how a smart education model, enabled by the Edu-Metaverse, can be developed to enhance better learning outcomes for students. Such a model should highlight smart pedagogy in the context of the Edu-Metaverse, together with a smart teaching environment, multimodal teaching resources, and AI-enabled assessment. In this study, we focused on the teaching of college English to 60 students from Zhejiang Open University. We investigated the effectiveness of a smart education model, which was empowered by the Edu-Metaverse, in enhancing better learning outcomes for the students, using a combination of qualitative and quantitative research. After the one-semester-long experiment, questionnaires were sent out to complement the interview findings. It was found that the students who engaged in the smart education model in the Edu-Metaverse yielded higher scores in oral English, vocabulary and grammar, reading comprehension, English-to-Chinese translation, and writing than those who engaged in traditional instruction. Therefore, this study suggests that a smart education model enabled by the Edu-Metaverse, which is characterized by a highly immersive experience, multimodal interaction, and a high degree of freedom for resource sharing and creation can help learners to realize deep learning, develop their capacity for high-order thinking, and help them to become intelligent individuals in an online learning space. In order to facilitate this smart learning, we make the following suggestions for educational institutions: (1) teachers should improve the design of teaching scenarios, (2) teachers should focus on learning assessment that is based on core literacy, and (3) teachers’ knowledge of the architecture of the Edu-Metaverse should be enhanced.
Enhancing privacy preservation and trustworthiness for decentralized federated learning

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning framework, where multiple data owners cooperate to train a global model without any aggregation server. In addition to protecting data privacy, it also avoids a single point of failure, and reduces the communication traffic congestion due to the central server. However, there are still many inherent privacy and security issues, e.g., training samples could be revealed by inferring gradients and malicious participants tend not to execute federated learning tasks as intended. Even worse, existing works rarely simultaneously consider a fundamental issue that data owners may join in and drop out of a DFL task at any time. In this paper, we propose PTDFL, a privacy-enhanced and trustworthy decentralized federated learning scheme. Specifically, we firstly design an efficient gradient encryption algorithm to protect data privacy, and then devise a succinct proof without trapdoors to ensure the correctness of the gradients. Meanwhile, we design a novel local aggregation strategy without trusted third party to ensure that the aggregated result is trustworthy. Moreover, our PTDFL is also supportive of data owners joining in and dropping out during the whole DFL task. Finally, we provide privacy and security analysis, implement a prototype of the PTDFL, and conduct extensive experiments on a real dataset. The results show that our PTDFL achieves more efficiency than prior works.
LSTM-Markov based efficient anomaly detection algorithm for IoT environment

The seamless integration of wireless and IoT device in normal day to day life and for smart homes has enabled more security and privacy needs. The attack or unauthorized access to these devices may result in issues in decision making inside the smart environment. Attacks and anomalies in open IoT system could provide false alarms and cause delay in processing the information’s. The problem statement considered is, Anomaly detection systems, on the other hand, can be targets of attacks, h/w s/w failures, and thus fall short of their objectives. Because these are power-hungry devices carrying highly sensitive data, an effective attack and anomaly alert system is critical in an IoT-based environment. The present algorithms require high training and additional memory to identify the anomalies in the network, which is not practically feasible to simple edge-based computing devices. The anomalies and false information inside the network are handled with effective anomaly detection algorithm designed in this paper. An efficient anomaly detection method in real-time sensor is identified through markov and LSTM based network and the outliers in the data is clearly removed through the proposed approach. The proposed approach is tested with the real-time DHT sensor monitoring room temperature and room humidity. The proposed methodology provides 96.03% effective anomalies detection with 92.48% high training accuracy. The methodology showcase improved with 6.54% of effective anomaly rejection and 5.13% of training accuracy when compared with KNN algorithm.
BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs

Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving. Though this task has attracted a large amount of research efforts, it is still challenging to flexibly cope with arbitrary (single or multiple) camera sensors equipped on the autonomous vehicle. In this paper, we present BEVSegFormer, an effective transformer-based method for BEV semantic segmentation from arbitrary camera rigs. Specifically, our method first encodes image features from arbitrary cameras with a shared backbone. These image features are then enhanced by a deformable transformer-based encoder. Moreover, we introduce a BEV transformer decoder module to parse BEV semantic segmentation results. An efficient multi-camera deformable attention unit is designed to carry out the BEV-to-image view transformation. Finally, the queries are reshaped according to the layout of grids in the BEV, and upsampled to produce the semantic segmentation result in a supervised manner. We evaluate the proposed algorithm on the public nuScenes dataset and a self-collected dataset. Experimental results show that our method achieves promising performance on BEV semantic segmentation from arbitrary camera rigs. We also demonstrate the effectiveness of each component via ablation study.