Reviewerly/exHarmony huggingface dataset:
Face Mask Detection in Realtime Environment using Machine Learning based Google Cloud

An outbreak of the coronavirus (Covid-19) has spread around the world, causing widespread health concerns. The pandemic's reach has spread to nearly every development sector. There have been numerous steps attempted to prevent the in areas where Covid-19 has spread, wearing a face mask is mandatory. The Covid-19 transmission could not have been controlled without it. To top it all off, seeks to detect finegrained wearing states of the face mask: make sure your face is covered with the right mask Without a disguise. There are two very difficult jobs in our job: first, because of an online market for augmented data sets as well as large-scale training datasets. A face mask identification app for mobile devices is presented in this paper. In its entirety, Cloud-based machine learning technology known as Google Cloud ML API automates the process using TensorFlow files to train the model The ML's effectiveness is demonstrated in this research model. The progress of cloud technology is also examined in this paper. Over traditional coding methods, machine learning is becoming increasingly popular.
Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis

Multimodal Emotion Recognition for Conversation (ERC) is a challenging multi-class classification task that requires recognizing multiple speakers’ emotions in text, audio, video, and other modalities. ERC has received considerable attention from researchers due to its potential applications in opinion mining, advertising, and healthcare. However, the syntactic structure characteristics of the text itself have not been considered in this study. Taking into account this, this paper proposes a conversational affective analysis model (DSAGCN) combining dependent syntactic analysis and graph convolutional neural networks. Since words that reflect emotional polarity are usually concentrated exclusively in limited regions, the DSAGCN model first employs a self-attention mechanism to capture the most effective words in the dialogue context and obtain a more accurate vector representation of the emotional semantics. Then, based on speaker relationships and dependent syntactic relationships, the multimodal sentiment relationship graphs are constructed. Finally, a graph convolutional neural network is used to complete the recognition of multimodal emotion. In extensive experiments on two real datasets, IEMOCAP and MELD, the DSAGCN model outperforms the existing models in terms of average accuracy and f1 values for multimodal emotion recognition, especially for emotions such as “happiness” and “anger”. Thus, dependent syntactic analysis and self-attention mechanism can enhance the model’s ability to understand emotions.
YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network

The current development of the satisfiability logical representation in Discrete Hopfield Neural Network has two prominent perspectives which are systematic and non-systematic logic. In general, systematic logic was reported to provide us with more consistent and very predictable behaviour whereas non-systematic logic provides broader solution space with a non-predictable pattern. However, the main weaknesses of the existing satisfiability logical rule are the non-flexibility of the logical composition and a low capability to represent the feature's intersection between systematic and non-systematic logic. In this paper, a flexible logical rule called Y-Type Random 2 Satisfiability was proposed by combining the structure of the systematic and non-systematic logic, as a hybrid satisfiability logical rule. The proposed logical rule enumerates randomly based on the structure of either first-order, second-order, or both orders of the clause. The proposed logical rule will be implemented into Discrete Hopfield Neural Network with the aim of minimizing the cost function. The behaviour of the proposed logical rule is compared with the existing satisfiability logic in terms of various performance evaluation metrics. The results show the improvement of the solution capacity for our proposed logic in terms of synaptic weight assessments, various error analyses, energy evaluation, the effects of different states, similarity index, and variability of states. The findings confirm that the proposed logical rule serves as a flexible symbolic instruction for the Discrete Hopfield Neural Network by obtaining the optimal synaptic weights and more variation of neuron states in solution spaces.
Critically Analyzing the Concept of Internet of Things (IOT) and How It Impacts Employee and Organizational Performance

It connects people, organizations, and smart objects, the internet of things (IoT) has the potential to profoundly alter the way we live, work, and interact, and it has the potential to reshape a broad variety of industrial areas. The purpose of this research article is to offer a critical examination of the Internet of Things (IoT) and how much it affects individual and organizational performance in the workplace. We examine the literature on the Internet of Things, including what it is, why it is important, what it has historically been, its benefits, pros and cons, security and privacy issues, and its impact on organizational performance, and we conclude that a better recognizing of how the Internet of Things will impact the ways that organizations and employees conduct their business is required.
Research and application of XGBoost in imbalanced data

As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but its classification effect in the case of data imbalance is often not ideal. Aiming at this problem, an attempt was made to optimize the regularization term of XGBoost, and a classification algorithm based on mixed sampling and ensemble learning is proposed. The main idea is to combine SVM-SMOTE over-sampling and EasyEnsemble under-sampling technologies for data processing, and then obtain the final model based on XGBoost by training and ensemble. At the same time, the optimal parameters are automatically searched and adjusted through the Bayesian optimization algorithm to realize classification prediction. In the experimental stage, the G-mean and area under the curve (AUC) values are used as evaluation indicators to compare and analyze the classification performance of different sampling methods and algorithm models. The experimental results on the public data set also verify the feasibility and effectiveness of the proposed algorithm.
Artificial intelligence humor in service recovery

With the growing prevalence of artificial intelligence (AI) technologies in the tourism and hospitality industry, the phenomenon of AI service failure emerges, which merits empirical attention. Via six scenario-based experiments, this study verifies why and how humorous responses from AI agents affect customers' tolerance of AI service failure. Findings confirm the positive impact of humorous response on customer tolerance. Results further reveal the mediating effects of perceived warmth and perceived competence along with boundaries (customer inoculation and time pressure) of the humor effect. These findings enhance knowledge of AI service failure and recovery by delineating the recovery effect of AI agents' humorous responses. This research also offers tourism companies a new tool, humorous response, for resolving AI service failures.
SDN and NFV for QoE-driven multimedia services delivery: The road towards 6G and beyond networks

The concept for developing the future mobile communication systems has been explored in recent years with a vision towards 5G and 6G systems. Meeting performance targets such as higher data rate transmission, lower end-to-end latency, higher capacity, lower cost, and satisfied Quality of Experience (QoE) for delivered services is the key for the success of 5G and 6G networks. Within these performance targets and associated enabling technologies, QoE and the overall management of multimedia services is the one which has been less well addressed for 5G in the past. This paper provides a comprehensive and ground-breaking discussion regarding QoE management in the context of future softwarized 5G/6G networks. We introduce a generalized QoE provisioning ecosystem for emerging multimedia streaming services in softwarized 5G/6G and beyond networks. We explore potential network softwarization and communication technologies that will enable the end-user’s QoE in 5G/6G. We further provide promising use case scenarios regarding (a) QoE-driven management of multimedia services over multi-access cloud/edge softwarized and virtualized 5G networks, (b) QoE monitoring for large traffic variations in business, residential and public areas in 5G networks, along with a Machine Learning (ML)-based softwarized controller for QoE provisioning over 5G/6G networks. We build on the achievements of 5G networks to provide the roadmap towards the development of 6G and beyond networks in terms of requirements, use case scenarios and service classes. We also present potential technologies that will be dominant through 2025–2030 in shaping the vision of 6G and beyond networks including (a) self-driving 3D network architecture, (b) network management, automation and orchestration, and (c) pervasive artificial intelligence. Moreover, we provide challenges and research directions in 6G and beyond wireless communication systems. The paper provides the first roadmap towards the management, orchestration and monitoring of multimedia 3D services in 6G and beyond networks. The comprehensive goal of this paper is to encourage researchers from the industry and academia to work together towards the realization of softwarized 6G and beyond networks while tackling the critical research challenges regarding the management of emerging 3D multimedia services and applications. The comments made by the authors in this paper are expectations on 6G, and the standardization of 6G where these aspects will be decided in 6G networks and beyond, will be started soon by different standardization bodies and fora.
Blockchain solutions for carbon markets are nearing maturity

<h2>Summary</h2> Carbon markets could hasten climate change mitigation by driving investment towards efficient decarbonization activities, but they face problems around trust, transparency, and uptake. Blockchain offers a foundational technology upon which new carbon markets can be built which address these shortcomings. This sector is still nascent, fragmented, and clouded by technology hype, all of which obscures objective judgement of its performance and suitability. Here, we survey the current blockchain ecosystem, identifying 39 organizations that are developing blockchain solutions for carbon markets across four use cases: emissions-trading schemes, voluntary carbon markets, Article 6 of the Paris Agreement, and the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). We develop and apply a technology readiness level (TRL) scale, and we find that most projects are still proofs of concept (TRL ≤ 3); however, one system has now reached maturity (TRL 9). Addressing the common barriers that face developers could allow more blockchain solutions to mature and potentially facilitate globalized carbon markets with greater efficiency, transparency, and accessibility.
InPars: Unsupervised Dataset Generation for Information Retrieval

The Information Retrieval (IR) community has recently witnessed a revolution due to large pretrained transformer models. Another key ingredient for this revolution was the MS MARCO dataset, whose scale and diversity has enabled zero-shot transfer learning to various tasks. However, not all IR tasks and domains can benefit from one single dataset equally. Extensive research in various NLP tasks has shown that using domain-specific training data, as opposed to a general-purpose one, improves the performance of neural models. In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks. We show that models finetuned solely on our synthetic datasets outperform strong baselines such as BM25 as well as recently proposed self-supervised dense retrieval methods. Code, models, and data are available at https://github.com/zetaalphavector/inpars.
Improved differential evolution algorithm based on the sawtooth-linear population size adaptive method

The main parameters that affect the performance of a differential evolution algorithm are the scaling factor, crossover rate, and population size. To adaptively adjust the population diversity and balance the exploration and exploitation abilities of a differential evolution algorithm during the evolution process, this study proposes a sawtooth-linear population size adaptive (SLPSA) method and an improved scaling factor and crossover rate control method. Subsequently, an improved differential evolution algorithm called SLDE is proposed. The SLPSA method constructs an external archive for storing the abandoned trial vectors and periodically adds vectors from external archive to the population during the iterative process, thereby periodically increasing the diversity of the population, which enhances the algorithm’s ability to jump out of locally optimal values. Based on a distance-based parameter adaptation method, the update formulas for the scaling factor weight and crossover rate weight are improved. By using 84 benchmark functions from CEC 2011, CEC 2014, CEC 2015, and CEC 2017, the SLDE performance was verified experimentally by analyzing and comparing the proposed SLDE to six state-of-the-art differential evolution algorithms. The experimental results demonstrated that the SLDE performance was significantly better than that of the six state-of-the-art differential evolution algorithms.
Multitentacle Federated Learning Over Software-Defined Industrial Internet of Things Against Adaptive Poisoning Attacks

Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT. To address this problem, this article proposes a multi-tentacle federated learning (MTFL) framework, which is essential to guarantee the trustness of training data in SD-IIoT. In MTFL, participants with similar learning tasks are assigned to the same tentacle group. To identify adaptive poisoning attacks, a tentacle distribution-based efficient poisoning attack detection (TD-EPAD) algorithm is presented. And also, to minimize the impact of adaptive poisoning data, a stochastic tentacle data exchanging (STDE) protocol is also proposed. Simultaneously, to protect the tentacle’s privacy in STDE, all exchanged data will be processed by differential privacy technology. A MTFL prototype system is implemented, which provides extensive ablation experiments and comparison experiments, demonstrating that the accuracy of the global model under attack scenario can be improved with 40%.
MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection

RGB-D Salient Object Detection (RGB-D SOD) aims at detecting remarkable objects by complementary information from RGB images and depth cues. Although many outstanding prior arts have been proposed for RGB-D SOD, most of them focus on performance enhancement, while lacking concern about practical deployment on mobile devices. In this paper, we propose mobile asymmetric dual-stream networks (MoADNet) for real-time and lightweight RGB-D SOD. First, inspired by the intrinsic discrepancy between RGB and depth modalities, we observe that depth maps can be represented by fewer channels than RGB images. Thus, we design asymmetric dual-stream encoders based on MobileNetV3. Second, we develop an inverted bottleneck cross-modality fusion (IBCMF) module to fuse multimodality features, which adopts an inverted bottleneck structure to compensate for the information loss in the lightweight backbones. Third, we present an adaptive atrous spatial pyramid (A2SP) module to speed up the inference, while maintaining the performance by appropriately selecting multiscale features in the decoder. Extensive experiments are conducted to compare our method with 15 state-of-the-art approaches. Our MoADNet obtains competitive results on five benchmark datasets under four evaluation metrics. For efficiency analysis, the proposed method significantly outperforms other baselines by a large margin. The MoADNet only contains 5.03 M parameters and runs 80 FPS when testing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> image on a single NVIDIA 2080Ti GPU.
Deep Adaptively-Enhanced Hashing With Discriminative Similarity Guidance for Unsupervised Cross-Modal Retrieval

Cross-modal hashing that leverages hash functions to project high-dimensional data from different modalities into the compact common hamming space, has shown immeasurable potential in cross-modal retrieval. To ease labor costs, unsupervised cross-modal hashing methods are proposed. However, existing unsupervised methods still suffer from two factors in the optimization of hash functions: 1) similarity guidance, they barely give a clear definition of whether is similar or not between data points, leading to the residual of the redundant information; 2) optimization strategy, they ignore the fact that the similarity learning abilities of different hash functions are different, which makes the hash function of one modality weaker than the hash function of the other modality. To alleviate such limitations, this paper proposes an unsupervised cross-modal hashing method to train hash functions with discriminative similarity guidance and adaptively-enhanced optimization strategy, termed Deep Adaptively-Enhanced Hashing (DAEH). Specifically, to estimate the similarity relations with discriminability, Information Mixed Similarity Estimation (IMSE) is designed by integrating information from distance distributions and the similarity ratio. Moreover, Adaptive Teacher Guided Enhancement (ATGE) optimization strategy is also designed, which employs information theory to discover the weaker hash function and utilizes an extra teacher network to enhance it. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed DAEH against the state-of-the-arts.
BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection

The feature selection problem is one of the pre-processing mechanisms to find the optimal subset of features from a dataset. The search space of the problem will exponentially grow when the number of features increases. Hence, the feature selection problem is classified as an NP-hard problem, and exact algorithms cannot find the optimal subset at a reasonable time. As a result, approximate algorithms like meta-heuristic algorithms are extensively applied to solve the problem. The feature selection problem is a discrete (binary) optimization problem; consequently, a discrete meta-algorithm can be employed to find the optimal subset of features. One of the recently introduced meta-heuristic algorithms is Marine Predator Algorithm (MPA), which has shown good solutions to many continuous optimization problems. In this study, a novel Binary Marine Predator Algorithm using Time-Varying Sine and V-shaped transfer functions (BMPA-TVSinV) is proposed to find the optimal subset of features in datasets. The proposed algorithm applies two new time-varying transfer functions to convert the continuous search space to the binary one. These transfer functions considerably improve the performance of BMPA-TVSinV. Several well-known datasets with high-dimensional features and three coronavirus disease (COVID-19) datasets have been selected to compare the results of BMPA-TVSinV with some recently introduced binary meta-heuristic algorithms for the feature selection problem. The results show the superiority of BMPA-TVSinV in achieving high classification accuracy and feature reduction rate. The source code of BMPA-TVSinV for feature selection problem is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/115315-bmpa-tvsinv-a-binary-metaheuristic-for-feature-selection . • A Binary Marine Predator Algorithm (BMPA-TVSinV) is proposed for feature selection. • Two novel time-varying Sine and V-shaped transfer functions are applied in BMPA. • The proposed algorithm is evaluated by high-dimensional and COVID-19 datasets. • BMPA-TVSinV archives a higher accuracy and feature reduction rate on datasets.
Metaverse: welcome to the new fraud marketplace

Purpose The purpose of this study is to examine the risks of the metaverse ecosystem. This study provides an overview of the metaverse and its evolution and discusses the various fraud risks it poses for organizations (including boards of directors, forensic accountants, auditors and accountants). Given the advantages of the metaverse and the growing interest it is attracting from organizations, this paper sheds light on the importance of mitigating its risks. Design/methodology/approach Based on a systematic review of the literature on the metaverse and analysis of the fraud triangle, this study examines the different fraud risks it poses. More specifically, this study analyzes 21 articles on the metaverse published between 2021 and 2022 and attempts to answer the following research questions: What are the risks inherent in the metaverse? What are the fraud risks associated with it? What are the opportunities and pressures it brings? What is the rationalization underlying its use? This study conducts the analysis on two levels, that of the individual (user) and that of the organization. This paper summarizes the findings of publications on the metaverse in 2021 and 2022 to discover its various definitions and the opportunities and risks it represents. Findings This paper offers an insightful discussion of the advantages and risks the metaverse can bring. Because this analysis shows that any organization could be vulnerable to metaverse risks, this study provides organizations with strategies to deter, detect and prevent fraud and reputational risks. Regulatory bodies, financial authorities, board of directors and fraud investigators should all consider these risks before investing in the metaverse. Originality/value This paper adds new insights to the scarce research on the metaverse and cybersecurity by exploring the opportunities and risks it presents. It has several implications for organizations, boards of directors, management and regulatory authorities.
Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities.