Reviewerly/exHarmony huggingface dataset:
Discriminating WirelessHART Communication Devices Using Sub-Nyquist Stimulated Responses

Reliable detection of counterfeit electronic, electrical, and electromechanical devices within critical information and communications technology systems ensures that operational integrity and resiliency are maintained. Counterfeit detection extends the device’s service life that spans manufacture and pre-installation to removal and disposition activity. This is addressed here using Distinct Native Attribute (DNA) fingerprinting while considering the effects of sub-Nyquist sampling on DNA-based discrimination. The sub-Nyquist sampled signals were obtained using factor-of-205 decimation on Nyquist-compliant WirelessHART response signals. The DNA is extracted from actively stimulated responses of eight commercial WirelessHART adapters and metrics introduced to characterize classifier performance. Adverse effects of sub-Nyquist decimation on active DNA fingerprinting are first demonstrated using a Multiple Discriminant Analysis (MDA) classifier. Relative to Nyquist feature performance, MDA sub-Nyquist performance included decreases in classification of %CΔ ≈ 35.2% and counterfeit detection of %CDRΔ ≈ 36.9% at SNR = −9 dB. Benefits of Convolutional Neural Network (CNN) processing are demonstrated and include a majority of this degradation being recovered. This includes an increase of %CΔ ≈ 26.2% at SNR = −9 dB and average CNN counterfeit detection, precision, and recall rates all exceeding 90%.
Design and Coverage Path Planning of a Disinfection Robot

Eliminating pathogen exposure is an important approach to control outbreaks of epidemics such as COVID-19 (coronavirus disease 2019). To deal with pathogenic environments, using disinfection robots is a practicable choice. This research formulates a 3D (three-dimensional) spatial disinfection strategy for a disinfection robot. First, a disinfection robot is designed with an extensible control framework for the integration of additional functions. The robot has eight degrees of freedom that can handle disinfection tasks in complex 3D environments where normal disinfection robots lack the capability to ensure complete disinfection. An ingenious clamping mechanism is designed to increase flexibility and adaptability. Secondly, a new coverage path planning algorithm targeted at the spraying area is used. This algorithm aims to achieve an optimal path via the rotating calipers algorithm after transformation between a 2D (two-dimensional) array and 3D space. Finally, the performance of the designed robot is tested through a series of simulations and experiments in various spaces that humans usually live in. The results demonstrate that the robot can effectively perform disinfection tasks both in computer simulation and in reality.
Digital Transformation of Educational Institutions Accelerated by COVID-19: A Digital Dynamic Capabilities Approach

During the Coronavirus crisis (COVID-19) that started in 2019 and at the extensive quarantine regulations, educational institutions, companies, and individuals have reacted by shifting their teaching and learning activities to virtual spaces. Yet, although the use of online learning has increased, it has not been able to achieve the long-promised transformative effect. The COVID-19 crisis has the potential to boost online education overall or at least enable better preparation of the system for the next crisis. Ultimately, to make a digital transformation sustainable, appropriate skills are required. In this study, we adapt the dynamic capabilities foundations creating a theoretical approach to explain how educational institutions have responded to the changing environmental conditions during the COVID-19 pandemic.
Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data

This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network-a multilayer perceptron based on dispersed data. The idea is to train local models that have identical structures based on local tables; however, due to different sets of conditional attributes present in local tables, it is necessary to generate some artificial objects to train local models. The paper presents a study on the use of varying parameter values in the proposed method of creating artificial objects to train local models. The paper presents an exhaustive comparison in terms of the number of artificial objects generated based on a single original object, the degree of data dispersion, data balancing, and different network structures-the number of neurons in the hidden layer. It was found that for data sets with a large number of objects, a smaller number of artificial objects is optimal. For smaller data sets, a greater number of artificial objects (three or four) produces better results. For large data sets, data balancing and the degree of dispersion have no significant impact on quality of classification. Rather, a greater number of neurons in the hidden layer produces better results (ranging from three to five times the number of neurons in the input layer).
Clustered Federated Learning Based on Momentum Gradient Descent for Heterogeneous Data

Data heterogeneity may significantly deteriorate the performance of federated learning since the client’s data distribution is divergent. To mitigate this issue, an effective method is to partition these clients into suitable clusters. However, existing clustered federated learning is only based on the gradient descent method, which leads to poor convergence performance. To accelerate the convergence rate, this paper proposes clustered federated learning based on momentum gradient descent (CFL-MGD) by integrating momentum and cluster techniques. In CFL-MGD, scattered clients are partitioned into the same cluster when they have the same learning tasks. Meanwhile, each client in the same cluster utilizes their own private data to update local model parameters through the momentum gradient descent. Moreover, we present gradient averaging and model averaging for global aggregation, respectively. To understand the proposed algorithm, we also prove that CFL-MGD converges at an exponential rate for smooth and strongly convex loss functions. Finally, we validate the effectiveness of CFL-MGD on CIFAR-10 and MNIST datasets.
Review of audio deepfake detection techniques: Issues and prospects

Abstract In the past years, multimedia content has improved in realism and plausibility owing to the development of deep learning techniques, particularly the generative adversarial networks and variational auto‐encoders. Though digital content, especially digital movies shot from a certain viewpoint gives a true representation of reality, yet the ubiquitous usage of content manipulation techniques casts doubt on its veracity. Deepfaking an AI based tampering technique, is able to map facial and acoustic features of a source person onto the target with an intention to make target say or enact the things that has not happened in real. Numerous approaches have been proposed in the literature for detection of image and video deepfakes. With technological advancement, researchers have also started to examine audio deepfakes and ways to detect them. As there is currently no comprehensive overview of audio deepfake generation and detection techniques, this paper aims to provide a survey of the relevant literature in this area. This survey paper intends to help research fraternity about the available audio generation and detection approaches for design of reliable detection models in future to classify fake and real audios.
Pandemics and Infodemics: How COVID-19 is Reshaping Content Regulation?

The debate about tackling online misinformation and disinformation is not unique to the COVID-19 pandemic. However, the health crisis has elevated the danger of misleading information circulated on social media. Labelled as ‘infodemic’, the problem of COVID-19-related misinformation and disinformation prompted governments and social media platforms around the globe to impose various speech restrictions. The public and private policy frameworks aiming to curb the spread of the infodemic were adopted in a state of emergency and without proper scrutiny. Yet, they are shaping the future of content regulation, possibly affecting freedom of speech and other democratic values for years to come. This chapter looks at the key problematic aspects of actions taken by governments and social media platforms to address COVID-19-related misinformation and disinformation and discusses the possible long-term effects of these measures.
A Hardware-Efficient Perturbation Method to the Digital Tent Map

Digital chaotic systems used in various applications such as signal processing, artificial intelligence, and communications often suffer from the issue of dynamical degradation. This paper proposes a solution to address this problem in the digital tent map. Our proposed method includes a simple and optimized hardware architecture, along with a hardware-efficient perturbation method, to create a high-performance computing system that retains its chaotic properties. We implemented our proposed architecture using an FPGA (Field-Programmable Gate Array) and the 1’s complement fixed-point format. Our results demonstrate that the implemented digital circuit reduces logical resource consumption compared to state-of-the-art references and exhibits pseudo-random nature, as confirmed by various statistical tests. We validated our proposed pseudo-random number generator in a hardware architecture for particle swarm optimization, demonstrating its effectiveness.
Autoencoder and Incremental Clustering-Enabled Anomaly Detection

Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no known examples of anomalous behaviour. Moreover, we are limited to the machine’s voltage, amperage, and current readings, drawn from a retrofitted power outlet in 60-s samples. No rich sensor data or previous insights are available as a training basis, limiting our ability to leverage the existing work. We design a system to monitor the behaviour of electrical appliances. This system requires special consideration as different power cycles from the same machine can exhibit different behaviours, and it accounts for this by clustering unseen cycle patterns into siloed training datasets and corresponding learned parameters. They are then passed in real-time to an autoencoder ensemble for reconstruction-based anomaly detection, using the error in reconstruction as a means to flag anomalous points in time. The system correctly identifies and trains appropriate cycle clusters of data streams on a real-world machine dataset injected with stochastic, proportionate anomalies.
Distributed Anonymous e-Voting Method Based on Smart Contract Authentication

With the development of science and technology, the traditional centralized ballot management will lead to the risk of an opaque voting process and tampering of back-end data, and it can no longer meet the requirements of e-voting transparency and anonymity, while the distributed blockchain technology, with its features of being transparent and tamper-proof, can well solve the problems in traditional e-voting and make the ballot firmly in the hands of users. To make each node reach consensus, each node can access the complete shared ledger, and malicious nodes can analyze the transaction information in the ledger to obtain users’ voting information, which makes anonymity impossible to guarantee. We propose a blockchain technology-based voting record synchronization model and an anonymous authentication model, using zk-SNARK and Merkle tree technology, that achieve user authentication and anonymous voting. In this scheme, the user’s random identity address is inserted into the Merkle tree, and when voting, it is only necessary to prove that the user’s identity address is in the tree to complete anonymous voting without revealing the user’s real identity. This scheme meets the basic requirements of electronic voting and ensures the reliability and security of voting.
JSUM: A Multitask Learning Speech Recognition Model for Jointly Supervised and Unsupervised Learning

In recent years, the end-to-end speech recognition model has emerged as a popular alternative to the traditional Deep Neural Network—Hidden Markov Model (DNN-HMM). This approach maps acoustic features directly onto text sequences via a single network architecture, significantly streamlining the model construction process. However, the training of end-to-end speech recognition models typically necessitates a significant quantity of supervised data to achieve good performance, which poses a challenge in low-resource conditions. The use of unsupervised representation significantly reduces this necessity. Recent research has focused on end-to-end techniques employing joint Connectionist Temporal Classification (CTC) and attention mechanisms, with some also concentrating on unsupervised presentation learning. This paper proposes a joint supervised and unsupervised multi-task learning model (JSUM). Our approach leverages the unsupervised pre-trained wav2vec 2.0 model as a shared encoder that integrates the joint CTC-Attention network and the generative adversarial network into a unified end-to-end architecture. Our method provides a new low-resource language speech recognition solution that optimally utilizes supervised and unsupervised datasets by combining CTC, attention, and generative adversarial losses. Furthermore, our proposed approach is suitable for both monolingual and cross-lingual scenarios.
Backward Compatible Identity-Based Encryption

In this paper, we present a new identity-based encryption (IBE) system that is named Backward Compatible Identity-based Encryption (BC-IBE). Our BC-IBE is proposed to solve the problem caused by the out-of-synchronization between users' private keys and ciphertexts. Encryption systems such as revocable IBE or revocable Attribute-based Encryption (ABE) often require updating private keys to revoke users after a certain time period. However, in those schemes, an updated key can be used to decrypt the ciphertexts created only during the current time period. Once the key is updated and the previous keys are removed, the user, the owner of the updated key, will lose access to the past ciphertexts. In our paper, we propose BC-IBE that supports backward compatibility, to solve this problem. In our proposed system, user's private keys and ciphertexts can be updated periodically with time tags, and these processes can be used to revoke users who do not receive an updated key as the other revocable encryption does. However, in our proposed system, a private key newly issued to a user is backward compatible. This means that it decrypts not only the ciphertexts at the present time period but also all past ciphertexts. This implies that our proposed scheme guarantees the decryption of all encrypted data even if they are not synchronized. Compared to the existing revocable identity-based encryption system, our proposed BC-IBE has the advantage of simplifying key management and securely delegating ciphertext updates. Our proposed scheme only requires a single backward-compatible private key to decrypt all past ciphertexts created. Moreover, the ciphertext update process in our proposed scheme does not require any special privileges and does not require decryption. This means that this process can be securely delegated to a third-party server, such as a cloud server, and it prevents the potential leakage of secrets. For those reasons, BC-IBE is suitable for a system where users are more dynamic, such as the Internet-of-Things (IoT) network, or a system that regularly updates the data, like cloud data storage. In this paper, we provide the construction of BC-IBE and prove its formal security.
Communication-Efficient Federated Learning with Adaptive Consensus ADMM

This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication costs are two primary challenges that hinder the efficacy of federated learning. To overcome these challenges, we have designed an algorithm, FedACADMM, which applies the adaptive consensus alternating direction method of multipliers (ACADMM) to federated learning clients (i.e., the edge mobile devices) to tackle the heterogeneity problem in federated networks. Importantly, the cost per round of communication for FedACADMM remains consistent with FedAvg and FedProx without adding any extra workload. Furthermore, our experimental results demonstrate that FedACADMM outperforms baseline methods with a realistic set of federated datasets, displaying enhanced convergence robustness. Notably, in highly heterogeneous scenarios, FedACADMM exhibits significant stability and rapid convergence, requiring at least 75% fewer communication rounds to achieve the target accuracy.
Borno-Net: A Real-Time Bengali Sign-Character Detection and Sentence Generation System Using Quantized Yolov4-Tiny and LSTMs

Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue. In this paper, we propose an end-to-end system that can detect Bengali sign characters from input images or video frames and generate meaningful sentences. The proposed system consists of two phases. In the first phase, a quantization technique for the YoloV4-Tiny detection model is proposed for detecting 49 different sign characters, including 36 Bengali alphabet characters, 10 numeric characters, and 3 special characters. Here, the detection model localizes hand signs and predicts the corresponding character. The second phase generates text from the predicted characters by a detection model. The Long Short-Term Memory (LSTM) model is utilized to generate meaningful text from the character signs detected in the previous phase. To train the proposed system, the BdSL 49 dataset is used, which has approximately 14,745 images of 49 different classes. The proposed quantized YoloV4-Tiny model achieves a mAP of 99.7%, and the proposed language model achieves an overall accuracy of 99.12%. In addition, performance analysis among YoloV4, YoloV4 Tiny, and YoloV7 models is provided in this research.
Classification of Arabic Poetry Emotions Using Deep Learning

The automatic classification of poems into various categories, such as by author or era, is an interesting problem. However, most current work categorizing Arabic poems into eras or emotions has utilized traditional feature engineering and machine learning approaches. This paper explores deep learning methods to classify Arabic poems into emotional categories. A new labeled poem emotion dataset was developed, containing 9452 poems with emotional labels of joy, sadness, and love. Various deep learning models were trained on this dataset. The results show that traditional deep learning models, such as one-dimensional Convolutional Neural Networks (1DCNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) networks, performed with F1-scores of 0.62, 0.62, and 0.53, respectively. However, the AraBERT model, an Arabic version of the Bidirectional Encoder Representations from Transformers (BERT), performed best, obtaining an accuracy of 76.5% and an F1-score of 0.77. This model outperformed the previous state-of-the-art in this domain.
ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
Temperature Control for Mass Concrete Construction of Wentang Bridge 5# Pier Pile Cap in Winter Construction

Abstract In order to ensure the construction quality of mass concrete structures in winter, and avoid and prevent harmful temperature cracks of mass concrete, a calculation method of temperature field of hydration heat in winter based on finite element method is proposed in this paper. First, the three-dimensional finite element model of the temperature field of hydration heat is established; Then, the temperature variation of winter hydration heat temperature field of Wentang Bridge is calculated through experiments. The results show that the maximum measured temperature of the core point is 44.2 °C, and the corresponding calculated temperature is 53.7 °C. The maximum internal and external temperature difference measured is 24.7 °C, and the corresponding calculated temperature is 22.4°C. The research results prove that the calculation model can better simulate the concrete hydration process.
A Multivariate Time Series Anomaly Detection Method Based on Clustered Particle Swarm Optimization

Abstract Due to the advent of 5G and the integration of sensors, sensor nodes need to collect data from multiple sources simultaneously.Traditional anomaly detection methods for single attribute time series detect multivariate data, low detection accuracy, and significant node energy consumption. To avoid these problems, this paper provides an improved FCM multivariate time series clustering method based on the sliding window. Based on the CPSO optimal weight, the reconstruction error realizes anomaly detection. Experiments on different datasets demonstrate the effectiveness of the algorithm. Moreover, compared with the three classical methods, the results show that the proposed algorithm has higher detection accuracy and accuracy.