Reviewerly/exHarmony huggingface dataset:
Context-aware Emotion Detection from Low-resource Urdu Language Using Deep Neural Network

Emotion detection (ED) plays a vital role in determining individual interest in any field. Humans use gestures, facial expressions, and voice pitch and choose words to describe their emotions. Significant work has been done to detect emotions from the textual data in English, French, Chinese, and other high-resource languages. However, emotion classification has not been well studied in low-resource languages (i.e., Urdu) due to the lack of labeled corpora. This article presents a publicly available Urdu Nastalique Emotions Dataset ( UNED ) of sentences and paragraphs annotated with different emotions and proposes a deep learning (DL)-based technique for classifying emotions in the UNED corpus. Our annotated UNED corpus has six emotions for both paragraphs and sentences. We perform extensive experimentation to evaluate the quality of the corpus and further classify it using machine learning and DL approaches. Experimental results show that the developed DL-based model performs better than generic machine learning approaches with an F1 score of 85% on the UNED sentence-based corpus and 50% on the UNED paragraph-based corpus.
Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection

The key to video anomaly detection is understanding the appearance and motion differences between normal and abnormal events. However, previous works either considered the characteristics of appearance or motion in isolation or treated them without distinction, making the model fail to exploit the unique characteristics of both. In this brief, we propose an appearance-motion united auto-encoder (AMAE) framework to jointly learn the prototypical spatial and temporal patterns of normal events. The AMAE framework includes a spatial auto-encoder to learn appearance normality, a temporal auto-encoder to learn motion normality, and a channel attention-based spatial-temporal decoder to fuse the spatial-temporal features. The experimental results on standard benchmarks demonstrate the validity of the united appearance-motion normality learning. The proposed AMAE framework outperforms the state-of-the-art methods with AUCs of 97.4%, 88.2%, and 73.6% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets, respectively.
Impact of blockchain technology on the optimal pricing and quality decisions of platform supply chains

We investigate how blockchain technology influences optimal pricing and quality decisions under two different supply chain structures. In both cases, the supplier is the leader and plays a Stackelberg game with downstream platform(s). Specifically, the supplier determines the wholesale prices and quality levels of the products, and the platform(s) decides the selling prices. Based on our analysis, we find that when consumer acceptance of blockchain is low, among the two products in the single platform, the products with blockchain technology have not only a high quality but also a low price. However, in the case of the two competitive platforms, although blockchain will also enable the platform that uses this technology to provide products with a high quality and low price, its advantages are not obvious. Nevertheless, in comparing the two cases, when consumer acceptance is high, the two competing platforms tend to provide products with a high quality and low price, regardless of whether or not the products use blockchain technology. Our conclusions reveal that although blockchain will benefit a supplier and consumers, in some cases, the supplier will have an incentive to decrease the quality level of the products with blockchain technology, which will eventually negatively affect consumers.
A novel image encryption algorithm with deep neural network

A novel image encryption algorithm based on deep neural network (DNN) is proposed. First, a new encryption unit with the deep neural network (EDNN) is designed. The EDNN is a multilayer forward neural network, and the weight matrices of the EDNN are composed of some scrambled discrete cosine transform (DCT) coefficients matrices to encrypt directly the original image. Then, the original image can be recovered by a decryption unit with the deep neural network (DDNN). For the DDNN, its network structure is symmetric with the EDNN, and the original image can be recovered with two pursuit algorithms corresponding to two activation functions of the EDNN, respectively. The experimental results show the effectiveness of the EDNN for image encryption, and demonstrate that the proposed method can effectively resist brute force attack, statistical attack, differential attack and cutting attack.
Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues

The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks.
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection

Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we aim to mine comprehensive co-salient features with democracy and reduce background interference without introducing any extra information. To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects. Then a comprehensive prototype based on the response maps can be generated as a guide for final prediction. To suppress the noisy background information in the prototype, we propose a self-contrastive learning module, where both positive and negative pairs are formed without relying on additional classification information. Besides, we also design a democratic feature enhancement module to further strengthen the co-salient features by readjusting attention values. Extensive experiments show that our model obtains better performance than previous state-of-the-art methods, especially on challenging real-world cases (e.g., for CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for maximum E-measure, and 3.7% for S-measure) under the same settings. Source code is available at https://github.com/siyueyu/DCFM.
MFA: TDNN with Multi-Scale Frequency-Channel Attention for Text-Independent Speaker Verification with Short Utterances

The time delay neural network (TDNN) represents one of the state-of-the-art of neural solutions to text-independent speaker verification. However, they require a large number of filters to capture the speaker characteristics at any local frequency region. In addition, the performance of such systems may degrade under short utterance scenarios. To address these issues, we propose a multi-scale frequency-channel attention (MFA), where we characterize speakers at different scales through a novel dual-path design which consists of a convolutional neural network and TDNN. We evaluate the proposed MFA on the VoxCeleb database and observe that the proposed framework with MFA can achieve state-of-the-art performance while reducing parameters and computation complexity. Further, the MFA mechanism is found to be effective for speaker verification with short test utterances.
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation

Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical for the generator to keep the source content and prevent it from unnecessary modifications. The self-supervised contrastive learning has already been successfully applied in the I2I. By constraining features from the same location to be closer than those from different ones, it implicitly ensures the result to take content from the source. However, previous work uses the features from random locations to impose the constraint, which may not be appropriate since some locations contain less information of source domain. Moreover, the feature itself does not reflect the relation with others. This paper deals with these problems by intentionally selecting significant anchor points for contrastive learning. We design a query-selected attention (QS-Attn) module, which compares feature distances in the source domain, giving an attention matrix with a probability distribution in each row. Then we select queries according to their measurement of significance, computed from the distribution. The selected ones are regarded as anchors for contrastive loss. At the same time, the reduced attention matrix is employed to route features in both domains, so that source relations maintain in the synthesis. We validate our proposed method in three different I2I datasets, showing that it increases the image quality with-out adding learnable parameters. Codes are available at https://github.com/sapphire497/query-selected-attention.
Source free domain adaptation for medical image segmentation with fourier style mining

Unsupervised domain adaptation (UDA) aims to exploit the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled target domain. Existing UDA techniques typically assume that samples from source and target domains are freely accessible during the training. However, it may be impractical to access source images due to privacy concerns, especially in medical imaging scenarios with the patient information. To tackle this issue, we devise a novel source free domain adaptation framework with fourier style mining, where only a well-trained source segmentation model is available for the adaptation to the target domain. Our framework is composed of two stages: a generation stage and an adaptation stage. In the generation stage, we design a Fourier Style Mining (FSM) generator to inverse source-like images through statistic information of the pretrained source model and mutual Fourier Transform. These generated source-like images can provide source data distribution and benefit the domain alignment. In the adaptation stage, we design a Contrastive Domain Distillation (CDD) module to achieve feature-level adaptation, including a domain distillation loss to transfer relation knowledge and a domain contrastive loss to narrow down the domain gap by a self-supervised paradigm. Besides, a Compact-Aware Domain Consistency (CADC) module is proposed to enhance consistency learning by filtering out noisy pseudo labels with shape compactness metric, thus achieving output-level adaptation. Extensive experiments on cross-device and cross-centre datasets are conducted for polyp and prostate segmentation, and our method delivers impressive performance compared with state-of-the-art domain adaptation methods. The source code is available at https://github.com/CityU-AIM-Group/SFDA-FSM.
Practical and secure multifactor authentication protocol for autonomous vehicles in <scp>5G</scp>

Abstract Autonomous vehicles (AV) can not only improve traffic safety and congestion, but also have strategic significance for the development of the transportation industry. With the continuous updating of core technologies such as artificial intelligence, sensor detection, synchronous positioning, and high‐precision mapping, the development of AV has been promoted. When 5G network is combined with Internet of Vehicles, the problems of AV can be solved by taking advantage of 5G ultra‐large bandwidth, low latency and high reliability. However, when the user controls the vehicle remotely, a real‐time and reliable authentication process is needed, while minimizing the overhead of security protocols. Therefore, this article proposes a practical and secure multifactor user authentication protocol for AV in 5G network. By introducing non‐interactive zero‐knowledge proof technology and physical uncloning function, the protocol completes mutual authentication and key agreement without revealing any sensitive information. The article proves the security of the protocol through BAN logic and the simulation of Scyther. And it can resist malicious attacks and provide more security features. The informal security analysis shows that the protocol can meet the proposed security requirements. Finally, we evaluate the efficiency of the protocol, and the results show that the protocol can provide better performance.
Binary Golden Eagle Optimizer with Time-Varying Flight Length for feature selection

The concept of any method to resolve feature selection issues is to identify a subset of the original features. However, determining a minimal feature subset is considered an NP-hard problem. Many existing feature selection methods, namely particle swarm optimization (PSO), grey wolf optimization (GWO) etc., are complex and consume more time. This article proposes a new wrapper-based method that employs Binary Golden Eagle Optimizer with Time Varying Flight Length (BGEO-TVFL) to reduce the feature selection issues. The proposed method provides optimal feature selection based on Golden Eagle Optimizer (GEO). GEO is a recently presented swarm-based meta-heuristics algorithm that imitates the smartness of Golden Eagles (GEs). GEO algorithm performs on continuous space; however, the feature selection is in discrete space. Hence, the continuous space has transformed into a discrete space by employing transfer functions. Here, eight different transfer functions have been applied to determine the best transfer function and test the BGEO-TVFL. Moreover, a time-varying flight length (TVFL) has been employed to balance the exploration and exploitation in GEO. Finally, the performance of the proposed BGEO-TVFL has been determined by focusing on various metrics under different UC Irvine (UCI) datasets. The best variant has been selected and analyzed the performance with existing standard filter feature selection approaches and wrapper based feature selection methods. The existing methods include Bat Algorithm (BAT), Ant Colony Optimization (ACO) algorithm, PSO, GWO, Genetic Algorithm (GA), Cuckoo Search (CS), Golden Eagle Optimizer (GEO), ReliefF, Information Gain (IG), Correlation-based feature selection (CFS), and Gain Ratio (GR) respectively. In addition, the performance of BGEO-TVFL is also compared and analyzed with state-of-art methods to determine its superiority. Furthermore, the proposed BGEO-TVFL is analyzed with existing algorithms by employing 30 functions from CEC’2017, CEC’2018 and CEC’2020 benchmarks. As a result, the proposed BGEO-TVFL has achieved a better result than existing methods and is suitable for dealing with dimensionality reduction issues.
BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network

Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recognizing Bengali sign language (BSL) alphabets to overcome the issue of generalization. The proposed method has been evaluated with three benchmark datasets such as ‘38 BdSL’, ‘KU-BdSL’, and ‘Ishara-Lipi’. Here, three steps are followed to achieve the goal: segmentation, augmentation, and Convolutional neural network (CNN) based classification. Firstly, a concatenated segmentation approach with YCbCr, HSV and watershed algorithm was designed to accurately identify gesture signs. Secondly, seven image augmentation techniques are selected to increase the training data size without changing the semantic meaning. Finally, the CNN-based model called BenSignNet was applied to extract the features and classify purposes. The performance accuracy of the model achieved 94.00%, 99.60%, and 99.60% for the BdSL Alphabet, KU-BdSL, and Ishara-Lipi datasets, respectively. Experimental findings confirmed that our proposed method achieved a higher recognition rate than the conventional ones and accomplished a generalization property in all datasets for the BSL domain.
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

Abstract The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors’ perspectives for the clinical implementation of ML.
Simulation and Performance Analysis of Arc Guard Systems

In the recent decade, there has been a significant advancement in arc-flash protection systems, which now include anything from arc-resistant gear to circuit breakers that have instantaneous settings. The use of arc-flash relays, which operate on the principle of light detection, has grown more widespread, particularly in medium-voltage systems. Arc flash relays are just recently being used in low-voltage applications, according to the manufacturer. Light-based arc flash relays perform well in medium-voltage applications, but their dependability in low-voltage applications is questionable. In addition, the complexity of wiring and installation should be kept to a minimum in all applications. Researchers and developers working on arc flash detection technology will be addressed by the authors in order to overcome some of these challenges.
A pipeline and comparative study of 12 machine learning models for text classification

Text-based communication is highly favoured as a communication mean, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models’ performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. B analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public corpus. Statistical tests and explainability techniques (SHAP) are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%. All data, models, and code used in this work are available on GitHub at https://github.com/Angione-Lab/12-machine-learning-models-for-text-classification.