Reviewerly/exHarmony huggingface dataset:
An adaptively balanced grey wolf optimization algorithm for feature selection on high-dimensional classification

Feature selection, which aims to screen out redundant and irrelevant features from datasets, is integral to machine learning and data mining. Grey Wolf Optimization (GWO) is a recent meta-heuristic algorithm based on swarm intelligence and has wide applicability to various optimization problems due to its fast convergence and few parameters. However, since the wolf pack is always dominated by the three leading wolves (i.e., α, β and δ), the GWO algorithm suffers from weak exploration throughout the whole optimization process and easily stagnates into local optima. In this paper, an Adaptively Balanced Grey Wolf Optimization (ABGWO) algorithm is proposed to seek out the optimal feature subset for high-dimensional classification. Specifically, to improve the exploration ability of GWO, a random wolf is introduced to cooperate with α, β and δ. A novel level-based strategy is further adopted to select the random wolf. Besides, to dynamically modulate the exploration and exploitation ability in different optimization stages, an adaptive coefficient is introduced to regulate the leadership of α, β, δ and the randomly-selected wolf. Finally, the improvement of exploration and exploitation is validated on 12 high-dimensional datasets provided by Arizona State University and University of California Irvine, and the superiority of ABGWO is further verified by comparing it with seven state-of-the-art feature selection approaches on the aspect of classification accuracy, size of feature subset and computational time.
Mining Cryptocurrency-Based Security Using Renewable Energy as Source

Cryptocurrency mining and blockchain technology using renewable energy as the main electricity source has gained attention for sustainable development in financial areas. However, very few studies have been reported concerning the power usage of cryptocurrencies using renewable energy. In this article, we report the effect of overclocking and undervolting on power usage and the hash rate for mining dogecoin with solar energy as renewable energy. The mining rig used in this work consists of different graphics processing units (GPUs) and non-LHR (lite hash rate) cards. The UnMineable software has been used for mining dogecoin as well as for wallet integration. The results indicate that mining dogecoin with solar energy as renewable energy consumes 2000 Watts power with overclocking and 1700 Watts power with undervolting technique. This work implicates the potential future of crypto-mining with renewable energy and the hardware configuration associated with it, which is expected to reduce e-waste and improve sustainable development. To reduce the e-waste and high electricity consumption, we have introduced two important techniques named GPU optimization and use of renewable energy for mining, which helps the miners to reduce the e-waste and electricity consumption significantly at the same time getting most out of the GPU by not having any impact on the environment.
Breast Cancer Detection and Classification Empowered With Transfer Learning

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.
COVID-19 and the Key Digital Transformation Lessons for Higher Education Institutions in South Africa

COVID-19 has left an unmistakable impression on the world stage. It has altered the global socio-economic landscape, forcing individuals to adapt and embrace new ways of doing business, as well as new ways of life. One of the most significant effects of the pandemic was to hasten the adoption of digital technologies by many areas of the global economy. Campus closures were observed in the higher education sector, putting an end to long-standing face-to-face teaching and learning. This necessitated the most rapid paradigm shift ever seen in this industry to continue educating and learning. An abrupt change to online learning, which is primarily reliant on digital technologies, occurred. As a result, the purpose of this article is to investigate and identify ten critical digital transformation lessons from COVID-19 for South African higher education institutions. Secondary data gathered from the literature research were used to meet the study’s goal. The study concluded by laying out the most important digital transformation lessons for South Africa, including the notion that the country should strive to build digital capabilities because digital technology gaps remain wide, among other things.
AI-enabled image fraud in scientific publications

Destroying image integrity in scientific papers may result in serious consequences. Inappropriate duplication and fabrication of images are two common misconducts in this aspect. The rapid development of artificial-intelligence technology has brought to us promising image-generation models that can produce realistic fake images. Here, we show that such advanced generative models threaten the publishing system in academia as they may be used to generate fake scientific images that cannot be effectively identified. We demonstrate the disturbing potential of these generative models in synthesizing fake images, plagiarizing existing images, and deliberately modifying images. It is very difficult to identify images generated by these models by visual inspection, image-forensic tools, and detection tools due to the unique paradigm of the generative models for processing images. This perspective reveals vast risks and arouses the vigilance of the scientific community on fake scientific images generated by artificial intelligence (AI) models.
Face Editing Based on Facial Recognition Features

Face editing generates a face image with the target attributes without changing the identity or other information. Current methods have achieved considerable performance; however, they cannot effectively retain the face’s identity and semantic information while controlling the attribute intensity. Inspired by two human cognitive characteristics, namely, the principle of global precedence and the principle of homology continuity, we propose a novel face editing approach called the information retention and intensity control generative adversarial network (IricGAN). It includes a learnable hierarchical feature combination (HFC) function, which can construct a sample’s source space through multiscale feature mixing; it can guarantee the integrity of the source space while significantly compressing the network. Additionally, the attribute regression module (ARM) can decouple different attribute paradigms in the source space to ensure the correct modification of the required attributes and preserve the other areas. The gradual process of modifying the face attributes can be simulated by applying different control strengths in the source space. In face editing experiments, both qualitative and quantitative results demonstrate that IricGAN achieves the best overall results among state-of-the-art alternatives. Target attributes can be continuously modified by refeeding the relationship of the source space and the image, and the independence of each attribute can be retained to the greatest extent. IricGAN: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nanfangzhe/IricGAN</uri> .
Transfer Learning for Wireless Networks: A Comprehensive Survey

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing

Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively.
A study on image processing with data analysis

Image analysis is from images extracting meaningful information; mainly through digital image processing techniques from digital images. In this context, Tasks menu coded the tags are as simple as reading or a person from their face as sophisticated as identification. This paper presentation is made on various techniques used in analyzing images,like Computer view is from digital images or videos Dealing with how computers can gain a higher level of understanding Is an intermediate field of science, Machine vision (MV) is for applications such as automation Used to provide imaging based automation analysis and analysisTechnology and methods. Study, process control and robot guidance. Format authentication is the automatic recognition of methods and regulations in data. This includes statistical data analysis, signal processing, and Contains applications in image analysis. The dividing a digital several, the innovations are detailed in more detail in the text.
An extensive study on pre-trained models for program understanding and generation

Automatic program understanding and generation techniques could significantly advance the productivity of programmers and have been widely studied by academia and industry. Recently, the advent of pre-trained paradigm enlightens researchers to develop general-purpose pre-trained models which can be applied for a broad range of program understanding and generation tasks. Such pre-trained models, derived by self-supervised objectives on large unlabelled corpora, can be fine-tuned in downstream tasks (such as code search and code generation) with minimal adaptations. Although these pre-trained models claim superiority over the prior techniques, they seldom follow equivalent evaluation protocols, e.g., they are hardly evaluated on the identical benchmarks, tasks, or settings. Consequently, there is a pressing need for a comprehensive study of the pre-trained models on their effectiveness, versatility as well as the limitations to provide implications and guidance for the future development in this area. To this end, we first perform an extensive study of eight open-access pre-trained models over a large benchmark on seven representative code tasks to assess their reproducibility. We further compare the pre-trained models and domain-specific state-of-the-art techniques for validating pre-trained effectiveness. At last, we investigate the robustness of the pre-trained models by inspecting their performance variations under adversarial attacks. Through the study, we find that while we can in general replicate the original performance of the pre-trained models on their evaluated tasks and adopted benchmarks, subtle performance fluctuations can refute the findings in their original papers. Moreover, none of the existing pre-trained models can dominate over all other models. We also find that the pre-trained models can significantly outperform non-pre-trained state-of-the-art techniques in program understanding tasks. Furthermore, we perform the first study for natural language-programming language pre-trained model robustness via adversarial attacks and find that a simple random attack approach can easily fool the state-of-the-art pre-trained models and thus incur security issues. At last, we also provide multiple practical guidelines for advancing future research on pre-trained models for program understanding and generation.
Distributed Offloading for Cooperative Intelligent Transportation Under Heterogeneous Networks

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.
A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs

The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the existing knowledge graph still has some problems which form hot research topics in recent years. Numerous methods have been proposed based on various representation techniques. Graph Neural Network, a framework that uses deep learning to process graph-structured data directly, has significantly advanced the state-of-the-art in the past few years. This study firstly is aimed at providing a broad, complete as well as comprehensive overview of GNN-based technologies for solving four different KG tasks, including link prediction, knowledge graph alignment, knowledge graph reasoning, and node classification. Further, we also investigated the related artificial intelligence applications of knowledge graphs based on advanced GNN methods, such as recommender systems, question answering, and drug-drug interaction. This review will provide new insights for further study of KG and GNN.
Attention-based aspect sentiment classification using enhanced learning through cnn-Bilstm networks

Deep neural networks (dnn) techniques for aspect-based sentiment classification have been widely studied. The success of these methods depends largely on training data which are often inadequate because of the rigor involved in manually tagging large collection of opinionated texts. Attempts have been made to transfer knowledge from document-level to aspect-level sentiment task. However, the success of this approach is also dependent on the model because aspect sentiment data like other type of texts contain complex semantic features. In this paper, we present an attention-based deep learning technique which jointly learns on document and aspect-level sentiment data and which also transfers learning from the document-level data to aspect-level sentiment classification. It basically consists of a convolutional layer and a bidirectional long short-term memory (Bilstm) layer. The first variant of our technique uses convolutional neural network (cnn) to extract high-level semantic features. The output of the feature extraction is then fed into the Bilstm layer which captures the contextual feature representation of the texts. The second variant applies the Bilstm layer directly on the input data. In both variants, the output hidden representation is passed to an output layer using softmax activation function for sentiment polarity classification. We evaluate our model on four standard benchmark datasets which shows the effectiveness of our approach with improvements over baselines. We also conduct ablation studies to show the effect of the different document-level weights on the learning techniques.
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics of deep segmentation networks trained on inaccurately annotated data. We observe a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an “early-learning” phase, before eventually memorizing the false annotations. However, in contrast to classification, memorization in segmentation does not arise simultaneously for all semantic categories. Inspired by these findings, we propose a new method for segmentation from noisy annotations with two key elements. First, we detect the beginning of the memorization phase separately for each category during training. This allows us to adaptively correct the noisy annotations in order to exploit early learning. Second, we incorporate a regularization term that enforces consistency across scales to boost robustness against annotation noise. Our method outperforms standard approaches on a medical-imaging segmentation task where noises are synthesized to mimic human annotation errors. It also provides robustness to realistic noisy annotations present in weakly-supervised semantic segmentation, achieving state-of-the-art results on PASCAL VOC 2012. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/Kangningthu/ADELE
IoMT-Based Platform for E-Health Monitoring Based on the Blockchain

With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of patient’s health. To meet the constraint of medical secrecy and confidentiality of information, we will use the Blockchain as a secure system. Our system will use the data collected by several smart sensors such as blood pressure, SPO2 concentration, and EEG signals. These encrypted data will be collected by an embedded Raspberry PI 4 platform (working as a smart data relay) before being processed (on a backend server) and then saved in an embedded Blockchain node. The preliminary results show the effectiveness of the proposed platform as a candidate of a low-cost example of secured Electronic Health Record (EHR).