Reviewerly/exHarmony huggingface dataset:
Use of artificial intelligence in the library services: prospects and challenges

Purpose Artificial intelligence (AI) is one of the emerging technologies of this time. It has been associated with several trades like business, defense, health and education, but its role in library services will foster intelligent decisions. AI is a widely used technology in library services that can transform the best services in the age of information technology. This paper aims to highlight the use of AI in library operations. Several research have been undertaken on this subject, but that only address a few applications. This paper highlights the pros and cons of AI in library services. AI and libraries have a substantial nexus; nevertheless, the use and awareness of AI in library services are still creating question marks addressed in this paper. This study will help the policy stakeholder, librarians and scholars in the field to address these issues before the deployment of AI in library services. Design/methodology/approach This study is based on a qualitative method using content analysis techniques. Extant literature was explored on the subject and was analyzed for this study. This study highlights the pros and cons of AI in library services and its possible solutions. Findings The findings of this study show that AI is a vibrant technology that can be used in library services; however, some obstacles like adequate funds, the attitude of librarians and technical skills are a few obstacles that hamper AI in library operations. The findings also reveal that using AI in library operations will accelerate libraries in the right direction. Furthermore, this study highlights various applications that can be deployed without spending costs. Originality/value This study covers the understanding of AI in library services that will help the librarian's information professionals leverage AI in library scenarios. Furthermore, the practical implication of AI in library services will bring positive change in implementing AI. Social implications: This study will motivate library professionals to take advantage of AI in library services and further accelerate library operations in the right direction.
Blockchain-based solution for Pharma Supply Chain Industry

Modern pharma supply chain systems are complicated, involving manufacturers, suppliers, and consumers that span across continents. To date, there is a considerable lack of transparency in the movement and sale of pharmaceutical products sold online. Lack of transparency, mistrust in collaboration, and unwillingness to share data can be huge challenges for this global industry. In this paper, we propose a blockchain-based framework for the online sale of pharma products, in a decentralized manner with no intermeies such as hospitals or pharmacies. The proposed solution is based on utilizing Ethereum smart contracts, to monitor the interaction between participants, trigger events that are logged to help the participants to keep track and be informed about sale transactions, and ensure payment dispersal securely. In addition, smart contracts regulate the interaction between sellers and consumers by monitoring the status of IoT containers, including pharmaceuticals, and fully notifying consumers. Our smart contracts handle special cases related to consumer refunds in case of breach of contract terms to ensure the safe delivery of medicines.
Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach

The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people’s sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people’s sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters.
A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning

Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.
Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification.
The use of mobile learning applications in higher education institutes

Nowadays, many people, especially students, enjoy spending their free time with mobile devices. The main reason for this is that an intelligent device based on current information presents the desires and requirements of students. A nowadays-promising technology to overcome the problems in m-learning, mobile devices provide reliable, customized and guaranteed dynamic computing environments for all users. This article presents a new pedagogical architecture for a mobile learning system in an extended cloud-computing environment enriched with smart devices. This architecture can be applied everywhere where there is a need for intensive teaching and learning in higher education. The system architecture was described together with the discussion of knowledge assessment methods possibilities and potential issues. In addition, a solution to the problem of developing applications for organizing mobile learning using the visual programming environment MIT App Inventor is proposed. The results of experiments on the use of mobile education were analyzed and presented in tabular diagram form.
Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.
Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review

A micro-credential is a proof of the student's knowledge, skills, and experience that can be used to progress towards a larger credential or degree that focuses on a particular field of study in the shortest amount of time. Micro-credentials are a new area in the education sector that has expanded significantly over recent years and have become a popular idea in the higher education sector. Since the Covid-19 pandemic, micro-credentials are the most recent innovation in online education, gaining traction in public and private universities throughout the world. This has resulted in many universities developing strategies to offer micro-credential-driven courses. Higher education institutions (HEIs) need to validate micro-credentials, but the validation is a long-drawn-out and cumbersome process, so blockchain technology can be used to easily validate the detailed information on each students’ micro-credentials. Unfortunately, to date, only scant scholarly research has been conducted on blockchain-based micro-credentialing systems in HEIs. This study provides a detailed overview of the state-of-the-art in the field of managing micro-credentials using blockchain technology. We start by outlining the various requirements that need to be met in a blockchain-based micro-credentialing system. We then use a systematic literature review (SLR) to retrieve relevant studies published between 2016–2022 and compare them to the defined requirements. We also analyse the relevant studies to determine the research gaps. This review will offer insight into micro-credentialing systems that have been proposed for HEIs over recent years.
An Improved YOLOv5 Method for Small Object Detection in UAV Capture Scenes

Aiming at the problem of a large number of small dense objects in high-altitude shooting and complex background noise interference in the captured scenes, an improved object detection algorithm for YOLOv5 UAV capture scenes is proposed. A Feature Enhancement Block (FEBlock) is first proposed to generate adaptive weights for different receptive field features by convolution, assigning major weights to shallow feature maps to improve small object feature extraction ability. The FEBlock is then integrated into Spatial Pyramid Pooling (SPP) to generate Enhanced Spatial Pyramid Pooling (ESPP), which performs feature enhancement for the result of each maximum pooling; and creates new features containing multi-scale contextual information with better feature characterization capability by weighting fused contextual features. Secondly, the Self-Characteristic Expansion Plate (SCEP) is proposed, which achieves the fusion and expansion of feature information through compression, non-linear mapping, and expansion with its own module, further improving the network’s capacity for feature extraction and generating a new spatial pyramid pooling (ESPP-S) by splicing with ESPP. Finally, a shallower feature map is added as a detection layer to the YOLOv5 network model’s large, medium, and small detection layers to improve the network’s detection performance for medium and long-range objects. Experiments were conducted on the VisDrone2021 dataset, and the results showed that the improved YOLOv5 model improved mAP0.5 by 4.6%, mAP0.5:0.95 by 2.9%, and precision by 2.7%. The mAP0.5 of the model trained at the input resolution of 1024 × 1024 reached 56.8%. The experiments show that the improved YOLOv5 model can improve object detection accuracy for UAV capture scenes.
Textual Context-Aware Dense Captioning with Diverse Words

Dense captioning generates more detailed spoken descriptions for complex visual scenes. Despite several promising leads, existing methods still have two broad limitations: 1) The vast majority of prior arts only consider visual contextual clues during captioning but ignore potentially important textual context; 2) current imbalanced learning mechanisms limit the diversity of vocabulary learned from the dictionary, thus giving rise to low language-learning efficiency. To alleviate these gaps, in this paper, we propose an end-to-end enhanced dense captioning architecture, namely Enhanced Transformer Dense Captioner (ETDC), which obtains textual context from surrounding regions and dynamically diversifies the vocabulary bank during captioning. Concretely, we first propose the Textual Context Module (TCM), which is integrated into each self-attention layer of the Transformer decoder, to capture the surrounding textual context. Moreover, we take full advantage of the class information of object context and propose a Dynamic Vocabulary Frequency Histogram (DVFH) re-sampling strategy during training to balance words with different frequencies. The proposed method is tested on the standard dense captioning datasets and surpasses the state-of-the-art methods in terms of mean Average Precision (mAP).
A Secure Traffic Police Remote Sensing Approach via a Deep Learning-Based Low-Altitude Vehicle Speed Detector through UAVs in Smart Cites: Algorithm, Implementation and Evaluation

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI’s field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles’ speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view.
A Survey of Intelligent Detection Designs of HTML URL Phishing Attacks

Phishing attacks are a type of cybercrime that has grown in recent years. It is part of social engineering attacks where an attacker deceives users by sending fake messages using social media platforms or emails. Phishing attacks steal users’ information or download and install malicious software. They are hard to detect because attackers can design a phishing message that looks legitimate to a user. This message may contain a phishing URL so that even an expert can be a victim. This URL leads the victim to a fake website that steals information, such as login information, payment information, etc. Researchers and engineers work to develop methods to detect phishing attacks without the need for the eyes of experts. Even though many papers discuss HTML and URL-based phishing detection methods, there is no comprehensive survey to discuss these methods. Therefore, this paper comprehensively surveys HTML and URL phishing attacks and detection methods. We review the current state-of-art deep learning models to detect URL-based and hybrid-based phishing attacks in detail. We compare each model based on its data preprocessing, feature extraction, model design, and performance.
Crime Analysis Using Computer Vision Approach with Machine Learning

Depending on the seriousness of the offence, any deliberate act that causes harm to oneself or another, as well as damage to or loss of property, qualifies as a crime for the purposes of criminal law. The number and diversity of unlawful activities are increasing at an alarming rate, necessitating the creation of efficient enforcement tools by law enforcement agencies. Due to their slowness and inefficiency, traditional crime-solving methods are no longer effective in today’s high-crime climate. As a result, we may be able to reduce the workload of police personnel and contribute to crime prevention if we develop ways for reliably anticipating crime in advance of its occurrence. In order to do this, we suggest using ML and computer vision technologies and methodologies. Throughout this study, we describe the results of several cases in which such strategies were used, which sparked our interest in further study. The fundamental reason for the change in crime detection and prevention strategies is based on the prior and subsequent statistical observations made by the authorities. Machine learning and computer vision may help law enforcement and other authorities detect, prevent and solve crimes more quickly and accurately, and this is the primary goal of this research effort. With the use of artificial intelligence and computer vision, law enforcement agencies might be transformed.
AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual​ deepfakes detection

Recent advances in the field of machine learning and social media platforms facilitate the creation and rapid dissemination of realistic fake content (i.e., images, videos, audios). Initially, the fake content generation involved the manipulation of either audio or video streams but currently, more realistic deepfakes content is being produced via modifying both audio–visual streams. Researchers in the field of deepfakes detection mostly focus on identifying fake videos exploiting solely visual or audio modality. However, there exist a few approaches for audio–visual deepfakes detection but mostly are not evaluated on a multimodal dataset with deepfakes videos having the manipulations in both streams. The unified approaches evaluated on the audio–visual deepfakes dataset have reported low detection accuracies and failed when the faces are side-posed. Therefore, in this paper, we introduced a novel AVFakeNet framework that focuses on both the audio and visual modalities of a video for deepfakes detection. More specifically, our unified AVFakeNet model is a novel Dense Swin Transformer Net (DST-Net) which consists of an input block, feature extraction block, and output block. The input and output block comprises dense layers while the feature extraction block employs a customized swin transformer module. We have performed extensive experimentation on five different datasets (FakeAVCeleb, Celeb-DF, ASVSpoof-2019 LA, World Leaders dataset, Presidential Deepfakes dataset) comprising audio, visual, and audio–visual deepfakes along with a cross-corpora evaluation to signify the effectiveness and generalizability of our unified framework. Experimental results highlight the effectiveness of the proposed framework in terms of accurately detecting deepfakes videos via scrutinizing both the audio and visual streams.