Reviewerly/exHarmony huggingface dataset:
Hybrid Ant Particle Swarm Genetic Algorithm (APSGA) for Task Scheduling in Cloud Computing

In cloud computing technology, task scheduling is one of the research challenges. For these various algorithms, works such as particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO) and genetic algorithm (GA). PSO is inspired by the bird’s movement, and ACO is based on the behaviour of ants. GA works based on the natural evolution process. This paper presents the hybrid of PSO-ACO-GA for task scheduling on virtual machines of cloud computing known as ant particle swarm genetic algorithm (APSGA). Here, GA and PSO will perform iteration to get the task basis on fitness value and further ACO will distribute the task on specific virtual machines. This paper has achieved improved results for parameters such as CPU utilization, makespan and execution time. Our proposed algorithm has achieved makespan that is reduced by 27.1%, 19.45% and 21.24% with compare to PSO, ACO and GA, respectively. It has achieved maximum of CPU utilization and execution time.
Association Rule Mining with Apriori Algorithm for Pediatric Foot Disorders

An association rule mining has potential to discover important information of disorder in data. The purpose of this study was to show analysis process of the clinical data to acquire significant information effectively between the foot disorder groups and biomechanical parameters related to symptom by the association rule discovery. The first clinical health records of the total 279 pediatric patients diagnosed with the complex foot disorder, including pes planus basically, were used for study. The Apriori algorithm was applied to discover rule of the foot disorder groups. As the results, we were able to discover 8 rules of the complex foot disorder and confirm major information. In next study, another data mining methodology like neural network will be applied with a careful preprocessing for better analysis of the pediatric foot disorder from now on.
Construction of Terrain Information Extraction model in the Karst Mountainous Terrain Fragmentation Area Based on UAV Remote Sensing

Rapid extraction of land use information plays an important role in data support for land construction and development and natural resource management. For south China the Karst mountainous terrain, object type is complicated, and lead to a difficult scene classification and information extraction, this paper use of Rotor Drone acquisition topography broken zone optical remote sensing image and image matching point cloud data, the comprehensive utilization of two-dimensional spectral information in remote sensing and image matching point cloud data in three-dimensional space structure information. The object oriented rule multi-feature extraction method was used to establish the ground object information in the terrain fragmentation area, and to realize the classification of building land, cultivated land, forest land and other ground objects. At the same time, the maximum likelihood method and K-mean method were used to classify visible images in the study area, and the accuracy of the classification was compared with that of object-oriented rule extraction. The results show that: (1) the ground object information extraction based on three-dimensional point cloud data and two-dimensional visible remote sensing image has a high accuracy, with the overall accuracy of 91.8&#x0025; and the Kappa coefficient of 0.89, which is simple and convenient for multi-factor extraction in complex terrain. (2) Compared with the single visible image for ground object extraction, the overall accuracy is improved by 22.91&#x0025;, and the Kappa coefficient is improved by 0.29. The addition of image matching point cloud data increases the feature of height factor extraction, which is more conducive to the extraction of ground object and land category information in the terrain fragmentation area.
Individualized tourism recommendation based on self-attention

Although the era of big data has brought convenience to daily life, it has also caused many problems. In the field of scenic tourism, it is increasingly difficult for people to choose the scenic spot that meets their needs from mass information. To provide high-quality services to users, a recommended tourism model is introduced in this paper. On the one hand, the tourism system utilises the users' historical interactions with different scenic spots to infer their short- and long-term favorites. Among them, the users' short-term demands are modelled through self-attention mechanism, and the proportion of short- and long-term favorites is calculated using the Euclidean distance. On the other hand, the system models the relationship between multiple scenic spots to strengthen the item relationship and further form the most relevant tourist recommendations.
Diversity and Inclusion in Cyber Security Early Careers

Her research interests cover diversity management, family firms' internationalization and generational diversity, in particular.Her current research projects are: (1) in family firms' internationalization and the impact that different generations have on this multidimensional process and (2) in the relationship between talent management practices for diverse talent groups and firm performance, with generation diverse talent as a core focus.
Insight-Based Vocalization of OLAP Sessions

Carrying out OLAP analyses in hands-free scenarios requires lean forms of communication between the users and the system, based for instance on natural language. In this paper we introduce VOOL, a framework specifically devised for vocalizing the insights resulting from OLAP sessions. VOOL is self-configurable, extensible, and is aware of the user's intentions expressed by OLAP operators. To avoid overwhelming the user with very long descriptions, we pursue the vocalization of selected insights automatically extracted from query results. These insights are detected by a set of modules, each returning a set of independent insights that characterize data. After describing and formalizing our approach, we evaluate it in terms of efficiency and effectiveness.
Requirement Analysis of Student Individual Learning Based on the Idea of Lifelong Learning

We are currently experiencing a knowledge-chasing society in which acquiring new knowledge and solving new problems are necessary and required skills for people to survive in human society in the future. Therefore, in such a circumstance, it is of certain research value to analyze the requirement satisfaction of Student Individual Learning (SIL) based on the idea of lifelong learning and discover problems in the process of SIL. However, existing studies generally have a few shortcomings such as the too narrow scope of study groups and the undiversified research objects. To this end, this paper took English learning as an example to research the requirement analysis of SIL based on the idea of lifelong learning. In the texts, the paper constructed a requirement satisfaction (RS) model of SIL, gave the calculation method of the learning benefit of SIL, and elaborated on the strategy optimization and selection method of SIL. The effectiveness of the proposed model was verified by experimental results attained in the study.
Augmented Reality Remote Maintenance in Industry: A Systematic Literature Review

Augmented reality (AR) is a promising technology for supporting industrial maintenance applications. Two major types of AR technology are used for maintenance applications. One of those is AR remote maintenance, a technology that connects remote experts to on-site technicians to work collaboratively on industrial maintenance applications. This seems especially valuable for nonstandardized tasks. Although several recent systematic literature reviews (SLRs) on AR for maintenance applications have been published, the growing body of literature calls for an ever-differentiated view of the knowledge base of AR remote maintenance. Therefore, this paper aims to map AR remote maintenance literature by conducting an SLR, characterizing the literature, describing applications in industry, and making suggestions for further research. Based on the analysis of 89 articles from the last two decades, this paper contributes the following findings: 1) the research field has a strong engineering focus on system development. 2) scholars share a common understanding of AR remote maintenance, despite using heterogeneous terminology; 3) the prevailing study design only allows for limited comparison of prototypes and applications; 4) transferability to industrial maintenance professionals is limited, due to the study design; 5) AR remote maintenance appears to raise business model opportunities for product-service systems; and 6) the diversity of AR remote maintenance applications indicates the technology’s industrial versatility. Overall, the maturity of the research field is increasing; however, it is still at an early stage. Based on these findings, we made two proposals for advancing the AR remote maintenance research field.
A Survey on CLIP-Guided Vision-Language Tasks

Multimodal learning refers to the representation of different modalities using a unified model. Modalities include images, text, audio, etc. In this article, we will first introduce the basic approach of CLIP which is a vision language model with the power of connecting different modalities, and then present different models inspired by CLIP on various downstream tasks. In the end, we conclude with a summary of the prospects and limitations of multimodal learning.
Review of text emotion detection

Emotion is one of the essential characteristics of being human. When writing essays or reports, people will add their own emotions. Text sentiment detection can detect the leading emotional tone of a text. Text emotion detection and recognition is a new research field related to sentiment analysis. Emotion analysis detects and identifies emotion types, such as anger, happiness, or sadness, through textual expression. It is a subdomain of NLP. For some applications, the technology could help large companies' Chinese and Russian data analysts gauge public opinion or conduct nuanced market research and understand product reputation. At present, text emotion is one of the most studied fields in the literature. Still, it is also tricky because it is related to deep neural networks and requires the application of psychological knowledge. In this article, we will discuss the concept of text detection and introduce and analyze the main methods of text emotion detection. In addition, this paper will also discuss the advantages and weaknesses of this technology and some future research directions and problems to be solved.
An Automated System to Preprocess and Classify Medical Digital X-Rays

Considering, the reality (fact) that innumerable medical (digital) images are collected at hospitals and numerous medical tests centres on a daily basis, a clear manifestation is that these images must be composed, stacked and accessed accurately for future references (CBIR). Sole motivation (reasoning) for proposition of this attempted work is to give a provision for categorizing or classifying medical X-rays automatically at macro-level (global level) to aid lab technicians (analysts) in their day job. GLCM is employed to draw out features or characteristics of (images) X-rays and resultant is utilized in building classification model by taking an advantage of SVM. Six different classes (groups) of X-ray images are taken, namely chest, foot, skull, neck, palm and spine. The proposed attempt to put forward a medical X-ray image classification process involves pre-processing of X-rays with an aim of making them fit for further processing. Digital X-rays in this current research are subjected to Pre-processing using a filtering operation (median filter), histogram filtering (or equalization) and CLAHE. The upshots of each are recorded. Subsequently, segmentation (connected component labelling), feature extraction (GLCM), classification (SVM). Lately post implementation, the outcomes vividly depict around 91% accuracy is acquired utilizing median filter accompanying GLCM and SVM whereas pre-processing images using, histogram equalization (HE) yielded 89% accuracy. Third blend (combination) of CLAHE, GLCM and SVM outshined with 96% accuracy. Consequently, CLAHE in estimation (comparison) to other Pre-processing methodologies outperformed classification results.
Modelling and Simulating a High-Resolution High-Fidelity Model for Three Discoveries in the Måløy Slope, Norwegian North Sea

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Logic and Accuracy Testing: A Fifty-State Review

Abstract Pre-election logic and accuracy (L $$ { \&amp; }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>&amp;</mml:mo> </mml:math> A) testing is a process in which election officials validate the behavior of voting equipment by casting a known set of test ballots and confirming the expected results. Ideally, such testing can serve to detect certain forms of human error or fraud and help bolster voter confidence. We present the first detailed analysis of L $$ { \&amp; }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>&amp;</mml:mo> </mml:math> A testing practices across the United States. We find that while all states require L $$ { \&amp; }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>&amp;</mml:mo> </mml:math> A testing before every election, their implementations vary dramatically in scope, transparency, and rigorousness. We summarize each state’s requirements and score them according to uniform criteria. We also highlight best practices and flag opportunities for improvement, in hopes of encouraging broader adoption of more effective L $$ { \&amp; }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>&amp;</mml:mo> </mml:math> A processes.
Word by Word Labelling of Romanized Sindhi Text by using Online Python Tool

Sindhi is one of the most ancient languages in the world and it has its own written and spoken scripts.After the rigorous study it was found that a lot of research work has been done in different languages, but word by word labelling of Sindhi language had not been done yet.In this research study, word labelling was done on 100 sentences of Romanized Sindhi texts using Python online tool.The dataset was collected from different sources which include Sindhi newspaper, blogs and social media webpages.From this dataset, a rule-based model has been applied for the Parts-of-Speech (POS) tagging of the Romanized Sindhi sentences.A total of 624 words of Romanized Sindhi texts were tested and successfully tagged by the SindhiNLP tool in which 482 words were tagged as nouns and pronouns, 92 words tagged as verbs and 50 words tagged as determinants.
Current Status and Future Development of Quantum Cryptographic Protocols

Quantum computing has the capability of parallel computing and is superior to classical computing in solving some specific problems. Once a large-scale quantum computer is developed, the security of classical cryptographic algorithms and protocols, which is based on the assumption of computational complexity, will be severely challenged. Quantum cryptography is a new cryptosystem; its security is based on the principles of quantum mechanics, and can resist the attack of quantum computing. This paper focuses on the nearly 40 years development of quantum cryptographic protocols, including quantum key distribution (QKD), quantum secure direct communication, quantum secret sharing, quantum identity authentication, two-party secure computation, and quantum private query, and summarizes the problems in the process of development. The analysis shows that the quantum cryptographic protocols are in an unbalanced state: QKD is far ahead of other protocols and other protocols are difficult to achieve breakthroughs. In the future, practical quantum protocols for digital signature and two-party secure computation are core issues that needs to be addressed urgently. Therefore, research on quantum and post-quantum cryptography should be conducted synchronously, cross-over study and talent cultivation for the quantum science and cryptography disciplines should be strengthened, and the examination and evaluation mechanism of relevant basic research needs to be optimized.
From intelligent agents to trustworthy human-centred multiagent systems

The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.
Towards Understanding the Effect of Node Features on the Predictions of Graph Neural Networks

Graph Neural Networks (GNNs) have shown powerful performance on various graph-related tasks. GNNs learn better knowledge representations by aggregating the features of neighboring nodes. However, the black-box representations of deep learning models make it difficult for people to understand GNN’s inherent operation mechanism. To this end, in this paper, we propose a model-agnostic method called GNN Prediction Interpreter (GPI) to explain node features’ effect on the prediction of GNN. Particularly, GPI first quantifies the correlation between node features and GNN’s prediction, and then identifies the subset of node features that have an essential impact on GNN’s prediction according to the quantitative results. Experiments demonstrate that GPI can provide better explanations than state-of-the-art methods.
Segregating Satellite Imagery Based on Soil Moisture Level Using Advanced Differential Evolutionary Multilevel Segmentation

Soil Moisture aid analysts in study of soil science, agriculture and hydrology. Satellite imagery for soil moisture estimation is recorded through earth satellites. By segmenting these satellite imageries based on soil moisture content, we can effortlessly identify regions of wetter condition and regions of dry condition. Differential evolution (DE) is a popular evolutionary approach that is used to optimize problems like image segmentation. In this work, an Advanced Differential Evolution (aDE) technique is introduced which has enhanced performance in comparison to traditional DE approach. This approach is combined with Renyi's entropy for performing multilevel segmentation on the imagery. The resultant segmented images obtained on using the proposed technique is of enhanced quality.
Development of a Keyphrase Extraction Method Based on a Probabilistic Topic Model

The article considers the task of topic modeling. A new method for extracting keywords has been developed based on topic modeling to analyze a collection of documents describing the goods of an online store. A comparative analysis of the basic method for extracting keywords and the proposed method was carried out. Illustrative results are presented that describe the advantages of this approach. The resulting solution can be used to simplify site navigation and search for relevant products.
Multi-stream Information-Based Neural Network for Mammogram Mass Segmentation

Mass segmentation is the first step in computer-aided detection (CAD) systems for classification of breast masses as malignant or benign, and it greatly impacts the accuracy of CAD systems. This paper proposes a model called region-based graph convolution and the atrous spatial pyramid pooling network (RGC-ASPP-Net), by considering mass context information, such as the features of location and size of mammogram masses, to yield better segmentation results for the CAD systems of mammogram diagnosis. Specifically, it introduces ASPP module in its skip-connection layer, to capture multi-scale mass context information. Then, it constructs a graph convolution module based on the clustering results of mass positions, for taking factors of the location of mammogram masses into account during the process of segmentation. We evaluated our model on the CBIS-DDSM dataset for conducting segmentation tasks, and the results demonstrate that our model RGC-ASPP-Net outperforms PSPNet, DeepLabV3+, AUnet and ASPP-FC-DenseNet by a large margin in terms of segmentation performance.
Multidimensional racism classification during COVID-19: stigmatization, offensiveness, blame, and exclusion

Transcending the binary categorization of racist texts, our study takes cues from social science theories to develop a multidimensional model for racism detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of BERT and topic modelling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19. Our study contributes to enriching the scholarly discussion on deviant racist behaviours on social media. First, a stage-wise analysis is applied to capture the dynamics of the topic changes across the early stages of COVID-19 which transformed from a domestic epidemic to an international public health emergency and later to a global pandemic. Furthermore, mapping this trend enables a more accurate prediction of public opinion evolvement concerning racism in the offline world, and meanwhile, the enactment of specified intervention strategies to combat the upsurge of racism during the global public health crisis like COVID-19. In addition, this interdisciplinary research also points out a direction for future studies on social network analysis and mining. Integration of social science perspectives into the development of computational methods provides insights into more accurate data detection and analytics.
Piracema: a Phishing snapshot database for building dataset features

Phishing is an attack characterized by attempted fraud against users. The attacker develops a malicious page that is a trusted environment, inducing its victims to submit sensitive data. There are several platforms, such as PhishTank and OpenPhish, that maintain databases on malicious pages to support anti-phishing solutions, such as, for example, block lists and machine learning. A problem with this scenario is that many of these databases are disorganized, inconsistent, and have some limitations regarding integrity and balance. In addition, because phishing is so volatile, considerable effort is put into preserving temporal information from each malicious page. To contribute, this article built a phishing database with consistent and balanced data, temporal information, and a significant number of occurrences, totaling 942,471 records over the 5 years between 2016 and 2021. Of these records, 135,542 preserve the page's source code, 258,416 have the attack target brand detected, 70,597 have the hosting service identified, and 15,008 have the shortener service discovered. Additionally, 123,285 records store WHOIS information of the domain registered in 2021. The data is available on the website https://piracema.io/repository.