A potent cryptographic mechanism called Secure Multi-Party Computation (SMPC) has evolved that allows numerous participants to work together and execute data analytic tasks while maintaining the privacy and secrecy of their individual data. In several fields, like healthcare, finance, and the social sciences, where numerous stakeholders must exchange and evaluate sensitive information without disclosing it to others, collaborative data analysis is becoming more and more common. This study gives a thorough investigation of SMPC for group data analysis. The main goal is to give a thorough understanding of the SMPC’s guiding ideas, protocols, and applications while stressing the advantages and difficulties it presents for fostering safe cooperation among various data owners. In summary, this study offers a thorough and current examination of Secure Multi-Party Computation for Collaborative Data examination. It provides a thorough grasp of the SMPC deployment issues as well as the underlying ideas, protocols, and applications. The goal of the article is to function as a useful resource for researchers, professionals, and decision-makers interested in using SMPC to facilitate group data analysis while protecting confidentiality and privacy.

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA.

Cryptography, optimization, simulation, and machine learning are just a few of the industries that might be completely transformed by quantum computing. This abstract gives a thorough introduction to quantum computing with an emphasis on its algorithms, architectures, and applications. In conclusion, this abstract offers an in-depth analysis of quantum computing, including its algorithms, structures, and applications. It highlights the revolutionary potential of quantum computing in tackling difficult issues that are beyond the scope of conventional computers, laying the groundwork for further research and understanding of this quickly developing topic.

Remote sensing (RS) object detection is extensively applied in the fields of civilian and military. The important role of remote sensing is to identify objects like planes, ships, harbours airports, etc., and then it can attain position information and object classification. It is of considerable importance to use RS images for observing the densely organized and directional objects namely ships and cars parked in harbours and parking areas. The object detection (OD) process involves object localization and classification. Due to its wide coverage and longer shooting distance, Remote sensing images (RSIs) have hundreds of smaller objects and dense scenes. Deep learning (DL), in particular convolution neural network (CNN), has revolutionized OD in different fields. CNN is devised to automatically learn the hierarchical representation of data, which makes them fit for feature extraction. Hence, the study proposes a new white shark optimizer with DL-based object detection and classification on RSI (WSODL-ODCRSI) method. The purpose of the WSODL-ODCRSI model is to classify and detect the presence of the objects in the RSI. To accomplish this, the WSODL-ODCRSI model uses a modified single-shot multi-box detector (MSSD) for the OD process. The next stage of OD is the object classification process, which takes place with the use of the Elman Neural Network (ENN) algorithm. The WSO algorithm is exploited as a parameter-tuning model for improving the object classification results of the ENN approach. The stimulated study of the WSODL-ODCRSI algorithm has been established on the benchmark data set and the outcomes underlined the promising performance of the WSODL-ODCRSI model on the object process of classification

In today’s working environment, various organizations confront the significant challenges of productivity and performance. However, higher education institutes (HEIs) are also not free from this massive issue. Therefore, the present study investigates employee productivity and performance through work engagement (WEE) and organizational factors. The modes of study are quantitative and based on cross-sectional data. The study collects the response from academic and administrative staff from public and private HEIs of Saudi Arabia. The study applies convenience sampling and successfully proceeds 254 valid cases to conclude the findings. The applied structural equation model (SEM) path analysis demonstrates a positive and significant effect of WEE comprising vigour, dedication, and absorption factors on employee productivity (EPD). In addition, employee performance (EP) is predicted through organizational factors such as management support (MS), learning culture (LC), work environment (WE) and organizational commitment (OC). Finally, the study finds a significant and positive effect of EPD on EP among the employees of HEIs. The study findings would be guidelines for policymakers and the top management of higher education commissions to advance the knowledge and skills of EPD and EP of the organizations. The study would support achieving job tasks and goals through developing WEE and organizational factors for productivity and performance. Lastly, the findings will augment the literature and provide empirical evidence from Middle East countries. The study provides a vigorous model which connects the WEE theory and organizational factors towards EPD and EP in an integrated way.