This study was based on changes in the work environment, from a pandemic to a post-pandemic state where employee performance began to increase. This study aimed to analyze the performance of employees who have improved based on environmental factors and communication through motivation as a mediator variable. This study was quantitative descriptive research with data collection using questionnaires. The population was employees of PT. Givaudan Indonesia, as an observation unit referred to as census techniques. Data analysis used Partial Least Square (PLS) based on the Structural Equation Model (SEM). The result of the study was the work environment has a positive and significant effect on motivation. The work environment has a positive but not significant effect on employee performance. Communication has a positive but not significant effect on motivation. Communication has a positive and significant effect on employee performance. Motivation has a negative and insignificant effect on employee performance. The work environment has a negative and insignificant effect on employee performance through motivation. Communication negatively and insignificantly affects employee performance through motivation. The work environment and communication simultaneously have a positive and significant effect on motivation. Work environment and communication as well as motivation simultaneously have a positive and significant effect on employee performance. The conclusion of this study shows that the work environment and communication affect performance, while motivation does not affect employee performance.

In order to achieve the aim of a cashless society, Indian government is patronizing and encouraging the usage of digital payment. In this work, an exploratory study related to digital payment using ‘credit and debit card’ and at ‘point of sales’ have been carried out considering several dimensions by drawing the inferences from the existing datasets. Inference related to ‘cards growth rate’ proved instrumental during study and presented the deep insight on the growth trend. Further, this work presents the popular digital payment modes, and comparisons among them is drawn to present the distinct features that justify the suitability of one over the other. Results revealed unprecedented emergence of COVID-19 had adverse impact on the overall growth of digital payment systems. This work will be profoundly helpful to the stakeholders involved in digital payment industry, and policy making to deeply understand the digital payment trend and formulate the futuristic policies. 

The safety of online transactions is paramount in the modern world, mainly since technology develops at a dizzying rate. This study aims to shed light on the numerous threats that users of online transaction systems face. The study used a mixed-methods research strategy to investigate the experiences and perspectives of 400 individuals from various backgrounds. Worryingly, the results show a significant knowledge gap on the many types of cyber hazards. The research reveals a troubling lack of awareness about various cyber risks, including fraud, phishing, and identity theft. It highlights the user’s common functional difficulties. The study proposes a novel framework named COTSEF: A Comprehensive Framework for Enhancing Security in Online Transactions to enhance online transaction security alongside these findings. This comprehensive framework aims to provide a safer and more dependable environment for online commerce by mitigating the identified risks and challenges. The demographic breakdown of the users is also investigated, with the results indicating the increased vulnerability of some age groups and professions to various hazards. It also highlights the need for educational activities to address the significant need for more awareness about data protection rules. The study is a critical resource for policymakers, corporations, and educational institutions, offering actionable insights for developing more secure and user-friendly online transaction systems.

In visual domain adaptation, the goal is to train effective classifiers for the target domain by leveraging information from the source domain. In unsupervised domain adaptation, the source domain provides labeled data while the target domain lacks labels. However, it is crucial to recognize that the source and target domains have different underlying distributions despite sharing the same label space. Directly applying source domain information to the target domain often leads to poor performance due to the distribution gap between the two domains. Unsupervised do- main adaptation aims to bridge this gap and improve performance. We introduce a comprehensive UDADFSP (Unified Domain Adaptation with Discriminative Features and Similarity Preservation) de- signed explicitly for unsupervised domain adaptation to tackle these challenges. Our framework focuses on incorporating discriminative and invariant features. We employ clustering with entropy regularization on the unlabeled target domain to refine the neighbor relationships. This step significantly enhances the alignment between the target and source domains, facilitating a more effective adaptation. Furthermore, we seamlessly incorporate discriminative features while preserving similarity in the source and target domains. We carefully balance the discrimination and similarity aspects by considering linear and non-linear data representations. Extensive testing demonstrates that learning discriminative and similarity features in the same feature space yields significant improvements over several state-of-the-art domain adaptation techniques. In a comparative evaluation, our approach surpasses several existing methods across four diverse cross-domain visual tasks and the Amazon re- view sentiment analysis task.

The increasing frequency and sophistication of cyberattacks targeting institutions have necessitated proactive measures to prevent losses and mitigate damages. One of these measures is to monitor the dark web. The dark web is a complex network of hidden services and encrypted communication protocols, with the primary purpose of providing anonymity to its users. However, criminals use the dark web to sell stolen data, launch zero-day attacks, and distribute malware. Therefore, identifying suspicious activity on the dark web is necessary for businesses to counter these threats. 
An analysis of dark web monitoring as an emerging trend in cyber security strategy is presented in this article. The article presents a systematic review of (a) why dark web surveillance enhances businesses' cybersecurity strategies, (b) how advanced tools and technologies are used to monitor dark web data in the commercial sector, (c) the key features of threat monitoring frameworks proposed by researchers, and (d) the limitations and challenges associated with dark web monitoring solutions. In summary, the proposed work involves analyzing various sources of information related to the topic and presenting a thorough assessment of the need and challenges of dark web surveillance to enhance the security measures of businesses.