Recent Advances in Computer Science and Communications - Current Issue

Prior research on abstractive text summarization has predominantly relied on the ROUGE evaluation metric, which, while effective, has limitations in capturing semantic meaning due to its focus on exact word or phrase matching. This deficiency is particularly pronounced in abstractive summarization approaches, where the goal is to generate novel summaries by rephrasing and paraphrasing the source text, highlighting the need for a more nuanced evaluation metric capable of capturing semantic similarity.

In this study, the limitations of existing ROUGE metrics are addressed by proposing a novel variant called ROUGE-SS. Unlike traditional ROUGE metrics, ROUGE-SS extends beyond exact word matching to consider synonyms and semantic similarity. Leveraging resources such as the WordNet online dictionary, ROUGE-SS identifies matches between source text and summaries based on both exact word overlaps and semantic context. Experiments are conducted to evaluate the performance of ROUGE-SS compared to other ROUGE variants, particularly in assessing abstractive summarization models. The algorithm for the synonym features (ROUGE-SS) is also proposed.

The experiments demonstrate the superior performance of ROUGE-SS in evaluating abstractive text summarization models compared to existing ROUGE variants. ROUGE-SS yields higher F1 scores and better overall performance, achieving a significant reduction in training loss and impressive accuracy. The proposed ROUGE-SS evaluation technique is evaluated in different datasets like CNN/Daily Mail, DUC-2004, Gigawords, and Inshorts News datasets. ROUGE-SS gives better results than other ROUGE variant metrics. The F1-score of the proposed ROUGE-SS metric is improved by an average of 8.8%. These findings underscore the effectiveness of ROUGE-SS in capturing semantic similarity and providing a more comprehensive evaluation metric for abstractive summarization.

In conclusion, the introduction of ROUGE-SS represents a significant advancement in the field of abstractive text summarization evaluation. By extending beyond exact word matching to incorporate synonyms and semantic context, ROUGE-SS offers researchers a more effective tool for assessing summarization quality. This study highlights the importance of considering semantic meaning in evaluation metrics and provides a promising direction for future research on abstractive text summarization.

Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures.

The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access.

These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape.

Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

This study investigates the impact of language complexity on Keystroke Dynamics (KD) and its implications for accurate KD-based user authentication system performance in smartphones.

This research meticulously analyzes keystroke patterns using 160 volunteers, including both frequently typed and infrequently typed texts. Our analysis of 12 anomaly detection algorithms reveals that a simple text-based KD system consistently outperforms its complex counterpart with superior Equal Error Rates (EERs).

As a result, the Scaled Manhattan anomaly detector achieves an EER of 2.48% for simple text and an improvement over 2.98% for complex text. The incorporation of soft biometrics further enhances algorithmic performance, emphasizing strategies to build resilience into KD-based user authentication systems.

Throughout this study, the importance of text complexity is emphasized, and innovative pathways are introduced to strengthen KD-based user authentication paradigms.

The consistency of the magnetorheological process insisted that the Inconel^® 718 material should be furnished. This process involves every material from the categories of soft and hard materials.

In this study, a cylindrical ferromagnetic work-part was finished using the magnetorheological finishing method.

The strength of the magnetic flux controls the density and forces in processes that are assisted by magnetic fields. The mechanism was studied parametrically in this research work using response surface methodology.

Optimal process parameters were determined using response surface methodology to accurately execute the finishing procedure. Each parameter's percentage consumption to the process's finishing output was also estimated. The finishing of an industrial extrusion punch was carried out using the optimum parameters obtained from the parametric analysis.

The RSM optimisation of process parameters is validated using the batch gradient descent (BGD) algorithm which is the best fit and innovation algorithm for these types of optimization solutions. The mathematical model that BGD provides confirms the RSM mathematical model.

The optimized parameters are useful in controlling the capability of the MR finishing process in various industrial applications.

Data is rapidly expanding in today's digital age. The reason for the expansion of data is due to social media sites. The internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, etc., concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis.

This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it.

The objective of the current overview is to better understand the market, gauge public opinion, and make strategic decisions. In addition, enterprises, governments, and scholars can all benefit from conducting a sentiment analysis.

In this study, we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper, we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume.

Regardless of the fact that the English language has traditionally been the focus of research in this field, other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination.

The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

The rapid development of wireless communications and mobile computation has given rise to the novel Internet of Things (IoT) systems, which is causing considerable research attention and industrial development. However, the lack of synchronization between the timers of IoT devices compromises the network's security.

The purpose of this patent application is to present a technique for synchronizing the timepieces of IoT gadgets and establishing a secure channel for the transmission of data from source to destination.

This study proposes a Synchronization Selection Method (SSM) for IoT systems to enhance network security and reduce packet loss.

The method utilizes time-lay synchronization and RSA algorithm-based secure channel establishment. Time lay is a technique that was developed for IoT devices to achieve efficient clock synchronization of sensor nodes. Before synchronizing the sensor nodes' timings, the cluster leaders initiate the process. Utilizing a finite number of nodes, the proposed method was executed in MATLAB.

Time-lay synchronization involves all network nodes synchronizing their clocks with a third-party clock. In the context of time-lay synchronization, the term “third-party clock” refers to a single specific point that contains the time signal that all nodes in the network use as a reference. This third-party clock is outside of the network nodes and acts as the standard for the precise and synchronized time within the network. Therefore, it can be deduced that each of the techniques possesses its advantages and disadvantages. Each of the synchronization techniques has the potential to significantly benefit the IoT by offering smart clock synchronization that is more secure. Experimental results demonstrate that the proposed method improves throughput and reduces packet loss compared to existing techniques.

The potential of this patent is highly significant for solving the synchronization problem of IoT devices and enhancing network security with decreased network packet loss.

The SSM would be assessed using the parameters of packet loss and throughput.