Reviewerly/exHarmony huggingface dataset:
Text Summarization and Translation of Summarized Outcome in French

Automatic text summarization is increasingly required with the exponential growth of unstructured text through increasing internet and social media usage across the globe. The various approaches are outcomes of extraction-based and abstraction-based. In Extraction-based summarization, the extracted content from the original data, is typically presented in the same or slightly modified form without significant paraphrasing or restructuring. Abstractive methods involve building an internal representation of the original content and then using that representation to generate a summary that may not be present in the original text verbatim. They employ natural language processing techniques, such as language generation models, to paraphrase and rephrase sections of the source document to create a more human-like summary. Abstraction in abstractive summarization is indeed a challenging task because it requires not only linguistic and syntactic understanding but also a deeper semantic understanding of the original content. The evaluation of automatically summarized text through criteria of precision and recall, and various evaluation methods, datasets used for domain based and generic text summarization. This paper proposes the Sentence Length Impact (SLI) algorithm to summarize English text content, which gives 92% accuracy and translating the same in French.
Efficient Domination In Fuzzy Graphs and Intuitionistic Fuzzy Graphs in Strong and weak forms

This work defines the concepts of strong efficient dominating set and intuitionistic fuzzy graph. We also introduce an intuitionistic fuzzy graph and a strong efficient dominating number of fuzzy graphs. The strong efficient dominant number in fuzzy graphs has some limitations that are studied, and intuitionistic fuzzy graphs are derived..
Deep Learning Techniques for Image Recognition and Object Detection

Particularly in the fields of object identification and picture recognition, deep learning approaches have transformed the science of computer vision. This abstract provides a summary of recent developments and cutting-edge methods in deep learning for applications like object identification and picture recognition. The automated identification and classification of objects or patterns inside digital photographs is known as image recognition. Convolutional neural networks (CNNs), for example, have displayed outstanding performance in image identification tests. By directly learning hierarchical representations of visual characteristics from raw pixel data, these algorithms are able to recognize complex patterns and provide precise predictions. The ability for models to learn sophisticated visual representations straight from raw pixel data has transformed applications like object identification and picture recognition. The development of extremely accurate and effective systems has been accelerated by advances in deep learning architectures and large-scale annotated datasets. Further advances in object identification and picture recognition are anticipated as deep learning develops, with applications in a variety of fields including autonomous driving, surveillance, and medical imaging.
Hand Gesture and Voice Assistants

Hand gesture mouse control for systems has received a lot of attention in recent years Because of its many uses and capability to efficiently connect with machines via human- computer interaction, hand gesture mouse control for systems has attracted a lot of attention in recent years. Most visual hand motion detection systems can only be used in specific settings due to the effects of lighting and complicated backgrounds. Together with the quick advancement of computer vision and machine learning, the need for human- machine interaction is also rising. Despite the complicated history, the suggested methodology provides a straightforward but efficient way to perform rapid manual tracking. In addition to hand tracking and gesture detection, this system also eliminates motion blur. The detected gesture is transformed into particular functional inputs, such clicking and mouse movement, to control other programmes. The project's voice assistant feature is one of its additional features. The voice assistants can be instructed verbally or by text. Voice-based intelligent assistants need an activating word, sometimes referred to as a wake word, before the command may be given.
A combination of optimized threshold and deep learning-based approach to improve malaria detection and segmentation on PlasmoID dataset

Malaria is a life-threatening parasitic disease transmitted to humans by infected female Anopheles mosquitoes. Early and accurate diagnosis is crucial to reduce the high mortality rate of the disease, especially in eastern Indonesia, where limited health facilities and resources contribute to the effortless spread of the disease. In rural areas, the lack of trained parasitologists presents a significant challenge. To address this issue, a computer-aided detection (CAD) system for malaria is needed to support parasitologists in evaluating hundreds of blood smear slides every month. This study proposes a hybrid automated malaria parasite detection and segmentation method using image processing and deep learning techniques. First, an optimized double-Otsu method is proposed to generate malaria parasite patch candidates. Then, deep learning approaches are applied to recognize and segment the parasites. The proposed method is evaluated on the PlasmoID dataset, which consists of 468 malaria-infected microscopic images containing 691 malaria parasites from Indonesia. The results demonstrate that our proposed approach achieved an F1-score of 0.91 in parasite detection. Additionally, it achieved better performance in terms of sensitivity, specificity, and F1-score for parasite segmentation compared to original semantic segmentation methods. These findings highlight the potential of this study to be implemented in CAD malaria detection, which could significantly improve malaria diagnosis in resource-limited areas.
Computer Vision: Advances in Image and Video Analysis

The topic of computer vision has emerged as one that is fast developing, altering how we examine and comprehend pictures and movies. Image and video analysis has significantly advanced in recent years, opening the door for applications in a variety of industries including healthcare, robotics, surveillance, and autonomous systems. An overview of current developments in computer vision methods, algorithms, and techniques used in image and video analysis is given in this abstract. In conclusion, there have been tremendous improvements in image and video analysis in the field of computer vision. Recurrent neural networks (RNNs) and CNNs are two examples of deep learning approaches that have been used to enhance accuracy, resilience, and efficiency in a variety of applications. A richer comprehension of visual material has resulted from the integration of spatial and temporal data with semantic analysis. These developments have enormous potential for use in a variety of fields, influencing the direction of computer vision and its social effects.
Intelligent Robotics: Navigation, Planning, and Human-Robot Interaction

The development of robotic systems that are able to independently navigate their environments, effectively plan their activities, and communicate naturally with people has given rise to the field of research known as intelligent robotics. The objective of this abstract is to give a summary of the developments in intelligent robotics with regard to planning, navigation, and human-robot interaction. As a result, the fields of navigation, planning, and human-robot interaction have seen notable breakthroughs in intelligent robots. Robots are now capable of navigating across complicated areas with efficiency because to the development of reliable navigation algorithms. Robots may now use planning strategies to make wise judgments and carry out activities on their own. Additionally, research on human-robot interaction has concentrated on creating user-friendly interfaces that allow for seamless collaboration between humans and robots. These developments open the way for intelligent robots to become fundamental elements of our society, improving output, security, and quality of life across a range of fields. But more study is still needed to address issues like long-term autonomy, environment adaptation, and the moral ramifications of widespread use of intelligent robots.
Optimal site selection for establishing solar power plant based on solar radiation using GIS

Although many nations employ solar energy as a backup energy source, not every site is equally conducive to the production of solar power. This is caused by the unequal dispersion of solar energy as well as a number of environmental conditions. The framework incorporates a number of Geographic Information Systems (GIS) features, including model builder and area classification. The framework used to derive the five sub-criteria weights determines the ideal locations for plant in the area based on the quantum of potential photovoltaic electricity production (PVOUT) that could possibly produce from Slope, exposure to sunlight, near to highways and closeness to a residential area. Finding the best locations for solar plant side identification in the “Anantapur” district using multicriteria decision analysis (MCDA).
Parallel and Distributed Computing for High-Performance Applications

The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs.
Identification of Phishing Attacks using Machine Learning Algorithm

Phishing is a particular type of cybercrime that allows criminals to trick people and steal crucial data. The phishing assault has developed into a more complex attack vector since the first instance was published in 1990. Phishing is currently one of the most prevalent types of online fraud behavior. Phishing is done using a number of methods, such as through emails, phone calls, instant chats, adverts, pop-up windows on websites, and DNS poisoning. Phishing attacks can cause their victims to suffer significant losses, including the loss of confidential information, identity theft, businesses, and state secrets. By examining current phishing practises and assessing the state of phishing, this article seeks to assess these attacks. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack media, and attacking strategies. Here, we categorise websites as real or phishing websites using machine learning techniques including Random Forest, XGBoost, and Logistic Regression. Additionally, the proposed anatomy will aid readers in comprehending the lifespan of a phishing attack, raising awareness of these attacks and the strategies employed as well as aiding in the creation of a comprehensive anti-phishing system.
Privacy-Preserving Data Mining and Analytics in Big Data

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets.
Secure Multi-Party Computation for Collaborative Data Analysis

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.
Comparison of Dense Net and over Logistic Regression in Predicting Leukemia Classification with Improved Accuracy

This study compares the performance of densenet and support vector machines (SVMs) in the diagnosis of leukemia disease, with the aim of improving the accuracy of the classification results. Materials and Method The Kaggle website is where the dataset was found. The dataset consists of 20 samples per group in JPG files with a resolution of 96 dpi and 512×512 pixel size.The sample size is determined using a pretest power of 80%, a threshold of 0.05, and a confidence interval of 95%. Results: For leukemia, dense net is 96.5%, whereas logistic regression is 89%. The significance levels for Densenet and logistic regression are data with p=.000 (p&lt;0.05) statistical significance difference respectively. Conclusion: Based on the findings, I believe that densenet performs superior to logistic regression.
Deep learning-based image captioning for visually impaired people

Vision loss can affect people of all ages. Severe or complete vision loss may occur when the eye or brain parts that need to process images are damaged. In this paper, in order to facilitate the blind, deep learning algorithms are used to caption the image for the blind person in which the blind can know about the object, distance and position of object. Whenever an image is captured via the camera, the scenes are recognized and predicted by the machine. After the prediction, it will be sent as an audio output to the user. Thus, with the help of this paper an artificial vision to the blind, can be achieved and help them to gain confidence while travelling alone.
Blockchain Technology: Applications and Challenges in Computer Science

In the growth of computer science blockchain technology has emerged as a disruptive force that is enhancing various area of software and the way data is managed, stored, and safeguarded. This essay offers a thorough examination of the uses and difficulties of blockchain technology in computer science. In this essay, blockchain technology has shown itself to be a game-changing innovation with numerous computer science applications. It has the enormous potential to completely transform sectors including finance, supply chain management, healthcare, voting systems, and IoT devices. The software technology is completely achieved with blockchain technology and the main security issue is solved with energy use and scalability. The new opportunities are examined and addressed with this innovation sector for creating effective solutions.
Video Transcript Summarizer

In today’s world, a large number of videos are uploaded in everyday, which contains information about something. The major challenge is to find the right video and understand the correct content, because there are lot of videos available some videos will contain useless content and even though the perfect content available that content should be required to us. If we not found right one it wastes your full effort and full time to extract the correct usefull information. We propose an innovation idea which uses NLP processing for text extraction and BERT Summarization for Text Summarization. This provides a video main content in text description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain good results with informative, concise, and readable multi-line video description and summary in a human evaluation.
KIS.API: Towards External Communication

Abstract The objective of this chapter is to provide a concise overview of KIS.API, which can be used for effective communication with external applications. In particular, the chapter starts with a general overview of KIS.API along with preliminary steps pertaining to KIS.API user registration and authorization in KIS.MANAGER. Subsequently, it is shown how to access essential functionalities explained in the preceding chapters through the KIS.API framework. Thus, the reader will be able to access the asset and user groups as well as the assets themselves. Another crucial point of this chapter is concerned with accessing data through Datapoints as well as calculated Datapoints and KPIs. Subsequently, the mechanism of Rule engine is confronted with KIS.API. In particular, it is show how to access information about the rules designed in KIS.MANGER as well as how to trigger the rules from external applications. With all the above-listed components, the concept of websockets is introduced along with sample applications concerning two-way communication with KIS.ME. Finally, the chapter is summarized with a set of training exercises concerning KIS.API-oriented skills.
Rounding Meets Approximate Model Counting

Abstract The problem of model counting, also known as $$\#\textsf{SAT}$$ , is to compute the number of models or satisfying assignments of a given Boolean formula F . Model counting is a fundamental problem in computer science with a wide range of applications. In recent years, there has been a growing interest in using hashing-based techniques for approximate model counting that provide $$(\varepsilon , \delta )$$ -guarantees: i.e., the count returned is within a $$(1+\varepsilon )$$ -factor of the exact count with confidence at least $$1-\delta $$ . While hashing-based techniques attain reasonable scalability for large enough values of $$\delta $$ , their scalability is severely impacted for smaller values of $$\delta $$ , thereby preventing their adoption in application domains that require estimates with high confidence. The primary contribution of this paper is to address the Achilles heel of hashing-based techniques: we propose a novel approach based on rounding that allows us to achieve a significant reduction in runtime for smaller values of $$\delta $$ . The resulting counter, called $$\textsf{ApproxMC6}$$ (The resulting tool $$\textsf{ApproxMC6}$$ is available open-source at https://github.com/meelgroup/approxmc ), achieves a substantial runtime performance improvement over the current state-of-the-art counter, $$\textsf{ApproxMC}$$ . In particular, our extensive evaluation over a benchmark suite consisting of 1890 instances shows $$\textsf{ApproxMC6}$$ solves 204 more instances than $$\textsf{ApproxMC}$$ , and achieves a $$4\times $$ speedup over $$\textsf{ApproxMC}$$ .
Solving String Constraints Using SAT

Abstract String solvers are automated-reasoning tools that can solve combinatorial problems over formal languages. They typically operate on restricted first-order logic formulas that include operations such as string concatenation, substring relationship, and regular expression matching. String solving thus amounts to deciding the satisfiability of such formulas. While there exists a variety of different string solvers, many string problems cannot be solved efficiently by any of them. We present a new approach to string solving that encodes input problems into propositional logic and leverages incremental SAT solving. We evaluate our approach on a broad set of benchmarks. On the logical fragment that our tool supports, it is competitive with state-of-the-art solvers. Our experiments also demonstrate that an eager SAT-based approach complements existing approaches to string solving in this specific fragment.
Recognition method for underwater imitation whistle communication signals by slope distribution

Underwater bionic covert communication has been extensively studied. However, there are little researches on bionic communication signals recognition. We propose a recognition method for bionic binary orthogonal keying modulated signals (BBOKMSs) coded by time frequency (TF) contour. The BBOKMSs realize the covert communication by mimicking the real dolphin whistles with a series of segmented chirp rate modulated signals. We first put forward a TF contours extraction method by the dynamic adaptive threshold calculated by the cross sampling. Next, the slope of TF curve is modeled based on Gaussian kernel probability density estimation (PDE) for the slope distribution. Based on the characteristics of BBOKMSs, the characteristic parameters of PDE curve are calculated and used to recognize the BBOKMSs by support vector machine (SVM). Finally, the numerical simulations are carried out to measure the effectiveness of the recognition method proposed under different SNRs, coding parameters, and channels in this paper. It is shown that when the modulation slope is higher than 20 kHz/s and the code length is longer than 20 ms, the recognition accuracy can reach more than 90% at a SNR of −5 dB in the constructed underwater aquatic channel.