Reviewerly/exHarmony huggingface dataset:
Detection of Cancer using Contrast Stretching

In day-to-day life, cancer is becoming a very common reason for human death throughout the world. But we can save the life of a person if we detect it at a very early stage. The survival rate for cancer patients increases up to 40 to 50 percent if it gets detected at the earliest possible. A physical test is not only time consuming but also incurs huge cost to the patients. Therefore, the investigation through the software proves to be the most convenient way to get this process done. In this study, we have used pre-processing, image extraction, and image enhancement to considered image samples. We aim to detect cancer in different parts of the body by checking CT scans and MRI scan images. A non-linear filter, the median filter are employed for eliminating the noise from the raw photos in this investigation. Once the image is filtered from noise, the contrast stretching approach is applied, which simply increases the image&#x2019;s contrast. This allows clinicians to quickly detect the existence of cancer on considered patient&#x2019;s body. The performance evaluation of proposed method is done against the Clah filters based on number of criteria to prove its supremacy.
FLIP60-AR based Application for Elders

Yoga and exercise are beneficial to one’s overall health. They improve the function of our heart and lungs, help us lose weight, and make our muscles strong and able to go for more extended periods. Working exercise is a natural mood enhancer. It keeps one active and healthy and aids in the battle against illness.This paper focuses on developing an android based application that encourages older adults to practice yoga and simple exercises. As a result, mobile phones are widely used to train ourselves, and they can serve as an outstanding health guide. Whether done inside or outdoors, exercises become more exciting with the aid of AR. Also, it improves the user experience. In this project, an app called Flip60 has been developed, which encompasses various types of yoga postures, exercises, and augmented reality models of activity with realistic views. The user can pick between an augmented reality model and a regular video. A recommendation system was included, which proposes acceptable videos based on data provided to the application.
A Big Data Encryption Method based on Lorenz and Feistel Structures

With the development of information technology, the information network has entered the era of big data. The dissemination and storage of a large amount of data will not only create value, but also increase the risk of data leakage. Data privacy protection is an important research topic in the field of network security, and the existing encryption methods make it difficult to give attention to the confidentiality and efficiency of big data encryption at the same time. Therefore, this paper proposes a hybrid encryption method based on the Lorenz system and Feistel structure, which is applied to large data encryption. It takes advantage of the randomness of a chaotic system, the confidentiality of hybrid encryption, and the high efficiency of symmetric encryption to ensure the speed and security of large data encryption at the same time.
Intrusion Bit Detection in Data Packet Transmitted Over Covert channels using K-NN Machine Learning Algorithm

bstract: K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning techniques. The algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well good category by using an algorithm. This algorithm can be used for Regression as well as for Classification but mostly it is used for Classification problems. If the encrypted data is transferred over the covert channel, the number of intruders, cryptanalysis attack and want to know data pattern by analysing the data bit pattern. If the intruder inserts any bit inside the data bit transferred to the receiver, then the receiver encryption system detects this bit and neglects this data for the decryption process.
Robot Operating Systems (ROS): The Fundamentals of ROS and Its Remarkable Performances in the World of Drones

Abstract: A structured communication mechanism is offered by the open-source operating system ROS. With the integration of its framework and toolkits, and a sophisticated operating system for software development, robot deployment is effective. The development of the unique robotic drone applications and a thorough description of how to program the drone using Ros are covered in this paper's brief discussion of the evolution of the Robot Operating System (Ros). Later in the article, the implementation of using a motion controller to control a drone's motion with basic hand motions is shown. However, there has been a significant increase in the commercial use of drones recently, and many drones have invaded public spaces, potentially invading people's right to privacy. To correct this mistake, we have used the ROS to deploy a drone that does not intrude in forbidden places.
Face Mask Detection: A Deep Learning Concept

Face Mask Detection program developed by OpenCV, Keras / Tensor Flow uses Deep Learning ideas and laptop Vision to detect face masks on continuous still images such as video streaming. Within the gift situation thanks to COVID-19, there are no requests for the acquisition of a mask to save the area that is currently in dire need of transport, crowded areas, accommodations, major manufacturers and various businesses to guarantee the safety. Also, lack of big image data with a mask has made this task even more difficult and difficult. Following the outbreak of the global COVID-19 epidemic, there was an urgent need for preventive measures, with a mask on the face. The primary purpose of the project is to determine whether there is a face mask on people&#x2019;s faces in video and photos streamed live. We used the depth of learning to build our face detection model. The features used for object detection are the Single shot detector (SSD) due to its performance with precision and high speed. Apart from this, we used basic concepts to transfer learning to neural networks without ultimately excluding the presence or absence of facial image in a photo or video stream. Test results show that our model works at 100% efficiency with 99% accuracy of test and memory, respectively. Our mask detector did not use any data from the inserted images. The model is accurate, and as we are accustomed to using the design of MobileNetV2, savings are calculated collaboratively and therefore make it a lot easier to move the model to embedded programs like Google Coral &amp; Raspberry Pi etc. This program will be used for time programs that require the acquisition of face masks for security operations due to the emergence of COVID-19. The project is used with the installed plans for use at airports, train stations, offices, school premises and marketplaces to ensure that the local unit of community safety tips is followed.
Reliable Visibility Algorithms for Emergency Stop Systems in Smart Industries

Automated machinery and robots working with humans are the norm in modern smart industries. A previous work in this area proposed a tool for improving the safety of such work places: an emergency system which halts those machines that are visible from an emergency stop button when it is pressed [1]. The solution presented in this paper improves the reliability of the aforementioned one at the expense of a higher computational complexity. Furthermore, two algorithmic optimizations are presented to mitigate the extra computational cost as it is shown by the results collected from the set of experiments conducted.
Towards the Use of Augmented Reality for Physics Education

Physics is around us everywhere. Young students should be taught the physics concepts from n early age. The goal of the work presented in the paper is to make Physics education more interactive and engaging through using an AR serious game. The game introduces the student to some physics concepts.
Tiempos de educación abierta, a distancia y en línea Una reflexión necesaria en el contexto presente para definir la educación del futuro

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Implementation of an English Word Learning AR Application for Infant English Education

In this study, we proposed an English word learning application for infant education using markerless augmented reality (AR) by implementing the object detection method algorithm. We created our dataset to make infants learn familiar English words. Subsequently, we obtained an English word learning AR application that allows infants to intuitively learn English words. Furthermore, we conducted a system evaluation questionnaire survey on the operability, readability, functionality, and learning effect of the proposed application for 30 subjects to evaluate this study. As a result, we obtained high evaluations on many items. However, 73% of the subjects answered positively to the functionality evaluation, which was slightly lower than the other evaluation items.
Comparison Study on SPN Type Light Weight Cryptography Algorithms for IoT

Today Internet of Things (IoT) are essential need for society due to its vast application of multiple domains that collects the sensible information from the various domain and send it to server. There are many complicated developments in placing the IoT device for retrieving and sending data. Nearly hundreds of light weight algorithms are existing with particular applications but still not sufficient to tiny devices. This paper focuses on how to secure the device by the cryptographic algorithm namely light weight cryptographic algorithm and elucidates a comparison study of the existing algorithm based on Substitution Permutation Network (SPN). The study shows PRINCE algorithm yields a better result when compared to others. In addition, this paper emphasize a new study field of light weight cryptography to boost IoT security.
Research on Recognition Method of Textual Implication

Text implication recognition aims to judge the semantic and logical relationship between two paragraphs of text. The reasoning process involves syntactic analysis, vocabulary understanding, logical reasoning, social experience, and common sense. It is a way to judge whether the computer is to a certain extent. The challenging research task of "understanding" text semantics is also one of the more important benchmark tasks in the field of natural language processing. It is found that in the past ten years, the total number of publications in this field has been increasing year by year, and the popularity is also increasing. At present, there are mainly text implication relationship recognition methods based on similarity, text implication relationship recognition methods based on alignment, and deep neural networks. The textual implication relationship recognition method. This article summarizes the different methods.
C# Realization of Online Stability System for Primary Art Classroom based on Multi-dimensional Information Technology Integration Platform Technology

The article mainly discusses the integration of information technology and primary school art guiding, and analyzes the importance of the integration of information technology and primary school art guiding. After fully considering the system requirements, choosing the two-tier C/S model as the development model of the marking system requires the implementation of top-level reconstruction, integration and refinement, linkage application, and multiple interaction strategies to achieve course objectives, course content, teaching methods, and exhibition evaluation. The in-depth integration of the four aspects of the method, the design goals and principles of the art online scoring system, and the analysis of the hardware architecture and logical architecture of the system.
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks From a System Theoretic Viewpoint

Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well known tools for predicting time series.Even though these solutions were developed independently in distinct communities, they share some similarities when considered as probabilistic structures.So in this paper we first consider HMC and RNN as generative models, and we embed both structures in a common generative unified model (GUM).We next address a comparative study of the expressivity of these models.To that end we assume that the models are furthermore linear and Gaussian.The probability distributions produced by these models are characterized by structured covariance series, and as a consequence expressivity reduces to comparing sets of structured covariance series, which enables us to call for stochastic realization theory (SRT).We finally provide conditions under which a given covariance series can be realized by a GUM, an HMC or an RNN.
A Survey of Commercial Password Detection Methods

Commercial password has been widely used in China's information services, and their role and importance in information security are gradually increasing.In recent years, as the application of quantum computing in computer technology becomes more and more extensive, the performance of computers continues to improve, and cryptographic penetration technology has emerged in an endless stream.Some known cryptographic algorithms are no longer secure in the new environment, such as the MD5 cryptographic algorithm with obvious security risks and the cracked RSA public key password algorithm, etc.Therefore, it is essential to detect and evaluate the security of commercial passwords.Based on a large number of literature at home and abroad and the practical work experience of Beijing Software Testing and Quality Assurance Center, this study comprehensively analyzes and combs the commercial password detection technology and forms a relatively complete research review on commercial password detection methods.The specific contents include traditional cryptanalysis methods, key penetration attack methods, side-channel attack methods, quantum computing cryptanalysis, etc.The research results provide important theoretical support for the commercial password security detection of the Winter Olympic information system, enhance the technical ability of the inspectors to the commercial password detection, and ensure the comprehensiveness of the information system security detection.
Text to Video GANs:TFGAN, IRC-GAN, BoGAN

Generative adversarial networks (GANs) have demonstrated high accuracy on image generation tasks. A large number of studies have applied image generating models to video generation as well. However, because of the complexities of video generation, it's not that trivial to use GANs in the video domain. In the video generation, the resulting content has to be spatially and temporally coherent. Moreover, generating videos from text is even more challenging since besides maintaining the temporal and spatial coherence, semantic consistency also needs to be maintained. In this paper, we have compared three recently proposed text-to-video GAN architectures. Text-Filter Conditioning Generative Adversarial Network (TFGAN) is the first architecture, which employs a superior feature fusion method in which firstly the discriminative convolutional filters are produced from text features and then convolved with image features in the discriminator. The Introspective Recurrent Convolutional GAN (IRC-GAN) is the second architecture, which leverages mutual-information introspection to maintain semantic consistency between the generated videos and the input text. The third model is Bottom-up GAN (BoGAN) which introduces three levels of losses viz, region level loss, frame-level loss, and video level loss.
Detecting Non-control-flow Hijacking Threats with Deep Autoencoding Gaussian Mixture Model

Non-control-flow hijacking attacks have been an emerging threat for current computer system security. Attackers leverage data manipulation in memory to corrupt the integrity of the target program without invalidating the normal control-flow during execution. Consequently, current sandox-based methods towards mitigating control-oriented attacks cannot identify non-control-flow hijacking attacks. In this paper, we proposed a data-driven methology to distinguish the abnormity for non-control-flow hijacking detection. We adopt a deep autoencoding gaussian mixture model architecture that leverages the hardware events and corresponding contextual information during execution. The proposed framework has shown an outstanding performance through comparative experiments with previous literatured works.
Multidimensional Information Security Guarantee Strategy for Scientific Information of University Administrative Management System based on Data Matrix Algorithm

Multidimensional information security guarantee strategy for scientific information of university administrative management system based on data matrix algorithm is studied in the paper. Because of its application of computer high-density media storage devices and also high-speed flexible and easy-to-use systems, the automation of the quality management system is transformed from theory foundation becomes possible. With this theoretical background, this paper designs the novel security guarantee strategy for scientific information of the university administrative management system. The optimization process of general nonnegative matrix decomposition is iterative updating solution, and the final convergence solution depends on the initial value of the matrix. Then the updated function is used to enhance the traditional methods. Through the testing on the experiment, the performance is validated.
GNN-Guided Track Branch Formation For Multiple Hypothesis Tracking

Multiple Hypothesis Tracking (MHT) has shown its powerful performance in multiple target tracking applications. To reduce the number of formed track hypotheses, MHT usually defines three correlation gates called extrapolation, initiation and correlation gate, and create track hypotheses based on the observation falling in the gate. However, in closely spaced multitarget scenarios, observations of new target may fall within the initiation gate of existing track for a long time, leading to the difficulty of track initialization. Similarly, the track may also break due to the gating rules. In this paper, we propose to utilize global nearest neighbor (GNN) association result to guide track branch formation in MHT and format track branches based on the GNN association result. Simulation experiments show that the proposed GNN-guided approach is able to solve the above problems and controls the computational complexity well.
Biased random walk based graph neural network

Graph neural networks (GNNs) are approaches that extend deep learning neural networks on graph data. Research on graph neural networks has made tremendous progress today. Graph neural networks are usually categorized as spectral-based models and spatial-based models. The spectral-based method has been widely recognized by the academic community due to its solid theoretical foundation. However, the existing spectral-based models induced by the Laplacian matrix usually cannot achieve satisfactory results in experiments due to their insufficient expressive ability. We theoretically derive an unbiased Laplacian matrix based on biased random walks. As a graph shift operator, it is more general than unbiased Laplacian. Based on biased Laplacian, we propose a more powerful spectral-based graph neural network BiGNN. And it achieves better simulation results than traditional spectral-based graph neural networks on Cora, Citeseer and PubMed datasets.
Calculating Requirements Similarity Using Word Embeddings

Finding similar requirements from a requirements dataset is an important problem in requirements engineering (RE). Automatic processing and representation of requirements documents has become a necessity for requirements engineers (REs) due to the inability of one person (or a even small group of people) to completely oversee the requirements of a system manually because of the system's scale. This paper outlines a novel framework which can help REs to realize document similarity using word embeddings. This framework takes a collection of documents as input and produces a list of similarity ratings for each document. The similarity ratings provide REs an intuition of what could be related or how the requirements search space can be reduced for further RE activities. The framework uses TF-IDF to produce a list of important terms for each document, filters out totally unique terms, then uses spaCy's deep learning word vectorization to calculate similarity between requirements documents.