Reviewerly/exHarmony huggingface dataset:
Honing Soft Skills Via Zoom-Mediated Learning

The paper considers the results of the research aimed at repelling public speaking anxiety in university students through the computer-mediated learning via Zoom. Online classes conducted in a comfortable home environment ensure the successful development of communication skills. Recognizing their own emotions on Zoom screen individuals are able to take control of the emotions that enhance their emotional intelligence levels that brings them to repel public speaking anxiety, thus allowing them to consequently hone their soft skills. The subject matter of the research is a questionnaire database filled in by the 2nd- and 3d-year students of Moscow State Institute of International Relations, Moscow, Russia, to identify how online classes via Zoom decrease the anxiety level and promote comfortable learning environment.The objective of the research is to identify how computer-mediated classes via Zoom decrease the anxiety level in students and contribute to developing their soft skills. The methods applied are a questionnaire, sampling, content analysis, and categorisation.The sample included international affairs university students who are learning online via Zoom. The online questionnaire to identify the public speaking skills via Zoom was virtually disseminated to the students in order to draw insights from the anxiety effect on the education process and eventually creating comfortable computer-mediated learning environment. The findings of this research point out at the repelled anxiety rates in university students that impact on their successful public speaking skills and effective communication during online classes.The paper summarizes the results of the study that can be widely applicable in psychological studies, self-directed learning, online-education.
Conditional independences and causal relations implied by sets of equations

Real-world systems are often modelled by sets of equations with exogenous random variables. What can we say about the probabilistic and causal aspects of variables that appear in these equations without explicitly solving for them? We prove that, under a solvability assumption, we can construct a Markov ordering graph that implies conditional independences and a causal ordering graph that encodes the effects of soft and perfect interventions by making use of Simon's causal ordering algorithm. Our results shed new light on discussions in causal discovery about the justification of using graphs to simultaneously represent conditional independences and causal relations in models with feedback.
Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis

Knowledge management in image processing is essential task for improving the medical images where as case-based reasoning (CBR) is the technique used for prediction purpose. This CBR is a type of Artificial Intelligence used for classification and regression problems to predict the upcoming results based on previous training samples. Information Extraction is a method for improving the visualization of the image by manipulating pixels and statistical elements. Image Interpretation is essential technique to identify the features in a object related to shape, color, texture, shadows, size, and patterns. In this chapter, the digital image processing role is discussed as well as knowledge management using CBR and information extraction using feature extraction algorithm and OBIA (Object-Based Image Analysis) for image segmentation. Feature/information extraction is implemented with the data set of cervical cancer using GLCM, HOG, and SURF algorithms. Machine learning algorithms and data science are the best tools for medical diagnosis for predicting the cancer state at the earliest. Data analysis is one of the initial steps to predict the knowledge of the data set. Information computing is a big deal about the process of hardware and software application based on the data analyzing, data storing, data retrieval, and manipulation of existing records of the patient. This chapter is entirely focused on data science, computational excellence, information computing, and society for information extraction of cervical cancer diagnosis. The Pap smear image is a microscopic image which is used for the entire chapter.
Large-Scale Optimization With Machine Learning Applications

This thesis aims at developing efficient algorithms for solving some fundamental engineering problems in data science and machine learning. We investigate a variety of acceleration techniques for i ...
Cloud Computing – Cryptography

Cloud Cryptography is encryption that safeguards data stored within the cloud. Several measures are being placed within cloud cryptography which adds a strong layer of protection to secure data to avoid being breached, hacked or affected by malware. Any data hosted by cloud providers are secured with encryption, permitting users to access shared cloud services securely and conveniently. Cloud Cryptography secures sensitive data without delaying the delivery of information.Cryptography within the cloud employs coding techniques to secure information which will be used or hold on within the cloud. It permits users to handily and firmly access shared cloud services, as any information that's hosted by cloud suppliers is protected with coding. Cryptography within the cloud protects sensitive information while not delaying info exchange. Cryptography within the cloud permits for securing essential information on the far side your company IT atmosphere, wherever that information is not any longer beneath your management. Cryptography knowledgeable Ralph sociologist Poore explains that “information in motion and knowledge at rest square measure best protected by cryptanalytic security measures. within the cloud, we have a tendency to don’t have the posh of getting actual, physical management over the storage of knowledge, that the solely manner we will make sure that the data is protected is for it to be hold on cryptographically, with USA maintaining management of the cryptanalytic key.”
Implementation of Covid-19 Social Distance Detection and Suspicious Human Behavior Recognition using Machine Learning

Detection of suspicious activities in public transport areas using video surveillance has attracted an increasing level of attention. In general, automated offline video processing systems have been used for post-event analysis, such as forensics and riot investigations. However, very little has been achieved regarding real-time event recognition. In this paper, we introduce a frame- work that processes raw video data received from a fixed color camera installed at a particular location, which makes real- time inferences about the observed activities. These supervised machine learning techniques are used for detection and tracking of Covid-19 social distancing between one or more person’s movements in public places and these observations can be done by the CCTV videos. First, the proposed framework obtains 3-D object-level information by detecting and tracking people and luggage in the scene using a real-time blob matching technique. Based on the temporal properties of these blobs, behaviors and events are semantically recognized by employing object and interobject motion features. A number of types of behavior that are relevant to security in public transport areas have been selected to demonstrate the capabilities of this approach. Examples of these are abandoned and stolen objects, fighting, fainting, and loitering. Using standard public data sets, the experimental results presented here demonstrate the out- standing performance and low computational complexity of this approach.
The Role of Hume-Rothery's Rules Play in the MAX Phases Formability

MAX phases are a family of layered, hexagonal-structure ternary carbides or nitrides of a transitional metal and an A-group element. What makes this type of material fascinating and potentially useful is their remarkable combinations of metallic and ceramic characteristics; as well as the indispensable role in 'top-down' synthesis of their 2D counterparts, MXenes. To enhance the efficiency in the successful search for potential novel MAX phases, the main efforts could go toward creating an information-prediction system incorporating all MAX phases' databases, as well as generally valid principles and the high-quality regularities. In this work, we employ structure mapping methodology, which has shown its merit of being useful guides in materials design, with Hume-Rothery parameters to provide guiding principles in the search of novel MAX phases. The formable/non-formable data on MAX phases can be ordered within a two-dimensional plot by using proposed expression of geometrical and electron concentration factors.
Image Generation for Real Time Application Using DCGAN (Deep Convolutional Generative Adversarial Neural Network)

As the technology keeps developing the unimaginable possibilities keep happening. And it leads to easy use of our daily life. In image processing when the CNNs came to our life it makes the world to turn around and makes the human work easier in all organization. Convolutional Neural Network were mainly used in computer vision, mainly in face recognition, image classification, action recognition, and document analysis, but these gets difficult when comes to dataset. Gathering dataset for machine learning is time consuming operation, at that point the new technique called GAN were introduced. It can predict that whether the image is real or not, which is a next level improvement of machine learning techniques. Our aim is to improve the creativity of the machine and generate different type of images which will be useful in the fields like animation and designing. Here in our paper, we will use the Deep Convolutional Generative Adversarial Networks (DCGAN) where it will be used to generate new images that are not in the dataset. And it's been a huge success in terms of creating new images. MNIST dataset and Anime dataset are used here, by using the DCGAN in it and try to create pictures that are similar to the datasets.
Cyber Attack! A Story-driven Educational Hacking Game

There is an urgent need for cyber-security professionals as large-scale data breaches and hacks are becoming daily occurrences. However, several perceived barriers prevent the young generation from pursuing a cyber-security-related career. One of the major barriers is the perception that cyber-security is a purely technical field that requires expertise in computer science. Attracting young people to join the cyber-security workforce requires innovative pedagogical methods that go beyond the traditional cyber-security education, especially to make the field more accessible. We postulate that game-based learning can motivate young generations to learn more about cyber-security and reduce the perceived barriers that prevent them from considering cyber-security-related career paths. To pursue this thesis, we developed Cyber Attack, a story-driven cyber-security video game that incorporates various gamification elements that focus on fundamental cyber-security concepts. This paper details the design principles we followed for the early version of Cyber Attack.
Improvement in Efficiency of The State-Of-The-Art Handwritten Text Recognition Models

In the past few years, the research in the discipline of Handwritten Text Recognition (HTR) has been fast-tracked as many researchers in computer vision are pursuing this discipline. Most of the deep learning models are likely to have vanishing gradient errors when processing paragraph images like scanned images. The most crucial problem with these models is that they have many parameters, which require a large amount of data and resources. So, the most recent offline HTR follows the Convolutional Recurrent Neural Network (CRNNs). Recently developed neural network architecture was used to get better results, namely Gated Convolutional Neural Network (Gated-CNN), which has fewer layers and parameters. The HTR based on Gated-CNN can outperform the CRNN based HTR. This research surpasses the state-of-the-art HTR system on five different handwritten datasets: Bentham, IAM, RIMES, Saint Gall, and Washington. This research needs low computational resources to use in real life, such as smartphones and robots.&#x0D;
FPGA Based Unicode Data Compression Using Static Dictionary Technique

With the increasing demands for large amounts of data in the digital era for information storage, processing and transfer, the demand for smaller and faster data storage memories have also exponentially increased. In order to avoid the bottleneck between the larger data storages that require larger bandwidths for data and avoid loss of information we have to choose correct data compression techniques which reduces the redundant data storage and in turn reduces the hardware required for data storage and processing. There are two different types of data compression techniques/algorithms: (1) Lossy data compression algorithms (2) Loss less data compression algorithms. While Lossy compression algorithms are faster, they involve loss of data/information to certain level during de-compression or reconstruction. Whereas Lossless compression algorithm are relatively, they can perfectly reconstruct the complete information from the compressed data. We already have several data compression software and tools like 7-zip, WinZip etc., which compress files, audio, and video at software level. The aim of this paper is to present a hardware-based technique to perform Unicode data compression on text data using static dictionary technique where the input data stream is scanned, indexed for the positions of characters with a reference character and replace with certain symbols which are the ASCII values of the Unicode characters in the text and their differences from the static dictionary designed based on a hash table reducing the redundant data and thus reducing the data size.
Implementation Outcomes of Literacy Movement trough The Habituation, Development and Learning Stages for Indonesian Elementary School Students

The Indonesian government has implemented awareness of literacy's importance through the school literacy movement at every level of education. The literacy movement, especially at the elementary school level, is carried out in three stages. There are habituation, development, and learning. The literacy movement's implementation since 2016 at the elementary school level has carried out well. However, there are still some obstacles. The method used is a systematic literature review. It examines various reports of literacy stakeholders in their implementation against each context. These reports found different effective strategies for successfully implementing the literacy movement, which can use as references. It then discovered that various obstacles became the gap in literacy education in Indonesia to be minimized further. This systematic literature review highlights a need to implement meaningful and innovative strategies by utilizing stakeholders such as teachers, school principals, parents, communities, and teacher institutes to provide professional context-based input related to the literacy movement
Implications of Expanding the Use of Information Technology Tools in Accounting of Finance Transactions

Blockchain is a technology that springs within its theoretical parameters from the conceptual frameworks of accounting that we encounter in the emerging of today's business . In this paper, in a simplified approach, we try to present a sketch of a set of aspects of the directions to be followed in the implementation of this technology and the likely impact on the accounting of operations in the real estate sector. Currently undergoing a rapid evolution, marked by the applicability to unprecedented levels of information technology (IT) tools on the accounting of entities, we consider it necessary to develop multidisciplinary approaches to the use of new technologies in accounting. Blockchain opens the door for new approaches to accounting information processing. It is now possible to perform multi-dimensional processing of financial - accounting information, processing that goes beyond and develops the traditional accounting paradigm.
Optimised multicomponent geothermometer MulT_predict

Geothermometry is used for reservoir temperature estimation since the 1960s. Different kind of solute, gas, and isotopic geothermometers has been evolved and further developed. We are focusing on solute geothermometery, using the multicomponent approach by Reed and Spycher (1984) and combined it with an optimisation process suggested by Nitschke et al. (2017). Therefore, IPhreeqC by Parkhurst and Appelo (2013) and Matlab were coupled. Thus, the geochemical output of IPhreeqC is numerically evaluated and optimised with Matlab. Sensitive parameters, e.g. pH-value, and aluminium concentration etc. are varied simultaneously to minimise the temperature difference between multiple mineral phases, used as geothermometer. MulT_predict with its implemented optimisation leads to more accurate temperature estimations with lesser variance of error.
의미망 전략과 암기 전략이 학습자의 인지 수준에 따라 영어 어휘 습득에 미치는 영향

We conducted this study to evaluate the efficacy on vocabulary acquisition of semantic mapping versus memorization strategies. Three classes of 4th and 6th graders at Seoul D Elementary School were divided into semantic mapping strategy group, memorization strategy group and one control group and given three weekly experimental classes over ten weeks. We conducted vocabulary tests to evaluate changes in receptive and productive vocabulary abilities. The data was analyzed using the one-way ANOVA and t-test of SPSS. Students were also given a questionnaire to study the effects of affective attitude. The results of the receptive and productive vocabulary tests showed more improvement in both test strategies over the control. Semantic mapping showed the most improvement of the three strategies. Semantic mapping strategy is particularly effective in improving long-term memory and productive vocabulary and upper level learners showed more improvement followed by middle level and lower level. Affective attitude showed the opposite order. This study suggests that the effectiveness of vocabulary learning strategy should be based on the level of learners and the learning situation.
Supply chain information sharing and performance improvements

Facing the environment changes characterized by increasing level of uncertainties and demand requirements, firms can no longer achieve competitive advantage alone. To survive, the only rescue would be to adhere to a supply chain membership, and share information with their trading partners. Furthermore, the adoption of IT has increased the exchanges of information between firms, which is leading them to think about the pay-offs of implementing technologies and the extent of sharing information. However, even if the majority of the state of the art agreed on the positive impact of information sharing on supply chain performance, there still is a need of understanding the factors that reinforces such outcome. Hence, in order to disclose the drivers of supply chain performance, by sharing information, we conduct a systematic literature review. The findings reveal that supply chain collaboration is a technique “par excellence” that boosts the impact of information sharing on supply chain performance, and results in better improvements.
Radar Signal Sorting System Based on Multi-domain Feature Extraction and Decision Tree Algorithm

With the rapid development of radar technology, radar signal types are becoming more and more complex and changeable. Moreover, the real-time and accuracy of traditional radar sorting systems are facing increasingly severe challenges. This paper proposes a radar classification method based on the classification and regression tree (CART). After multi-domain feature extraction of the radar signal, the decision tree model is trained and verified by the new data according to the Gini impurity minimum criterion. Finally, the radar signal can be effectively recognized. The simulation results demonstrate that the proposed radar signal sorting system's recognition accuracy can reach 98.9%, which is 18.7% higher than that of the decision tree model without feature extraction.
FACIAL EMOTION AND FACE MASK DETECTION

In today's world mostly work is going through the machine and their uses are increasing constantly they are helping in manpower and making our work easy and faster. Machine learning is giving the machine a perc eption, which can lead them to do a variety of tasks such as the variety of detections regarding human face like face mask detection combined with facial emotions. During the development of this project, we used deep learning neural network over images for clarification for different emotions and with masks, without masks. Our model will detect a person is wearing a mask or not if ‘ it will detect the mask and if ‘ it will detect emotion in a face.
Security Issue & Challenges In Cloud Computing

Cloud Computing Is A New Computing Method That Is Gaining Traction In The Field Of Computer Science. Cloud Computing Is A Kind Of Computing Based On The Internet And Represents The Next Stage Of The Internet's Development. Although It Has Garnered Much Attention In Recent Years, Security Concerns Are A Key Impediment To Cloud Computing Development. It Moves User Data And Application Software To Distant Data Centres, I.E., The Cloud, Over Which The User Has No Control And Where Data Management May Be Less Secure. However, This Unique Characteristic Of Cloud Computing Presents A Slew Of Security Concerns That Must Be Addressed And Properly Understood. This Expansion Of The Cloud Computing Ecosystem Imposes Additional Security Problems On Cloud Developers. Additionally, The Article Covers Current Cloud Security Methods And Approaches. This Article Will Educate Academics And Professionals On Various Security Risks And The Models And Methods To Address Them.