Reviewerly/exHarmony huggingface dataset:
Correlation of cytomorphology with flowcytometric immunophenotyping in patients of acute leukemia in tertiary care hospital

Background: Morphological diagnosis of leukemias may sometimes not correlate with flow cytometry diagnosis. Classification of hematological neoplasm by the World Health Organization gives priority to cytogenetic, molecular biology, and even patient history, in an attempt to classify patients primarily regarding prognosis. Cytomorphology and immunophenotyping complement each other. Morphology is burdened by a high degree of subjectivity. That is why correlation of information provided by the two techniques is still absolutely necessary. The aim of our study was to correlate between cytomorphological findings and immunophenotyping results on a group of patients investigated for acute leukemia. Materials and Methods: Study was conducted in department of pathology in Chirayu Medical College and Hospital, Bhopal from December 2018 to September 2019. Cases were diagnosed as acute leukemia based on complete blood count and bone marrow aspirate/peripheral smear.These cases were sent to flowcytometry for immunophenotyping for further confirmation. Results: Total 74 cases of acute leukemias were diagnosed, out of which 30 were Acute myeloid leukemia and 44 were Acute lymphoblastic leukemia. There was 95.95% correlation between diagnosis on cytomorphology and flowcytometry. Two cases remain unclassified on cytomorphology which turn out to be Acute myeloid leukemia on flowcytometry. One case of Acute myeloid leukemia on cytomorphology was diagnosed as Acute lymphoblastic leukemia on flowcytometry. Conclusions: Inclusion of flowcytometry in routine diagnostic workup of acute leukemia ensures proper characterization as well as management. Major challenges for the near future are the standardization of technical procedures, data interpretation, and reporting.
STUDENT SPEAKING ACTIONS IN UNDERSTANDING THE MEANING OF ENGLISH COMMUNICATION HOMOPONS IN THE LECTURE ROOM

The meaning of homophones is a part of the vocabulary that is difficult to understand because the sound when the pronunciation is the same but the meaning is different. For example, the words to, two and too spoken by English users will sound the same. This causes missinterpretation in giving meaning. Along with that, basically advances in communication technology have made it easier for students to learn English related to the meaning of the homophone. Students can also learn this through various applications such as Facebook, Snack Video, Tik Tok as a teaching resource in exploring the meaning of homophones in English. In addition, students can also communicate in English with various countries based on this communication technology. The problem is that although advances in communication technology have provided space and opportunities for students to master English related to the meaning of homophones, there are still many students who do not understand the use of the meaning of homophones in English. The purpose of this study was to determine the extent to which students understand the meaning of the use of homophone words such as to, two and too to students in the lecture room. This research consists of three study rules. First, the rule of study uses the rules of a questionnaire to 500 students from various universities. Apart from that, the reviewer also used the rules of in-depth interview (In-Depth Interview) to 5 informants. The analysis of the study uses quantitative and qualitative rules where the reviewer will emphasize student speech and explanations supported by information from study informants. The objective of research about ability of the student university level how to read and heard the homophone word by the lecturer. From the analysis of the data, it can be concluded that the students easily understanding read the homophone and difficult the homophone, like to, two and too. The rule family institution teaching homophone English language to their children can improve about English language ability especially understanding homophone, like to, two and too. Besides that, communication technology can help the student learning English language about homophone.
Storage and encryption file authentication for cloud-based data retrieval

The amount of data that must be processed, stored, and modified rises as time passes. An enormous volume of data from a wide range of sources must be stored on a safe platform. Maintaining such a large volume of data on a single computer or hard drive is impracticable. As a result, the cloud is the ideal platform for storing any quantity of data. An advantage of storing data in the cloud is that it may be accessed at any time and from any device. However, the security of data stored in the cloud is a big concern. Because of this, despite the benefits, most users are reluctant to move their papers to the cloud. The data should be encrypted before sending it off to the cloud service provider to avoid this issue. It's a great way to increase the security of your papers. According to a new technique presented in the system, data may be searched across encrypted files without compromising the privacy and security of various data owners. Implementing the pallier homomorphic encryption method makes it possible to perform computations on encrypted data without decryption.
Research on Teaching Design of Geochemistry and Biology under the Background of Ecological Environment and Information Technology

The current social situation is that the rapid development of science and technology has brought about rapid economic growth, but the wanton development of ecological environmental resources, resulted in environmental deterioration, so that human survival faces major challenges. This is not only the current state of life but also an urgent problem that people will work together to solve. Therefore, in order to cultivate environmental awareness, make students intuitively and clearly understand the benign operation of ecological environment and the close relationship between people and the environment, this paper designs and studies the existing information technology to design biology teaching on the basis of the sustainable development of ecological environment and encourages students to strengthen the protection of the environment. This paper studied and designed the practical inquiry, analysis, and questionnaire survey. The experimental results show that the students in information technology teaching perform better than the traditional students, which can stimulate students’ enthusiasm, arouse their interest in learning, and deepen students’ understanding. The number of outstanding students increased by 5, and the number of failed students decreased by 8. The research significance of this paper is to provide new teaching design ideas for the existing biology teaching mode so that the development of information technology can benefit biology teaching.
VISUEL - A Web Dynamic Dashboard for DataVisualization

Data visualization aims to explore and analyze data quickly, interactively, and intuitively using visual representations. Faced with the constant growth of data in terms of volume and diversity, visualization techniques must confront the challenge of dealing with increasingly large datasets in terms of representation, interaction, and performance. Therefore, these techniques must be able to effectively convey the characteristics of the information space and inspire discovery.In this article, we present VISUEL, a web dynamic dashboard for data visualization. VISUEL supports multiple coordinated views, integrating visualization techniques such as scatter plots, parallel coordinates, and box plots, and interactive schematic maps to represent information enriched with spatial references.VISUEL is fully interactive, supporting traditional interactions like filtering, selection, brushing and linking, and zooming, among others. It also allows the user to configure the visual representation of their data, by selecting the color and shape of the representations.We illustrate the usefulness of this tool using real-life data related to the wine industry in Argentina. Important aspects of our case study are discovered through the construction and analysis of multiple views.
Vehicle Classification and Tracking Based on Deep Learning

Traffic volume is gradually increasing due to the development of technology and the concentration of people in cities. As the results, traffic congestion and traffic accidents are becoming social problems. Detecting and tracking a vehicle based on computer vision is a great helpful in providing important information such as identifying road traffic conditions and crime situations. However, vehicle detection and tracking using a camera is affected by environmental factors in which the camera is installed. In this paper, we thus propose a deep learning based on vehicle classification and tracking scheme to classify and track vehicles in a complex and diverse environment. Using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.
Detection of Phishing and User Awareness Training in Information Security: A Systematic Literature Review

Phishing is a method of online fraud where attackers are targeted to gain access to the computer systems for monetary benefits or personal gains. In this case, the attackers pose themselves as legitimate entities to gain the users&#x0027; sensitive information. Phishing has been significant concern over the past few years. The firms are recording an increase in phishing attacks primarily aimed at the firm&#x0027;s intellectual property and the employees&#x0027; sensitive data. As a result, these attacks force firms to spend more on information security, both in technology-centric and human-centric approaches. With the advancements in cyber-security in the last ten years, many techniques evolved to detect phishing-related activities through websites and emails. This study focuses on the latest techniques used for detecting phishing attacks, including the usage of Visual selection features, Machine Learning (ML), and Artificial Intelligence (AI) to see the phishing attacks. New strategies for identifying phishing attacks are evolving, but limited standardized knowledge on phishing identification and mitigation is accessible from user awareness training. So, this study also focuses on the role of security-awareness movements to minimize the impact of phishing attacks. There are many approaches to train the user regarding these attacks, such as persona-centred training, anti-phishing techniques, visual discrimination training and the usage of spam filters, robust firewalls and infrastructure, dynamic technical defense mechanisms, use of third-party certified software to mitigate phishing attacks from happening. Therefore, the purpose of this paper is to carry out a systematic analysis of literature to assess the state of knowledge in prominent scientific journals on the identification and prevention of phishing. Forty-three journal articles with the perspective of phishing detection and prevention through awareness training were reviewed from 2011 to 2020. This timely systematic review also focuses on the gaps identified in the selected primary studies and future research directions in this area.
An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9%, outperforming three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.
Maritime English Learning Materials Based on Standard Training Certification and Watchkeeping for Seafarers (STCW) Curriculum and Intercultural Competence (IC)

The objectives of this study is to investigate 1) the current existing conditions of Maritime English learning materials of Deck Department of Maritime Education and Training (MET), 2) the Maritime English learning materials needed by the students of Deck Department of Maritime Education and Training (MET), and 3) the intercultural competences needed by the students. This study is conducted by qualitative method. The respondents of the research are the active students, lecturers, active and retired seafarers, cadets, port authority, and administration staff. They are taken by purposive random sampling. The data are collected by questionnaire, documentary sheet, and in depth interview. The data are analyzed by descriptive qualitative. The results of the study show that 1) generally the existing Maritime English Materials mostly are not in line with the STCW’10 and need to be developed in order to meet the minimum standard of Standard Training Certifications and Watchkeeping for Seafarers (STCW’2010), 2) there are various topics of Maritime English learning materials needed by the students in terms of both English language and content or professional subjects, 3) and the various contents of intercultural competences are also needed by the students before they go to work at sea in order to effectively communicate with other crew of different cultural background.
GA-Net-Pyramid: An Efficient End-to-End Network for Dense Matching

Dense matching plays a crucial role in computer vision and remote sensing, to rapidly provide stereo products using inexpensive hardware. Along with the development of deep learning, the Guided Aggregation Network (GA-Net) achieves state-of-the-art performance via the proposed Semi-Global Guided Aggregation layers and reduces the use of costly 3D convolutional layers. To solve the problem of GA-Net requiring large GPU memory consumption, we design a pyramid architecture to modify the model. Starting from a downsampled stereo input, the disparity is estimated and continuously refined through the pyramid levels. Thus, the disparity search is only applied for a small size of stereo pair and then confined within a short residual range for minor correction, leading to highly reduced memory usage and runtime. Tests on close-range, aerial, and satellite data demonstrate that the proposed algorithm achieves significantly higher efficiency (around eight times faster consuming only 20–40% GPU memory) and comparable results with GA-Net on remote sensing data. Thanks to this coarse-to-fine estimation, we successfully process remote sensing datasets with very large disparity ranges, which could not be processed with GA-Net due to GPU memory limitations.
Personified Virtual Assistants

As the presence of smart objects increases in everyday life, computing grows out of the image of a desktop computer and becomes more open to new types of interaction and interfaces. Beyond this, smartphones, smartwatches, smart TVs, videogames and especially smart speakers offer voice interaction. This research presents how users perceive the relationship with these voice interfaces and personified virtual assistants. It also considers how their adoption is taking place, what are their opinions and expectations and what kinds of problems they encounter. A literature review and field research was carried out through the use of an online questionnaire and Focus Group. The findings illustrate that the humanlike personality presence has a critical role in the adoption of virtual assistants, as well as it has positive effects on user perception. However, the use of voice user interfaces still causes discomfort while in a public environment. The significant concerns users face are not technical problems or usability issues, but worries about the influences of the virtual assistants on social behavior and impacts on day-to-day living.
Assist Home Training Table Tennis Skill Acquisition via Immersive Learning and Web Technologies

Sports applications in Virtual Reality (VR) have become immensely popular for training skill-based sports like table tennis. However, the existing researches do not focus on designing an intuitive system for efficient communication between the trainee and the coach. We developed a VR table tennis training system for table tennis skill acquisition that focuses on helping coaches to convey a trainee&#x0027;s mistake clearly. Our system consists of a VR training system where trainees can learn a skill gradually and a web-based feedback annotative tool for coaches. Trainees can examine their mistakes through a tablet or an immersive VR world.
Secure Complaint Management System against Women Harassment at Workplace Using Blockchain Technology

Since the Industrial era, women are playing a significant role in the workforce to move the world forward. Their increasing contribution in various fields has earned a fortune for the global economy. Despite that, women constantly face more obstacles than men in the workplace. When half of the population are mistreated because of gender inequality, the economy of any nation is supposed to collapse. One of the biggest barriers for women in their careers is workplace harassment. Workplace harassment may include physical, verbal or nonverbal harassment that not only have an adverse effect on a woman's career, mental health and physical health but also organizational reputation. A common way to make a complaint in most organizations is to fill up a complaint form, email or go directly to the competent authority and complain. But victims often hesitate to complain because their identity might get revealed or their documentary evidence might be tampered. As a result, most of the harassers get through very easily. To resolve this problem, this paper presents a blockchain-based anonymous, transparent and secure platform where women can easily complain against their harassers. To keep the platform secure and reliable, a two-level hierarchical model is introduced, where level-1 is the Human Resources (HR) and level-2 is the Higher Authority. In level-1, victims can anonymously complain to HR and in Level-2, victims can complain with their identity revealed to higher authority. This way, the proposed platform ensures women of a healthy work environment and provides all necessary support to stand up against injustice in the workplace.
University campus: New self-study opportunities in the digital era

Globalisation, technological and digital changes as well as COVID-19 have caused numerous changes in higher education including migration of all learning, teaching, and assessment into online domains. This means that all key skills, such as team-work, project planning, innovative, research and self-study skills should be developed mainly by means of individual research practice. It is crucial that university campuses provide learners with all the possible assistance they may need. In these circumstances, the purpose of the article is to identify new opportunities for a university campus to support students in their self-study and research projects. Based on the literature review, we suggest four groups of such opportunities – they are measures that various universities implement in their practice. We also discuss possible challenges and perspectives that university campuses may face in the future.
Benchmarking Benchmarking

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This special issue of</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Design&amp;Test</i> is about benchmarking machine learning (ML) applications. A special issue on benchmarking is long overdue. The last one I can find is one I edited way back in 2000. That issue covered the ITC99 benchmarks that I put together and introduced at a special session of the 1999 International Test Conference. These were used for comparing automatic test pattern-generation (ATPG) algorithms.
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking

This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network. The former learns coarse primary information with low cost while the latter conveys residual information for high fidelity to original representations. The proposed parallel architecture is effective to maintain complementary information since fixed-point arithmetic can be utilized in the quantized network and the lightweight model provides precise representations given by a compact channel-pruned network. We incorporate the hybrid representation technique into an online visual tracking task, where deep neural networks need to handle temporal variations of target appearances in real-time. Compared to the state-of-the-art real-time trackers based on conventional deep neural networks, our tracking algorithm demonstrates competitive accuracy on the standard benchmarks with a small fraction of computational cost and memory footprint.
Evaluation of Artificial Intelligence Models and Wireless Network Applications for Enterprise Sales Management Innovation under the New Retail Format

Enterprise sales are distinct from other types of technology sales. It refers to the procurement of big contracts that involve long sales cycles, multiple decision-makers, and a higher level of risk when compared to traditional sales. It has its very own way of doing the decision-making process. In the current age of Internet technology, almost every field has adapted or upgraded to Artificial Intelligence (AI) technology. Likewise, enterprise sales also use this vibrant technology to enrich the sales representatives’ work. It does not mean that it affects the employees’ jobs, but it makes them much more manageable. The New Retail Format is the concept of selling products through the blending of contexts of online and offline modes. Also, enterprise sales management defines the management of product sales in business-to-business (B2B) transactions. In this research, Fuzzy Multicriteria Decision-Making is used by the customer to choose various products in an enterprise sales store. Additionally, Particle Swarm Optimization is utilised in this application to monitor the stock (product) availability and sales of the available products between two larger organizations. In this article, the proposed model incorporates AI and Wireless Sensor Networks (WSN) for enterprise sales under a new retail format. In this research, Fuzzy Multicriteria Decision-Making with a Particle Swarm Optimization Algorithm is implemented to analyze the process of enterprise sales. The proposed algorithm is compared with the existing fuzzy clustering method. From the result, it is observed that the proposed algorithm has achieved 99.78%, whereas the existing system has given only 93.46% of accuracy.