Reviewerly/exHarmony huggingface dataset:
An image encryption algorithm using two-dimensional chaotic map and DNA coding

By improving the typical Sine map and Logistic map, a 2D chaotic map with good pseudo-randomness and a large chaotic range of parameters is derived. Based on the characteristics of DNA coding, an efficient image encryption algorithm is designed by combining chaotic map with DNA coding. Firstly, the image is divided into 8-bit planes by bit level operation, and the bit plane is divided into two modules by parity separation strategy. Then, the different DNA coding rules are used to encode the image, and then the DNA XOR is used to conduct the diffusion operation. After the diagonal diffusion on the image, the inverse diagonal diffusion is carried out. The image is divided into four blocks, and the diffusion operation is carried out within the four blocks. Simulation results and security analysis show that the proposed image encryption scheme has certain advantages compared with some existing methods.
Multimedia English Online Learning Behavior Intelligent Monitoring System Based on Face Recognition

To better promote students’ English learning effect, aiming at the shortcomings of current online learning monitoring, this paper introduces face recognition technology and designs a multimedia English online learning behavior intelligent monitoring system. optimize the system hardware configuration, improve the performance of monitoring equipment, further improve the system software function and simplify the system operation process. Finally, experiments show that the multimedia English online learning behavior intelligent monitoring system based on face recognition can accurately identify students’ learning behavior in a complex environment, improve the monitoring quality and promote the effect of English learning.
Silicon-based true random number generators

True random number generator (TRNG) is an essential hardware security primitive that has been used in a variety of security, hazard games, gambling, computer simulation, statistical testing and industrial labeling applications. In cryptology, they can be used to produce a digital key stream for encrypting or signing messages, generate nonces for authentication in secure communication or for chip manufacturing and as seed for generation of device -specific identities. Monte Carlo simulations of physical phenomena and neural networks also employ TRNGs to generate many initial parameters. Due to the economy of scale, standard CMOS-compatible or solid-state TRNGs with full entropy, high throughput and small footprint are attractive for integration into security solutions of commercial products. This chapter presents a few modern solidstate TRNG circuits. The design and their performances are detailed. Besides, on -chip post -processing techniques to improve the randomness and security of TRNGs are reviewed and discussed. Furthermore, we also illustrate the randomness evaluation methods in the literature.
Screen Ontologies or Teaching the Virus a Lesson: A Few Things that Work in Online Education and a Few that Don’t

The recent global disruption in education due to the COVID-19 virus has led to a significant increase, if not an explosion, in education studies. Research into and application of digital pedagogical strategies in general and (vision-based) online teaching in particular have reached new heights. Many studies focus on the strengths of this new online education and, indeed, much of the data points to its success. However, other studies elicit slightly less positive data. The study at hand will take both of these scientific strands seriously and try to interpret them. This will be achieved in the first place through the analysis of a number of significant studies on online teaching and commenting on their methodologies and basic premises. Secondly, this will be achieved through a rendition of the author’s own teaching experience under COVID-19 conditions. Then one of the sine-qua-nons of online education will be examined, namely the screen, a central device left unaddressed by most other research. Using an ontological interpretative approach, the study will demonstrate why at least some of the promises of online education simply cannot be kept. Lastly, the study will list ways in which a thoroughly reflected approach to online education can nevertheless play an important role in preparing students for a post-screen and post-postdigital world.
Female Urinary Microbiome Analysis and Artificial Intelligence Enhances the Infectious Diagnostic Yield in Precision Medicine

We assessed the diagnostic yield of metagenomics urine sample testing in patients with urological symptoms. We conducted microbiome analysis of 86 female urine samples that included 17 healthy controls and 69 patients. Natural language processing (NLP), a subfield of artificial intelligence, was used to create a pathogen identification tool, Xplore-AI, to assess the potential pathogens in all of the samples. Meanwhile, report summaries that were written by infectious disease experts were compared to the NLP results to investigate its accuracy. The results showed that the NLP system reported 97% of patient samples had at least one pathogen over three standard deviations from values found in in healthy controls. Similarly, 84% of patients had two or more classified pathogens. These diagnostic percentages were consistent with the infectious disease expert summaries. However, some pathogens like Aerococcus urinae were present in 13 patient samples, but only reported in one summary. In conclusion, this study demonstrated the high diagnostic yield in females with urological symptoms following metagenomic analysis and the ability of using an NLP-based system to identify pathogens to improve the accuracy of the reportable species.
Smart Clothes for Security Personnel

In national security, details matter butwaiting to get them right would lead to failure. Developing a centralised system to monitor the groups of security personnel would help in making vital decisions on time. Hence using IOT technology, we create a smart cloth which transmits live location and psycho-physiological data of security forces to a permissioned database where the data is analysed to understand the state of troops. These sensitive data are protected from tampering usingblockchain.
Interpretation of Hand Spelled Banking Helpdesk Terms for Deaf and Dumb Using Deep Learning

The hand sign language uses visual-manual modality to share a certain message. Specially abled people having hearing and speaking disabilities interact more naturally in hand sign language rather than verbal language. According to one of the Census study, 2.21% out of 121 crore population in India are ‘disabled’, out of which 19% are having a hearing disability and 7% are having speech disability. Since everyone cannot communicate in sign language as it is a lesser-known language, it often leads to communication gap. So, the automated Sign Language Interpreter (SLI) helps to meet this communication gap as a manual sign language translator is not a convenient option because of its privacy issues and lack of availability. This paper proposes an Indian Hand Sign Language Interpreter which operates upon a vision-based approach that uses Machine Learning and Deep Learning techniques to locate the hand gesture region accurately for extracting the features and finally interpreting the respective meaning. The experimentation for performance metrics such as accuracy and loss using various activation functions helped to analyzed the performance of the model. The system successfully identifies a number of hand spelled words and thus eases the communication among people.
A Single Precision Floating Point Multiplier for Machine Learning Hardware Acceleration

A single precision floating-point multiplier based on FPGA is designed. The booth-2 algorithm is used for the encoding operation, and the Wallace tree structure is used to complete the accumulation of partial products. A 32-bit single precision floating-point multiplier based on IEEE754 standard is designed. The multiplier can be used as the basic structure of hardware multiplier to accelerate Convolutional Neural Networks algorithm.
An overview on human-computer interaction

Human-Computer Interaction (HCI) is a multidisciplinary area of research that focuses on computer technology design and, in particular, the interaction between people (users) and computers. The use of computers had always raised the issue of interfacing. The methods by which humans interact with computers have progressed significantly. HCI began with computers and has now grown to include virtually all aspects of information technology design. This paper's goal is to provide a broad overview of the field of Human-Computer Interaction. The fundamental concepts and terminology are covered, as well as a review of current technologies and recent developments in the area, typical architectures used in the design of HCI systems, including unimodal and multimodal configurations, and HCI applications. In addition, this paper provides a complete list of references for each HCI idea, technique, and application.
Possibilities of using gamification in distance education

The article discusses the problems of using game mechanisms (Gamification) to increase motivation and attract students participating in distance educational activities. Distance learning has become the dominant form of education at various levels of education during the COVID-19 pandemic. It is highly probable that in the future such courses will also represent a significant and larger than hitherto part of the educational offer of universities and other educational institutions. With the spread and massive introduction of distance learning, the role and importance of teachers has increased, who in a relatively short time had to adjust the materials and content of training to the conditions and opportunities of online education. Maintaining the attention and participation of distance learning participants was a significant challenge and challenge. The aim of the work is to clarify the concept of gamification and discuss the mechanisms of play and their possible use in the context of learning. The effectiveness of gamification has been discussed in the light of selected theories of psychology in relation to the technological possibilities of online education. The article also presents examples of selected applications and platforms that can be useful for creating educational materials and tasks performed in a distance form at the level of professional education.
Long-Term Electronic Archives: Modernization and Integration

Over a period of about twenty years, the IIS SB RAS has created information systems of historical orientation: the Electronic Archive of Academician A.P. Ershov, the Photo Archive of the Siberian Branch of the RAS, the Archive of the newspaper “Science in Siberia”, the Open Archive of the SB RAS, etc. Each of the resources has its own specifics but in general their content is based on the general social and territorial basis of scientific and public activities of the SB AS USSR/RAS and the Novosibirsk Akademgorodok. In this report we will look at some of the problems of integrating/disintegrating disparate electronic resources into a common platform using existing and emerging tools.
Transforming a Course into the Online Delivery Mode on a Global Platform: Benefits and Challenges

This paper describes developing an online alternative of an offline course within the framework of a newly created “Smart Materials” Master’s degree program. For an engineering education course with an experimental component, the authors accomplished significant modifications of course materials meant to be delivered online. From course benchmarking and discussions with the management team, it was clear that just creating an online copy of an existing offline course is not a successful approach. The authors of the course had to unwind an entire Master’s program that course belonged to. The process of the online course development required blending the best components of the respective offline program into an integrated and standalone course that resembles its offline counterpart but is an independent academic product. The most challenging and time-contributing aspect of this online course development was transformation of experiments with labs on chips and numerical simulations software into compact and intuitive videos. The course attracted audience from 70 + countries with over 90% of positive feedback.
Sentiment Analysis of Online Comments through Semantic Networks and ROST Text Mining Software

Collecting the evaluation and feelings of tourists on the image of tourist destinations through network texts, and using data mining methods to get the feelings of tourists during their play has become an important way to study the image of tourism. Take Gulangyu Island in Xiamen as an example, take the text data of tourist review websites as samples, and use ROST ContentMining software to extract high-frequency feature words of Gulangyu tourism online reviews to understand the content and themes of Gulangyu tourism image; use content analysis methods to construct social networks and semantic networks, And conduct sentiment analysis to analyze the degree of satisfaction of tourists with Gulangyu tourism.
Research on Endoscopic Image Enhancement Algorithm Based on Contrast Fusion

Image enhancement can improve doctors' perception of gastrointestinal tissues and blood vessels. This paper introduces an image enhancement method for medical endoscopy, which includes three stages: (1) improved contrast enhancement algorithm, using multiple iterative sharpening mask to enhance the edge contour of gastrointestinal tissues and blood vessels; (2) improved limiting threshold of limiting contrast adaptive histogram equalization (CLAHE); (3) improved contrast image fusion to preserve the good characteristics of improved contrast enhancement algorithm and improved CLAHE. The proposed method is applied to large data sets of low contrast white light endoscopes. The experimental results show that the endoscopic image enhancement algorithm based on contrast fusion is a feasible method to solve the defects between different image enhancement algorithms, because the image fusion results can effectively reduce the defects of contrast enhancement algorithm and CLAHE compared with the original image, such as detail loss, local contrast loss and pixel value loss.
Re-imagining Blended Learning. An Experience-Led Approach to Accelerate Student Future Skills Development

This paper argues that the changes to education and the workplace, brought about by the use of blended learning and working during the Pandemic, can accelerate the development of students digital skills. Adopting the EU definition of blended learning, the paper shows that a tripartite understanding of blended learning between schools, industry and policymakers is needed to secure sustainability and transferability of digital skills from school to the workplace. The challenges and opportunities of a European digital literacy programme, the Digital Schools Awards, are offered as a way to consolidate the development of digital literacy across Europe so that the experiences of blended learning and teaching during the Pandemic can be harnessed and advanced. To be sustainable, blended learning must appeal to students and their teachers’ pedagogical and curricular needs. The paper, therefore, promotes a continuum approach to blended learning where a range of developmental and progressive strategies are suggested. Then, a rationale for future work and skills is presented that draws on this continuum to support blended learning and working as a lifelong practice. A multistakeholder, peer to peer approach to the future of learning and skills development will, we argue, positively impact the way 21st-century citizens can educate, learn and work at a cross-cultural, multi-societal and institution level.
Defect Prediction Technology of Aerospace Software Based on Deep Neural Network and Process Measurement

In order to ensure high reliability, the efficiency of traditional aerospace software testing is often low. With the rapid development of machine learning, its powerful data feature extraction ability has great potential in improving the efficiency of aerospace software testing. Therefore, this paper proposed a software defect prediction method based on deep neural network and process measurement. Based on the NASA data set and combined with the software process data, the software defect measurement set is constructed. 35 measurement elements are used as the original input, and multiple single-layer automatic coding networks are superimposed to form the deep neural network model of software defect. The model is finally trained by the layer-by-layer greedy training method to realize software defect prediction. Experimental verification shows that the prediction method has a good prediction effect on aerospace software defects, and the accuracy rate reached 90%, which can greatly improve the efficiency and effect of aerospace software testing.
Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic

Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access ‘travel recommendation correlated with social media is used. In the proposed system the system analyzes the user’s social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named ‘Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)’. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.
Data Augmentation for Layperson’s Medical Entity Linking Task

Due to the vast amount of health-related data on social media, it is beneficial to monitor health-related issues experienced by the users, such as monitoring adverse drug effects. This problem is known as the Medical Entity Linking (MEL) task, which identifies informal medical terms and normalizes them to corresponding formal medical concepts (Medical Concept Normalization, or MCN). One of the challenges of MEL when working with layperson language is the scarcity of training data and the low coverage of laypersons' health vocabulary. We apply data augmentation methods to existing data sets to increase the size of training corpora and expand the coverage of colloquial health vocabulary. We treat the informal medical phrase identification task as a sequence labelling task for Named Entity Recognition (NER). An extensive evaluation of NER shows that data augmentation may introduce noise that affects the performance of identifying informal medical phrases. Our further investigation on false positive in NER task finds that most of the false positive terms that occurred as a result of augmentation could be identified as medical entities and correctly normalized to SNOMED-CT medical concepts. In contrast, the augmentation techniques in MCN task can significantly improve performance on both the CADEC and PsyTAR data sets, particularly for paraphrase-based augmentation and combinations of augmentation techniques.
Face Based Attendance System Using Convolutional Neural Network

Over the past years Automatic face recognition (AFR) technologies have shown improvements in performance. Hence these systems are widely used for security and commercial applications. An automated system for face recognition during a real time background for a university to mark the attendance of their employees. So Smart Attendance using Real Time Face Recognition may be a world solution which comes with day-to-day activities of handling employees. The task is extremely difficult because the important time background subtraction during a picture remains a challenge. To detect real time face are used and an easy fast Principal Component Analysis has won’t to recognize the faces detected with a high accuracy rate. The matched face is employed to mark attendance of the worker. Our system maintains the attendance records of employees automatically. It becomes difficult task to enter attendance entry manually in logbooks. Hence, we have come with a module in which attendance of employee will be marked automatically by detecting their face by using face recognition techniques. This enrolling may be a onetime process and their face are going to be stored within the database. During enrolling of face we require a system since it's a onetime process. You can have your own roll number as your employee id which can be unique for every employee. The presence of every employee is going to be updated during a database. Proposed system results proved that it has better performance than manual attendance system Attendance is marked after employee identification. This product gives far more solutions with accurate leads to user interactive manner instead of existing attendance and leave management systems.
Intelligent Vision in Healthcare

During the last decade, an exponential development has been observed in the field of intelligent vision technologies. These have shown profound success in a wide range of application domains such as robotics, bio-metric identification, intruder detection, health care, agriculture, and many more. With advancement in vision technologies, medical diagnosis has been revolutionized with automatic analyses. Specifically, deep learning methods have illustrated remarkable performance in disease assessment. Moreover, the abundance of medical data has aided in developing intelligent tools for interpretation and combat of diseases. For a high-precision vision system, feature representation is one of the important tasks. Many techniques have been developed for representation of images so that a machine learning model can be trained. Therefore, this chapter focuses on the various feature representation techniques in the field of intelligent vision for healthcare system. Moreover, limitations and other challenges are also discussed for the future development.
Trust-based gateway selection in a multi-tiers blockchain architecture

In a multi-tiers blockchain (BC) architecture, we find heterogeneous BC with different security levels that need to communicate. This communication is ensured by node on each BC tier, called the gateway. This node has a sensitive role in the network, it represents a weak point and a source of attacks propagation between networks. In this paper, we focus on the selection of the gateway node using a distributed trust model to evaluate nodes behaviors inside each BC network. Our solution is blockchain-based and will be implemented above the blockchain without affecting its initial structure. The trust model is inspired from the Spence's model where signals, cost and rewards are used to incite nodes to collaborate and to act honestly. In addition, we introduce penalties for nodes acting maliciously or being selfish. Moreover, the gateway undergoes severe penalties in case he changes his behavior after election. Nodes can complain about the current gateway, their complaints are considered if they are trustworthy and quite numerous. Then, we propose a 2 phases algorithm based on the proposed trust model for a dynamic selection of the gateway node. The 2 phases ensure the election of an honest node with the optimal performances.