Reviewerly/exHarmony huggingface dataset:
Application of Double Pictures to Audio Watermarking using Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) Methods

One way to control the development of arbitrary distribution of audio is to apply audio watermarking. Audio Watermarking is used to identify the ownership rights of audio files by embedding a watermark while paying attention to the audio quality and the watermark's resistance to amplify, resampling, and invert attacks. Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) methods are frequency-domain methods that have a good robustness. The DCT and SVD methods are combined with inserting and extracting watermarks using two images as watermarks. From the test results, the effect of inserting an audio watermark with two images does not significantly impact the durability of the watermark and the quality of the audio inserted with the watermark. However, the alpha value has a significant effect. The higher the alpha value, the lower the audio SNR value by an average of 1.93 dB from an alpha value of 0.0008 and 0.001 for each type of classical, jazz, and rock audio genre.
Recent advances in language &amp; knowledge engineering

Language &amp; Knowledge Engineering is essential for the successfully development of artificial intelligence. The technologies proposed in international forums are meant to improve all areas of our daily life whether it is related to production industries, social communities, government, education, or something else. We consider very important to reveal the recent advances Intelligent and Fuzzy Systems applied to Language &amp; Knowledge Engineering because they are the base for the society of tomorrow. Thus, the aim of this special issue of Journal of Intelligent and Fuzzy Systems is to present a collection of papers that cover recent research results on the two wide topics: language and knowledge engineering. Even if the special issue is structured into these two general topics, we have covered specific themes such as the following ones: Natural Language Processing, Knowledge engineering, Pattern recognition, Artificial Intelligence and Language, Information Processing, Machine Learning Applied to Text Processing, Image and Text Classification, Multimodal data analysis, sentiment analysis, etc.
Tensor Methods for Clinical Informatics

Tensors are higher-order, multiway generalizations of matrices. Many datasets in machine learning problems can be naturally organized in tensor form, and leveraging this multilinear structure usually leads to better results, since it makes use of additional information within the data that would otherwise be neglected. For this reason, tensor methods have seen a surge of interest in recent years and have become increasingly common in bioinformatics. This survey aims at providing a brief introduction to the main mathematical tools used to handle tensors, along with an overview of many biomedical areas in which these ideas have found fruitful applications in recent years.
Yoti seals key ID deal with UK Post Office

Facial recognition specialist Yoti has won a major contract to supply a suite of digital identity products to the UK Post Office, whose 11,500 branches makes it Britain's biggest retail network.
Property Registration and Ownership Transfer using Blockchain

The high-value property like Land, Home, related to real estate it is essential to have exact records that recognize the present proprietor and give evidence that he is surely the proprietor. Such a record can be utilized to protect the owner’s privileges, prevent sale fraud and to make sure that the ownership is correctly transferred to a new owner after sale. Thus it is essential to maintain correct and complete information and prevent illegal or unjustified, fraudulent changes. Many efforts have been taken already for providing data security to sensitive information. Blockchain is the technology that gives high security to the data. Blockchain technology can store an immutable history of transactional records, so no one can ever doubt the authenticity; records are permanently linked to the system so no one can ever interfere with a record of their own. This paper gives a comprehensive system on blockchain technology as it can not only be used in financial assets but anything which has some value.
CBProf: Customisable Blockchain-as-a-Service Performance Profiler in Cloud Environments

Blockchain technologies, e.g., Hyperledger Fabric and Sawtooth, have been evolving rapidly during past years and enable potential decentralised innovations in a substantial amount of business applications, e.g. crowd journalism, car-sharing and energy trading. The development of decentralised business applications has to face challenges in selecting suitable blockchain technologies, customising network protocols among distributed peers, and optimising system performance to meet application requirements. Also, manually testing and comparing those different technologies are time-consuming. Therefore, an effective tool is needed for profiling the performance characteristics of blockchain services in different cloud environments. In this paper, we present the Customisable Blockchain-as-a-Service Performance Profiler (CBProf), a tool we developed for automating blockchain deployment and performance profiling in cloud environments. We also provide the implementation and functionality demonstration of this tool.
An Analysis on the Hardships of Crypto Currency to be Accepted as a Mainstream Currency in Future

Money and finance are changing right in front of our eyes. Digital assets, as well as innovative financial channels, instruments, and systems, are redefining financial transactions and generating new capital channels. Rather of relying on traditional financial institutions to validate and guarantee your transactions, cryptocurrency users' computers connected to the currency's network confirm them. It is impossible to expand the money supply above a predetermined algorithmic rate since the currency is safeguarded and encrypted. The algorithmic rate is known to all users. As a result, no cryptocurrency can be produced or "mined" beyond the ceiling limit set by each algorithm. Despite the fact that there are over 1000 cryptocurrencies on the market, Bitcoin is the most well-known and widely used.
Research on Human Falling Recognition Based on Inertial Sensors

This article aims to recognize human fall behavior based on wearable inertial sensors. The experiment in this paper mainly adopts data fusion algorithm, which can extract various features that can represent activities in time domain, frequency domain and time-frequency domain from the original data of human motion to effectively distinguish activities. In addition to considering the validity of the data, we also need to consider the way the data relates to the real situation and the comfort of the user in real life. Experimental data shows that in the data collection process, in order to obtain datasets that are easy to calculate, accurate and effective, two aspects need to be considered: the location of the data collection device and the frequency of data collection. Experimental results show that feature extraction has a great influence on the accuracy of activity recognition. Using 6 features of the elderly specifically selected for activity recognition, the original sensor data is directly trained through LSTM-RNN, and the accuracy of activity recognition can reach 92.28%.
Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules)

General methods for the evolution of programs with modular structure have long been sought by genetic programming researchers, in part because modularity has long been considered to be essential, or at least helpful, for human programmers when they develop large-scale software projects. Multiple efforts have been made in this direction, and while success has been demonstrated in specific contexts, no general scheme has yet been demonstrated to provide benefits for evolutionary program synthesis that are similar in generality and significance to those provided by modularity in human software engineering. In this chapter, we present and analyze a new framework for the study of the evolution of modularity, called GLEAM (Genetic Learning by Extraction and Absorption of Modules). GLEAM’s flexible architecture and tunable parameters allow researchers to test different methods related to the generation, propagation, and use of modules in genetic programming.
Developing Metrics for NASA Earth Science Interdisciplinary Data Products and Services

Metrics are measures that are able to produce quantifiable information. There are many applications of metrics in Earth science data and services; for example, metrics are frequently used to track service performance and progress. In short, developing, collecting and analyzing metrics are essential activities to better support Earth science research, applications, and education. As one of the largest repositories of Earth science data in the world, NASA’s Earth Science Data and Information System (ESDIS) Project supports twelve Distributed Active Archive Centers (DAACs). Standard metrics have been developed by the ESDIS Metrics System (EMS). These metrics are collected and analyzed routinely at each DAAC. As it is expected that the total data volume will continue to grow rapidly, and the timely developed technologies (e.g., cloud computing, AI/ML) will continue to improve data discovery and accessibility, opportunities for developing new data services for the Earth science community will also arise, especially in interdisciplinary research and applications. However, developing such metrics has become a challenge because multiple datasets are often needed. Current metrics are designed for a single predefined dataset or service, a disadvantage for collecting metrics for interdisciplinary data services. In this paper, we assess current metrics using one of the NASA DAACs, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), as an example, to discuss challenges and opportunities, along with recommendations for developing metrics addressing interdisciplinary satellite data products and services. <strong>Highlights</strong> <ul><li>Overview of NASA GES DISC Earth science datasets and services</li><li>Overview of existing metrics collection methods and analysis tools with examples</li><li>Discuss challenges and opportunities in collecting metrics for Earth science interdisciplinary data and services</li></ul>
An efficient hybrid encryption approach with bit shuffling for image data security

In the current scenario there is the need of data security to make the communication more secure and reliable. In this paper image data security has been discussed. The major problem is to provide security in less time with minimum loss of data. In this paper a combination of RSA, RC4 and blowfish (R2B) algorithms have been used for the data encryption and decryption purpose. For experimentation different images have been considered from different databases. The experimentation has been considered based on mean squared error (MSE), encryption time and overall processing time. The results have been considered based on different iterations to reduce any computational biasness. The results clearly show the improvement in error rates, encryption time and overall processing time as comparison to the traditional approaches.
Vocal Data Assesment To Envision Distinctive Features of An Individual

There is a lot of audio data generated on a day to day bases, which goes to waste without undergoing due processing. If we process this data, it can be beneficial for a multitude of purposes. Vocal data is unstructured, which makes it even harder for processing. This data has to undergo thorough pre-processing to convert it to a machine-understandable form. We aim to perform analysis of human voice to extract meaningful data and make a prediction of their age, gender, and accent. The developed system uses the Mel-frequency Cepstral Coefficient (MFCC), zero-cross-rate(ZCR), chroma_stft, spectral_centroid, spectral_bandwidth, and spectral_rolloff algorithms as a tool for Feature Extraction. The algorithms used for making inferences are support vector machine (SVM), K-nearest neighbors, and SVR. The work can be extended even further by combining video data with the audio data for analysis. The system can also be improved by increasing the number of languages it can detect.
Probabilistic Grayscale Visual Cryptography Scheme Using Multi-Pixel Encoding

This paper proposed a general implementation of multi-pixel encoding method based on grayscale probabilistic matrix which help improve the performance of grayscale probabilistic visual cryptography. Firstly, we presented an algorithm to generate the probabilistic matrix for grayscale palette of any size. Then, we realized a new way of multi-pixel encoding by introducing the idea of probability reduction. Meanwhile, the multitone-based analysis by synthesis technique was used to improve the perceptual quality of the reconstructed images. Experimental results show that our scheme effectively improves the smoothness and expressiveness of grayscale probabilistic visual cryptography.
Correction to: Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts.
Single step method for two-frame phase-shifting interferometry with unknown phase shift

Abstract A procedure to recover the phase from two interferograms with unknown phase step is proposed. A least-square approach is used to solve simultaneously for the illumination background and the introduced phase shift in a local window. This allows to calculate the interferogram phase of the pixel at the center of the window with the standard arc-tangent formula. This process is carried out sequentially across the interferogram field until all pixels are recovered. The proposed scheme is fast and robust, and unlike others methods eliminates the need to implement prior steps of background suppression or interferogram normalization. The developed procedure may be used in problematic environments where vibrations or other disturbances difficult to acquire a full set of phase shifted interferograms with constants phase shifts. The developed procedure is validated in real and simulated data with good results.
Apex Frame Spotting Using Convolutional Neural Networks with Continuous Labeling

Apex frame is the frame containing the highest intensity changes of facial movements in a sequence of video. It plays a crucial role in the analysis of micro-expressions, which generally have minute facial movements. This frame is hard to be identified that requires a laborious and time-consuming effort from highly skilled specialists. Therefore, a convolutional neural networks-based technique is proposed to automate apex frame detection using a novel continuous labeling scheme. The network is trained using ascending and descending labels according to the linear and exponential functions, pivoted on the ground truth apex frame. Two datasets, CASME II and SAMM databases are used to verify the proposed algorithm, where the apex frame is determined according to the maximum label intensity and a sliding window of the maximum label intensity. The results show that a linear continuous label with the sliding window approach produced the lowest average error of 14.37 frames.
RETRACTED ARTICLE: Using tissue-like P system to solve the nurse rostering problem at the Medical Centre of the National University of Malaysia

The nurse rostering problem (NRP) is a complex optimization issue, and its solution involves selecting groups of nurses and assigning sets of shifts to them; therefore, constructing timetables for healthcare personnel is normally a difficult-to-handle problem. The current paper proposes a new method called “Tissue-like P System” for solving the nurse rostering issue at the Medical Center of the National University of Malaysia (called University Kebangsaan Malaysia (UKM) in Malaysian language). It is a difficult-to-solve real issue, which has been manually tackled throughout the history. The main problem is assigning duties to a nurses’ group keeping in view the rules and regulations. We demonstrated that the proposed algorithm showed significant impact. Moreover, comparative evaluation of the proposed algorithm was accomplished using conventional methods for the same dataset, and it was found that the proposed method was considerably more effective.
Intellectual analysis of information about users of social networks

Social networks began to play an important role in the informatization of society. Experts from all over the world are researching social network data to solve various tasks, such as creating popular content, conducting advertising campaigns, meeting the information needs of society, ensuring state security, etc. The analysis of social networks is understood as the solution of such tasks as determining the tonality of the text, determining the target portrait of the audience, searching for associative rules, calculating community performance indicators and data visualization. The article considers the relevance of solving the problem, analyzes the results of previous work, examines the audience's reaction to content, builds a target portrait of subscribers of various communities, examines the relationship between user interests. The initial data of the study are social networks, or rather informational messages, opinions, subnets and communities, individual users, external nodes.The paper considers the classification of social network analysis systems (such as Brand Analytics, IQBuzz, Agorapulse, Semantic Force, Talkwalker) according to the following criteria: users, analysis methods, objects of analysis, data sources, features.To determine the audience's reaction to the content, the method of determining the tonality of the text was applied by analyzing comments to the content. The cluster analysis method was used to determine the target profile of users in a particular community. To find patterns between the user's interests in the work, the frequency analysis of sets of elements was considered. The search for associative rules was carried out using the Apriori algorithm. As a result, the works are presented in the form of graphs and diagrams. In the course of the work, an integrated approach to solving problems was used, which made it possible to create an automated information and analytical system that can be used as analytical tools in this area.
Prestige

Prestige is the high value attributed to an individual due to their social position or deeds. It is related to feelings of admiration and respect for someone. The perception of prestige is commonly related to tourism. It is understood that travel is an activity performed by those who have already met their basic needs related to food, housing and health, among others. The traveller is therefore someone who can go further, meeting their non-essential needs. In this sense, the non-essentiality of its nature attributes considerable glamour to tourism. Therefore, the motivation of a trip can be related to the enrichment of the. The prestige of a trip is found through the exaltation of different – for example, socioeconomic, intellectual and emotional – characteristics of the traveller. In addition, other characteristics related to the traveller can be deserving of social honour, such as vitality, disposition, knowledge, courage and cultural adaptability.
Research on Intelligent Information System Using Computer Big Data and Artificial Intelligence Technology

Based on computer big data technology, the article analyses the importance and current situation of enterprise management, discusses the functional requirements of enterprise management system based on Intranet/Internet, and develops an enterprise management system based on the coding system. The system has been tried out in some research institutes and enterprises and has achieved satisfactory results.
Syntactic Analysis of the Sentence Pattern in Online News Article at The Jakarta Post’s Website

This study aimed (1) to know the sentence patterns used in news desk at The Jakarta Post’s website and (2) to find out the construction of the sentence patterns in news desk at the Jakarta Post’s Website. The method of this research was descriptive qualitative. The data was the one clause sentences fromonline news articles posted in The Jakarta Post’s website. The sentences were taken randomly fromthe articles posted in the month of October and November 2021. The analysis was oriented to syntactic analysis. The object is the sentence pattern that covers the Subject, Predicate, Object, Complement and the Adjunct. The research findings showed that the sentence pattern in online news articles at The Jakarta Post’s website is manifold. There were 9 sentence patterns found and among these 10 sentence patterns, four include the basic sentence pattern, for example, SPA and SPC while another did not. The findings also showed that the constructions of the sentence patterns in online news article The Jakarta Post Website were theoretically structured.
Online School Future: Challenges and Expectations of Modern Education in Indonesia

Rapid technological advances and integration into human life whose life is different, including learning and education will continue to be innovative and adaptable. This paper is intended to discuss the future of virtual schools in the face of challenges and opportunities to achieve modern and eye education in Indonesia. To that end, we have carried out a series of data collection with an electronic search system for several publications, both national and international, in scientific journals, books, and websites that actively discuss the problem of virtual learning in the future. After data collection, we further reviewed it systematically and included a coding system, in-depth interpretation, sharp evaluation and conclusion drawing that answered the research questions with high quality of validity. Based on the existing evidence in the discussion of understanding the results. So the result is a better future for online education in Indonesia along with rapid technological advances and efficiency in all fields. Now online education still needs state recognition and formalities so that more online schools appear in the country. Thus, these findings become important inputs for future educational studies with similar teaching activities.