Reviewerly/exHarmony huggingface dataset:
Design of digital piano audio signal transmission

In recent years, the collective teaching mode of digital piano has gradually become a research hotspot of piano teaching. Based on the problems encountered in digital piano teaching and actual operation, this paper proposes a kind of digital piano classroom, piano audio signal network transmission plan, realizes the network function of digital piano collective classroom, and provides a feasible method for digital piano The operating platform, in turn, helps to improve the collective sound effect of the digital piano.
High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network (Adv. Sci. 13/2022)

Spintronic Multi-Level Memory Unit In article number 2103357 by Kun Zhang, Yue Zhang, and co-workers, a spintronic multi-level memory unit (MLMU) with high on/off ratio is constructed by integrating a magnetic tunnel junction chain and a diode in parallel. This MLMU can enable high-accuracy artificial intelligence applications, such as a deep neural network accelerator for image classification. Just as described in the cover design, the MLMUs are regarded as building blocks to assemble the artificial-intelligence brain.
Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph

The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility of the same event in different social networks, this paper takes the COVID-19 outbreak as the background and selects the platforms Weibo and TikTok as the research objects. In this paper, first, we use the transfer learning model to apply the knowledge obtained in the source domain of Weibo to the target domain of TikTok. From the perspective of text information, we propose an improved TC-LDA model to measure the similarity between the two domains, including temporal similarity and conceptual similarity, which effectively improves the learning effect of instance transfer and makes up for the problem of insufficient sample data in the target domain. Then, based on the results of transfer learning, we use the improved single-pass incremental clustering algorithm to discover and filter popular topics in streaming data of social networks. Finally, we build a topic knowledge graph using the Neo4j graph database and conduct experiments to predict the evolution of popular topics in new emergencies. Our research results can provide a reference for public opinion monitoring and early warning of emergencies in government departments.
Image Retrieval Using Digital Image Inpainting Techniques

Image retrieval is an inverse problem in digital image processing. In this paper, the authors deal with restoration of image using digitally image inpainting methods. In this inpainting technique, one can extract a missing an important part or can fill unwanted parts of an image, so as to recover original information without loss. They are using semi-automatic and fast image inpainting methods and compared their result in terms of image quality assessment parameters such as PSNR, MSE, as well as required timing constraints. The observational as well as statistical results show that the fast image inpainting technique is superior in all respects for any kinds of multimedia image application.
Research on the architecture of energy big data operation service system and algorithm simulation based on decision tree

Through the energy big data platform, realize the intelligent management of energy, improve energy utilization efficiency, improve energy production system and develop renewable energy, and finally build the energy Internet, promote the application of renewable energy and complete the intelligent peak shaving of energy. The energy big data platform realizes intelligent energy management, builds a control platform that can manage and ensure the efficient operation of the center. The author compares the actual energy data from 2018 to 2021 with the decision tree algorithm and achieves good results.
Model Design in Bayesian Spectral Deconvolution

Bayesian spectral deconvolution is a powerful tool for analyzing complex spectra with overlapping peaks. It can not only decompose the overlapping peaks and estimate parameters such as the position, height, and width of each peak, but also estimate the number of included peaks from the spectral data themselves through Bayesian model selection. To estimate the number of peaks, we prepare models with different numbers of peaks for a given spectrum, and calculate the evidence for these models. The number of peaks in the model with the highest evidence is adopted as an estimate of the number of peaks in the given spectrum. However, when the difference in evidence between models with different numbers of peaks is small, low-precision estimates of the evidence lead to a high error probability in the peak number estimation. In this study, we propose an appropriate model design that will increase the difference in evidence between different peak number models. This allows us to reduce the error probability even with low-precision estimates.
Metrics to quantify software developer experience

The quality of the developers has a direct impact on the software product. Besides the code they write, an experienced developer may define architectural solutions essential to the system's maintainability. This condition is necessary to develop new features and fix defects in a timely and cost-effective manner. Some works have studied the impact of developer experience and use metrics to study the relationship with the maintainability and evolution of software. In this systematic mapping, we present a catalog of metrics that have been used to quantify developers' experience, found in 34 works. These metrics are classified by the type of experience they represent and the purpose of using them. The results showed that most of the selected studies use metrics based on counting developer activities in code.
Tree-Cutting Detecting System Using Residual Neural Networks

Trees are significant in that there are products that originate from them. Wood is the greatest product of trees together with other products like timber and paper. Carbonized wood produces charcoal that is energy used for cooking. Trees, as indicated above, have quite a range of importance and usage to the human beings, hence they are not usable while still intact. Due to this reason, people end up cutting down many trees to meet their needs like timber and charcoal. The problem is finding the culprits who cut down trees for their own selfish needs. This paper discusses a solution to this challenge. This is the Tree-cutting Detection System designed to alert the forest authorities when there is tree-cutting going on in the forest. There is detecting device placed in the forest to listen to whether there are chainsaw sounds or not. When it detects chainsaw sounds, meaning there is tree-cutting going on, the forest authorities are alerted through an alarm and the location of the device that signal them is given so as to access the scene easily and faster. The development methodology adopted in the implementation stage was an agile approach. The solution was the designed and developed through several iteration phases of feedback and improvements of functionalities. Finally, testing of the system done severally during development and after the system completed.
A Novel Frame Work to Improve Security and Performance Issues in Healthcare System using Cloud Computing

Cloud computing became a huge servicing platform to many domains for organizational growth. Virtualization, autonomic, utility computing and service oriented architecture made cloud computing robust. One of the major contributions of cloud computing to the health care systems is prominent one. In this paper we propose a framework that depicts various security and performance issues related to health care domain with the support of cloud computing. Beginning with a device of well known statistics protection the board procedures got from norms of the ISO 27000 own family the principle statistics protection tactics for medical care associations utilising distributed computing could be diagnosed thinking about the number one risks with admire to allotted computing and the sort of facts treated. The distinguished cycles will help a well being with worrying association utilising distributed computing to zero in on the most significant isms methods and lay out and work them at a becoming degree of development thinking about restricted property. We examine dangers and emergencies for medical care suppliers and talk about the effect of distributed computing in such situations. The research is led in an all encompassing manner, considering hierarchical and human angles, medical, it-associated, and utilities-associated takes a chance in addition to joining the angle on the overall gamble the executives. We ruin down risks and emergencies for medical care suppliers and study the impact of dispensed computing in such situations. The research is directed in a complete manner, thinking about hierarchical and human viewpoints, scientific, it-associated, and utilities-associated gambles as well as consolidating the angle on the general gamble the board. On this paper, we assessment about the unique types of problems and problems related with distributed computing in particular execution troubles and disbursed garage protection troubles.
A Glimpse into Artificial Intelligence in Animal Physiology and Allied Sciences

Artificial Intelligence (AI) has developed as an interdisciplinary science based on computers and is concerned with building machines and equipment which use human intelligence to perform a particular task. The role of AI is manifold in our day-to-day lives. With high penetration amongst people in different societies, AI has transformed the way of living and has the potential to act as a vehicle to disseminate information regarding animal health, production, and reproduction aspects. AI has already made an immense contribution in veterinary and allied sciences by helping in devising various applications used in research and simulation aids. In addition, it has been put in to use efficiently in the field of veterinary sciences thereby hastening diagnosis, treatment, and prognosis of various animal diseases. The history of AI, its applications as software packages in statistics, bioinformatics, simulation apps, and a list of various equipment used for analytical, clinical, and livestock farm purposes are elaborated in this article. Despite playing a vital role, AI has to be further refined in such a way to target the rural livestock farmers to improve animal health and production in developing countries that are in dire need of meeting food security requirements amidst the current scenario of population explosion.
Evaluation method of power grid investment benefit based on matter-element extension model

With the improvement of the national economy, the power load has increased steadily. In order to meet the growing demand for electricity from the people, grid companies need to continuously invest in expanding the scale of the distribution network and optimizing the structure of the distribution network. This paper constructs a power grid investment benefit decision-making index system after the power transmission and distribution price reform from six aspects: policy indicators, safety indicators, economic indicators, competitive indicators, risk indicators and social indicators. By evaluating the investment benefits of the power grid, it helps the power grid to pay more attention to investment benefits and improve investment capabilities.
Reflexive essay about a piece of music as a way to develop the creative potential of students

The subject of the study is the influence of writing a reflexive essay about a listened piece of music on the development of a person's creative potential. The purpose is a theoretical substantiation of the possibility of developing the creative potential of a person when writing reflexive essays and empirical confirmation of the proposed approach based on data from a pilot study conducted at the Children's art school in Khanty-Mansiysk (n=22). The theoretical methods of research were analysis, generalization, systematization. Empirical methods: experiment, testing, qualitative analysis of the texts of the essays of the investigated.&#x0D; The authors conduct a brief analysis of methods for determining the level of creative potential and justify the possibility of using the survey method. The conditions that must be observed when selecting works and writing essays are described. Arguments are given in favor of the fact that writing an essay is not an interpretation of a musical work, and listening to the work itself is not part of psychotherapeutic methods. The results of the pilot study are presented, which with a certain probability indicate that the writing of the essay had an impact on the development of the creative potential of the person.
Testing Polish Secondary-School Students’ Written Social Communication Competence Based on a Timed Composition Task

Abstract This study experimentally evaluated Polish school students’ competence at written communication, understood as one of the forms of social communication. The task, performed online, was designed to see if the students were able to select the most important information from a given base text and to compose new texts on this basis to comply with three different length requirements, and how they behaved under the pressure of the time allocated to complete the sub-tasks. Results from an overall pool of 500 secondary school students from a diverse sample of schools (in the city of Poznań and the surrounding Wielkopolska province) that differed in terms of type and ranking suggest an overall poor competence at various composite skills involved in written social communication: information selection, summarization, logical structuring, and cohesive embellishment. Participants clearly exhibited various problems with concisely formulating thoughts, properly complying with instructions, etc. Composing a short, written message (based on a provided base text) and/or freely embellishing and reformulating information clearly caused them considerable difficulty. The article closes with some suggestions for how the methods used could potentially be improved in future studies of this type.
Time-Frequency Analysis and Type Identification of High-Density Communication Countermeasure Electronic Signals

With the development of communication technology, the electronic signals of communication countermeasures become more and more complex. Facing the increasingly complicated and changeable environment of such signals, the existing cluster analysis algorithms cannot identify the type of communication countermeasure electronic signals ideally. Therefore, this paper carries out the time-frequency analysis and type identification of high-density communication countermeasure electronic signals. The signals were analyzed in both time and frequency domains to extract the time frequency features of the signals more accurately. In this way, the authors captured the variation of non-stationary communication electronic signals in both time and frequency domains. Next, the pulse repetition interval (PRI) transform, a typical pulse repetition frequency (PRF) type identification algorithm, was modified, and applied to the type identification of communication electronic signals, followed by an illustration of the type identification of high-density communication countermeasure electronic signals. After that, several experiments were carried out to compare the absolute errors and relative errors of different time-frequency analysis methods, and contrast the effects of the original and modified PRI transforms, revealing the effectiveness of the proposed approach.
Autonomous Fire Detection Alarm System At Forest

Fire mishaps are characterized as a bothersome occasion which radiates warmth, smoke or fire. Fire mishap is a significant type of mishap and can cause countless causalities due to the peril and hazard associated with protecting casualties out of the fire. While firefighting units participate in such circumstances there is a high chance to misfortune the life of fire personnel's. In regular day to day existence, it isn't possible to consistently depend on human watch for identifying and dousing fire at a fire mishap scene. In the event that a mechanized framework is made to watch the edge for fire mishaps, at that point we can have an early admonition framework. This will be compelling in a fire mishap in ventures and neighborhoods where the fire prospects are high. So as to accomplish this, we should think of a thought which can recognize a fire, find it and smother the fire preceding it represents a danger to anything around it. For dangerous circumstances, it is hopeful to send a firefighting robot that could rapidly and proficiently discover the fire and smother it. Compelling observing, fast acknowledgment, and stifling of fire are issues to be managed right away. To lessen the danger of losing life in such circumstances, fire mishap control framework can be utilized. The programmed framework is intended to maintain a strategic distance from further spreading of the fire that could prompt conceivable human causalities or harm to property.
Conclusions

Large-scale graph analysis is a critical task for big data applications. The distributed graph computing system is a successful paradigm for the large-scale graph analysis. It not only helps analysts achieve high scalability and efficiency, but also enables analysts to focus on the logic of analysis tasks through transparenting the tedious distributed communication protocols. In this book, we chose Pregel-like systems as a basic platform, and studied the deficiency of existing systems.
Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Abstract Background: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, we need methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods: In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare. Results: Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions: In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.
OHKWR: Offline Handwritten Kannada Words Recognition using SVM Classifier with CNN

In field of handwriting recognition, Robust algorithms for recognition and character segmentation are presented for multilingual Indian archive images of Devanagari and Latin scripts. These report basically suffer from their format organizations, low print and local skews quality and contain intermixed messages (machine-printed and manually written). In order to overcome these drawbacks, a character segmentation algorithm is proposed for kannada handwriting recognition. In this work, in initial steps we are obtained the segmentation paths by using the characters of structural property and also the graph distance theory whereas overlapped and connected character are separated. Finally, we are calculated results by using the SVM classifier. In proposed recognition of character, they are three new geometrical shapes based on new features such as center pixel of character is obtained by first and second feature and third feature is calculation purpose we are used in neighborhood information of text pixels. Benchmarking results represent that proposed algorithms have best work identified with other contemporary methodologies, where best recognition rates and segmentation are obtained.
Understanding work engagement through multi-level investigations: From antecedents to outcomes

Work engagement is a valuable organizational resource, and it has many positive outcomes. In today’s dynamic and competitive business environment, organizations can only be successful when they have an engaged workforce. The current study examines how supervisors support and fairness is important for employee’s work engagement and how the role of perceived organizational support is significant in an employee's workplace. In line with Organizational Support Theory (OST) and Conservation of Resource Theory (CRT), the current study is an empirical attempt to explain antecedents that could increase work engagement and resultantly fetch productivity and profit in the context of South Asia (the case of Pakistan) by considering the outcomes. This cross-sectional study draws data through structured questionnaires from 310 employees of eight mega retail stores by using simple random sampling. The outcomes of Structural Equation Modeling (SEM) analysis revealed a positive effect of supervisors’ support and organizational fairness on work engagement through the mediation of Perceived Organizational Support (POS) and explained the impact of work engagement on task performance and career satisfaction of employees. The current study tested the model for work engagement; future research might test the model using other employee factors (employee sustainability or motivation) in order to test continuous employee behaviors in their workplaces.