Reviewerly/exHarmony huggingface dataset:
Residual Tuning: Toward Novel Category Discovery Without Labels

Discovering novel visual categories from a set of unlabeled images is a crucial and essential capability for intelligent vision systems since it enables them to automatically learn new concepts with no need for human-annotated supervision anymore. To tackle this problem, existing approaches first pretrain a neural network with a set of labeled images and then fine-tune the network to cluster unlabeled images into a few categorical groups. However, their unified feature representation hits a tradeoff bottleneck between feature preservation on labeled data and feature adaptation on unlabeled data. To circumvent this bottleneck, we propose a residual-tuning approach, which estimates a new residual feature from the pretrained network and adds it with a previous basic feature to compute the clustering objective together. Our disentangled representation approach facilitates adjusting visual representations for unlabeled images and overcoming forgetting old knowledge acquired from labeled images, with no need of replaying the labeled images again. In addition, residual-tuning is an efficient solution, adding few parameters and consuming modest training time. Our results on three common benchmarks show consistent and considerable gains over other state-of-the-art methods, and further reduce the performance gap to the fully supervised learning setup. Moreover, we explore two extended scenarios, including using fewer labeled classes and continually discovering more unlabeled sets, where the results further signify the advantages and effectiveness of our residual-tuning approach against previous approaches. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuyudut/ResTune</uri> .
Audio steganography with enhanced LSB method for securing encrypted text with bit cycling

Data security pressing issue, particularly in terms of ensuring secure and reliable data transfer over a network. Encryption and seganography play a fundamental role in the task of securing data exchanging. In this article, both steganography and cryptography were combined to produce a powerful hybrid securing stego-system. Firstly, a text message is encrypted with a new method using a bits cycling operation to give a cipher text. In the second stage, an enhanced LSB method is used to hide the text bits randomly in an audio file of a wav format. This hybrid method can provide effectually secure data. Peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structural similarity (SSIM) were employed to evaluate the performance of the proposed system. A PSNR was in range (60-65) dB with the enhanced least significant bit (LSB) and the SSIM had been invested to calculate the signal quality, which scored 0.999. The experimental results demonstrated that our algorithm is highly effective in securing data and the capacity size of the secured text. Furthermore, the time consumption was considerably low, at less than 0.3 seconds.
On the Capacity of Quantum Private Information Retrieval From MDS-Coded and Colluding Servers

In quantum private information retrieval (QPIR), a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The QPIR capacity is the maximal achievable ratio of the retrieved file size to the total download size. In this paper, the capacity of QPIR from MDS-coded and colluding servers is studied for the first time. Two general classes of QPIR, called stabilizer QPIR and dimension-squared QPIR induced from classical strongly linear PIR are defined, and the related QPIR capacities are derived. For the non-colluding case, the general QPIR capacity is derived when the number of files goes to infinity. A general statement on the converse bound for QPIR with coded and colluding servers is derived showing that the capacities of stabilizer QPIR and dimension-squared QPIR induced from any class of PIR are upper bounded by twice the classical capacity of the respective PIR class. The proposed capacity-achieving scheme combines the star-product scheme by Freij-Hollanti <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> and the stabilizer QPIR scheme by Song <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> by employing (weakly) self-dual Reed–Solomon codes.
A Study on Climate Change with Mayfly Algorithm Optimization

Mayfly algorithm (MA) is an optimal way to achieve this goal.It is a newly developed algorithm that combines the key benefits of PSO, GA and FA, resulting in a hybrid of PSO-GA-FA.Compared with the other seven transformation methods, the performance evaluation performed using the CEC test operations demonstrates the dominance of MA in terms of accumulation rate and velocity.Although this requires rapid integration, it is not yet used in many engineering optimization fields, so we propose to use it for antenna optimization tasks.Predictions for the next century 15 to 95 Cm, the 50 cm 'best estimate' indicates a significant increase in sea level rise over the last century, with records from both the Atlantic and the Pacific in the Northern Hemisphere showing sea increase.However, in most cases, of some sites This condition is reduced due to surface resurfacing or regeneration.Rise is the height of the average level of the ocean surface and the increase in wavelength around it.The mayfly optimization algorithm (MOA) was proposed with a better hybridization of the particle swarm optimization (PSO) and the differential evolution (DE) algorithms.The velocity would be relevant to the Cartesian distance among the relevant individuals.
The Construction of Folk Sports Featured Towns Based on Intelligent Building Technology Based on the Internet of Things

With the emergence of the Internet of Things, technology and Internet thinking have entered traditional communities, and combined with traditional technologies, many new and better management methods and solutions have been born. Among them, the concept of intelligent buildings is also known to people. Based on big data technology, cloud computing technology, and Internet of Things technology, smart buildings provide smart and convenient devices and services for smart device users. The Internet of Things technology is entering our lives at an unimaginable speed. It has been applied in many fields. Smart home, smart transportation, smart medical, smart agriculture, and smart grid are widely used in the Internet of Things technology. The application of Internet of Things technology to the construction of folk sports characteristic towns is of great significance. The construction of folk sports characteristic towns and the protection of intangible cultural heritage have the same purpose and interoperability of elements as the development of traditional cities. From the perspective of protecting folk culture and intangible cultural heritage, it is effective to promote the development of small towns with folk custom characteristics. Based on the research on the construction of folk-custom sports towns, this paper proposes a series of data model analysis and analyzes the proportion of sports preferences in the survey of volunteers in the folk-custom sports towns. The final result of the research shows that the ball games sports personnel accounted for the largest proportion, with 156 people accounting for 48.15%. This shows that about half of the people like ball sports, which proves that ball sports should be the mainstay of folk sports towns, and other sports should be supplemented by other sports.
Design of Network-Assisted Teaching of Ideological and Political Courses for College Students Based on Android System

In order to be able to provide learning resources for learners anytime and anywhere and improve the quality of online teaching, a network-assisted teaching system for college students’ ideological and political courses based on the Android system is designed. The characteristics of the Android system platform and its hierarchical structure are analyzed, and the overall structure of the network-assisted teaching system was designed based on it and through the construction of the system user login module, learning resources module, operation management module, unit test module, and teaching interface display module, so as to achieve the effect of network assisted ideological and political course teaching. The preferences of students were fully considered, the needs of students with different resource spaces were matched, and the genetic algorithm is used to share the information of network teaching resources to meet the needs of ideological and political teaching. According to the experimental results, the system designed in this paper has high accuracy in the recommended content of ideological and political education resources and the coverage of resource recommendation is relatively high, indicating that the application effect of the system is better.
A Chinese Named Entity Recognition Model of Maintenance Records for Power Primary Equipment Based on Progressive Multitype Feature Fusion

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment (namely, main transformer, circuit breaker, and isolating switch) are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method.
A partial image encryption scheme based on DWT and texture segmentation

nowadays, image encryption is an interesting research area due to the rapid growth of communication technologies and the increasing demands for safeguarding the privacy of the transmitted images as unencrypted images are vulnerable to interception. Partial encryption is used to overcome the challenge of encryption the large size of digital images, due to the large computational overhead and long processing time needed. In this research, one level Discrete Wavelet Transform is applied to a colored image to divide the image to four sub-bands; then a texture segmentation using a Gabor filter and K-means clustering is applied to lowest frequency band. To achieve that, the texture segmentation is used as a new method for scrambling the image based on the features of another image. Finally, the scrambled segments of the image are encrypted using interchanged AES and RC4 Algorithms. Each one of the image pixels is then XORed using one of the two algorithms and the key of the other algorithm. The proposed work is evaluated using several security and performance analysis tests. The results show that the proposed work is resistant agents many kinds of attack and promising in real-time image transmission and encryption.
A Bibliometric Approach to Track Research Trends in Computer-Aided Early Detection of Cancer Using Biomedical Imaging Techniques

The paper’s primary goal is to conduct a comprehensive bibliometric analysis of scholarly publications related to computer-aided early detection of cancer using biomedical imaging techniques. Among many diseases, cancer is the second major cause of death in the world. Nevertheless, with advancements in cancer screening, the survival rate for many types of cancer is enhancing. If cancer is noticed in its early stage, it sets out the best chances for healing. Screening involves a physical examination, laboratory tests, imaging tests, and biopsy. According to research, imaging tests are the most accurate cancer diagnosis methods. This work seeks to acquire results from scholarly articles extracted from the Scopus database for the last two decades by analyzing growth in publications and sources, author’s endowment, keyword analysis, article citation frequencies, etc. To exhibit the bibliometric study, open-source tools, namely BiblioShiny, VOSviewer, Word Cloud, are utilized. The analysis indicates, 78.52% of publications are of article and review type. Breast cancer, segmentation, melanoma, and deep learning are often used keywords in scholarly articles. Substantial work has been done in China, followed by Germany and the USA under the Computer Science area; also, it shows the elevation in several publications since 2019. Our study will furnish a broad range of perceptions for upcoming researchers by tracking the research study trends. This bibliometric analysis will be worthwhile for an apprentice to survey ongoing research under cancer detection using biomedical images.
Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Video acquisition has become more convenient as science and technology have progressed, and the development of mobile Internet has resulted in a large amount of video data being generated every day. The question of how to analyze these videos automatically has become urgent. Among them, the study of sports movement recognition in video has important theoretical implications in sports research as well as practical application value. This paper proposes a PSO-NN-based sports action recognition model. Kernel principal component analysis is used to extract and analyze the characteristics of sports movements. The improved neural network is used to identify common human postures in sports, and the classification and block background estimation method is used to detect human targets. The feature extraction of targets is completed according to the edge features, and the feature extraction of targets is completed according to the edge features. Finally, the feature vectors are trained using a backpropagation neural network (BPNN), and the parameters of the BPNN are chosen using the PSO algorithm to create a classifier for sports action recognition. The results show that this model improves the accuracy of sports video recognition and is an effective method of sports action recognition when compared to the comparison model.
Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua

Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
Multi-state comparison of machine learning techniques in modelling reference evapotranspiration: A case study of Northeastern Nigeria

Monthly reference evapotranspiration (ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) was predicted for Bauchi and Maiduguri stations located in the northeastern semiarid region of Nigeria. The data for 34 years (1983-2016) were used including maximum and minimum temperature, relative humidity, and wind speed. The models were developed using artificial neural networks (ANN), support vector regression and multiple linear regression (MLR). The most influential weather parameters and the best computing technique were also investigated. FAO Penman-Monteith (FAO-56-PM) is regarded as the sole method for estimating ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , it is therefore employed in this study as the benchmark ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . Two statistical indicators of root mean square error (RMSE) and determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) were used to assess the performance of the models. The results showed that relative humidity has better performance in single input models but inclusion of wind speed can produce best performance for the 3 inputs models. However, the study revealed that ANN had the better ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> prediction capability in both stations, and 3 inputs model with minimum temperature, relative humidity, and wind speed led to superior efficiency. The general results demonstrated that the ANN, SVR and MLR can be employed for reliable estimation of ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> in the study stations.
Small Object Detection Based on SSD-ResNeXt101

Object detection is closely related to video analysis and image retention, which has attracted many researchers to research in this field. Traditional object detection methods are built with hand-crafted features and shallow trainable architectures. The resulting accuracy of traditional object detection is very much influenced by the selected features. The development of the field of Artificial Intelligence (AI), especially Deep Learning (DL), has made DL a powerful model for object detection. This is because DL has semantic analysis capabilities, high-level, deeper features, which are problems that often arise in traditional object detection. However, there are a few things that need to be fixed regarding the application of object detection in the real world because there are many small objects and varied backgrounds. Manual labeling of small objects is quite a time consuming and costly. The lack of a dataset to train small objects greatly affects the accuracy of the Convolutional Neural Network (CNN) model that was built. Single Shot Multi box Detector (SDD) as an object detection framework can detect objects of different sizes. To improve SSD accuracy in detecting small objects, in this paper, we replaced the SSD backbone using ResNeXt101. The experimental results yield better accuracy than the previous SSD framework with ResNet101. SSD (ResNeXt101) reach accurate 67.17% while SSD (ResNet101) with accurate 66.09%.
Bytecode Similarity Detection of Smart Contract across Optimization Options and Compiler Versions Based on Triplet Network

In recent years, the number of smart contracts running in the blockchain has increased rapidly, accompanied by many security problems, such as vulnerability propagation caused by code reuse or vicious transaction caused by malicious contract deployment, for example. Most smart contracts do not publish the source code, but only the bytecode. Based on the research of bytecode similarity of smart contract, smart contract upgrade, vulnerability search and malicious contract analysis can be carried out. The difficulty of bytecode similarity research is that different compilation versions and optimization options lead to the diversification of bytecode of the same source code. This paper presents a solution, including a series of methods to measure the similarity of smart contract bytecode. Starting from the opcode of smart contract, a method of pre-training the basic block sequence of smart contract is proposed, which can embed the basic block vector. Positive samples were obtained by basic block marking, and the negative sampling method is improved. After these works, we put the obtained positive samples, negative samples and basic blocks themselves into the triplet network composed of transformers. Our solution can obtain evaluation results with an accuracy of 97.8%, so that the basic block sequence of optimized and unoptimized options can be transformed into each other. At the same time, the instructions are normalized, and the order of compiled version instructions is normalized. Experiments show that our solution can effectively reduce the bytecode difference caused by optimization options and compiler version, and improve the accuracy by 1.4% compared with the existing work. We provide a data set covering 64 currently used Solidity compilers, including one million basic block pairs extracted from them.
An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

Performance evaluation of counselors plays a vital role for education industry (schools, colleges, universities, and vocational colleges). The problem can be stated with the phrases; how to design reasonable and appropriate performance indicators? The objective of this research is to design an effective performance evaluation. The purpose of this study is to explore a new method of performance evaluation that combines strategic goals with personal development goals. The purpose of performance evaluation is to better motivate the enthusiasm of counselors. With the methodology, a new issue faced at modern colleges and universities is being resolved. Therefore, for explaining methodology, this study has carried out the application analysis of the fusion particle swarm algorithm (FPSA) in the performance evaluation of instructors. First, on the basis of comprehensive analysis of performance evaluation, it discusses the advantages and disadvantages of university performance evaluation. Secondly, particle swarm and fuzzy comprehensive evaluation methods are used in the research of instructor performance evaluation. Pass the superiority of this assessment system. Index parameter evaluation is from 2.5 to 3.0. The range indicates an excellent value. In result this improved particle swarm can be compared with the state of the art (Liu et al., 2019). In conclusive remarks, this study is to provide state-of-the-art study for the current research on the topic of instructor performance appraisal in colleges and universities.