Reviewerly/exHarmony huggingface dataset:
Near lossless image compression using discrete cosine transformation and principal component analysis

An innovative method to obtain 2-Dimensional images which are nearly lossless in compression using DCT (discrete cosine transformation) and PCA (Principal Component Analysis) is proposed. All the DCT transformed blocks are individually quantized, then an algorithm called Inverse of DCT is applied on those quantized values, produced miscalculations in the remaining code is sequenced. The expansion of logical and mechanical requests of high-goal sight and sound content has incredibly expanded the information volume in big business server farms and workers on the internet. Our half and half methodology take advantage of (I) PCA to diminish the dimensionality of the picture, and (ii) DTT calculation to improve the picture quality The parameters of these processes are set employing compression with the help of metrics which is based on performance. A different process is performed on the values that accomplish the relationship between DCT values with high energy. The result goes under further entropy coding and gets compressed and gives a result that has a near-lossless standard of JPEG images.PCA is an arithmetical scheme that uses orthogonal conversions to turn a hypothetically interrelated set of records into a linearly uncorrelated set of data that contain principal value components. The PCA will be under and same as the total no. of available variables in the unique dataset. Furthermore, the PCs are arranged in such a way that the very first factor has the biggest possible alteration in the data, and every following component has the next highest variance.
Community Participation in Reducing the Risk of Flood Disaster in Tuminting District Manado City

Floods have undesirable impacts including physical, social, economic, and environmental impacts, such as (1) Physical impacts are damage to public facilities, such as offices caused by flooding, and (2) Social impacts include death, health risks, mental trauma, disruption of educational activities, and other basic needs, (3) Economic impacts include material loss, disruption of economic activities such as people unable to go to work and others and (4) Environmental impacts include water pollution such as pollutants brought by the flood. Based on the results of the research and discussion above related to community participation in flood control in Tuminting sub-district, the following conclusions can be drawn: (1) Community participation in Tuminting sub-district in the implementation of development is good in providing ideas/ideas, providing material and financial assistance and participation in contributing energy is quite good. Although there are still some residents who have not been actively involved. The enthusiasm of the community in the Tuminting Sub-district to attend meetings in development planning is quite good, and working together in handling the flood problem is quite good, (2) The efforts of the Tuminting subdistrict government and the Tuminting, Sumampo, and Mahawu sub-districts in disaster mitigation activities and inviting the community to participate in flood disaster management is very good.
Bug Triage Automation Approaches

Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to a bug report. A personnel (triager) has to analyze the developers' profiles and bug reports for the purpose of making a suitable assignment. Manual bug triage consumes time and effort, so automating this process is a necessity. The previous research studies addressed the triage problem as an information retrieval problem, where the new bug report is the query. Other researchers tackled this problem as a classification problem and utilized traditional machine learning or deep learning techniques. A handful of research studies handled this problem as an optimization problem and utilized optimization algorithms such as Hungarian. This paper briefs and analyzes the previous bug triage approaches in addition to conducting an empirical comparison among five of the previous approaches.
Augmented Reality Enhanced Analytics to Measure and Mitigate Disengagement in Teaching Young Children

The research identifies novel AR-based metrics to address disengagment in young children during learning in class-rooms and to provide better insights to teachers to make learning by AR more engaging, fun and stimulating. Util-ising AR applications and mobile devices to measure and mitigate students' disenagagement in their learning, studying how novel AR-based metrics can be analysed and contribute to better understand disengagement amongst young children during learning. Two research goals have been de-fined to guide this research to investigate the use of AR enhanced analytics to address disengagement in young chil-dren. Key contributions of this research include: 1) Capture of data from device sensors, user interactions and learning outcomes within the augmented reality experience., and 2) Deriving metrics from this sensory data to provide five cat-egories of AR Enhanced Analytics (AREA) measures rep-resenting the quality of the learning experience; namely (a) Learning Analytics, (b) Interaction Analytics, (c) Spatial Analytics, (d) Sensory Analytics and (d) Emotion Analyt-ics. The novel metrics will be validated for detailed and relevant sensing of the learning environmental and student behavioural stimuli including; a) position/location, b) time duration, c) interaction / engagement with virtual assets, d) experience recall, e) sentiments (audio and video), biometric (temperature, heart beat) and f) movements (using device sensors such as accelerometer, gyroscope and magnetome-ter, and through feature points tracked visually). AR-based metrics can be visualised and presented to support teachers in managing their classrooms and their teaching practice to address factors as associated with student disengagement.
Research on detection method of mask wearing based on improved YOLOv5

The network model of existing mask wearing detection methods have large number of parameters and calculation. To improve these shortcomings, this paper proposed a lightweight mask wearing detection method based on improved YOLOv5. This method replaced the original convolution module by Ghost module reducing the model parameters and computation. This method used lightweight attention mechanism and Transformer self-attention module to make the model more focused on important information to improve accuracy. This method used Alpha-CIoU loss function to enhance the accuracy of bounding box regression. At last, this method used knowledge distillation to boost the detection performance of small model. The results of the experiment show that this method reduced the number of parameters, computation amount and model size to 57.3%, 51.83% and 58.54%, and improved the mAP by 0.7%.
Life Is Short, Train It Less: Neural Machine Tibetan-Chinese Translation Based on mRASP and Dataset Enhancement

This paper highlights a multilingual pre-trained neural machine translation architecture as well as a dataset augmentation approach based on curvature selection. The multilingual pre-trained model is designed to increase the performance of machine translation with low resources by bringing in more common information. Instead of repeatedly training several checkpoints from scratch, this study proposes a checkpoint selection strategy that uses a cleaned optimizer to hijack a midway status. Experiments with our own dataset on the Chinese-Tibetan translation demonstrate that our architecture gets a 32.65 BLEU score, while in the reverse direction, it obtains a 39.51 BLEU score. This strategy drastically reduces the amount of time spent training. To demonstrate the validity of our method, this paper shows a visualization of curvature for a real-world training scenario.
Improving Human Activity Recognition Models by Learnable Sparse Wavelet Layer

Modern machine learning algorithms for human activity recognition based on artificial neural networks often require a large amount of labelled training data to generalize between human subjects and training contexts. Large degrees of freedom make them susceptible to overfitting and often computationally intensive to implement on portable hardware. In this work, we introduce wavelet-based learnable filters as a feature extraction layer that greatly improves the generalization capability of the detector model. Our evaluations on six benchmark datasets show significant improvements in macro F1 score when our wavelet-based learnable filter layer is prepended to three state-of-the-art human activity recognition models. As a side effect, in many cases we could drastically reduce the required model size to achieve competitive performance on the benchmark dataset, which is an important requirement for use in wearable computing.
Class-wise Attention Reinforcement for Semi-supervised Meta-Learning

Meta-learning aims to learn some common knowledge quickly from the limited labeled examples. Semi-supervised meta-learning is developed to improve the learning performance of the learner using limited labeled data and available unlabeled data. This paper proposes a new semi-supervised meta-learning method called the class-wise attention reinforcement (CWAR) method from the distinction of class representation and the credibility of pseudo-labeling. Firstly, we propose a class-wise attention (CWA) module to generate the class-wise attention weight vectors and apply them to prototype, query set, and unlabeled auxiliary set, reinforcing the attention on critical features in the task. Then, we use the random data augmentation method to assign pseudo labels to unlabeled data. Moreover, to mitigate the effects of misclassification and noise-influenced samples on the prototype, generating weight as the coefficient to calculate the new prototype. Experimental results on two popular benchmarks demonstrate the proposed method’s effectiveness and have highly competitive performance compared with the state-of-the-art.
A General Framework for Accurate and Private Mean Estimation

In this letter, we present a differentially private algorithm which accurately estimates the mean of an underlying population with given cumulative distribution function. Our algorithm outperforms the former algorithms in two aspects. First, our algorithm is capably of handling more general types of probability distributions, possibly with a very heavy tail. Second, for light-tailed distributions, our algorithm achieves a better level of accuracy with fewer samples.
Pedestrian Re-identification in Video Surveillance System with Improved Feature Extraction

In this work, we present a comparison between using different pedestrian re-identification (re-id) architectures. We have investigated the advantages of using more complex and deeper convolutional neural networks (CNNs) at the feature extraction stage. The re-id network is based on the summary network presented by (Ahmed and Marks 2015) which we have modified and enhanced. The comparison is done by replacing the feature extraction portion of the network. The newer improved models performed better than the baseline model and resulted in an accuracy of above 96% on our dataset and an accuracy of 92.09% on CUHK03 test dataset. The network takes 2 images as input and, outputs a confidence level indicating whether or not the 2 images depict the same person. The 2 images both go through a CNN with shared weights and the resulting 2 feature maps are used to compare and classify the 2 images as a positive or a negative match.
A Secure Group Data Encryption Scheme in Intelligent Manufacturing Systems for IIoT

In recent years, there are many industries that imported intelligent systems to help them make the intelligent factory and get product record analysis to evaluate product rate. These intelligent systems could generate product records, store them to the on-line database, and provide product rate analysis from these records. Due to the rapid development of internet of things (IoT), the stockholder can construct its own smart factory with the smart intelligent system to develop its own industrial internet of things (IIoT) architecture. With the help of IIoT, the smart intelligence system can collect data information with IoT sensors embedded into each machine in the production line. However, there are some security issues arising between smart intelligent systems and IoT devices. In addition, the authors also discovered that there are fewer methodologies to talk about the data security during the machine transmitting its censored data to the other machines under the same network environment.
A Novel Method of Transmitting Text Messages via Images using Chaotic Encryption

Various methods of chaotic encryption have been proposed to securely transmit images using a private key. These images are first encrypted using a private key, transmitted over a channel and then decrypted using the same private key. In this paper, we propose a novel low-complex method for transmitting a text using the ideas and methods of chaotic encryption. In this method, the text is first converted to an image after which the image is encrypted using a chaotic encryption technique. At the receiving end, the image is first decrypted and then the text is retrieved from the image using a technique that happens to be the inverse of the technique used to convert the text to the image. The strength of our proposed scheme has been demonstrated by 15 standard randomness tests, benchmarked by National Institute of Standards and Technology (NIST) SP-800-22 Test Suite, also capable of generating a high amount of unpredictability in the generated key, making it difficult for the adversary to decipher the message.
Local Binary Pattern Symmetric Centre Feature Extraction Method for Detection of Image Forgery

A new method for extracting the image from the copy-move forging was created by changing the Gabor filter and the Local Binary-Pattern Center Symmetric (LBP-CS) is given in this article. In this article the LBP- CS is a modification of the local binary pattern which is free of the illumination and rotational effect differences, which is commonly used as a feature extractor for the core detecting stage. This method is used to find Center Symmetric Local Binary Pattern values for every Gabor picture (sub-bands) from the previous stage, based on the Gabor filter in different scales and orientations in the input image. This article contains information about the mechanism to detect a forgery in the copy-move and a flowchart of the system proposed. The input image is pre-processed and the Gabor Filter and LBP-CS function is extracted with various scales and guidelines. There are five different types of passive image forgery detection methods. Pixel-based techniques draw attention to the pixels in a digital image. Copy-move processes include things like splicing, resampling, and statistical approaches. The similitude of the original and the forgery image was calculated using Euclidean distance in the next step. In the last study, the output of the method proposed is compared to the excising function extraction method.
Automating Software Re-engineering: Introduction to the ISoLA 2022 Track

Software Engineering is often viewed as the discipline of developing new software systems from scratch. In practice, however, it is much more common that software development starts from a system to be modified or substituted. Software re-engineering is a difficult, error-prone, and expensive task. Therefore, automating software re-engineering emerges as an attractive opportunity. Formal methods can help in automating software re-engineering, but, current formal approaches to software development and analysis tend to look at software as a static object and to focus on green-field scenarios. In this track we look at formal approaches that take the re-engineering aspect into account.
High-Quality Article Classification Based on Named Entities of Knowledge Graph and Multi-head Attention

With the number of all kinds of self-media articles explosive growth, it is of great research significance and practical application value to recommend high-quality articles to users. This paper explores how to combine named entities of knowledge graph and multi-head attention mechanism with quality article identification, not only the contents of articles. For the article classification task of CCKS 2022, we proposed an incorporating named entity information approach based on the NEZHA model with continual pre-training combined, multi-head attention and BiGRU. Five-fold cross-validation solved the problem of less training data, warm-up strategy handled the problem of unstable effect at the beginning of training and focal loss treated the unbalanced samples. Then we selected the top three results in terms of F1 scores for voting fusion. Finally, data augmentation was applied in accordance with certain rules in the fusion results. Our method achieves F1 score of 86.51% on the test set of the task and ranks the second place in the competition.
Local Energy Trading Platform Based on Privacy-Preserving Blockchain with Linkable Ring Signature

The growing renewable energy source technologies and distributed communication infrastructure call for local energy trading (LET) platform in smart grid to assist prosumers and consumers to trade energy locally and efficiently. Blockchain is essentially a decentralized data storage and processing technology that can be used for transactions of any kind, which makes local energy transactions transparent, traceable and decentralized controlled. However, blockchain essentially presents a conflict between traceability and identity anonymity of transactions, which further brings a challenge for the conjunction of blockchain and the adaptation to various distributed fields, such as smart grid system. This paper proposes a blockchain-based regulated local energy trading platform. Identity anonymity is achieved by replacing traditional signature with ring signature. The traditional signature verification exposes signer's identity while ring signature verification in the latter includes all the identities of the ring, which hides real transaction identity. Further, when suspicious transaction happens, regulator is able to track down the malicious user by asking all the ring members to sign the suspicious transaction. Also, the proposed platform can resist quantum computing attack. We present the comprehensive concept, system design and simulation of the local energy trading platform based on a public blockchain, FISCO BCOS.
Image Super-Resolution Reconstruction of Pancreatic Carcinoma Based on Edge Repair Generative Adversarial Network

High-resolution medical imaging of pancreatic carcinoma is of great significance for the early diagnosis of the disease. Despite breakthroughs in accuracy and the capable of generating realistic textures, there are still some problems remain unsolved in current medical image super-resolution reconstruction methods: the edge details are often accompanied with unpleasant artifacts, how do we recover finer textures? To achieve this, we propose an Edge Repair Generative Adversarial Network (ERGAN). The model adds an edge repair network on the basis of the generator and the discriminator network, performs edge detection and repair on the input low-resolution images, then fuses the output edge feature map with the shallow feature map generated by the generator. Finally, the reconstructed images are sent to the discriminator through the up-sampling layer to evaluate the reconstruction effect. In this paper, the model is evaluated on the pancreatic carcinoma data set CPTAC-PDA published on the website of the Cancer Imaging Archive (TCIA). The experimental results show that the images reconstructed by the network model proposed in this paper not only improve the evaluation indicators, but also has a clearer cross-sectional outline and more obvious detailed features.
Security Analysis of User Authentication and Methods

Recent studies on authentication protocols and login systems have drawn much attention due to their importance and purpose. All around the world, there are types of usage of the internet, devices, personal or corporate computers, web servers, etc. which summarizes that technology is a part of daily life and routine. Similarly, login and authentication are important aspects of every system software. These systems and software tend to keep logs, databases, files, algorithms, secrets, etc. which are related to either one user or an entire company or group of people. To protect these details of importance so they can be kept away from being stolen and modified, authentications and login are required so that it is only accessible to read/write who is the owner or who is authorized to do so. The application widely concentrates on the types of authentications around the World Wide Web and with the help of the scripting language PHP. PHP plays a vital role in this project due to its name and importance in the wide global market of Development and debugging. Nowadays every website around the globe uses a process to identify the logged-in user data and its accessibility. Our project purely focuses on some of these methods.
Z-transform: basics and main properties

The z-transform can be seen as the analogous mathematical transformation for discrete-time systems to the Laplace transform in continuous-time systems.
Less is more: Low-precision feature selection for wearables

Nowadays, the amount of data produced daily has significantly increased due to the growth in the number of wearable devices. Similarly, this increase is also visible in the interest of developing machine learning algorithms with reduced precision computations, due to the limitations of such devices. This work studies the effect of using low precision operations in the context of feature selection, a preprocessing step that is becoming necessary to deal with the increasing data dimensionality. This study focuses specifically on feature selection methods based on Mutual Information (one of the most popular and widely-used metrics in this area) and how low precision computations can be carried out obtaining experimental results similar to those achieved by double-precision over several low- and high-dimensional datasets. We observe that the use of 16-bit fixed-point representation makes it possible to obtain feature rankings with high similarity to those obtained in double- precision. Even the rankings obtained with 8 bits and then used in subsequent classification tasks, lead to similar accuracy (no significant difference) to the one obtained when using the 64-bit representation in certain situations.
STUDENT FOLK-INSTRUMENTS ORCHESTRA IN THE CONTEXT OF HOLISTIC TRAINING OF A MUSICIAN

The article considers a comprehensive approach to the education of a musician as a competent specialist in various areas of implementation of the acquired educational, practical and theoretical experience. The article contains a review of modern main areas of scientific and methodological research, issues and aspects of pedagogical research, a review of important works and areas of work with the folk-instruments orchestra. Various forms of work with the student orchestra are researched, which expands the musical worldview, raises the professional and artistic level, improves the professional skills of ensemble and orchestral playing, promotes the mastering of basic knowledge and skills of students for successful future pedagogical and performing activities.