Reviewerly/exHarmony huggingface dataset:
IFHE: Intermediate-Feature Heterogeneity Enhancement for Image Synthesis in Data-Free Knowledge Distillation

Data-free knowledge distillation (DFKD) explores training a compact student network only by a pre-trained teacher without real data. Prevailing DFKD methods mainly consist of image synthesis and knowledge distillation. The synthesized images are crucial to enhance the student network performance. However, the images synthesized by existing methods cause high homogeneity on intermediate features, incurring undesired distillation performance. To address this problem, we propose the Intermediate-Feature Heterogeneity Enhancement (IFHE) method, which effectively enhances the heterogeneity of synthesized images by minimizing the loss between intermediate features and pre-set labels of the synthesized images Our IFHE outperforms the SOTA results on CIFAR-10/100 datasets of representative networks.
StreamOps: Cloud-Native Runtime Management for Streaming Services in ByteDance

Stream processing is widely used for real-time data processing and decision-making, leading to tens of thousands of streaming jobs deployed in ByteDance cloud. Since those streaming jobs usually run for several days or longer and the input workloads vary over time, they usually face diverse runtime issues such as processing lag and varying failures. This requires runtime management to resolve such runtime issues automatically. However, designing a runtime management service on the ByteDance scale is challenging. In particular, the service has to concurrently manage cluster-wide streaming jobs in a scalable and extensible manner. Furthermore, it should also be able to manage diverse streaming jobs effectively. To this end, we propose StreamOps to enable cloud-native runtime management for streaming jobs in ByteDance. StreamOps has three main designs to address the challenges. 1) To allow for scalability, StreamOps is running as a standalone lightweight control plane to manage cluster-wide streaming jobs. 2) To enable extensible runtime management, StreamOps abstracts control policies to identify and resolve runtime issues. New control policies can be implemented with a detect-diagnose-resolve programming paradigm. Each control policy is also configurable for different streaming jobs according to the performance requirements. 3) To mitigate processing lag and handling failures effectively, StreamOps features three control policies, i.e., auto-scaler, straggler detector, and job doctor, that are inspired by state-of-the-art research and production experiences at ByteDance. In this paper, we introduce the design decisions we made and the experiences we learned from building StreamOps. We evaluate StreamOps in our production environment, and the experiment results have further validated our system design.
Personal Values of Middle Stage Students in Relation to Their Academic Achievement

The present study was conducted on middle stage students to analyze the relationship between their personal values and academic achievement which are very vital for success in life. On the basis of descriptive survey method, the sample of 520 middle stage students was drawn from Kanpur Nagar and Kannauj districts of U.P. with the help of stratified random sampling technique. Personal value scale prepared by Dr. Madhulika Verma and Vindeshwari Waxar Pawar was utilized for collection of data. The academic achievement was measured through the previous year annual exams results scores. With the help of SPSS Pearsons product moment correlation coefficient ‘r’ was calculated between the two variables. Critical Ratio ‘CR’ was calculated to assess the significant difference of personal values between high and low academic achievers. Result revealed that there is significant difference in the mean personal values scores between high achievers and low achievers among middle stage students. Based on the correlation analysis, it's concluded that there is a statistically significant positive correlation between personal values and academic achievement among middle stage students.
Assessing Patterns

This chapter explains how using graphs to display visual data helps the understanding and exploration of data and allows people to make an initial assessment of potential patterns. Frequency distribution graphs are visual depictions of frequency distributions involving just one variable and are used to describe a single sample. The chapter also considers the features and functions of other types of graphs, such as scatterplots. It discusses the implications of sample error within descriptive statistics and graphs. Due to the expansion of computing capacity and technology, biological scientists today have a wide range of analytical techniques available to them.
Open-world story generation with structured knowledge enhancement: A comprehensive survey

Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structured knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematic taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study.
A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

Abstract Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT‐based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real‐time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short‐Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT‐based monitoring system than other solutions from the related literature.
Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning

Deep neural networks can be potentially vulnerable to adversarial samples. For example, by introducing tiny perturbations in the data sample, the model behaviour may be significantly altered. While the adversarial samples can be leveraged to enhance the model robustness and performance with adversarial training, one critical attribute of the adversarial samples is the perturbation rate. A lower perturbation rate means a smaller difference between the adversarial and the original sample. It results in closer features learnt from the model for the adversarial and original samples, resulting in higher-quality adversarial samples. How to design a successful attacking algorithm with a minimum perturbation rate remains challenging. In this work, we consider pruning algorithms to dynamically minimise the perturbation rate for adversarial attacks. In particularly, we propose, for the first time, an attribution based perturbation reduction method named Min-PR for white-box adversarial attacks. The comprehensive experiment results demonstrate Min-PR can achieve minimal perturbation rates of adversarial samples while providing guarantee to train robust models. The code in this paper is available at: https://github.com/LMBTough/Min-PR .
Unsafe behaviour detection with the improved YOLOv5 model

Abstract In industrial environments, workers should wear workwear for safety considerations. For the same reason, smoking is also prohibited. Due to the supervision of monitoring devices, workers have reduced smoking behaviours and started wearing workwear. To meet the requirements for detecting these behaviours in real‐time monitoring videos with high speed and accuracy, the authors proposed an improved YOLOv5 model with the Triplet Attention mechanism. This mechanism strengthens the connection between channel and spatial dimensions, focuses the network on important parts, and improves feature extraction. Compared to the original YOLOv5 model, the addition of the mechanism increases the parameters by only 0.04%. The recall rate of the YOLOv5 model is enhanced while its prediction speed is maintained with only a minimal increase in parameters. Experiment results show that, compared to the original model, the improved YOLOv5 has a recall rate of 78.8%, 91%, and 89.3% for detecting smoking behaviour, not wearing helmets, and inappropriate workwear, respectively.
Research on WeChat Tampering and Forensics Based on Android System

The research of WeChat tampering technology has important practical value in improving the accuracy of WeChat forensics. This paper presents a forensics method for tampering with WeChat data on the Android system. Based on the analysis of WeChat data characteristics, SQLite database key cracking and data storage principle, this paper discusses WeChat text records, images and attachments, communication mechanism, AMR voice file tampering and tamper detection technology, and verifies it through experiments. The experiment shows that this method can effectively identify the tampered WeChat data, and can be directly applied to the forensics practice. It has high practicability and important reference value for digital forensics.
An Efficient and Secure Health Data Propagation Scheme Using Steganography-Based Approach for Electronic Health Networks

Electronic health (e-health) networks enable users to enjoy convenient, flexible, and low-cost medical services at home, so they attract great attention and spread into the market quickly. In e-health networks, large amounts of various health data including personal privacy information and physiological signals are transmitted, which raises security risks. To protect the health data transmitted in e-health networks, steganography-based solutions have been widely researched. Although existing steganography-based solutions successfully hide health data in physiological signals such as electrocardiograms (ECG), forward secrecy is not fully considered. This means that adversaries are able to extract users’ health data hidden in previous stego signals by using compromised long-term secrets. Moreover, to reduce communication overhead, compression techniques are introduced in some steganography-based methods. However, the imperceptibility and embedding capacity of these solutions are sacrificed. To solve the above issues, in this study, we adopt Singular Value Decomposition (SVD) and the Bose-Chaudhuri-Hocquenghem (BCH) codes to design an efficient and secure health data propagation scheme based on steganography and compression. In our design, the BCH codes are used to update the encryption key and change the embedding locations in each steganography process, thus achieving forward secrecy and further enhancing the security of steganography. Moreover, a two-stage compression method is proposed in our scheme to compress the signals during signal processing and compression phases, which effectively reduces the communication overhead. Security analysis and the experimental results show that our proposed scheme enhances security while achieving an elaborate balance between imperceptibility, embedding capacity, and compression.
Psychology of art in a transitive world: the aesthetics of change

Background. The modern changeable and uncertain situation actualizes the problem of art, which is able to reflect the emotional experiences of people, harmonize their ideas about themselves and the world, and also play the role of a certain coping strategy that helps to overcome uncertainty and variability. Objective. The study aims to work out the theoretical and methodological analysis of trends that have existed in the art of the XX–XXI centuries. Methods. Comparative analysis of the concepts of the psychology of art in the XX–XXI centuries and the new aesthetic concepts of the last decades of the XXI century is carried out. Results. The results obtained showed that contemporary art combines the aesthetics of everyday life and the “torn” aesthetics of abrupt changes. Crystallization of artists and viewers’ experiences connects them with a commonality of emotions and attitudes to the situation. The mechanism of both creativity and catharsis is the mutual “spots of time”, leading from disharmony to harmony between the human and the world. Conclusion. The changed situation throughout the world leads to change in the “language” of art, which obtains new mechanisms of emotional impact. Aesthetics of transitivity connects aesthetic transformations with everyday life, making new images familiar. This leads to the fact that these images become a new type of aesthetics and help to get used to the transformations.
Using Pre-Trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study

Purpose: This study addresses the limitations of current short abstracts of DBPEDIA entities, which often lack a comprehensive overview due to their creating method (i.e., selecting the first two-three sentences from the full DBPEDIA abstracts). Methodology: We leverage pre-trained language models to generate abstractive summaries of DBPEDIA abstracts in six languages (English, French, German, Italian, Spanish, and Dutch). We performed several experiments to assess the quality of generated summaries by language models. In particular, we evaluated the generated summaries using human judgments and automated metrics (Self-ROUGE and BERTScore). Additionally, we studied the correlation between human judgments and automated metrics in evaluating the generated summaries under different aspects: informativeness, coherence, conciseness, and fluency. Findings: Pre-trained language models generate summaries more concise and informative than existing short abstracts. Specifically, BART-based models effectively overcome the limitations of DBPEDIA short abstracts, especially for longer ones. Moreover, we show that BERTScore and ROUGE-1 are reliable metrics for assessing the informativeness and coherence of the generated summaries with respect to the full DBPEDIA abstracts. We also find a negative correlation between conciseness and human ratings. Furthermore, fluency evaluation remains challenging without human judgment. Value: This study has significant implications for various applications in machine learning and natural language processing that rely on DBPEDIA resources. By providing succinct and comprehensive summaries, our approach enhances the quality of DBPEDIA abstracts and contributes to the semantic web community.
A Design of High School English Listening and Speaking Teaching Activities Based on Thematic Meaning

The English Curriculum Standards for General Senior High Schools (2017) claims that themes provide the range of subject matter or context for language learning, and students 'exploration of the thematic meaning should be the most important content of language learning. The teaching activity design of English listening and speaking class should also be theme-based to help students explore and express the theme meaning in the process of listening input and oral output. However, in the current teaching of English listening and speaking in senior high schools, teachers' interpretation of listening texts is limited to the superficial level such as vocabulary and grammar, and lack of exploration of the meaning of the theme, which is not conductive to the cultivation of students' core competence. Moreover, the transition of listening and speaking activities is not natural enough, resulting in students cannot transfer and use language knowledge in the new context. Based on this, this paper aims to explain the design principles of the teaching activities of English listening and speaking based on thematic meaning and discuss how to carry out it effectively so as to offer some advices for English listening and speaking teaching.
KDD on the CIFAR10 dataset by meta models for binary classification approaches through IP

An ensemble classifiers have been successfully applied to image classification problems. This research finds the efficiency of ensemble model on tiny images for real world problems based on the following metrics like accuracy, precision, recall, receiver operating characteristic curve (ROC), precision recall curve (PRC), kappa, F-Measure, MCC, performance analysis of deviations on like Bagging, Logit Boost (LB), Iterative Classifier Optimizer (ICO), Classification Via Regression (CVR), Multi Class Classifier Updateable (MCCU), and Random Committee (RC) of selected meta models. The RC gives best outcome compare with other models such as 90% of accuracy, 90% of precision, 0.90 of recall, 0.96 of ROC, 0.94 of PRC, 0.80 of kappa, 0.82 of MCC with low deviations. The ICO gives worst outcome compare with other models such as 65% of accuracy, 0.65 of precision, 0.65 of recall, 0.74 of ROC, and 0.74 of PRC with high deviations. This work finds and evolves that the Random Committee model performs well based on comparison of several metrics with other models.
Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

As a newly developed metaheuristic algorithm, the artificial bee colony (ABC) has garnered a lot of interest because of its strong exploration ability and easy implementation. However, its exploitation ability is poor and dramatically deteriorates for high-dimension and/or non-separable functions. To fix this defect, a self-adaptive ABC with a candidate strategy pool (SAABC-CS) is proposed. First, several search strategies with different features are assembled in the strategy pool. The top 10% of the bees make up the elite bee group. Then, we choose an appropriate strategy and implement this strategy for the present population according to the success rate learning information. Finally, we simultaneously implement some improved neighborhood search strategies in the scout bee phase. A total of 22 basic benchmark functions and the CEC2013 set of tests were employed to prove the usefulness of SAABC-CS. The impact of combining the five methods and the self-adaptive mechanism inside the SAABC-CS framework was examined in an experiment with 22 fundamental benchmark problems. In the CEC2013 set of tests, the comparison of SAABC-CS with a number of state-of-the-art algorithms showed that SAABC-CS outperformed these widely-used algorithms. Moreover, despite the increasing dimensions of CEC2013, SAABC-CS was robust and offered a higher solution quality.
Sentiment Analysis of Flagship Smartphones on Social Media Using Python TextBlob And Naive Bayes Algorithm

Social media plays a crucial role in the advancement of organizations, industries, and businesses nowadays. Almost everyone is connected to social media. Each individual can interact and exchange knowledge due to the fusion of technology and social relationships. Sentiment analysis is a technique that allows extracting information from users expressing emotions, perspectives, and opinions on the internet. One strategic sector for implementing sentiment analysis is the technology sector, especially the smartphone industry. The wide range of smartphone variants available today poses a problem for individuals in finding the best smartphone product. The sentiment analysis of flagship smartphones conducted in this article aims to find the best solution between two flagship smartphones from renowned manufacturers, namely the Samsung S22 Ultra and the Xiaomi 12 Pro. The data is collected from various social media platforms such as Twitter, YouTube, and GSMArena. The collected data is then analyzed using Python TextBlob, and the analysis results in negative, positive, and neutral sentiments displayed through various visualizations. The final outcome is the assessment of Net Brand Reputation, which evaluates the reputation of a brand across multiple social media platforms.
Image Clustering: Utilizing Teacher-Student Model and Autoencoder

Complete and high-quality labeled dataset is indispensable for image classification. Considering the often arduous task of data labeling, clustering algorithms are commonly utilized in the preliminary stages of data classification to perform preliminary categorization. Traditional clustering algorithms, such as K-means and Gaussian Mixture Model, often struggle to effectively cluster images. In this paper, we propose a novel image clustering method utilizing teacher-student model and autoencoder. Specifically, the teacher model is a fully connected autoencoder and the student model consists of multiple convolutional autoencoders. We firstly obtain sub-datasets by applying the K-means algorithm to cluster the output of the teacher model, then utilize the student model to achieve clustering through an iterative data exchange method where the same data can converge. In addition, the proposed method can filter low-quality data to a certain extent by recording the frequency of exchanges because clustering is achieved during multiple exchanges of data. Experiments on a synthetic dataset and three benchmark image datasets (MNIST, FashionMNIST and USPS) show that our method can achieve satisfactory clustering results.
YOLO-Tiny-attention: An Improved Algorithm for Fault Detection of Wind Turbine Blade

An improved YOLO-Tiny-attention lightweight deep learning method is developed due to the difficulties of deploying deep learning algorithms for wind turbine blade aerial image identification and damage classification to edge computing platforms. The algorithm incorporates the Spatial-Channel Attention Module, which improves the spatial attention of the network to target localization and the channel attention to classification. The extra Yolo head is added to make the network detect multi-scale targets with higher precision. Our method demonstrates a higher detection accuracy compared to YOLOv7-Tiny, particularly with regards to small-sized objects. The whole detection process speed on a RTX3090 GPU reaches 92 FPS while ensuring detection precision (>90%), and the speed on an NVIDIA Jetson Xavier NX edge computing platform reaches 23 FPS, meeting the requirements in wind turbine blade real-time inspection.