Reviewerly/exHarmony huggingface dataset:
Partial Feedback Control for Knowledge Distillation

Knowledge distillation transfers dark knowledge from cumbersome teachers to lightweight students. Recently, some mainstream knowledge distillation methods impose strict constraints on students to expect similar output to the teacher network. Nevertheless, these approaches severely increase the burden of the student networks learning and constraint them to fully receive the teacher's knowledge without self-thinking, which significantly restricts their ability to learn independently. In this paper, we propose a simple yet effective Partial Feedback Control for Knowledge Distillation (PFC-KD), which performs partial supervision on the student network at the penultimate layer. Specifically, we utilize the deviation of feature maps of the student network from that of the teacher network as a feedback signal, and select features with large variances as beneficial knowledge that students need to learn in that iteration. Extensive experiments conducted on image classification task and object detection task with a significant results improvement, which demonstrates the effectiveness of our proposed PFC-KD. Code is available at https://github.com/SCUT-BIP-Lab/PFC-KD.
Ideological asymmetries in online hostility, intimidation, obscenity, and prejudice

To investigate ideological symmetries and asymmetries in the expression of online prejudice, we used machine-learning methods to estimate the prevalence of extreme hostility in a large dataset of Twitter messages harvested in 2016. We analyzed language contained in 730,000 tweets on the following dimensions of bias: (1) threat and intimidation, (2) obscenity and vulgarity, (3) name-calling and humiliation, (4) hatred and/or racial, ethnic, or religious slurs, (5) stereotypical generalizations, and (6) negative prejudice. Results revealed that conservative social media users were significantly more likely than liberals to use language that involved threat, intimidation, name-calling, humiliation, stereotyping, and negative prejudice. Conservatives were also slightly more likely than liberals to use hateful language, but liberals were slightly more likely than conservatives to use obscenities. These findings are broadly consistent with the view that liberal values of equality and democratic tolerance contribute to ideological asymmetries in the expression of online prejudice, and they are inconsistent with the view that liberals and conservatives are equally prejudiced.
A Robust Gray-code Pattern Decoding Method for 3D Scanning with Structured Light

Structured light 3D imaging based on Gray code requires accurate pixel light and dark judgment for high-quality reconstruction. This paper proposes a pixel point classification method for fringe images based on the U-Net network to achieve robust and efficient pixel binary classification. To make the network more effective in learning and utilizing the structural information of the fringes projected by the projector, a multi-channel network input is designed. To address the challenge of obtaining a large number of fringe images for network training in real scenes, this paper utilizes computer graphics to construct a virtual 3D structured light imaging system and produce usable datasets. The proposed method achieved a pixel classification error rate of 0.44% and a mean square error of 285.019 on the test data, outperforming the traditional method's 1.10% and 713.578, respectively. The proposed method can accurately recognize and classify pixel values of highly reflective regions in an image. And it can generate high dynamic range images with wider dynamic range and richer image details.
Research on the application of Bot Chat Technology in the field of medical assistance

With the development of artificial intelligence and natural language processing technology, bot chat technology has emerged and been applied to various fields of human life. For example, the recent rise of ChatGPT technology has brought great convenience to people's life. Through it, people can realize convenient information acquisition and communication, and provide creative ideas and inspiration for people. However, in the field of medical assistance and patient care, ChatGPT lacks human professional knowledge and experience, as well as personalized and targeted diagnosis for patients. Therefore, the research on bot chat technology in the field of medical assistance and patient care needs to be further studied. This paper first studied the challenges and needs of the medical service field in the market, as well as the practice and application of bot chat technology in the medical service field. On this basis, the key technologies involved in the implementation of bot chat were briefly described. Secondly, this paper studies the application design framework and implementation core modules of bot chat technology in the field of medical services. Finally, the application was tested and evaluated. The app provides users with functions such as medical consultation, medical knowledge and online diagnosis, aiming to facilitate People's Daily life, disseminate medical knowledge and relieve the pressure on hospitals.
The Conceptual on the Impact of Digital Competence on Non-academic Staff's Job Performance in the Vietnam Higher Educational Context

Amid a digitally growing world, digital competence has emerged as a critical concept in discussing what kinds of proficiency individuals should have. It is believed that, in addition to professional competence, digital competence is required for the workforce to improve their work results when much of their daily work uses technologies. Nonetheless, a high rate of the Vietnamese force faces a low level of digital competence, which aligns with the global trend. Meanwhile, previous studies have mainly concentrated on developing digital competence frameworks and proposing roadmaps to apply them; this current study may help seek answers to the questions of how the digital competence of the non-academic staff impacts their job performance. This proposed study may use survey quantitative research design to serve descriptive and evaluative research purposes based on the snowball sampling techniques for data collection. This study could be a fitting response to explain to what extent digital competence impact job performance among the non-academic staff in Vietnam universities by surveying full-time members from a private university. In addition, the findings from this study may assist educational organizations in selecting strategies for improving job performance based on a better understanding of the impact of digital competence on it.
Research on Intelligent Maintenance Technology for Industrial Internet Equipment

With the integration of the new generation of information technology and traditional industries, industrial Internet equipment is moving towards automation and intelligence. The rapid development of information technology has also brought many security and management challenges. Research on industrial Internet intelligent security operation and maintenance technology can effectively improve the reliability and operation efficiency of equipment and reduce labor costs. This paper takes the rotating bearing equipment as an example, uses random forest and other algorithms to evaluate the equipment safety decline state, and uses the long-short-term neural network model (LSTM) to predict the degradation state of the equipment, and uses less resources for equipment operation and maintenance to avoid failures lead to serious consequences. At the same time, the classification model can resist false data injection to some extent, which shows the effectiveness of the work in this paper.
Intelligent Decision Making Algorithm Based On Hybrid Cuckoo Algorithm and Deep Learning

The current intelligent decision-making algorithms are not comprehensive enough in data search, resulting in poor quality decision results. For this reason intelligent decision algorithms based on hybrid cuckoo algorithms and deep learning are proposed. Integrated intelligent decision data space. Global search of data in this space based on the hybrid cuckoo algorithm. Establish a non-interference decision data allocation matrix, and use deep learning technology to generate intelligent decision algorithms based on the results of the interference decision data allocation matrix. Experiments show that the highest reward value of the decision results obtained by the algorithm reaches 4.73, which substantially improves the quality of the decision results and has high practical application value.
A 2D Human Pose Estimation Method Based On Visual Transformer

Two-dimensional human pose estimation is the basis of human behavior understanding, but predicting a reasonable three-dimensional human pose sequence is still a challenging problem. To solve this problem, a pose estimation model named DEFormer based on ViT (Vision Transformer) is proposed, which uses a coordinate representation of key points' distribution perception to reduce quantization errors, and combines the original encoding module with an efficient encoding module to construct a lighter two-stage model. Experimental results show that on the CrowdPose dataset and a self-constructed campus scene human motion dataset, the DEFormer lightweight pose estimation model achieves a maximum average accuracy of 85.9% for human pose estimation, demonstrating more accurate pose estimation performance.
LLE-Net: A Deep Network with Overexposure Fusion for Low-Light Enhancement

Inspired by Retinex theory, this paper proposes a deep network (LLE-Net) to enhance low-light image,which decomposes image into reflectance and illumination, and then adjusts the illumination to reconstructs image precisely. A fusion mechanism is introduced to avoid reconstructed image overexposure. Experiments results on public datasets show the advantages of our method compared with other state-of-the-art methods in qualitative and quantitative perspective.
The Role of Education and Science-Driven Tools in Scaling Up Photovoltaic Deployment

Accelerating the deployment of Photovoltaic (PV) systems is a key contributing factor in achieving climate neutrality. Even though solar power is one of the cheapest energy sources and its deployment is growing rapidly around the world, an even faster growth is required to achieve existing climate goals. Besides the role that finance and permitting can play as enablers or barriers to this, the key elements to enable fast PV deployment are the use of education, and science and data-driven tools to empower citizens, installers, and investors to make their decisions based on robust scientific evidence. This perspective article aims to summarize the key concepts presented and discussed during the side event at COP27 on PV resources towards climate neutrality. The article will accomplish this by highlighting two key aspects: (1) the advantages of using solar-related education and data-driven tools, and (2) showcasing the significance of education, improved data and tools, community involvement, and PV mapping in expediting the deployment of PV systems.
Design and Implementation of Single to Multi-cloud Securities in Network

The adoption of cloud computing has skyrocketed in a lot of companies. One of the biggest selling points of cloud computing is how cheap it is and how easily you can access your data. Since users routinely save sensitive data with cloud storage providers despite the fact that they may not be reliable, cloud computing security is a vital concern in the cloud computing ecosystem. Due to concerns around current service failure and damaging entrants in the single-cloud, consumers are predicted to shift away from engaging with “single cloud” suppliers. Multiple clouds, or “inter-clouds” as they are sometimes called, are becoming increasingly popular. In this study, we investigate the problem of securing multiple cloud environments and discuss some potential approaches to the problem. Researchers have paid less effort to studying how to use several cloud providers while keeping data secure than they have to studying individual clouds. Multi-clouds can reduce vulnerability to some types of attacks, hence this initiative aims to increase their adoption. However, there are still major worries about the security and trustworthiness of cloud-based data storage. We present DEPSKY, a system that encrypts, encodes, and replicates data over numerous clouds to increase availability, integrity, and confidentially stored in the cloud. Our infrastructure, together with Planet Lab, has been deployed to four commercial clouds, where users from all around the world can use the service. Our protocols improved perceived availability and, for most cases, access latency when compared to those of individual cloud providers.
Machine Learning-Based Fake News Detection System Using Blockchain

Now a days, news on print and digital media has become very essential for everyone to stay aware with current happenings in the world. The increased amount of IT penetration and digitization has resulted in more number of people accessing various social media offerings like YouTube, Facebook, Twitter, Yahoo, etc. With the increased usage of social media, the amount of fake news also has increased disproportionately high. Fake news may cause economic and social problems. It is very difficult to find source of fake news and to make the concerned accountable for it. The accountability of authentic news is decreasing day by day. In this paper, we are proposing a Blockchain-based platform which acts as a media to share news. The source of news and each interaction will be recorded in the Blockchain and it is immutable. Users have read access to the news and they can also give rating to news based on its authenticity. Authors/journalists have write access, so that they can post their contents to media. Because all the interactions are available on Blockchain, it is easy to trace back the source of fake news if any.
Prediction of Breast Cancer Using Convolution Neural Network

The Breast cancer is the a type of a carcinogenic cells that is developed in the breast and is more commonly found in women. This type of cancer has been found more fatal amongst women after the lung cancer. The paper proposes a study where it helps in identifying the breast cancer at faster rate using Convolution Neural Network (CNN). The paper uses a technique of identifies and differentiates the mammography pictures into three types, first one to be benign, second one is malignant, and the third one to be normal. This technique helps the medical professional in identifying the type of cancer. The CNN model used uses VGG-16 architecture for cancer detection and classification. The proposed system uses huge dataset of 275,000 RGB image patches. The proposed prediction is a deep learning technique where it’s architecture has two stages, first is image processing and the second is image classification. The result of using this technique reaches the accuracy level of 95.72% in classifying the different types of cancer.
Risk identification for business blockchain-based technology adoption via an advanced fuzzy MRDM architecture

Blockchain technology holds considerable amount of potential for all types of industries by executing transactions in a verifiable, efficient, and permanent channel. It has been widely viewed as a standard requirement for making industry ready for the future, but when it comes to practical applications, it still arouses numerous risks/challenges that need to be addressed. Therefore, it is essential to address this gap and establish a comprehensive and effective practical framework to align the information technology revolution with sustainable value creation. The purpose of this research is to realize to what extent an enterprise legacy system’s transformation benefits a blockchain-based system and to minimize its specific risk through a hybrid fuzzy MRDM (multiple rule-based decision making) model that integrates data envelopment analysis with rough set theory (DEA-RST) and the fuzzy DEMATEL approach grounded on a questionnaire derived from domain experts. We aim to point out the inherent risks of blockchain-based technology adoption and to assist senior engineers in designing or adopting a suitable architecture for practical operation and planning of any future integration and development. The potential risk evaluation of business blockchain adoption reveals that the priority improvement sequence based on dimensions is smart contract risk, value transfer risk, and standard risk. Furthermore, law and regulation are the most critical criteria.
Using n-ary multi-modal logics in argumentation frameworks to reason about ethics

Autonomous behaviors may raise ethical issues that agents must consider in their reasoning. Some approaches use deontic logics, while others consider a value-based argumentation framework. However, no work combines both modal logic and argumentation to reason about ethics. Hence, we propose a new argumentation framework where arguments are built from a n-ary multi-modal logic. It allows to express different kinds of operators, e.g. nullary choice or moral worth operators, dyadic deontic operators or mental states. However the standard attacks based on logical contradictions are no longer sufficient to catch an intuitive meaning for attacks. Hence, we enrich standard attacks by characterizing how oppositions between modal operators arise. Furthermore we show the standard logic-based attacks have a quasi-symmetry property, i.e. when an argument attacks another, this argument is necessarily attacked by another one. Our modal attacks do not have this property, which is highly relevant to decide a dilemma.
Detection of Content-Based Image Retrieval Using OCR Tools

In the field of image processing content-based image retrieval is having research scope to retrieve the images which is stored in any location on the network. In the content-based image retrieval system to check images which are deposited on the network based on image attributes like image size, name of the image, shape, and color of the image. This work gives a novel method for system retrieve the image based on the text query given by the user. The scope of the paper also extends the scope to finding the similar combined images containing text and also graphical shapes. In the proposed system we can retrieve the images by using Optical Character Recognition system. The important scope of the proposed methodology is to produce accurate result even in the case of invariance to difficult graphics and background features, text design, and different font size. The approach has been tested for retrieving images of English text based on the provided text. We put forth a brand-new method for creating a content-based picture retrieval system based on locating images with the same textual information within an image.
Texture Feature Extraction and Classification Using Machine Learning Techniques

Texture, a crucial aspect of an image, is something made up of components that are related to one another. Reliable feature extraction in image files requires the use of a texture-based categorization method, which is significant. This study proposes an effective method for classifying textures using machine learning (ML) approaches. Using these ML classifiers, which are in the form of artificial intelligence (AI), programmers can predict results exactly without providing instructed to do so explicitly. The proposed study focuses on the creation of own dataset in the form of CSV file, to do so Haralick features (contrast, dissimilarity homogeneity, energy, and correlation) extracted from the Brodatz texture dataset. Different ML algorithms are used like: K-Nearest Neighbor, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and AdaBoost Classifier which are experimented on the created dataset to classify the texture of Brodatz dataset. Proposed approach exhibits better results with 100% accuracy with less computation time as compared to previous work in the literature.
Sentimental Analysis-Based Recommended System for Products Using Machine Learning

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on Amazon.com and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product.
Cloud Risks and Solutions Review

Recently, cloud computing has been the most powerful research area due to its flexibility and efficiency. Thus, it provides dynamic, easily scalable services to the environment. Although cloud computing promises to make life much easier for both organizations and consumers, there are still quite a few security issues. Security is a top priority when transferring data between a client and a server. An effective model should be put into place in the cloud environment to guard against unauthorized users. As a result, cloud security has recently gained attention in the IT industry. This study describes a survey of various cloud computing security issues and possible solutions for remediation Another name for cloud security is cloud computing security. It is a collection of rules, procedures, tools, and controls for a virtual infrastructure made up of hardware, software, and apps. Web security, network security, field database security, and other closely related Stated differently, cloud security is closely related to information security, computer security, and IT security. Every person and business is starting to use IT infrastructure on a daily basis, thus security is a major worry in this context. Numerous approaches, including collective control, detective control, preventive control, and preventive control, are used to handle cloud computing security. Sound and safe cloud security policies depend on conducting penetration and vulnerability tests on the cloud. A well-known term in the IT and computing industries, cloud computing is becoming more and more prevalent across a wide range of businesses and establishments. Various areas of Cloud Computing are described in this study. There are different models and architectures and different rules, regulations, and frameworks for cloud computing security. This article talks about different areas of security and offers a new method to many cloud providers to ensure it. This method is related to Face ID and emphasizes the importance of its application to computers. The article also discusses other security issues related to the Cloud.
Practical Certificate-Less Infrastructure with Application in TLS

We propose highly efficient certificate-less (CL) protocols for the infrastructure used by authenticated key exchange (AKE). The construction is based on elliptic curves (EC) without pairing, which means it can be easily supported by most industrial cryptography libraries on constrained devices. Compared with other pairing-free CL solutions, the new CL-AKE protocol enjoys the least number of scalar multiplications over EC groups. We use a unified game-based model to formalize the security of each protocol, while most previous works only assess the security against a list of attacks, provide informal theorems without proper modeling, or use separate models for protocols in different stages. We also present an efficient integration of the core protocols into the TLS cipher suites and a stand-alone implementation for constrained devices. The performance is evaluated on constrained devices in real-world settings, which further confirms the efficiency of our proposal.