Reviewerly/exHarmony huggingface dataset:
Research on Curriculum Construction in Higher Vocational Colleges Based on Modern Educational Technology

In order to effectively promote educational innovation, actively promote teaching reform, talent cultivation mode reform, and system reform, share high-quality teaching resources, and comprehensively improve the quality of education and teaching, this article proposes a study on the construction of
Innovative Practice of Blended Higher Education Sports Teaching Model Based on Online Teaching Platforms

Currently, there are issues in higher education sports teaching that require reform and innovation. This study systematically explores a blended higher education sports teaching model based on online platforms through literature research, instructional design, and empirical research. The research se
Research on Student Learning Behavior in Smart Classroom Based on Multimodal Data

The smart classroom empowered by intelligent technology provides rich educational multimodal data for monitoring, evaluating, providing feedback, and early warning of students' learning behavior. This study focuses on multimodal data of students' learning behavior in smart classrooms, and constructs
Rewriting with Acyclic Queries: Mind Your Head

The paper studies the rewriting problem, that is, the decision problem whether, for a given conjunctive query $Q$ and a set $\mathcal{V}$ of views, there is a conjunctive query $Q'$ over $\mathcal{V}$ that is equivalent to $Q$, for cases where the query, the views, and/or the desired rewriting are acyclic or even more restricted. It shows that, if $Q$ itself is acyclic, an acyclic rewriting exists if there is any rewriting. An analogous statement also holds for free-connex acyclic, hierarchical, and q-hierarchical queries. Regarding the complexity of the rewriting problem, the paper identifies a border between tractable and (presumably) intractable variants of the rewriting problem: for schemas of bounded arity, the acyclic rewriting problem is NP-hard, even if both $Q$ and the views in $\mathcal{V}$ are acyclic or hierarchical. However, it becomes tractable if the views are free-connex acyclic (i.e., in a nutshell, their body is (i) acyclic and (ii) remains acyclic if their head is added as an additional atom).
A Study on the Evaluation of Intermediate Students' Computational Thinking Based on SOLO Classification Theory

Computational thinking, as a crucial criterion for assessing students' ability to independently solve problems, has long been a core concept of international interest. With the growing demand for IT talent in China, the evaluation of computational thinking among secondary vocational school students
A Study on the Cultivation Mode of Graduate Students of Fine Arts in Academic Disciplines by Research for Learning

The training of discipline art graduate students has become an important part of higher art education. At present, the training mode of disciplinary art graduate students mainly includes the dual tutor system, which is based on the on-campus tutor system and the off-campus practice tutor system. Thi
Research on the Construction and Quality Assessment of Smart English Classroom for Personalized Learning in Secondary Schools

Traditional secondary school English classrooms are often conducted by teachers according to curriculum standards, fixed textbooks, and schedules, which cannot fully meet the learning pace and needs of each student. Intelligent-supported personalized learning in secondary school English refers to a
Application Research on Enhancing the Learning Effectiveness Level of Engineering College Students Based on STEM Education

This study aims to explore the STEAM threshold, form a set of more perfect engineering class vocational classroom teaching mode, which greatly stimulate the engineering class vocational students classroom vitality, improve engineering quality, and improve the social competitiveness, engineering clas
Dynamic Update Exploration and Discussion of Basic Geographic Information Data Based on Multi-Source Data Fusion

Basic geographic information data is an important part of natural resources investigation and monitoring, and the basic guarantee for realizing the management function of "two unifications". Aiming at the problem of low integration efficiency caused by different types of geographic information data,
Construction of E-commerce Visual Network Online Teaching System Based on Web

Under the background of digital economy, the integration trend of e-commerce and digital marketing is obvious, which leads to the new demand of e-commerce talents' compounding and specialization. Among them, visual marketing, as the key link of e-commerce digital marketing, is facing a huge talent g
Innovation and Digital Transformation in UAE Public Services: Reactive to Proactive

In this era, technology is constantly changing the world; governments must deliver efficient, accurate, innovative, and proactive services. Several case studies confirmed the importance of proactive and intuitive public services over reactive services. Machine learning, AI, IoT, and blockchain are expected to increase this importance in the fourth industrial revolution. Quality of life and wellbeing measurements are also helping governments design and deliver services that help people live better. Proactive and intuitive services focus on delivering services to customers before they ask for it, using available data from both the public and private sectors. On the other hand, traditional reactive services are provided by opening the door and waiting for the customer to come, ask, request, fill forms, submit documents, pay, and then provide the service. The Reactive-Proactive service delivery framework can help design, develop, and deliver services that improve people's wellbeing. The framework will also help visualize the service delivery maturity model. Case studies analysis and in-depth interviews analysis using text analytics were used to clarify and validate the framework's components. The results show that proactive service design requires other components such as ICT infrastructure, data, human capabilities, leadership vision, and supported policies. The Reactive-Intuitive Service Delivery Framework can also be used to mentor government entities on how to transform reactive services into intuitive cognitive services.
Numerical analysis of aerodynamic and structure characteristics of an autonomous unmanned flying car for high-rise building rescue operations in urban area

Using numerical commercial software, this article presents a type of autonomous unmanned flying car capable of saving people in burning high-rise buildings in urban areas. This kind of flying vehicle is maneuverable and fast, and it has the ability to anchor to the building balconies to receive people in distress. Initial, a computational simulation is performed to investigate the aerodynamic properties of the propeller without and with a guard ring. A full-scale model evaluation based on Buckingham-Pi theory is then performed to evaluate the aerodynamic and structural characteristics of the proposal. Numerical simulation results show that the proposed design is feasible.
Digital Ecosystems, Digital Skill, and Business Performance: A Study on Graduate Entrepreneurs

Pandemic COVID-19 has affected the business ecosystem. Nevertheless, the world of digital delivers opportunities for business optimization especially for SMEs to survive these challenges. Even though the digital ecosystem may represent either an opportunity or a challenge to SMEs, the result depends on how SMEs strategically undertake. Therefore, this paper aims to examine the relationship between the digital ecosystem and digital skills on business performance among university graduate entrepreneurs. Three different sectors were chosen namely manufacturing and retail sectors. Respondent profiles, descriptive statistics and correlation analysis and multiple regression were utilized in this study. The findings revealed experience in business is seen as one of the important factors that can affect the performance of a business. A multiple regression analysis depicted that there was a significant influence between all digital ecosystem factors (e-readiness, knowledge management ICT utilization) on business performance. The digital skill also shows high significant influence on business performance. Therefore, it is suggested universities in Malaysia need to equip and prioritize students with more digital ecosystems and skills in their curricula.
A Verifiable Multi-Secret Sharing Scheme Based on ℓ-Intersection Pair of Cyclic Codes

A verifiable multi-secret sharing (VMSS) scheme addresses various security issues, helping in cheating detection and cheater identification. The dimension of the intersection of a given pair of cyclic codes over a finite field [Formula: see text] is represented by [Formula: see text]. In this paper, we propose a VMSS scheme based on an [Formula: see text]-intersection pair of cyclic codes. The proposed scheme is very useful for sharing a large number of secrets. If [Formula: see text] is the number of secrets in a threshold [Formula: see text] scheme, then for [Formula: see text], the computational complexity and the public values required in the reconstruction phase are much less compared to many existing schemes in the literature. Overall, the proposed scheme comprises nearly all the features, like being verifiable, testing participants’ integrity, checking Verifier frauds, and most importantly, it is of multi-use type.
A Machine Learning Based Video Summarization Framework for Yoga-Posture Video

Video summarization techniques aim to generate a concise but complete synopsis of a video by choosing the most informative frames of the video content without loss of interpretability. Given the abundance of video content and its complex nature, there has always been a huge demand for an effective video summarization technique to analyze various dynamic posture centric videos. Yoga session video summarization is one of the interesting application areas of dynamic posture centric video analysis that is lately drawing the attention of computer vision researchers. The majority of available general video summarizing methods fail to detect key yoga poses in a yoga session video effectively, as they do not consider posture-centric information while extracting key frames. In this paper, we propose a machine learning based video summarization framework, which is capable of extracting a series of key postures in a yoga session video by tracking a few key-posture points corresponding to vital parts of the human body. Compared to the widely used FFMPEG tool, the proposed method appears to have a higher proportion of matched keyframes but a lower proportion of missing key-frames and redundant non key-frames with respect to the ground truth set, demonstrating its potential as an effective yoga posture video summarizer.
Attention-Residual Convolutional Neural Network for Image Restoration Due to Bad Weather

Image quality degrades due to various reasons. In some circumstances, different weather conditions like fog, mist or rain have an impact on image visibility. Dust and pollution in the air can reduce the clarity of images taken outside. Thus, input images with poor visibility may reduce the effectiveness of computer vision related applications. The automated traffic monitoring systems, computer vision based smart systems used in different vehicles are few examples of such applications. Image restoration is essential of such applications for accurate implementation. In the proposed work, an end-to-end network is designed to restore images affected by rain, fog, dust and pollution. An integrated Convolutional Neural Network (CNN) with channel attention method is proposed for image restoration. In the proposed work a CNN is designed to reduce the loss between input degraded image and clear ground-truth image. Channel attention technique based on Style-based Recalibration Module (SRM) is applied on convolutional feature maps to improve the visibility of the restored image. The model is trained on a synthesized dataset and it is then evaluated on both synthesized and real-world out-door and traffic images. The experimental results demonstrate that the proposed method is more effective to several state-of-the-art methods both quantitatively and visually.
Classification of Text and Non-text Components Present in Offline Unconstrained Handwritten Documents Using Convolutional Neural Network

Identification of text parts and non-text parts present in offline unconstrained handwritten manuscripts is an essential step toward the construction of an effective optical character recognition (OCR) system. To address the said issue researchers mostly extracted handcrafted features which capture the texture information in order to recognize text or non-text components separately. In presence of noise, these types of feature descriptors badly suffer. Therefore, in this paper, a Convolutional Neural Network (CNN) is designed to separate these extracted components. To evaluate the developed model, an in-house dataset of 150 pages is created. In this dataset, the present model has achieved 85.07% accuracy. The performance of the present model is compared with three recent works where it has outperformed these existing works.
Machine Learning In Fraud Detection and Prevention

Digital fraud has become a threat to businesses of all kinds. Under the IOT scenario, financial fraud refers to unauthorized mobile transactions using mobile platforms to fraudulently obtain funds through identity theft or credit card theft It has now become imperative for an organization to pay attention emphasize fraud and people suppression. Digitization has revolutionized the daily tasks we perform at the click of a button. In the real world, financial fraud detection under the IOT scenario requires advanced planning since financial fraud results in financial loss. Thus, we primarily analyzed financial fraud mechanisms using machine learning and deep learning techniques such as complex algorithms for fraud pattern detection. But fraudsters are also becoming smarter by the day and must be constantly monitored to prevent fraud and stay ahead of fraudsters. It is important to look at salient patterns that can help distinguish genuine behavior from fraudulent behavior. Client information such as Geo-location, authentication, session, and device IP address can be captured and monitored. The application of machine learning and artificial intelligence will play an ever-important role in detecting and identifying fraudulent patterns.
Restaurant Review Sentiment Analysis: An Automated Approach to Customer Feedback Analysis

The rapid growth in the area of online food Provider Like Swiggy, Zomato, Uber eats, etc. took the growth of Restaurants and QSR service of India to Massive Revenue Sector. as the Restaurants have moved online So the online Reviews play Significant Role to empower the Business, There is Huge Data size of Reviews on Swiggy , Zomato and Google Reviews So Both the Owners and Potential Customer are Confused How They See this Review as, all platforms Comes with the idea of Stars Rating But They Don't Justified The Sentiments of Customers So we have Come with This Algorithm Which use The Power of Natural Language Processing (NLP) and Machine Learning(ML) to Solve this Problem.
"MERN Stack Unveiled: A Research Study on the Technology's Architecture and Benefits"

The MERN stack, is a modern web development paradigm that has gained great popularity for its adaptability, efficiency, and versatility. Express.js, a web application framework based on Node.js, and MongoDB, a NoSQL database, combine to create a dependable and responsive backend infrastructure. React, a dynamic JavaScript library, on the other hand, enables programmers to create interesting and interactive user interfaces. These elements are combined by Node.js, which serves as the runtime environment for JavaScript, enabling fluid communication and data flow. This research paper offers a thorough examination of the architectural underpinnings of the MERN stack, illuminating its advantages and practical applications. Because of its performance, agility, and usability, we emphasize how appealing it is to developers. The MERN stack's crucial role in the creation of various platforms, including e-commerce websites, content management systems, and social media networks, is further illuminated by our examination of its real-world applications. Through demonstrating case studies and project examples, we show how businesses have streamlined their web development efforts using the MERN stack, producing better user experiences and scalable solutions. The MERN stack is positioned as a crucial and adaptable option for companies and developers looking to develop contemporary, strong, and flexible web solutions by carefully examining the larger context of related projects.
Probabilistic Computing Based on Random Devices

Recently, a new type of computing called as probabilistic computing approach has shown its broad application in various problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit (p-bit) constitutes a basic building block. This computing scheme tackles computationally hard problems in specific domains that can be efficiently solved using probabilistic algorithms compared to conventional deterministic computers. Here, we broadly review several aspects of this computing scheme starting from its building blocks and system network to working principles/algorithms and a family of problems that can be solved. By leveraging emerging nano-devices together with existing CMOS technology, we believe probabilistic computers can not only provide a valuable new approach to tackling computationally hard problems but also reveal the unknown power of probabilistic samplings by improving the speed and energy efficiency significantly.
Domain Generalization for Foreground Segmentation Using Federated Learning

Foreground segmentation is one of the main applications in computer vision, as it separates an object in an image from its background. State-of-the-art models are achieving great metrics in terms of object segmentation tasks. However, when these models are subject to domain shift, their performance massively degrades. This is due to poor generalization. Domain generalization can be achieved by training the model using multiple datasets from different domains. However, most of the existing foreground segmentation models are unable to learn effectively from multiple datasets. If the training of multiple datasets is done simultaneously, the model cannot learn the different distributions, as each distribution requires its own set of parameters. Moreover, if the training is done sequentially, the models will struggle to perform well on all datasets due to a problem called catastrophic forgetting. This paper proposes a deep learning model and a novel training procedure to learn from multiple domains while overcoming the mentioned problems effectively. The proposed model is an encoder with a multi-head decoder that combines shared and isolated parameters for different domains. The proposed training procedure is similar to federated learning, where client models are created such that each client learns from a specific domain. After that, the main model learns the knowledge gained by each client using knowledge distillation with regularization to prevent the main network from forgetting the previously gained knowledge. Experiments demonstrate the effectiveness of our proposed work in comparison with state-of-the-art models.
Эколого-геохимическая оценка состояния почвенного покрова на территории города Уфы

The relevance. The need for constant monitoring of the state of natural environments because of Ufa increasing industrialization. This study is especially relevant due to the presence of enterprises of various industries (petrochemical, energy, metal construction, timber processing, chemical) located on a fairly small territory of the millionaire city. The aim. To determine the degree of technogenic pollution of the soil cover from industrial facilities in the study area. It is necessary to assess the impact of industrial emissions, as well as emissions from stationary and mobile sources for development of measures aimed at reducing the impact of chemical elements on the environment. Methods. Instrumental neutron activation analysis, atomic absorption spectroscopy, kappametry, mathematical statistics, mapping (spatial distribution of indicator geochemical indicators and content of the main pollutants). Results. The paper introduces the results of the analysis of the content of 29 chemical elements in the soil cover on the territory of Ufa according to the study of 104 samples. For each industrial area of the city, the priority elements-pollutants from the list of the analyzed ones were identified. Areas with high traffic congestion, as well as areas near oil refineries, are characterized by increased concentrations of As and LREE group (La, Ce, Nd). Elevated levels of Br and Sc are observed in areas near energy complexes. Increased concentrations of REE (Nd, Tb, Lu) were detected around the objects of the timber industry complex. A special situation is observed with the concentrations of some metals (Cr, Zn, Co). The entire territory of the city is characterized by increased concentrations of elements of the transition metal group due to the presence of a large metalworking complex and heavy automobile traffic. Using statistical methods of data interpretation, associations of chemical elements were identified and divided by possible genesis: mainly natural (Ca-Sr), mixed (Cs-Rb, Br-Ta-Na), mainly technogenic (Sb-Zn, Ba-As-Cr-Hg, Th-Lu-Hf-Yb, Ce-La-Nd). The constructed maps-schemes of spatial distribution, the total indicator of pollution and magnetic susceptibility determined that the territory of Ordzhonikidze district and the territory of the Dema railway station can be considered as the most susceptible to anthropogenic influence.
Chaotic Secure Communication by Event-Triggered Extended High-Gain Observers

The chaotic secure communication issue is addressed in this research using event-triggered extended high-gain observers. The communication system is based on two-channel communication, in which the transmitter system transmits the output of the chaotic system in one channel and the encrypted message that is obscured by the states of the chaotic system in the second channel. An event-triggering mechanism is introduced for each channel to decide the irregular time instants to transmit out the signal such that the resource-demanding continuous communication is avoided. In the receiver system, two event-triggered extended high-gain observers are proposed respectively to recover the chaotic system's states and unknown nonlinear function simultaneously, as well as the continuous encrypted message signal, from intermittently received signals. Based on the estimated signals, the message signal is decrypted by the inverse function of the encryption function. Theoretical analysis demostrates that Zeno phenomenon is not present and that the signal estimate errors diminish quickly to the area around the origin. Finally, simulation experiments are offered to demonstrate how well the proposed strategy works.
MAKING THE CLASSROOM MORE DYNAMIC WITH ICT: USING MAGNIFYING LENSES ATTACHED TO A SMARTPHONE FOR WOOD IDENTIFICATION

MAKING THE CLASSROOM MORE DYNAMIC WITH ICT: USING MAGNIFYING LENSES ATTACHED TO A SMARTPHONE FOR WOOD IDENTIFICATION