Reviewerly/exHarmony huggingface dataset:
Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques

Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval.
Towards Constructive Cost Analysis for demand based Reusable Domain Specific Components

The prevailing software development methodology embraced by a majority of organisations is characterised by its agility. The neglect of normal analysis and design procedures is a consequence of the significant pressures associated with designing a product within specified time and budget constraints. This phenomenon could potentially result in a death of software of superior quality, while simultaneously impeding the constructive reuse of components. In the majority of component approaches, the demand of domain specific sofwatre components occurs during the later stages. In this paper, various components can be identified as demand based reusbale domain specific software components, which might also help in reusing these components in the subsequent increments. The strategy for extraction of components &amp; procedure for reusing the existing components is described and a sample case to realize the same is presented.Still there is a dire need to early identify the demand based domain specific sofwatre components and perform the constructive cost analysis for the reusable domain specific software components. The issues related to the estimation of cost reuse measures are still challenging. This paper presents the constructuve cost analysis for the demand based reusable domain specific sofwtare components and proposes reuse measures for the family of applications with the quantized values. By analyzing these cost measures, the budget and effort in the development can be reduced. The results are estimated from the HR Portal domain specific softwrae application as a case study and its respective scenario has been explored in a better manner.
Trusted Knowledge Infusion Model-based Recommender System for IoT based B2B applications

The exponential growth and usage of Internet, social sites, and e-commerce results extensive amount of information everywhere. It becomes very difficult for the people to search something, so they go for the offered suggestions which are pertinent for them rather than searching from a potentially overwhelming number of options. Recommender Systems are the solutions for such difficulties. There are many powerful Recommender Systems available for e-commerce, websites, books, tourism, and documents, but recommendations for IoT-based applications need of new discoveries. Traditional recommendations methods are not sufficient for big data based scalable and heterogeneous IoT environment. In this paper, we propose a knowledge infusion model-based hybrid recommendation model for IoT-based B2B applications. The proposed model is analyzed with a real dataset, and the evaluation represents that model performs well in terms execution time, RMSE, precision and F1-score as compared with the existing models.
Autoencoder with Orthogonal Variant

Autoencoder is a widely used neural architecture for dimensionality reduction. It can be considered similar to the principal component analysis (PCA) methodology. However, the final distribution of the components between classes does not establish orthogonality between them, which can result in a reduced separation between different classes. To address this issue, we present a modified autoencoder architecture with an orthogonal variant. The error minimization equation has been changed to ensure orthogonality between the final components. Our experiments show that the proposed orthogonal autoencoder architecture generates a final distribution with more separability than the PCA numerical process and a typical autoencoder architecture. This makes it a promising approach for applications that require high separability between different classes.
Implementasi Metode Profile Matching Dalam Penentuan Jurusan Siswa Madrasah Aliyah

The selection of majors at the Madrasah Aliyah (MA) level is a crucial step in students' educational development, and its influence on their future is significant. However, due to the abundance of major options, students often face numerous choices and challenges. Counseling services provided by guidance teachers (BK) sometimes encounter difficulties due to the multitude of factors to consider, resulting in less-than-optimal services. To address this challenge, this research attempts to implement a Decision Support System (DSS) using the Profile matching method by utilizing data already collected from each student during their learning process, including grades and interests. These two factors are then used as aspects in the calculation process using profile matching. This research explains the steps of implementing Profile matching while considering criteria for each major. The test results show that the DSS with the Profile matching method provides faster and more objective major recommendations, making it an alternative for madrasah, especially BK teachers, in determining students' majors. The hope is to create a synergy between BK teachers and students to enhance motivation and academic achievement while helping students gain a better understanding of their future career choices.
KEMAMPUAN MENULIS PUISI DENGAN PENDEKATAN KONTEKSTUAL

The purpose of this study was to describe the methods, media, and student responses in learning poetry in class X SMK PAB 10 Patumbak. This research was conducted using a descriptive qualitative method. Data collection techniques are observation, essay tests, and documentation. The research data is information about poetry learning in class X, with 30 students in the experimental class and 30 students in the control class; data sources are from researchers, students, and events in learning to write poetry. The data analysis technique uses a contextual approach that is perfect for learning to write poetry in class X SMK PAB 10 Patumbak. The results of this study found that the learning method used by researchers was a contextual approach using essay tests. This method can arouse students' enthusiasm and interest in participating in learning, and the media researchers use relates to the surrounding natural environment. The results of student scores with an average achievement in control class learning with a score of 65, while experimental class learning with a score of 80 indicate that students' writing skills in contextual learning are better. Student responses were grouped into two, namely, positive and negative responses. Students' positive responses were shown by the activity of writing poetry in the experimental class using a contextual approach, namely being active in asking and answering questions posed by researchers, and the negative responses of students in the control class were seen by the lack of enthusiasm of students towards the use of the lecture method by researchers and the placement of class schedules at the end meetings where the condition of students is tired and hungry.
Evaluation and Analysis of Disparity Between Self-perception and Actual Performance for Chinese Citizen's Digital Literacy

Digital literacy can be defined as the critical and creative use of digital technologies to achieve one's goals related to leisure, learning, employment and participation in society. Evaluation of digital literacy involves different measurement methods, which mainly generate two kinds of implications: self-perception and actual performance. This paper evaluates the digital literacy of 665 Chinese citizens (demographically and geographically dispersed) on the basis of 15 descriptors in a domestic framework, using methods of five-scale questionnaire (self-perception), Q&A structural test and simulated digital tasks (actual performance). The results indicate that there exists noticeable disparity between self-perceived digital literacy and observed digital proficiency (except descriptor Critical Thinking), because people get used to over-rate their own digital literacy. It calls for more education to publicize the nature of digital literacy and what kinds of skills, competences and awareness it contains in the diverse social and cultural contexts. Besides, it also finds the operational skill of structured processing among Chinese people is fairly weak, which requires additional attention of educators to formulate and implement remedies. Due to a handful of existing limitations, further studies will continue on the aspects of disparity sources and evaluation instruments.
Edukasi Siswa SMAN 1 Batulayar Melalui Penyuluhan Potensi Energi Terbarukan Menuju Sekolah Ramah Lingkungan

Senior High School (SHS) 1 Batulayar is located at Jl. Otomotif Raya, Senteluk, Batulayar, West Lombok Regency, West Nusa Tenggara 83355, Indonesia, is in the Senggigi tourism buffer area. In the 2023 academic year, SHS 1 Batulayar only received 39 new students who were accommodated in 1 class (study group). Seeing these conditions is a problem faced by SHS 1 Batulayar in getting students. As a teaching team in the Department of Electrical Engineering, we are inspired to educate students and teachers, one of which is providing information on the potential of new renewable energy towards environmentally friendly schools. Educational material in the form of knowledge about the new energy sector in accordance with Law No. 30 of 2007 concerning Energy and PP No. 70 of 2009 concerning energy conservation and Minister of Environment and Forestry Regulation No. P.52/MENLHK/SETJEN/KUM.1/9/2019, movement for environmental care and culture in schools, especially in the field of EBT, energy conservation towards the Adiwiyata (Green School) program. The PkM group consists of lecturers, technicians and students from the Electrical Engineering study program specializing in electrical power systems and telecommunication. PkM participants were 25 classes X and More attention to SHS 1 Batulayar which has less number of students.
PROPOSAL FOR A SERIOUS GAME ON THE THEME OF PERSONAL INFORMATION

With the advent of the Internet, our society has evolved into a place where personal information can be easily shared and collected. To explore people's attitudes towards this reality, we developed an interactive online game to analyze these
"ATTITUDES OF LIBRARIANS TOWARDS EMERGING TECHNOLOGIES – EFFECTS OF A MULTILINEAR SCENARIO"

A study examined the attitudes of 457 librarians in Germany and Switzerland towards emerging technologies that are likelyto change the face of their profession. A multilinear narrative enabled participants to live through the future with
Aplikasi Perhitungan Pemenuhan Gizi pada Orang Sakit Menggunakan Metode Harris Benedict

The National Health System states that all efforts in health development in Indonesia are directed towards achieving a higher degree of health which enables people to live more productively both socially and economically. With the increasing socio-economic status, public health services, lifestyle changes, increasing life expectancy, then in Indonesia there is a shift in the pattern of disease. Efforts to improve health for sick people can be done by maintaining food intake and meeting nutritional needs for sick people. Fulfillment of nutrition for the sick is fulfilling the nutritional needs needed by the body, such as calories, carbohydrates, protein, and fat. To overcome this problem, it is necessary to have an application to speed up the process of calculating nutrition fulfillment for sick people. Application is the application of a system design to process data that uses the provisions of certain programming languages and also a computer program created to help and facilitate human work. In the calculation process the application is made using the Harris Benedict method. Where the Harris Benedict method is used to do calorie calculation needed by sick people based on height, weight, age, activity factor and gender. In its application, this application uses the Microsoft Visual Basic programming language, with a Microsoft Access database. The final result of this application is in the form of nutritional information and food advice for sick people.Kata Kunci—
Exploration and application of mixed reality technology in modern home design

The exploration and application of Mixed Reality (MR) technology in modern home design has become a research field that attracts much attention. MR technology has brought unprecedented innovation opportunities for home design. By integrating virtual elements into the real environment, designers can cooperate with customers in a more interactive and visual way, making it easier for them to understand design concepts and choices. This improves the customization of the design, strengthens the user experience and is expected to improve customer satisfaction. MR technology also plays a key role in the visualization of the design process. Designers can create virtual models to present different design schemes in the actual environment, thus reducing the number of design modifications and saving time and resources. This is expected to improve the efficiency and accuracy of the design. This paper first introduces the concept of MR technology, then summarizes the application of MR technology in modern home design, and finally discusses the challenges and future prospects of MR technology for reference by peers.
Human behaviour analysis based on spatio-temporal dual-stream heterogeneous convolutional neural network

At present, there are still many problems to be solved in human behaviour analysis, such as insufficient utilisation of behaviour characteristic information and slow operation rate. We propose a human behaviour analysis algorithm based on spatio-temporal dual-stream heterogeneous convolutional neural network (STDNet). The algorithm is improved on the basic structure of the traditional dual-stream network. When extracting spatial information, the DenseNet uses a hierarchical connection method to construct a dense network to extract the spatial feature of the video RGB image. When extracting motion information, BNInception is used to extract temporal features of video optical flow images. Finally, feature fusion is carried out by multi-layer perceptron and sent to Softmax classifier for classification. Experimental results on the UCF101 dataset show that the algorithm can effectively use the spatio-temporal feature information in video, reduce the amount of calculation of the network model, and greatly improve the ability to distinguish similar actions.
CenterCoop: Center-Based Feature Aggregation for Communication-Efficient Vehicle-Infrastructure Cooperative 3D Object Detection

Vehicle-Infrastructure Cooperative (VIC) 3D object detection is a challenging task for balancing communication bandwidth and detection performance. Intermediate fusion is recently studied to reach a better balance by transferring feature maps. Existing works mainly perform spatial-wise fusion and adopt feature compression to alleviate bandwidth cost by high-resolution feature maps, which would inevitably lead to information loss. Besides, overlapping observations between the two sensors would lead to near-duplicate detections, making trivial improvement to cooperative task while causing unnecessary bandwidth cost. To mitigate these problems, we propose a novel feature aggregation framework called CenterCoop, which first encodes the informative cues from the whole Bird's Eye View (BEV) context into compact center representations, enabling feature aggregation at sequence-level to significantly reduce the communication cost. Furthermore, to tackle the redundancy of transmitted data, we incorporate communication-aware regularization which enforces the network to extract complementary and beneficial cues for collaboration task. From an information-theoretic perspective, the proposed auxiliary constraints facilitate cooperative-view independence mining, resulting in enlarged perception range within the limited bandwidth. Extensive experiments on the DAIR-V2X dataset demonstrate the superior performance-bandwidth trade-off of CenterCoop, which achieves the state-of-the-art detection performance with less than 10% bandwidth cost.
A Four-Dimensional Space-Based Data Multi-Embedding Mechanism for Network Services

In the age of data science and connected devices of all kinds, users have gained a lot of convenience. However, the massive data generated by users from the communication network is faced with security problems such as intrusion, tampering, hijacking, etc. According to the properties of covert transmission, the network with data covert embedding becomes an effective means to solve these challenges. In this paper, we propose a four-dimensional space-based data multi-embedding mechanism to protect data in the network. Specifically, a feature extractor is first used to extract vectors for embedding. Next, a Schmitt-based four-dimensional space is constructed, two of which are used to embed robust data to hide confidential information. The other two dimensions are used to embed fragile data to detect whether the signal has been tampered with. According to the experimental comparison with other methods, it shows that the method proposed in this paper achieves satisfactory performance.
Surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle, Inyo and San Bernardino Counties, California

First posted December 5, 2023 For additional information, contact: Geology, Minerals, Energy, & Geophysics Science CenterU.S. Geological SurveyBuilding 19, 350 N. Akron Rd.P.O. Box 158Moffett Field, CA 94035 The surficial geologic map of the Owlshead Mountains 30' x 60' quadrangle depicts the distribution and characteristics of surficial-deposit materials and neotectonic deformation for an area of approximately 5,000 square kilometers (km2) located in the western Basin and Range Province of eastern California. The map represents a new compilation of the surficial geology that encompasses deposits within the late Pliocene to Quaternary. The map is based primarily on new mapping conducted between 2001 and 2009. Map compilation was supported by field observations distributed across the map area, combined with reference to several published and unpublished mapping sources that mostly emphasized neotectonic deformation. The surficial-deposit units included in the map follow a classification scheme that systematically denotes depositional process, relative age, and any secondary sedimentologic or morphologic characteristics. Identification, correlation, and age estimation of map units are based primarily on the relative degree of development of certain time-dependent characteristics such as surface morphology, including local dissection and surface preservation, surface clast modification, and degree of soil development; these characteristics are implicitly incorporated into unit designations. The map represents a detailed and regionally uniform synthesis of the late Neogene geology for this large area that provides a framework applicable to many interpretative studies, such as regional patterns of deposition and dissection; surface drainage development and evolution; and the distribution, style, and timing of neotectonic deformation.
Federated Learning Empowered Recommendation Model for Financial Consumer Services

In recent years, recommendation systems have gained popularity in the financial services industry. These systems offer personalized recommendations to consumers based on their distinct preferences, behaviors, and historical data. Centralized data storage and processing used in traditional recommendation systems can raise privacy and security concerns. In light of these challenges, in this paper, a federated learning-empowered recommendation model (FLRM) is proposed that utilizes federated learning and blockchain technology. In our proposed model, the central server coordinates model aggregation and communicates with the blockchain network. In FLRM, financial institutions hold their data in private blockchains while participating in the federated learning process. Federated learning provides a solution to these challenges by enabling privacy-preserving collaborative model training across multiple distributed data sources. Blockchain technology enhances the security and transparency of the federated learning model by providing a decentralized and tamper-proof mechanism for data storage and management. By decentralizing the recommendation system, FLRM enhances user privacy, reduces data transfer overhead, and builds trust through transparency. The integration of smart contracts in this proposed model facilitates secure and automated transactions. The proposed approach represents a significant step forward in creating a more secure, privacy-preserving, and effective recommendation system model.
A Novel Personalized Recommendation Model For Computing Advertising Based on User Acceptance Evaluation

Nowadays, computing advertising has been an intelligent Internet application which provides personalized advertising service to customers. But how to suggest suitable advertising contents to users relies on effective mining of user preference characteristics. Conventionally, machine learning-based methods were most intuitive solutions to predict unknown user features. Nevertheless, such kind of approaches highly relied on massive labelled samples, and also cost much time in algorithm training. In realistic engineering application, running efficiency acts as the top priority. To deal with this issue, this paper proposes a novel personalized recommendation model for computing advertising based on user acceptance evaluation. Firstly, the functional requirements in personalized recommendation of computing advertising is analyzed, and the perceived behavior of the algorithm ethical risks generated in computing advertising is analyzed based on collaborative filtering. Then, based on the user experience risks generated in recommendation process, a user acceptance value for personalized recommendation is calculated. We also conduct some experiments on real-world data to make empirical assessment for the proposal. It can be concluded that recommendation effect of the proposal is better than that of machine learning algorithm and ant colony algorithm.