Reviewerly/exHarmony huggingface dataset:
Literature Survey: The Potential of Integrating Immersive Experience and Aesthetic Experience in Virtual Reality Historical Event

This study intends to explore the potential of integrating of two major theories that stemmed from two entirely different disciplines, namely the human-computer interaction and the arts. Indeed, there are vast gaps when two different theories, such as technology and art, are to be unified to develop a new element that complements to both disciplines. Empirically, virtual reality as immersive technological innovations are progressively developed and evaluated among various disciplines. Virtual reality technically is customarily to immersive experience as a measurement tool while virtual three-dimensional animated environment in virtual reality is an aesthetic object that requires an aesthetic-friendly method as its measurement tool. The proposition of evaluation is to measure the user immersive experience and aesthetic experience when dealing with an art object that imbues with digital technology. Therefore, an integrative multidimensional evaluation is required to evaluate virtual reality environment. This study uses scientific literature survey which research papers are retrieved from Google Scholar, ScienceDirect, and Scopus. Thus, this paper reports on the potentials of integrating the immersive experience and aesthetic experience theories to evaluate a virtual reality historical event. It explains the concept of immersive experience and aesthetic experience in immersive virtual reality applications in general and museum digital preservation practices in particular. Then focuses on the concept idea of how to integrate immersive experience and aesthetic experience in virtual reality historical event application by discovering the construct and element from both theories. Thus, the finding unveils the possibilities of integration immersive experience and aesthetic experience in terms of integrating the construct and elements, contributing to contemplate new possibilities and discoveries in both fields and become catalysts for future research. This initial concept idea could become fundamental conceptual design reference of developing virtual reality system in museum context used by designers, developers and museum professionals. In the conclusion, this study concluded that there is a significant and lucrative potential to integrate both theory in developing immersive virtual reality systems. Hence recommend to a new integrative framework development that is applicable to immersive product development.
Variance-aware spatial decoupling attention for stereo matching

Abstract The stereo matching algorithms which are based on feature matching build cost volume to regression disparity. Such cost volume shows a regular data distribution along the disparity dimension. The pixels with proper matching should have a single-peak distribution, while the pixels in the ill-conditioned regions have a multi-peak distribution. Using the variance of disparity dimension data, ill-conditioned regions can be identified. In this paper, an attention mechanism based on variance features is proposed, which promotes the neural network to deal with complicated regions. By decoupling the space of the image, a linear complexity attention mechanism is realized. It can be embedded into the algorithm based on feature matching with a slight increase in inference time, and a suitable trade-off is achieved in precision and speed. By embedding the module into BGNet, the accuracy of the designed model has been improved by 10 %, meanwhile, competitive performance has been achieved.
PENGARUH KEMAMPUAN MENGGAMBAR MOTIF TERHADAP HASIL BELAJAR KRIYA BATIK SISWA KELAS XI DI SMKN 8 PADANG

This research is a quantitative research. The data were analyzed using descriptive statistical analysis. This research aimed to determine the effect of ability to drawing motifs of class XI at SMKN 8 Padang and the factors that influence the ability to drawing motifs. The population in this research were students of class XI majoring in Textile Crafts department at SMKN 8. Meanwhile, the sample was selected based on "purposive sampling" which is taking samples based on special characteristics that are in accordance with the research objectives. Sample were 30 students of class XI from Textile Crafts department. The questionnaire is used to collect the data. Questionnaires are arranged based on the variable of drawing ability, the motives are described based on variable indicators, then developed into items of questions. While the data's achievement are taken by teacher subject in final grade documentation. The results of this study indicate that the relationship between the ability to draw motifs with batik craft learning outcomes is significant. The average score of ability to drawing motifs (3.00) while the average score of learning achievement (3.00).
BlockFIR: Blockchain based First Information Report System

India is experiencing a sharp rise in criminal activity. This is a serious problem, as many of these crimes go unreported. Although there is an online platform for the police to store First Information Reports (FIR) and Non-Cognizable Reports (NCR), most FIRs are still written by hand. This is inefficient and can lead to errors. Additionally, the complainant must typically be at the police station to report a cognizable offense. This can be inconvenient and time-consuming, especially for victims who live in rural areas. In 2009, the Crime and Criminal Tracking Network and Systems (CCTNS) were launched as an efficient e-governance system. This system has helped to improve the reporting of crimes, but it is still a centralized system. This means that it is vulnerable to cyberattacks and can be easily shut down by a single point of failure. Therefore, a fully decentralized system is required to ensure no single point of failure and that complaints are handled safely and securely to prevent unauthorized access. This paper proposes a blockchain-based solution called BlockFIR to manage complaints against cognizable and non-cognizable offenses. Using this system, complaints can be registered by users. The police stations will be able to see complaints registered in their jurisdiction, register FIRs/NCRs accordingly, and take action on them. Through a prototype implementation using Go-Ethereum (Geth), smart contracts, and Django web server, we demonstrate the practical use of BlockFIR. We show that our system can be easily used by users, police personnel, and Higher Authorities to improve the current systems in India.
Kernel Size Dependent Outlier Separation in Images

The ability of a novel filtering technique to separate outliers of different scales is demonstrated in a systematic way. The filter utilizes a kernel level modified Z distribution to find outliers separating the input image into a filtered image and an outlier image at the end of every iteration. The length-scale of outliers extracted in the outlier image at every iterarion is shown to depend on the kernel size used in the filtering process. The outlier image, formed of the outlers flagged by the filtering process is shown to contain valuable information about the input image. The ability to detect outliers at different length scales even under low contrast scenarios can be significant in discovering early stage calcifications and nodules as outliers in tissues. The possibility of such discoveries is demonstrated by showing that the filter successfully separates artifacts of certain length scales when applied to an ultrasound image of a thyroid tissue sample iteratively.
Hybrid Classical Quantum Neural Network based classification of Photonic Band Gap crystal structure defects

This paper emphasizes an effort to effectively identify and categorize structural defects in Photonic Band Gap (PBG) Crystals, which are vital in material characterization and structure analysis. These PBG materials have unique optical characteristics that are quite advantageous in the design of photonic devices and solar cells however, their imperfections may exert an adverse impact on these characteristics. The challenging task of recognizing and classifying these PBG crystal defects with high accuracy and efficiency can be handled by using machine learning techniques. We propose the use of a Hybrid Classical Quantum Neural Network (HCQNN) model that leverages the traits of quantum computing to efficiently classify the types of structural defects in the PBG crystals. The quantum part of the HCQNN model is contrived with a quantum node encompassing three qubits, Angle Embedding layer and Basic entangling layer with 500 numbers of shots. The quantum node interface is integrated with generalized classical dense layers, and the entire HCQNN model is trained with a classical optimizer. The experimental results demonstrate that the proposed HCQNN architecture outperforms existing machine learning and Artificial Neural Network models in terms of classification accuracy, precision, and other critical model parameters.
Automated Traffic Monitoring Control for Improved Visibility On Curve Hilly Road Using Internet-of-Things

Traffic management is a frequent problem these days due to the rapid growth of urban population. There are more accidents due to improper traffic management. Accidents happen frequently, especially in hilly areas, but people still don't care about safety when driving on roads. In addition to poor driving skills, terrain problems such as blind spots, increased number of U-turns, poor lighting (lack of streetlights), and lack of support equipment are the causes of accidents. Due to increased urbanization, population growth, the creation of varied living spaces, and the widespread distribution of vehicles, roads are in good condition and modernized, but there are still significant human casualties from accidents. In order to solve this problem, IoT-based traffic control system in mountainous area was developed. A screen can be set up at the curves and the sensors are placed on the roads. The design is implemented to control traffic in hilly areas using technologies available in the market and using IoT.
Exploring the Potential of Federated Learning CNN for Interactive Virtual Tours of UNESCO Cultural Heritage Sites: A Case Study

Cultural heritage places play a significant role in our shared history and identity, and their survival needs to be preserved and promoted. A federated learning CNN model for interactive virtual tours of culturally significant locations is presented in this paper. Utilizing the Federated Averaging technique and the MoblieNet CNN architecture, we trained the model on a simulated federated learning environment with 20 devices. Ten thousand photographs from each of the four UNESCO World Heritage Sites—Angkor Wat, Stonehenge, Machu Picchu, and Petra—comprised the total 40,000 enhanced images used to train the model—a testing set of 4,000 photos with equal representation from each site. The model's parameter values ranged from 0.93 to 0.97, yielding % overall accuracy of 95.4%. The option to customize the tour based on individual interests and preferences, which was not possible in the centralized CNN model, was appreciated by the participants utilizing the federated learning CNN approach. The participants also well-liked the model's privacy and security features, increasing confidence in the system's operation. The findings of this research show that the federated learning CNN model is very accurate and precise for item recognition and categorization in cultural heritage locations. The model's personalized and secure user interface may improve the virtual tour experience and foster system trust.
Role of Blockchain in Health Application using Blockchain Sharding

Due to the enhanced security and privacy provided by blockchain technology, it has been used in multiple sectors including healthcare. Health records are one of the most important assets in health care systems that must be efficiently and accurately provided to the participants. The immutability, decentralization, and security that the block chain technology offers the system have the potential to revolutionize the healthcare industry. Although the scalability in block chain systems is a major issue due to the ledger technology and consensus mechanisms. To statement the scalability problem in block chains the sharding of the blockchain can be an important approach. In the sharding of the blockchains, network is virtually partitioned into small set of group’s i.e. shards, which carry out parallel processing of the transactions resolving the issues of the scalability in blockchains. In this research, we provide a sharding paradigm for healthcare data management that offers decentralization with improved scalability. The study proposes a health application that uses sharding methodology in which the formation of the shards is based on the patient requests. The proposed system can handle a large scale of healthcare data by maintaining higher transaction speeds with better efficiency and proves a promising solution for enhancing scalability of blockchain based health applications.
Similarity-based Multi-Modal Lecture Video Indexing and Retrieval with Deep Learning

Video digitization is considered as the emerging technologies essential for the applications of video compression and video recording. There are diverse approaches feasible for effectively retrieving the videos in the conventional research work. This research work presents a video retrieval scheme based on the processing of multimodal lecture videos by performing an efficient similarity checking between the query text with the appropriate videos. Initially, different types of lecture videos are obtained through the standard online resources. Then, in the training phase, the selection of optimal key frames takes place and also it gets indexed as each segment using Rat Swarm Optimizer (RSO) under the optimal video segmentation phase. Further, the feature pool generation is made with the extraction of deep features like text and image using BERT and Convolutional Neural Network (CNN) from the segmented video frames. These features are stored in the feature database. Further, in the testing phase, the text query is obtained and also its features is acquired and undergo cosine similarity checking for retrieving the appropriate videos for respective queries. The main objective is to maximize the variance of retrieved videos. The analysis outcomes demonstrate the effective performance of the developed video retrieval and indexing system by comparing with the conventional models.
CL3: Generalization of Contrastive Loss for Lifelong Learning

Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem of catastrophic forgetting, the phenomenon of forgetting past knowledge while learning new concepts. In this work, we propose a novel knowledge distillation algorithm that makes use of contrastive learning to help a neural network to preserve its past knowledge while learning from a series of tasks. Our proposed generalized form of contrastive distillation strategy tackles catastrophic forgetting of old knowledge, and minimizes semantic drift by maintaining a similar embedding space, as well as ensures compactness in feature distribution to accommodate novel tasks in a current model. Our comprehensive study shows that our method achieves improved performances in the challenging class-incremental, task-incremental, and domain-incremental learning for supervised scenarios.
Boundless Conversations: AI-Powered Video Interactions across Domains, Languages, and Time

We present novel web applications that blend AI and video communication, offering an interactive way to engage with videos, transcending time and distance constraints. Known as Time Offset Interaction Application (TOIA), the platform transforms passive video viewing into dynamic conversations using personal recordings or YouTube clips. Our work expands TOIA with two unique experiences: 1) "The Elephant in the Room" project, stimulating dialogues on sensitive topics like childbirth, sex, and death; 2) Support for multilingual interactions, fostering a global collection of diverse personal narratives. Through AI and interactive media, we aim to broaden the horizons for sharing human experiences and perspectives.
A topological counterpart of well-founded trees in dependent type theory

Within dependent type theory, we provide a topological counterpart of well-founded trees (for short, W-types) by using a proof-relevant version of the notion of inductively generated suplattices introduced in the context of formal topology under the name of inductively generated basic covers. In more detail, we show, firstly, that in Homotopy Type Theory, W-types and proof relevant inductively generated basic covers are propositionally mutually encodable. Secondly, we prove they are definitionally mutually encodable in the Agda implementation of intensional Martin-Loef's type theory. Finally, we reframe the equivalence in the Minimalist Foundation framework by introducing well-founded predicates as the logical counterpart for predicates of dependent W-types. All the results have been checked in the Agda proof-assistant.
Voting based double-weighted deterministic extreme learning machine model and its application

This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is affected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and the model performance is unstable. The V-DPELM model proposed in this paper can greatly alleviate the limitations of traditional models because of its direct determination of weight structure and voting mechanism strategy. Through extensive simulations on various real-world classification datasets, we observe a marked improvement in classification accuracy when comparing the V-DPELM algorithm to traditional V-ELM methods. Notably, when used for machine recognition classification of breast tumors, the V-DPELM method demonstrates superior classification accuracy, positioning it as a valuable tool in machine-assisted breast tumor diagnosis models.
Self-adaptive Cooperation Scheme in a Parallel ACO Algorithm for Binary Combinatorial Problems

The ant colony optimization (ACO) is widely used for combinatorial optimization problems, although it can suffer from fast convergence to local minima. In order to provide a versatile implementation of ACO, we present a parallel multicolony strategy with an improved cooperation scheme for binary combinatorial problems. Our proposal is based on a self-adaptive method, which assigns appropriate run-time cooperation levels to each problem based on its size and available computational resources. We evaluate this proposal with problems with different levels of cooperation and number of processes. All these configurations combined show its flexibility as a versatile solver for this type of problems
Изучение процесса деформации различных видов теплоизоляционных материалов при воздействии низких температур и влаги

Introduction. Refrigerating machines are used in many areas of industry to produce artificial cold. To maintain optimal operating temperatures, increase energy efficiency and reduce energy loss, thermal insulation is used during the operation of refrigerating machines. Thermal insulation is the structural elements of the refrigerator, which reduce the heat transfer process, and plays the role of the main thermal resistance in the structure. The main characteristics of thermal insulation materials are: heat conductivity, density, moisture absorption, hydrophobicity, frost resistance, strength and flammability. Depending on the conditions in which the refrigerator will be located, it is possible to determine which material will be preferable as thermal insulation. At the moment, there are many thermal insulation materials, which in turn puts the consumer at a dead end, because in most cases, the description of the characteristics of this material provides information only about its positive qualities. At the same time, its other features, which make its use insufficiently effective, are kept silent. Materials and methods. In this article, the analysis of different types of thermal insulation materials, which are priority in the design of cold storage chambers, is carried out. In order to understand which material has the characteristics we need, it is necessary to recreate the conditions under which this material will be used. To do this, several basic laboratory influences are used to create unfavorable conditions for materials. Results. As a result of these interactions, tests were performed on materials that demonstrated the strengths and weaknesses of materials under different physical conditions. Conclusions. Based on the above results, it will become clear which materials are priority as thermal insulation of the refrigerator, and which need additional processing.
Analysis of the Relationship Between Returns of Nasdaq Composite and Bitcoin

Purpose: The article aims to investigate the relationship between the returns of the NASDAQ Composite stock index and the Bitcoin cryptocurrency. Theoretical framework: According to the literature, it is obvious that cryptocurrencies are very volatile, especially during the economic instability period. There is a belief that when uncertainty is in the economy, investors prefer alternative investment opportunities. There is a need to prove that. Design/Methodology/Approach: The study employs two different models, the ARMAX and the GARCH, to analyze the data from March 2018 to March 2023. The results of the analysis suggest a significant relationship between the returns of the NASDAQ Composite and Bitcoin. These results have important implications for investors and policymakers. Findings: The findings suggest that investors need to be aware of the potential risks and benefits associated with investing in both assets, particularly in times of economic uncertainty. Policymakers may also need to consider the impact of traditional stock markets and the overall economy on cryptocurrencies. Research, Practical &amp; Social implications: The research suggests that investors should be careful with cryptocurrencies. Originality/Value: The results are based on the time series analysis that makes the research original. Because there are few examples of time series and volatility analysis of cryptocurrencies.
Power trading Raft consensus mechanism considering green certificate and carbon emission weights

The consensus mechanism is a critical technology in the power trading blockchain distributed power trading platforms, which are gradually being applied and promoted to achieve the “dual carbon” goal. Green-certificate trading and carbon emission trading systems help mobilize power generation enterprises to increase the generation of and investment in renewable energy, thereby becoming important factors in distributed power trading. Therefore, they should also be considered as factors in the consensus mechanism. This article first evaluates the green-certificate weight based on whether nodes possess green certificates, evaluates the carbon emission weight based on the carbon emission settlement of nodes in the previous cycle, and constructs a mechanism to evaluate a node’s weight. An improved Raft consensus algorithm based on node weight is then proposed. Nodes with greater weights are more likely to become leading nodes, promoting active compliance and green consumption by nodes and reducing carbon emissions. A simulation shows that the improved Raft consensus mechanism enhances the reliability of leading nodes, improves the efficiency of the distributed power transaction, increases the generation of renewable energy, and encourages the consumption of new energy.