Reviewerly/exHarmony huggingface dataset:
Dense Monocular Depth Estimation with Densely Connected Convolutional Networks

Deployment of depth estimating models in computer vision applications like obstacle avoidance, scene reconstruction, and camera pose estimation has become a fundamental task in the present day. Most of the depth images generated by existing monocular depth estimation models have blurry approximations of the depth and resolution, especially in low-textured regions. However, the existing depth estimation models that provide better predictions of depth generally take multiple images of the same scene as an input for generating depth maps. This paper presents a transfer learning approach with densely connected convolutional neural networks that take only an RGB image as an input for deeper and high-quality prediction of depth. In the proposed solution, an encoder-decoder architecture is leveraged for extracting features from an RGB image and generating a depth map for that corresponding RGB image. The densely connected convolutional network with 161 layers (Densenet-161) is used as an encoder, and the decoder is made up of five upsampling blocks and one transposed convolutional layer. The evaluation results obtained after training the model on the benchmark NYU V2 depth dataset ( Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor Segmentation and Support Inference from RGBD Images. In: Proceedings of the IEEE Conference on Computer Vision, pp. 746-760(2012).) have shown that the proposed approach for monocular depth estimation with the encoder-decoder architecture of lesser complexity, trained with fewer parameters and iterations, performs better than the existing state-of-the-art techniques as the value of the average Root Mean Square Error (RMSE) between the predicted and ground-truth depths, calculated in the evaluation phase of the proposed approach, is 0.505 which is less than the RMSE values obtained in case of all the mentioned monocular depth estimation methods. Furthermore, the depth maps generated by the proposed model have a good quality resolution and have minimal effect on the surrounding conditions like the texture of the walls, illumination effects, etc.
GAN technique for reading QR code embedded in 3D printed object

This paper presents a novel technique for accurately reading QR codes embedded in 3D printed objects using a generative adversarial network (GAN). The QR code is made of material that emits near-infrared fluorescence and is embedded inside the 3D printed object. The object is made of resin, so a bright pattern image can be captured from the outside by irradiating near-infrared light because resin transmits near-infrared light. However, if the pattern is embedded too far below the surface of the object, the captured image of the pattern is blurred, making it difficult to correctly recognize the code. In addition, if the surface of the object is curved or irregularly shaped, reading the code becomes even more difficult. In the experiment, we used this blurred image as a training image and the original clear QR code image as a truth image to train a GAN, and simulated using the proposed method to recognize an irregularly blurred image. The experimental results for the correct embedded code demonstrated that 100% accurate readout is possible under all experimental conditions. We found that when the embedded code is different from the correct code, it is not erroneously recognized as the correct code under all other conditions when the GAN is only trained a few times. This indicates that the proposed technique works accurately the GAN is trained for a few epochs.
Design of context-based healthy catering O2O recommendation system

With the improvement of people's living standards, the public has become more and more concerned about healthy diet. A healthy diet is the first line of defense for human health. The existing recommendation services on catering mostly consider personal preference and usually ignore the people's demand for healthy diet. To solve such problem, we consider the healthy diet recommendation system with balanced healthiness and personalization, where the online and offline interactions of food and beverage are more contextualized and diversified, and the personalized needs of users are context sensitive. In particular, we study the process and system implementation of healthy diet recommendation, and consider the actual user needs of healthy diet recommendation system. By integrating two algorithms of knowledge filtering and collaborative filtering by process, we design a modular system architecture framework and realize the acquisition of contextual information and ontology module with rule-based inference.
Researches on slope steering characteristics of tracked vehicle

The hydraulic-mechanical differential steering enables the tracked vehicle to traverse the complex terrain environment. Therefore, this paper analyzes the principle of the mechanical differential steering of the tracked vehicle, establishes the mathematical model of the variable pump quantitative motor, then the dynamic model of the slope center steering is analyzed. The variation law of required traction force and azimuth angle under different braking forces and slope inclinations is analyzed to determine whether stable steering can be achieved based on MATLAB/Simulink.
A study on the estimation of global horizontal irradiance via deep learning technique

For grid-connected photovoltaic (PV) systems, accurate solar irradiance forecasting is crucial, especially in cases of intermittent environmental conditions, in order to ensure grid operation, scheduling, and grid energy management. Solar irradiance, temperature, and other meteorological factors significantly influence PV generation and also make it intermittent in nature. Time series analysis provides an efficient way to map temporal patterns, like hourly, monthly, yearly, and seasonal variations, and within solar irradiance data. In this paper, time series forecasting of 24hourly future observations of global horizontal irradiance (GHI) is done using a multilayer perceptron (MLP) of deep learning framework. Different hyperparameters like hidden layers, hidden units of each layer, activation function, optimizer, and lag observations/steps are tuned to develop the best model that fits the GHI historical data. Further dropout regularization is used to avoid overfitting the model. The model is validated using validation data that the model has never seen before. RMSE, MSE, MAE, and NRMSE errors, aiming at minimizing forecasting errors on test data, are used to select the optimal value of hyperparameters.
A Study on Main Causes of Ripple Effect of Supply Chain Disruption Risk Using AHP Method

This study aims to analyze the main causes of ripple effect and their degree of risk propagation under supply chain disruption. To achieve the purpose of this study, we derived the priority of main causes for managing ripple effect focusing on domestic manufacturing companies and logistics & distribution companies using AHP method. The main results of this study are as follows. As a result of multiplying the weights of the 1st and 2nd layers, it was confirmed that the structural vulnerability factor of the supply chain had the highest weight value. In addition, the results of group comparison by the type of industry, business size, and manufacturing category are as follows. First, the uncertainty of suppliers and buyers was found to be important for logistics and distribution companies to manage the ripple effect. Second, while large enterprises evaluated the sub-factors of supply chain structural vulnerability as the most important, SMEs(Small and Medium-sized Enterprises) were identified to require more careful management of complexity and dependence on suppliers and buyers. Third, unlike consumer goods manufacturing, the three major factors of assembly and processing manufacturing are all related to supply chain structural vulnerability. This study provides academic implications by identifying the factors of the ripple effect and quantifying the degree of risk propagation. In addition, it is proposed to effectively manage the main causes of ripple effects according to industry type, business size, and characteristics of each company.
Incorporating a New Pseudorandom Number Generator in AES Algorithm to Improve Its Security Level

The implementation of the multiplicative inverses of elemental polynomials under an irreducible polynomial over GF(pn) played an important role in cryptography. In the AES algorithm, the multiplicative inverses under the first irreducible polynomial over GF(28) have been used for the first time in 1999 to design its substitution box. The new PRNG RC4-MI in which the multiplicative inverses under the two irreducible polynomials over GF(73) are being used accepts the AES key as its own key and generates random bytes. Undertaking exclusive OR operation of RC4-MI bytes with AES cipher bytes, one gets a new sequence of cipher bytes thatexhibits randomness quantitatively better than that of the original sequence of AES cipher bytes.
Research and Implementation of Importing Distributed Cluster Data into Cloud Platform Based on JMS

This project researches and implements the problem of concurrent data repeated submission during the integration of distributed cluster system to a cloud platform. In this paper, three different solutions are proposed. Through analysis and comparison, the JMS service provided by Weblogic, which has an automatic failover function, is selected as the final solution to prevent concurrent duplicate data submission. And this solution only takes little code change effort on the existing system. When different users select duplicate records for concurrent submission, the message queue function of the Weblogic JMS service is used to record the records to be submitted. At the same time, the duplicate records are removed to avoid the problem of concurrent duplicate submission. Finally, the records can be imported in to the corresponding cloud platform through WebService technology.
KerbNet: A QoE-aware Kernel-Based Backdoor Attack Framework

Deep neural networks are vulnerable to backdoor attacks, where a specially-designed trigger will lead to misclassification of any benign samples. However, existing backdoor attacks usually impose conspicuous patch triggers on images, which are easily detected by humans and defense algorithms. Existing works on invisible triggers, however, either have reduced attack success rate or yield detectable patterns to visual inspections. In this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KerbNet</small> , a kernel-based backdoor attack framework, which applies kernel operations to clean samples as the trigger to incur misclassification. The kernel-processed samples achieve a high attack success rate while appearing natural with high Quality-of-Experience (QoE). We carefully design the kernel trigger generation algorithm by exploiting the neural network structure to propagate the influence of the trigger to the target misclassification label under the QoE constraint. We conduct extensive experiments on five datasets, i.e., MNIST, GTSRB, CIFAR-10, CelebA, and ImageNette to evaluate the effectiveness and practicality of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KerbNet</small> under the impact of various factors, including neuron-residing layer, kernel size, base image, loss function, model structure, and so on. We also show that our proposed attacks can evade state-of-the-art defense strategies and visual inspections. Code will be available after publication.
Demo: Real-Time WebXR Edge-based Object Detection for AR

Web-based extended reality (WebXR) can enable lightweight, easy-to-access, and cross-platform augmented reality (AR) experiences. Context awareness is one key feature of AR. Supporting this in browser-based WebXR applications is challenging as typical object detection algorithms are too computationally demanding to be run in-browser, leading to slow response times and decreased battery life. In this demo, we show a WebXR AR application that uses a technique of WebRTC-based video streaming to obtain a usable video stream on an edge server to perform object detection.
Development of E-Module Based on Problem Based Learning Assisted with Scratch Applications to Improve Students Computational Thinking Skills

This study aims to develop a valid, practical, and effective problem-based learning e-module to improve the computational thinking skills of fifth-grade elementary school students. The research method used was Research and Development, using the ADDIE development model. The research instruments used were validation questionnaires, practicality questionnaires for students and teachers, and pretest and posttest question sheets to measure students' abilities. While the data analysis technique to test the validity and practicality using a Likert scale, while the product effectiveness test was carried out using the Pretest-Posttest Control Group Design and analyzed using SPSS. The results of the research showed that the validity results based on lesson plans obtained a value of 94.44% (very valid), material expert validators 90.48% (very valid), linguists 92.86% (very valid), and media experts 89.6% (very valid). The practical results can be seen from the student response of 90.42% or very practical, and the teacher's response of 93,52 % or very practical. At the same time, the effectiveness test from the results of the pretest and posttest obtained that count&gt; table, namely 15,499 &gt; 1,711 and Sig (2-tailed) 0,000 &lt;0.05, so it can be concluded that there is an average difference in students' computational thinking abilities from the results of the pretest to the results of the posttest in the experimental class. Furthermore, the effectiveness test based on N-gain was obtained at 60.6203 or 60.6%, including the quite effective category. So, the development of PBL-based e-modules assisted by scratch applications to improve the computational thinking skills of Grade V elementary school students is declared valid, practical, and effective.
Comparison of the TF-IDF Method with the Count Vectorizer to Classify Hate Speech

Hate speech is a form of expression used to spread hatred and commit acts of violence and discrimination against a person or group of people for various reasons. Cases of hate speech are very common in social media, one of which is Twitter. The goal to be achieved is to create a system that can classify a tweet on Twitter into hate speech (HS) or non-hate speech (NONHS) classes. The method used is Support Vector Machine by comparing the features of TF-IDF and Count Vectorizer. And the parameters compared are seen from accuracy, precision, recall, and f1-score. Results obtained, overall, by using the TF-IDF feature, the Support Vector Machine algorithm gets high results compared to the Count Vectorizer feature, with an accuracy value of 88.77%, 87.45% precision, 88.77% recall, and f1-score of 87.81%.
Quantum computing for manufacturing and supply chain optimization: enhancing efficiency, reducing costs, and improving product quality

The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.
Issues and Challenges When Metaverse Replaces the Workplace

This study aims to develop a conceptual framework that describes the essentials needed when an organization uses the metaverse as an alternative to virtual offices. Recent discussion in the existing literature widely concludes that hybrid working offers high productivity, wellbeing, and employee mental health. However, not all work types can be done through metaverse, while certain parts of work might be finished in the metaverse. Another discussion also offers that the metaverse as a place for leisure rather than work. Drawing from a review of the current literature and interviews with three senior leaders, we provide detailed insight ranging from essential needs to strategy for maximizing the metaverse as a virtual office, an action list to be taken by the top management team, and modifications to organizational policies and practices that can be considered for implementation. The specific outcome to be targeted in this research is to understand the opportunities and challenges of the future of work and workplace.
Online Fake Review Detection Based on Machine Learning Techniques

In E-Commerce client’s reviews can assume a huge part in deciding income of an association. As the vast majority of individuals require review about an item prior to spending their cash on that item. So individuals went over different audits in the site however these surveys are genuine or counterfeit isn’t distinguished by the user .In survey sites some great review are added by the item organization individuals itself to create bogus positive item review. They give great surveys for some, various items fabricated by their own firm. Client will not ready to see if the survey is genuine or counterfeit. The suggestion motor creates benefits dependent on client profiles and previous authentic studying movement for clients who have as of late joined the framework and unequivocally permitted web history. Consolidate the data separating strategy with the client profiles gained from the present community sifting procedure to give customized audit proposals. The proposed concentrate on utilizes a mixture AI framework to suggest web surveys. The framework first works utilizing Natural Language Processing (NLP) to extricate elements and train the module. The technique might direct investigations dependent on the client’s very own set of experiences. We recommend an item viewpoint surveys system in this paper, featuring fundamental components of items to expand the convenience of the various assessments. For example, given an item’s client reviews, we utilize a feeling classifier to identify item attributes and decide shopper assessments on these components. Then, at that point, utilizing a concurrent thought of viewpoint recurrence and the impact of client surveys given to every perspective over their unfit feelings, we foster an angle positioning calculation to deduce the importance of perspectives. We then, at that point, gauge these variables to get the item’s general grade. The proposed outfit model beats a few current methodologies, thus giving a clever answer for handle information lop-sidedness and component pruning troubles in the space of phony survey ID.
Using Virtual Reality to Foster Creativity for Co-design a New Self-Expression and Relaxation Virtual Environment for Students

Virtual Reality (VR) may improve mental health through immersive self-expression and relaxing environments. However, co-design for these outcomes is challenging due to participants' limited experience with VR and unfamiliarity with self-expression and relaxation techniques. In this paper, we proposed VRelax, a virtual environment that will be designed to support stress relief through self-expression. We are using OpenBrush as a technology proof to provide participants with a self-expression VR experience before co-design sessions. Smartwatches and inertial sensors are used to collect heart rate and accelerometer data to evaluate participants' stress levels during VR experience. We hypothesize self-expressive VR experience will foster participants’ creativity in designing relaxation environments, and the multimodal data collected could identify which parts of the VR experience are more relaxing or stressful. Our findings will have implications to improve the co-design sessions by using VR as a technology proof and improve the design of VR experiences for relaxation.
Guess the Gesture: Uncovering an Intuitive Gesture-based User Interface for 3D Content Interaction in Virtual Reality

This paper presents a novel approach to co-designing an immersive virtual reality (VR) experience where users can participate in a guessability process for a full gesture-based user interface for 3D content interactions using hand controllers. The proposed VR system includes animated visual prompts to allow users to create their own gestures for 13 different object interactions. A pilot study with two participants demonstrated that the visual prompts helped users understand expected interactions and enhanced their creativity in gesture design while maintaining an enjoyable user experience. Hand motion data and user feedback were collected and will be analyzed to provide quantitative insights into gesture efficiency and economy. This paper contributes to developing an intuitive and interactive gesture-based interface for VR and sets the foundation for future studies with more participants to gather additional gesture design ideas.
Bibliophilism of the Nobility as an Element of the Cultural Environment of the Central Chernozem Region in the First Half of the 19th Century

Introduction. The article examines the issue of the development of book collecting as a special feature that is formed in the provincial Russian reader in the first half of the XIX century. An important part of the formation of reading culture in the provincial environment was the gradual transition from traditional values to new behavioral and spiritual practices. Materials and methods. An important part of the formation of the reading culture in the provincial environment was the gradual transition from traditional values to new behavioral and spiritual practices. Historical experience shows that in Russia, as in European countries, reading is an attribute of the life of modernized groups of the population. The dominant social structures in established societies, based on ingrained patterns of behavior, were closely associated with oral communication. And only in modernized communities does the need for the printed word appear as a universal source of information transmission. Results. Drawn into the processes of social and economic modernization ofRussia in the XVIII century, the nobility intensively assimilated European culture, including its information transmitter – the works of foreign writers. As a result, by the end of the century, reading becomes an important component of the lifestyle of the upper class. At the same time, the development of foreign literature contributed to the transfer of Western socio-economic and cultural ideas to the Russian soil, catalyzed theprocess of intellectual "Europeanization" of the upper class. Conclusion. The prevalence of Frenchlanguage literature during this period was characteristic of the majority of the nobility and testified to the active involvement of the upper class in the process of "gallomania". The high level of education of the owners allowed them to have rich collections of literature in foreign languages, expanding the horizons of bibliophiles. The main role here was played by the works of French enlighteners, who formed the freethinking views of landowners, educating the ideas of freedom and equality.