Reviewerly/exHarmony huggingface dataset:
Java Based Object Recognition Application for Visually Impaired

It was difficult to detect items in real time and turn them into an auditory output. Many real-time object identification apps have been made possible by recent developments in computer vision. This essay explains a basic Android application. that would make it easier for those who are blind to comprehend their surroundings. Data about the surrounding area was recorded using the phone's camera. where the object detection API of Tensor flow was used to perform real-time object recognition. Android's text-to-speech feature was then used to turn the objects that were recognized into an audio output. Complex algorithm offline processing is now easy thanks to Tensor flow lite. It was discovered that the suggested system's overall accuracy was close to 90%. Keywords- computer vision, machine learning, TensorFlow, TensorFlow Lite, object acknowledgment, and Android.
InstaBlox: An Instagram Automation Tool by using a Selenium Webdriver

Instagram is a popular social media platform where users can share their photos and videos with their followers. With the rise of automation, many users are seeking ways to streamline their Instagram activities, including posting pictures, commenting, liking photos, and managing their followers. Selenium is widely used for automating web tasks, while Python is a popular choice for programming automation. With Selenium, we can automate various actions on Instagram, such as logging in, navigating to user profiles, posting photos, leaving comments, liking posts, and following or unfollowing other users. Before we can start automating Instagram with Selenium, we need to install Selenium and a compatible web driver like Chrome Driver or Gecko Driver. By identifying and interacting with the HTML elements on the site, Selenium allows us to automate our interactions with Instagram. In a similar vein, we have the ability to automate various Instagram actions by utilizing Selenium. For instance, we can employ Selenium to discover and engage with different elements on the platform. This involves locating the file selection input element and utilizing the transmit keys() function to input the file path and simulate clicking the upload button. Keywords: – Instagram Automation, selenium, python, account analytics, MERN stack, Account management system.
Quantization Effects on a Convolutional Layer of a Deep Neural Network

Over the last few years, we have witnessed a relentless improvement in the field of computer vision and deep neural networks. In a deep neural network, convolution operation is the load bearer as it performs feature extraction and dimensionality reduction on a large scale. As the models continue to go deeper and bulkier for better efficiency and accuracy there is a rapid increment in storage requirements too. The problem arises when performing computation with efficient numerical representations for embedded devices. Transitioning from floating-point representation to fixed-point could potentially reduce computation time, storage requirements, and latency with some accuracy loss. In this paper, an analysis of the effects of quantization of the first convolutional layer on the accuracy, and memory storage requirement with varying bit-width for fixed-point integer values of network parameters has been carried out. The approach adopted is post-training quantization with a mixed-precision format to avoid model re-training and minimize accuracy loss by using root-mean-square-error (RMSE) as a performance metric. Various combination has been analyzed and compared to find the optimal precision to implement on a resource-constraint device. Based on the analysis, the suggested bit-width of I/O data for this implementation is selected as <10,5> and mid-data be <20,10> instead of <16,8> and <32,16> respectively. This combination of bit-widths has reduced memory consumption such as BRAM by 10%, DSPs by 98.6% and FFs by 40.27% with some accuracy loss.
Motion Planning and Navigation of a Dual-Arm Mobile Manipulator in an Obstacle-Ridden Workspace

This paper presents a design of velocity controllers for a 2-link dual-arm mobile manipulator which is required to move from its starting configuration to a final position while targeting to avoid multiple fixed circular obstacles of random sizes and positions, and observing all mechanical singularities which are associated with the system. With the help of Lyapunov-based control scheme (LbCS), nonlinear time-invariant continuous velocity-based control laws are formulated which enable the center of the car-like mobile structure to converge to a predetermined target position and the links attain a final orientation. The method also guarantees stability associated with the system proving the use of direct method of Lyapunov. The computer simulations illustrate the effectiveness of the proposed technique.
Machine Learning Robustness in Predictive Maintenance Under Adversarial Attacks

Predictive maintenance (PdM) techniques can increase industrial productivity and reduce maintenance costs by predicting the remaining useful life (RUL) of complicated machines. However, PdM systems involve industrial internet of things (IIoT) devices and machine learning (ML) algorithms, which are prone to adversarial attacks. In this work, first PdM is developed based on four ML classification models: Random Forest (RF), Light Gradient-Boosting Machine (LGBM), Gaussian Naive Bayes (GNB), and Adaptive Boosting (AdaBoost) classifier. Second, the robustness of the ML models under three adversarial attacks is evaluated, using the NASA turbofan engine dataset: Zeroth Order Optimization (ZOO), Universal Adversarial attack, and HopSkipJump attack. Results indicate RF as the most efficient classifier, reaching 96.35% of classification accuracy. Moreover, RF is proven to be the most robust of the examined classifiers under the considered attacks, displaying comparative resilience of up to 83.58% higher, compared to other models.
Developing Information Gateway for Intelligent Decision-Making and Stability in Solar Power Generation

Abstract This study has developed a specialized information gateway for inverters, utilizing relays, data converters, and single-board computers, among other components. Upon receiving data from the gateway, the server processes it to generate an intelligent solar monitoring system. The platform utilizes deep learning RNN and LSTM algorithms to forecast power generation at solar power plants, allowing for real-time monitoring of weather and power generation. By comparing actual and expected power generation data, the system can adjust equipment maintenance and cleaning schedules. It is designed to automatically alert staff members to take appropriate action when anomalous power generation data is continuously transmitted. Additionally, the system sends an alert for on-site inspection and removal of any abnormal situations to increase the stability of solar power generation. This study employs deep learning and IoT data collection to provide the knowledge necessary for intelligent decision-making and increased stability in solar power generation. JEL classification numbers: C43, F68, H41. Keywords: Solar energy, IoT monitoring, RNN, LSTN, Abnormal return.
Vulnerability Detection via Typestate-Guided Code Representation Learning

Machine learning, including deep learning, has found success in various domains. Recently, the focus has shifted to using deep learning, like graph neural networks, for static vulnerability detection. Existing methods represent code as an embedding vector and train models on safe and vulnerable code patterns to predict vulnerabilities. However, these models lack precise bug detection, as they prioritize coarse-grained classification over understanding vulnerability semantics, such as typestate properties. This paper introduces an innovative typestate-guided code embedding technique for accurate static vulnerability detection. We select and retain feasible typestate sequences extracted from typestate analysis using self-supervised contrastive learning in a pretrained path embedding model. This reduces the need for labeled data in training downstream models for vulnerability detection. Evaluation on real-world projects showcases our approach’s superiority over recent learning-based approaches. It outperforms them by substantial margins across various metrics like precision, recall and F1 Score.
Exploring the Reasons behind Modest Economic Performance

Living standards in Benin have remained low over the last decades because of sustained demographic pressures and the absence of a growth-enhancing structural transformation. Its two leading sectors have not been engines of sustained growth. The cotton sector has been constrained by institutional instability and political interference. Cross-border trade with Nigeria has nurtured informality but also corruption, tax evasion, and political capture. Adjustment to the decline of the agricultural sector has been passive, its labour migrating to informal activities and low-productivity sectors. Capital deepening is absent, and misallocation of resources has resulted in efficiency losses and slow technological change. Weaknesses in domestic resource mobilisation and inefficiencies in expenditure management have prevented the government from addressing key challenges of economic and social development, like providing good-quality physical infrastructure, significantly improving the performance of the education and healthcare systems, or fighting persistent poverty and inequality. Benin needs to reorient its development strategy and deal with its specific institutional weaknesses.
Penguatan Digitalisasi Layanan Dokumen Kependudukan Melalui PLAVON Kabupaten Sidoarjo

Technological advances force every government to digitize its activities, especially in the aspect of public services. Public service is a matter that describes the government of a region. Population documents that must be owned by every community need to be properly provided by the local government. The Sidoarjo Regency Government strengthens population service by using a digital portal named Plavon. The Department of Population and Civil Registration of Sidoarjo Regency continues to innovate and develop the portal properly and is able to provide services. However, the adaptation process sometimes does not work properly due to a lack of understanding and outreach to the community about new service models. The research method used is descriptive qualitative, with data collection techniques carried out by observation, interviews, and documentation. Facilities and infrastructure can be used properly in providing services through the Plavon portal. Service timeliness can also work well. Ease of access runs satisfactorily and is easily understood by users.
Digital Voting Application using Blockchain Technology

Building a secure electronic voting system that offers the fairness and privacy of current voting schemes, while providing the transparency and flexibility offered by electronic systems has been a challenge for a long time. The development of a robust and secure e- voting system is crucial to ensure the integrity and authenticity of electoral processes. In this context, the integration of blockchain technology with face detection and Aadhar card verification presents an innovative solution. This system combines the power of blockchain's immutability and transparency with biometric authentication through face recognition and Aadhar card validation. By leveraging blockchain, the system can maintain a tamper-proof ledger of votes, ensuring their verifiability and integrity. The incorporation of face detection and Aadhar card verification enhances security by ensuring that only eligible voters can participate, thereby reducing the risk of fraudulent voting. This abstract introduces a novel e-voting system that promises to enhance the trust and reliability of the electoral process through blockchain technology, face detection, and Aadhar card authentication. Key Words: Blockchain, Electronic Voting System and Evoting, face recognisition.
Web Scraping and its Applications

In the age of information, the plenitude of data on the World Wide Web has come an inestimable resource for various industries and research domains. This research paper explores the world of web scraping and its different operations in our data-rich period. It delves into the elaboration, tools, and challenges of web scraping, emphasizing its ethical and legal confines. Through real- world case studies, we illustrate its vital part in disciplines similar to e-commerce, finance, social media, healthcare, journalism, and academia. While web scraping unlocks unknown data- driven perceptivity, it prompts conversations on sequestration and data power. This paper concludes by considering arising technologies and the transformative eventuality of web scraping in driving data- driven invention across industries Keywords- web scraping, python, library, Selenium, Data Extraction.
PORPHYRY-COPPER MINERALIZATION OF TAL’NIKOVOYE ORE FIELD (OKHOTSK SEGMENT OF THE OKHOTSK-CHUKOTKA VOLCANOGENIC BELT)

The Talnikovoye ore field where previous academic research and exploration work revealed porphyry copper mineralization is described. Our data show that mineralization is confined to the intrusions of Turonian granodiorite and quartz diorite (91 Ma, U-Pb metohod), as well as the accompanying hydrothermal-explosive breccias. Biotite-epidote-chlorite propylites are widespread within the ore field; in the southern part, phyllic alteration is superimposed on the halo of K-feldspar alteration. The ore occurs in zones of intense quartz, chlorite-epidote-quartz (with chalcopyrite and molybdenite), sulfide-potassium feldspar-quartz (with chalcopyrite and bornite), and sulfide-quartz-sericite (with chalcopyrite) stockwork veining with copper-molybdenum mineralization. They are characterized by moderate concentrations of Cu (0.1–0.3 %, reaching 1.1 %) and Mo (up to 0.1 %), low concentrations of Au (up to 0.1 g/t) and Ag (up to 2.6 g/t) and have a Cu-Mo-(Au, Ag, Pb, Zn, Sb, As) geochemical signature. Fluid inclusion data suggest the formation of mineralized veinlet quartz during the magmatic-hydrothermal transition (430–150 °С) with solutions at high (50 wt % NaCl equiv.), medium and low concentration (5–18.9 wt % NaCl equiv.) involved under the cooling-dilution scenario. Values of the main geochemical indicators, such as Cu/Mo (30–60) and Cu/Au (&gt; 1 × 105) ratios allowed us to assign the Tal’nikovoye ore field to the porphyry copper-molybdenum type characteristic of continental-margin volcano-plutonic belts formed at the mafic island-arc basement. The geochemical parameters of porphyry granitoids indicate their formation in a setting of subduction-to-transform plate boundary transition.
ORE POTENTIAL OF THE LATE MESOZOIC ARIADNENSKY MASSIF OF ULTRAMAFIC, MAFIC AND GRANITOID ROCKS (SIKHOTE-ALIN OROGENIC BELT)

In the south of the Russian Far East, a new promising type of sources of strategic and critically important metals (titanium, gold, platinum, niobium, hafnium, copper, antimony, etc.) has been identified: those are complex ores and placers spatially and genetically related to the late mesozoic (cenomanian) synorogenic intrusions of ultramafic-mafic rocks of the Sikhote-Alin orogenic belt. An example of this is the Ariadnensky massif of ultramafic, mafic and granitoid rocks with manifestations of gold-titanium mineralization in its mafic part, gold-antimony mineralization in the exocontact, and gold-copper mineralization in the near-intrusive zone. The main mineralogical and geochemical features of these ore formations have been determined. The typomorphic properties of native gold from ultramafic rocks have been revealed. It is shown that mantle and crustal processes were involved in the formation of different types of ore. The materials obtained provide new opportunities for the reassessment of the strategic metals resource potential of Primorye.
Study of the Application of VQA Deep Learning Technology to the Operation and Management of Urban Parks† - Analysis of SNS Images -

This research explores the enhancement of park operation and management by analyzing the changing demands of park users. While traditional methods depended on surveys, there has been a recent shift towards utilizing social media data to understand park usage trends. Notably, most research has focused on text data from social media, overlooking the valuable insights from image data. Addressing this gap, our study introduces a novel method of assessing park usage using social media image data and then applies it to actual city park evaluations. A unique image analysis tool, built on Visual Question Answering (VQA) deep learning technology, was developed. This tool revealed specific city park details such as user demographics, behaviors, and locations. Our findings highlight three main points: (1) The VQA-based image analysis tool
PSYCHOLOGICAL FACTORS AFFECTING ENGLISH SPEAKING PERFORMANCE AT ELEVENTH GRADE STUDENTS’ IN SMK AL-INAYAH TEBO

This research is about students who have some problems in speaking English. This research was conducted at the Vocational High School Al-inayah Tebo. The purpose of this study was to determine the factors that influence students’ difficulties in learning English skills. The design of this research used qualitative descriptive method. The subjects of this research were students and teachers of eleventh grade at the Vocational High School Al-inayah Tebo. The research data were collected using observation and interviews. The result showed that students experienced several problems that were influenced by their psychological factors in speaking English, such as : Lack of motivation, Anxiety, Lack of confidence, Shyness, Fear of making mistake, Learning environment Factors, Performance condition factors. From these problems, the most dominant factor was fear of making mistakes. Most of the students stated that they were afraid to speak because they could not pronounce it. Most of them are afraid if they make mistake so they better not speak English.&#x0D;
Methodological recommendations for editors of scientific publishing divisions of academic institutions: experience of the “Akademperiodyka” Publishing House of the NAS of Ukraine

The recommendations are based on the practical experience of experts of the “Akademperiodyka” Publishing House of the NAS of Ukraine. The publication contains theoretical and applied material, the mastery of which will help to improve the quality of subediting and ensure a high culture of academic publications. The recommendations will be an asset to the employees of the publishing divisions of academic institutions and the employees of the scientific periodicals’ editorial offices who participate in the prepublishing of scientific and popular scientific publications and take care of academic-and publishing activities topics.
Privacy-Preserving Data Synthesis via Differentially Private Normalizing Flows with Application to Electronic Health Records Data

Medical data often contain sensitive personal information about individuals, posing significant limitations to it being shared or released for downstream learning and inferential tasks. We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated and released. We apply the technique to an electronic health records dataset containing patients with pulmonary hypertension. We assess the learning and inferential utility of synthetic data by comparing the accuracy of hypertension predictions and the variational posterior distribution of the parameters in a physics-based model. The results suggest that synthetic data generated via NF with DP can yield good utility at a reasonable privacy cost. Our study provides evidence and adds to the growing literature on the feasibility of generating synthetic medical data for sharing or obtaining inferences from medical data using deep generate models with formal privacy guarantees.
RL-HAT: A New Framework for Understanding Human-Agent Teaming

This paper presents a novel framework for human-agent teaming grounded in the principles of Reinforcement Learning (RL). Recognizing the need for a unified language across various disciplines, we utilize RL concepts to provide a standard for the understanding and evaluation of diverse teaming strategies. Our framework extends beyond traditional RL constructs, integrating aspects such as belief states, prior knowledge, social considerations, situational awareness, and mental models. A particular focus is placed on the role of ethics and trust in effective teaming. Additionally, we discuss how sensor data, perception models, and actuator modules can be incorporated, emphasizing the adaptability of our framework to a broad range of tasks and environments. We believe this work forms a substantial contribution to the field of human-agent teaming, establishing a solid foundation for future research and application.
Progressive 3D Reconstruction for Collaborative Construction of Digital Twins

Digital twins are increasingly useful in metaverse-related applications, but their construction process usually involves deep technical expertise and costly resources. This position paper demonstrates an early prototype (see Figure 1) of a web-based digital twin authoring system to enable untrained users to collaboratively build digital twin environments. The system aims to explore the combination of photogrammetry and GAN-based machine learning models to enable near-real-time collaboration between capture client (scanning objects using common smartphone cameras) and editing client (constructing 3D scenes on thin client devices) users.
Intelligent Hotel Guidance System via Face Recognition Technology

In modern large hotels, due to a large number of rooms and complex layouts, it is difficult for customers to find rooms, which increases a lot of workloads for hotel attendants to guide. In this paper, a hotel intelligent guidance system based on face recognition is designed. After entering the customer’s facial photos, the room guidance and customer management are carried out through face recognition. With this, hotels can move toward card-free management, green environmental protection, and save on resources. With these improvements, hotel management will be card-free and green. Each monitoring device of the system adopts dual STM32 core architecture, in which STM32H7 is responsible for face recognition, while STM32L4 is the main control chip, which is responsible for data exchange, guest room guidance and other work. The monitoring master not only guides, but also uploads customer check-in information to the cloud platform to facilitate the management of the hotel. The system adopts contactless information collection and guidance, which improves the intelligence and humanization of the hotel, and has a good application prospect.