Reviewerly/exHarmony huggingface dataset:
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion

High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared without privacy protection. In this paper, we propose a novel differentially private data releasing method called Differentially Private Data Publishing with Gaussian Optimized Model Inversion (DPGOMI) to address this issue. Our approach involves mapping private data to the latent space using a public generator, followed by a lower-dimensional DP-GAN with better convergence properties. We evaluate the performance of DPGOMI on standard datasets CIFAR10 and SVHN, as well as on a facial landmark dataset for Autism screening. Our results show that DPGOMI outperforms the standard DP-GAN method in terms of Inception Score, Fréchet Inception Distance, and classification performance, while providing the same level of privacy. Our proposed approach offers a promising solution for protecting sensitive data in GAN training while maintaining high-quality results.
Tracking Aided Drone Bird Classification Using YOLO and LSTM

In this work, we propose a system that uses thermal imaging and deep learning techniques to detect and classify objects in real-time. We use thermal images provided in the ICIP challenge dataset, which are then fed into a (You Look Only Once) YOLOv5 object detection model to detect and locate objects in the scene. The detected objects are then tracked using a centroid tracking algorithm to generate trajectories of the objects. We then use the trajectory data to train a Long Short-Term Memory (LSTM) model for object classification based on the object's movement pattern. We demonstrate the effectiveness of our system on a dataset of thermal images captured in a variety of scenarios. The results show that our system can accurately detect and classify objects in real-time, even in challenging conditions such as low-light environments.We have also used ResNet-18 model to identify the different types of distortions in the dataset. Our approach has potential applications in surveillance, security, and search and rescue operations.
Quantitative interpretations of potential fields: from parametric to geostructural recalculations

The relevance. Need to development of an applied algorithm for combined interpretation of heterogeneous geophysical data based on unified physical analogy, focused on solving the inverse problem of potential theory. The wave approximation of the cross-section of mining massif fits into the paradigm of dissipative structuring of nonequilibrium media acts as a special case of the above analogy. The task of the study is to adapt this algorithm to recalculations of both potential and non-potential geofields. In the list of nonpotential geofields the remote sensing data and digital terrain model are the most interesting as the data with non-regulated access. Practical significance of the research is associated with the approbation of a family of algorithms in production projects of both geological and engineering nature. The aim. To minimize the family of equivalent solutions to the inverse problem by mutual verification of heterogeneous models of the anomaly-forming source. The objects. Development of the geofields combined interpretation algorithm and recalculation of geofields in depth for tracing elements of stratification and geological fractures. The methods. Formed by software and algorithmic development, which is based on deterministic analytical representations: computation of Cauchy integral, iterative application of direct solution, spectrum estimation. The results. Reduced to the set of parametric cross-sections derived with recalculations with minimum a priori geological information with final geological consideration. The combination of visualizations of analytical continuation, singular points and results of automated fitting admits the reconstruction of geostructural model of parametric cross-section with significant reliability. Extension of standard methods of quantitative interpretation by means of wave approximations includes quasi-seismic halftone visualizations verifying the mentioned reconstructions.
Deep Learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping

Automatic speaker recognition (ASR) plays the major role in many applications including forensics, dictionary learning, voice verification, biometric systems, and so on. The performance of these application depends on efficiency of ASR system. However, the conventional ASR systems were developed using standard machine learning algorithms, which resulted in low recognition performance. Therefore, this work is focused on development of deep learning-based ASR system. Initially, voice features are extracted using Mel-frequency cepstral coefficients (MFCC), which analyzed the spectral properties of various voice samples. Then, self-organized feature map (SOFM) is applied to reduce the number of available features, which selects the best features using Euclidian similarity between features. Further, deep learning convolutional neural network (DLCNN) model is used to train the features and forms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASR-Net system resulted in superior recognition performance as compared to conventional systems.
Humanoid robot control system based on human pose detection

With the rapid progress of science and technology, an increasing number of robots are used in society. Compared with multi-legged crawling robots, humanoid robots have gained more flexibility and reliability with a structure similar to the human body, and have been widely used. Therefore, the proper pose control of the humanoid robot is particularly critical. This paper proposes and designs a humanoid robot control system based on human pose detection. The system detects the posture image of the human body by using the OpenPose method, and then uses the space vector method to calculate the rotation angle information of the human body joints. After being processed, the information is sent to the steering gear controller by the Raspberry Pi. After the controller solves and processes the signal, the pose of the robot is controlled by driving the steering gear
A path planning for cultural tourism service robot combining improved A* algorithm and dynamic window approach

To meet the needs of the guidance of algorithm search in a complex environment, the optimality of global path in a static environment, and the security of real-time obstacle avoidance in a dynamic environment for path planning of cultural and tourism service robots, an algorithm based on the fusion of improved A * algorithm and Dynamic Window Approach is proposed. Firstly, based on the traditional A * algorithm, evaluation function are improved to improve the algorithm's search directionality to a certain extent; Secondly, the concept of safe distance is introduced, and a cubic broken line optimization method is proposed, which eliminates redundant nodes and inflection points, and only retains necessary key path points, greatly reducing the number of inflection points and improving the smoothness of the path. Subsequently, a dynamic obstacle vertical distance cost function was added to the evaluation function of the dynamic window approach to effectively reduce conflicts and collision risks between robots and dynamic obstacles. Finally, the improved A * algorithm is integrated with the dynamic window approach, selecting critical path points as temporary target points for the dynamic window approach. The dynamic window approach is used in segments for local real-time path correction, ensuring the optimal global path and avoiding collisions with unknown obstacles, ultimately achieving a safe and fast destination.
Real-time gigahertz free-space quantum key distribution within an emulated satellite overpass

Satellite quantum key distribution (SatQKD) intermediated by a trusted satellite in a low-Earth orbit to ground stations along the satellite’s path allows remote users to connect securely. To establish a secure connection, a SatQKD session must be conducted to each user over a dynamically changing free-space link, all within just a few hundred seconds. Because of the short time and large losses under which the QKD protocol will be implemented, it has not yet been possible to form a complete key by transmitting all the relevant information required within a single overpass of the satellite. Here, we demonstrate a real-time QKD system that is capable of forming a 4.58-megabit secure key between two nodes within an emulated satellite overpass. We anticipate that our system will set the stage for practical implementations of intercontinental quantum secure communications that can operate over large networks of nodes and enable the secure transmission of data globally.
Low-Resource Indic Languages Translation Using Multilingual Approaches

Machine translation is effective in the presence of a substantial parallel corpus. In a multilingual country like India, with diverse linguistic origins and scripts, the vast majority of languages need more resources to produce high-quality translation models. Multilingual neural machine translation (MNMT) has the advantage of being scalable across multiple languages and improving low-resource languages via knowledge transfer. In this work, we investigate MNMT for low-resource Indic languages—Hindi, Bengali, Assamese, Manipuri, and Mizo. With the recent success of massively multilingual pre-trained models for low-resource languages, we explore the effectiveness of using multilingual pre-trained transformers—mBART and mT5 on several Indic languages. We perform fine-tuning on the pre-trained models in a one-to-many and many-to-one approach. We compare the performance of multilingual pre-trained models with multiway multilingual translation trained from scratch using a one-to-many and many-to-one approach.
Penerapan Elementor sebagai Perangkat Pengembangan Platform Pengawalan Rancangan Kerja PT. PVI

In the pursuit of optimizing information systems and website development for PT. PVI (Renstra), an extensive analysis was conducted on the use of CMS WordPress supplemented with the Elementor add-on as the primary tool for website construction. This analysis sought to assess the feasibility and capabilities of Elementor basic in meeting Renstra's requirements. Various critical aspects were evaluated, including account creation, login and logout systems, as well as form display and integration. Elementor basic, while providing the convenience of a user-friendly drag-and-drop design interface, falls short of fully satisfying Renstra's needs. A significant challenge encountered was Elementor basic's inability to create a seamless account creation system integrated with login and logout features. This posed a hindrance for website users seeking to input data, as they were unable to smoothly implement login functionalities within Elementor basic. To address these challenges, several recommendations are proposed. Firstly, Renstra should consider upgrading to Elementor Pro, which offers an array of comprehensive features, including streamlined form integration and efficient account creation. Secondly, designing an efficient login system within Elementor Pro to enable users to effortlessly create accounts, log in, and access their work progress is paramount. Thirdly, meticulous attention should be given to integrating form submission and the Progress Bar to enable users to submit data effortlessly and view their progress directly on the website, eliminating the need for manual Progress Bar adjustments. Fourthly, leveraging Elementor Pro's additional design features can significantly enhance the website's overall appearance, ensuring it aligns with user requirements. Finally, Renstra should explore the development of supplementary features to enrich the user experience, such as enhanced work progress management, progress notifications for registered users, or more restricted access for specific users. Implementing these recommendations will enable PT. PVI to enhance the efficiency and effectiveness of their information system and website. This, in turn, will enable them to provide superior services to their users, enhance transparency in progress reporting, and ultimately achieve their goal of optimizing their corporate information system.
Speech Therapy Supported by AI and Smart Assistants

Speech impairments can be extremely debilitating for individuals in many areas of their lives. Speech therapy is a field that aims to solve these disorders by taking into account multiple factors and following patients over an extended period of time. Technology can represent a powerful support system for people affected by these impairments; more specifically, Artificial intelligence (AI) can come in handy when it comes to monitoring therapies and helping children perform daily exercises to improve their condition. This research work aims at illustrating how a smart voice assistant, Amazon Alexa, and a web application called “e-SpeechT” can seamlessly work together to support every phase of speech therapy. In particular, it explores how the AI algorithms that characterize these systems can improve the overall interaction paradigm and their medical feasibility.
THE IMPACTS OF THE BRAZILIAN CRYPTOASSETS LAW IN NFT SOLUTIONS

This article aims to establish the applicability of Law No. 14,478, enacted on December 21, 2022 (hereafter referred to as the Brazilian Cryptoassets Law), to the trading of non-fungible tokens (NFTs) that utilize Blockchain and Smart Contract technologies. The article covers (i) the use of Smart Contracts and Blockchain in relation to NFTs, (ii) the current state-of-the-art NFT solutions, and (iii) the key articles and legal obligations outlined in the Brazilian Cryptoassets Law. It's important to note that the Brazilian Cryptoassets Law applies differently to various NFT solutions depending on their intended use, which is the distinguishing factor in determining whether the law is applicable to a particular NFT transaction.
An Improved Sampling Dijkstra Approach for Robot Navigation and Path Planning

The task of path planning is extremely investigated in mobile robotics to determine a suitable path for the robot from the source point to the target point. The intended path should satisfy purposes such as collision-free, shortest-path, or power-saving. In the case of a mobile robot, many constraints should be considered during the selection of path planning algorithms such as static or dynamic environment and holonomic or non-holonomic robot. There is a pool of path-planning algorithms in the literature. However, Dijkstra is still one of the effective algorithms due to its simplicity and capabilities to compute single-source shortest-path to every position in the workspace. Researchers propose several versions of the Dijkstra algorithm, especially in mobile robotics. In this paper, we propose an improved approach based on the Dijkstra algorithm with a simple sampling method to sample the workspace to avoid an exhaustive search of the Dijkstra algorithm which consumes time and resources. The goal is to identify the same optimal shortest path resulting from the Dijkstra algorithm with minimum time and number of turns i.e., a smoothed path. The simulation results show that the proposed method improves the Dijkstra algorithm with respect to the running time and the number of turns of the mobile robot and outperforms the RRT algorithm concerning the path length.
Developing Management Information System for Construction Equipment Maintenance

Sudden breakdown of equipment is one of the main factors impacting the delay of the infrastructure project. The key cause for the rapid breakdown of construction equipment is improper equipment maintenance. This essay aims at delivering a solution to monitor the regular maintenance of equipment and its replacement parts, by introducing a management information system. The data on maintenance schedule for different locally utilized equipment is acquired from dealers and construction equipment showroom. The data collection process is interrupted by the employment of a graphical user interface to frame the software architectural design. The proposed software architecture has the potential to be developed into an application in the future, allowing for the efficient maintenance of construction equipment as well as the mitigation of construction delays caused by an equipment failure on the construction site.
A Survey on the Satisfaction of 5th and 6th Grade Students in P Primary School and T School in Huaihua City

Puplis; Rural primary school; The satisfaction of a need
A profile of hematological malignancies diagnosed in a tertiary care center of Assam: Highlighting the experience from a resource constraint perspective

Background: Hematological disorders require a systematic approach, from clinical examination to a complete blood count, followed by a bone marrow study for diagnosis. To effectively plan, treat, and prognosticate, immunophenotyping and molecular studies are essential to incorporate. Aims and Objectives: The aim of our study is to understand the pattern of neoplastic hematological conditions encountered in Assam and share our experience during the diagnosis. Materials and Methods: This was a single-centered, observational study carried out for 18 months (January 2022–June 2023). A thorough hematological workout was done, including cytochemical staining and conventional polymerase chain reaction for the BCR-ABL transcript, after satisfying inclusion and exclusion criteria. The data were analyzed and correlated before arriving at the final diagnosis. Results: The total of 38 neoplastic cases confirmed by bone marrow study. Out of 38 cases, 14 were chronic myeloid leukemia, 10 were chronic, 4 were in the blastic phase, and BCR-ABL was positive in 13 cases. Two cases were pediatric, of which 1 was in the blastic phase. All 8 cases of plasma cell dyscrasia presented with anemia, bone lesions, and hypercalcemia. The acute leukemia category consisted of 4 acute myeloid leukemia cases, of which 1 was acute promyelocytic leukemia. A total of 10 acute lymphoblastic leukemia (ALL) cases, where 1 case was T-ALL and the rest was B-ALL. Two cases showed a subleukemic presentation in peripheral blood. Conclusion: The experience of pathologists and their meticulous approach can greatly aid in accurate diagnosis, but the immunophenotyping facility and molecular hematology setup can provide most of the relevant information for the best decision-making.
Multimodal Linguistic Landscape in Lombok: A Case Study of Product Branding

A study of writing a language in a public area is known as the linguistic landscape. This research aims to map language choices (monolingual, bilingual, and multilingual) on food products in Lombok and analyze the interconnections between the implementation of national, regional, and foreign language policies on food products in Lombok. The multimodal approach can describe more about the language landscape, including the meaning and function of public writing. This multimodal concept views signs as a form of visual communication that is definite and non-arbitrary. Micro, small, and medium enterprises (MSMEs) are explored as a case study in creative linguistic marketing about local interactions and global forces on the linguistic landscape. These MSMEs use various semiotic sources of regional languages, foreign languages, local wisdom, and inclusive spirit as an introduction and production process. Through linguistic analysis of landscapes, researchers examine systems of signs, images, and texts within a semiotic framework. The uniqueness of this study is what makes it unique to researchers because it has rich local meaning in context. This research describes not only MSMEs but also semiotic landscapes that contribute to understanding changes in sociolinguistic patterns and creativity in the branding of MSME products in the Lombok region.&#x0D;
Effect of Social Media on Performance of SMEs: Empirical Evidence from United Arab Emirates

This chapter discusses the mediating effects of national culture on the relationship between social media marketing tools and SMEs’ performance in Abu Dhabi UAE. The characteristics of social media marketing such as interactive, fast and cheap make it more attractive to most companies to use social media as their marketing channel. The study specifically looks at how social media platforms like Facebook, WhatsApp, Twitter, and email affect the operations of SMEs in the United Arab Emirates. The research employed a quantitative approach and used survey questionnaires to collect the data from the respondents among Abu Dhabi SMEs managers and owners. SPSS and AMOS were used to analyze the data. The data revealed that the direct relationships between Facebook, WhatsApp, Twitter and Email and SME Performance. Also, the results confirmed that national mediate the relationship between Facebook, WhatsApp, Twitter and Email and SME Performance. Thus, it is hoped that the proposed research model improves the UAE SMEs performance.
Python Essential Commands and Functions

In this chapter, you will learn the essential Python commands and functions you will need to produce the illustrations shown in this book. You will learn how to use Python’s basic plotting functions, set up a plotting area, create a set of two-dimensional coordinate axes, and use basic plotting primitives (the dot, the line, and the arrow), which are the building blocks you will use to construct images throughout this book. In Chapter 2 , you will learn how to use these primitives to build two-dimensional images and then translate and rotate them. In Chapter 3 , you will extend these concepts to three dimensions. You will also learn about colors, how to apply text to your plots, including the use of Latex commands, and the use of lists and arrays. By the last chapter, you will be able to create images such as those of Saturn in Figures 1-1a and 1-1b. These were created by Program SATURN in Chapter 10 using different input angles that describe the orientation of Saturn relative to the Sun.
Demonstration: The Sun

This chapter demonstrates how you can use Python to plot and label curves that represent mathematical functions. Our focus of interest will be the Sun. We will plot Max Planck’s spectrum of radiation, which will be used to represent the energy spectrum emitted by the Sun. Then we will calculate the Sun’s total power output and the amount that reaches Earth, which is called the solar constant. The scientific aspects of this section are quite interesting, as is the history of their development. The major benefit from a Python programming aspect is seeing how Python can be used to set up plots, perform numerical integration, display numerical data, and put labels and notes on the plots. Plus you will learn some interesting facts about solar physics. Much of this chapter relates to photons, which were discussed in Chapter 11. All illustrations in this chapter were produced using Python.