Reviewerly/exHarmony huggingface dataset:
Towards Leveraging Artificial Intelligence for NoSQL Data Modeling, Querying and Quality Characterization

In the last two decades, NoSQL databases emerged and formed an umbrella category grouping well over a hundred databases of different characteristics, and providing a new take on scalability, availability, consistency and data modeling, with the aim of conquering the classic one-size-fits-all solution represented in traditional databases. NoSQL databases' heterogeneity, flexibility and high performance allowed them to encompass the volume, velocity and variety challenges brought by the Big Data era and to fulfill the complex requirements of real-time applications. These advantages have given them a competitive edge in the database market. However, despite these added bonuses, a wider adoption of NoSQL has been hindered by a few challenges. The “schema-less” nature of NoSQL databases allowing data to be directly ingested, without defining a schema a priori, can be a deterrent for many users given its potential impact on data management and integrity, application complexity and system integration. Additionally, the heterogeneous nature of NoSQL databases creates a complex and diverse landscape of query languages and interfaces requiring extensive and wide-ranging expertise. In an effort to breach the standardization and democratization of NoSQL databases, I propose an approach integrating artificial intelligence techniques in NoSQL life cycles, namely: data modeling, querying and quality assessment. This approach offers multi-faceted contributions to the literature and the industry, including: a uniform design methodology for NoSQL databases, an intelligent bi-directional mapping between higher and lower level NoSQL schema representations, the generation of logical and physical NoSQL data models from requirements specified in natural language, intelligent and bi-directional generation of a NoSQL query from a prompt expressed in natural language, and a multi-stakeholder NoSQL database-agnostic quality characterization framework. With this approach, I aim to pave the way towards a fully integrated, artificially intelligent system capable of undertaking the processes of data modeling, natural language querying and quality assurance of NoSQL databases. This objective, when achieved, would not only increase the adoption of NoSQL databases by both experts and novices, but would also potentially positively impact the cost and time constraints of NoSQL database handling, as well as their evolvability, maintainability and portability.
Quadcopter Stability Control System Using PID And Kalman Filter

Quadcopter is an Unmanned Aerial Vehicle (UAV) that uses 4 motors arranged crosswise as a propulsion system. The quadcopter's capabilities are supported by a main component called the flight controller or control system. The operation of this unmanned aircraft is controlled automatically through a program run from the GCS (Ground Control Station) so that the quadcopter can fly according to the desired destination. The control system is needed to maintain the balance of the quadcopter while flying and maneuvering to remain stable. PID (Proposional Integral Derivative) method as a counterweight when manoeuvring on the y-axis (pitch) and x-axis (roll) and Kalman Filter as processing the resulting sensor output. The results of this study show that setting the value for the proportional constant (Kp), integral constant (Ki) and derivative constant (Kd) determines the quadcopter can fly or maneuver well. The gain values used are Kp of 0.135, Ki of 0.135 and Kd of 0.003. However, the output generated by the gyroscope in pitch and roll still has noise caused by the vibration of the mover during flight and maneuvering. So the Kalman filter is needed to find out the value that is close to the actual output of the gyroscope.
Environmental Geochemistry of the Surface Sediments of Sabkhat Daryana, NE Libya

The present paper discriminates between nearshore and farshore sabkha sediments at Daryana Coast,NE Libya, using geochemical viewpoints. The nearshore sediments are mostly sand with low amounts ofmud (Al). High contents of heavy metals found in these sediments can be due to their incorporation incarbonate phases. The farshore sediments are mostly sandy mud with higher contents of Al and lowercontents of heavy metals, except higher contents of Pb. The heavy metals are mainly of anthropogenicorigin (EF &amp;gt; 1.5). Generally, Igeo values indicate that the study area is unpolluted with most heavymetals (except with As and Hg).
File Upload and Popup Dialogs

In this chapter, we will show you how to handle file upload and popup dialogs. Most popup dialogs, such as “Choose file to upload,” are native windows rather than separate browser windows. This can be a challenge for testing because Selenium only drives browsers. If one popup window is not handled properly, test execution will halt.
Agro-Based Mobile Apps Adoption among Bangladeshi Farmers at the ‘Hamlet’ of Bangladesh – A Case Study of Chandpura Village of Barisal

Farmers in Bangladesh are gradually adopting and using ICT-based agricultural applications. This study attempted to identify the factors influencing the use of agro-based mobile applications by farmers in Chandpura village of Barisal district. The study examined farmers' access to ICT in terms of smartphone ownership, internet access, their ICT use behaviour for agriculture, and their knowledge and use of agro-based mobile applications. The research was conducted following the principles of qualitative methods in case studies. For data collection and analysis, in-depth interviews, semi-structured questionnaires, and narrative data analysis were utilised. The results showed that farmers were primarily influenced by personal factors such as need, age, education, and satisfaction. The youth were more receptive to innovations because they were more educated and adaptable. In addition, technological factors, such as access to a device and the internet, digital knowledge, ease of use, and a support network, played a significant role in this regard. While the study helped us understand the significance of ICT technology for the continued development of the agricultural sector, market and weather information were neglected in agro apps for small villages where disease, market, and weather information took precedence. Before implementing any technology, it is crucial to conduct research on the population and their specific needs. The implication is that the study helped us comprehend the significance of ICT technology for the advancement of the agriculture sector, even in small villages, and how implementing an ICT-based "agricultural advisory service" supported and benefited farmers. Keywords: Agro-based mobile apps, farmers, agriculture, information and communication technology (ICT), smartphone and internet.
Quality Evaluation of Summarization Models for Patent Documents

Several recently developed neural network models have shown their potential for automated text summarization. However, the evaluation results of these models on summarization of long text are fairly close in almost every major evaluation parameter. None of these models including large language models GPT-3.5 and GPT-4 can well summarize long text without manual interventions. In this paper, we report the evaluation results of several state-of-the-art neural network models on text summarization under different configurations of the input, which includes 1630 U.S. patent documents. Based on the evaluation results, we proposed a strategy for improving the text summarization in long text and demonstrated its effectiveness with new cases.
Mobile application and cloud system for Telecommunication natural disasters anticipation

In this paper, a mobile and cloud-based platform will be created called telecommunication natural disaster anticipation (TELENADIA). The TELENADIA application is currently only created specifically to display information related to flood natural disasters, so that the features contained in it are made based on the needs of application users regarding information on the causes of flooding, so that the information contained in the application is river water level, rainfall, and wind speed. The information is displayed based on the results of data processing stored in the cloud, the data stored in the cloud is the result of data retrieval from the hardware of the flood detection device, which is sent using an antenna from a place that is difficult to get a signal. The platform will go through a testing and verification phase to determine that the application and cloud used are in line with the paper objectives.
Communication-Efficient Federated Learning with Sparsity and Quantization

Google proposed federated learning in 2016, which aims to solve the problems of data silos and privacy leakage in machine learning. However, as the difficulty of the target task increases and the model performance requirements progressively improve, these have to consume more communication costs to increase the model parameters. In order to solve this problem, this paper proposes the Federated Learning with Sparsity and Quantization(FedSQ) method, where we select the gradients with a large rate of change in the gradients of the participants in each round for transmission, and utilize binary compression to quantize the selected model parameters, while adopting a suitable aggregation algorithm to replace the original FedAvg, so that it does not excessively impair the model accuracy in the case of high-fold compression. Finally, we verify the effectiveness of the scheme proposed in this paper, which can improve the quality of model training under model compression.
The Use of a Physics-based DNI Model to Enhance the National Solar Radiation Database (NSRDB)

Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI computation and forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is implemented in the National Solar Radiation Database (NSRDB) to assess 5-minute DNI in 2-km pixels over the contiguous United States. The DNI computation for 2019-2021 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM), Surface Radiation Budget Network (SURFRAD), National Renewable Energy Laboratory (NREL), Solar Radiation (SOLRAD), and University of Oregon (UO). The results show that the cloudy-sky DNIs from the NSRDB are improved as evaluated using absolute percentage error and mean absolute error.
Leveraging Visual Attention for out-of-distribution Detection

Out-of-Distribution (OOD) detection is a crucial challenge in computer vision, especially when deploying machine learning models in the real world. In this paper, we propose a novel OOD detection method leveraging Visual Attention Heatmaps from a Vision Transformer (ViT) classifier. Our approach involves training a Convolutional Autoencoder to reconstruct attention heatmaps produced by a ViT classifier, enabling accurate image reconstruction and effective OOD detection. Moreover, our method does not require additional labels during training, ensuring efficiency and ease of implementation. We validate our approach on a standard OOD benchmark using CIFAR10 and CIFAR100. To test OOD in a real-world setting we also collected a novel dataset: WildCapture. Our new dataset comprises more than 60k wild animal shots, from 15 different wildlife species, taken via phototraps in varying lighting conditions. The dataset is fully annotated with animal bounding boxes and species.
Self-supervised Learning of Contextualized Local Visual Embeddings

We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised convolutional-based method that learns representations suited for dense prediction tasks. CLoVE deviates from current methods and optimizes a single loss function that operates at the level of contextualized local embeddings learned from output feature maps of convolution neural network (CNN) encoders. To learn contextualized embeddings, CLoVE proposes a normalized mult-head self-attention layer that combines local features from different parts of an image based on similarity. We extensively benchmark CLoVE’s pre-trained representations on multiple datasets. CLoVE reaches state-of-the-art performance for CNN-based architectures in 4 dense prediction downstream tasks, including object detection, instance segmentation, keypoint detection, and dense pose estimation. Code: https://github.com/sthalles/CLoVE.
MOSAIC: Multi-Object Segmented Arbitrary Stylization Using CLIP

Style transfer driven by text prompts paved a new path for creatively stylizing the images without collecting an actual style image. Despite having promising results, with text-driven stylization, the user has no control over the stylization. If a user wants to create an artistic image, the user requires fine control over the stylization of various entities individually in the content image, which is not addressed by the current state-of-the-art approaches. On the other hand, diffusion style transfer methods also suffer from the same issue because the regional stylization control over the stylized output is ineffective. To address this problem, We propose a new method Multi-Object Segmented Arbitrary Stylization Using CLIP (MOSAIC), that can apply styles to different objects in the image based on the context extracted from the input prompt. Text-based segmentation and stylization modules which are based on vision transformer architecture, were used to segment and stylize the objects. Our method can extend to any arbitrary objects, styles and produce high-quality images compared to the current state of art methods. To our knowledge, this is the first attempt to perform text-guided arbitrary object-wise stylization. We demonstrate the effectiveness of our approach through qualitative and quantitative analysis, showing that it can generate visually appealing stylized images with enhanced control over stylization and the ability to generalize to unseen object classes.
A Review on Online Phishing Detection Using Machine Learning

Abstract: The rapid growth of the internet and the increasing dependence on online services have led to a significant rise in cyber threats, with phishing attacks being a prevalent and pervasive threat. Phishing attacks often involve the use of deceptive techniques to trick users into divulging sensitive information, such as login credentials, credit card details, and personal information. Detecting and preventing phishing attacks in real-time has become a critical challenge for individuals, businesses, and organizations. This research paper presents an approach for online phishing detection using machine learning techniques. The primary objective is to develop a system that can automatically identify and classify phishing websites and emails, thereby enhancing cyber security and protecting users from falling victim to these malicious activities. Online phishing detection has gained significant importance, which will only grow with the amount of dependency on cyberspace, the proposed system provides an easy solution, during cases when the user is unsure about the authenticity of the website visited, they can try to copy the Uniform Resource Locator (URL) and paste the link into the online phishing detection system. Through the system process, it will help the user to identify whether given links were legitimate website or it is a phishing website. Therefore, the user will not be in a doubtful situation the whole day in wondering whether the information they gave in a certain website is safe or not. Providing complex decision with simplicity, the system will help the user to detect each variable of URL given accurate.
LANGaware: biomarker workflows for classifying neurodegenerative and affective disorders across languages and severity levels

Abstract Background A wide range of neurodegenerative disorders impair communication faculties, manifested as subtle changes in speech and language. Following trends in Machine Learning (ML), Text and Multimedia Analysis, recent works pursue classification of such disorders by developing biomarkers useful for cognitive status description. However, investigations that gauge biomarker applicability on cases of radically different impairment / symptom severity have been under‐investigated. In this work, we build upon previous research and evaluate LANGaware’s biomarker suite in the drastically different tasks of Dementia and Depression classification. For both cases, we use appropriate Healthy Controls and evaluate the proposed biomarker workflow under a multi‐language experimental investigation. Method We utilize audio recordings and transcripts from patient responses to verbal cognitive assessment tasks. These are analyzed with LANGaware’s multimodal biomarker pool, mapping raw data to biomarkers scores that quantify vocal, linguistic and grammatical usage proficiency, structure and patterns, by applying both statistical analysis and explainable template matching. Biomarkers activations are fed to ML workflows composed of gradient boosting learners that employ feature selection, filtering and ensemble‐based learning to arrive at configurations best suited for discriminating the disorder of interest. The pipeline is evaluated using a standard train‐test and cross‐validation setup. Result Experimental results indicate that our method achieves weighted F1 test scores of 82.49% for Greek Dementia classification, using a train / test dataset of 1271 / 624 instances and 80.28% on 570 / 243 English data. We use the same pipeline and biomarker pool for Depression classification, reaching 74.03% and 71.36% performance scores, obtained from 73 / 31 and 652 / 42 available train‐test data, for Greek and English respectively. Conclusion The above findings show that the proposed biomarker pool generalizes across neurodegenerative and affective disorders of radically different severity. As a result, they constitute valuable decision support tools for accurate, automatic and explainable early diagnosis, facilitating proactive care, improved symptom management and better patient quality of life. Future research efforts include extending our experimental evaluation of LANGaware biomarkers to additional disorders and diseases, as well as expanding the biomarker set to enable analysis of additional modalities.
Linguistic Aspects of Arabic Football Discourse

The work is devoted to the linguistic aspects of the Arabic football discourse. The study focuses on the study of discourse markers of official documents; discourse markers, comparisons and metaphors in articles in the Arab sports media; comparisons and metaphors used by commentators during Qatar World Cup matches, regional tournaments and club championships. The relevance of the work lies, firstly, in the absence of such studies in the Russianspeaking segment of the science of the discourse of the Arabic language, and secondly, in the general importance of football in the life of Arab society and the active development of the football industry in the Middle East.
Priority Based Scheduler for Asymmetric Multi-core Edge Computing

Edge computing technology has gained popularity due to its ability to process data near the source or collection device, benefiting from low bandwidth utilization and enhanced security. Edge devices are typically equipped with multiple devices that employ asymmetric multi-cores for efficient data processing. To ensure optimal performance, it is crucial to carefully assign tasks to the appropriate cores in asymmetric multi-core processors. However, the current Linux scheduler needs to consider the capabilities of individual cores when assigning tasks. Consequently, high-priority tasks may be assigned to energy-efficient cores, while low-priority tasks end up on high-performance cores. This sub-optimal task assignment negatively impacts the overall system performance. To address this issue, a new algorithm has been proposed. This algorithm considers both the core’s capabilities and the task’s priority. However, due to the asymmetric nature of the cores, prior knowledge of each core’s speed is necessary. The algorithm fetches the priorities of the tasks and classifies them into high, medium, and low-priority categories. High-priority tasks are scheduled on high-performance cores, while medium and low-priority tasks are allocated to energy-efficient cores. The proposed algorithm demonstrates superior performance for high-priority tasks compared to the existing Linux task scheduling algorithm. It significantly improves task scheduling time by up to 16%, thereby enhancing the system’s overall efficiency.
APPLICATION OF BAGGING CART IN THE CLASSIFICATION OF ON-TIME GRADUATION OF STUDENTS IN THE STATISTICS STUDY PROGRAM OF TANJUNGPURA UNIVERSITY

The timeliness of graduation is used as the success of students in pursuing education which can be seen from the time taken and measured by the predicate of graduation obtained. The characteristics of students who tend to graduate not or on time can be analyzed using classification techniques. Classification and Regression Tree (CART) is one of the classification tree methods. There is a weakness in CART, which is less stable in predicting a single classification tree. The weaknesses in CART can be improved by using Ensemble methods, one of which is Bootstrap Aggregating (Bagging) which can reduce classification errors and increase accuracy in a single classification model. This study aims to classify and determine the accuracy of Bagging CART in the case of the accuracy of student graduation classification. The number of samples used is 140 data on the graduation status of Untan Statistics Study Program students from Period I of the 2017/2018 academic year to Period II of the 2022/2023 academic year. The variables used are the timeliness of graduation which is categorized into two namely Not and On Time, Gender, Semester 1 GPA, Semester 2 GPA, Semester 3 GPA, Semester 4 GPA, Region of Origin Domicile, High School Accreditation, Entry Path, Scholarship, and first TUTEP. A good classification can be seen from the accuracy value. The CART method obtained an accuracy value of 70%. While using the CART Bagging method obtained an accuracy value of 85.71%. Based on the accuracy value obtained, the application of the CART Bagging method can increase accuracy and correct classification errors on a single CART classification tree by 15.71% by resampling 25 times.
PECULIARITIES OF THE FORMATION OF INFORMATION CULTURE OF FUTURE SPECIALISTS OF DIGITAL DESIGN (EDUCATION AND PROFESSIONAL PROGRAM “DIGITAL DESIGN TECHNOLOGY”)

Abstract: the article examines the concept of “information culture” and defines approaches to defining the information culture of a specialist, analyzes the educational and professional program “Digital Design Technologies” of the State Tax University, specialty 122 “Computer Science”. The content part of each educational discipline of professional training was revealed and their analysis was carried out. Ways to improve the educational and professional program “Digital Design Technologies” of the State Tax University, specialty 122 “Computer sciences” have been identified.