Reviewerly/exHarmony huggingface dataset:
Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were constructed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which characterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.
Innovative Practice of Teaching Mode of Blended and Comprehensive Practice of Peking Opera Course on the Platform of Smart Education of Beijing

The school’s activities have the characteristics of “eclectic, all-round development, teaching students in accordance with their aptitude, learning for use, and combining eastern and western education culture”. The school pays attention to the cultivation of students’ independent learning awareness and individual differences, so that each child can develop independently and shape himself. Under such a management philosophy and educational goal, the key words of curriculum culture and classroom culture are proposed, namely student-oriented, independence, open, and creativity. A “bright • colorful” curriculum unique to Dengshikou Primary School is constructed.
Build a Digital Base for Education and Build a High-Quality Education Support System

Alibaba Cloud Digital Base Solution for Education is an educational infrastructure operating system and ecological base designed and developed according to the characteristics of the educational public service industry. It is applied to build a new educational infrastructure system with an optimized structure by perfecting the organization, enriching the application.
Application of virtual reality technology in second language classroom teaching in colleges and universities

The design and implementation of teachers’ classroom teaching strategies is the key to the success of second language classroom teaching. In order to improve the quality of second-language classroom teaching in universities and enhance the interactivity in the teaching process, the application of virtual reality technology in second-language classroom teaching in universities is studied. Firstly, an integrated ware platform is designed for second language classroom teaching in universities, which consists of an integrated ware library and a database. Then virtual reality technology is used to design an integrated ware library within the platform and collect information such as various teaching media resources designed in the classroom teaching content into the integrated ware library; Utilize 3DS Max software in virtual reality technology to construct three-dimensional models of teaching scenes and entities; Choose to use linear difference method to render 3D model colors; From a visual perspective, enhance the realism of the rendered model through image enhancement technology and color contrast enhancement technology. According to the functions of the physical object, various interactive events are added to the created teaching scene and the three-dimensional model of the entity and stored in the integrable ware library to achieve panoramic roaming of the second language classroom teaching scene in universities. The experimental results show that the teaching platform designed by this method can accurately construct three-dimensional models of teaching scenes and objects with good visual effects, providing users with a more realistic sensory experience and effectively improving students’ mastery of teaching content.
e-CNY and Digital Economy in China

This paper highlights the inescapable trend of digital currency growth by examining past currency patterns, and explores the ties between digital currency and the digital economy. With the case of China, this paper forecasts the future improvement pattern of digital money. In the midst of the fast growth of private cryptocurrencies, China developed the digital currency with the goal of serving the digital economy and committing to the globalization of the digital renminbi and the digital economy. While Europe and the United States were experiencing continued stagnation in 2022, China has entered the cycle of loose monetary and fiscal policy. China views digital fiat money and digital economic development as a breakthrough to drive the transformation of the current economic system, new inventive growth, and the safety of the whole supply chain in the context of the scissors gap. It has become one of the forefront countries in the digital field, with numerous experiences that can be learned by countries around the world. This paper also creates the concept and calculation formula of the e-CNY Index, sets the year 2022 as the first year of the e-CNY with the initial value of the index at 100; it also gives suggestions for creating and calculating the China-style Digital Economy Index. The importance of these indices as new tools for tracking and evaluating the digital macroeconomy in China is presented, providing a promising direction for future in-depth research on digital fiat currency.
Automated government form filling for aged and monolingual people using interactive tool

Purpose The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English.
CNN Based Facial Recognition with Age Invariance

Abstract : As the world has seen exponential expansion over the previous decade, there has been an unusual increase in the crime rate as well as an increase in the number of criminals/missing persons. Face recognition can extract the individualistic characteristics of the human face. A straightforward and adaptable biometric technology is face recognition. The technology used to recognize and identify faces in images or videos is called face detection and recognition. The process of removing facial features has gotten easier as technology has advanced. This study describes the use of an automated security camera for realtime face recognition. With this system, we can instantly identify and detect the faces of the criminals in a live video feed captured by a camera. Criminal records typically include the offender's picture and personal information. Thus, we are able to use these photos in addition to his information. The security camera's recorded footage is transformed into frames. After a face is identified in a frame, it undergoes pre-processing and feature extraction. The characteristics of the real-time image processing are compared to those of the images kept in the criminal database.
Causal Factor Disentanglement for Few-Shot Domain Adaptation in Video Prediction

An important challenge in machine learning is performing with accuracy when few training samples are available from the target distribution. If a large number of training samples from a related distribution are available, transfer learning can be used to improve the performance. This paper investigates how to do transfer learning more effectively if the source and target distributions are related through a Sparse Mechanism Shift for the application of next-frame prediction. We create Sparse Mechanism Shift-TempoRal Intervened Sequences (SMS-TRIS), a benchmark to evaluate transfer learning for next-frame prediction derived from the TRIS datasets. We then propose to exploit the Sparse Mechanism Shift property of the distribution shift by disentangling the model parameters with regard to the true causal mechanisms underlying the data. We use the Causal Identifiability from TempoRal Intervened Sequences (CITRIS) model to achieve this disentanglement via causal representation learning. We show that encouraging disentanglement with the CITRIS extensions can improve performance, but their effectiveness varies depending on the dataset and backbone used. We find that it is effective only when encouraging disentanglement actually succeeds in increasing disentanglement. We also show that an alternative method designed for domain adaptation does not help, indicating the challenging nature of the SMS-TRIS benchmark.
Improved UAV-to-Ground Multi-Target Tracking Algorithm Based on StrongSORT

Unmanned aerial vehicles (UAV) are essential for aerial reconnaissance and monitoring. One of the greatest challenges facing UAVs is vision-based multi-target tracking. Multi-target tracking algorithms that depend on visual data are utilized in a variety of fields. In this study, we present a comprehensive framework for real-time tracking of ground robots in forest and grassland environments. This framework utilizes the YOLOv5n detection algorithm and a multi-target tracking algorithm for monitoring ground robot activities in real-time video streams. We optimized both detection and re-identification networks to enhance real-time target detection. The StrongSORT tracking algorithm was selected carefully to alleviate the loss of tracked objects due to factors like camera jitter, intersecting and overlapping targets, and smaller target sizes. The YOLOv5n algorithm was used to train the dataset, and the StrongSORT tracking algorithm incorporated the best-trained model weights. The algorithm’s performance has greatly improved, as demonstrated by experimental results. The number of ID switches (IDSW) has decreased by sixfold, IDF1 has increased by 7.93%, and false positives (FP) have decreased by 30.28%. Additionally, the tracking speed has reached 38 frames per second. These findings validate our algorithm’s ability to fulfill real-time tracking requisites on UAV platforms, delivering dependable resolutions for dynamic multi-target tracking on land.
A Comparison Study Between Otsu’s Thresholding, Fuzzy C-Means, and K-Means for Breast Tumor Segmentation in Mammograms

Currently, the technique of digital mammography is considered the first screening tool used for detecting breast cancer in early stages. The shape and margin of masses in mammograms play a crucial role in the classification of benign and malignant tumors. Several machine learning (ML) algorithms have demonstrated high performance in breast mass edge segmentation from mammograms. The main objective of this paper is to present a comparative study of three algorithms of ML where: C-means clustering, K-means clustering, and Otsu’s thresholding for automatically detecting the lesion edges on mammogram images. Two groups (C = 2) are assigned to clustering as output for each method. In order to achieve high accuracy in contour extraction, only relevant pixels are selected as inputs. This study will highlight the advantages and disadvantages of these algorithms, which are expected to improve the detection of breast masses contours in mammographic images. The mini-MIAS database is used to evaluate the obtained results which show a higher performance in terms of segmentation accuracy when using fuzzy C-means algorithm. However, a faster lesion extraction process can be achieved by utilizing the K-means clustering and Otsu’s threshold.
Analysis of the Impact of Automatic Content Reflow on the Speed of Full Page Rendering

Now more and more information is transmitted via the Internet, including through websites. The speed with which sites are displayed to users affects their attitude to the resource they are viewing, and therefore, the speed also affects the success of the resource itself. For example, search engines will be less likely to offer users resources with lower behavioral factors. Despite this, the display speed of many sites is low. This state of affairs harms both the end user of information and the owners of Internet resources. To fix the download speed, you can apply content reflow to optimize the html document. Manually making such edits is a rather time-consuming process. It can be accelerated by automating content reflow through special algorithms. The aim of the work is to optimize the structure of the HTML page to increase the speed of its full display by the browser. A list of services is provided that allows you to check the page loading speed. The text describes the "mirror" script, designed to create copies of web site pages, to test html content optimization algorithms. Descriptions of algorithms developed to solve the problem of low download speed are given. The article presents the results of content optimization on the example of two sites. The full page rendering speed measurement model provided by the Google PageSpeed Insights tool was used, which includes the following metrics: First Contentful Paint (FCP), Speed Index (SI), Total Blocking Time (TBT), Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS). The developed algorithms for automatic reflow of html documents show their effectiveness in optimizing page loading speed according to Lighthouse 9 and 10 versions.
Pelatihan Keterampilan Bahasa Inggris Komunikatif bagi Pedagang Souvenir di Sentra Kerajinan Gerabah Sasak

This community service program aims to improve English communication skills for pottery souvenir traders who sell around the Sasak Pottery craft center, Penujak Village, Central Lombok Regency. The implementation of this service uses a transfer of knowledge-based training method consisting of preparation, implementation, and evaluation activity. The service activity partners are souvenir traders at the Sasak Pottery Craft Center, totaling 30 people. The evaluation instrument for this activity uses an English conversation test and is analyzed descriptively. The results of the implementation of the activities show that: 1) The training participants, consisting of souvenir traders at the Sasak Pottery craft center, have gained knowledge both theoretically and practically regarding good communication skills in English. 2) Souvenir traders have been able to use English as a means of communication to promote and offer various types of handicraft products to foreign guests. It is hoped that this training activity can positively contribute to improve the residents' welfare, especially for makers and/or sellers of pottery souvenirs around Penujak village.
Pelatihan Pembuatan Media Pembelajaran Berbasis Website Terintegrasi Model Pembelajaran Project Based Learning bagi Guru SMAN 2 Tondano Minahasa

This community service aims to increase knowledge and competence in creating website-based learning media integrated with the project-based learning (PjBL) learning model for teachers at SMAN 2 Tondano, Minahasa Regency. Implementing this service used training, which included lectures, discussions, simulations, and mentoring. The activity evaluation instrument used a questionnaire and was analyzed descriptively. The results of this service showed that the ability of ICT teachers at SMAN 2 Tondano increased in creating website-based learning media integrated with the PjBL model. The implementation of training and mentoring activities received a positive response from participants, as evidenced by the enthusiasm and activeness of teachers in participating in the training. The evaluation results showed that the level of satisfaction and increase in teacher knowledge after participating in the training was in the very good category. Teachers were very interested in using website-based learning media because of its advantages and integration with the PjBL learning model.
Interpretable Hierarchical Deep Learning Model for Noninvasive Alzheimer’s Disease Diagnosis

Alzheimer’s disease is one of the leading causes of death in the world. Alzheimer’s is typically diagnosed through expensive imaging methods, such as positron emission tomography (PET) scan and magnetic resonance imaging (MRI), as well as invasive methods, such as cerebrospinal fluid analysis. In this study, we develop an interpretable hierarchical deep learning model to detect the presence of Alzheimer’s disease from transcripts of interviews of individuals who were asked to describe a picture. Our deep recurrent neural network employs a novel three-level hierarchical attention over self-attention (AoS3) mechanism to model the temporal dependencies of longitudinal data. We demonstrate the interpretability of the model with the importance score of words, sentences, and transcripts extracted from our AoS3 model. Numerical results demonstrate that our deep learning model can detect Alzheimer’s disease from the transcripts of patient interviews with 96% accuracy when tested on the DementiaBank data set. Our interpretable neural network model can help diagnose Alzheimer’s disease in a noninvasive and affordable manner, improve patient outcomes, and result in cost containment. History: Rema Padman served as the senior editor for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/2881658/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2020.0005 ). The study involves secondary use of already-collected data. None of the authors were part of the original study team. The authors had no interaction with living individuals and had no access to protected health information (PHI) or private identifiable information about living individuals.
Introduksi Sistem Aplikasi STUNNING bagi MGMP di Lingkungan Dinas Pendidikan Provinsi Jawa Timur

This community service activity aims to introduce STUNNING as a tool for evaluating high school student learning-based information technology. The method for implementing this service activity uses seminars, which are part of a series with the Opening of the 2023 Matching Fund Activities at Dinamika University. This activity was attended by 45 participants from subject teachers who were members of the MGMP administrators within the East Java Provincial Education Office and attended by several media crews, including D'Media, BTV, MNCTV, Merro, and Analisapost.com. The evaluation instrument for this activity uses a questionnaire, which is then analyzed descriptively. The results of this service show that the introduction of the STUNNING application system has been successfully implemented and can achieve the stated goals. Participants seemed enthusiastic and welcomed the presence of STUNNING to evaluate learning for high school students. This is also reinforced by the results of the participant questionnaire, which shows an average score of 3.63 from a range of 1-4 scores.
Keypoint regression strategy and angle loss based YOLO for object detection

Abstract The YOLOv4 approach has gained significant popularity in industrial object detection due to its impressive real-time processing speed and relatively favorable accuracy. However, it has been observed that YOLOv4 faces challenges in accurately detecting small objects. Its bounding box regression strategy is rigid and fails to effectively leverage the asymmetric characteristics of objects, limiting its ability to enhance object detection accuracy. This paper proposes an enhanced version of YOLOv4 called KR–AL–YOLO (keypoint regression strategy and angle loss based YOLOv4). The KR–AL–YOLO approach introduces two customized modules: an keypoint regression strategy and an angle-loss function. These modules contribute to improving the algorithm’s detection accuracy by enabling more precise localization of objects. Additionally, KR–AL–YOLO adopts an improved feature fusion technique, which facilitates enhanced information flow within the network, thereby further enhancing accuracy performance. Experimental evaluations conducted on the COCO2017 dataset demonstrate the effectiveness of the proposed method. KR–AL–YOLO achieves an average precision of 45.6%, surpassing both YOLOv4 and certain previously developed one-stage detectors. The utilization of keypoint regression strategy and the incorporation of robust feature fusion contribute to superior object detection accuracy in KR–AL–YOLO compared to YOLOv4.
Research on depth measurement calibration of light field camera based on Gaussian fitting

Abstract Optical field imaging technology does not require a complicated optical path layout and thus reduces hardware costs. Given that only one single exposure of a single camera can obtain three-dimensional information, this paper proposes an improved calibration method for depth measurement based on the theoretical model of optical field imaging. Specifically, the calibration time can be reduced since the Gaussian fitting can reduce the number of refocused images used to obtain the optimal refocusing coefficient calibration. Moreover, the proposed method achieves the same effect as the multiple refocusing calibration strategy but requires less image processing time during calibration. At the same time, this method's depth resolution is analyzed in detail.
Software Testing: A Prediction Techniques

Prediction of software defects using machine learning techniques has attracted more attention from researchers due to its importance in producing successful software. On the other side, it reduces the cost of software development and facilitates procedures to identify the reasons for determining the percentage of defect-prone software in the future. There is no conclusive evidence for specific types of machine learning that will be more efficient and accurate in predicting software defects. However, some of the previous related work proposes ensemble learning techniques as a more accurate alternative. The paper presents the resample technique with three types of ensemble learners; Boosting, Bagging, and Rotation Forest, using eight base learners tested on seven types of benchmark datasets provided in the PROMISE repository. Results indicate that accuracy has been improved using ensemble techniques more than single leaners especially in conjunction with Rotation Forest with the resample technique in most of the algorithms used in the experimental results.