Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF PREDICTIVE TOOLS TO IDENTIFY CRITICAL MINERAL ENRICHMENT IN HIGH-ALUMINA UNDERCLAYS OF THE APPALACHIAN AND ILLINOIS BASINS, USA

The USGS Earth Mapping Resources Initiative (Earth MRI) works to identify mineralized areas or deposits across the United States that may host critical minerals. Aluminum-rich clays associated with coal horizons have the potential to be low-grade, large-volume, critical mineral resources. Past studies show that alumina is routinely 20-40% in these clay layers, and preliminary geochemical data indicate that some clay layers host anomalously high (>300 ppm) rare earth element (REE) concentrations. However, understanding of the distribution of metals in varying lithologies and stratigraphic positions is limited by data density and a lack of modern geochemical data. Ongoing work through Earth MRI includes geochemical analyses, mapping of known deposits, and determination of the stratigraphic and lithologic intervals with the greatest promise of hosting these elevated critical mineral resources. As part of a multi-year study, a stratigraphic and spatially representative set of underclay samples were collected from eight states in the Appalachian and Illinois basins, geochemically analyzed by the USGS, and then processed with Orange, an open-source machine learning and statistical analysis software. Using Orange, Principal Component Analysis (PCA) and cross-plots were created to determine the elements that correlate with higher REE values. The trends indicate that in these samples higher REE totals (>375 ppm) have strong positive correlations with several elements, including Al, P, Th, and Sr, and weakly positive correlations to several others. Higher total REEs were inversely correlated with Mg. These initial results were then compared with handheld X-Ray Fluorescence (hhXRF) measurements and used to inform the sampling strategy for a second round of sample collection. As a final step, results from each round of sampling will be compared to determine if the screening exercise resulted in higher overall REE totals using the major elemental associations. If successful, this workflow can enable quick-look or field-level selection of samples using the hhXRF and development of a semi-quantitative predictive tool to help locate potential REE deposits.
Cooperative Area Coverage Path Planning for Multiple UAVs Over Large Areas

With the popularity and development of Unmanned Aerial Vehicle (UAV) technology, the UAV coverage path planning problem has also emerged. In this paper, we propose a new model of UA V coverage path planning problem, which uses multiple UAVs to cover a certain large area cooperatively, and considers the energy constraint of UA Vs on flight distance, UA V energy replenishment and UA V recovery in the coverage process. In this paper, we propose a two-stage approach to solve the problem. In the first stage, we propose a region decomposition method based on a regular triangular grid, which converts the search region into a graph composed of nodes and edges. In the second stage, we use mixed linear integer programming (MILP) method to plan the coverage path based on the graph generated in the first stage. We use the actual parameters such as the drone's own camera, speed, energy, etc. to dissecte a rectangular area and accurately solve the MILP model, in the process of solving we designed three different scenarios to verify the feasibility and versatility of the model, and the solution results meet the expectations. We also observed the effect of UA V base location on the solving speed and the effect of UA V flight altitude on the model solving target value.
Compression Techniques for Low Power Hardware Accelerator Design: Case Studies

Deep learning algorithms are playing a vital role in wide range of Artificial Intelligence-based applications. Since the CPU/GPU-based solutions are not suitable for low power applications like IoT due to their high-power requirements, a dedicated hardware called hardware accelerator is needed for a given AI-based processing. Further, the hardware accelerators are essential in reducing the inference-time latency. The FPGA/ASIC-based CNN accelerator implementations are in research focus now a days owing to their high performance per Watt in variety of domains such as vision, voice, text. In this paper, the k-means algorithm based technique for the CNN model compression and Winograd based techniques for reducing the number of multiplications reported in the literature are discussed as case studies for model compression, and hardware optimization.
Novel Feature Extraction Methods to Detect Age from Handwriting

Age detection from handwritten documents is a crucial research area in many disciplines such as forensic analysis and medical diagnosis. Furthermore, this task is challenging due to the high similarity and overlap between individuals’ handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of age-related features suggested by a graphologist, to detect the age of the writers, have been proposed. These features include irregularity in slant, irregularity in pen pressure, irregularity in textlines, and the percentage of black and white pixels. Support Vector Machines (SVM) classifier has been used to train, validate and test the proposed approach on two different datasets: the FSHS and the Khatt dataset. The proposed method has achieved a classification rate of 71% when applied to FSHS dataset. Meanwhile, our method outperformed state-of-arts methods when applied to the Khatt dataset with a classification rate of 65.2%. Currently, these are the best rates in this field.
Web-Based Employee Attendance Information System

Along with the times, where computers are no longer valuable, the integrity to maximize the use of computers is increasing. Almost all organizations and companies ranging from small to large scale have started using computers to support their operational activities. However, often many companies ignore or do not maximize the use of computers in their operations, Attendance information system can help companies in maximizing the use of computers. With web-based, the attendance information system becomes one of the company's strengths in managing existing human resources. The implementation can be simply recording employee data ranging from general data to specific data such as the absence of an employee from entering to leaving. Using this employee attendance information system can help companies, especially admins in terms of recording employee absent data and making employee attendance report data and providing information to employees.
Analisis Literasi , Komunikasi Dan Penalaran Matematika Terhadap Hasil Belajar Siswa Selama Pembelajaran E-Learning Pada Materi Matriks Kelas XI SMA di Sumatera Utara

This study aims to analyze literacy, communication and mathematical reasoning on student learning outcomes during E-Learning learning on Matrix material for Class XI SMA in North Sumatra. This research method uses quantitative and qualitative approaches. The results showed that the sig value for the influence of literacy on learning outcomes was 0.26 &gt; 0.001 and the t value was -2.252 &lt; 0.26, which means that there was no influence of literacy on learning outcomes, then the sig value for the effect of communication on learning outcomes was 0.055 &gt; 0.001 and the t-count value is 1.940&gt; 0.055, which means that there is an effect of communication on learning outcomes. Furthermore, the sig value for the effect of student reasoning on learning outcomes is &lt;0.001 and the t-count value is 7.867&gt; 0.001, which means that there is an effect of reasoning on learning outcomes. In conclusion, there is no influence of literacy on learning outcomes, and there is an effect of communication and mathematical reasoning on learning outcomes during e-learning learning on matrix material in class XI high school students in North Sumatra.&#x0D; Keywords: E-Learning, Literacy Skills, Communication, Reasoning
Comparison of K-Nearest Neighbor And Modified K-Nearest Neighbor With Feature Selection Mutual Information And Gini Index In Informatics Journal Classsification

With the rapid development of informatics where thousands of informatics journals have been made, a new problem has occured where grouping these journals manually has become too difficult and expensive. The writer proposes using text classification for grouping these informatics journals. This research examines the combinations of two machine learning methods, K-Nearest Neighbors (KNN) and Modified K-Nearest Neighbors with two feature selection methods, Gini Index (GI) and Mutual Information (MI) to determine the model that produces the higherst evaluation score. The data are informatics journals stored in pdf files where they are given one of 3 designated labels: Information Retrieval, Database or Others. 252 data were collected from the websites, neliti.com and garuda.ristekbrin.go.id. This research examines and compares which of the two methods, KNN and MKNN at classifying informatics journal as well as determining which combination of parameters and feature selection that produces the best result. This research finds that the combination of method and feature selection that produces the best evaluation score is MKNN with GI as feature selection producing precision score, recall score and f1-score of 97.7%
Sedimentary features of the Miocene strata of the Jinding Zn-Pb ore concentration area, the SE Tibetan Plateau: Fillings of a peripheral foreland basin in the oblique collision belt

兰坪中-新生代盆地位于青藏高原东南缘的三江造山带中段, 受印度-欧亚大陆斜向碰撞相关构造的强烈影响, 导致地层序列及时代归属难辨, 区域地质与成矿作用认识存在诸多分歧。先期完成的区域地质调查发现, 在金顶铅锌矿集区内常见的含石膏岩地层并非前人认为的古新统云龙组的一部分, 而是角度不整合于前古新统之上的一套新地层, 暂定名为中新统金顶砂泥岩。系统的剖面测量和详细的沉积学研究表明, 金顶砂泥岩从下到上由洪积相砾岩、辫状河三角洲相粉-细砂岩夹泥岩和砾岩以及湖泊相粉砂岩夹泥岩组成, 石膏层位于砾岩之上。辫状河三角洲相中频繁出现灰绿色、灰黑色粉砂质泥岩及黄铁矿结核层, 可能是频繁的区域性挤压作用造成的逆冲-走滑断层使上三叠统基底岩系中古油气藏破裂、泄露所致; 该套地层内广泛发育的同沉积逆断层(视正断层)、滑脱褶皱和液化砂脉等软沉积物变形构造, 指示沉积盆地发育期间地壳发生了NEE-SWW向缩短。金顶砂泥岩是印度-欧亚大陆碰撞背景下近东西向挤压构造诱发并控制的周缘前陆盆地沉积, 时代为中新世。这一认识为深入理解大陆斜向碰撞带构造演化及大规模金属成矿作用提供了新的资料。
Design and Development of a Searchable Database of Veterinary Curriculum

Curriculum maps visually present all components of a curriculum by providing a broad overview of what is taught, in what order, and how it is taught. As an analytical tool for organizing, managing, and evaluating curriculum, a curriculum map, therefore, is a powerful tool in identifying gaps and redundancies in a curriculum. Common approach used in curriculum mapping is to map learning objectives whether at the course or lecture level to program outcomes or competencies. This process is extremely laborious and time consuming, and fails to provide a comprehensive visual representation of the curriculum. Further, it may not reflect the realities of classroom teaching as it relies on intended learning objectives. In this study, we argue that machine learning and text mining techniques could be utilized to create a comprehensive overview of the curriculum using real classroom data. This work-in-progress tool will have three distinct components 1) searchable curriculum, 2) network modeling of the curriculum, and 3) outcome mapping. This paper reports the processes we followed to create a searchable database of the veterinary medicine curriculum.
Diagnosis of Malaria using Machine Learning Models

Malaria is a disease caused by the protozoan parasite plasmodium vivax, and spreads through the bites of infected mosquitoes. It causes close to 19,000 deaths annually in India alone, a testament to the truly devastating impact this disease can have if not treated properly. We recognize that an early diagnosis is paramount with regards to the treatment of any disease, especially one as potentially deadly as malaria. This work aims to facilitate the diagnosis of malaria by utilizing Machine Learning models to classify patients as infected or otherwise. The data we use to train our models consists of cell image data, more specifically, images of blood cells collected from patients. Using our predictive models, we attempt to accurately classify blood cells on the grounds of whether they are parasitized or uninfected. We also compare the performance metrics of the models we employ and seek to find the one that performs optimally and provides the most accurate predictions in the process. Since the data is not skewed, Classification Accuracy is a suitable metric for measurement of the performance of various models and will be used in our implementation.
Bayesian Nonparametric Scalar-on-Image Regression via Potts-Gibbs Random Partition Models

Teo, Mica Shu Xian Wade, SaraScalar-on-image regression aims to investigate changes in a scalar response of interest based on high-dimensional imaging data. We propose a novel Bayesian nonparametric scalar-on-image regression model that utilises the spatial coordinates of the voxels to group voxels with similar effects on the response to have a common coefficient. We employ the Potts-Gibbs random partition model as the prior for the random partition in which the partition process is spatially dependent, thereby encouraging groups representing spatially contiguous regions. In addition, Bayesian shrinkage priors are utilised to identify the covariates and regions that are most relevant for the prediction. The proposed model is illustrated using the simulated data sets.
DIGITAL TRANSFORMATION OF HIGHER EDUCATION, KEY FEATURES AND FUNCTIONS OF SMART UNIVERSITY 4.0

The concept of a SMART university currently being developed in the popular understanding should meet the requirements of accelerated learning, use the achievements of digitalization and be based on the principles of self-development, adaptability, measurability, digital tools and distance education.However, this concept, supported in most modern methodological and scienti c works, seems to be insuf ciently complete and systematized.The article presents an extended joint contextual economic-technological and historical analysis of the evolution of informatics, the sphere of knowledge and technology, which made it possible to clarify the basic requirements for the new educational system and its elements, digitalization tools, its main characteristics and functions, as well as to raise a number of questions that require additional scienti c consideration and veri cation in practice.The existing international education system is a product and simultaneously an element of the rst industrial revolution, functioning on the basis of the principles of ow-type production established more than a hundred years ago, and solves the problem of maximum penetration of a typical set of knowledge into the masses of the population, rather than maximizing the quality of the obtained knowledge and its individualization.New industrial revolution based on wide digitalization and networking, which we are witnessing, requires modernization of most economic institutions, including education system, and offers for this modernization not only a new set of technologies, but also new principles of exibility, individualization, maximization of ef ciency. The subject of electronic education has been developing in different countries for quite a long period. By now the necessary set of technologies and organizational elements has been created. However, it is during the 2019-2021 pandemic that suf cient conditions have emerged for the non-alternative widespread implementation of a new holistic education system based on remote mode and adaptive digital content. The article considers the modernization of the education system, the creation of the Digital SMART University or University 4.0. The modernization of the educational system has many parallels with the digital transformation of other sectors of the economy. This makes it possible and necessary to use the experience and knowledge accumulated in other areas of economic activity, their systematization and analysis of applicability.
Monocular Image-based Path Planning Selection Scheme for Planetary Exploration Rover

The autonomous navigation system is required for planetary exploration rovers to drive to the destination. The conventional navigation systems always perform processing with a large load. Therefore, it takes long time to traverse to the destination. It is necessary for rovers itself to select the behavior modes of navigation according to the environment so that a rover drives faster and efficiently. In this paper, a method is proposed in which the rover autonomously selects the path planning method based on the monocular images. A simulation study is conducted to evaluate the effectiveness of the proposed method.
One-Shot Triple-Resource Trade-Off in Quantum Channel Coding

We analyze a task in which classical and quantum messages are simultaneously communicated via a noisy quantum channel, assisted with a limited amount of shared entanglement. We derive direct and converse bounds for the one-shot capacity region, represented by the smooth conditional entropies and the error tolerance. The proof is based on the randomized partial decoupling theorem, which is a generalization of the decoupling theorem. The two bounds match in the asymptotic limit of infinitely many uses of a memoryless channel and coincide with the previous result obtained by Hsieh and Wilde. Direct and converse bounds for various communication tasks are obtained as corollaries, both for the one-shot and asymptotic scenarios.
Cloud Storage Client Forensic: Analysis of MEGA Cloud

In the current decade, the use of cloud storage services is drastically increasing because it provides large data storage with various functionalities. Consequently, it is easier for someone to download, upload, change, and share data on multiple platforms. Meanwhile, cloud storage forensics is of paramount importance for service providers to rectify security issues. However, the unique characteristics of cloud service introduce numerous challenges like resource pooling, on-demand services, investigation, and legal matters. Cloud storage forensic deals with phenomena involving the cloud infrastructure and corresponding services in civil proceedings and criminal investigations. This paper presents a forensic analysis of the MEGA, a cloud service provider, using the Windows 10 operating system and android platform. In this investigation, we recognize various artifacts such as uploading, log in, downloading, file timestamps, and file sharing that could be forensically recovered.
Analisis Teks Fiksi Bermuatan Nilai Pendidikan Karakter dalam Buku Teks Bahasa Indonesia SMP

This study aims to determine the values of character education in the Indonesian language textbooks for SMP class VII published by the Ministry of Education and Culture of the Republic of Indonesia in 2017. This study uses a qualitative approach with content analysis methods. Sources of data in research this is the book text Languages Indonesia class VII. Technique collection of data is done with the analysis is thorough and recording of the load values of characters are scattered in all parts of the book especially be true in the text of fiction. Check the validity of the data based on semantic validity and reliability is interrater reliability. The use of textbooks in schools has a role important for teachers and participants of learners. The book text is used as a support in the process of learning, then from the expected book text used in each learning in school, especially the eyes of subjects Languages Indonesia. Books Text Languages Indonesia in addition to exposing the material subjects, but also there are values of characters in the text are presented with the purpose to be able to take benefit by the participant students. The results of the study have demonstrated that, there are the values of the characters that are on; (1) the value of hard work was obtained 24 quotes, (2) the value of social care was obtained 18 quotes, and (3) the value of friendly or communicative was obtained 15 quotes.
Perbandingan Pengelompokan Metode PSO K-Means Dan Tanpa PSO Dalam Pengelompokan Data Alert

With increasing knowledge and increasing internet crime, an Intrusion Detection System (IDS) is needed, one of which is Snort which can detect attacks. An attack notification is needed to let administrators know if an attack has occurred. The grouping of alerts uses the PSO method on K-Means and continues with the calculation of the risk value to label the threat level, namely low, medium, high in each group. The Whatsapp bot will send groups of alerts that have high and medium labels only. A notification will appear on the Whatsapp application. The results obtained in this study by grouping the attack data, namely, the accuracy obtained by the system using the Particle Swarm Optimization method on K-Means obtained better results than only using the K-Means method.
Relative Position Relationship Learning Network for Scene Graph Generation

Scene graph generation (SGG) aims to detect objects along with their relationships in images. It is well believed that the position of objects is a significant consideration when analyzing object relationships. However, current SGG methods generally adopted the absolute positions of objects, which are less effective to describe relationships between two objects when the two objects are placed into different positions of one image. In this paper, we propose a relative position relationship learning network (RPRL-Net) to explicitly represent relationships between different positional objects. Specifically, RPRL-Net develops relative positional self-attention (RPSA) modules to analyze context features from objects by exploring relative positional information between pairwise objects. Afterward, RPRL-Net integrates absolute positional features, relative positional features, and context features of object pairs to predict the final predicates. We conducted comprehensive experiments on the Visual Genome dataset. The experimental results compared with the state-of-the-art demonstrate the superiority of RPRL-Net.
Curvable Image Markers: Toward Trackable Markers for Every Surface

Augmented reality (AR) possesses great potential for productivity, entertainment and communication, inclusive the mobile context. Although the strength of AR is the blending of physical and virtual environments, current mobile applications seem to focus only on overlaying the virtual part upon the physical one. It is argued that an important direction to explore is how to enhance the interaction in AR such that physical elements can act upon the virtual ones. ArUco is a popular choice in academia for tracking due to its relatively cheap processing cost and requiring only RGB input, making it also suitable for mobile devices. A fiducial marker like ArUco, however, is flat. To move towards enabling it for uneven surfaces, this paper proposed adapting the markers for curved surfaces to be suitable on bandages for warping around objects for tracking. It also presents an image marker making use of neighborhood features to improve its appeal for AR purposes.