Reviewerly/exHarmony huggingface dataset:
Mapping and Petro-Structural Study of Northern Komborodougou Formations, in Korhogo Gold District: A Substantial Contribution to the Discovery of a Potential Gold Mineralization in Birimian Greenstone Belts

The Gold District of Korhogo, in the northern region of Côte d'Ivoire, holds enormous potential for gold mineralizations, some of which are under exploration phase and others in exploitation phase (example of Tongon Gold Mine).Dormant since 1998, Mapping Services of most of the West African countries hardly provide geological maps at the scales of: 1/200,000, 1/50,000 and 1/25,000.This situation of unavailability of detailed geological maps does not help mining operators in the selection of prolific areas and also in the interpretation of in soil geochem anomies or gold mineralizations.Consequently, during the years 2020 and 2021, we have undertaken a campaign of geological mapping and petro-structural study of the northern sector of Komborodougou, located in the southern extension of Banfora Birimian Belt.This work, which allowed the realization of a geological map at 1/20,000 scale, reveals that: the mapped area includes three (3) major lithological units: 1) a volcano-sedimentary unit made up of metasediments (metaarenites, metasiltites and metaflyschs) and metavolcanites (metabasalts), which are metamorphosed and outcrop in the form of schists; 2) a metaplutonic and intrusive unit composed on the one hand of quartz-diorites and metagabbros, and on the other hand of granites and granodiorites; 3) and finally, a unit of dikes formed by
Quantum Computing in Graphene

Quantum computing, based on different principles than classical computing, has raised high expectations regarding the increase of computational speed in nano-sized quantum systems. Therefore, the search for implementations of quantum logic gates in photons, spin states, atom/ion traps or superconducting materials, for example, is a very active research area. Graphene has demonstrated already the possibility of implementing reversible logic gates, therefore becoming a compelling candidate for quantum computing applications. The paper presents several proposals of quantum logic gates implementation in graphene, which could work at room temperature and require only current measurements as readout procedures; examples of such quantum gates are Hadamard, C-NOT, C-phase and Toffoli gates. Besides these gates, it is shown that quantum algorithms, such as the modified Deutsch-Jozsa algorithm, can be implemented also in graphene.
Research on Performance Optimization Technique of Dynamic Translation System Based on Apparatus Knowledge Algorithm

With the appearance of the era of knowledge economy, web message technique has been applied in a wider range. In the new burgeon period, English schooling is an important tool for students' knowledge and future employment burgeon. In English teaching and knowledge, the application of English online platforms provides greater convenience for students' knowledge, improves students' English theory and impacts English application ability. The appearance of the English online translation platform has broken through the restrictions of the traditional translation mould and achieved the simultaneous improvement of efficiency and quality. At the same time, the appearance of various advanced mobile terminal equipment also provides more possibilities and opportunities for the burgeon of English online translation platforms. Therefore, it is necessary to conduct in-depth research on the design and implementation process of the English online translation platform to promote the burgeon of the translation industry. The method used in this paper is apparatus knowledge algorithm, which is a major advantage in solving current numbers mining issues. By using latter-day artificial intelligence technique and moulding various mathematical moulds, numbers issues can be better utilized and solved.
Horse Racing Decision and Analysis Based on Data Mining and Big Data Analysis

Nowadays, the horse racing betting market is developing rapidly, which makes the prediction of the outcome of horse racing has become a new challenge. This research mainly discusses horse racing decision-making based on data mining and big data analysis technology. The main functions of the system designed in this study can be divided into two main parts: publishing competitions and participating in competitions. Publishing the competition means that the organizer of the competition publishes its own competition through the system. The content of the release mainly includes the description of the competition, the time of the holding, the rewards, etc., and the most important part is that the organizer must provide the information used in the competition data; and participating in the competition means that the participants of the competition obtain all relevant information of the competition through this platform, the most important thing is the data of the competition, and after the data is processed by data mining, the prediction results of the data are obtained., And submit the results to the system, and the system will automatically score the results. For the first 10 games, the accuracy of predicting the winning percentage can reach up to 91%. This research will promote the development of the horse racing industry.
A Novel Outdoor Edge Server Design with Hybrid Air Cooling and Refrigeration

Edge servers are trending up to 24% of total global server deployment in 2024. Several emerging applications are required to deploy with edge servers in harsh outdoor edge environments where special system design is required to address the challenges of extended temperature, waterproof ratings of IP65 or above, corrosion resistance, lightening protection, antifreeze and so on, compared with data center server. In parallel, as more AI services extended to edge from datacenter, high TDP CPUs and GPUs deployed in data center have been relocated in footprint constraint, -40~55+℃ extended temperature edge environments which calls for advanced thermal solutions with air cooling optimal techniques, heat exchangers, and refrigeration. This paper introduces a novel outdoor Xeon Scalable Processor edge server design with hybrid cooling solution of two-layer air cooling by the inner and outer circulations mechanism, companioned with refrigeration techniques. This hybrid cooling solution optimal for energy efficiency and edge server reliability, efficiently addressed the challenges of redeployment from datacenter to extended temperature edge environments for IT devices or components whose working temperature is limited within 5~35℃ data center or 5~45℃ High Temperature Ambient (HTA) data center level. Engineering design artwork results include thermal simulation, validation test data as well as filed deployment cases are also summarized in this paper.
Development of a Guitar Playing Robot Using Strumming Technique and Evaluation of its Performance

As an example of human-machine interaction, research on musical instrument ensembles between humans and machines has been conducted. In this study, we propose a robot system that can play an acoustic guitar. There have been several previous studies of guitar robots, but they have been limited by (1) the heat generation problem of the string pressing mechanism, (2) the difficulty of independently pressing the strings, and (3) the difficulty of controlling the strings. (2) The strings are independently plucked, and the stroking technique is not the same as that used by humans. (3) It is not practical because of these problems. In this study, we solved these problems and developed a guitar robot system that can play in concert with humans. In an evaluation experiment using subjects who could distinguish between the performances of humans and robots, the correct answer rate was about 70%, and we confirmed that even humans could not distinguish between the two performances. The effectiveness of the proposed system was demonstrated.
Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation

White matter hyperintensities (WMHs) are lesions with unusually high intensity detected in T2 fluid-attenuated inversion recovery (T2-FLAIR) MRI images, commonly attributed to vascular dementia (VaD) and chronic small vessel ischaemia. The Fazekas scale is a measure of WMH severity, widely used in radiology research. Although stand-alone WMH segmentation methods have been extensively investigated, a model encapsulating both WMH segmentation and Fazekas scale prediction has not. We propose a novel multi-task multiple instance learning (MTMIL) model for simultaneous WMH lesions segmentation and Fazekas scale estimation. The model is initially trained only for the segmentation task to overcome the difficulty of the manual annotation process. Afterwards, volume-guided attention (VGA) obtained directly from instance-level segmentation results figure out key instances for the classification task. We trained the model with 558 in-house brain MRI data, where only 58 of them have WMH annotations. Our MTMIL method reinforced by segmentation results outperforms other multiple instance learning methods.
Implementation of Cooperative Learning Methods in Improving Reading Comprehension

Background: Taggart consists of one cycle of one action, which begins with planning, action, observation, and reflection. This research was conducted in the odd semester of the 2017/2018 academic year, namely in October 2017. The subjects of this research were students of class X Catering Services (JB) SMK Negeri 1 Ciamis, totaling 31 people. The object of research is students' reading comprehension.&#x0D; Objectives: The purpose of this study was to determine the implementation of the Cooperative Learning Method in teaching reading comprehension, especially reading Factual Report texts to Class X Catering (JB) students at SMK Negeri 1 Ciamis, Ciamis District, Ciamis Regency, 2017/2018 Academic Year.&#x0D; Methods: This study uses qualitative data analysis and quantitative data analysis. Quantitative methods were used to analyze the data on students' reading test results (pre-test and post-test) which could be used to analyze statistical data on descriptive text learning materials. While the qualitative method was used to analyze data from observations, student questionnaires, and interviews. The data obtained from quantitative and qualitative methods are then compared and matched to obtain the conclusions from the research.&#x0D; Results: This type of research is Classroom Action Research (CAR) and the design of this research refers to Kemmis and Mc. Taggart's model consists of one cycle of one action, which begins with planning, action, observation, and reflection. This research was conducted in the odd semester of the 2017/2018 school year, namely in October 2017.&#x0D; Conclusion: The results showed that the Cooperative Learning Method could improve students' reading skills, especially in factual report texts.
Polytopic Planar Region Characterization of Rough Terrains for Legged Locomotion

This paper studies the problem of constructing polytopic representations for planar regions from depth camera readings. This problem is of great importance for terrain mapping in complicated environment and has great potentials in legged locomotion applications. To address the polytopic planar region characterization problem, we propose a two-stage solution scheme. At the first stage, the planar regions embedded within a sequence of depth images are extracted individually and then merged to establish a terrain map containing only planar regions in a selected frame. To simplify the representations of the planar regions that are applicable to foothold planning for legged robots, we further approximate the extracted planar regions via convex polytopes at the second stage. With the polytopic representation, the proposed approach achieves a great balance between accuracy and simplicity. Experimental validations with RGB-D cameras are conducted to demonstrate the performance of the proposed scheme. The proposed scheme successfully characterizes the planar regions via polytopes with acceptable accuracy. More importantly, the run time of the overall scheme is less than 10ms (i.e., > 100Hz) throughout the tests, which strongly illustrates the advantages of our approach developed in this paper.
Pengembangan Koleksi di Perpustakaan Universitas Panca Bhakti

Collection development is a tool for identifying the needs of the library. The needs in question are not onlypresent needs but cover the next 5 years. In addition, the development of the collection covers the needs of thelibrary as a whole, not only the needs of active users. Thus, this research describes the Development ofCollections in the Panca Bhakti University Library. This research uses qualitative methods with data collectiontechniques using interview and observation techniques. The results of this study are several processes carriedout by the Panca Bhakti University Library including: 1. Survey of Library Needs of The Library, 2. Concept ofCollection Development, 3. Poses Collection Procurement, 4. Weeding Collection, 5. Evaluation of Activities
Fuzzy Evaluation Model of Teaching Quality of Physical Education Course Based on Deep Reinforcement Learning

Because there are many factors affecting teaching evaluation, the evaluation results are difficult to reach a high level. Therefore, a fuzzy evaluation model of teaching quality of physical education course based on deep reinforcement learning is designed. On the basis of clarifying the requirements of teaching quality evaluation, the factor analysis method is used to preprocess the teaching data, the Bartlett spherical test is used to verify it, and the data meeting the verification requirements are K-mean clustered. Finally, based on the clustered data results, the fuzzy evaluation model is constructed according to the idea of minimum membership weighted average deviation. The test results show that the evaluation results of the design model have high adaptability with the actual results, and can meet the evaluation needs.
CCOM: Cost-Efficient and Collusion-Resistant Oracle Mechanism for Smart Contracts

Smart contracts, that allow parties to establish agreements based on predefined rules without a trusted third-party, have been explored in various applications. However, the main drawback of smart contracts is that they cannot access the external data required to trigger the execution of inner logic. The Oracle technology is an interactive bridge between on-chain smart contracts and off-chain data, which is designed to introduce external data into the blockchain system. A superior oracle mechanism should achieve reliable data acquisition with easy data parsing, deployable services, high system efficiency, and cost-effectiveness. The current smart contracts oracle mechanisms either rely on a trusted third-party or introduce high computation overhead and difficulty in deployment. This paper, for the first time, proposes a decentralized and efficient on-chain oracle mechanism CCOM. In our scheme, a prisoner’s contract is introduced for users who want to obtain specific information. The user introduces two oracles to complete the same task of obtaining data, and the contract can prevent oracles from collusion. Rational oracles will not collude but honestly submit the correct result to increase self-interest. We also demonstrate that the proposed scheme can resist a single potentially malicious oracle service and prevent collusion from occurring. Finally, we perform experiments on Ethereum test network Rinkeby and show that our scheme is time-efficient and cost-effective.
IoT Devices Classification Base on Network Behavior Analysis

IoT devices classification from the network traffic have attract increasing attention due to the manage requirement of the growing IoT applications. Current statistic feature based methods needs to sample most of the packets and requires much computation during the feature extracting. The features are easily affected by the network environment and user operations. This paper proposes a network behavior analysis (NBA) based IoT devices classification scheme which takes the sequence of network session features as input and learns the behavior feature with LSTM for IoT devices classification. NBA bases on the network behavior analysis without parsing the packet of network traffic, thus can handle the traffic using encryption protocol. In addition, we built a testbed with IoT devices for evaluation. The traffic generated in the testbed was captured for evaluation. The result of the experiment indicates that by incorporating destination and interval features, our method can accurately classify IoT and non-IoT devices and achieves the accuracy over 99%.
Multi-level Temporal-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition is a crucial and challenging task, which has promoted remarkable progress in diverse fields. Nevertheless, how to capture long-range temporal relationships remains a challenging problem, which is vital to reducing the ambiguity of indistinguishable actions. Towards this end, we propose a novel Multi-Level Temporal-Guided Graph Convolutional Network (ML-TGCN) to tackle the above problem. We leverage the multi-level temporal-guided mechanism to learn diverse temporal receptive fields for mining the discriminative motion patterns. Moreover, most current approaches cannot effectively explore the comprehensive spatial topology due to the skeleton graph is heuristically predefined, thus we propose a cross-space GCN to capture global context and maintain strengths of GCNs (i.e., hierarchy and local topology) jointly beyond the physical connectivity. The experimental results on the challenging datasets NTU RGB+D and Kinetics-Skeleton verify that ML-TGCN can achieve state-of-the-art performance.
Human Motion Recognition Method in Physical Education Based on Wearable Perception

The traditional human behavior recognition technology takes a long time to recognize human behavior. Aiming at the shortcomings of traditional methods, this paper designs a new human action recognition method in physical education based on efficient wearable sensor network. Firstly, the human action recognition characteristics of physical education teaching are extracted. Secondly, the human action recognition model of physical education teaching is constructed based on wearable perception, so as to realize human action recognition. The results show that the shortest action recognition time of physical education teaching people designed in this paper is 0.169 s. The designed physical education teaching human action recognition method has good recognition effect and certain application value.
Blockchain and Culture Education

With the rapid development of Internet technology, the content of cultural education is rapidly transferred from offline to online. The transformation solves the problem of time and space limitation, yet brings new challenges such as digital copyright protection, quality certification of curriculum resources, education evaluation and certification, personalized learning services, and education quality supervision. The emergence of blockchain technology provides strong support to solve the above challenges in cultural education. This chapter focuses on the solutions based on blockchain for copyright deposit, transaction, and value evaluation, as well as education certification, learning incentive and credit cashing modes, and the subject-object evaluation model for online education and learning. Three application scenarios and application cases are tackled, including educational authentication and certificate management, multiple evaluations for online learning, copyright certificate storage and trading, and knowledge securitization. The discussion and research in this chapter could be referenced for further development of blockchain in cultural education.
DCRNNX: Dual-Channel Recurrent Neural Network with Xgboost for Emotion Identification Using Nonspeech Vocalizations

The human voice, especially nonspeech vocalizations, inherently convey emotions. However, existing efforts have ignored such emotional expressions for a long time. Based on this, we propose a Dual-channel Recurrent Neural Network with Xgboost (DCRNNX) to solve emotion recognition using nonspeech vocalizations. The DCRNNX mainly combines two Backbone models. The first model is a two-channel neural network model based on the Deep Neural Network (DNN) and Channel Recurrent Neural Network (CRNN). Channel 1 is constructed by CRNN, and the other model is constructed by Xgboost. Additionally, we employ a smoothing mechanism to integrate the outputs of the two classifiers to promote our DCRNNX. Compared with the baselines, DCRNNX combines not only multiple features but also combines multiple models, which ensures the generalization performance of DCRNNX. Experimental results show that our method achieves 45% and 42% UAR (Unweighted Average Recall), on the development dataset. After model fusion, DCRNNX achieves 46.89% UAR and 37.0% UAR on development and test datasets, respectively. The performance of our method on the development dataset is nearly 6% better than the baselines. Especially, there is a considerable gap between the performance of DCRNNX on the development and the test set. It may be the reason for the differences in emotional characteristics of the male and female voices.
Deep Learning Approach to the Quality Restoration for JPEG Images

The JPEG codec is still the dominant and common image compression format despite the continuous development of new image compression techniques. In order to raise the compression ratio, distortion caused by some quantization operation within the encoding process is unavoidable. Therefore, this paper proposed a deep learning based JPEG decoder to decrease image quality degradation by JPEG encoding quantization. Most of the image processing problems, such as image denoising, image super-resolution, and even style conversion, can be handled well in the spatial domain by deep learning using the fully convolutional networks (FCN). However, because the image is converted from the spatial domain to the frequency domain by the Discrete Cosine Transform (DCT) during JPEG encoding, this paper addresses the issue of FCN might not be suitable for processing frequency-spatial domain directly. Then, this study develops two learning models more suitable for inverse DCT (IDCT) with fully connected layers and transposed convolutional layers than the results with FCN. Finally, we constructed a JPEG decoder model based on deep residual networks. The experimental results showed that the FCN does not handle the frequency domain data well, and our proposed decoder has shown better results in peak signal to noise ratio (PSNR), structural similarity index (SSIM), and multi-scale SSIM (MS-SSIM) than the general JPEG decoder, giving evidence that the learning based JPEG decoder is capable of restoring some of the image quality degradation caused by JPEG quantization.