Reviewerly/exHarmony huggingface dataset:
3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data

Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas with mineral potential. These 3D geological models are the typical data source for 3D prospective modeling. Geological data sets from multiple sources are used to construct 3D geological models. Since in practice there is a significant imbalance in the ratio of mineralized to non-mineralized classes, the classification results will be biased in favor of the more observed classes. Borderline-SMOTE (BLSMOTE) is an oversampling technique used to solve the problem of unbalanced datasets and works by generating synthetic data points along the boundary line between the minority and majority classes. This helps to create a more balanced dataset without introducing too much noise. Non-mineralized samples can be generated by randomly selecting non-mineralized locations, which means that uncertainties are generated. In this paper, we take the shallow-forming low-temperature hydrothermal deposit Guizhou Lannigou gold deposit as an example to extract the ore-controlling elements and establish a 3D geological model. A total of 50 training samples are generated using the sampling method described above, and 50 mineralization prospects are generated using Random Forests. A return–risk analysis was used to explore the uncertainties associated with synthetic positive samples and randomly selected negative samples, and to determine the final mineral potential values. Based on the evaluation metrics G-mean and F-value, the model using BLSMOTE outperforms the model without the synthetic algorithm and the models using SMOTE and KMeansSMOTE. The optimal model BLSMOTE18 has an AUC of 0.9288. The methodology also performs superiorly with different levels of class imbalance datasets. Excluding the predictions where the results highly overlap with known deposits, five target zones were circled for the targets using a P-A plot, all of which have obvious metallogenic geological features. Among them, Target1 and Target2 have good potential for mineralization, which is of great significance for future mineral exploration work.
Biometric-Based Password Management

Major threat for the user's identity stem from selecting weak passwords or re-using the same password for different systems. Modern password managers are designed to address this human factor. But in most cases this is achieved at cost of using a single master secret to either derive access keys to protected services, or to encrypt a credentials database. Despite wide adoption, this boils down to security and availability of this master secret. We propose a technology to derive cryptographically-strong (of sufficient length and entropy) master secret from user's biometrics, such as face and voice. If applied to password manager scenario, this allows to amend or even completely replace master secret to avoid related risks. While general approach (using fuzzy extractors) is known, the unique part of the presented technology is small hint size (58KB for 128 bits key) and low computational complexity (it takes 125 msec to extract the key on Galaxy S22 phone in the worst case). Experimental results show that FAR and FRR are close to $$0\%$$ for wide range of cryptographic keys lengths (from 80 to 256 bits). All computations are performed on-device, which means the technology is privacy-friendly: user's biometrics never leaves the phone. The technology does not require storing any sensitive data on the device, that is important advantage in comparison with traditional biometric authentication solutions.
Towards a Unified Abstract Architecture to Coherently and Generically Describe Security Goals and Risks of AI Systems

We propose a unified abstract architecture for describing IT security goals and risks within AI systems. The architecture facilitates effective interdisciplinary communication among AI developers, data scientists, and security professionals. The architecture is abstract enough to cover a wide range of AI methods (not limited to machine learning) while it can still be used to sufficiently describe and map existing AI-specific attacks. It emphasises the importance of identifying at-risk processes or at-risk data within the AI system for a targeted increase of the overall system's security. This systematic approach could help to optimise resource allocation while achieving desired protection targets for AI systems.
Privacy-Preserving NN for IDS: A Study on the Impact of TFHE Restrictions

The rapid growth of the Internet ecosystem has led to an increase in malicious attacks, such as Distributed Denial of Service (DDoS), which pose a significant threat to the availability of shared services. Intrusion Detection Systems (IDSs) play a crucial role in detecting and responding to these attacks. However, the use of intelligent systems like IDSs raises significant concerns regarding privacy and the protection of network data. Homomorphic Encryption (HE) has emerged as a promising cryptographic technique for enabling privacy-preserving IDSs, but it has some limitations. In this work, we analyse the impact of HE, specifically the TFHE scheme, on the performance of Neural Networks (NNs) for DDoS attack detection, and provide the first study on assessing the impact that TFHE restrictions may have in NNs training and inference. Our findings show that TFHE restrictions have a minor impact on the performance of NNs, with the models complying with TFHE restrictions achieving performance metrics comparable to plaintext-based NNs. This suggests that high-performing NNs can be achieved, for DDoS attack detection, without exposing plaintext data. Additionally, we also observed that TFHE-compliant models exhibit a learning pace similar to traditional NNs. Therefore, our results highlight the potential of TFHE in enabling privacy-preserving NNs for DDoS attack detection, but further research is needed to gain a deeper understanding of its limitations. This may be done by exploring other metrics, and datasets, and by assessing the computational overhead in real-world scenarios.
Penerapan Sistem Pendukung Keputusan dalam Penentuan Alat Bantu Media Pembelajaran Fisika Terbaik Menggunakan Metode PSI

Technology in the Revolutionary Era 4.0 and Society 5.0 made changes in the manual learning system to digital. Along with the development of technology that makes technology and humans very difficult to separate. There are many ways to acquire learning knowledge, one of which is to use learning media tools that are already available on mobile phones. There are so many physics learning media tools available that make students confused in determining the best media to use. In determining physics learning media tools when learning online there are several criteria for Storage Capacity, Application Features, Number of Materials, Number of Language Choices, Number of Users and Rating Reviews. Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the Preference Selection Index (PSI) method which is very helpful in generating the best weight and preference values ​​from alternative data and criteria so that the final result is that the best physics learning media tool is in Alternative F4 with a value of 0.79563, namely the PhyWiz application.
Penerapan Algoritma Gronsfeld Dengan Pembangkit Kunci Quicksort Untuk Mengamankan Pesan Rahasia

The numerous issues often encountered in securing confidential messages revolve around message leaks. Message leaks occur through various means, such as eavesdropping. Eavesdropping happens when unauthorized parties manage to access and obtain information from ongoing communications. One way to maintain the confidentiality of secret messages is through encryption and decryption, known as cryptography, achieved by transforming the original message (plaintext) into a secret message (ciphertext). In this study, the results of the encrypted character ciphertext are v, v, w, ^, ™, ʽ, ¦, ¥, -, ¼, Ý, ó, ⁒, x, [], ", [], #, S, a, I, p, p. These findings can serve as an alternative solution to safeguard message confidentiality, ensuring only the message owner and those with knowledge of the key can access it. The Gronsfeld Cipher cryptographic method can also enhance the security of the Quicksort algorithm, a cryptographic algorithm, by generating a more randomized text message that does not reveal patterns connecting it to the original text message.
Penerapan Data Mining Dalam Penentuan Proritas Pemesanan Produk Berdasarkan dengan Data Penjualan Barang Menggunakan Algoritma Apriori

Products cannot be separated from the buying and selling process carried out by customers and sellers. Products that are a priority are, of course, products that are favored by consumers or customers. However, companies often make mistakes in the process of determining which products to order for sale. Errors in the process of determining product orders can result in losses for the company. Therefore, it is necessary to handle this problem properly and appropriately. Therefore, the settlement that should be done should be done by processing the data on the sale of these goods. Data mining is a method that can be used for data processing. But the data processing process carried out in data mining is not done independently. The a priori algorithm is one of the algorithms found in the data mining association technique. The a priori algorithm is widely used to carry out the analysis process on sales data to find sales patterns in the company. The results of the study were 3 items that became the priority of ordering, namely ordinary pants, shorts and t-shirts. With a combination of item sets, if you buy ordinary pants, you also buy shorts with a support value of 10% and a confidence value of 20%. Other item combinations that you get: If you buy regular pants, you also buy T-shirts with a support value of 30% and a confidence value of 60% and the last combination of set items. If you buy shorts, you also buy T-shirts with a support value of 30% and a confidence value of 60%.
Game Edukasi Visual Novel Bermain Pantun dan Peribahasa Menggunakan Ren'py Berbasis Python

In the digital era, technological developments, especially games, are very promising to learn. This can be used as an interactive learning tool for every game fan. Learning media using games can be an alternative for elementary school students to get to know rhymes and proverbs easily and fun. An interactive program that can arouse students' interest in knowing rhymes and proverbs is needed to eliminate boredom when studying them. The researcher uses the SDLC method to create a visual novel game application to play rhymes and proverbs with Python-based Ren'py. Learning to play rhymes and proverbs through visual novels is done by choosing from different problems from the novel's plot. The results obtained in making this game is an interactive novel game by displaying each dialogue which can then be answered by the user and the selected dialogue can determine the flow of the game.
Penerapan Metode Posenet untuk Deteksi Ketepatan Pose Yoga

When someone does yoga activities, usually, someone follows yoga guidelines in the form of yoga classes and yoga self-guidance, which is usually in the form of images, videos, and audio. But along with the rapid development of technology, now there are many yoga guides and easy to get. Yoga activities can now be done independently without approaching a yoga instructor. The purpose of this study is to help yogis to be able to do yoga correctly and to follow yoga guidelines with the correct poses with a system that contains yoga poses and also as the main feature of correcting yoga poses that yogis do to get yoga poses correctly. The resulting system will capture yoga poses and detect whether the pose is correct according to the yoga pose guidelines. Based on the verification of the distance between the device or the device that has a camera, it is placed at a distance of 200cm – 250cm between the device and the yogis. The device's height is placed in a place that has a height half the size of the yogi's whole body or at the height of 85cm. The test results using the BlackBox method get no errors. Besides that, the system can run well in a room with 7-watt LED lights, and no objects block the yogis, so the camera catches the whole body.
Kombinasi Metode Certainty Factor dan Fuzzy Tsukamoto dalam Pradiagnosa Penyakit Gagal Ginjal Kronis

Expert systems aim to combine human knowledge with systems, that is, so that computers can solve problems in the same way that experts usually do. Expert systems can also be used in diagnosing disease to determine the type of disease suffered as an initial diagnosis based on the symptoms to be followed up. In this study, the method used to develop an expert system for chronic kidney failure was using the Fuzzy Tsukamoto and Certainty Factor methods. The data search process starts from the symptoms experienced by the user and lab results of anemia, creatinine and eGFR then the final results obtained from this study are an Expert System Application for pre-diagnosing Kidney Disease with the Tsukamoto Method and Certainty Factor. The results obtained from this study, namely the certainty factor method obtained a patient's disease confidence level of 99.48% where according to these results according to experts and with the Fuzzy Tsukamoto method the results obtained for the stage of chronic kidney failure were 73.9 where these results were included in the VV High Risk category.
Sequential Action Retrieval for Generating Narratives from Long Videos

In this paper, we propose a novel event retrieval method called Sequential Action Retrieval, which is a work in progress, towards generating video and text narratives of long-term events from long videos. Summarizing events of user interest from long videos is a challenging topic. Our proposed method aims at detecting long-term human activities defined by a sequence of action elements. By searching action elements from a semantic video graph that structures the appeared objects and their relationships in videos, our method is able to recognize complex action events such as object owner changes involving two or more people. We conducted an initial evaluation of event-related person detection on the Narrative dataset. We introduce a new evaluation metrics, KP-IDF1 to evaluate the accuracy of the appearances of related persons. Our proposed method achieves 76.4% of KP-IDF1 for the case of bicycle theft.
Imbalanced node classification with Graph Neural Networks: A unified approach leveraging homophily and label information

The homophily assumption in graph theory posits that nodes with similar characteristics have a higher tendency to form connections. This principle has rendered Graph Neural Networks (GNNs) as vital tools for graph representation learning. However, many real-world graphs may exhibit a phenomenon often termed as neighbor class imbalance, which is characterized by frequent connections between dissimilar nodes, a scenario reflecting low homophily. Classical GNNs tend to overlook this issue, leading to a significant decline in performance. Prior research has attempted to address this challenge by employing high-order neighborhoods and filtering out dissimilar neighbors, yet they have paid little attention to homophily degree estimation and label utilization. In this work, we initially explore the performance of classical GNNs on a synthetic graph with varying homophily degrees, designated as SynG-N. Following this, we introduce a novel method, HLA-GNN, which integrates homophily degree estimation and label utilization to enhance classical GNNs. The degrees of homophily between node pairs are estimated using a limited set of ground-truth labels, which can be integrated into classic GNNs to guide the message aggregation process. Drawing on the label propagation algorithm, we combine the partially observed class labels to enhance the original feature space. Here, the observed class labels are randomly masked as a feature augmentation and training signal. Our experimental results on eight datasets with varying degrees of homophily underscore the effectiveness of our method. HLA-GNN achieves a 12.69%∼34.19% improvement on low-homophily graphs, while maintaining competitive results in homophilous settings.
Virtual-Violence: A Brand-New Dataset for Video Violence Recognition

As the development of intelligent terminals, a large number of online videos are uploaded to the video platforms. Thus, violence detection has been a significant part of online video content review. Suitable datasets are an important prerequisite for deep learning network training. However, considering the particularity of violence actions, it is both time and labor consuming to collect sufficient and various data. To address the problem, we generate the Virtual-Violence, a brand-new dataset containing 809 videos in total. Besides, we propose a violence detection model based on global-local visual and audio contrastive learning, which demonstrates the Virtual-Violence can improve the performance on the current datasets. Experimental results prove Virtual-Violence performs well on violence recognition task.
Strip-FFT Transformer for Single Image Deblurring

The purpose of image deblurring is to restore the origin image from the blurred image. With the development of deep learning, better performence for image deblurring can be obtained through the deblurring methods based on CNNs, while limited ability to model the global relationship, therefore its treatment of the correlation between the original resolution pixels is relatively weak. The hot Transformer approaches have a better ability to model the global context in the early stage, howerver, the disadvantage is that it is computational complexity. In addition, using only spatial features for image deblurring may lead to poor recovery of frequency domain information from the deblurred images, and frequency domain information is also key features for image deblurring. Therefore, we propose the SFT (Strip-FFT Transformer) method, which uses a hybrid architecture of CNNs and transformers to reduce the computational complexity, and a strip-fft Attention Block that integrates attention and Res-FFT mechanism to simultaneously process spatial and frequency domain information. After experiments, it is proved that SFT can obtain state-of-the-art effect in dynamic scene deblurring with relatively low memory consumption and computational complexity.
Code Switching in English Teaching Learning Processes of English as a Foreign Language

This research was done because code-switching was a phenomenon that exists in teaching-learning English as a foreign language, where the teachers used more than one language to communicate including in the teaching-learning processes of English as the foreign language at SMAN 2 Bukittinggi. The design of this research was descriptive qualitative research. The instruments of this research were interview for analyzing the functions of teacher’s code-switching and the observation for analyzing the types and the frequencies of teacher’s code-switching. The population of the research was all the English teaches. The sample was one of the English teacher eleventh’s grade at SMAN 2 Bukittinggi taken by randomly technique where the teacher was no-native of English speaker. The result of the research showed that in the three types of teacher’scode-switching they were 139 times inter sentential switching, 256 times intra sentential switching, 79 times tag switching. Besides, in the functions of code-switching were in 115 times of total frequencies by different modified of the functions were in classroom management (24,35%), inserting humor (2,60%), clarification of information (47,83%), increasing students’ comprehension (18,26%), and interpreting the lesson (6,96%). Then, distributions of teachers’ languages were 474 times or frequencies, the languages distribution were Pure English (44, 30%), Indonesian (29, 96%), and local language (25, 74%). Thus, this research found about three factors of teacher’s code switching that were the types of codeswitching, the functions of code switching, and the frequencies of teacher’s code-switching.
Design and Implementation of an Online Electronic Platform to Manage Education Activities in the Computer Science Department at Mosul University

In this work, data for electronic activities of lecturers in Computer Science Department was organized using a relational database management system to exploit relations between entities for facilitating access, view, and manipulation of data developed using Microsoft Visual C# programming language to obtain an easy-to-use graphical user interface (GUI). In addition, the Sap Crystal Report program was used to prepare reports for the electronic activities of the lecturers under study. The electronic system would reduce mistakes caused in the manual use of lecturers’ data. Additionally, it would speed up data access in case of viewing, updating or deleting the data without any errors. This project consisted of four phases, namely, system analysis, design, implementation and evaluation. Experiments explained that the suggested database system is more appropriate for lecturers’ information management, as more than 80% of the lecturers studied were satisfied with this project. This type of on-line platform system and its applications can be applied for stakeholders in various ways.
A Stable Long-Term Tracking Method for Group-Housed Pigs

In recent years, computer vision technologies have been increasingly applied to livestock farming for improving efficiency and reducing the labor force in surveillance. Tracking of group-housed pigs is an important task for monitoring the daily behaviors of pigs, which can be used to preliminarily evaluate the health status of pigs. Most researchers directly apply existing multi-object tracking algorithms to this task, but often suffer from tracking failures due to false detection, stacking, occlusion, video jamming, etc. It usually produces a lot of incorrect ID switches that are disastrous for follow-up tasks. In this paper, we propose a group-housed pigs tracking method that can achieve stable long-term tracking. As the identity and number of monitored pigs remain unchanged during a feeding period, we introduce a new object matching mechanism with a classifier, which avoids most incorrect ID switches and effectively improves the matching accuracy. Thus, our tracking method is more robust to complex posture variations of the pig and achieves stable long-term tracking. The experimental results on real videos captured in a pigs farm prove the effectiveness of our method.