Reviewerly/exHarmony huggingface dataset:
Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China

In the big data era, mineral explorations need to accommodate for the growth in spatial dimensions and data dimensions, as well as the data volume and the correlation between data. Aiming to overcome the problems of limited and scattered data sources, chaotic data types, questionable data quality, asymmetric data information, and small sample sizes in current mineral prospecting data, this paper improved traditional 3D prediction methods based on the characteristics and actual needs of relevant mineral prospecting data. First, for the regions with incomplete data, a new 3D prediction method based on transfer learning was proposed. Meanwhile, random noise was adopted to compensate for the limited sample size in mineral prediction. By taking the Huayuan Mn deposit in Hunan Province as the study area, 22 proposed ore-controlling variables were divided into six groups for comparative tests under different combinations, and each group was further divided into the 3D CNN prediction method and the transfer learning prediction method. After the similarities between the regional metallogenic backgrounds were proven, the convolution kernel of the Minle area was transferred to that of the Huayuan area with poor data. Then, both were used to train a 3D prediction model to realize the training and transfer of the spatial correlation between the spatial distribution of ore-controlling factors and the manganese ore. The results indicated that the accuracy of the transfer learning model in test 6 could reach 100%, with good stability of the transfer learning prediction model and a high convergence speed. By comparing the 3D-predicted targets before and after the transfer learning of tests 5 and 6, it was found that the 3D CNN model of test 5 still performed well, but the transfer learning model of test 6 was superior. In verifications based on superposition with the basin model and the growth fault model, the prediction results were consistent with the geological characteristics of the research area. To sum up, the 3D CNN prediction method has advantages in mineral prediction when big data are available, and transfer learning based on the 3D CNN algorithm helps to realize 3D deep mineral prospect prediction in the case of incomplete data.
68 landmarks are efficient for 3D face alignment: what about more?

This paper proposes a 3D face alignment of 2D face images in the wild with noisy landmarks. The objective is to recognize individuals from their single profile image. We first proceed by extracting more than 68 landmarks using a bag of features. This allows us to obtain a bag of visible and invisible facial keypoints. Then, we reconstruct a 3D face model and get a triangular mesh by meshing the obtained keypoints. For each face, the number of keypoints is not the same, which makes this step very challenging. Later, we process the 3D face using butterfly and BPA algorithms to make correlation and regularity between 3D face regions. Indeed, 2D-to-3D annotations give much higher quality to the 3D reconstructed face model without the need for any additional 3D Morphable models. Finally, we carry out alignment and pose correction steps to get frontal pose by fitting the rendered 3D reconstructed face to 2D face and performing pose normalization to achieve good rates in face recognition. The recognition step is based on deep learning and it is performed using DCNNs, which are very powerful and modern, for feature learning and face identification. To verify the proposed method, three popular benchmarks, YTF, LFW, and BIWI databases, are tested. Compared to the best recognition results reported on these benchmarks, our proposed method achieves comparable or even better recognition performances.
Design and Evaluation for Improving Lantern Culture Learning Experience with Augmented Reality

AbstractAbstractMany forms of Intangible Culture Heritage (ICH) are fast disappearing. The Lantern Festival is an important part of Chinese tradition and a critical ICH of Taiwan. However, few young people attend ICH activities. Given the importance of ICH education, this study describes what participants learned after lantern exhibitions that combined Augmented Reality (AR), mobile websites, and Paper-based Exhibition Brochures. This study evaluated what a cohort of college students (n = 115) learned about ICH through experiments and questionnaires. The results show that the public understood ICH better through digital technology. All participants thought that applying AR to ICH was good for the public to gain more information on lantern culture, although it took a lot of time to learn the new technology. The results also encouraged the application of AR to engage in ICH education. Therefore, a large number of digital technologies that are popular among young people should be used as part of ICH education in order to improve their cultural awareness and learning motivation.Keywords: Intangible cultural heritageaugmented realityChinese lantern culturetechnology enhanced learningcultural awarenessHuman-Computer Interaction Disclosure statementJian Kang is employed by Shandong Jianzhu University Design Group Co., Ltd. The remaining authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.Additional informationFundingThis study was supported by Taipei City Government and Ministry of Education of the Republic of China [PHA1101038].Notes on contributorsChun-Ching ChenChun-Ching Chen is an associate professor at the Department of Interaction Design, National Taipei University of Technology, Taiwan. His research interests focus on human–computer interaction, user interface design, user behavior, and human–computer system applications.Xin KangXin Kang is an assistant professor at the School of Design, NingboTech University, Ningbo, Zhejiang, China. Her research interests focus on digital art, design thinking, user experience, and human-computer interface design.Xin-Zhu LiXin-Zhu Li is a PhD Candidate at College of Design, National Taipei University of Technology, Taiwan. His research scope is User Experience, Chinese traditional lantern craft, Technology Education, and human-computer system applications.Jian KangJian Kang is a Project Chief Engineer at the Department of Energy Design, Shandong Jianzhu University Design Group Co., Ltd. His research interest is human-computer Interaction.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
A Novel Framework for Human Action Recognition Based on Features Fusion and Decision Tree

Image-based detection of human actions has recently emerged as a hot research area in the fields of computer vision and pattern recognition. It is concerned with detecting a person's actions or behavior from a static image. In this article, we are using a decision tree to develop an action recognition technique. In order to enhance the clarity of the video frames, the proposed method begins by implementing the HSI color transformation in the initial stage. Subsequently, it utilizes filters to minimize noise. The silhouette is extracted using a statistical method. We use SIFT and ORB for feature extraction. Next, using a parallel process, extract the shape and texture features needed for fusion applying name length control features. Additionally, the best high-dimensional data for classification is explored using vectors and the t-distributed stochastic neighbour embedding (t-SNE).The final step involves the features to be input into a decision tree, where they will be sorted into relevant human actions based on those final characteristics. The recognition rate of the UT interaction data used in the experimental process is 94.6%.
Artificial Neural Network for Human Object Interaction System Over Aerial Images

Recognition of human and object interaction is an important milestone in image understanding and event analysis. Recognizing interactions between humans and objects is the most promising way in visual classification to analyze activities or events happening at any place. Many researchers have invested their efforts in the field of activity recognition between humans and objects. However, some challenges are still open due to incorrect interaction inferences, occlusion between a person and target objects, unrelated target objects, or unclear activities. The major goal of this research project is to provide a useful system for categorising event classification and human-object interaction. Preprocessing, feature extraction, feature optimization, and classification using an artificial neural network are the four main processes of the proposed method. The Games Action dataset, which contains aerial photos, has been used to test the proposed technique. Results demonstrate the effectiveness of the suggested system.
Design and Implementation of Electronic Health Records using Ethereum Blockchain

Blockchain system is a technology for storing data in a way that it makes hard and impossible to change or hack to steal the data. It is a key technology containing the distributed database and continuously growing records with a decentralized architecture. Ethereum is a decentralized, open-source blockchain network with smart contract capabilities. So, Ethereum Blockchain Network is explored along with its components and transactions flow. Ethereum has many applications in the healthcare sector. The highly sensitive (EHR) is the current method used in medical for transmitting and storing medical records. Due to a lack of trustworthy and dependable health data sharing mechanisms, the most EHR sharing data is still done through the mail. Blockchain is a decentralized architecture that offers cryptographic assurances of data confidentiality, privacy, and access control through smart contracts. A permissioned blockchain system, which is one of the principles employed in Ethereum is a solution for a peer-to-peer network for safely executing and checking application code of a decentralized blockchain platform. In order to store the huge amount of records in blockchain requires lot of money and the other challenge is authorized sharing of health records. In the proposed system to overcome this challenge Interplanetary File System (IPFS) is used for the cost reduction.
Disentangling Representations in Restricted Boltzmann Machines without Adversaries

A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. Methods for disentangling representations often rely on an adversarial scheme, in which representations are tuned to avoid discriminators from being able to reconstruct information about the data properties (labels). Unfortunately adversarial training is generally difficult to implement in practice. Here we propose a simple, effective way of disentangling representations without any need to train adversarial discriminators, and apply our approach to Restricted Boltzmann Machines (RBM), one of the simplest representation-based generative models. Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the two-dimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of log-likelihood of the data, associated to the disentanglement of their representations.
Sensitive region-aware black-box adversarial attacks

Recent research on adversarial attacks has highlighted the vulnerability of deep neural networks (DNNs) to perturbations. While existing studies generate adversarial perturbations spread across the entire image, these global perturbations may be visible to human eyes, reducing their effectiveness in real-world scenarios. To alleviate this issue, recent works propose to modify a limited number of input pixels to implement adversarial attacks. However, these approaches still have limitations in terms of both imperceptibility and efficiency. This paper proposes a novel plug-in framework called Sensitive Region-Aware Attack (SRA) to generate soft-label black-box adversarial examples using the sensitivity map and evolution strategies. First, a transferable black-box sensitivity map generation approach is proposed for identifying the sensitive regions of input images. To perform SRA with a limited amount of perturbed pixels, a dynamic l0 and l∞ adjustment strategy is introduced. Furthermore, an adaptive evolution strategy is employed to optimize the selection of generated sensitive regions, allowing for the execution of effective and imperceptible attacks. Experimental results demonstrate that our SRA achieves an imperceptible soft-label black-box attack with a 96.43% success rate using less than 20% of the image pixels on ImageNet and a 100% success rate using 30% of the image pixels on CIFAR-10.
The engagement of academic libraries in open science: A systematic review

Open science is reshaping the environment of scholarly communication. In this context, academic libraries are beginning to redefine or expand their functions and role. Using a systematic review and meta-synthesis approach, this study analyses 65 literature related to the engagement of academic libraries in open science. The results show that the existing research mainly focuses on four aspects of open science: open access, research data management, open educational resources, and citizen science. Based on these themes, academic libraries have implemented a range of practical activities, playing the roles of open science service providers, advocates or educators, policy makers, publishers or knowledge producers, etc. At the same time, however, academic libraries are facing a number of challenges caused by inadequate security of internal resources and insufficient support from the external environment.
A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.
Nursing Students’ Experience of Using HoloPatient During the Coronavirus Disease 2019 Pandemic: A Qualitative Descriptive Study

This study examined nursing students' experiences of using HoloPatient to learn COVID-19-related patient care.In this qualitative descriptive study, focus group interviews were held virtually with 30 nursing student participants in South Korea. Data were analyzed using a mixed content analysis.Participants reported satisfaction associated with having gained patient assessment and critical thinking skills, self-confidence, and knowledge about the care of patients with COVID-19.HoloPatient in nursing education can improve learning motivation, critical thinking skills, and confidence. Efforts should be made to engage users by providing an orientation, supplementary materials, and an environment conducive to learning.
Coarse-to-fine sparse self-attention for vehicle re-identification

Existing vehicle re-identification (Re-ID) methods usually combine global features and local features to meet the challenge of inter-class similarity and intra-class variance, but almost all existing methods rely on extra auxiliary networks to locate the vehicle parts for feature mining, which are inefficient, cost-ineffective and time-consuming. Self-attention mechanism, averaging model's attention weights on the similar vehicle parts, can guide the model to identify more discriminative vehicle parts for identification, thus extracting expensive local features becomes unnecessary. However, the computational cost of the original self-attention mechanism is unacceptable and the attention map is less discriminative when processing high-resolution feature maps (i.e. 384*384 or larger), which limits the performance gains of the challenging vehicle Re-ID. In this paper, we propose a lightweight coarse-to-fine sparse self-attention mechanism for vehicle Re-ID. Our method decomposes the self-attention process to a coarse stage and a fine stage. In coarse stage, the pixel-level feature map is transformed to patch-level feature map and the dependencies between similar vehicle parts are captured in a global context. In fine stage, the details of vehicle parts are captured in a local context. In addition, multi-head structure is introduced to capture diverse but robust attention information. Experiments on VeRi and VERI-Wild show that our method not only outperforms recent vehicle Re-ID methods relying on expensive auxiliary networks, but also outperforms the original self-attention mechanism in accuracy with much lower computation and memory cost.
Genetic Algorithms: Brief review on Genetic Algorithms for Global Optimization Problems

An intelligent bionic algorithm with great global optimization potential, the genetic algorithm evolved in a manner analogous to the natural process of genetic evolution in living creatures. This paper first explains the foundation of genetic algorithms, which is based on Darwin's “survival of the fittest” principle, then outlining the algorithm's primary features and briefly discussing its drawbacks. The structural and thorough view of genetic algorithms is presented in this review work. Furthermore, taxonomy and the applications and application-specific genetic operators. This review paper's challenges and issues, as well as their answers, address every facet of GAs.
Face Recognition Accuracy Improving Using Gray Level Co-occurrence Matrix Selection Feature Algorithm

Information security is becoming increasingly important as technology develops, but it also faces many challenges. Face recognition is one of the technologies that can be used to face these challenges. The facial recognition system has been studied extensively, but its accuracy has not been optimized. Therefore, further studies are needed to increase accuracy. The feature extraction process can be used to improve accuracy. This method is used as a reference to extract important information in an image and distinguish one image from another. Several feature extraction methods are available for face recognition, one of which is the Gray Level Co-occurrence Matrix (GLCM) method. This research goal to improve the accuracy of facial recognition by trying a number of features in GLCM. Face recognition uses Backpropagation because this research only proves the effect of GLCM features, not to find maximum accuracy. Based on the trials that have been carried out, the best test accuracy results without GLCM feature extraction are 73%. While the test using extraction with 4 GLCM features is 89% when the GLCM neighbor distance is 1 pixel, so there is an increase in accuracy of 16%.
COLIEE 2022 Summary: Methods for Legal Document Retrieval and Entailment

We present a summary of the 9th Competition on Legal Information Extraction and Entailment (COLIEE 2022). The competition consists of four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law component includes an information retrieval task (Task 3) and an entailment/question answering task (Task 4). Participation was open to any group, using any approach. Ten different teams participated in the case law competition tasks, most of them in more than one task. We received competition submissions from 9 teams for Task 1 (26 runs) and 5 teams for Task 2 (14 runs). On the statute law task, there were 11 different teams participating, most in more than one task. Five teams submitted a total of 15 runs for Task 3, and 6 teams submitted a total of 18 runs for Task 4. We summarize the technical details of all approaches, describe our official evaluation, and provide an overall analysis on our data and submission results.
UAV Chasing Based on YOLOv3 and Object Tracker for Counter UAV Systems

This paper proposes a vision-based unmanned aerial vehicle (UAV) chasing system that can be embedded in a pursuer UAV (pUAV) to protect the attack from an evader UAV (eUAV).The proposed UAV chasing system consists of two parts, i.e., the UAV tracking and control signal generation parts.Combining a deep learning-based object detector, you only look once version three (YOLOv3), and the existing object trackers, the proposed UAV tracking algorithm can improve the tracking performance of pUAV within affordable computational complexity.Then, the control signals of the pUAV are generated by utilizing the predicted bounding box area of the eUAV and the proportional-derivative control method.Following the object tracking benchmark performance criteria, various combinations of YOLOv3 and object trackers are examined and compared.From the evaluation results, the UAV tracking algorithm with the highest performance is chosen, whose average success rate and average precision rate for object tracking are 2.86% and 5.61% higher than YOLOv3, respectively.Through the field test, it is verified that the proposed UAV chasing system outperforms the YOLOv3 system in terms of bounding box misalignment (33% accuracy improvement) and computational complexity (71% reduction).