Reviewerly/exHarmony huggingface dataset:
Nighttime Traffic Sign and Pedestrian Detection Using <scp>RefineDet</scp> with Time‐Series Information

Abstract Object detection is one of the most important tasks in computer vision‐based automation, such as advanced driver assistance systems in driving automation. It is preferable to detect traffic‐related objects at a far distance that appear small in the recorded scene in order to ensure maximum road safety while driving. As drivers tend to miss more traffic‐related objects at nighttime driving, this work focuses on nighttime in‐vehicle camera images. Because videos were recorded using an in‐vehicle camera, objects to be detected in this study, such as traffic signs and pedestrians, occupy a small size in the frame when far away from the own vehicle. Furthermore, it is necessary to take into account time‐series information to detect objects in sequential frames. Therefore, this research proposes an object detection model that combines the RefineDet small object detection model and the TSSD video detection model. Experimental results confirm the effectiveness of the proposed model. Moreover, a publicly available benchmark dataset is used to confirm the performance of the proposed model regardless of daytime or nighttime images. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Research article classification with text mining method

Summary In this article, it is aimed to classify research articles according to their subjects by using text mining method. The data set used for the study consists of research articles that are obtained by writing Python code with a state‐of‐the‐art web mining method. The collection of texts includes articles from the “Medicine”, “Social Sciences”, “Basic Sciences” and “Engineering” topics on the DergiPark‐Academic website. The data set consists of articles written in English and Turkish. In the first phase of text mining, text preprocessing and feature selection steps were applied to text data. Then the articles are classified according to their subject areas by employing Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) algorithms. In the final phase of text mining, the performance of the algorithms is evaluated with respect to the machine learning performance criteria. The results show that the working times of the algorithms were close to each other. The fastest running algorithm is NB, while the slowest running algorithm is SVM. On the other hand, SVM dominates with the best accuracy results among others. Algorithm performances in both Turkish and English languages do not differ remarkably in terms of accuracy and speed.
Characterizing and optimizing Kernel resource isolation for containers

Container-based virtualization has become increasingly popular as a lightweight alternative to hypervisor-based virtualization in cloud computing. Isolation is a fundamental property for consistent and reliable performance for cloud environment. However, the isolation between containers is much weaker than virtual machines as containers on the same host share one underlying host kernel. Existing works have mainly focused on the isolation problems at physical resources (e.g. CPU) level and almost not discussed with kernel resources (e.g. lock). In this paper, we perform a study to quantify kernel resource isolation for containers with a new microbenchmark, KRIBench. Then we describe kernel resource isolation issues and identify several kernel resources competition behind the poor isolation. Furthermore, we design and implement Valve, a general and flexible system that reduces kernel resources competition through limiting usage of system calls. Valve adopts Pareto-based container identification to locate misbehaving containers and supply–demand model to manage usage of system calls. The evaluation results demonstrate that our system can effectively enhance the kernel resource isolation for containers with negligible performance overhead.
HCFN: Hierarchical cross-modal shared feature network for visible-infrared person re-identification

Compared with traditional visible-visible person re-identification, the modality discrepancy between visible and infrared images makes person re-identification more challenging. Existing methods rely on learning efficient transformation mechanisms in paired images to reduce the modality gap, which inevitably introduces noise. To get rid of these limitations, we propose a Hierarchical Cross-modal shared Feature Network (HCFN) to mine modality-shared and modality-specific information. Since infrared images lack color and other information, we construct an Intra-modal Feature Extraction Module (IFEM) to learn the content information and reduce the difference between visible and infrared images. In order to reduce the heterogeneous division, we apply a Cross-modal Graph Interaction Module (CGIM) to align and narrow the set-level distance of the inter-modal images. By jointly learning two modules, our method can achieve 66.44% Rank-1 on SYSU-MM01 dataset and 74.81% Rank-1 on RegDB datasets, respectively, which is superior compared with the state-of-the-art methods. In addition, ablation experiments demonstrate that HCFN is at least 4.9% better than the baseline network.
<scp>ER‐DeepSORT</scp>: Pedestrian Multiobject Tracking with Enhanced Reidentification

Abstract Pedestrian multiobject tracking is the major research branch in the field of computer vision. In complicated scenarios with frequent scale changes and occlusion, the existing multiobject tracking methods based on detection have unsatisfactory tracking accuracy because of the low robustness of reidentification. This article proposed a multiobject tracking method to improve the reidentification module in YOLOv5‐DeepSORT at a more fine‐grained level. The feature extraction network for the Re ‐ID part of this algorithm is built based on Res2Net and group convolution. This network's hierarchical connection structure effectively improved the network's ability to extract multiscale features, and at the same time increased the receptive field of each network layer. The PCB network structure with evenly divided feature maps is used in the output part of the backbone network to enhance the influence of local features on the overall network performance. Based on this, the reidentification model is trained on the public datasets Market‐1501 and DukeMTMC‐reID using triplet loss. ER‐DeepSORT is an algorithm that combined the improved reidentification module of this article into DeepSORT. This article compared ER‐DeepSORT with YOLOv5‐DeepSORT under the original reidentification module to evaluate the tracking effect in MOT16 test sequence, the experimental results showed that ER‐DeepSORT improved MOTA by 5.4% and MOTP by 2.2% on the Market‐1501 datasets, and improved MOTA by 9.6% and MOTP by 2.7% on the DukeMTMC‐reID datasets. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach

Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals.
DOCS: A Data Ownership Confirmation Scheme for Distributed Data Trading

Data assets trading can encourage owners to distribute data and achieve large-scale data aggregation to promote the development of the supply chain system. Blockchain is a promising platform for constructing a decentralized data marketplace. The data may face risks in the marketplace, such as illegal theft, malicious tampering, or illegal distribution in the transactions process. The data ownership confirmation in a blockchain-empowered marketplace has attracted much attention in recent years. However, challenges still remain, including maintaining data integrity, traceability of illegal data, and accountability. In this paper, we propose a new data ownership confirmation scheme (DOCS) in the transaction scenario of blockchain-empowered distributed data assets trading. It integrates smart contracts, data-embedding technology, and data fingerprint to realize ownership confirmation and protection of data assets in transactions. DOCS ensures reliable mapping between on-chain data ownership information and off-chain data entities, which assists with the accurate prosecution of the illegal distribution of data assets. We demonstrate that DOCS can have desirable security properties in multiple attack models.
Lightweight MobileNetV2 offline handwritten Chinese character recognition based on attention mechanism

Over recent years, offline handwritten Chinese character recognition which based on convolutional neural network has become a research hotspot, and the accuracy rate is constantly improving. At the same time, there are problems such as large model size, many calculation parameters and model redundancy. In response to this, this paper proposes a lightweight and improved MobileNetV2 offline handwritten Chinese character recognition model based on attention mechanism. By adding an attention mechanism to the pooling layer of CNN, the L2 norm-constrained Softmax classification function is used in the final classification. In this paper, a research experiment is designed to compare the improved model with the traditional method and the classic CNN model. Experiments show that on the ICDAR-2013 competition test set, the accuracy of this method reaches 96.12%, and the model size is 48.34MB, which is improved in both recognition rate and volume compared with previous methods.
Maximal margin hyper-sphere SVM for binary pattern classification

In this paper, we propose a novel maximal margin hyper-sphere support vector machine (MMHS-SVM) for binary pattern classification. Our proposed MMHS-SVM aims to find two hyper-spheres simultaneously by solving a single quadratic programming problem and is consistent between its predicting and training processes. An essential difference that distinguishes it from other hyper-sphere SVMs is that the optimization model is constructed by maximizing the sum of the square distance between centers of two hyper-spheres, but not the sum of squares distances from the center of hyper-sphere to all examples of the opposite class. Such a principle of structural risk in our MMHS-SVM not only helps us grasp the critical samples and eliminate a large number of redundant samples, but also reduces the test cost due to the sparsity. In addition, an effective SMO-typed algorithm is designed to decrease the high time complexity and storage. Finally, a large number of experiments verify the above statements again. The experimental results on several artificial and publicly available benchmark datasets show the feasibility and effectiveness of the proposed method.
A modified secure hash design to circumvent collision and length extension attacks

This paper introduces an improved version of the secure hash algorithms, SHA-1, and SHA-2. The proposed work produces a strengthened secure hash design using the fusion between the SHA-1 and SHA-2 hash standards. This design helps to protect the SHA-1 and SHA-2 against collision and length extension attacks. The proposed design is incorporated in the round steps of the hash functions using the function manipulators of the SHA-1 and SHA-2. Moreover, the proposed design employs a padding method that is different from the one used in the SHA-1 and SHA-2 construction. The proposed design is verified by the official test vectors that were confirmed by the National Institute of Standard and Technology. Moreover, the avalanche effect, hamming distance, and bit-hit properties were studied using different test cases. The proposed design gives over 52 percent of the avalanche effect and over 84 hamming distance. The proposed design was tested against collision attack and produced unique hash values for real collided examples and shows resistance against length extension attack.
GDN: Guided down-sampling network for real-time semantic segmentation

Real-time semantic segmentation has attracted wide attention in the computer vision field, and its performance depends on both rich semantic information and high-resolution information. Most networks fastly reduce image resolution or prune feature channels in the coding stage to improve the computing speed and concatenate the shallow feature to the deep feature in the decoding stage for filling in the high-resolution details. Here, we propose a new real-time semantic image segmentation method called guided down-sampling network. The guided down-sampling decomposes the original image into a group of compressed images that replace the original image as the input of the encoding layers. This operation reduces the size of the feature map and meanwhile retains the most spatial information of the original image. Furthermore, a two-branch sub-network is designed to extract semantic information and restore high-resolution image details from compressed images for better supervising feature learning. Our network is tested on the Cityscapes dataset on a single Nvidia GeForce GTX 1080Ti GPU, and the competitive results of 113 FPS and 75.6% mIoU have been achieved. The code is available at ( https://github.com/ldrunning/segmentation).
Artificial Intelligence-Oriented User Interface Design and Human Behavior Recognition based on Human–Computer Nature Interaction

This work is to explore the application of intelligent algorithms based on deep learning in human–computer interaction systems, hoping to promote the development of human–computer interaction systems in the field of behavior recognition. Firstly, the design scheme of the human–computer interaction system is presented, and the establishment of the robot visual positioning system is emphasized. Then, the fast-region convolutional neural networks (fast-RCNN) algorithm is introduced, and it is combined with deep convolutional residual network (ResNet101). A candidate region extraction algorithm based on ResNet and long short-term memory network is proposed, and a residual network (ResNet) for spatial context memory is proposed. Both algorithms are employed in human–computer interaction systems. Finally, the performance of the algorithm and the human–computer interaction system are analyzed and characterized. The results show that the proposed candidate region extraction algorithm can significantly reduce the loss value of training set and test set after training. In addition, the corresponding accuracy, recall, and F-value of the model are all above 0.98, which proves that the model has a good detection accuracy. Spatial context memory ResNet shows good accuracy in speech expression detection. The detection accuracy of single attribute, double attribute, and multi-attribute speech expression is above 89%, and the detection accuracy is good. In summary, the human–computer interaction system shows good performance in capturing target objects, even for unlabeled objects, the corresponding grasping success rate is 95%. Therefore, this work provides a theoretical basis and reference for the application of intelligent optimization algorithm in human–computer interaction system.
Mineral potential modelling of orogenic gold systems in the granites-tanami Orogen, Northern Territory, Australia: A multi-technique approach

In this study we adopted fuzzy inference system (FIS), transformed predictor map-based random forest (RF) and feed-forward deep neural network (FF-DNN) approaches to mineral potential modelling (MPM) of the Granites-Tanami Orogen (GTO), Northern Territory, Australia, to (i) predict gold prospectivity, and (ii) compare the performance of these models to those previously generated in the study area. The current study, which utilized 19 predictor maps previously developed by Roshanravan et al. (2020) in the framework of a mineral systems approach, took into consideration the learnings gained and results obtained from previous approaches to modelling the gold potential of the GTO (Roshanravan et al., 2020: continuous fuzzy gamma, geometric average, data-driven index overlay and non-transformed predictor map-based RF; Roshanravan et al., 2021: cuckoo optimization algorithm). Importantly, our study area extending over > 46,000 km2 in the GTO, represents one of the few, if not the only, areas worldwide that has been subjected to eight different modelling techniques, an approach that delivered unique insights that cannot be achieved by utilizing a single technique. For example, our multi-technique approach enabled us to study what effect predictor maps may have on model performance and resulting mineral potential maps. In the case of data-driven MPM with RF, arguably-one of the most popular machine learning techniques, the effect of non-transformed (i.e., original) and transformed (i.e., derivative) predictor maps has never been evaluated simultaneously. To achieve this, we generated a transformed predictor map-based RF potential model with the aim of comparing it to the non-transformed predictor map-based RF model of Roshanravan et al. (2020). The FF-DNN and Mamdani-type FIS approaches to MPM were carried out specifically for the purpose of comparing and further investigating the modelling results. With respect to the Mamdani-type FIS, the concentration-area (C-A) fractal technique served to more objectively determine thresholds of the underlying mathematical functions, a novel approach that aids in reducing bias resulting from human input and expert opinion. The thresholded results demonstrate that the RF potential map generated with transformed predictor maps outperformed all other data-driven and continuous models. Overall, our multi-technique approach to MPM and the comparing and contrasting of a large set of resulting gold potential models, not only offered insights that helped to develop and calibrate new tools and techniques but also delivered what we believe are more robust gold exploration targets. As a result, we strongly recommend to using a multi-technique approach to MPM, which we hope will soon replace the currently accepted and widely utilized single-technique approach.
Let it go! Point of release prediction for virtual throwing

In this paper, we present a novel real-time physical interaction system that allows users to throw a virtual ball without using an intermediary device such as a controller. A dataset of throwing motions was captured from six actors, from which ground truth Point of Release (PoR) frames were calculated. We trained a PoR prediction model using motion features extracted from arm joints, and developed a detection algorithm to predict the PoR of a throwing motion in real-time. Evaluation of the system with pre-recorded throwing motion data results in detection errors of less than 50 ms. Qualitative results from six users of the real-time system in VR indicate that the task of throwing without a controller was very natural, although the system performed better for underarm than overarm throws. Finally, we report the relative importance of different joints and motion features for the PoR prediction task.
Understanding Chinese secondary school students’ perceptions of mobile-assisted language learning

Given the paucity of research on mobile-assisted language learning (MALL) in secondary schools in China, this retrospective case study explored the psychological processes underlying the non-voluntary MALL experiences of Chinese secondary school students during a lockdown to contain the spread of COVID-19. Drawing on prior technology acceptance studies, we proposed a research model comprising nine latent constructs, and tested it with data collected through a questionnaire survey (N = 265). The results of a structural modeling test showed that subjective norms and attitudes toward MALL were significant predictors of the behavioral intention to use it, whereas perceived usefulness and perceived ease of use mediated other factors that influence attitudes. The findings support the use of the technology acceptance model in investigating MALL acceptance. Implications for the better implementation of MALL, especially in the context of secondary schools in China are also discussed.
Using Artificial Intelligence Methods to Classify Different Seismic Events

Abstract The classification of seismic events is crucial for monitoring underground nuclear explosions and regional unnatural seismic events. To classify tectonic earthquakes, explosions, and mining-induced earthquakes, we established 36- and 60-dimensional network-averaged datasets and single-station datasets through feature extraction and spectral amplitude analysis. Using different artificial intelligence (AI) methods, including the support vector machine (SVM), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), residual neural network, and long short-term memory fully convolutional network (LSTM-FCN), we constructed two-class and three-class models, analyzed the change in the classification with epicentral distances, and evaluated the generalizability of different classifiers. The results showed that the accuracies of different AI models with the feature extraction dataset were higher than those achieved with the spectral amplitude dataset, indicating that the feature extraction method can more clearly highlight the differences between different types of seismic events. The accuracies with the network-averaged dataset were 5%–8% higher than that achieved using the single-station dataset. The earthquake and mining-induced earthquake classifiers constructed by different AI methods had the best performance, followed by the earthquake and explosion classifier, and the explosion and mining-induced earthquake classifier, with average accuracies of 97.4%–98.4%, 96.5%–97.6%, and 88.8%–90.6%, respectively. In the model generalization evaluation, the test accuracies and F1-Scores of the two-class models with the 36-dimensional network-averaged dataset exceeded 90%. Among the five AI methods, XGBoost and LSTM both performed well in classification of different datasets, indicating that these models have good application prospects for seismic event classifications.