Reviewerly/exHarmony huggingface dataset:
A novel coal-gangue recognition method in underground coal mine based on image processing

ABSTRACTThis study proposes a coal-gangue recognition method based on Retinex and extreme learning machine. The traditional Retinex has many limitations, including halo artifacts, excessive enhancement, and noise amplification in dark areas. To handle these, first, a modified multi-scale Retinex image enhancement algorithm based on hue, saturation, value color space, and bidimensional empirical mode decomposition named modified multi-scale Retinex algorithm with color restoration (MMSRCR) is proposed herein. On-site images are used to verify the effectiveness of the MMSRCR. Second, a preliminary segmentation method for coal-gangue images based on the optimal entropy threshold optimized by the improved quantum genetic algorithm is designed. The proposed segmentation method performs excellent stability compared with other traditional methods. Third, the adhesion part of the ore is further segmented based on the morphology opening operation and watershed algorithm. Then, the feature space of each enclosed area is extracted using the chain code and line segment tables. The improved extreme learning machine completes the classification of these areas, and the coal-gangue visual ratio recognition method is proposed. Finally, an experimental platform for visual ratio recognition is built to verify the proposed method. The experimental results show that the proposed method can accurately identify the visual ratio of coal-gangue.KEYWORDS: Coal-gangue recognitionretinexquantum genetic algorithmoptimal entropy threshold segmentationextreme learning machine AcknowledgementsThe authors would like to thank the support of the National Natural Science Foundation of China (grant numbers: 52174152 and 52074271), the Natural Science Foundation of Jiangsu Province (grant number: BK20211245), China Postdoctoral Science Foundation (grant numbers: 2019M661974 and 2020T130696) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.Disclosure StatementNo potential conflict of interest was reported by the authors.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe work was supported by the National Natural Science Foundation of China (grant numbers: 52174152 and 52074271), the Natural Science Foundation of Jiangsu Province (grant number: BK20211245), China Postdoctoral Science Foundation (grant numbers: 2019M661974 and 2020T130696) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
SIFT Application Separates Motion Characteristics and Identifies Symbols on Tires

Deep learning techniques have aided in the transfer of content-based image retrieval issues from manually constructed local features like scale-invariant feature transformations (SIFT) to features obtained from convolutional neural networks (CNNs). Existing image-based CNN features, which are taken directly from the entire picture, are not adequate for identifying small areas of overlap. These will have an impact on the performance of partial duplicate picture detection. In this work, we systematized the SIFT characteristics, compared them to other algorithms, and then employed it to separate the motion characteristics and recognize the characters on the tire tread. The experimental findings suggest that after enhancing the input data, the training process of digital characters may be identified with high accuracy.
Single Photon Emitters with Polarization and Orbital Angular Momentum Locking in Monolayer Semiconductors

Excitons in monolayer transition metal dichalcogenide are endowed with intrinsic valley-orbit coupling between their center-of-mass motion and valley pseudospin. When trapped in a confinement potential, e.g., generated by strain field, we find that intralayer excitons are valley and orbital angular momentum (OAM) entangled. By tuning trap profile and external magnetic field, one can engineer the exciton states at ground state, and realize a series of valley-OAM entangled states. We further show that the OAM of excitons can be transferred to emitted photons, and these novel exciton states can naturally serve as polarization-OAM locked single photon emitters, which under certain circumstance become polarization-OAM entangled, highly tunable by strain trap and magnetic field. Our proposal demonstrates a novel scheme to generate polarization-OAM locked/entangled photons at nanoscale with high degree of integrability and tunability, pointing to exciting opportunities for quantum information applications.
Design of a Web Portal for Assertive Diagnosis and Cognitive Evolution in Children with Special Abilities

A world beyond the five senses is what opens the doors to new expectations for children with special abilities, as they lack a variety of factors due to a prejudiced society with middle-aged life stereotypes. We will use the SCRUM method to make the appropriate changes in a short time this established to norms and laws both in education and health, what we develop is a web application that generates a diagnosis for cognitive evolution in children with special abilities. The number of special children who tried the software have made great strides in terms of individual performance. The studies carried out serve the teachers as health professionals who attend to the child with a report, in order to also contribute to the individual and social improvement that the children with special abilities will present through the therapies they need.
Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.
Category-Level Assignment for Cross-Domain Semantic Segmentation in Remote Sensing Images

Deep learning-based semantic segmentation has made great progress in understanding very-high-resolution (VHR) remote sensing images (RSIs). However, large-scale applications are still limited. The main reason is that diverse imaging modes and geographical differences make it difficult to transfer a model trained in the source domain to the target domain. To solve this problem, unsupervised domain adaptation (UDA) for VHR RSIs has received some attention, but the accuracy of cross-domain semantic segmentation still needs to be improved. Currently, one reasonable proposal for improving accuracy is to take a close look at the category-level information. In this paper, we reveal an integer programming mechanism for modeling the category-level relationship between the source and target domains. The mechanism is based on the solution of the assignment problem, and thus, the proposed method is called category-level assignment for UDA (ClA-UDA). In ClA-UDA, a category-level assignment problem with additional constraints is defined for UDA tasks, and the solution is provided. Based on the solution, an assignment-based image-to-image transferring algorithm (AIT) is first proposed to transfer the source-domain images based on the style of the target-domain images. AIT minimizes a weighted discrepancy, and provides an analytical solution for the transfer. Two assignment-based alignment losses are then introduced to align the source and target domains based on the category-level relationship in a concise way. To validate the performance of ClA-UDA, three VHR remote sensing image datasets are employed, and six UDA tasks are designed. Extensive experiments are conducted, and the results demonstrate the superiority of ClA-UDA compared to the existing methods.
Quantization Backdoors to Deep Learning Commercial Frameworks

Due to their low latency and high privacy preservation, there is currently a burgeoning demand for deploying deep learning (DL) models on ubiquitous edge Internet of Things (IoT) devices. However, DL models are often large in size and require large-scale computation, which prevents them from being placed directly onto IoT devices, where resources are constrained, and 32-bit floating-point (float-32) operations are unavailable. Commercial framework (i.e., a set of toolkits) empowered model quantization is a pragmatic solution that enables DL deployment on mobile devices and embedded systems by effortlessly post-quantizing a large high-precision model (e.g., float-32) into a small low-precision model (e.g., int-8) while retaining the model inference accuracy. However, their usability might be threatened by security vulnerabilities. This work reveals that standard quantization toolkits can be abused to activate a backdoor. We demonstrate that a full-precision backdoored model which does not have any backdoor effect in the presence of a trigger—as the backdoor is dormant—can be activated by (i) TensorFlow-Lite (TFLite) quantization, the only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">product-ready</i> quantization framework to date, and (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beta released</i> PyTorch Mobile framework. In our experiments, we employ three popular model architectures (VGG16, ResNet18, and ResNet50), and train each across three popular datasets: MNIST, CIFAR10 and GTSRB. We ascertain that all trained float-32 backdoored models exhibit no backdoor effect <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">even in the presence of trigger inputs</i> . Particularly, four influential backdoor defenses are evaluated, and they fail to identify a backdoor in the float-32 models. When each of the float-32 models is converted into an int-8 format model through the standard TFLite or PyTorch Mobile framework's post-training quantization, the backdoor is activated in the quantized model, which shows a stable attack success rate close to 100% upon inputs with the trigger, while it usually behaves upon non-trigger inputs. This work highlights that a stealthy security threat occurs when an end-user utilizes the on-device post-training model quantization frameworks, informing security researchers of a cross-platform overhaul of DL models post-quantization even if these models pass security-aware front-end backdoor inspections. Significantly, we have identified Gaussian noise injection into the malicious full-precision model as an easy-to-use preventative defense against the PQ backdoor. The attack source code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/quantization-backdoor</uri> .
Validate the Users’ Comfortable Level in the Virtual Reality Walkthrough Environment for Minimizing Motion Sickness

Motion sickness is a common scenario for users when they are exposed to a virtual reality (VR) environment.It is due to the conflict that occurs in the brain that tells the user that they are moving in the environment, but the fact is that the user's body is sitting still causing them to get symptoms of motion sickness like nausea and dizziness.Therefore, motion sickness has become one of the main reasons why users still do not prefer to use VR to enhance their productivity.Motion sickness can be overcome by increasing the user's comfort level of walkthrough in the VR environment.Meanwhile, a popular VR simulation which is widely used in many industries is a walkthrough in a VR environment at a certain speed.This paper is focused on presenting the result of walkthroughs in a VR environment using movement speed and based on frame rates performance and adopting the unified theory of acceptance and use of technology (UTAUT) model construct variables namely performance expectancy (PE) and effort expectancy (EE) to measure the user's comfort level.A mobile VR, 'VR Terrain' application software was developed based on the proposed framework.The application software was tested by 30 users by moving around in a VR environment with 4 different movement speeds that were implemented into four colored gates using a head-mounted display (HMD).A descriptive and coefficient analysis was used to analyze all the data.The blue gate revealed the most comfortable, outperforming all other three gates.Overall, the most suitable speed to use for VR walkthrough is 4.0 km/h.The experiment result may be used to create a parameter for the VR developers to reduce the VR motion sickness effect in the future.
University Students' Satisfaction and Evaluations of Synchronous Online Learning for Physical Education Courses

Physical education major in higher education has been negatively impacted during COVID-19 due to the need for social distancing. Many universities have adopted hybrid or fully remote learning models to avoid these risks in this environment. Recognizing the importance of online education for physical education, this research focuses on Korean university students' satisfaction and evaluation with synchronous online learning using Zoom. The study implemented a mixed method, including exit course surveys (n=60) and individual interviews (n=4). The data was collected in the 2022 fall semester. Study results showed synchronous online education provided a flexible learning environment for physical education students. Also, university students were satisfied with synchronous online education and were willing to take these courses again next semester. Furthermore, they evaluated synchronous online education as effective in providing real-time interaction between the instructor and students. However, the interview data showed some challenges of online learning using Zoom for physical education courses. Finally, several implications are discussed for effectively constructing synchronous online physical education for higher education.&#x0D; &#x0D; Received: 20 February 2023 / Accepted: 20 April 2023 / Published: 5 May 2023
SAN-T2T: An automated table-to-text generator based on selective attention network

Abstract Table-to-text generation aims to generate descriptions for structured data (i.e., tables) and has been applied in many fields like question-answering systems and search engines. Current approaches mostly use neural language models to learn alignment between output and input based on the attention mechanisms, which are still flawed by the gradual weakening of attention when processing long texts and the inability to utilize the records’ structural information. To solve these problems, we propose a novel generative model SAN-T2T, which consists of a field-content selective encoder and a descriptive decoder, connected with a selective attention network. In the encoding phase, the table’s structure is integrated into its field representation, and a content selector with self-aligned gates is applied to take advantage of the fact that different records can determine each other’s importance. In the decoding phase, the content selector’s semantic information enhances the alignment between description and records, and a featured copy mechanism is applied to solve the rare word problem. Experiments on WikiBio and WeatherGov datasets show that SAN-T2T outperforms the baselines by a large margin, and the content selector indeed improves the model’s performance.
Calibration method for fringe projection profilometry based on rational function lens distortion model

The calibration of the fringe projection profilometry (FPP) system is susceptible to the camera lens distortion. This paper presents a calibration method based on the rational function lens distortion model. In this method, we integrate the rational function into the mathematical expression of the out-of-plane height to mitigate the impact of lens distortion. With arbitrarily arranged measurement components, we can flexibly measure the three-dimensional (3D) shape of objects with no expensive auxiliaries, high-precision setups, or complicated projector calibration. First, we project modulated sinusoidal fringe patterns with high frequency to the concentric circle grids calibration board to accurately estimate the unwrapped phase value, the pixel coordinate, and the out-of-plane height at each feature point. Then, the 3D coordinates can be recovered by determining the coefficients of the high order phase, coordinate, and height (PCH) mapping. Experiments on computer simulation and real data demonstrate that the modified calibration contributes to the flexibility and accuracy of the 3D shape measurement.
Attribute-Based Data Sharing Scheme Using Blockchain for 6G-enabled VANETs

The advent of 6G communications technology will bring about a transition from the “Internet of Everything” to the “Intelligent Connection of Everything”. 6G-enabled vehicular ad hoc networks (VANETs) will enjoy lower latency, higher speed, and greater capacity network services. Nevertheless, achieving secure data sharing will be an even tougher challenge. Given this, we propose an attribute-based data sharing scheme with blockchain for 6G-enabled VANETs. First, we propose an efficient multi-tree-based user revocation mechanism. With the Chinese remainder theorem, our mechanism supports user batch revocation and batch joining. Second, we achieve distributed data storage by utilizing the blockchain and smart contracts. To solve the problem of insufficient storage capacity on the blockchain, we adopt a combination of on-chain and off-chain storage. Third, to reduce the computation burden on users, our proposal supports online/offline encryption and verifiable outsourced decryption. Meanwhile, our mechanism supports policy hiding, data revocation, and cross-domain data sharing. The proposed scheme is proven to satisfy the indistinguishability under chosen plaintext attack (IND-CPA) in the standard model. Theoretical analysis shows that our mechanism outperforms existing schemes in functionality and security. Simulation experiments show that our proposal is efficient and suitable for 6G-enabled VANETs.
Non-invasive Haemoglobin Estimation Using Different Colour and Texture Features of Palm

Anaemia, caused due to lack of blood haemoglobin levels, is one of the most common diseases which affects billions of people across the world. According to WHO statistics, India is one of the developing countries with highest prevalence of anaemia. Conventional invasive methods are cost-prohibitive and difficult to administer globally which essentially demands non-invasive, accurate, and low-cost approaches for screening of anaemia. The current work targets to combine cutting edge computational approaches with the age-old practice of rough estimation of blood haemoglobin levels by observing pallor in the palm to develop a non-invasive reliable anaemia detection system. The proposed system works with the principle of inducing pallor changes in palm with suitable pressure application and release, measuring the rate of change of colour and performing time-domain analysis thereof to correlate with blood haemoglobin concentration. The entire event of colour changes in palm induced through a customized device, is videophotographed using smartphone camera sensor and is processed and analysed through a set of image processing and analysis techniques. Different handcrafted colour and texture feature extraction techniques are applied on some of the dominant frames considering different colour models on the video samples. The set of features selected through feature selection techniques are provided as input to multi-layer perceptron (MLP) networks comprising of different activation functions and optimizers. The proposed system ensures an accurate estimation of blood haemoglobin level with an average RMSE of 0.597 as determined based on palm pallor video samples of 41 participants.
Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, existing temporal grounding datasets are not carefully designed to evaluate the compositional generalizability. To systematically benchmark the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. When evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization. Based on this insight, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into hierarchical semantic graphs, respectively, and learns fine-grained semantic correspondence between the two graphs. Furthermore, we introduce a novel adaptive structured semantics learning approach to derive the structure-informed and domain-generalizable graph representations, which facilitate the fine-grained semantic correspondence reasoning between the two graphs. Extensive experiments validate the superior compositional generalizability of our approach.
An Efficient Certificateless Signature Scheme With Provably Security and Its Applications

The Internet of Things (IoT) is helpful in making people's life more convenient and efficient. To ensure that the nodes within IoT can interact securely, a certificateless signature (CLS) can be used to protect message authentication in the IoT. Recently, some concrete constructions of CLS schemes have been proposed in the literature, but through our analyses, we demonstrate that some existing CLS schemes cannot keep their claimed security because of various security flaws. For example, a valid signature can be forged by any Type I adversary by replacing the corresponding user's public key. In this article, we describe the security flaws that need to be addressed and introduce a novel CLS scheme without using bilinear mapping. The existential unforgeability in our proposed scheme can be proved based on the hardness of the elliptic curve discrete logarithm problem in the random oracle model. The comparison with existing CLS schemes shows that our scheme not only enjoys more security features but also is more efficient in computation. Moreover, we introduce an identity authentication protocol as the application of our proposed CLS scheme, which achieves mutual authentication and anonymity in communications.