Reviewerly/exHarmony huggingface dataset:
BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain Technology

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book’s interacting entities on blockchain because every consumer could retrace the book’s renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants.
Applications of Blockchain Technology in Modern Education System – Systematic Review

The development of new technologies and also the provision of great connections are becoming increasingly important in all aspects of our everyday situations. This advanced technology also brings a variety of flaws, most importantly security-related problems. Distributed ledger technology, amongst the most prominent technologies in the previous few years, is gaining traction due to its ability to just provide privacy in a variety of domains, including logistics management, transportation, as well as other industries. The educational industry should also make use of the important features blockchain technology offers. This technology is now being considered by schools and universities, particularly universities, as an approach to enhance the teaching-learning process and encourage the participation of all stakeholders including such undergraduates, professors, and even family members. E-transcripts, digitized certificates and credentials, data storage, plus access control will all benefit from it. This detailed research analysis discusses the adaptability of blockchain technology in the education sector.
Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Following the Bayesian inference framework, this paper investigates the problem of distributed particle filtering over a sensor network to achieve consensus. The objective of the posterior-consensus strategy is to fuse the posterior probability distribution functions (PDFs) at different sensor nodes, so that an agreement of belief can be established in terms of the Kullback-Leibler average (KLA). To facilitate the consensus process and reduce the communication load, the local PDFs are approximated with weighted meshes and transmitted between neighboring nodes. The mesh representations are constructed by resorting to a grid partition of the state space, such that the PDF can be approximated by a linear combination of indicator functions. To derive a particle representation of the fused PDFs, a novel importance density function is designed to draw particles with respect to the information from all neighboring nodes. The weights of the particles are calculated via the recursive solution of the KLA. The effectiveness of the proposed filtering approach is demonstrated through two target tracking examples.
Audit 4.0-based ESG assurance: An example of using satellite images on GHG emissions

As Environment, Social, and Governance (ESG) information has become an essential resource for investors, regulators have attempted to assure its quality. While more companies offer ESG disclosure, it is usually not fully substantiated with supporting information. Assurance is needed to verify that ESG reports are free of substantive errors. However, traditional financial audit approaches are less effective in providing ESG assurance due to the difference in the nature of evidence, difficulty in measuring and verifying ESG measures, and ever-changing ESG performance. To increase the effectiveness of ESG audit approaches, this study proposes Audit 4.0-based ESG assurance, which uses Big Data and emerging technologies of Audit 4.0 to capture evidence from the physical world and provide accurate assurance on ESG reports in a timely manner. We further perform a case study as a proof of concept of the proposed approach, which explores the integration of satellite-based methane estimation as ESG audit evidence. The use of satellite images provides opportunities for regulators and companies to monitor and assure ESG reports in a continuous manner.
Quantifying protocol efficiency: A thermodynamic figure of merit for classical and quantum state-transfer protocols

Manipulating quantum systems undergoing non-Gaussian dynamics in a fast and accurate manner is becoming fundamental to many quantum applications. Here, we focus on classical and quantum protocols transferring a state across a double-well potential. The classical protocols are achieved by deforming the potential, while the quantum ones are assisted by a counterdiabatic driving. We show that quantum protocols perform more quickly and accurately. Finally, we design a figure of merit for the performance of the transfer protocols---namely, the protocol grading---that depends only on fundamental physical quantities, and which accounts for the quantum speed limit, the fidelity, and the thermodynamics of the process. We test the protocol grading with classical and quantum protocols, and show that quantum protocols have higher protocol grading than the classical ones.
ZERO: A Generic Open-Source Extended Reality Eye-Tracking Controller Interface for Scientists

Virtual reality and eye-tracking technologies are nowadays standard research tools. A growing number of researchers from different disciplines are utilizing these technologies. Currently, access to eye-tracking hardware in virtual reality glasses is usually provided as APIs to call functions of the eye-tracking device. Proper implementation is device-specific and left to the user. Especially non-computer scientists are left alone with this problem, which impedes eye-tracking research in virtual reality for many scientists. This paper describes a generic open-source interface for everyone to efficiently and easily utilize common eye trackers in virtual reality. The interface is published under a friendly CC BY 4.0 license that allows for integration, modification, and extension of the code. It includes a standardized interface for several eye-tracking devices in virtual reality, is ready to be used out of the box, and allows easy addition of APIs from other manufacturers. The code is available at https://bitbucket.org/benediktwhosp/zvsl-zero
Dynamic data-free knowledge distillation by easy-to-hard learning strategy

Data-free knowledge distillation (DFKD) is a widely-used strategy for Knowledge Distillation (KD) whose training data is not available. It trains a lightweight student model with the aid of a large pretrained teacher model without any access to training data. However, existing DFKD methods suffer from inadequate and unstable training process, as they do not adjust the generation target dynamically based on the status of the student model during learning. To address this limitation, we propose a novel DFKD method called CuDFKD. It teaches students by a dynamic strategy that gradually generates easy-to-hard pseudo samples, mirroring how humans learn. Besides, CuDFKD adapts the generation target dynamically according to the status of student model. Moreover, we provide a theoretical analysis of the majorization minimization (MM) algorithm and explain the convergence of CuDFKD. To measure the robustness and fidelity of DFKD methods, we propose two more metrics, and experiments shows CuDFKD has comparable performance to state-of-the-art (SOTA) DFKD methods on all datasets. Experiments also present that our CuDFKD has the fastest convergence and best robustness over other SOTA DFKD methods.
SGCL: Spatial guided contrastive learning on whole-slide pathological images

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL.
Machine Learning-Based Social Media News Popularity Prediction

The Internet has surpassed print media such as newspapers and magazines as the primary medium for disseminating public news because of its rapid transmission and widespread availability. This has made the study of how to gauge interest in online stories a pressing concern. Mashable News, one of the most popular blogs in the world, is the main source for the dataset used in this study, which was collected at the UCI data repository. Random forest, logistic regression, gaussian naive bayes, k-means and multinomial naive bayes are the four kinds of machine learning algorithms used to forecast news popularity based on the number of times an item has been shared. Gaussian naive bayes provides the most accurate predictions, at 92%. The findings suggest that Gaussian naive bayes method improves prediction and outlier detection in unbalanced data.
Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.
Lightweight Authentication Mechanism for Industrial IoT Environment Combining Elliptic Curve Cryptography and Trusted Token

With the promotion of Industry 4.0, which emphasizes interconnected and intelligent devices, several factories have introduced numerous terminal Internet of Things (IoT) devices to collect relevant data or monitor the health status of equipment. The collected data are transmitted back to the backend server through network transmission by the terminal IoT devices. However, as devices communicate with each other over a network, the entire transmission environment faces significant security issues. When an attacker connects to a factory network, they can easily steal the transmitted data and tamper with them or send false data to the backend server, causing abnormal data in the entire environment. This study focuses on investigating how to ensure that data transmission in a factory environment originates from legitimate devices and that related confidential data are encrypted and packaged. This paper proposes an authentication mechanism between terminal IoT devices and backend servers based on elliptic curve cryptography and trusted tokens with packet encryption using the TLS protocol. Before communication between terminal IoT devices and backend servers can occur, the authentication mechanism proposed in this paper must first be implemented to confirm the identity of the devices and, thus, the problem of attackers imitating terminal IoT devices transmitting false data is resolved. The packets communicated between devices are also encrypted, preventing attackers from knowing their content even if they steal the packets. The authentication mechanism proposed in this paper ensures the source and correctness of the data. In terms of security analysis, the proposed mechanism in this paper effectively withstands replay attacks, eavesdropping attacks, man-in-the-middle attacks, and simulated attacks. Additionally, the mechanism supports mutual authentication and forward secrecy. In the experimental results, the proposed mechanism demonstrates approximately 73% improvement in efficiency through the lightweight characteristics of elliptic curve cryptography. Moreover, in the analysis of time complexity, the proposed mechanism exhibits significant effectiveness.
A Fast and Accurate Prediction of Distortions in DCT-Based Lossy Image Compression

Since the number of acquired images and their size have the tendency to increase, their lossy compression is widely applied for their storage, transfer, and dissemination. Simultaneously with providing a relatively large compression ratio, lossy compression produces distortions that are inevitably introduced and have to be controlled. The properties of these distortions depend on several factors such as image properties, the coder used, and a parameter that controls compression, which is different for particular coders. Then, one has to set a parameter that controls compression individually for an image to be compressed to provide image quality appropriate for a given application, and it is often desirable to do this quickly. Iterative procedures are usually not fast enough, and therefore fast and accurate procedures for providing a desired quality are needed. In the paper, such a procedure for two coders based on discrete cosine transform is proposed. This procedure is based on a prediction of mean square errors for a given quantization step using a simple analysis of image complexity (local activity in blocks). The statistical and spatial–spectral characteristics of distortions introduced by DCT-based coders are analyzed, and it is shown that they depend on the quantization step and local content. Generalizing the data for sets of grayscale test images and quantization step values, it is shown that the MSE can be easily predicted. These predictions are accurate enough and can be used to set the quantization step properly, as verified by experiments performed using more than 300 remote sensing and conventional optical images. The proposed approach is applicable to the lossy compression of grayscale images and the component-wise compression of multichannel data.
FusionPillars: A 3D Object Detection Network with Cross-Fusion and Self-Fusion

In the field of unmanned systems, cameras and LiDAR are important sensors that provide complementary information. However, the question of how to effectively fuse data from two different modalities has always been a great challenge. In this paper, inspired by the idea of deep fusion, we propose a one-stage end-to-end network named FusionPillars to fuse multisensor data (namely LiDAR point cloud and camera images). It includes three branches: a point-based branch, a voxel-based branch, and an image-based branch. We design two modules to enhance the voxel-wise features in the pseudo-image: the Set Abstraction Self (SAS) fusion module and the Pseudo View Cross (PVC) fusion module. For the data from a single sensor, by considering the relationship between the point-wise and voxel-wise features, the SAS fusion module self-fuses the point-based branch and the voxel-based branch to enhance the spatial information of the pseudo-image. For the data from two sensors, through the transformation of the images’ view, the PVC fusion module introduces the RGB information as auxiliary information and cross-fuses the pseudo-image and RGB image of different scales to supplement the color information of the pseudo-image. Experimental results revealed that, compared to existing current one-stage fusion networks, FusionPillars yield superior performance, with a considerable improvement in the detection precision for small objects.
Typomorphic Features and Source of Native Gold from the Sykhoi Log Area Placer Deposits, Bodaibo Gold-Bearing District, Siberia, Russia

The Bodaibo gold-bearing district in the Lena gold province of Siberia is one of the largest and oldest placer gold-bearing provinces in the world. Approximately 1650 tons of gold has been extracted from the region. Precise studies on the source of these unique placer deposits are lacking and still controversial. Native gold from four different locations was gathered to investigate its morphology, chemical signatures, structure and inclusions. Some data on primary bedrock mineralization were obtained from the published literature. The linear weathering crusts developed along the zones of disjunctive dislocations near the Sukhoi Log gold deposit were researched. If they coincided with zones of low-grade veinlet-disseminated gold–quartz–sulphide mineralization with small gold grain sizes, a supergene replacement of primary mineralization was known to have occurred, accompanied by the formation of gold-rich rims and an increase in the size, content and purity of gold. Such mineralization associated with linear weathering crusts can be a source of local eluvial–proluvial placers, while placers of large valleys are formed due to low-sulphide gold–quartz lodes.
Reduced-reference image deblurring quality assessment based on multi-scale feature enhancement and aggregation

Image deblurring is a basic task in the field of computer vision, and has attracted much attention because of its application prospects in traffic monitoring and medical imaging, etc. Due to the inherent weakness of the model, it is difficult to obtain well-pleasing deblurred images for all the visual contents so far. Therefore, how to objectively evaluate the quality of these deblurred results is very important for the rapid development of image deblurring. In recent years, numerous convolutional neural networks based quality assessment methods have been proposed to automatically predict the quality of synthetic and authentic distorted images, producing results that are mildly consistent with subjective perception. However, they are limited in Image Deblurring Quality Assessment (IDQA). For IDQA, it is more meaningful to predict the quality difference of blurry-deblurred image (BDI) pair than to make prediction on single deblurred image. Inspired by this, we propose a novel reduced-reference image deblurring quality assessment method based on multi-scale feature enhancement and aggregation. Firstly, the multi-scale features of BDI pair are generated from a versatile vision Transformer. Secondly, the discrepancy information is exploited to implicitly enhance the initial deep features. Finally, the enhanced features of different scales are aggregated and then mapped to the quality difference of BDI pair. Experimental results on four challenging datasets demonstrate that the proposed method is superior to the state-of-the-art quality assessment methods.
Unsupervised sub-domain adaptation using optimal transport

We focus on domain adaptation, a branch of transfer learning that concentrates on transferring knowledge from one domain to another when the data distributions differ. Specifically, we investigate unsupervised domain adaptation methods, which have abundant labeled examples from a source domain and unlabeled examples from a target domain available. We aim to minimize the distribution divergences between the domains using optimal transport with subdomain adaptation. Previous methods have mainly focused on reducing global distribution discrepancies between the domains, but these approaches cannot capture fine-grained information and do not consider the structure or geometry of the data. To handle these limitations, we propose Optimal Transport via Subdomain Adaptation (OTSA). Our method utilizes the sliced Wasserstein metric to reduce transportation costs while preserving geometrical data information and the Local Maximum Discrepancy (LMMD) to compute the local discrepancy in each domain category, which helps capture relevant features. Experiments were conducted on six standard domain adaptation datasets, and our method outperformed the majority of baselines. Our approach increased the average accuracy when compared with baselines on the OfficeHome (67.7% to 68.31%), Office-Caltech10 (91.8% to 96.33%), IMAGECLEF-DA (87.9% to 89.9%), VisDA-2017 (79.6% to 81.83%), Office31 (88.17% to 89.11%), and PACS (69.08% to 83.72%) datasets, respectively.
An artificial bee colony algorithm with a cumulative covariance matrix mechanism and its application in parameter optimization for hearing loss detection models

Artificial bee colony (ABC) algorithms which are applied to various complex problems for their competitive performance and simple structures are a relatively popular paradigm of evolutionary algorithms (EAs). Although many variants based on the basic ABC algorithm have been developed, most variants have limited performance on non-separable problems. This is mainly because these variants usually focus on only individual quality information, but neglect population distribution information. To address this limitation, in this paper, we propose a new ABC with a cumulative covariance matrix mechanism (CCoM-ABC), in which the population distribution information in all previous generations is involved to guide the evolution process. Specifically, the CCoM-ABC algorithm applies a cumulative covariance matrix (CCoM) mechanism by which the population information of every generation is involved to improve the probability of finding the optimum. It also applies an adaptive selection mechanism by which the proper coordinate system used to generate candidate solutions can be dynamically selected between a natural one and an eigen one during the search process. Furthermore, for improving the search ability, a new search equation is adopted in the scout bees phase. A series of experiments are conducted on the CEC2014 to evaluate the performance of the proposed CCoM-ABC algorithm, and the results show that the CCoM-ABC outperforms state-of-the-art ABC variants and EAs in terms of convergence speed, solution accuracy, and numerical scalability. For validating the practicality of the proposed CCoM-ABC algorithm, we further apply it to optimize the parameters of the neural network for hearing loss (HL) detection. This study involves 60 brain images consisting of 20 left-sided HL, 20 right-side HL, and 20 healthy control images. The results show that this model outperforms state-of-the-art related methods in terms of overall accuracy by about 2%.