Reviewerly/exHarmony huggingface dataset:
A robust detection and localization technique for copy-move forgery in digital images

Image forensic analysis becomes a major role in the field of digital image security due to tampering and forgery. The image forgery violates the authenticity and ownership of digital images. Copy-move forgery considers a significant kind of image forensic analysis algorithm. In this kind, the forger copies a part of an original image and then pastes it into the selected position from the same image. The purpose of forgery is to hide or highlight a specific region of the original image. To detect copy-move forgery, there are two traditional techniques: block-based and keypoint-based. The main drawback of the keypoint-based technique is the insufficient features for the small and flat regions, which causes undetected forgery. In contrast, the block-based technique has intensive processing. Therefore, this paper proposes a robust scheme that overcomes the drawbacks of the above techniques and maintains their advantages. This scheme adopts three connected stages, the first detects the initial duplicated regions using the SURF-HOG detector and descriptor. Subsequently, the second stage localizes the primary matched regions by SLIC segmentation and then selects the suspicious neighbor regions to be combined with primary regions to obtain the active regions. In the third stage, the block-based technique adopts overlapping Zernike moments to extract sufficient key points from the produced active regions. In the final stage, the duplicated regions are classified into authentic or forged regions. The proposed scheme provides not only forgery detection but also localization and recognition for the duplicated regions. The experimental results show that the proposed scheme is fast and has high accuracy for forgery detection and localization, at least 93.75, and 7.25 in terms of True Positive and False Positive Rates. Moreover, the scheme has high robustness under various conditions and attacks such as geometric transformation attacks and compound photometric attacks. The proposed scheme can be used in sensitive applications such as cybercrime detection and adopted as evidence in the courts.
Measurement-device-independent quantum key distribution with passive time-dependent source side channels

While measurement-device-independent (MDI) quantum key distribution (QKD) allows two trusted parties to establish a shared secret key from a distance without needing to trust a central detection node, their quantum sources must be well-characterized, with side-channels at the source posing the greatest loophole to the protocol's security. In this paper, we identify a time-dependent side-channel in a common polarization-based QKD source that employs a Faraday mirror for phase stabilization. We apply the recently developed numerical proof technique from [Phys. Rev. A 99, 062332 (2019)] to quantify the sensitivity of the secret key rate to the quantum optical model for the side-channel, and to develop strategies to mitigate the information leakage. In particular, we find that the MDI three-state and BB84 protocols, while yielding the same key rate under ideal conditions, have diverging results in the presence of a side-channel, with BB84 proving more advantageous. While we consider only a representative case example, we expect the strategies developed and key rate analysis method to be broadly applicable to other leaky sources.
Secure access microcontroller system based on fingerprint template with hyperchaotic encryption

Authentication systems based on biometric and microcontroller systems are used around the world to control the access of users in restricted sites such as in offices, banks, or hospitals. Moderns authentication systems use cloud connectivity using the Internet. Nevertheless, biometric data are considered confidential, which is susceptible to hacker attacks with the aim of fraud, extortion or identity theft. In this work, we propose a low-cost secure control access microcontroller system that uses the Secure Hash Algorithm 1 (SHA-1) and hyperchaotic encryption of fingerprint template to increase the security against cybernetic attacks. The main findings can be briefly described as (1) chaotic encryption of the fingerprint template provides strong security by using the hyperchaotic 2D Lotka–Volterra map with just one round of diffusion–confusion; (2) the fingerprint template injected to SHA-1 increases the robustness against differential attack by combining a unique 160-bit hash key with the personal key; (3) the authentication system implemented in a low-cost embedded system based on 32-bit microcontroller provides high security at low cost; (4) several security analysis of data generated in the embedded authentication system validates the effectiveness of proposed scheme. Based on the high security level achieved in proposed authentication scheme, the system provides double padlock in secure access biometric systems providing robustness against malicious attacks, avoid identity theft and the unauthorized access in highly restricted areas.
An Improved Spherical Vector and Truncated Mean Stabilization Based Bat Algorithm for UAV Path Planning

Unmanned aerial vehicles have a wide range of applications. An intelligent optimization algorithm based on the traditional bat algorithm (BA) is investigated in this paper for UAV flight path planning in a static complex environment. The primary goal of this work is to develop a safer flight path while considering the feasibility of the UAV and the requirements for safe operation. This research proposes an improved spherical coordinate and truncated average stable strategy-based bat optimization algorithm (TMS-SBA). The algorithm uses the UAV’s motion space to encode the operator, and by substituting a new bat for the worst of the old one after each iteration to increase population diversity, the algorithm can converge quickly in a complex environment while maintaining stable operation. In addition, the flight path is smoothly generated by using B-spline curves to make the planned path suitable for UAV. MATLAB simulation experiments show that, compared with other traditional swarm intelligent algorithms, TMS-SBA can successfully generate feasible and effective optimal solutions in complex environments and plan shorter, safer, and more accessible flight paths for UAV.
Blockchain-driven win–win strategy of economy and environment in the asymmetric competitive supply chain

In reality, it is difficult for some consumers to distinguish the attributes of green products according to the shopping experience or social trust. The blockchain has a transparent technical feature, which helps to enhance the shopping experience of consumers and identify the accurate attributes of product. In order to explore the condition of technical application, this study constructs the game-theoretical model to analyze the technical condition from the aspects of firms’ profit, environmental benefit, and market effect, including two competitive manufacturers and a retailer. Moreover, this paper also investigates the equilibrium condition of government subsidy, consumer trust, and competition intensity, respectively. The main results are as follows. On the one hand, this paper finds the equilibrium condition based on specific conditions among firms. On the other hand, the study shows the equilibrium strategy with market effect, which has a feature of “four-high”, including the high price, high quantity, high profit, and high environmental benefit. Based on these results, the paper discusses some managerial implications to guide blockchain technology and green technology of firms. The contribution of this study is the combinative analysis of blockchain technology transparency and the equilibrium strategy for the competitive manufacturers, which reveals the value of transparency and the value of blockchain service.
CrossRectify: Leveraging disagreement for semi-supervised object detection

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and unlabeled data with pseudo labels predicted by the detector itself, but their performances are always limited. Through experimental analysis, we reveal the underlying reason is that the detector is misguided by the incorrect pseudo labels predicted by itself (dubbed self-errors). These self-errors can hurt performance even worse than random-errors, and can be neither discerned nor rectified during the self-labeling process. In this paper, we propose an effective detection framework named CrossRectify, to obtain accurate pseudo labels by simultaneously training two detectors with different initial parameters. Specifically, the proposed approach leverages the disagreements between detectors to discern the self-errors and refines the pseudo label quality by the proposed cross-rectifying mechanism. Extensive experiments show that CrossRectify achieves outperforming performances over various detector structures on 2D and 3D detection benchmarks.
Illegal activity detection on bitcoin transaction using deep learning

Forensic investigations increasingly leverage artificial intelligence (AI/ML) to identify illegal activities on bitcoin. bitcoin transactions have an original graph (network) structure, which is sophisticated and yet informative. However, machine learning applications on bitcoin have given limited attention to developing end-to-end deep learning frameworks that are modeled to exploit the bitcoin graph structure. To identify illegal transactions on bitcoin, the current paper extracts nineteen features from the bitcoin network and proposes a deep learning-based graph neural network model using spectral graph convolutions and transaction features. The proposed model is compared with two state-of-the-art techniques, viz., a graph attention network (GAT2) and an extreme gradient boosted decision tree (XGBOOST) trained on convoluted features for classification of illegal transactions on bitcoin. To understand the efficacy of the proposed model, a dataset is collected consisting of 13310125 transactions of 2059 entities having 3152202 bitcoin account addresses and belonging to 28 categories of users. Two sets of experiments are performed on the datasets: labeling transactions as legal or illegal (binary classification) and identifying the originator of the transaction to one of the twenty-eight types of entities (multi-class classification). For fast and accurate decisions, binary classification is appropriate, and for pinpointing the category of bitcoin users, a multi-class classifier is suitable. On both the tasks, the proposed models achieved a maximum of 92% accuracy, validating the methodology and suitability of the model for real-world deployment.
Long-Strip Target Detection and Tracking with Autonomous Surface Vehicle

As we all know, target detection and tracking are of great significance for marine exploration and protection. In this paper, we propose one Convolutional-Neural-Network-based target detection method named YOLO-Softer NMS for long-strip target detection on the water, which combines You Only Look Once (YOLO) and Softer NMS algorithms to improve detection accuracy. The traditional YOLO network structure is improved, the prediction scale is increased from threeto four, and a softer NMS strategy is used to select the original output of the original YOLO method. The performance improvement is compared totheFaster-RCNN algorithm and traditional YOLO methodin both mAP and speed, and the proposed YOLO–Softer NMS’s mAP reaches 97.09%while still maintaining the same speed as YOLOv3. In addition, the camera imaging model is used to obtain accurate target coordinate information for target tracking. Finally, using the dicyclic loop PID control diagram, the Autonomous Surface Vehicle is controlled to approach the long-strip target with near-optimal path design. The actual test results verify that our long-strip target detection and tracking method can achieve gratifying long-strip target detection and tracking results.
Peer-to-peer energy trading and smart contracting platform of community-based virtual power plant

Traditional centralized transactions require a control center for user demand matching, settlement and other processes. However, with the increase in the penetration rate of distributed energy in the community, the explosive increase in the number of transactions leads to a decrease in efficiency and it is difficult to guarantee user privacy and information security. The smart contract technology based on blockchain technology has the characteristics of decentralization, traceability and tamper resistance, and these key factors show unique advantages in distributed energy transactions. This paper explores Ethereum and smart contract technology, designs a peer-to-peer energy sharing mechanism with reward and punishment incentives and establishes a smart contract trading platform for smart community-based virtual power plant (CVPP). This paper verifies the functionality and effectiveness of smart contract. The results show that when the supply and demand ratio changes, the user can conduct energy transactions according to the contract without a third-party organization, which solves the problem of trust between the two parties and achieves the expected effect and runs successfully. In addition, the simulation results show that the peer-to-peer transaction based on smart contracts reduces the energy cost per household and increases the total benefit of CVPP.
Attribute Verifier for Internet of Things

Identity management, authentication, and attribute verification are among the main concerns in many Internet of Things (IoT) applications. Considering the privacy concerns, attribute verification became more important in many applications. Many of the proposed models in this field suffer from privacy and scalability issues as they depend on a centralized entity. In this paper, we proposed a decentralized attribute verifier based on a challenge-response approach. To address various IoT attribute verification requirements, the proposed model provides two modes of attribute verification, namely 1-out-of-n verification and n-out-of-n verification modes, in which the participants can prove the nossession of one or all of the given target attributes.
Has My Release Disobeyed Semantic Versioning? Static Detection Based on Semantic Differencing

To enhance the compatibility in the version control of Java Third-party Libraries (TPLs), Maven adopts Semantic Versioning (SemVer) to standardize the underlying meaning of versions, but users could still confront abnormal execution and crash after upgrades even if compilation and linkage succeed. It is caused by semantic breaking (SemB) issues, such that APIs directly used by users have identical signatures but inconsistent semantics across upgrades. To strengthen compliance with SemVer rules, developers and users should be alerted of such issues. Unfortunately, it is challenging to detect them statically, because semantic changes in the internal methods of APIs are difficult to capture. Dynamic testing can confirmingly uncover some, but it is limited by inadequate coverage.
GLITCH: Automated Polyglot Security Smell Detection in Infrastructure as Code

Infrastructure as Code (IaC) is the process of managing IT infrastructure via programmable configuration files (also called IaC scripts). Like other software artifacts, IaC scripts may contain security smells, which are coding patterns that can result in security weaknesses. Automated analysis tools to detect security smells in IaC scripts exist, but they focus on specific technologies such as Puppet, Ansible, or Chef. This means that when the detection of a new smell is implemented in one of the tools, it is not immediately available for the technologies supported by the other tools — the only option is to duplicate the effort.
Enriching Compiler Testing with Real Program from Bug Report

Researchers have proposed various approaches to generate test programs. The state-of-the-art approaches can be roughly divided into random-based and mutation-based approaches: random-based approaches generate random programs and mutation-based approaches mutate programs to generate more test programs. Both lines of approaches mainly generate random code, but it is more beneficial to use real programs, since it is easier to learn the impacts of compiler bugs and it becomes reasonable to use both valid and invalid code. However, most real programs from code repositories are ineffective to trigger compiler bugs, partially because they are compiled before they are submitted.
Data Leakage in Notebooks: Static Detection and Better Processes

Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimating the model's accuracy during offline evaluations, possibly leading to deployment of low-quality models in production. Such leakage can happen easily by mistake or by following poor practices, but may be tedious and challenging to detect manually. We develop a static analysis approach to detect common forms of data leakage in data science code. Our evaluation shows that our analysis accurately detects data leakage and that such leakage is pervasive among over 100,000 analyzed public notebooks. We discuss how our static analysis approach can help both practitioners and educators, and how leakage prevention can be designed into the development process.
Zero-shot cross-lingual transfer language selection using linguistic similarity

We study the selection of transfer languages for different Natural Language Processing tasks, specifically sentiment analysis, named entity recognition and dependency parsing. In order to select an optimal transfer language, we propose to utilize different linguistic similarity metrics to measure the distance between languages and make the choice of transfer language based on this information instead of relying on intuition. We demonstrate that linguistic similarity correlates with cross-lingual transfer performance for all of the proposed tasks. We also show that there is a statistically significant difference in choosing the optimal language as the transfer source instead of English. This allows us to select a more suitable transfer language which can be used to better leverage knowledge from high-resource languages in order to improve the performance of language applications lacking data. For the study, we used datasets from eight different languages from three language families.
An optimized optical diffractive deep neural network with OReLU function based on genetic algorithm

The diffractive deep neural network (D2NN) has demonstrated advantages in artificial intelligence image processing tasks, such as classification and lens-free imaging. Due to the lack of nonlinear layers, the nonlinear representation and generalization capability of D2NN needs to be improved. To solve this problem, we introduce an Optical Rectified Linear Units (OReLU) function utilizing beam splitting, sensing, and modulation of optoelectronic devices. We experimentally verified the functionality of the proposed OReLU and analyzed the impact of the actual error on the whole network. The simulation result shows that such a nonlinear layer increases the sparsity of the network, which reduces the interdependence between parameters and also improves the classification accuracy of the network. Since the model's convergence speed and classification accuracy are influenced by the truncation threshold associated with the nonlinear function, we also optimize the threshold factor to achieve the highest classification accuracy of 97.97% and 87.85% on the MNIST and Fashion-MNIST datasets using the genetic algorithm.
Deep learning-based real-time 3D human pose estimation

Human body pose estimation represented by joint rotations is essential for driving the virtual characters. The present paper developed a novel end-to-end point-to-pose mesh fitting network (P2P-MeshNet) to directly estimate the body joint rotations. P2P-MeshNet provided a strong collaboration between the deep learning network, an inverse kinematics network for body pose estimation (IKNet-body), and the self-correcting network, an iterative error feedback network (IEF). The introduced P2P-MeshNet was then applied to the free mocap (FreeMocap) dataset covering OpenPose 3D joint locations reconstructed from multi-view OpenPose 2D joint locations. The generated joint rotations were tested using the mean per joint position error (MPJPE), as well as the percentage of correct keypoints (PCK) along with the area under the PCK curve (AUC) with a threshold range of 0–60 mm after Procrustes aligned. Based on the compared metrics, P2P-MeshNet with 11.31 mm and 99.7% in estimate error and success rate as well as an AUC of 80.9 demonstrated a more consistent tool for future human body pose estimation. The runtime performance of 100 frames per second implied its potential application prospects.
A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution

High-resolution DEMs can provide accurate geographic information and can be widely used in hydrological analysis, path planning, and urban design. As the main complementary means of producing high-resolution DEMs, the DEM super-resolution (SR) method based on deep learning has reached a bottleneck. The reason for this phenomenon is that the DEM super-resolution method based on deep learning lacks a part of the global information it requires. Specifically, the multilevel aggregation process of deep learning has difficulty sufficiently capturing the low-level features with dependencies, which leads to a lack of global relationships with high-level information. To address this problem, we propose a global-information-constrained deep learning network for DEM SR (GISR). Specifically, our proposed GISR method consists of a global information supplement module and a local feature generation module. The former uses the Kriging method to supplement global information, considering the spatial autocorrelation rule. The latter includes a residual module and the PixelShuffle module, which is used to restore the detailed features of the terrain. Compared with the bicubic, Kriging, SRCNN, SRResNet, and TfaSR methods, the experimental results of our method show a better ability to retain terrain features, and the generation effect is more consistent with the ground truth DEM. Meanwhile, compared with the deep learning method, the RMSE of our results is improved by 20.5% to 68.8%.