Reviewerly/exHarmony huggingface dataset:
PNB Based Differential Cryptanalysis of Salsa20 and ChaCha

This paper introduces significant improvements over the existing cryptanalysis approaches on Salsa20 and ChaCha stream ciphers. For the first time, we reduced the attack complexity on Salsa20/8 to the lowest possible margin. We introduced an attack on ChaCha7.25. It is the first attack of its type on ChaCha7.25/20. In our approach, we studied differential cryptanalysis of the Salsa20 and ChaCha stream ciphers based on a comprehensive analysis of probabilistic neutral bits (PNBs). The existing differential cryptanalysis approaches on Salsa20 and ChaCha stream ciphers first study the differential bias at specific input and output differential positions and then search for probabilistic neutral bits. However, the differential bias and the set of PNBs obtained in this method are not always the ideal combination to conduct the attack against the ciphers. The researchers have not focused on the comprehensive analysis of the probabilistic neutrality measure of all key bits concerning all possible output difference positions at all possible internal rounds of Salsa20 and ChaCha stream ciphers. Moreover, the relationship between the neutrality measure and the number of inverse quarter rounds has not been scrutinized yet. To address these study gaps, we study the differential cryptanalysis based on the comprehensive analysis of probabilistic neutral bits on the reduced-round Salsa20 and ChaCha. At first, we comprehensively analyze the neutrality measure of 256 key bits positions. Afterward, we select the output difference bit position with the best average neutrality measure and look for the corresponding input differential with the best differential bias. Considering all aspects, we present an attack on Salsa20/8 with a time complexity of 2241.62 and data complexity of 231.5, which is the best-known single bit differential attack on Salsa20/8 and then, we introduced an attack on ChaCha7.25 rounds with a time complexity of 2254.011 and data complexity of 251.81.
Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Abstract Noisy intermediate-scale quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the quantum approximate optimization algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.
Mobile image restoration via prior quantization

In the photograph of mobile terminal, image degradation is a multivariate problem, where the spectral of the scene, the lens imperfections, the sensor noise, and the field of view together contribute to the results. Besides eliminating it at the hardware level, the post-processing system, which utilizes various prior information, is significant for correction. However, due to the content differences among priors, the pipeline that directly aligns these factors shows limited efficiency and unoptimized restoration. Here, we propose a prior quantization model to correct the degradation introduced in the image formation pipeline. To integrate the multivariate messages, we encode various priors into a latent space and quantify them by the learnable codebooks. After quantization, the prior codes are fused with the image restoration branch to realize targeted optical degradation correction. Moreover, we propose a comprehensive synthetic flow to acquire data pairs in a relative low computational overhead. Comprehensive experiments demonstrate the flexibility of the proposed method and validate its potential to accomplish targeted restoration for mass-produced mobile terminals. Furthermore, our model promises to analyze the influence of various priors and the degradation of devices, which is helpful for joint soft-hardware design.
Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Abstract Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques ( K ‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p ‐value for the t ‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.
Computational Analysis: Unveiling the Quantum Algorithms for Protein Analysis and Predictions

The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In this research, we explore the application of quantum algorithms, specifically Grover’s algorithm, in improving the accuracy and efficiency of PPI prediction. By harnessing the inherent parallelism and quantum search capabilities of Grover’s algorithm, we aim to enhance the identification of interacting protein pairs from large-scale datasets. We demonstrate the effectiveness of using this algorithm through an extensive approach, comparing the performance of Grover’s algorithm with classical machine learning algorithms. Our results reveal that the quantum algorithm offers significant improvements in prediction accuracy, enabling the identification of previously undetected PPIs. Moreover, we discuss the advantages and limitations of using Grover’s algorithm in PPI prediction and provide insights into its potential for accelerating research in protein interaction networks. This research highlights the potential of quantum algorithms in advancing the field of bioinformatics and protein interaction analysis.
On the Defense of Spoofing Countermeasures Against Adversarial Attacks

Advances in speech synthesis have exposed the vulnerability of spoofing countermeasure (CM) systems. Adversarial attacks exacerbate this problem, mainly due to the reliance of most CM models on deep neural networks. While research on adversarial attacks in anti-spoofing systems has received considerable attention, there is a relative scarcity of studies focused on developing effective defense techniques. In this study, we propose a defense strategy against such attacks by augmenting training data with frequency band-pass filtering and denoising. Our approach aims to limit the impact of perturbation, thereby reducing the susceptibility to adversarial samples. Furthermore, our findings reveal that the use of Max-Feature-Map (MFM) and frequency band-pass filtering provides additional benefits in suppressing different noise types. To empirically validate this hypothesis, we conduct tests on different CM models using adversarial samples derived from the ASVspoof challenge and other well-known datasets. The evaluation results show that such defense mechanisms can potentially enhance the performance of spoofing countermeasure systems.
Challenges of Producing Software Bill of Materials for Java

Software bills of materials (SBOMs) promise to become the backbone of software supply chain hardening. We deep-dive into six tools and the SBOMs they produce for complex open source Java projects, revealing challenges regarding the accurate production and usage of SBOMs.
Impact of Data Augmentation on Hate Speech Detection

With the increase of social media platforms such as Facebook, Twitter, and YouTube, individuals from diverse cultures and societal backgrounds can communicate and express their viewpoints on several aspects of daily life. However, due to the differences in these cultures, along with the freedom of expression, hateful and offensive speech has increased and spread on these platforms. The detection of hate speech has significantly increased the interest of researchers in natural language processing (NLP). The OSACT5 shared task provides a new dataset that aims to detect the offensive language in addition to identifying the type of hate speech on Arabic social media. However, the available dataset is unbalanced, which leads to low performance, especially in the F1 score. Therefore, in this paper, we focused on overcoming such a problem by augmenting the text data. We fine-tuned and evaluated various pre-trained deep learning models in addition to augmenting the data to achieve the best performance. We observed that data augmentation increases the F1 score. After fine-tuning the QARiB model and augmenting the data we achieved the best F1 score of 0.49.
EDite-HRNet: Enhanced Dynamic Lightweight High-Resolution Network for Human Pose Estimation

Lightweight pose estimation models have been widely used in devices with different computing powers, providing convenience for numerous downstream tasks, such as gait estimation, behavior analysis, motion capture, etc. Although these lightweight methods can run on low-performance equipment, their estimation accuracy is low, which seriously affects the actual experience. In order to improve the prediction accuracy of the lightweight human pose estimation methods, we propose an Enhanced Dynamic Lightweight High-Resolution Network (EDite-HRNet) for human pose estimation. Specifically, we propose an Enhanced Dynamic Multi-scale Context (EDMC) block which enhances the features of the simple branch with multi-level features of the complex branch to realize multi-level features fusion. Moreover, inspired by GhostNet V2, we redesign the Enhanced Dynamic Global Context (EDGC) and the Enhanced Dynamic Multi-scale Context (EDMC) block by adopting GhostNet V2 module with DFC attention to replace ConvBN block in the original blocks. The experimental results on the two datasets (66.1% on the COCO2017 dataset and 86.8% on the MPII dataset), demonstrate that our network achieves the state-of-the-art performance with a slight increase in model complexity.
Gaussian Synthesis for High-Precision Location in Oriented Object Detection

In aerial image scenes, the objects have properties of arbitrary orientation, large-scale range, and dense distribution. Thus, the object detector uses oriented bounding box (OBB) to locate objects, which is more complex and challenging than horizontal bounding box (HBB) detector. Mainstream OBB detectors mostly use one-to-many label assignment strategy to predict multiple bounding boxes for the same object, and filter out repeat predictions by non-maximum suppression (NMS). NMS ranks with confidence and drops the detection box with IoU higher than the threshold, which is easy to get the local optimum result. The clustered synthesis method gets more accurate results than the original NMS, but applying it to the OBB detector leads to border shift, which arises from the angular discontinuity problem. Therefore, we use Gaussian OBB (G-OBB) to deal with the angular discontinuity and thus eliminate the offset generated by direct synthesis. G-OBB is not an easy to understand and describe representation. For this reason, we analyze the properties of G-OBB, and design a decoding method to convert a G-OBB to a rotated rectangular box, further discussing its conditions. Based on the decoding method, we propose a Gaussian synthesis algorithm (GauS), which transforms the OBB into Gaussian space, followed by synthesis, and finally transforms the synthesis result back into a new OBB. We have derived the synthesis and decoding methods, and further verified their effectiveness. The extensive experiments on several existing models show that GauS takes very little computation and improves detector’s high-precision performance. Extensive experiments verify the effectiveness, stability, and universality of the proposed algorithm. In addition, The RTMDet using GauS achieves a performance of 81.61 AP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> and gains a 0.39% improvement in mAP, which achieves the SOTA performance. Our implementation is available at: https://github.com/lzh420202/GauS.
Traffic Object Detection in Virtual Environments

As Virtual Reality (VR) technology becomes more advanced, VR can create scenes that closely resemble the real world. Therefore, automotive industry has started to use VR environments as test bed for autonomous driving algorithms. However, to the best of our knowledge, there is no systematic evaluation for the performance of real-world object detectors in VR worlds. To solve this issue, we used Unreal Engine to construct a virtual environment simulating real street view and AirSim plugin to generate bounding box ground truths. We employed the state-of-the-art object detectors YOLOv7 and YOLOv8 to detect common traffic objects. Our experiments can reveal the strengths and weakness of each detector.
Explainable production planning under partial observability in high-precision manufacturing

Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners.
A knowledge-driven co-evolutionary algorithm assisted by cross-regional interactive learning

Differential evolution (DE) and Estimation of distribution algorithm (EDA) exhibit complementary superiority in solving complex continuous optimization and engineering problems. The design of appropriate strategies coordinated with the two algorithms to balance exploration and exploitation is conducive to obtaining high-precision solutions. A knowledge-driven co-evolutionary algorithm assisted by a cross-regional interactive learning mechanism (KCACIL) is proposed to achieve a comprehensive collaboration between the algorithms, diverse strategies, and cross-regional individuals. Various elite-guided mutation strategies and a self-feedback strategy based on successful experience in light of implicit knowledge are devoted to fulfilling self-learning and cross-regional interactive learning to accomplish individual collaboration and knowledge transfer in the three regions. Reinforcement learning based on ε−greedy and simulated annealing is employed as feedback on the cross-regional individual information to promote the collaboration between opposition-based learning, interaction learning mechanism, and the revised strategy of inferior solutions with small Q values and high distance density. The dynamic self-adaptive adjustment strategies of multiple parameters are adopted to balance diversity and convergence. KCACIL is verified on the CEC 2014, 2017, 2020 benchmark test suites, and engineering applications. Experimental results indicate KCACIL is superior to the state-of-the-art comparison algorithms.
Temporal transaction information-aware Ponzi scheme detection for ethereum smart contracts

In recent years, the frenetic advances of blockchain techniques have promoted the large-scale application of cryptocurrency and attracted significant attention in the mushrooming applications of decentralized finance (DeFi). To guarantee the health of a DeFi ecosystem, it is critical to reduce the transaction risks in a DeFi system. In particular, as a representative DeFi ecosystem platform, Ethereum’s transaction process is mainly carried out with the help of smart contracts. Due to (pseudo)anonymity, the transaction process of Ethereum users is challenged by severe fraud threats. Ponzi scheme is the typical one. Previous studies have used machine learning methods to build Ponzi scheme detection models based on learning from the identified static smart contract samples feature data. However, in the early stage of smart contract deployment, the Ponzi scheme is difficult to detect. With the progress of transactions, Ponzi scheme will gradually show its characteristics. The existing methods are still falling short in capturing the temporal features of smart contracts for detecting Ponzi schemes in the big data environment. The recognition rate of the current approaches needs to be further improved. In this paper, we propose TTPS, a Long Short-Term Memory (LSTM) Ponzi scheme detection method considering time series transaction information of smart contracts. TTPS considers both temporal account features and code features of smart contracts. Adaptive synthetic sampling (ADASYN) is employed to effectively extend the feature data of minority class Ponzi scheme small samples. LSTM is utilized to learn from the temporal feature data of Ponzi scheme samples for TTPS model training. Experimental results verify and demonstrate the effectiveness and efficiency of TTPS.
Real-Time Human Activity Recognition for VR Simulators with Body Area Networks

Due to the limited physical space and training facilities, we propose one efficient immersive training method to integrate a virtual reality (VR) simulation system with a body area network (BAN). With the Customized deep neural network algorithm, the body-worn inertial sensors are capable to recognize the activities of participants and avoid mismatched actions. Moreover, the neural networks have been utilized to provide greater access to physical actions of the VR real-time training environment. In this paper, a quaternion based deep neural network algorithm is developed and implemented for human activity recognition (HAR). We share the experience on the VR application that has the potential to fulfil multi-user immersive VR system on HAR.
Identifying the Correlation Between Online Exam Answer Trajectory and Test Behavior Based on Artificial Intelligence and Eye Movement Detection Technology

COVID-19 has brought many challenges to higher education, such as remote cheating derived from remote assessment, students are easy to obtain cheating channels and resources, and the burden of remote invigilators being large, all of which make the fairness of remote assessment highly critical question. In order to improve the problems existing in remote assessment, this paper proposes a deep learning artificial intelligence proctoring system (AIPS), which detects whether there is any abnormality in the answer through the webcam gaze and eye tracking. Behavior, combined with Long Short Term Memory (LSTM) to design a deep learning model that can be used to detect whether there is an abnormal answer in real-ter the test is over, a report can be output, including eye movement answer hot spots, test question answer order. This paper develops AIPS which is expected to assist invigilators to find candidates with abnormal answers through artificial intelligence assistance, so as to improve the fairness of online assessment.
Credit Card Fraud Detection Based on DeepInsight and Deep Learning

In this paper, we propose a credit card fraud detection method that leverages DeepInsight and deep learning. The proposed method employs the DeepInsight mechanism to convert non-image credit card transaction data into structured images. These images are then processed by a parallel convolutional neural network (CNN) deep learning model to extract crucial hidden features for credit card fraud detection. To evaluate the performance of our method, we utilize European credit card transaction data. The evaluation results are compared with those of related methods, demonstrating the superiority of our proposed method in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient.
CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation

Federated learning is proposed to solve data privacy and security issues for traditional machine learning, which requires the training dataset to be stored locally on a machine or data center for training. However, federated learning may have problems like Non-Independent and Identically Distributed (Non-IID) data and private security. Non-IID can lead to lower training accuracy than expected, and there may be a risk of privacy leakage in the data uploaded by clients. Therefore, this paper proposes CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation. Before training begins, we cluster all clients so that the data distribution between clients in each group is similar. This means only a random subset of clients from each cluster is selected in each training round instead of all clients participating in the training. We can use this method to adjust the data balance of participating training. Finally, we add differential privacy and secure aggregation to the clustering and training process to improve the privacy and security of the proposed clustered federated learning framework.