Reviewerly/exHarmony huggingface dataset:
A social friend selection method based on interest and social behavior

Social recommendation algorithms that integrate social information have become a research hotspot. However, the existing social recommendation has the problem that it is difficult to obtain reliable and clear friend relationships. Therefore, this paper proposes a social friend selection method FSISB based on interests and social behaviors. This method first defines four types of scenarios to find the explicit/hidden social friends of the target user, and alleviates the sparsity problem of social data by expanding the social scope; then by combining the user’s social structure and long-term/short-term interests to calculate the similarity of social friends, gets reliable and unambiguous social information and complete friend selection. Experiments show that the baseline model integrated with the FSISB method improves the recommendation performance by 0.4%-1.5% compared with the original method, and the performance improvement is more obvious in datasets with sparse social data, which verifies the positive impact of the FSISB method on alleviating data sparseness.
Neuron importance algorithm for continual learning

Most regularization-based continual learning methods are based on synapse importance. In contrast to them, we propose a neuron importance algorithm that uses Taylor criterion to calculate the importance of neurons. Then, for the fully connected layer and the convolutional layer, we propose two different approaches to convert neuron importance to synapse importance. Compared with existing neuron importance based method, the proposed algorithm is simple to implement, requiring only one forward propagation and one backward propagation to calculate the neuron importance. The effectiveness of the algorithm was validated on two datasets, 5-Split-MNIST and 5-Split-CIFAR10.
A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region

Abstract Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture.
CLFuzz: Vulnerability Detection of Cryptographic Algorithm Implementation via Semantic-aware Fuzzing

Cryptography is a core component of many security applications, and flaws hidden in its implementation will affect the functional integrity or, more severely, pose threats to data security. Hence, guaranteeing the correctness of the implementation is important. However, the semantic characteristics (e.g., diverse input data and complex functional transformation) challenge those traditional program validation techniques (e.g., static analysis and dynamic fuzzing). In this article, we propose CLFuzz, a semantic-aware fuzzer for the vulnerability detection of cryptographic algorithm implementation. CLFuzz first extracts the semantic information of targeted algorithms including their cryptographic-specific constraints and function signatures. Based on them, CLFuzz generates high-quality input data adaptively to trigger error-prone situations efficiently. Furthermore, CLFuzz applies innovative logical cross-check that strengthens the logical bug detection ability. We evaluate CLFuzz on the widely used implementations of 54 cryptographic algorithms. It outperforms state-of-the-art cryptographic fuzzing tools. For example, compared with Cryptofuzz, it achieves a coverage speedup of 3.4× and increases the final coverage by 14.4%. Furthermore, CLFuzz has detected 12 previously unknown implementation bugs in 8 cryptographic algorithms (e.g., CMAC in OpenSSL and Message Digest in SymCrypt), most of which are security-critical and have been successfully collected in the national vulnerability database (7 in NVD/CNVD) and is awarded by the Microsoft bounty program (2 for $1,000).
Semantic Template-based Convolutional Neural Network for Text Classification

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task.
Exploring the Relative Effects of Body Position and Locomotion Method on Presence and Cybersickness when Navigating a Virtual Environment

The primary goals of this research are to strengthen the understanding of the mechanisms underlying presence and cybersickness in relation to the body position and locomotion method when navigating a virtual environment (VE). In this regard, we compared two body positions (standing and sitting) and four locomotion methods (steering + embodied control [EC], steering + instrumental control [IC], teleportation + EC, and teleportation + IC) to examine the association between body position, locomotion method, presence, and cybersickness in VR. The results of a two-way ANOVA revealed a main effect of locomotion method on presence, with the sense of presence significantly lower for the steering + IC condition. However, there was no main effect of body position on presence, nor was there an interaction between body position and locomotion method. For cybersickness, nonparametric tests were used due to non-normality. The results of Mann-Whitney U tests indicated a statistically significant effect of body position on cybersickness. In particular, the level of cybersickness was significantly higher for a standing position than for a sitting position. In addition, the results of Kruskal-Wallis tests revealed that the locomotion method had a meaningful effect on cybersickness, with participants in the steering conditions feeling stronger symptoms of cybersickness than those in the teleportation conditions. Overall, this study confirmed the relationship between body position, locomotion method, presence, and cybersickness when navigating a VE.
Aspect-level Information Discrepancies across Heterogeneous Vulnerability Reports: Severity, Types and Detection Methods

Vulnerable third-party libraries pose significant threats to software applications that reuse these libraries. At an industry scale of reuse, manual analysis of third-party library vulnerabilities can be easily overwhelmed by the sheer number of vulnerabilities continually collected from diverse sources for thousands of reused libraries. Our study of four large-scale, actively maintained vulnerability databases (NVD, IBM X-Force, ExploitDB, and Openwall) reveals the wide presence of information discrepancies, in terms of seven vulnerability aspects, i.e., product, version, component, vulnerability type, root cause, attack vector, and impact, between the reports for the same vulnerability from heterogeneous sources. It would be beneficial to integrate and cross-validate multi-source vulnerability information, but it demands automatic aspect extraction and aspect discrepancy detection. In this work, we experimented with a wide range of NLP methods to extract named entities (e.g., product) and free-form phrases (e.g., root cause) from textual vulnerability reports and to detect semantically different aspect mentions between the reports. Our experiments confirm the feasibility of applying NLP methods to automate aspect-level vulnerability analysis and identify the need for domain customization of general NLP methods. Based on our findings, we propose a discrepancy-aware, aspect-level vulnerability knowledge graph and a KG-based web portal that integrates diversified vulnerability key aspect information from heterogeneous vulnerability databases. Our conducted user study proves the usefulness of our web portal. Our study opens the door to new types of vulnerability integration and management, such as vulnerability portraits of a product and explainable prediction of silent vulnerabilities.
Automated Test Suite Generation for Software Product Lines Based on Quality-Diversity Optimization

A Software Product Line (SPL) is a set of software products that are built from a variability model. Real-world SPLs typically involve a vast number of valid products, making it impossible to individually test each of them. This arises the need for automated test suite generation, which was previously modeled as either a single-objective or a multi-objective optimization problem considering only objective functions. This article provides a completely different mathematical model by exploiting the benefits of Quality-Diversity (QD) optimization that is composed of not only an objective function (e.g., t -wise coverage or test suite diversity) but also a user-defined behavior space (e.g., the space with test suite size as its dimension). We argue that the new model is more suitable and generic than the two alternatives because it provides at a time a large set of diverse (measured in the behavior space) and high-performing solutions that can ease the decision-making process. We apply MAP-Elites, one of the most popular QD algorithms, to solve the model. The results of the evaluation, on both realistic and artificial SPLs, are promising, with MAP-Elites significantly and substantially outperforming both single- and multi-objective approaches, and also several state-of-the-art SPL testing tools. In summary, this article provides a new and promising perspective on the test suite generation for SPLs.
IMPRINT: Interactional Dynamics-aware Motion Prediction in Teams using Multimodal Context

Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration.
Monocular vision-based online kinematic calibration method for five-axis motion platform

Abstract This article proposes an online kinematic calibration method for simultaneous five-axis motion for solving the problems of low kinematics accuracy and a highly complex kinematic calibration of the five-axis motion platform. First, the ArUco markers are used in a vision system for large stroke detection, while kinematics models of the five-axis motion platform are established based on the screw theory. This article proposes an online kinematic parameter identification method for simultaneous motion along five axes, using a monocular camera as a measurement tool. Furthermore, the stability and effectiveness of the identification algorithm are verified by simulation and experiment. Specifically, a process trajectory commonly used to conduct experiments verifies the scheme’s influence on the kinematic accuracy. Experimental results show that the proposed kinematic calibration method reduces the average position error of the five-axis motion platform by 88.59% and the average direction error by 84.54%, thus proving that the proposed kinematic calibration method can significantly improve the kinematic accuracy of the five-axis motion platform and verifying the applicability and effectiveness of the proposed scheme.
Precise image generation on current noisy quantum computing devices

Abstract The quantum angle generator (QAG) is a new full quantum machine learning model designed to generate accurate images on current noise intermediate scale quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the large Hadron Collider at CERN.
Concentration training of MR shooting game plus AI tech on college students

College students are the source of power for the country’s sustainable development, lack of concentration causes significant problem that leads to low learning acquisition for college students. The team uses literature, expert interviews, data statistics, and logical analysis to explore a solution that caters to college students’ preferences and has an above-notable concentration enhancement. Keeping close to the background of the times and fully integrating technological elements, the research is conducted on the inducing effect of MR shooting games on the concentration enhancement of college students. Comparing the experimental data, it is found that the MR shooting game has a more obvious intervention effect on the rehabilitation training and strengthening of the concentration of college students, and it and can be used as an effective tool for the induction of concentration enhancement of college students. At the same time, with the support of Internet+AI technology, it can have a subset considered enabling empowerment effect on MR shooting games, and realize self-iteration, migration, and the possibility of more functional expansion. It is helpful to support the improvement of concentration in more fields and the use and promotion of a vaster broader of work, and better serve the group of college students.
Afan Oromo Speech-Based Computer Command and Control: An Evaluation with Selected Commands

Speech-based computer command and control utilize natural speech to enable computers to understand human language and execute tasks through commands. However, there has been no study or development of a speech-based command and control system for Microsoft Word in Afan Oromo. The primary aim of this research is to investigate and develop a speech-based command and control system for Afan Oromo using a selected set of command-and-control words from MS Word. To accomplish this objective, a speech recognizer was developed using the HTK toolkit, employing a small vocabulary, isolated words, speaker independence, and HMM-based techniques. The translation of the selected MS command words from English to Afan Oromo was completed in order to develop this automatic speech-based computer command system. Audio recordings were obtained from 38 speakers (16 females and 22 males) aged between 18 and 40 years, based on their availability. Word-level speech recognition was performed using MFCC and data processing, which are widely used and are effective approaches in speech recognition. Out of a total of 64 MS command words, 54 words (84.37%) were used for training and 10 words (15.63%) were used for testing. Live and nonlive evaluation techniques were employed to assess the performance of the recognizer. The live recognizer, which considers variations in the environment, outperformed the nonlive recognizer due to the influence of neighboring phones. The performance results for the monophone tied state, triphone, and triphone-based recognizers were 78.12%, 86.87%, and 88.99%, respectively. Thus, the triphone-based recognizer exhibited the best performance among the nonlive recognizers. The challenges of limited resources in this research study were limited to investigate speech-based commands for computers using only selected MS commands, which play a crucial role in text processing. In order to evaluate a speech-based interface in a real environment, there were no components available for object-as-a-service. The experimental findings of this study demonstrated that if an adequate amount of language resources was available, a computer-based Afan Oromo speech-based interface for command-and-control purposes could be developed.
A mask-guided generation learning loss optimization method for person re-identification

Traditional person re-identification (ReID) methods based on generative adversarial networks often neglect the impact of background clutter during the image generation stage of data augmentation, which can have a negative effect on the accuracy of subsequent recognition results. To address this problem, this paper proposes a mask-guided generation learning loss optimization method, which consists of reconstructed image loss optimization and composite image loss optimization. By optimizing the loss function in the image generation stage, the proposed method generates more real images of the person’s body part to eliminate the impact of background clutter on recognition results in subsequent stages. The reconstructed image loss optimization uses a body mask to construct an attention transfer matrix, mapping the original image into a new feature map containing person-related but background-independent features. The composite image loss optimization uses VGG16 to extract fine-grained features from the composite image and calculates the local loss so that the generation network can further learn the fine-grained features of the body part. Extensive experiments on the Market- 1501 dataset show that the proposed method achieves 94.8% in Rank-1 and 85.5% in mAP, increasing the baseline results by 0.4% and 0.5%, respectively. The proposed method significantly improves the authenticity of the person’s body part in the generated image and the accuracy of ReID, providing high-quality training data for the model to discover more finegrained features. The proposed method provides a new idea for the application of body masks in ReID as well as a feasible technical route for future research.
Vehicle detection algorithm for highway driving scenarios

Detecting vehicles is a crucial task in autonomous driving systems for highways. However, current detection algorithms used in such scenarios may face issues such as overlooking or misidentifying small or faraway vehicles, as well as overall low detection accuracy in different situations. To tackle these challenges, this paper proposes a vehicle detection algorithm called CC-YOLO that specifically targets the highway driving scene. CC-YOLO is an improved version of YOLOv5s, leveraging the C2F module from YOLOv8 to replace the original C3 module in YOLOv5s’ Backbone and PAN-FPN. This replacement enhances the detection accuracy of vehicles while maintaining a lightweight model. In addition, the PAN-FPN is upgraded with a lightweight upsampling operator known as CARAFE to improve the recognition rate of small target vehicles. Finally, the detection head is enhanced with an ECA attention module to better learn vehicle features and improve the accuracy of vehicle detection. A vehicle detection dataset is constructed using real highway driving videos, and CC-YOLO is evaluated on this dataset. The experimental results indicate that, compared to YOLOv5s, CC-YOLO achieves an increased mAP of 6.8%, effectively improving vehicle detection accuracy in the highway driving scene.
An improved YOLOv5-based method for UAV object detection

Object detection based on unmanned aerial vehicle (UAV) images is very challenging. The multi-scale size and high density of objects in the UAV view bring great difficulties. To fully address this issue to unleash the potential of UAV applications, the YOLOv5-STD model is proposed. First, we add one more head to locate extremely small object detection by shallow image features; second, we use the attention mechanism to optimize the backbone by the transformer; third, we use SPD-Conv to avoid the loss of fine-grained image feature information. At the last, sufficient experiments on the dataset VisDrone 2022 have proven that the model has good performance, compared with the basic model, the improved model has an average improvement of about 7% in mAP@.5 metrics, and the ablation experiments have verified that its improvement skills have a positive effect on the model. This paper can help developers and researchers get a better experience in the analysis and processing of unmanned aerial vehicle images.