Reviewerly/exHarmony huggingface dataset:
Secure Model for Records Traceability in Airline Supply Chain Based on Blockchain and Machine Learning

With the enormous amount of sensitive data generated by supply chains and with the accession of Industry 4.0, the development of airline supply chain management (SCM) strategies and the preservation of privacy have become a necessity. The implementation of robust privacy strategies allows supply chains to protect their personal data from being lost, used, or accessed in an unauthorized way. Blockchain technologies (BT) allow data to be traded transparently and to automate transactions through smart contracts. Indeed, blockchain technology is attracting more and more attention as it ensures integrity and non-repudiation. However, secure supply chain existing models neglect the complexity of the airline supply chain management (ASCM), data confidentiality, and advanced and persistent attacks (APT) which make them exposed to many vulnerabilities. In this article, we propose a blockchain-based framework suitable for the ASCM context. Thus, we will develop an efficient model based on blockchain and machine learning to authenticate, validate, and secure transactions between suppliers and legitimate users on ASCM environments. The proposed solution reduces transaction costs, enhances security level by detecting and preserving anomalies, and fosters transparency and validity of transactions.
Pulse width dependent operations of a Ag<sub>2</sub>S island network reservoir

Abstract The rapid growth in demand for edge artificial intelligence increases importance of physical reservoirs that work at low computational cost with low power consumption. A Ag 2 S island network also works as a physical reservoir, in which various physicochemical phenomena contribute to a reservoir operation. In this study, we investigated its frequency dependence and found that diffusion of Ag + cations in a Ag 2 S island, which has a relaxation time of about 100 μ s, plays a major role when performance is improved. Modified National Institute of Standards and Technology (MNIST) classification task using an input pulse width of 100 μ s resulted in the accuracy of 91%. Iterative operations up to 10 million cycles revealed a small enough standard deviation of output, suggesting a potential for practical use of a Ag 2 S island network as a reservoir.
Heterogeneous Cross-Project Defect Prediction via Optimal Transport

Heterogeneous cross-project defect prediction (HCPDP) aims to learn a prediction model from a heterogeneous source project and then apply the model to a target project. Existing HCPDP works mapped the data of the source and target projects in a common space. However, the pre-defined forms of mapping methods often limit prediction performance and it is difficult to measure the distance between two data instances from different feature spaces. This paper introduced optimal transport (OT) theory for the first time to build the relationship between source and target data distributions, and two prediction algorithms were proposed based on OT theory. In particular, an algorithm based on the entropic Gromov-Wasserstein (EGW) discrepancy was developed to perform the HCPDP model. The proposed EGW model measures the distance between two metric spaces by learning an optimal transfer matrix with the minimum data transfer cost and avoids measuring the distance of two instances of different feature spaces. Then, to improve EGW performance, an EGW+ transport algorithm based on EGW was developed by integrating target labels. Experimental results showed the effectiveness of EGW and EGW+ methods, and proved that our methods can support developers to find the defects in the early phase of software development.
Deformable Density Estimation via Adaptive Representation

Crowd counting is the basic task of crowd analysis and it is of great significance in the field of public safety. Therefore, it receives more and more attention recently. The common idea is to combine the crowd counting task with convolutional neural networks to predict the corresponding density map, which is generated by filtering the dot labels with specific Gaussian kernels. Although the counting performance is promoted by the newly proposed networks, they all suffer one conjunct problem, which is due to the perspective effect, there is significant scale contrast among targets in different positions within one scene, but the existing density maps can not represent this scale change well. To address the prediction difficulties caused by target scale variation, we propose a scale-sensitive crowd density map estimation framework, which focuses on dealing with target scale change from density map generation, network design, and model training stage. It consists of the Adaptive Density Map (ADM), Deformable Density Map Decoder (DDMD), and Auxiliary Branch. To be specific, the Gaussian kernel size variates adaptively based on target size to generate ADM that contains scale information for each specific target. DDMD introduces the deformable convolution to fit the Gaussian kernel variation and boosts the model's scale sensitivity. The Auxiliary Branch guides the learning of deformable convolution offsets during the training phase. Finally, we construct experiments on different large-scale datasets. The results show the effectiveness of the proposed ADM and DDMD. Furthermore, the visualization demonstrates that deformable convolution learns the target scale variation.
Intelligent Warehouse Robot Path Planning Based on Improved Ant Colony Algorithm

To improve the safety and accuracy of path planning of intelligent warehouse robots, this paper establishes a storage shelf model, incorporates Poisson Distribution to simulate the influence of unknown factors, and establishes a three-color raster map. The pheromone update mechanism is optimized by considering the path safety, path length, and turning elements under the influence of unknown factors. The two models based on the three-dimensional shelves are simulated separately, and the planned paths are de-pointed and smoothed. The simulation results show that the improved algorithm can design the optimal route safely and effectively in the storage environment under the influence of unknown factors. The proposed algorithm not only solves the blind search and deadlock problems, but also has better performances than other algorithms, i.e., 4 iterations compared to 22 and 30 iterations, 3 turns compared to 9 and 7 turns, 8.468s running time compared to 16.974s and 13.754s.
Blockchain-Based Service-Oriented Architecture for Consent Management, Access Control, and Auditing

Continuity of care requires the exchange of health information among organizations and care teams.The EU General Data Protection Regulation (GDPR) establishes that subject of care should give explicit consent to the treatment of her personal data, and organizations must obey the individual's will.Nevertheless, few solutions focus on guaranteeing the proper execution of consents.We propose a serviceoriented architecture, backed by blockchain technology, that enables: (1) tamper-proof and immutable storage of subject of care consents; (2) a fine-grained access control for protecting health data according to consents; and (3) auditing tasks for supervisory authorities (or subjects of care themselves) to assess that healthcare organizations comply with GDPR and granted consents.Standards for health information exchange and access control are adopted to guarantee interoperability.Access control events and the subject of care consents are maintained on a blockchain, providing a trusted collaboration between organizations, supervisory authorities, and individuals.A prototype of the architecture has been implemented as a proof of concept to evaluate the performance of critical components.The application of subject of care consent to control the treatment of personal health data in federated and distributed environments is a pressing concern.The experimental results show that blockchain can effectively support sharing consent and audit events among healthcare organizations, supervisory authorities, and individuals.
Interaction Transformer for Human Reaction Generation

We address the challenging task of human reaction generation, which aims to generate a corresponding reaction based on an input action. Most of the existing works do not focus on generating and predicting the reaction and cannot generate the motion when only the action is given as input. To address this limitation, we propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attention. Specifically, temporal attention captures the temporal dependencies of the motion of both characters and of their interaction, while spatial attention learns the dependencies between the different body parts of each character and those which are part of the interaction. Moreover, we propose using graphs to increase the performance of spatial attention via an interaction distance module that helps focus on nearby joints from both characters. Extensive experiments on the SBU interaction, K3HI, and DuetDance datasets demonstrate the effectiveness of InterFormer. Our method is general and can be used to generate more complex and long-term interactions. We also provide videos of generated reactions and the code with pre-trained models at https://github.com/CRISTAL-3DSAM/InterFormer
Uncertainty-Driven Dense Two-View Structure From Motion

This work introducesan effective and practical solution to the dense two-view structure from motion (S <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> M) problem. One vital question addressed is how to mindfully use per-pixel optical flow correspondence betweentwo frames for accurate pose estimation—as perfect per-pixel correspondence between two images is difficult, if not impossible, to establish. With the carefully estimated camera pose and predicted per-pixel optical flow correspondences, a dense depth of the scene is computed. Later, an iterative refinement procedure is introduced to further improve optical flow matching confidence, camera pose, and depth, exploiting their inherent dependency in rigid S <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> M. The fundamental idea presented is to benefit from per-pixel uncertainty in the optical flow estimation and provide robustness to the dense S <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> M system via an online refinement. Concretely, we introduce a pipeline consisting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> an uncertainty-aware dense optical flow estimation approach that provides per-pixel correspondence with their confidence score of matching; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> a weighted dense bundle adjustment formulation that depends on optical flow uncertainty and bidirectional optical flow consistency to refine both pose and depth; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(iii)</i> A depth estimation network that considers its consistency with the estimated poses and optical flow respecting epipolar constraint. Extensive experiments show that the proposed approach achieves remarkable depth accuracy and state-of-the-art camera pose results superseding SuperPoint and SuperGlue accuracy when tested on benchmark datasets such as DeMoN, YFCC100 M, and ScanNet.
A Novel Time-Domain Graph Tensor Attention Network for Specific Emitter Identification

Specific emitter identification (SEI) is significant in military communication scenarios, cognitive radio, and self-organized networks. However, these methods only consider the feature of signals or the feature after signal transformation. In other words, the time-domain correlation of each feature and relationships between features are seldom taken into account. A novel method is, therefore, proposed, which includes a transformation to convert the specific emitter signal into a graph tensor and a model named time-domain graph tensor attention network (TDGTAN) to encode graph tensors for SEI. Specifically, the model includes two main parts. The first part is intrapropagation, which uses the relationship between different sampling points through message propagation in each graph. The other part is interpropagation, which propagates cross-layer messages between different graphs at the same sampling point, to realize the use of the relationship between different features. Extensive experiments are conducted on a real-world dataset, and the result shows that the proposed approach acquires higher accuracy (ACC) and intriguing anti-interference performance. In addition, the proposed model also has higher parameter utilization and calculation efficiency.
Multimodal cooperative self‐attention network for action recognition

Abstract Multimodal human behaviour recognition is a research hotspot in computer vision. To fully use both skeleton and depth data, this paper constructs a new multimodal network identification scheme combined with the self‐attention mechanism. The system comprises a transformer‐based skeleton self‐attention subnetwork and a depth self‐attention subnetwork based on CNN. In the skeleton self‐attention subnetwork, this paper proposes a motion synergy space feature that can integrate the information of each joint point according to the entirety and synergy of human motion and puts forward a quantitative standard for the contribution degree of each joint motion. In this paper, the results from the skeleton self‐attention subnetwork and the depth self‐attention subnetwork are integrated and they are verified on the NTU RGB+D and UTD‐MHAD datasets. The authors have achieved 90% recognition rate on UTD‐MHAD dataset, and the CS recognition rate of the authors’ method on the NTU RGB+D dataset reaches 90.5% and the recognition rate of CV is 94.7%. Experimental results show that the network structure proposed in this paper achieves a high recognition rate, and its performance is better than most current methods.
Enabling Accurate Data Recovery for Mobile Devices Against Malware Attacks

Mobile computing devices today suffer from various malware attacks. After the malware attack, it is challenging to restore the device’s data back to the exact state right before the attack happens. This challenge would be exacerbated if the malware can compromise the OS of the victim device, obtaining the root privilege. In this work, we aim to design a novel data recovery framework for mobile computing devices, which can ensure recoverability of user data at the corruption point against the strong OS-level malware. By leveraging the version control capability of the cloud server and the hardware features of the local mobile device, we have successfully built MobiDR, the first system which can ensure restoration of data at the corruption point against the malware attacks. Our security analysis and experimental evaluation on the real-world implementation have justified the security and the practicality of MobiDR.
The Degree of Practicing Creative Leadership by Academic Leaders at Jordanian Universities and Its Relationship to the Level of Teaching Performance

This study aimed to identify the degree to which academic leaders at official Jordanian universities practice creative leadership and its relationship to the level of teaching performance according to faculty members’ point of view. This study also aimed to identify if there are statistically significant differences in the degree of creative leadership and in the level of teaching performance due to the variables of faculty and years of experience. It also aimed to examine the correlation between creative leadership and teaching performance. A descriptive correlative approach was used in this study, and the study sample consisted of 362faculty members at Jordanian universities during the second semester of the academic year 2020/2021.This study found that the degree of practicing creative leadership by the academic leaders at the faculties of Jordanian universities was high, and it also showed that there were no statistically significant differences inthe degree of academic leaders’ practice of creative leadership at the faculties of Jordanian public universities due to the variables of faculty and years of experience. The level of teaching performance of faculty members at the faculties of Jordanian universities was also high, and there were no statistically significant differences in the level of teaching performance of faculty members. There was a positive relationship between the degree of practicing creative leadership and the level of teaching performance of faculty members at Jordanian universities. According to the results of this study, the researchers recommended that universities should adopt the concept of creative leadership because of its positive role in achieving the universities’ mission, vision, and objectives, and in motivating employees.
Promoting Adversarial Transferability via Dual-Sampling Variance Aggregation and Feature Heterogeneity Attacks

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing adversarial samples exhibit weak migration in the black-box case, especially on some adversarially trained defense models. Previous work for gradient-based optimization either optimizes the image before iteration or optimizes the gradient during iteration, so it results in the generated adversarial samples overfitting the source model and exhibiting poor mobility to the adversarially trained model. To solve these problems, we propose the dual-sample variance aggregation with feature heterogeneity attack; our method is optimized before and during iterations to produce adversarial samples with better transferability. In addition, our method can be integrated with various input transformations. A large amount of experimental data demonstrate the effectiveness of the proposed method, which improves the attack success rate by 5.9% for the normally trained model and 11.5% for the adversarially trained model compared with the current state-of-the-art migration-enhancing attack methods.
WOGAN at the SBST 2022 CPS tool competition

WOGAN is an online test generation algorithm based on Wasserstein generative adversarial networks. In this note, we present how WOGAN works and summarize its performance in the SBST 2022 CPS tool competition concerning the AI of a self-driving car.
Operationalizing machine learning models

Deploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. Although many studies focus on adapting ML software engineering (SE) approaches and techniques, few studies have summarized the status and challenges of operationalizing ML models. Model operationalization encompasses all steps after model training and evaluation, including packaging the model in a format appropriate for deployment, publishing to a model registry or storage, integrating the model into a broader software system, serving, and monitoring. This study is the first systematic literature review investigating the techniques, tools, and infrastructures to operationalize ML models. After reviewing 24 primary studies, the results show that there are a number of tools for most use cases to operationalize ML models and cloud deployment in particular. The review also revealed several research opportunities, such as dynamic model-switching, continuous model-monitoring, and efficient edge ML deployments.
A smart contracts repository for top trending contracts

Blockchain technology spread very quickly during the last few years and has become one of the most popular trends among the research and developers community. In particular, the Ethereum blockchain is one of the most supported and used for developing smart contracts, which are informatics protocols that provide a higher level of security than traditional contracts and reduce other transaction costs associated with the bargaining practice. Nowadays, the transaction number associated with smart contracts deploying increased widely, and it is difficult for researchers and developers to keep track of programming trends. This work proposes a dataset containing only specific categories of smart contracts associated with the most popular trends of the last five years. Mainly, the proposed collection of programs contains Token and Non-Fungible-Token programs, whose popularity has increased a lot since their first appearance on the blockchain.
Wasserstein generative adversarial networks for online test generation for cyber physical systems

We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test having a fitness function for determining failing tests. As a proof of concept, we evaluate WOGAN by generating roads such that a lane assistance system of a car fails to stay on the designated lane. We find that our algorithm has a competitive performance respect to previously published algorithms.
Teaching Methodology for Understanding Virtual Reality and Application Development in Engineering Major

This study proposed a virtual reality (VR) course that addresses the overall understanding and application of VR technology. After investigating previous studies, we found that two technologies must be applied to design a VR course that fits the latest trends. One is hardware technology dealing with the technical background, while the other is software utilization and development using the merits of VR technology. To accommodate these needs, we designed a VR course consisting of three steps: VR-related theory, TA-led content creation training, and team projects. Through this course, students will improve their ability to develop applications that apply to their research fields after studying the technical background and courses of VR. We conducted a semester-long study with nine students to verify the proposed method and then evaluated them through an in-depth interview and a questionnaire with a five-point Likert scale consisting of nine items. Considering this feedback, we have added several steps to improve the educational effect among students.
How Blockchain Is Transforming Accounting, Auditing and Finance: A Systematic Review

Blockchain technology (BT) has been receiving increasing attention from the academics and practitioners, in terms of its emergence, evolution, transformation, potential disruptions, technical aspects, and implications on accounting, auditing and finance practices. Through a review of the 51 papers published from 2015 to 2021 in Scopus indexed academic journals in accounting and auditing. Based on the analysis of the selected papers, this chapter charts the current knowledge on BT, examines key themes identified from the literature, and recommends opportunities for future research. The chapter finds that the innovation and ensuing disruption of BT is still in an emerging phase, particularly the scope and influence in the accounting, auditing, and finance practice and research. The findings of this chapter can be used by the key stakeholders involved in professional practice in the accounting and auditing domain. The chapter offers avenues for future research seeking to develop theory and align theory-practice.