Reviewerly/exHarmony huggingface dataset:
Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals.
A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm

Labeled data in insufficient amounts and missing categories are two observable features for high maneuvering target trajectory data. However, the existing research achievements are insufficient for solving these two problems simultaneously during data classification. This study proposed a two-stage semi-supervised trajectory data classification algorithm. By pre-training the autoencoder and combining it with the Siamese network, a two-stage joint training was formed, which enabled the model to deal with missing categories by clustering and maintaining the classification ability under the missing label categories. The experimental simulation results showed that the performance of this algorithm was better than the classical semi-supervised algorithm label propagation and transferred learning when the amount of various labeled data was as low as 1–5. The two-stage training model also had a good effect on the problem of missing categories. When 75% of the types were missing, the purity could still reach 82%, which was about eight percentage points higher than the directly trained network. When two problems appeared simultaneously, compared with the directly trained network, the performance improved by about three percentage points on average, and the purity was consistently higher than the clustering results. In summary, this algorithm was more tolerant of the problems of labeled datasets, so it was more practical.
Teaching a Hands-On CTF-Based Web Application Security Course

American philosopher John Dewey, in one of his most famous theories about the hands-on approach to learning, said that practical problem-solving and theoretical teaching should go hand-in-hand. This means students must interact with their environment to adapt and learn. Today, we almost take for granted that laboratory classes are an essential part of teaching science and engineering. Specific to cybersecurity, an integral piece of any training is the opportunity to work in an interactive hands-on environment: problem-solving skills are best developed in this fashion. In this paper, we present a hands-on web application security course based on OWASP Top 10 that allows students to learn through real-life experience. The virtual laboratories provided in our course simulate common vulnerabilities and issues mapped directly from OWASP Top 10, allowing students to be well-prepared for most of the critical security risks to web applications that arise in the real world. To examine how practical knowledge affects the learning experience and to measure the effectiveness of the proposed solution, we gathered learning data (such as the number of tries and the execution time for each exercise) from our cybersecurity course applied to a group of students at our university. Then, we examined correlations between students’ results and gathered statistics. In our research, we made use of a CTF-based approach, which is known as a valuable pedagogical tool for providing students with real-life problems and helping them gain more practical skills, knowledge, and expertise in the cybersecurity field.
Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers.
Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design

Instructional framework based on a knowledge graph makes up for the interdisciplinary theme design ability of teachers in a single discipline, to some extent, and provides a curriculum-oriented theme generation path for STEAM instructional design. This study proposed a dynamic completion model of a knowledge graph based on the subject semantic tensor decomposition. This model can be based on the tensor calculation of multi-disciplinary curriculum standard knowledge semantics to provide more reasonable STEAM project-based learning themes for teachers of those subjects. First, the STEAM multi-disciplinary knowledge semantic dataset was generated through the course’s standard text and open-source encyclopedia data. Next, based on the semantic tensor decomposition of specific STEAM topics, the dynamic generation of knowledge graphs was realized, providing interdisciplinary STEAM learning topic sequences for teachers of a single discipline. Finally, the application experiment of generating STEAM learning themes proved the effectiveness of our model.
IP dLedger - Decentralized ledger for intellectual property administration

With the rapid growth of big data, the applications of intellectual property (IP) rights and prior art documents are increasing rapidly. Patent examiners are usually overworked to evaluate inventions referring to a large volume of prior arts in a short period. Such increasing workloads and documents may cause administrative inefficiencies and generate low-quality IPs that hamper technological innovation. Moreover, today's centralized IP administration system is inefficient in processing speed, security, and cost. Inefficiencies of IP administration in each national IP office impede speedy and efficient cooperation for international application and registration. This study proposes an IP decentralized ledger (IP dLedger) based on the blockchain to significantly reduce IP administration costs and improve IP use. IP dLedger connects the on-chain (blockchain) to the off-chain (centralized database) systems using IP codes that combine hash values and timestamps. Similar to the web hypertext method, this connective feature of IP dLedger enables users who click on identification information registered in a blockchain network to access, through a hyperlink, a centralized database of enormous volumes of related IP information. Therefore, IP dLedger can reduce IP administration costs, increase IP usage, and facilitate safe and convenient use of the IP data.
Weighted least-squares fitting of circles with variance component estimation

Although the least-squares (LS) circle fit has been widely used, the weighted LS fitting of circle is not thoroughly investigated, in particular, when the prior weight information is only partly known. Based on the Gauss–Helmert model (GHM), we first investigate the invariance on translation and rotation. The results show that though the translational invariance holds, the rotation invariance is broken except for some specific weight structures. As the main finding of this paper, we develop the VCE theory directly adapting to the nonlinear GHM representation of the circle fitting problem where the weight information is not exactly known. In the simulated example and the real applications, we show that: (1) The conclusions about the invariance of translation and rotation are validated. (2) The estimated variance components can perfectly represent the uncertainty of different point groups or different coordinate components from the statistical perspective.
Face photo–sketch synthesis via intra-domain enhancement

Face photo–sketch synthesis plays an increasingly important role in the field of law enforcement and entertainment. However, most of the existing methods only focus on how to cross the modality differences between the photo domain and the sketch domain, while neglecting the image quality enhancement of the generated faces. In this paper, we propose a novel intra-domain enhancement (IDE) method for the face photo–sketch synthesis task. Our method is composed of two steps to cope with the inter-domain generation gap and the intra-domain quality defect respectively, i.e. inter-domain face generation and intra-domain face enhancement. In the inter-domain face generation stage, we apply a probabilistic graphical model which is capable of synthesizing the coarse face sketch from photos with low quality. In the intra-domain face enhancement stage, a Multi-Layer feature Aggregation (MLA) based Generative Adversarial Networks (GAN) is designed to enhance facial details in the sketches and further refine the synthesized image quality. Our first phase helps bridge the modality gap between photos and sketches and ensures the shape consistency between them. The second phase aims to improve the detail-quality and resolution of the synthesized sketches. Extensive experimental results on public face sketch databases outperform the state-of-the-art face sketch synthesis methods.
Isomorphic model-based initialization for convolutional neural networks

Modern deep convolutional neural networks(CNNs) are often designed to be scalable, leading to the model family concept. A model family is a large (possibly infinite) collection of related neural network architectures. The isomorphism of a model family refers to the fact that the models within it share the same high-level structure. Meanwhile, the models within the model family are called isomorphic models for each other. Existing weight initialization methods for CNNs use random initialization or data-driven initialization. Even though these methods can perform satisfactory initialization, the isomorphism of model families is rarely explored. This work proposes an isomorphic model-based initialization method (IM Init) for CNNs. It can initialize any network with another well-trained isomorphic model in the same model family. We first formulate the widely used general network structure of CNNs. Then a structural weight transformation is presented to transform the weight between two isomorphic models. Finally, we apply our IM Init to the model down-sampling and up-sampling scenarios and confirm its effectiveness in improving accuracy and convergence speed through experiments on various image classification datasets. In the model down-sampling scenario, IM Init initializes the smaller target model with a larger well-trained source model. It improves the accuracy of RegNet200MF by 1.59% on the CIFAR-100 dataset and 1.9% on the CUB200 dataset. Inversely, IM Init initializes the larger target model with a smaller well-trained source model in the model up-sampling scenario. It significantly speeds up the convergence of RegNet600MF and improves the accuracy by 30.10% under short training schedules. Code will be available.
Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with control depth. In principle, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the regularized model selection algorithm to identify the optimal depth with just a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.
ITUFP: A fast method for interactive mining of Top-K frequent patterns from uncertain data

Top-K Uncertain Frequent Pattern (UFP) mining is an interesting topic in data mining. The existing TUFP algorithm supports static mining of Top-K UFPs; however, in the real world, users need to repeatedly change the K threshold to extract the information according to the requirements of their application. In interactive environments, the TUFP algorithm needs to re-scan the database and create the UP-Lists and CUP-Lists from scratch which is very time-consuming. In this paper, a fast method called ITUFP is proposed for interactive mining of Top-K UFPs. The proposed method uses a new data structure called IMCUP-List to store information of patterns efficiently. It creates the UP-Lists with a single database scan, extracts the patterns by generating IMCUP-Lists, and stores all the lists. When K changes, the proposed algorithm only updates the IMCUP-Lists without having to create the lists from scratch. Accordingly, ITUFP conforms to the ''build once, mine many" principle, where the UP-Lists and IMCUP-Lists are created only once and used in mining with different K values. This is the first study on interactive mining of Top-K UFPs. Extensive experimental results with sparse and dense uncertain data prove that the proposed method is very efficient for interactive mining of Top-K UFPs.
PROV-FL: Privacy-preserving Round Optimal Verifiable Federated Learning

Federated learning is a distributed framework where a server computes a global model by aggregating the local models trained on users' private data. However, for a stronger data privacy guarantee, the server should not access the local models except the aggregated one. One way to achieve this is to use a secure aggregation protocol that comes with the cost of several rounds of interactions between the server and users in the absence of a fully trusted third party (TTP). In this paper, we present PROV-FL, an efficient privacy-preserving federated learning training system that securely aggregates users' local models. PROV-FL requires only one round of communication between the server and users for aggregating local models without a TTP. Based on the homomorphic encryption and differential privacy techniques, we develop two PROV-FL training protocols for two different, namely single and multi-aggregator, scenarios. PROV-FL enjoys the verifiability feature in which the server can verify the authenticity of the aggregated model and efficiently handles users' dynamic joining and leaving. We evaluate and compare the performance of PROV-FL by running experiments on training CNN/DNN models with a diverse set of real-world datasets.
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation

Recent works have demonstrated that deep learning models are vulnerable to backdoor poisoning attacks, where these attacks instill spurious correlations to external trigger patterns or objects (e.g., stickers, sunglasses, etc.). We find that such external trigger signals are not necessary, as highly effective backdoors can be easily inserted using rotation-based image transformation. Our method constructs the poisoned dataset by rotating a limited amount of objects and labeling them incorrectly; once trained with it, the victim's model will make undesirable predictions during run-time inference. It exhibits a significantly high attack success rate while maintaining clean performance through comprehensive empirical studies on image classification and object detection tasks. Furthermore, we evaluate standard data augmentation techniques and five different backdoor defenses against our attack and find that none of them can serve as a consistent mitigation approach. Our attack can be easily deployed in the real world since it only requires rotating the object, as shown in both image classification and object detection applications. Overall, our work highlights a new, simple, physically realizable, and highly effective vector for backdoor attacks. Our video demo is available at https://youtu.be/6JIF8wnX34M
Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation

The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and split learning (SL) are introduced to solve this problem as well as data management and ownership issues with their different strengths and weaknesses. The recent proposal of federated split task-agnostic (F eSTA) learning tries to reconcile the distinct merits of FL and SL by enabling the multi-task collaboration between participants through Vision Transformer (ViT) architecture, but they suffer from higher communication overhead. To address this, here we present a multi-task distributed learning using ViT with random patch permutation, dubbed p -F eSTA. Instead of using a CNN-based head as in F eSTA, p -F eSTA adopts a simple patch embedder with random permutation, improving the multi-task learning performance without sacrificing privacy. Experimental results confirm that the proposed method significantly enhances the benefit of multi-task collaboration, communication efficiency, and privacy preservation, shedding light on practical multi-task distributed learning in the field of medical imaging.
Dynamic PET images denoising using spectral graph wavelet transform

Positron emission tomography (PET) is a non-invasive molecular imaging method for quantitative observation of physiological and biochemical changes in living organisms. The quality of the reconstructed PET image is limited by many different physical degradation factors. Various denoising methods including Gaussian filtering (GF) and non-local mean (NLM) filtering have been proposed to improve the image quality. However, image denoising usually blurs edges, of which high frequency components are filtered as noises. On the other hand, it is well-known that edges in a PET image are important to detection and recognition of a lesion. Denoising while preserving the edges of PET images remains an important yet challenging problem in PET image processing. In this paper, we propose a novel denoising method with good edge-preserving performance based on spectral graph wavelet transform (SGWT) for dynamic PET images denoising. We firstly generate a composite image from the entire time series, then perform SGWT on the PET images, and finally reconstruct the low graph frequency content to get the denoised dynamic PET images. Experimental results on simulation and in vivo data show that the proposed approach significantly outperforms the GF, NLM and graph filtering methods. Compared with deep learning-based method, the proposed method has the similar denoising performance, but it does not need lots of training data and has low computational complexity.
Communicating Missing Causal Information to Explain a Robot’s Past Behavior

Robots need to explain their behavior to gain trust. Existing research has focused on explaining a robot’s current behavior, yet it remains unknown yet challenging how to provide explanations of past actions in an environment that might change after a robot’s actions, leading to critical missing causal information due to moved objects. We conducted an experiment (N = 665) investigating how a robot could help participants infer the missing causal information by replaying the past behavior physically, using verbal explanations, and projecting visual information onto the environment. Participants watched videos of the robot replaying its completion of an integrated mobile kitting task. During the replay, the objects are already gone, so participants needed to infer where an object was picked, where a ground obstacle had been, and where the object was placed. Based on the results, we recommend combining physical replay with speech and projection indicators (Replay-Project-Say) to help infer all the missing causal information (picking, navigation, and placement) from the robot’s past actions. This condition had the best outcome in both task-based—effectiveness, efficiency, and confidence—and team-based metrics—workload and trust. If one’s focus is efficiency, then we recommend projection markers for navigation inferences and verbal markers for placing inferences.
Context matters: Methods for Bitcoin tracking

Bitcoin and other cryptocurrencies are well-known for their privacy properties that allow for the “anonymous” exchange of money. Bitcoin tracking with taint analysis remains challenging as it does not account for the change in Bitcoins' ownership or the usage of Privacy-Enhancing Technologies (PETs) to obscure Bitcoins' movement, and often produces unessential incidents with transactions unlikely to be related to the targeted activity. In this paper, we propose to improve the Bitcoin taint analysis tracking process that adapts to the context of address ownership and avoid following unrelated transactions. First, we introduce an approach in which we incorporate Bitcoin taint analysis with address profiling. Second, we propose two context-based taint analysis strategies. Third, we introduce a set of metrics using hypothesised behaviours related to illegal Bitcoins and recognisable patterns within the blockchain. We conducted an experiment using sample data from known Bitcoin theft cases to illustrate and evaluate the approach. The results on address profile integration reveal distinct transaction behaviours in tracking theft cases following all the metrics, such as address reuse, address size and transaction fee payment. One of the context-based tracking strategies, Dirty-First, shows positive potential for illustrating illegal Bitcoins’ spending and obscuring strategies. The majority of the six metrics we defined give distinct results in transaction behaviours between the theft cases and the control groups. Our context-based tracking methodology provides a solution for one of the shortcomings in the current Bitcoin tracking methodology and the next step for future cryptocurrency and cybercrime forensic research.