Reviewerly/exHarmony huggingface dataset:
Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot.
Kalman filters based on multibody models: linking simulation and real world. A comprehensive review

Abstract The Kalman filter algorithm estimates variables of linear systems combining information from real sensors and a mathematical model of the system. It may be applied to observe nonlinear systems by means of a linearization of the system model. Multibody system dynamics constitutes a methodology for the analysis and design of mechanical systems. During the last twenty years, many ways of employing a multibody model as the Kalman filter model have been explored. This paper gathers up diverse algorithms, from the first ones based on the continuous expressions of the filter, to the indirect methods that enable real-time implementations of the observation of mechanical systems with a large number of variables. A detailed explanation of the methods and a description of the strengths and weaknesses of each one is presented along this paper, including a benchmark evaluating the performance of the methods. An important aspect of the Kalman filter is the characterization of the system uncertainty by means of white Gaussian noise. Sometimes, the statistical properties of the noise are unknown. Several methods to determine these properties are described, and a new methodology to model systems perturbed by colored noise (time-correlated noise) is presented. In Kalman filters based on multibody models, the information from a real mechanical system can be employed to keep the model behaving like the actual system with a great level of accuracy, linking the simulation to the real behavior of the system.
URNet: An UNet-Based Model with Residual Mechanism for Monocular Depth Estimation

Autonomous vehicle systems rely heavily upon depth estimation, which facilitates the improvement of precision and stability in automated decision-making systems. Noteworthily, the technique of monocular depth estimation is critical for one of these feasible implementations. In the area of segmentation of medical images, UNet is a well-known encoder–decoder structure. Moreover, several studies have proven its further potential for monocular depth estimation. Similarly, based on UNet, we aim to propose a novel model of monocular depth estimation, which is constructed from the benefits of classical UNet and residual learning mechanisms and named URNet. Particularly, we employ the KITTI dataset in conjunction with the Eigen split strategy to determine the efficacy of our model. Compared with other studies, our URNet is significantly better, on the basis of higher the precision and lower error rate. Hence, it can deal properly with the depth estimation issue for autonomous driving systems.
Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor-Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV.
360°VR: Application for exercise and sport science education

OPINION article Front. Sports Act. Living, 20 March 2023Sec. Physical Education and Pedagogy Volume 5 - 2023 | https://doi.org/10.3389/fspor.2023.977075
Residue-based Label Protection Mechanisms in Vertical Logistic Regression

Federated learning (FL) enables distributed participants to collaboratively learn a global model without revealing their private data to each other. Recently, vertical FL, where the participants hold the same set of samples but with different features, has received increased attention. This paper first presents one label inference attack method to investigate the potential privacy leakages of the vertical logistic regression model. Specifically, we discover that the attacker can utilize the residue variables, which are calculated by solving the system of linear equations constructed by local dataset and the received decrypted gradients, to infer the privately owned labels. To deal with this, we then propose three protection mechanisms, e.g., additive noise mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages local differential privacy and homomorphic encryption techniques, to prevent the attack and improve the robustness of the vertical logistic regression model. Experimental results show that both the additive noise mechanism and the multiplicative noise mechanism can achieve efficient label protection with only a slight drop in model testing accuracy, furthermore, the hybrid mechanism can achieve label protection without any testing accuracy degradation, which demonstrates the effectiveness and efficiency of our protection techniques.
TCMal: A Hybrid Deep Learning Model for Encrypted Malicious Traffic Classification

Encryption protocols can protect personal privacy, but attackers can also use it to evade detection by intrusion detection systems. If an intrusion detection system can identify the malware family class to which the encrypted malicious traffic belongs, it can take targeted measures to mitigate the damage. Traditional payload-based methods are no longer effective for encrypted malicious traffic classification, while deep learning-based methods have made some progress. In this paper, we propose a hybrid deep learning model TCMal, which consists of two sub-networks: T-net and C-net. T-net can be unsupervised pre-trained through transformer for representation learning of raw packets, while C-net for flow-level feature extraction through convolutional neural network. The experimental results show that TCMal outperforms the other four models and a control group model on all three datasets(the average F1-Score is 91.71%, 95.56%, and 91.92% on the three datasets, 2.8%, 3.51%, and 5.66% higher than the second place model, respectively). TCMal also proves the feasibility of transformer in encrypted malicious traffic classification.
Medical Q&amp;A Statement NER Based on ECA Attention Mechanism and Lexical Enhancement

Medical NER is a basic task of information extraction in medical domain, and a key step of entity relationship extraction and medical health Q&A. To address the problems of small NER datasets, sparse entity types and difficult construction methods in medical domain, we built own large-scale medical Q&A statement dataset Ywbd-2022 based on medical Q&A statement pairs. The dataset contains nine categories of entities, and covers more than 94000 statement pairs using three annotation methods. Experiments show that the dataset has high availability and strong training ability. Meanwhile, the method based on vocabulary enhancement can avoid the decline of model performance and recognition accuracy caused by word segmentation errors. Therefore, in this paper, the vocabulary enhancement method is applied to the medical NER field, combined with a large untagged medical domain corpus for BERT pre-training, combined with ECA attention mechanism, fine-tuning on the professional data set, to obtain a medical NER domain-oriented vocabulary enhancement model BERT-SL-ECA. Experimentally, the Bert-SL-ECA model achieves optimal results from all three major CCKS conference evaluation datasets from 2017 to 2019, and achieved a F1 value of 96.64% on the Ywbd-2022 dataset.
Data Dimension Reduction makes ML Algorithms efficient

Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping. In this paper, we demonstrate that pre-processing not only speeds up the algorithms but also improves accuracy in both supervised and unsupervised learning. In pre-processing of DDR, first PCA based DDR is used for supervised learning, then we explore AE based DDR for unsupervised learning. In PCA based DDR, we first compare supervised learning algorithms accuracy and time before and after applying PCA. Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning. Supervised learning algorithms including support-vector machines (SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic Gradient Descent classifier (SGDC) and unsupervised learning algorithm including K-means clustering, are used for classification purpose. We used two datasets MNIST and FashionMNIST Our experiment shows that there is massive improvement in accuracy and time reduction after pre-processing in both supervised and unsupervised learning.
MFFAN: Multiple Features Fusion with Attention Networks for Malicious Traffic Detection

Malicious traffic detection is an important task in network security, which protects the target network from privacy leakage and service paralysis. The complexity of the network and the hierarchical structure of network traffic, i.e, byte-packet-flow, indicate the diversity of traffic information. Most of the existing work only uses one feature or statistical feature, and cannot learn network traffic from multiple perspectives, i.e, shortsighted, which results in the lack of important information in network traffic. Meanwhile, after obtaining multiple features, the effective fusion of multiple features is also an urgent problem to be solved. In this paper, we propose a Multiple Features Fusion with Attention Networks (MFFAN). According to the hierarchical structure of network traffic, we extract byte, packet, and statistical features from original traffic files to learn traffic from multiple perspectives, overcoming shortsighted. To effectively fuse multiple features, we use the self-attention to learn the intra-feature relationship with each feature and use the co-attention to learn the inter-feature relationship between features. We conduct experiments on the ISCIDS2012 dataset and CICIDS2017 dataset, and the results show that our model achieves an effective fusion of multiple features and high accuracy.
Efficient and Highly Accurate Differentially Private Statistical Genomic Analysis using Discrete Fourier Transform

As the amount of data containing human genome information increases, these data will be further utilized in medicine. However, if the statistics obtained from large-scale analyses are released unchanged, there is a risk of identifying individuals. Although there are several privacy-preserving techniques to release and utilize genomic statistics, most have the problem of poor accuracy at high privacy levels and do not provide correct results especially with an increased number of outputs. In addition, existing methods with relatively high accuracy are computationally intensive and hardly applicable to a large cohort such as those containing 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> SNPs. In this paper, we propose innovative differentially private methods with both efficiency and high accuracy to release the top K significant SNPs based on genomic statistics data. First, we enhance the Fourier perturbation algorithm (FPA), which was proposed in the context of histogram publication, for use with genomic statistics. Then, we propose a new extended FPA with more accurate privacy guarantees and provide a proof that this method achieves ε-differential privacy. Furthermore, we present novel methods combining DFT with the Laplace and exponential mechanisms. These methods take only $\mathcal{O}(m{\text{log}}m)$ time for a dataset containing m SNPs. We also theoretically guarantee that the value of sensitivity for these methods is smaller than that for existing methods and therefore can provide more accurate outputs. In fact, our proposed algorithms can be conducted in less than 20 seconds even for a large cohort, and our experiments using real data show that our methods can achieve 1.5 to 8 times higher accuracy than state-of-the-art methods especially when K is large. Because retrieving multiple significant SNPs from large cohorts in genomic analysis is preferred, our proposed methods are remarkably advisable rather than existing methods. Supplementary materials and the Python implementation of our experiments are available at https://github.com/ay0408/DP-DFT.
An Empirical Study on Source Code Feature Extraction in Preprocessing of IR-Based Requirements Traceability

In information retrieval-based (IR-based) requirements traceability research, a great deal of researches have focused on establishing trace links between requirements and source code. However, as the description styles of source code and requirements are very different, how to better preprocess the code is crucial for the quality of trace link generation. This paper aims to draw empirical conclusions about code feature extraction, annotation importance assessment, and annotation redundancy removal through comprehensive experiments, which impact the quality of trace links generated by IR-based methods between requirements and source code. The results show that when the average annotaion density is higher than 0.2, feature extraction is recommended. Removing redundancy from code with high annotation redundancy can enhance the quality of trace links. The above experiences can help developers to improve the quality of trace link generation and provide them with advice on writing code.
Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?

Software quality models aggregate metrics to indicate quality. Most metrics reflect counts derived from events or attributes that cannot directly be associated with quality. Worse, what constitutes a desirable value for a metric may vary across contexts. We demonstrate an approach to transforming arbitrary metrics into absolute quality scores by leveraging metrics captured from similar contexts. In contrast to metrics, scores represent freestanding quality properties that are also comparable. We provide a web-based tool for obtaining contextualized scores for metrics as obtained from one’s software. Our results indicate that significant differences among various metrics and contexts exist. The suggested approach works with arbitrary contexts. Given sufficient contextual information, it allows for answering the question of whether a metric value is good/bad or common/extreme.
LLDP: A Layer-wise Local Differential Privacy in Federated Learning

Federated learning (FL) combined with local differential privacy (LDP) has attracted considerable attention due to its privacy-preserving capability against inference-type attacks, e.g., model inversion attacks and membership inference attacks. However, the noise introduced by LDP reduces the global model performance, while decreasing the noise by setting a larger privacy budget sacrifices the privacy guarantees. In this paper, we propose a layer-wise LDP for the FL system, dubbed LLDP, which disturbs various layers of a local model according to clients’ self-assigned privacy budgets. With the deployment of LLDP, clients could train a highly accurate and rapid-converged global model without loosing privacy guarantees. Through extensive security analyses, the proposed LLDP scheme helps the entire local model achieve (ε,δ)-LDP, and the probability indistinguishability of the local model is achieved under the widespread semi-honest threat model. Ingenious experiments show that LLDP improves the global model prediction and convergence rate by 3.38% and 4.76% on the CIFAR-10 dataset compared to the state-of-the-art LDP method with the same privacy budget. In addition, given the same training target (loss value), LLDP requires a 26.67% lower privacy budget, providing stronger privacy guarantees against model inversion attacks.
A BiLSTM-Attention Model for Detecting Smart Contract Defects More Accurately

Smart contracts are applications running on the blockchain which control many virtual currencies. Since smart contracts are composed of code, they inevitably have defects. In recent years, many smart contract defects have caused lots of economic losses and harmful impacts. A contract that has defects may have some errors that cause unwanted results. As smart contracts cannot be modified once deployed, it is necessary to ensure that they are free from defects. In this paper, we focus on eleven defects of smart contracts and construct a deep learning-based model to detect these contract defects more accurately. Our model regards the smart contract’s operation codes as a sequential sentence and uses an Attention-based bidirectional long short term memory (BiLSTM-Attention) model to find smart contract defects. We evaluate our model’s and other models’ performance on 45622 real-world smart contracts. The experimental results show that our model can achieve higher accuracy (95.40%) and F1-score (95.38%). In addition, our model is highly efficient and can quickly detect large numbers of contracts.
Data Integrity Verification Scheme Based on Blockchain Smart Contract

In cloud computing circumstance, users upload data to the cloud server and verify data integrity through a third-party audit (TPA). However, verifying data integrity is still a computationally intensive and time-consuming operation. If there are illegal users or unreliable cloud servers, it can only be known from the verification result, resulting in invalid computation and time overhead. In order to solve the above problems, RSA algorithm is used to verify the legitimacy of the user, and when the verification passes, Merkle hash tree is used to filter unreliable servers. To prevent replay attacks, data integrity is verified through the bilinear mapping feature. Finally, the simulation results show that the scheme not only can detect the legitimacy of users, but also filter out unreliable servers, and effectively reduce the computational and time overhead of verifying data integrity.
Context-Aware Program Simplification to Improve Information Retrieval-Based Bug Localization

Information Retrieval-based Bug localization (IRBL) techniques have become a hot research topic in bug localization due to their few external dependencies and low execution cost. However, existing IRBL techniques have many challenges regarding localization granularity and applicability. First, existing IRBL techniques have not yet achieved statement-level bug localization. Second, almost all studies are limited to Java-based projects, and the effectiveness of these techniques for other widely used programming languages (e.g., Python) is still unknown. The reason for these deficiencies is that existing IRBL techniques mainly employ conventional NLP techniques to analyze the bug reports and have not yet fully exploited the stack trace attached to the bug reports. To improve IRBL techniques in terms of localization granularity and adaptability, we propose a context-aware program simplification technique—COPS—that is able to localize defective statements in suspicious files by analyzing the stack trace in bug reports, which enables statement-level bug localization for Python-based projects. Experiments using 948 bug reports show that our technique can localize the buggy statements with 102.6% higher Top@10, 56.2% higher MAP@10, and 95.6% higher MRR@10 than the baseline. Compared with the state-of-the-art techniques, COPS can improve 19.1% in MAP@10 and achieve 92% buggy statement coverage with a full scope search. Experimental results show that COPS has higher bug localization effectiveness than existing IRBL techniques; and that COPS achieves the same effectiveness with higher execution efficiency than state-of-the-art statement-level defect techniques.
Robust Secure Aggregation with Lightweight Verification for Federated Learning

Verifiable secure aggregation (VSA) is a critical procedure in federated learning (FL), where secure aggregation achieves local gradients aggregation while data confidentiality is preserved, and verifiability enables participants to verify the correctness of aggregated results returned by a central server (CS). Most of existing solutions for VSA employ cumbersome cryptographic primitives and techniques (e.g., homomorphic encryption, bilinear pairing, interactive proof systems), which impose high communication round complexity and computational costs on participants or CS. Besides, user dropouts occur commonly in cross-device FL as a result of unstable network connection, it is demanded to design particular mechanism to deal with such events. In this paper, we present a robust secure aggregation scheme with lightweight verification for FL, by utilizing Shamir’s secret sharing technique to design a random masking code to protect the confidentiality of local gradients and achieve resilience to possible user dropout. To support verifiability upon aggregation, we extend a multi-key homomorphic MAC to achieve verification over gradient vector space. We provide security analysis to show that our scheme can protect data confidentiality against collusion attacks, meanwhile ensure the verifiable results are unforgeable under the assumption pseudorandom functions exist. We implement our scheme to verify its correctness and feasibility, performance evaluation shows its advantages in terms of efficiency and functionality.