Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism for Federated Learning Participants Based on Statistical Analysis Features

Compared to traditional machine learning approaches, Google's innovative framework for federated learning has emerged as a prominent focus within the realm of collaborative efforts involving multi-source data and multiple users, all aimed at enhancing model performance. Distinguished by its high learning efficiency and robust privacy-preserving capabilities, this framework has garnered significant attention. In the context of cross-device federated learning, potential challenges can stem from participants who display a lack of enthusiasm for engaging in model training or who meticulously manipulate their submitted model parameters. These malicious behaviors carry the potential to undermine the integrity of the global model, thereby exerting a notable influence on the trajectory of the global model training process.To tackle these challenges, this paper introduces an incentive mechanism tailored for federated learning participants, grounded in the analysis of statistical features. By harnessing insights from statistical features, this mechanism dynamically adjusts participants' weights, thereby curbing the impact of malicious users. Under the assumption of rational participant behavior, the mechanism not only mitigates potential harm but also encourages proactive involvement. Empirical findings underscore the effectiveness of this adaptive mechanism in constraining the behavior of malicious participants in federated learning, thereby fortifying protective measures for the global model. The practical utility of this mechanism is demonstrated convincingly.
Simplification of an Additive Manufacturing Machine Implementation Using Its CAD Model and Mixed-Reality

This study presents the development and evaluation of a Mixed Reality (MR) tool called HoloPrint, designed to assist students in learning how to use a Filament Deposition Machine (FDM) for 3D printing. The HoloPrint tool combines a MR headset with a pedagogical scenario based on teacher feedback and is tailored to the expected competency profiles of students. The proof of concept phase focuses on understanding how users adopt this novel way of interacting with a combined virtual and real environment while accomplishing the scenario's objectives. A homogenous population of 81 first-year engineering students at INSA Toulouse was selected to participate in the study. The experiment was conducted during practical work sessions with 8 classes, each consisting of 10 students. The study evaluated the success rate of students using HoloPrint and assessed the tool's usability and usefulness through the System Usability Scale (SUS) questionnaire. Results indicate a high success rate of 98.8% for students using HoloPrint to print objects without assistance. The average SUS score for the tool was 79.69, indicating “Good” acceptability and nearing “Excellent.” Furthermore, prior experience with 3D printers or VR headsets did not significantly impact the success rate or SUS scores, suggesting the tool's effectiveness in aiding both novice and experienced students. In conclusion, the study demonstrates the potential of Mixed Reality as an effective and user-friendly tool for training students on a simple additive manufacturing machines, it enables the researchers to conduct further experimentation on more complex additive manufacturing machines.
Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recognition. To solve this problem, an optimized cooperative semisupervised learning radar emitter recognition method based on a small amount of labeled data is developed. First, a small amount of labeled data are randomly sampled by using the bootstrap method, loss functions for three common deep learning networks are improved, the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification. Subsequently, the dataset obtained after sampling is adopted to train three improved networks so as to build the initial model. In addition, the unlabeled data are preliminarily screened through dynamic time warping (DTW) and then input into the initial model trained previously for judgment. If the judgment results of two or more networks are consistent, the unlabeled data are labeled and put into the labeled data set. Lastly, the three network models are input into the labeled dataset for training, and the final model is built. As revealed by the simulation results, the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.
The Application of Digital Twins of the Subsystems of APCS of NPP at the Stage of Factory Tests

The task of executing factory tests of the subsystems, including in the Automated process control system (APCS) of a Nuclear power plant (NPP), in the absence of actual related subsystems, is considered. The methods for solving this problem using software and hardware modeling tools (digital twins) for the subsystems of upper and lower levels of APCS of NPP are proposed.
Problems in Applying Machine Learning Methods to Aviation

The report discusses potential applications of machine learning methods in aviation. Problems hindering the rapid implementation of machine learning methods in aviation, such as adversarial attacks, problems of data shift and data leakage, and the opacity of black box models, are investigated.
Reading between the lines: Automatic inference of self-assessed personality traits from dyadic social chats

Interaction through text-based platforms (e.g., WhatsApp) is a common everyday activity, typically referred to as “chatting”. However, the computing community paid relatively little attention to the automatic analysis of social and psycho-logical phenomena taking place during chats. This article proposes experiments aimed at the automatic inference of self-assessed personality traits from data collected during online dyadic chats. The proposed approach is multimodal and takes into account the two main components of chat-based interactions, namely what people type (the text) and how they type it (the keystroke dynamics). To the best of our knowledge, this is one of the very first works that includes keystroke dynamics in an approach for the inference of personality traits. The experiments involved 60 people and the results suggest that it is possible to recognize whether someone is below median or not along the Big-Five traits. Such a result suggests that personality leaves traces in both what people type it and how they type it, the two types of information the approach takes into account.
GPBP: Pipeline Extraction of Entities and Relations for Construction of Urban Utility Tunnel Knowledge Graph

In order to ensure the stable operation of the urban utility tunnel, the knowledge graphs can be applied to its inspection and maintenance process. Knowledge extraction is a key algorithm in the construction of knowledge graphs, including named entity recognition (NER) and relation extraction (RE) from textual data. For the construction of knowledge graphs in professional fields, it is necessary to design knowledge extraction algorithm based on text features. We collate extensive corpus from maintenance manuals and official documents related to urban utility tunnel, and create the dataset Standards for Operation and Maintenance of Urban Utility Tunnel (SOMUUT). In addition, we proposed a Global Pointer Based Pipeline approach (GPBP) for entity and relation extraction. we adopt global pointer to relation extraction and use two independent neural network structures to finish NER task and RE task, which achieves a better performance comparing with mainstream algorithms on SOMUUT.
Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction

Lexical complexity prediction (LCP) determines the complexity level of words or phrases in a sentence. LCP has a significant impact on the enhancement of language translations, readability assessment, and text generation. However, the domain-specific technical word, the complex grammatical structure, the polysemy problem, the inter-word relationship, and dependencies make it challenging to determine the complexity of words or phrases. In this paper, we propose an integrated transformer regressor model named ITRM-LCP to estimate the lexical complexity of words and phrases where diverse contextual features are extracted from various transformer models. The transformer models are fine-tuned using the text-pair data. Then, a bidirectional LSTM-based regressor module is plugged on top of each transformer to learn the long-term dependencies and estimate the complexity scores. The predicted scores of each module are then aggregated to determine the final complexity score. We assess our proposed model using two benchmark datasets from shared tasks. Experimental findings demonstrate that our ITRM-LCP model obtains 10.2% and 8.2% improvement on the news and Wikipedia corpus of the CWI-2018 dataset, compared to the top-performing systems (DAT, CAMB, and TMU). Additionally, our ITRM-LCP model surpasses state-of-the-art LCP systems (DeepBlueAI, JUST-BLUE) by 1.5% and 1.34% for single and multi-word LCP tasks defined in the SemEval LCP-2021 task.
A Knowledge-driven Cross-modal Memory Network for Radiology Report Generation

In previous studies, most work focused on generating high-quality reports by smoothing the alignment between cross-modal mappings. However, a common problem with current radiology report generation methods is that they ignore the interactions and connections between various medical findings that are critical to medical discovery. To overcome this problem, we propose a knowledge-driven cross-modal memory network. The network is built by data mining clinical reports and constructing knowledge graphs and their corresponding adjacency matrices based on the mined a prior knowledge. Combining information after graphical interaction through a cross-modal memory network with a knowledge graph adjacency matrix. Finally the Transformer generates a full report. Experiments have demonstrated that reports generated from cross-modal memory network models that incorporate prior knowledge are more clinically informative.
Small Sample Learning Mechanism Based on Knowledge-transferring for Fault Diagnosis

Deep learning-based methods have been applied in many fields which has received increased attention. It requires large annotated data to train a deep model with good generalization ability. However, it may be very difficult, time-consuming, and even expensive to collect and annotate practical industrial data, for example, for fault diagnosis in industrial systems. To address this issue, a novel small sample learning (SSL) mechanism based on Knowledge-transferring is proposed in this manuscript, which leverages the expert system and deep neural network simultaneously. Firstly, an annotator is constructed based on a belief rule base (BRB) expert system only with available small labeled samples. Then, the left unlabeled samples will be labeled through the annotator to form an augmented training dataset (ATD) with the available small labeled samples, which can realize the data representation of expert knowledge. Besides, a united optimization model based on CNN that combines data and knowledge is built, which has realized the transfer of expert knowledge. Finally, we demonstrate the capability of the proposed method on the dataset collected from the real experimental platform for rotating machinery fault diagnosis.
Is Chinese Spelling Check ready? Understanding the correction behavior in real-world scenarios

The task of Chinese Spelling Check (CSC) is crucial for identifying and rectifying spelling errors in Chinese texts. While prior work in this domain has predominantly relied on benchmarks such as SIGHAN for evaluating model performance, these benchmarks often exhibit an imbalanced distribution of spelling errors. They are typically constructed under idealized conditions, presuming the presence of only spelling errors in the input text. This assumption does not hold in real-world scenarios, where spell checkers frequently encounter a mix of spelling and grammatical errors, thereby presenting additional challenges. To address this gap and create a more realistic testing environment, we introduce a high-quality CSC evaluation benchmark named YACSC (Yet Another Chinese Spelling Check Dataset). YACSC is unique in that it includes annotations for both grammatical and spelling errors, rendering it a more reliable benchmark for CSC tasks. Furthermore, we propose a hierarchical network designed to integrate multidimensional information, leveraging semantic and phonetic aspects, as well as the structural forms of Chinese characters, to enhance the detection and correction of spelling errors. Through extensive experiments, we evaluate the limitations of existing CSC benchmarks and illustrate the application of our proposed system in real-world scenarios, particularly as a preliminary stage in writing assistant systems.
JiuJITsu: Removing Gadgets with Safe Register Allocation for JIT Code Generation

Code-reuse attacks have the capability to craft malicious instructions from small code fragments, commonly referred to as “gadgets.” These gadgets are generated by JIT (Just-In-Time) engines as integral components of native instructions, with the flexibility to be embedded in various fields, including Displacement . In this article, we introduce a novel approach for potential gadget insertion, achieved through the manipulation of ModR/M and SIB bytes via JavaScript code. This manipulation influences a JIT engine’s register allocation and code generation algorithms. These newly generated gadgets do not rely on constants and thus evade existing constant blinding schemes. Furthermore, they can be combined with 1-byte constants, a combination that proves to be challenging to defend against using conventional constant blinding techniques. To showcase the feasibility of our approach, we provide proof-of-concept (POC) code for three distinct types of gadgets. Our research underscores the potential for attackers to exploit ModR/M and SIB bytes within JIT-generated native instructions. In response, we propose a practical defense mechanism to mitigate such attacks. We introduce JiuJITsu , a security-enhanced register allocation scheme designed to prevent harmful register assignments during the JIT code generation phase, thereby thwarting the generation of these malicious gadgets. We conduct a comprehensive analysis of JiuJITsu ’s effectiveness in defending against code-reuse attacks. Our findings demonstrate that it incurs a runtime overhead of under 1% when evaluated using JetStream2 benchmarks and real-world websites.
Ensemble learning based software defect prediction

Currently, the cost to detect and solve software defects is a heavy burden on software projects. So, it is significant to predict software defects at the earlier stages of the software development lifecycle. In this study, seven commonly-used machine learning and deep learning algorithms were studied and the performance of defect classification on 4 representative public datasets from NASA and the PROMISE repository was demonstrated. Furthermore, three classical ensemble learning methods (bagging, boosting, and stacking) were used to improve the prediction performance. Six metrics, including accuracy, precision, f1-score, recall, the area under the receiver operating characteristic curve (AUC), and G-Mean were utilized to evaluate the performance. It was noted that ensemble learning exceeded all the other seven algorithms. Ensemble learning achieved the highest AUC of 0.99, the highest G-Mean of 0.96, and an average F1-score of 0.97. Under a time-sensitive scenario, the boosting method was a good choice as it spent less runtime and had a similar performance to the other two ensemble learning methods in most cases.
Effectiveness of Video-based Training for Face-to-face Communication Skills of Software Engineers: Evidence from a Three-year Study

Objectives. Communication skills are crucial for effective software development teams, but those skills are difficult to teach. The goal of our project is to evaluate the effectiveness of teaching face-to-face communication skills using AVW-Space, a platform for video-based learning that provides personalized nudges to support student's engagement during video watching. Participants. The participants in our study are second-year software engineering students. The study was conducted over three years, with students enrolled in a semester-long project course. Study Method. We performed a quasi-experimental study over three years to teach face-to-face communication using AVW-Space, a video-based learning platform. We present the instance of AVW-Space we developed to teach face-to-face communication. Participants watched and commented on 10 videos and later commented on the recording of their own team meeting. In 2020, the participants ( n = 50) did not receive nudges, and we use the data collected that year as control. In 2021 ( n = 49) and 2022 ( n = 48), nudges were provided adaptively to encourage students to write more and higher-quality comments. Findings. The findings from the study show the effectiveness of nudges. We found significant differences in engagement when nudges were provided. Furthermore, there is a causal effect of nudges on the interaction time, the total number of comments written, and the number of high-quality comments, as well as on learning. Finally, participants exposed to nudges reported higher perceived learning. Conclusions. Our research shows the effect of nudges on student engagement and learning while using the instance of AVW-Space for teaching face-to-face communication skills. Future work will explore other soft skills, as well as providing explanations for the decisions made by AVW-Space.
A prediction model of integrated learning based on FCM and SAGA

Existing methods for big data analysis and prediction face difficulties due to the high dimensionality and disorderliness of big data, which cause noise problems. Moreover, the proportion of base learners in integrated learning often leads to redundancy. To address these issues, this paper proposes an integrated learning prediction model based on FCM-SAGA, which dynamically adjusts the learners by using the SAGA algorithm to achieve the optimal solution of the selector. For feature selection, this paper adopts FCM clustering to reduce the dimensionality and ensure that data clusters with similar variation curves can be aggregated together. Finally, the dataset selects real incremental one-year data to verify the advantages of the proposed algorithm over other mainstream algorithms in terms of accuracy.
A Drone Detection Algorithm Based on Color-Edge Joint Information and Convolutional Neural Network

With the popularization of drones, drone detection is more and more important for industrial and agricultural management. The video surveillance is an important way for drone detection. However, it is difficult to achieving a balance between performance and computational burden for some old video surveillance platforms with low computation capability. In this paper a drone detection method based on color-edge joint information and convolution neural network is proposed. Firstly, the background is eliminated by frame difference based method to reduce environment disturbances. Secondly, the frame difference result is processed by edge abstraction and morphology operation to find the candidate targets. Finally, the color information and abstracted edge information of candidate targets are jointly supplied to a convolutional neural network to detect the drone target. Due to the effective way to select candidate targets and concise neural network architecture, the proposed method can achieve satisfying detection performance on the platforms with low computing capability. The experiment results demonstrate that the proposed method is effective for drone detection.
Visual Transformer for Image Splicing Localization

Image splicing, a prevalent form of image tampering with potential security implications, has prompted the need for reliable models to detect and locate manipulated regions. While convolutional neural network (CNN) based algorithms have shown promise, they often lack global modeling capabilities. In this paper, we propose an end-to-end image splicing and localization network based on a visual Transformer to address the uneven modeling of long-range dependencies between image pixels in existing algorithms. The model incorporates an encoder-decoder architecture that captures pixel relationships and local continuity, leveraging both shallow and deep features through skip connections. Multi-scale features are combined to enhance the accuracy of tampered area localization. The loss function utilizes weighted cross-entropy to locate splicing areas of various sizes and shapes. Evaluation on four public datasets demonstrates significant performance improvements over existing methods, with F1 scores increasing by 33.3%, 21.1%, 9.5%, and 5.4% on the IMD2020, CASIA2.0, Columbia, and NIST16 datasets, respectively. Furthermore, the model exhibits robustness to various post-processing operations. These findings highlight the efficacy of the proposed approach in addressing the challenges associated with image splicing localization. The developed model holds promise for enhancing security measures in image forensics and related applications.
Talent Flow Model Based on Graph Attention Network

Talent flow is a vital component of the region’s competitiveness and development. Accurately grasping talent trends can aid in the development of the region. Unlike text and image data, the characteristics of questionnaire data are more dependent on prior and potential correlations between each factor than on its order and position relationship. To efficiently extract features from questionnaire data and accurately predict talent flow, this paper proposes a novel talent flow prediction method based on the graph attention network. In our method, the data is transformed into graph data, the data feature is saved in the nodes, the adjacency matrix stores the link between the nodes, and an attention mechanism allocates the aggregate weight when the neighbor nodes are aggregated. The experiment demonstrates that the method proposed in this paper outperforms other algorithms for similar tasks both in accuracy and efficiency. It achieves an overall classification accuracy of 92.33% and more than 90% of the classification accuracy in each category prediction classification on our dataset.
Research and Implementation of PDF Specific Element Fast Extraction

In order to better understand the enterprise's bidding preference data, a Python platform-based algorithm that automatically recognizes and extracts specific data, extracts the project information in the PDF file of the government procurement contract, and constructs an enterprise portrait of the enterprise participating in government procurement. The algorithm utilizes the built-in library of Python, and our approach improves the algorithm in the open-source library by accurately locating the form pages to improve the parsing efficiency, thus improving the efficiency of information extraction. Designed for different types of government procurement PDF files, identify the text information and form information in the PDF file, the text data for specific content extraction, form information structured and stored in the database. Experiments show that: the algorithm can quickly, accurately, batch extraction of PDF files of specific text content and form information, and converted into structured data deposited into the database, to achieve the desired purpose; its conversion speed and extraction accuracy is significantly faster than the web page conversion tools, etc., and can be batch extraction operations.