Reviewerly/exHarmony huggingface dataset:
What we say and how we do: The role of metacognitive blindness in mathematics online learning using GeoGebra

During the Covid-19 pandemic, the learning system is shifted to online learning. Although it is held online, the ability of undergraduate students to metacognition plays an important role in learning mathematics. Students who have good metacognitive skills will have better performance in completing the tasks. Unfortunately, not all of them have this skill; moreover, they are often experiencing metacognitive failure when solving a mathematics problem. One of these failures is called metacognitive blindness that students do not realize the difficulty, error and mistakes in making the sketches. This study is intended to explore the characteristics of metacognitive blindness experienced by an undergraduate student who was studying Mathematics online during the pandemic situation. This research is qualitative with the type of case study. The researcher selected one out of fifteen undergraduate students as the subject using the purposive sampling technique. The finding reveals that the subject’s words in retelling the problem in his language and steps in operating GeoGebra were not synchronized. Furthermore, the subject said that the cuboids ABCD.EFGH with AB=2 cm. BC=1 cm and AE=1 cm. However, the subject sketched a rectangle with the same length and width of 2 units, then built it into a cuboid with a height of 1 unit; thus, the subject experienced error detection as one of the indicators of metacognitive blindness. Further research is needed to explore other metacognitive failures such as vandalism and mirage in the context of online learning.
Validity and practicality of the problem based learning e-module assisted by augmented reality on virus to improve student learning outcomes, retention, and science literacy

Technological developments have a significant impact on the world of education, especially in Indonesia. The progress that occurs is related to the current curriculum in Indonesia, namely K13. The substance of K13 focuses on bridging the mastery of 21st-century skills, namely scientific literacy. The low level of scientific literacy encourages the implementation of a quality learning process, one of which is learning biology material at SMAN 10 Malang. The purpose of this research is to determine the validity and practicality of development products in the form of e-module teaching materials assisted by augmented reality for learning the biology of viral material. The material includes Viruses for grade X senior high school students while perceiving the quality of the learning media produced mainly in terms of validity and practicality. This research uses the development research ADDIE model, with several stages of development, namely Analysis, Design, Development, Implementation, and Evaluation. The results of the validity test obtained an average score of 98.78% on the e-module and 100% on the augmented reality application showing a very good category. The result of the practicality test obtained an average score of 87,64% showing a very feasible category. Based on the results, the e-module assisted by augmented reality is valid and feasible to be implemented in the biology learning process on virus material to improve student learning outcomes, retention, and science literacy.
Private Multi-Winner Voting for Machine Learning

Private multi-winner voting is the task of revealing k-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine learning literature despite its prevalence in many domains such as healthcare. We propose three new DP multi-winner mechanisms: Binary, Tau, and Powerset voting. Binary voting operates independently per label through composition. Tau voting bounds votes optimally in their L2 norm for tight data-independent guarantees. Powerset voting operates over the entire binary vector by viewing the possible outcomes as a power set. Our theoretical and empirical analysis shows that Binary voting can be a competitive mechanism on many tasks unless there are strong correlations between labels, in which case Powerset voting outperforms it. We use our mechanisms to enable privacy-preserving multi-label learning in the central setting by extending the canonical single-label technique: PATE. We find that our techniques outperform current state-of-the-art approaches on large, real-world healthcare data and standard multi-label benchmarks. We further enable multi-label confidential and private collaborative (CaPC) learning and show that model performance can be significantly improved in the multi-site setting.
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps

In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Similarly to the seminal work of Nemirovski [26] and the recent approach of Piliouras et al. [28] in normal form games, our work is based on the fact that the update rule of the Proximal Point method (PP) can be approximated up to accuracy ε with only O (log 1/ε) additional gradient-calls through the iterations of a contraction map. Then combining the analysis of (PP) method with an error-propagation analysis we establish that the resulting first order method, called Clairvoyant Extra Gradient, admits near-optimal time-average convergence for general domains and last-iterate convergence in the unconstrained case.
Sampling an Edge in Sublinear Time Exactly and Optimally

Sampling edges from a graph in sublinear time is a fundamental problem and a powerful subroutine for designing sublinear-time algorithms. Suppose we have access to the vertices of the graph and know a constant-factor approximation to the number of edges. An algorithm for pointwise ε-approximate edge sampling with complexity has been given by Eden and Rosenbaum [SOSA 2018]. This has been later improved by Tetek and Thorup [STOC 2022] to . At the same time, time is necessary. We close the problem, by giving an algorithm with complexity for the task of sampling an edge exactly uniformly.
Self-supervised Visual-Semantic Embedding Network Based on Local Label Optimization

Image-text retrieval has always been an important direction in the field of vision-language understanding, which is dedicated to bridging the semantic gap between two modalities. The existing methods are mainly divided into global visual-semantic embedding and local region-word alignment. Although the local region-word alignment method has achieved remarkable results, this method based on fine-grained features often leads to low retrieval efficiency. At the same time, the method based on global embedding lacks extra semantic information, resulting in insufficient accuracy. In this paper, we propose a novel self-supervised visual-semantic embedding network based on local label optimization. Specifically, we generate a label for the entire image-text pair from the local information and use this label to optimize our embedding network, which can not only affect the retrieval efficiency but also significantly improve the retrieval accuracy. Experimental results on two benchmark datasets validate the effectiveness of our method.
zkChain: An Efficient Blockchain Privacy Protection Scheme Based on zk-SNARKs

As a distributed public ledger technology in a peer-to-peer network, blockchain has been significantly adopted in a variety of Internet interaction systems in the past few years, such as market supervision, copyright protection, digital identity, etc. However, most blockchain-based applications remain susceptible to privacy attacks. In the blockchain environment, all historical transactions are open access and transparent, and adversaries may illegally access private data, including transaction amounts, account addresses, and account balances. In this paper, we present an efficient blockchain privacy protection scheme based on zk-SNARKs, called zkChain. In zkChain, we construct zero-knowledge proof based on one-way hash function and zk-SNARKs scheme. The proof is publicly recorded in the ledger after verification by miners, and users use proofs to transfer digital assets. In the above process, zkChain can achieve strong privacy guarantee on account balance, transaction amount and account address. To demonstrate the practicality of zkChain, we conduct experiments based on libsnark and FISCO BCOS. Our empirical results demonstrate the practicality and effectiveness of zkChain, which takes about 5.9 s to generate a pair of keys, about 1.7 s to generate a proof, and about 0.005 s to verify a proof.
AP-GCL: Adversarial Perturbation on Graph Contrastive Learning

A serious ecological hazard of illegal transactions (money laundering, financial fraud, etc.) on the Bitcoin trading network. Anti-money laundering and fraud detection are essential instruments to address the problem. However, such datasets are generally extremely unbalanced in terms of positive and negative samples, and most of the data are unlabelled, with the illegal class accounting for just a minimal fraction of the total, which prevents supervised learning from learning a well-represented feature. We propose a self-supervised learning framework based on contrastive learning, in which two different augmented transformations are applied to the original graph data, perturbations are randomly attached to the node features of the upgraded views, and the model parameters and perturbations are updated by gradient descent to maximize the consistency of the single node in different views. The experimental result demonstrates that our model achieves excellent performance in all metrics and is comparable to supervised methods, which verifies the efficiency of the perturbation-based contrastive learning model.
Boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Correction

To solve the problem of boarding pass positioning with image tilt and sticking, we proposed a jointly boarding pass positioning and correction algorithm using multi-channel color components. First, a local threshold segmentation is performed on red and blue components in RGB color space to determine the general area of the boarding pass. Then, edge detection is performed on the blue component and pixel value standardization statistics on the three components to obtain partial edges and internal information of the boarding pass to refine the binary image. Next, the watershed algorithm is used to achieve precise positioning of the boarding pass. Finally, the segmentation and the boarding pass are completely rectified through perspective transformation. The results of segmentation and correction of boarding passes in actual scenarios show that the proposed algorithm can complete segmentation and correction under complex conditions such as boarding pass images sticking to each other, uneven illumination, and large-angle tilt. The average time spent on the test set is 0.049s, and the segmentation and correction success rate is 95.16 $$\%$$ . The proposed algorithm has high application value.
AMAD: Improving Adversarial Robustness Without Reducing Accuracy

The data augmentation method has been demonstrated as a ploy for enhancing model accuracy and adversarial robustness. However, it is well known that the traditional data augmentation methods have limited ability to defend against adversarial attacks. The most effective way to defend against adversarial attacks is still adversarial training. In addition, the trade-off between classification accuracy and robustness in adversarial training is also a popular research direction. In this paper, we propose a more effective method to combine one mixup algorithm with adversarial training to further enhance the robustness and accuracy of the model. Specifically, we align images in the feature space before the adversarial training. This method adds the features of another image on the basis of retaining the outline of one image. The images are trained by adversarial attack afterward. To verify the effectiveness of our method, we compare several other adversarial training methods. The experiments show our method achieves significant robustness and accuracy gains. Especially, our method makes an impressive trade-off between robustness and accuracy.
Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%.
Research on Intelligent Detection Method of Automotive Network Data Security Based on FlexRay/CAN Gateway

In order to improve the transfer rate of data information parameters by the car network host and avoid the occurrence of information false detection, this paper carries out research on the intelligent detection method of data security of the Internet of vehicles based on FlexRay/CAN gateway. According to the connection form of the FlexRay/CAN gateway, the EDF scheduling algorithm is solved, and the calculation expression of the known queue forwarding efficiency is combined to complete the vehicle network data forwarding processing based on the FlexRay/CAN gateway. According to the definition form of the vehicle network, the accurate data domain analysis results are obtained, and then the design of the vehicle network data security intelligent detection method based on the FlexRay/CAN gateway is completed by calculating the traffic detection coefficient. The experimental results show that compared with the traditional detection method, the FlexRay/CAN gateway can greatly improve the transfer rate of the data information parameters of the car network host, and has strong practical value in solving the problem of false detection of data information.
Large Scale Network Intrusion Detection Model Based on FS Feature Selection

Intrusion detection is one of the important means to ensure network security, aiming at the problem that the current network intrusion detection model cannot obtain the ideal network intrusion detection effect, a large-scale network intrusion detection model based on FS feature selection is designed. This study takes the NSL-KDD dataset as an example, and performs numerical and normalization processing on it. The main features are selected using the grey wolf optimization algorithm fused with cuckoo search. Taking the feature as input, the C4.5 algorithm is used to realize network intrusion detection. The results show that under the application of the constructed model, the accuracy rate (A), detection rate (D), and precision rate (P) are all greater than 90%, and the F1 score is greater than 1, indicating that the model has a good performance in network intrusion detection.
Network Security Evaluation Method of College Freshmen Career Counseling Service Based on Machine Learning

Because the career guidance service network for college freshmen is vulnerable to malicious attacks, network security is difficult to be guaranteed, therefore, a network security evaluation method based on machine learning is proposed for college freshmen career counseling services. According to the principle of index system, 3 primary indexes and 15 secondary evaluation indexes are selected. Three collectors are used to collect index data and implement pretreatment. Using the entropy weight method to calculate the index weights, taking this as input, the evaluation method based on BP neural network is used to obtain the network security trend. The experimental results show that, the average relative error, mean square error and root mean square error of the proposed method are low, which proves that the network security evaluation effect of the proposed method is better.
Anomaly Detection for Multi-time Series with Normalizing Flow

Various interconnected devices and sensors of cyber-physical systems interact with each other in time and space, and the multiple time series generated have interdependent implicit correlations and highly nonlinear relationships. Determining how to model the multiple time series and search anomaly measures through feature selection is the key to anomaly detection. Aiming at the complex interdependence between multi-time series, this paper proposes an anomaly detection model GNF, which applies a Bayesian network to model the structural relationships of multiple time series, and introduces a dependency encoder to obtain the representations of interdependency between multiple time series. Assuming that the anomalies are distributed in the low density area, the joint probability density of the time series can be decomposed into the product of conditional densities, and the data corresponding to the final low density is judged as abnormal. We have conducted experiments on real world datasets and demonstrated the effectiveness of GNF in anomaly detection.
Local Feature Acquisition Method of Multi-layer Vision Network Image Based on Virtual Reality

As an important part of image features, local image features reflect the changes of local information in the image, which are not easily disturbed by various changes such as noise, illumination, scale, and rotation. Faced with this situation, this paper proposes a method for local feature acquisition of multi-layer visual network images based on virtual reality. Based on virtual reality technology, the multi-layer visual network image is reconstructed in layers, and the reconstructed images are preprocessed by histogram equalization and denoising. The SUSAN algorithm and the SIFT algorithm are combined to realize the acquisition of local image features. The results show that compared with the original SUSAN algorithm and the original SIFT algorithm, the average running time of the researched method is shorter and the overlap error is smaller, which indicates that the researched method has lower time complexity and higher acquisition accuracy.
Examining the Multimodal and Multilingual Practices of Finnish Social Media Influencers

This chapter presents a qualitative study on the roles of multimodality and multilingualism in the online activities of Finnish social media influencers who create beauty- and fashion-focused content. Specifically, I analyse the ways in which these influencers utilise a combination of visual and linguistic representations on their social media, as well as the ways in which they combine the use of Finnish and English. The empirical analysis illustrates that there is variation in the multilingual practices that influencers utilise on different platforms, and that the use of these practices appears to change over time. The influencers also display a range of creative, multimodal practices that help them create and maintain a coherent brand across different social media channels.
Entity relation extraction based on pretrained model and multi-fork decoding tree

Aiming at the problems that the previous entity relation extraction model has insufficient dependencies between words, the recognition effect of overlapping entities is low, and the triples caused by single decoding force an unnecessary order, this paper proposes a deep learning-based method. The method uses a pre-trained model to extract sentence features, uses the Span method with stronger ability to extract overlapping entities for entity extraction, and uses a deep multi-fork decoding tree to implement parallel decoding. The experimental results on the CoNLL04 and ADE datasets show that compared with other relation extraction models, the F1 value of the model in this paper has a better improvement, and it also verifies the effectiveness and generalization ability of the model in this paper.
Mobile resources big data deduplication and offloading algorithm based on new computing network architecture

The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture.
Hierarchical path planning for autonomous forklifts in complex industrial environments

This article aims to quickly plan feasible and collision-free paths for autonomous forklifts operating in complex industrial environments. Inspired by the behaviors of forklifts drivers, a hierarchical path planning approach including two stages is designed, in which a topological map is inferred from the occupancy grid map and a coarse route is generated first, and then a heuristic search method is adopted to solve the planning problem in a short time. This is achieved through novel heuristic functions and exploration strategies under the guidance of the coarse route. Besides, with a trade-off between computational efficiency and accuracy, a multi-disk approximation method for non-convex shapes of vehicles is proposed for fast collision detection. Experiment results verify that the proposed algorithm can generate a feasible path through the narrow passages and meet the real-time requirement.