Reviewerly/exHarmony huggingface dataset:
Rethinking Label Flipping Attack: From Sample Masking to Sample Thresholding

Nowadays, machine learning (ML) and deep learning (DL) methods have become fundamental building blocks for a wide range of AI applications. The popularity of these methods also makes them widely exposed to malicious attacks, which may cause severe security concerns. To understand the security properties of the ML/DL methods, researchers have recently started to turn their focus to adversarial attack algorithms that could successfully corrupt the model or clean data owned by the victim with imperceptible perturbations. In this paper, we study the Label Flipping Attack (LFA) problem, where the attacker expects to corrupt an ML/DL model's performance by flipping a small fraction of the labels in the training data. Prior art along this direction adopts combinatorial optimization problems, leading to limited scalability toward deep learning models. To this end, we propose a novel minimax problem which provides an efficient reformulation of the sample selection process in LFA. In the new optimization problem, the sample selection operation could be implemented with a single thresholding parameter. This leads to a novel training algorithm called Sample Thresholding. Since the objective function is differentiable and the model complexity does not depend on the sample size, we can apply Sample Thresholding to attack deep learning models. Moreover, since the victim's behavior is not predictable in a poisonous attack setting, we have to employ surrogate models to simulate the true model employed by the victim model. Seeing the problem, we provide a theoretical analysis of such a surrogate paradigm. Specifically, we show that the performance gap between the true model employed by the victim and the surrogate model is small under mild conditions. On top of this paradigm, we extend Sample Thresholding to the crowdsourced ranking task, where labels collected from the annotators are vulnerable to adversarial attacks. Finally, experimental analyses on three real-world datasets speak to the efficacy of our method.
Secondary Matching Algorithm: a New Heterogeneous Image Matching Algorithm for the UAV Image and Satellite Remote Sensing Image

Existing image matching is mostly limited to homologous images, but there are few researches on heterogeneous image matching such as UAV images and satellite remote sensing images. Aiming at the slow speed and low accuracy of traditional image matching methods when matching small-scale UAV images with satellite remote sensing images, a secondary matching method based on image edge features is proposed. The gradient information calculates the normalized cross-correlation formula to obtain the first matching result, and then obtains the optimal matching result through the shape context algorithm. Experimental results show that this method can well overcome the influence of factors such as illumination, low resolution, rotation, etc., and match UAV images and remote sensing images in a short time. The average precision and recall of the algorithm can reach 67.75% and 64.66%. Compared with the traditional SURF algorithm, it has faster matching speed and better matching accuracy.
Hierarchical Modular Event Detection Based on Dependency Graph

Event Detection (ED) is a very important sub-task in the field of information extraction, which studies how to correctly identify the trigger words that trigger event generation from unstructured text containing event information. We can regard ED as a token-based multi-classification task and sequence labeling task. However, in the previous methods, the ED task is performed for fine-grained types of events, ignoring the more abstract information of coarse-grained event types, which leads to missing conceptual semantic information about the class hierarchy of events. We propose a new ED method (Hierarchical Modular Event Detection Based on Dependency Graphs, HMED) in this paper. First, we implement dynamic modeling of multi-order dependency label information between words, which is used to generate the fine-grained representations of event types. Then we design an upper-level conceptual module based on the characteristics of the ACE corpus to compute the coarse-grained representations of event types and fuse fine-grained and coarse-grained event conceptual semantic information through global attention. On the widely used ACE2005 corpus, our hierarchical module can significantly improve the performance when compared with the most current state-of-the-art results.
Learning to Adapt to Label-Scarce Image Domain via Angular Distance-Based Feature Alignment

Most recent domain adaptation (DA) methods deal with unsupervised setup, which requires numerous target images for training. However, constructing a large-scale image set of the target domain is occasionally much harder than preparing a smaller number of image and label pairs. To cope with the problem, a great attention is recently paid to supervised domain adaptation (SDA), which takes an extremely small amount of labeled target images for training (e.g., at most three examples per category). In the SDA setup, adapting deep networks towards target domain is very challenging due to the lack of target data, and we tackle this problem as follows. Given labeled images from source and target domains, we first extract deep features and project them to hyper-spherical space via l2-normalization. Afterwards, an additive angular margin loss is embedded so that deep features of both domains are compactly grouped on the basis of shared class prototypes. To further relieve domain discrepancy, a pairwise spherical feature alignment loss is incorporated. All of our loss functions are defined in the hyper-spherical space, and the advantage of each ingredient is analyzed in the literature. Comparative evaluation results demonstrate that the proposed approach is superior to existing SDA methods, achieving 60.7% (1-shot) and 64.4% (3-shot) average accuracies for the DomainNet benchmark dataset using the ResNet-34 backbone. In addition, by applying a semi-supervised learning scheme to a network initialized by our SDA method, we achieve the state-of-the-art performance on semi-supervised domain adaptation (SSDA) as well.
Application of Sentiment Classification of Weibo Comments Based on TextCNN Model

The Internet has become the main medium for the dissemination of network public opinion. By analyzing the emotional development trend of emergencies, it is possible to explore the evolution of public opinion and identify potential risks, and to provide decision support for the guidance and control of public opinion. This paper combines Word2vec and network and text convolutional neural network (Convolutional Neural Network, TextCNN) model to achieve text sentiment classification for Weibo comments. Formalize the comment text, and segment the processed data through the stuttering word segmentation library, that is, extract the keyword words, and use the Word2vec tool to train the word vector for each word segmentation, and obtain the word embedding weight matrix. The embedding layer of the CNN model. In this paper, the comment text is normalized, and the processed data is segmented through Jieba, that is, the keyword words are extracted, and the Word2vec tool is used to train the word vector for each word segmentation, and the embedding of the word embedding weight matrix CNN model is obtained.
A Method for Removing Complex Visible Watermarks of Electronic Map Based on Conditional Generative Adversarial Nets

With the rapid development and extensive application of the electronic map technology, complex visible watermarks have been widely used in e-maps and map tiles delivered by map service providers to protect copyright. To evaluate the robustness and effectiveness of visible watermarks, many researches on watermark removal have attracted much attention in recent years. However, most of the existing watermark removal methods have the problems of watermark residue or repaired area being indistinct and unreal. To address these difficulties, this paper presented a deep neural network model based on conditional generative adversarial networks (cGANs) to remove complex visible watermark for electronic map tiles. The model is mainly consisted of a generator and a discriminator. The generator adopts a U-Net structure with jump connections to generate watermark-free maps based on watermarked maps, and the discriminator uses a full convolutional network based on region discrimination model to distinguish the differences between the generated watermark-free maps and real watermark-free ones. Specifically, a perceptual loss function was introduced to enhance the structural details of the generated maps. The experiments show that the proposed method can remove not only simple textual watermarks, but also colorful graphic watermarks with complex structures as well, and it does not need manual intervention and can achieve fast batch removal of a wide variety of watermarks for electronic maps. In addition, the detailed information contained in map tiles can be effectively preserved while acquiring high-quality watermark-free electronic map tiles.
Integrating Software Issue Tracking and Traceability Models

Awareness of the importance of systems and software traceability, as well as tool support for traceability, have improved over the years. But an effective solution for traceability must align and integrate with an organization’s engineering processes. Specifically, the phases of the traceability process model (traceability strategy, creation, use and maintenance of traceability) must be aligned with the organization’s engineering processes. Previous research has discussed the benefits of integrating traceability into the configuration management process. In this paper, we propose Change Request management using traceability data. In our approach, new Change Requests (CRs) are created from the traceability model of the corresponding project. The created CRs contain a portion of the project’s overall traceability model that is relevant to that change. A proof-of-concept issue tracking system is proposed that uses a traceability model at its core.
Research on DNS Encryption Technology

The Domain Name System (DNS) has been proposed for nearly 40 years and has always been an important part of Internet communication. By default, DNS queries and responses are transmitted in unencrypted plaintext, which is easy to disclose users' private information. In order to ensure the privacy and security of users, researchers at home and abroad have produced four main encrypted DNS technologies, namely Domain Name System Security Extensions(DNSSEC), DNS over TLS (DoT), DNS over HTTPS (DoH), DNS over QUIC (DoQ). Firstly, this paper studies the current situation, advantages and problems of encrypted DNS technology. Then, a DNS encryption protocol based on HTTP/3 is proposed to ensure user experience and user privacy and security; Finally, the future development trend and research direction of encrypted DNS technology are discussed.
More Efficient Verifiable Functional Encryption

Functional encryption (FE for short) can be used to calculate a function output of a message when given the corresponding function key, without revealing other information about the message. However, the original FE does not guarantee the unforgeability of either ciphertexts or function keys. In 2016, Badrinarayanan et al. provide a new primitive called verifiable functional encryption (VFE for short), and give a generic transformation from FE to VFE using non-interactive witness-indistinguishable proof (NIWI proof). In their construction, each VFE ciphertext (resp. function key) consists of 4 FE ciphertexts (resp. function keys) generated from independent FE public keys (resp. secret keys) and a NIWI proof on the correctness. In this paper, we show that there is redundancy in their construction. Concretely, we give a new construction for VFE which uses only 3 FE ciphertexts and function keys, and prove the verifiability and security of the construction. Since the NIWI proof is also simpler in our scheme, our construction may lead to an about 25% decrease in both ciphertext/key size and encryption/decryption cost.
Towards Explaining DL Non-entailments by Utilizing Subtree Isomorphisms

We present our current state of research on explaining non-entailments by finding isomorphic subtrees of $$\mathcal{E}\mathcal{L}$$ -description trees. Our approach extends the set of abducible axioms that consist only of concepts to role restrictions as well. We argue how our approach could find solutions to abduction problems in scenarios where other methods cannot, and illustrate this via an example comparing our approach to existing ones.
Faster Ship Detection Algorithm in Large-Scene SAR Images

In recent years, deep learning has achieved great accuracy in SAR image ship detection tasks. However, for large-scene SAR images, the sliding window method will generate a large number of slices sent to the network, resulting in a waste of GPU resources. To solve this problem, we proposed a ship detection algorithm based on CFAR, which significantly reduces the number of slices and false alarms on land. Firstly, the sea regions to be detected are obtained under the prior guidance of Global Self-consistent Hierarchical High-resolution Shorelines(GSHHS), and remove major land areas in the SAR image. Secondly, the ship candidate region generation algorithm based on block G0-CFAR is implemented to obtain the candidate slices. Finally, the Yolov5 model is used to detect ships from the candidate slices. The validation experiment of sentinel-1 and Gaofen-3 large-scene SAR images shows that the proposed method can detect the ships in large-scene SAR images with 88% detection accuracy within 97s, and the number of image slices fed into GPU is greatly reduced. It has certain practical significance for ship detection in large-scene SAR images.
Clustering Semantic Predicates in the Open Research Knowledge Graph

When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/ . Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
The knapsack fuzzy chaos cryptosystem

The fuzzy set theory is employed as an influential in mathematics by many researchers. The fuzzy models are used as a fundamental tool to solve several problems in cryptography. This work proposed alternative version of the fuzzy chaotic cryptosystem through employing the Knapsack problem and the chaotic map that uses the Takagi-Sugeno (TS) Fuzzy model. The resulted outputs of the TS Fuzzy chaotic model are used to create the secure ephemeral key. A primitive variable is chosen to add the nonlinearity feature to the chaotic system. The orbit of the points is determined based on the fuzzy chaos system that is unpredictable through the randomly choice of the initial values. On the proposed Knapsack fuzzy chaotic (KFC) cryptosystem, the keys are generated, the masked ciphertext are computed based on the super-increasing sequence which is combined with a secure ephemeral key. One of several experimental results is implemented using the Lorenz map that has a chaotic behavior with two scrolled points. The security considerations of the proposed KFC cryptosystem are determined.
On <i>L</i> <sup>2</sup>-Consistency of Nearest Neighbor Matching

Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary statistics with matching methods and importance weighting. In this paper, we study nearest neighbor matching (NNM), which makes estimates of population quantities from biased samples by substituting unobserved variables with their nearest neighbors in the biased sample. We show that NNM is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L^{2}$ </tex-math></inline-formula> -consistent in the absence of smoothness and boundedness assumptions in finite dimensions. We discuss applications of NNM, outline the barriers to generalizing this work to separable metric spaces, and compare NNM to inverse probability weighting.
2D Winograd Circuits with Data Reuse for FaceNet Identification System

In this paper, we proposed 2D Winograd circuits with data reuse for FaceNet identification system. We optimize the FaceNet network and use the CASIA-WebFace database. In order to improve the computing efficiency of the system, we combined fast 2D Winograd algorithms for convolutional neural networks to optimize the operation of the convolution circuit, and increase the data reading performance through data reuse. Compared with the traditional FFT convolution operation, it can reduce 2.25 operational complexity.
Reinforcement Learning Based Online Active Learning for Human Activity Recognition

Online active learning (OAL), i.e., asking a user in a targeted and parsimonious way to provide annotation for activities they are currently engaged in, has been established as a meaningful way for bootstrapping human activity recognition (HAR) systems for real-world deployments. In this paper we extend on the idea of optimizing budgets of user-provided annotations by introducing a reinforcement learning based OAL approach. Our method decides on which data sample a user shall provide a label for using a continuosly updated base classifier and a reward function that takes into account the classifier’s confidence in form of its a-posteriori probability. We evaluate our approach on seven benchmark datasets and demonstrate recognition capabilities of the resulting classifiers that are superior to the state-of-the-art and reach the performance of fully supervised baseline systems for half the datasets. The presented approach has the potential to push the boundaries for real-world deployments of HAR systems.
SVIT: Hybrid Vision Transformer Models with Scattering Transform

Transformer architectures not only enable significant performance in natural language processing tasks, but also demon-strate comparable feature extraction ability as convolutional neural networks in computer vision tasks. Unlike words in sentences, it is difficult to specify a graphic token with definite semantic representations in pictures, which diminishes the impact of the self-attention mechanism. This work inves-tigates tokenization methods for Vision Transformer (ViT). Scattering transform shares a tiny network structure based on fixed wavelet filters. It is capable of providing adequate spatial and frequency-based tokenizations. So we propose hybrid ViT models with scattering transform called Scat-tering Vision Transformer (SViT). Experiments on image classification tasks suggest that scattering-transform-based tokenizations can improve performance of vanilla ViT, especially on small datasets or frequency-sensitive tasks such as satellite image classification. Our codes are available at https://github.com/TianmingQiu/scattering_transformer.
ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion

Real-world knowledge graphs (KG) are mostly incomplete. The problem of recovering missing relations, called KG completion, has recently become an active research area. Knowledge graph (KG) embedding, a low-dimensional representation of entities and relations, is the crucial technique for KG completion. Convolutional neural networks in models such as ConvE, SACN, InteractE, and RGCN achieve recent successes. This paper takes a different architectural view and proposes ComDensE which combines relation-aware and common features using dense neural networks. In the relation-aware feature extraction, we attempt to create relational inductive bias by applying an encoding function specific to each relation. In the common feature extraction, we apply the common encoding function to all input embeddings. These encoding functions are implemented using dense layers in ComDensE. ComDensE achieves the state-of-the-art performance in the link prediction in terms of MRR, HIT@1 on FB15k-237 and HIT@1 on WN18RR compared to the previous baseline approaches. We conduct an extensive ablation study to examine the effects of the relation-aware layer and the common layer of the ComDensE. Experimental results illustrate that the combined dense architecture as implemented in ComDensE achieves the best performance.
Emergency Rescue Action Recognition Algorithm Based on Recurrent -Adaptive Graph Convolutional Networks

In the context of emergency rescue training, aiming at the scene of action recognition and effect evaluation by video. In order to solve the problems of large consumption of existing methods and poor real-time detection effect, this paper uses the technology stack of lightweight OpenPose and Recurrent-Adaptive Graph Convolutional Networks (R-AGCN). The recognition accuracy is improved by introducing cyclic enhancement module and adaptive module, and it has certain advantages for continuous video recognition. Firstly, the first ten layers of VGG - 19 network are used to extract image features, and OpenPose is used to extract bone feature key point coordinates. R-AGCN is used to realize action recognition. The module enhances the influence of spatial dimension on temporal dimension and then improve recognition accuracy. Under the two evaluation criteria of NTU-RGB + D data set, the accuracy of this algorithm is 84.3 % and 94.9 %, respectively, and it also has good recognition effect in the actual scene.
Distribution and Statistical Source Identification of Heavy Metals and Radionuclides in Soil: A Case Study from a Proposed Uranium Mining Site, Jharkhand, India

Singhbhum Thrust Belt is well recognized for its uranium and copper deposits and thus radionuclides as well as metals can be anticipated in its environs inclusive of soil. The enrichment of metals and radionclides in the vicinity of the uranium mining areas can be attributed to their release due to mining activities and waste disposal. As a part of the baseline study of the Bagjata mining area, 10 soil samples were collected from the villages in proximity of the mining area and analyzed for radionuclides (U(nat), 226Ra, 230Th, 210Po) and metals (Fe, Mn, Zn, Pb, Cu, and Ni). The geometric mean activity of 226Ra, 230Th and 210Po was 23.24, 65.54 and 55.15 Bqkg−1, respectively while geometric mean concentration of U(nat), Fe, Mn, Zn, Pb, Cu and Ni was calculated to be 2.37, 26,106, 267.6, 53.2, 37, 159.6 and 141.4 mgkg−1, respectively. Cu and Ni surpassed the Maximum Allowable limits (MAL) in the majority of locations. Owing to uneven and sporadic mineralization of the region, widespread spatial variation in the heavy metals and radionuclides has been observed. The Principal Component Analysis (PCA) depicted the geogenic contribution as the main source of radionuclides and metals. The secondary sources of metals were illustrated to be anthropogenic sources like mining and related vehicular load. The association of the radionuclides with the Fe is also depicted from the study.