Reviewerly/exHarmony huggingface dataset:
Not Just Learning From Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, such a pattern of ‘learning from others’ struggles to handle the extreme intra-class variation, preventing FSS from being directly generalized to remote sensing scenes. To bridge the gap of intra-class variance, we develop a Dual-Mining network named DMNet for cross-image mining and self-mining, meaning that it no longer focuses solely on support images but pays more attention to the query image itself. Specifically, we propose a Class-public Region Mining (CPRM) module to effectively suppress irrelevant feature pollution by capturing the common semantics between the support-query image pair. The Class-specific Region Mining (CSRM) module is then proposed to continuously mine the class-specific semantics of the query image itself in a ‘filtering’ and ‘purifying’ manner. In addition, to prevent the co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (KMS) module to suppress the activation of known-class objects in the sample. Extensive experiments on the iSAID and LoveDA remote sensing datasets have demonstrated that our method sets the state-of-the-art with a minimum number of model parameters. Significantly, our model with the backbone of Resnet-50 achieves the mIoU of 49.58% and 51.34% on iSAID under 1-shot and 5-shot settings, outperforming the state-of-the-art method by 1.8% and 1.12%, respectively. The code is publicly available at https://github.com/HanboBizl/DMNet/.
Technique for Searching Data in a Cryptographically Protected SQL Database

The growing popularity of data outsourcing to third-party cloud servers has a downside, related to the serious concerns of data owners about their security due to possible leakage. The desire to reduce the risk of loss of data confidentiality has become a motivating start to developing mechanisms that provide the ability to effectively use encryption to protect data. However, the use of traditional encryption methods faces a problem. Namely, traditional encryption, by making it impossible for insiders and outsiders to access data without knowing the keys, excludes the possibility of searching. This paper presents a solution that provides a strong level of confidentiality when searching, inserting, modifying, and deleting the required sensitive data in a remote database whose data are encrypted. The proposed SQL query processing technique allows the DBMS server to perform search functions over encrypted data in the same way as in an unencrypted database. This is achieved through the organization of automatic decryption by specially developed secure software of the corresponding data required for search, without the possibility of viewing these data itself. At that, we guarantee the integrity of the stored procedures used and special tables that store encrypted modules of special software and decryption keys, the relevance and completeness of the results returned to the application. The results of the analysis of the feasibility and effectiveness of the proposed solution show that the proper privacy of the stored data can be achieved at a reasonable overhead.
Local Differential Privacy Based Membership-Privacy-Preserving Federated Learning for Deep-Learning-Driven Remote Sensing

With the development of deep learning, image recognition based on deep learning is now widely used in remote sensing. As we know, the effectiveness of deep learning models significantly benefits from the size and quality of the dataset. However, remote sensing data are often distributed in different parts. They cannot be shared directly for privacy and security reasons, and this has motivated some scholars to apply federated learning (FL) to remote sensing. However, research has found that federated learning is usually vulnerable to white-box membership inference attacks (MIAs), which aim to infer whether a piece of data was participating in model training. In remote sensing, the MIA can lead to the disclosure of sensitive information about the model trainers, such as their location and type, as well as time information about the remote sensing equipment. To solve this issue, we consider embedding local differential privacy (LDP) into FL and propose LDP-Fed. LDP-Fed performs local differential privacy perturbation after properly pruning the uploaded parameters, preventing the central server from obtaining the original local models from the participants. To achieve a trade-off between privacy and model performance, LDP-Fed adds different noise levels to the parameters for various layers of the local models. This paper conducted comprehensive experiments to evaluate the framework’s effectiveness on two remote sensing image datasets and two machine learning benchmark datasets. The results demonstrate that remote sensing image classification models are susceptible to MIAs, and our framework can successfully defend against white-box MIA while achieving an excellent global model.
Soft prompt enhanced joint learning for cross-domain aspect-based sentiment analysis

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, aiming to detect customer's opinion targets from reviews about products or services. The traditional supervised models have achieved promising results with annotated datasets. However, their performance dramatically decreases in cross-domain aspect term extraction tasks. Existing cross-domain transfer learning methods face two common limitations: (1) these works directly inject linguistic features into language models, making it challenging to transfer linguistic knowledge to the target domain; (2) they rely on the fixed predefined prompts, which is time-consuming to construct the prompts for all potential aspect term spans. To address the limitations, we propose a soft prompt-based joint learning method for cross-domain aspect term extraction in this paper. Specifically, by incorporating external linguistic features, the proposed method learns domain-invariant representations between source and target domains via multiple objectives, which bridges the gap between domains with varied distributions of aspect terms. Furthermore, the proposed method interpolates a set of transferable soft prompts consisting of multiple learnable vectors that are beneficial to detect aspect terms in the target domain. Extensive experiments are conducted on two groups of datasets and the experimental results demonstrate the effectiveness of the proposed method for cross-domain aspect terms extraction.
An application of kho-Kho optimization algorithm to estimation parameter of permanent magnet synchronous machine

Permanent magnet synchronous motors (PMSM) are among the popular motors used in industrial applications. Designing an appropriate control scheme to obtain the desired output for these motors depends on the motor parameters. In this aspect, this paper presents the application of a well-known meta-heuristics optimization scheme, i.e., the Kho-Kho optimization (KKO) algorithm to estimate the parameters of PMSM. The KKO algorithm introduced in [1] is a population-based meta-heuristic optimization scheme that replicated the chasing strategy of a defending team in a Kho-Kho game. To show the supremacy of the suggested scheme in determining the unknown parameters of the PMSM, a PMSM with known coefficients is considered and the parameter of the model is estimated using the KKO algorithm.
Transformer with Hybrid Attention Mechanism for Stereo Endoscopic Video Super Resolution

With stereo cameras becoming widely used in invasive surgery systems, stereo endoscopic images provide important depth information for delicate surgical tasks. However, the small size of sensors and their limited lighting conditions lead to low-quality and low-resolution endoscopic images and videos. In this paper, we propose a stereo endoscopic video super-resolution method using transformer with a hybrid attention mechanism named HA-VSR. Stereo video SR aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) videos. In our method, the stereo correspondence and temporal correspondence are incorporated into the HA-VSR model. Specifically, the Swin transformer architecture is utilized in proposed framework with hybrid attention mechanisms. The parallel attention mechanism is utilized by using the symmetry and consistency of left and right images, and the temporal attention mechanism is utilized by using the consistency of consecutive frames. Detailed quantitative evaluation and experiments on two datasets show the proposed model achieves advanced SR reconstruction performance, showing that the proposed stereo VSR framework outperforms alternative approaches.
An integrated recommender system for multi-day tourist itinerary

Planning a personalized itinerary that satisfies the preferences and limitations of a tourist can be a complex task. The challenge is further compounded by the lack of information about tourists' tastes, which makes it difficult to ensure their satisfaction, and the impracticality of extracting this information through extensive questioning. To overcome these challenges, this paper presents a novel integrated recommender system for predicting tourists' interests and planning multi-day itineraries. The system utilizes a hybrid prediction method based on interests of similar tourists to attractions, attraction categorization, and the similarity between touristic attractions, to predict the tourist's tastes. The optimization and planning problem is formulated as a multi-day categorized orienteering problem with count-dependent profits, with the goal of finding multiple paths between points of interest to maximize the cumulative profit. As the problem is NP-hard, a hybrid meta-heuristic algorithm combining Genetic Algorithm and Variable Neighborhood Descent is proposed to solve it. The performance of the hybrid prediction method and the hybrid meta-heuristic algorithm is evaluated on the MovieLens and Tisligirides datasets. Finally, the proposed integrated recommender system is thoroughly evaluated on a real-world dataset from Tehran, demonstrating its effectiveness in recommending personalized multi-day itineraries.
A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place

Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD1 or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day's predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach.
Prioritisation of research challenges in software technologies : A multi-factor approach

<ns3:p><ns3:bold>Background</ns3:bold>: Current and future key emerging technologies, e.g. quantum computing, augmented human, advanced Artificial Intelligence, sensing and mobility are linked by software technologies. Software is therefore spanning across a huge number of application domains, as it links and connects to diverse digital infrastructures and it opens up significant challenges in terms of security and privacy, behavioural optimisation, self-adaptation and reliability to mention some.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> This article presents one of the main results from the European project SWForum.eu (https://www.swforum.eu/), that focuses on the implementation of a multi-factor methodology to develop research and innovation roadmaps for software</ns3:p><ns3:p> technologies.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> The article also discusses the selection and prioritisation research and</ns3:p><ns3:p> innovation challenges and topics on software technologies, cyber security and digital infrastructures through the implementation of a multi-phase, multi-source and multi-factor methodology.</ns3:p><ns3:p> <ns3:bold>Conclusions:</ns3:bold> Software is pervasive and from the analysis it is concluded that several challenges are to be addressed in the upcoming years impacting software value chain, namely, generative AI, quantum computing, Edge and constrained re-</ns3:p><ns3:p> sources, or DevSecOps. This article identifies these main challenges and prioritises them towards a set of relevant factors for Europe.</ns3:p>
Design and implementation of a Python-based front-end system for websites

As the entrance to the organization's overall network, the richness of its display content directly affects the ability to promote the website system. To achieve the popularity and promotion of the website system, a Python-based website front-end system is designed. Using the soapy crawler framework, we improve the Python database operation mode, and define the execution criteria of the data mining process according to the total amount of web data samples stored, thus realizing the Python-based website data mining. On this basis, we determine the website design objectives, develop a complete security protection strategy according to the connection relationship between the main functional modules, and complete the overall design of the website front-end system. This paper analyzes the key design techniques of the front-end system of the website from three perspectives: redirection function, port closure function, and data detection function, so as to guarantee the richness of the website display content and promote the popularity and promotion of the website system.
BEV-S: a simple and robust look-around perception network

This study explores a novel Bird’s Eye View (BEV) method for autonomous vehicles. By simplifying the converter, we have minimized computation in the process of converting 3D maps to BEV maps, leading to more effective solutions. Our understanding of the BEV model and its advantages over current solutions was informed by related works on BEV and Semantic Segmentation. Through in-depth research on similar BEV algorithms and experiments, we identified key operations crucial for the accuracy of BEV maps while eliminating unrelated operations. As a result, we developed a new algorithm model for pure electric vehicles based on a previous transformer, retaining only key operations for a more concise model. Our model may achieve higher accuracy than previous models by ensuring that critical operations are not disrupted.
Face attribute editing based on self-attention mechanism and generative adversarial network

The purpose of face attribute editing is to control the specific attributes in the face image according to the specified face attribute tags, and obtain a synthetic face image that meets the specific attributes. In recent years, face attribute editing has been widely used in the fields of beauty, entertainment and face recognition. Based on the problem of inaccurate attribute editing in the current model, this paper proposes a face attribute editing method based on self-attention mechanism and generative adversarial network. Taking selective transfer generative adversarial network (STGAN) as the basic model, a self-attention mechanism is added after the convolutional layer of the decoder. At the same time, the loss function is improved, and the latent space encoding consistency loss is added to the original objective function. Experiments were carried out on the CelebA face dataset, and it was found that the model has improved compared with StarGAN, AttGAN and STGAN in terms of image generation quality and face attribute editing accuracy.
Iterative pseudo-labeling methods for improving speech recognition

In recent years, pseudo-labeling methods can reduce the difficulty of building speech recognition systems, in end-to-end automatic speech recognition (ASR). Iterative pseudo-labeling (IPL) is a classical semi-supervised algorithm that can efficiently perform multiple pseudo-labeling iterations on unlabeled data as acoustic models evolve. We incorporate the language model to generate pseudo-labeling based on IPL using the language model for decoding and data augmentation, and make new attempts on the selection of pseudo-labeling. The effectiveness of the improved approach is demonstrated by simulating low resources and standard settings and obtaining a word error rate better than IPL on the LIBRISPEECH test.
Blockchain-based label coverage storage query scheme

Blockchain technology realizes information traceability by constructing chain network structure; Through distributed storage and consensus mechanism, each transaction can be verified and shared. Hyperledger Fabric has the characteristics of deep encryption, convenient expansion, flexible deployment and pluggability, and provides a modular distributed ledger platform for the needs of different applications. Combining the blockchain technology with the storage and sharing requirements of railway engineering data and drawing information, this paper first introduces the related concepts of blockchain technology, Hyperledger Fabric and critical algorithms and puts forward a Coverage Tag Validation (CTV) for the storage of railway engineering data and drawing information, which can improve the query efficiency and optimize the results, and provide some reference for future research.
Adaptive resource scaling of service mesh in cloud computing

Cloud computing has become an essential part of modern computing infrastructure, with many organizations adopting cloud-based architectures to improve scalability, reliability, and cost-effectiveness. In this context, service mesh has emerged as an essential technology for managing service-to-service communication and security in cloud computing. However, service mesh can pose significant challenges for resource allocation and management due to the dynamic nature of cloud-native applications and growing microservice instances. Cloud applications are subject to unpredictable traffic patterns, changing workloads, and shifting resource demands, which make it difficult to allocate resources appropriately. In this paper, we propose an adaptive resource scaling approach for service mesh that leverages practical method based on a feedback loop that continuously monitors the proxies’ CPU utilization and CPU throttling to predict its future resource requirements. Results show that our approach reduces the resource consumption by up to 40% compared to static resource allocation strategies, while maintaining the Service Level Object.
Research on UAV path planning based on information fusion

In order to improve the accuracy of the unmanned aerial vehicle (uav) when performing specific tasks path planning, based on information fusion is proposed in this paper, artificial potential field method, first of all, to install the radar and photoelectric detection device of the unmanned aerial vehicle (uav) as an example, the radar can obtain more accurate distance information, photoelectric detection device can obtain Angle information of target is of high precision, In this paper, the target state information is obtained by radar and photoelectric detection device respectively, and the more accurate target position information is obtained by distributed heterogeneous sensor fusion method. Secondly, the path planning of uav is carried out by improved artificial potential field method. Finally achieve the purpose of uav tracking target.
Anomaly detection method of substation equipment based on CenterNet

Deep learning has significantly advanced object detection algorithms in the area of abnormal detection for substation equipment. However, there are still several challenges and problems that hinder its effective application in this domain. Firstly, the scale of data acquisition is limited, which may affect the performance of deep learning models. Secondly, there are various types and shapes of substation equipment with complex backgrounds, which will also affect the detection performance of deep learning models. This paper proposes an improved CenterNet-based method for target detection of substation equipment. Firstly, we embedded a Non-Local attention mechanism in the backbone network ResNet50. The integration of the attention mechanism can effectively improve the model's ability to prioritize significant targets and minimize the negative impact of intricate backgrounds on its overall performance. Then, the DIOU loss is used to replace the center point offset loss and width-height loss to accelerate network convergence and improve model performance. The proposed method exhibits superior performance in substation equipment anomaly detection, as evidenced by experimental results, with a detection accuracy increase of 10.8% over the original method and outperforming existing techniques like Faster R-CNN.
Roadmap of post-quantum cryptography standardization: Side-channel attacks and countermeasures

Quantum computing utilizes properties of quantum physics to build a fast-computing machine that can perform quantum computations. This will eventually lead to faster and more efficient calculations especially when we deal with complex problems. However, there is a downside related to this hardware revolution since the security of widely used cryptographic schemes, e.g., RSA encryption scheme, relies on the hardness of certain mathematical problems that are known to be solved efficiently by quantum computers, i.e., making these protocols insecure. As such, while quantum computers most likely will not be available any time in the near future, it's necessary to create alternative solutions before quantum computers become a reality. This paper therefore provides a comprehensive review of attacks and countermeasures in Post-Quantum Cryptography (PQC) to portray a roadmap of PQC standardization, currently led by National Institute of Standards and Technology (NIST). More specifically, there has been a rise in the side-channel attacks against PQC schemes while the NIST standardization process is moving forward. We therefore focus on the side-channel attacks and countermeasures in major post-quantum cryptographic schemes, i.e., the final NIST candidates.