Reviewerly/exHarmony huggingface dataset:
Decentralized tendering of construction projects using blockchain-based smart contracts and storage systems

Existing tendering methods for construction projects are centralized and rely on a third party's involvement during the process, hence, the methods raise critical concerns regarding data security, transparency, and traceability, as the bidders have to trust the organizer for the legitimacy and fairness of the process. This paper focuses on how the recent developments in decentralized blockchain technologies can be adopted for tendering of construction projects to improve the traditional process by eliminating intermediaries and ensuring transparency as well as data security. A novel, decentralized blockchain-based tendering system is presented for construction projects, leveraging (i) decentralized smart contracts to execute the tendering steps, including storage of the information throughout the process, management of the bid bonds, evaluation of the bids, and awarding of the contract according to the preset conditions, (ii) blockchain-integrated decentralized storage systems to exchange and store off-chain documents that are related to tendering, and (iii) decentralized application to facilitate the participants' interaction with the proposed system during the tendering process. The proposed system presents a new method for achieving a decentralized, secure, trustworthy, and transparent tendering environment among project participants. The outcomes of the system indicate the potential of future blockchain research for achieving autonomous and decentralized tendering sytems.
An Improved YOLO Model for UAV Fuzzy Small Target Image Detection

High-altitude UAV photography presents several challenges, including blurry images, low image resolution, and small targets, which can cause low detection performance of existing object detection algorithms. Therefore, this study proposes an improved small-object detection algorithm based on the YOLOv5s computer vision model. First, the original convolution in the network framework was replaced with the SPD-Convolution module to eliminate the impact of pooling operations on feature information and to enhance the model’s capability to extract features from low-resolution and small targets. Second, a coordinate attention mechanism was added after the convolution operation to improve model detection accuracy with small targets under image blurring. Third, the nearest-neighbor interpolation in the original network upsampling was replaced with transposed convolution to increase the receptive field range of the neck and reduce detail loss. Finally, the CIoU loss function was replaced with the Alpha-IoU loss function to solve the problem of the slow convergence of gradients during training on small target images. Using the images of Artemisia salina, taken in Hunshandake sandy land in China, as a dataset, the experimental results demonstrated that the proposed algorithm provides significantly improved results (average precision = 80.17%, accuracy = 73.45% and recall rate = 76.97%, i.e., improvements by 14.96%, 6.24%, and 7.21%, respectively, compared with the original model) and also outperforms other detection algorithms. The detection of small objects and blurry images has been significantly improved.
f-FNC: Privacy concerned efficient federated approach for fake news classification

Fake news and manipulated information affect the social, economic and emotional growth of the world's population. For the identification of fake news, several classification systems are available, but no such system was found fast, secure and reliable as per the need of the hour. In this work, an efficient framework based on the federated architecture for the classification of fake news was proposed, while maintaining the data privacy constraints for sensitive text news datasets. The proposed federated-Fake New Classification (f-FNC) framework utilized the distributed client–server architecture with data privacy of all client or connected edge devices. For the testing and evaluation of the proposed f-FNC framework, the non-identical data was gathered from several online resources and was disseminated in a pre-processed format. To test the validity of federated deep learning models, the experiments were performed under various scenarios such as traditional learning, federated learning single client, and federated learning multi-clients. The performance of f-FNC framework was evaluated through various computational parameters such as accuracy and loss validation along with available resource parameters including CPU and RAM utilization. It was observed from the resultant outcome that the proposed f-FNC framework worked significantly well in both single-client and multi-client (N-clients) scenarios in comparison to traditional distributed deep learning based classifiers. The additional features of low cost and data-privacy of edge devices with limited resources made this proposed framework unique and the best alternative to existing fake news classifier tools.
A Static Machine Learning Based Evaluation Method for Usability and Security Analysis in E-Commerce Website

Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economical market’s “brick-and-mortar” model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyze usability assessments using statistical methods and security assessments using online e-commerce security scanner tools to investigate e-business standards that consider the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B representing the ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times1$ </tex-math></inline-formula> ) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category.
DSCA: A dual-stream network with cross-attention on whole-slide image pyramids for cancer prognosis

The cancer prognosis on gigapixel Whole-Slide Images (WSIs) has always been a challenging task. To further enhance WSI visual representations, existing methods have explored image pyramids, instead of single-resolution images, in WSIs. Despite this, they still face two major problems: high computational cost and the unnoticed semantical gap in multi-resolution feature fusion. To tackle these problems, this paper proposes to efficiently exploit WSI pyramids from a new perspective, the dual-stream network with cross-attention (DSCA). Our key idea is to utilize two sub-streams to process the WSI patches with two resolutions, where a square pooling is devised in a high-resolution stream to significantly reduce computational costs, and a cross-attention-based method is proposed to properly handle the fusion of dual-stream features. We validate our DSCA on three publicly-available datasets with a total number of 3,101 WSIs from 1,911 patients. Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4.6%; (ii) our DSCA network is more efficient in computation—it has more learnable parameters (6.31M vs. 860.18K) but less computational costs (2.51G vs. 4.94G), compared to a typical existing multi-resolution network. (iii) the key components of DSCA, dual-stream and cross-attention, indeed contribute to our model’s performance, gaining an average C-Index rise of around 2.0% while maintaining a relatively-small computational load. Our DSCA could serve as an alternative and effective tool for WSI-based cancer prognosis. Our source code is available at https://github.com/liupei101/DSCA.
Electroencephalogram-based mental workload prediction for using Augmented Reality head mounted display in construction assembly: A deep learning approach

Augmented Reality Head-Mounted Displays (AR HMD) are considered a promising technology for assisting on-site construction tasks such as assembly. However, concerns about creating information overload and becoming a distraction to workers outweigh the potential benefits to productivity and task performance. Previous research investigated workers' mental workloads as impacted by AR-based displays for assembly tasks. Within that context, this paper proposes a Long Short-Term Memory (LSTM) based approach to predict mental workload when using AR HMD for construction assembly, to forecast users' cognitive status under such complex working condition. Thirty participants were recruited to finish a wood frame assembly using an AR HMD projecting a 3D conformal model on the workspace. The proposed method provided a reliable prediction of real-time electroencephalogram (EEG) signal and mental workload. The outcomes validated the feasibility of LSTM model for EEG-based mental workload prediction and specifically provided a usable method for evaluating AR HMD use in construction tasks.
Self-supervised contrastive learning on heterogeneous graphs with mutual constraints of structure and feature

Self-supervised learning on heterogeneous graphs has gained significant attention as it eliminates the need for manual labeling. However, most existing researches focus on predefined meta-paths that relies on domain knowledge, and they cannot handle the noises in graphs effectively. To address these problems, we propose a self-supervised contrastive learning method on heterogeneous graphs with mutual constraints of structure and feature called HeMuc. Specifically, in the high-order relation view, we exploit graph reachability to obtain the sequence of target nodes traversed by source nodes. Furthermore, we design degree and feature constraints to reduce the noises in topological structure. In the feature view, we reconstruct the graph structure using the similarity between node features and eliminate the dependence on the original graph. Finally, we propose a contrastive learning method by designing a new sampling strategy that combines the structure and feature information. The experimental results on the tasks of node classification and node clustering demonstrate that the proposed HeMuc outperforms the state-of-the-art methods. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/HeMuc.
Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector

Uterine smooth muscle hyperplasia causes a tumor called a uterine fibroid. With an incidence of up to 30%, it is one of the most prevalent tumors in women and has the third highest prevalence of all gynecological illnesses. Although uterine fibroids are usually not accompanied by symptoms, there are physical effects, such as impairment of the ability to conceive. To reduce morbidity, early detection and treatment are crucial. Ultrasound imaging is a common method used for pre-operative guidance and interventional therapy. Many applications of object detection are performing well with the advancement of deep learning in the field of medical image analysis. To ensure accuracy, computer-assisted detection can further solve the subjective problem generated by different doctors when they read images.Our research provides an improved YOLOv3 that combines the properties of EfficientNet and YOLOv3, which use a convolutional neural network to extract features, to detect uterine fibroid ultrasound images.Our approach attained an F1 score of 95% and an average precision of 98.38% and reached a detection speed of 0.28 s per image. We reviewed and analyzed several detection techniques and identified potential future research hotpots.This technique offers enough supplementary diagnostic tools for amateur or expert ultrasonologists and sets a solid foundation for future medical care and surgical excision.
Dual-branch spatio-temporal graph neural networks for pedestrian trajectory prediction

Pedestrian trajectory prediction is an important area in computer vision, with wide applications in autonomous driving, robot path planning, and surveillance systems. The core underlying technique of these applications is pattern recognition. A key challenge in this area is modeling social interactions between pedestrians, such as pedestrian view area and group behaviors. However, although many methods have been proposed to model social interactions, pedestrian view area and group behaviors have not been explored together to account for complex situations. Additionally, most existing studies require additional detectors and manual annotations to handle view area and group interactions, respectively. In this paper, we propose a dual-branch spatio-temporal graph neural network to automatically model view area and grouping together. Specifically, a spatio-temporal graph attention network (STGAT) branch is designed to handle pedestrian view area, and a spatio-temporal graph convolutional network (STGCN) branch is designed to model group interactions. The features of these branches are then fused to provide better feature representations, on which a temporal convolution operation (TCN) is performed for trajectory prediction. Experiments on public standard datasets demonstrate that the proposed method achieves very competitive performance and predicts socially acceptable trajectories in different challenging scenarios.
Dynamic Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

Aiming at the shortcomings of the traditional sparrow search algorithm (SSA) in path planning, such as its high time-consumption, long path length, it being easy to collide with static obstacles and its inability to avoid dynamic obstacles, this paper proposes a new improved SSA based on multi-strategies. Firstly, Cauchy reverse learning was used to initialize the sparrow population to avoid a premature convergence of the algorithm. Secondly, the sine-cosine algorithm was used to update the producers' position of the sparrow population and balance the global search and local exploration capabilities of the algorithm. Then, a Lévy flight strategy was used to update the scroungers' position to avoid the algorithm falling into the local optimum. Finally, the improved SSA and dynamic window approach (DWA) were combined to enhance the local obstacle avoidance ability of the algorithm. The proposed novel algorithm is named ISSA-DWA. Compared with the traditional SSA, the path length, path turning times and execution time planned by the ISSA-DWA are reduced by 13.42%, 63.02% and 51.35%, respectively, and the path smoothness is improved by 62.29%. The experimental results show that the ISSA-DWA proposed in this paper can not only solve the shortcomings of the SSA but can also plan a highly smooth path safely and efficiently in the complex dynamic obstacle environment.
Knowledge transduction for cross-domain few-shot learning

Cross-Domain Few-Shot Learning (CDFSL) aims to classify new categories from new domains with few samples. It confronts a greater domain shift than Few-Shot Learning (FSL). Based on the transfer learning framework, we propose a Knowledge Transduction method (KT) to alleviate domain shift and achieve few-shot recognition. First, a feature adaptation module based on feed-forward attention is constructed to learn domain-adapted features. The feature adaptation module weakens domain shift by transducing knowledge from an auxiliary dataset to the new dataset. Second, a feature transduction module based on deep sparse representation is developed to gather class semantics from limited support images. The feature transduction module transduces knowledge from support images to query images for few-shot recognition. In addition, a stochastic image augmentation method is proposed for FSL to train a more generalized model through consistency representation learning. Our method achieves competitive accuracy on four CDFSL datasets and four FSL datasets compared to state-of-the-art methods. The source code is available at https://github.com/XDUpfLi/KT.
Rethinking PASCAL-VOC and MS-COCO dataset for small object detection

The data and the algorithm are critical to deep learning-based small object detectors. In this paper, we rethink the PASCAL-VOC and MS-COCO dataset for small object detection. By visual analysis of the original annotations, we find that there are different labeling errors in these two datasets. To solve these problems, we build specific datasets, including SDOD, Mini6K, Mini2022 and Mini6KClean. The experimental results of several typical algorithms (e.g. SSD, YOLOv5, Faster RCNN and Deformable DETR) on the datasets show that data labeling errors (such as missing labels, category label errors, inappropriate labels) are another factor that affects the detection performance of small objects.
Bayesian evolutionary optimization for crafting high-quality adversarial examples with limited query budget

Due to the importance of security, the adversarial attack has become an increasingly popular area for deep learning, especially the black-box adversarial attack, which can only obtain the input and output of the target model, resulting in closer to the real world. Query-based methods are a common strategy for performing black-box attacks, and they can find a slight perturbation to make target model misclassify by continuously querying the target model to obtain the output of the target model. However, query-based methods usually suffer from a serious flaw that needs massive queries, which is unaccepted in the real world. Although some query-efficient methods have been proposed to alleviate the above problems, they greatly sacrifice the quality of adversarial examples due to their problem formulations. To generate high-quality adversarial examples with a limited query budget, we propose a Bayesian evolutionary optimization (BEO) based black-box attack method using differential evolution, where a Gaussian processes model is employed to approximate the real objective function. As a key component of the BEO, we use seven acquisition functions to sample the new solution to update the Gaussian processes model, and an information entropy based selection strategy is proposed to adaptively choose the acquisition function. Finally, an effectiveness validation study is carried out comparing the proposed method with five other black-box attack methods and one Bayesian optimization (BO) method using the CIFAR-10 and ImageNet datasets. Experimental results demonstrate that the proposed method can effectively generate adversarial examples using only 200 queries.
Unidirectional Updatable Encryption and Proxy Re-encryption from DDH

Updatable Encryption (UE) and Proxy Re-encryption (PRE) allow re-encrypting a ciphertext from one key to another in the symmetric-key and public-key settings, respectively, without decryption. A longstanding open question has been the following: do unidirectional UE and PRE schemes (where ciphertext re-encryption is permitted in only one direction) necessarily require stronger/more structured assumptions as compared to their bidirectional counterparts? Known constructions of UE and PRE seem to exemplify this “gap” – while bidirectional schemes can be realized as relatively simple extensions of public-key encryption from standard assumptions such as DDH or LWE, unidirectional schemes typically rely on stronger assumptions such as FHE or indistinguishability obfuscation (iO), or highly structured cryptographic tools such as bilinear maps or lattice trapdoors. In this paper, we bridge this gap by showing the first feasibility results for realizing unidirectional UE and PRE from a new generic primitive that we call Key and Plaintext Homomorphic Encryption (KPHE) – a public-key encryption scheme that supports additive homomorphisms on its plaintext and key spaces simultaneously. We show that KPHE can be instantiated from DDH. This yields the first constructions of unidirectional UE and PRE from DDH. Our constructions achieve the strongest notions of post-compromise security in the standard model. Our UE schemes also achieve “backwards-leak directionality” of key updates (a notion we discuss is equivalent, from a security perspective, to that of unidirectionality with no-key updates). Our results establish (somewhat surprisingly) that unidirectional UE and PRE schemes satisfying such strong security notions do not, in fact, require stronger/more structured cryptographic assumptions as compared to bidirectional schemes.
A Novel Medical Decision-Making System Based on Multi-Scale Feature Enhancement for Small Samples

The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology, and inconsistent scales. Diagnosis is quite difficult, especially for developing countries, where medical resources are inadequate per capita and there is a lack of professionals, and the time spent in the diagnosis process may lead to a gradual deterioration of the disease. To address these, we discuss an osteosarcoma-assisted diagnosis system (OSADS) based on small samples with multi-scale feature enhancement that can assist doctors in performing preliminary automatic segmentation of osteosarcoma and reduce the workload. We proposed a multi-scale feature enhancement network (MFENet) based on few-shot learning in OSADS. Global and local feature information is extracted to effectively segment the boundaries of osteosarcoma by feeding the images into MFENet. Simultaneously, a prior mask is introduced into the network to help it maintain a certain accuracy range when segmenting different shapes and sizes, saving computational costs. In the experiments, we used 5000 osteosarcoma MRI images provided by Monash University for testing. The experiments show that our proposed method achieves 93.1% accuracy and has the highest comprehensive evaluation index compared with other methods.