Reviewerly/exHarmony huggingface dataset:
Blockchain for Food Tracking

A food shortage, which has increased with the climate crisis, will be one of the biggest problems of the world, together with water scarcity, in the future and will damage the sustainability of the food supply system. With the effect of the COVID-19 pandemic, food resources are decreasing, and food prices are rising all over the world. The decrease in food sources increases the importance of food tracking even more. The exorbitant price increases after the COVID-19 pandemic are the most concrete indicators of this. Blockchain-based food tracking systems will be of critical importance because they will prevent exorbitant price increases with their contribution to food tracking processes, such as reliability and transparency. In this study, the establishment of a blockchain-based food tracking system in Turkey, its operation, and its results will be discussed. It was concluded that 97.54% of the participants using the established system found the application useful and wanted such an application to become widespread. In addition, comparing the performance data of the established blockchain-based system with other blockchain infrastructures, a value of 0.038 s for latency is 435 times better than Ethereum, one of the most popular blockchain infrastructures. A transmission per second value of 285, reception per second value of 335, and CPU load rate value of 19.22 are obtained with the proposed system.
A Survey of Blockchain Consensus Algorithms

Blockchain is a distributed and decentralized data storage technology, which has been widely concerned by the government, research institutions and industry in recent years. Blockchain technology realizes the trust problem between unknown peers without relying on the third party, which is expected to solve the problems of high cost, high delay and privacy leakage under the traditional centralized trust. Consensus algorithm is the core technology in blockchain, which describes how peers in blockchain network achieve data consistency and directly determines the operating efficiency of the entire blockchain system. In this paper, existing consensus algorithms are summarized and divided into three categories: consensus algorithm based on certain attribute value proof of peers, consensus algorithm based on peer voting mechanism and Paxos class consensus algorithm. This paper introduces the implementation details of three kinds of consensus algorithms in detail, conducts comparative research according to Mundell's impossible triangle theory, and gives the development direction of consensus algorithm. This paper provides reference for researchers to conduct in-depth research on block chain consensus algorithm.
Performance Analysis between YOLOv5s and YOLOv5m Model to Detect and Count Blood Cells: Deep Learning Approach

Blood cell identification and counting are essential nowadays for healthcare professionals and therapists treating a variety of diseases. Platelet detection and counting are commonly performed for various disorders such as COVID-19 and others. However, it is the most costly and time-consuming. Furthermore, it is not available everywhere. From that standpoint, it is necessary to develop an effective technological model for detecting and counting three fundamental kinds of blood cells: Platelets, Red Blood Cells (RBCs), and White Blood Cells (WBCs). So, a deep learning-based model is proposed in this study comparing two versions of YOLOv5 model such as YOLOv5s and YOLOv5m. It is found that the YOLOv5m model outperforms with 0.799 precision, where YOLOv5s produces 0.797 precision. The study suggests that the YOLOv5m model is highly capable of detecting and counting the blood cells individually. Doctors, physicians, and other clinicians will be capable to identify and quantify blood cells from real-time photos. It will save money and time by identifying and counting blood cells using real-time blood photos.
A Novel Hybrid Split and Federated Learning Architecture in Wireless UAV Networks

The ever-growing use of unmanned aerial vehicles (UAVs) as aerial users is becoming a major part of the sixth generation (6G) networks, which could provide various applications, like object detection and video surveillance, by exploiting machine learning (ML) algorithms. However, the training of conventional centralized ML algorithms causes high communication overhead due to the transmission of large datasets and may reveal user privacy. Hence, distributed learning algorithms, including federated learning (FL) and split learning (SL), are proposed to train ML models in a distributed manner via sharing model parameters rather than raw data. Due to the different learning structures, they have different communication and learning efficiency. We propose a new distributed learning architecture, namely hybrid split and federated learning (HSFL), by adopting the parallel model training mechanism of FL and the network splitting structure of SL. Through the simulations in wireless UAV networks, the HSFL algorithm is demonstrated to have higher learning accuracy than FL and less communication overhead than SL under non-IID data. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better channel quality and larger local model updates in each round. Numerical results demonstrate it achieves higher learning accuracy than the benchmark schemes, BC, MAB-BC, and MAB-BN2 UE selection schemes.
An Overview of Technologies for Improving Storage Efficiency in Blockchain-Based IIoT Applications

Since the inception of blockchain-based cryptocurrencies, researchers have been fascinated with the idea of integrating blockchain technology into other fields, such as health and manufacturing. Despite the benefits of blockchain, which include immutability, transparency, and traceability, certain issues that limit its integration with IIoT still linger. One of these prominent problems is the storage inefficiency of the blockchain. Due to the append-only nature of the blockchain, the growth of the blockchain ledger inevitably leads to high storage requirements for blockchain peers. This poses a challenge for its integration with the IIoT, where high volumes of data are generated at a relatively faster rate than in applications such as financial systems. Therefore, there is a need for blockchain architectures that deal effectively with the rapid growth of the blockchain ledger. This paper discusses the problem of storage inefficiency in existing blockchain systems, how this affects their scalability, and the challenges that this poses to their integration with IIoT. This paper explores existing solutions for improving the storage efficiency of blockchain–IIoT systems, classifying these proposed solutions according to their approaches and providing insight into their effectiveness through a detailed comparative analysis and examination of their long-term sustainability. Potential directions for future research on the enhancement of storage efficiency in blockchain–IIoT systems are also discussed.
An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.
A reciprocal learning strategy for semisupervised medical image segmentation

Semisupervised strategy has been utilized to alleviate issues from segmentation applications due to challenges in collecting abundant annotated segmentation masks, which is an essential prerequisite for training high-performance 3D convolutional neural networks (CNNs) .Existing semisupervised segmentation methods are mainly concerned with how to generate the pseudo labels with regularization but not evaluate the quality of the pseudo labels explicitly. To alleviate this problem, we offer a simple yet effective reciprocal learning strategy for semisupervised volumetric medical image segmentation, which generates more reliable pseudo labels for the unannotated data.Our proposed reciprocal learning is achieved through a pair of networks, one as a teacher network and the other as a student network. The student network learns from pseudo labels generated by the teacher network. In addition, the teacher network autonomously optimizes its parameters based on the reciprocal feedback signals from the student's performance on the annotated images. The efficacy of the proposed method is evaluated on three medical image data sets, including 82 pancreas computed tomography (CT) scans (training/testing: 62/20), 100 left atrium gadolinium-enhanced magnetic resonance (MR) scans (training/testing: 80/20), and 200 breast cancer MR scans (training/testing: 68/132). The comparison methods include mean teacher (MT) model, uncertainty-aware MT (UA-MT) model, shape-aware adversarial network (SASSNet), and transformation-consistent self-ensembling model (TCSM). The evaluation metrics are Dice similarity coefficient (Dice), Jaccard index (Jaccard), 95% Hausdorff distance (95HD), and average surface distance (ASD). The Wilcoxon signed-rank test is used to conduct the statistical analyses.By utilizing 20% labeled data and 80% unlabeled data for training, our proposed method achieves an average Dice of 84.77%/90.46%/78.53%, Jaccard of 73.71%/82.67%/69.00%, ASD of 1.58/1.90/0.57, and 95HD of 6.24/5.97/4.34 on pancreas/left atrium/breast data sets, respectively. These results outperform several cutting-edge semisupervised approaches, showing the feasibility of our method for the challenging semisupervised segmentation applications.The proposed reciprocal learning strategy is a general semisupervised solution and has the potential to be applied for other 3D segmentation tasks.
Thermal local Fisher information and quantum uncertainty in Heisenberg model

Abstract This study explores the spontaneous emergence of local Fisher information and local quantum uncertainty beyond entanglement in a noisy two-qubit Heisenberg XXX model with Dzyaloshinskii-Moria (DM) and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions. Local quantum Fisher information, local quantum uncertainty, and logarithmic negativity are used to examine two-spin non-locality. We find that by employing strong two-spin, Dzyaloshinskii- Moriya, and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions, we can prevent noise-induced losses in local Fisher information, local quantum uncertainty, and logarithmic negativity. The robustness of the two-spin non-localities is greater for a strong spin’s coupling strength than for the strong DM and KSEA interactions. Local Fisher information and local quantum uncertainty, as well as logarithmic-negativity, display asymmetric behaviors with respect to two-spin coupling and symmetric behaviors with respect to the Dzyaloshinskii-Moriya and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions. At high bath temperatures, the phenomenon of sudden-death two-spin entanglement emerges. Furthermore, by combining the parameters of the strong two-spin, DM, and KSEA interactions, the robustness of the produced two-spin non-localities can be improved.
Adaptive hierarchical hyper-gradient descent

Abstract Adaptive learning rate strategies can lead to faster convergence and better performance for deep learning models. There are some widely known human-designed adaptive optimizers such as Adam and RMSProp, gradient based adaptive methods such as hyper-descent and practical loss-based stepsize adaptation (L4), and meta learning approaches including learning to learn. However, the existing studies did not take into account the hierarchical structures of deep neural networks in designing the adaptation strategies. Meanwhile, the issue of balancing adaptiveness and convergence is still an open question to be answered. In this study, we investigate novel adaptive learning rate strategies at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining adaptive information at different levels. In addition, we show the relationship between regularizing over-parameterized learning rates and building combinations of adaptive learning rates at different levels. Moreover, two heuristics are introduced to guarantee the convergence of the proposed optimizers. The experiments on several network architectures, including feed-forward networks, LeNet-5 and ResNet-18/34, show that the proposed multi-level adaptive approach can significantly outperform many baseline adaptive methods in a variety of circumstances.
Client selection based weighted federated few-shot learning

With the advancement of technology, clients have a large amount of personal data. Human beings are paying more and more attention to the privacy and security of this part of data. Clients do not want to share private data, which directly leads to the existence of data islands. Especially in few-shot scenarios, due to the insufficient amount of personal data, constructing an effective few-shot model is difficult. To solve the above problems, we propose Federated Few-shot Learning (FedFSL) in this paper. We utilize Federated Learning (FedL) to ensure the privacy and security issues of joint training. Moreover, the global model obtained by FedL has the characteristics of universally applicable to all clients. That universality satisfies the scenario of universally applicable to all meta tasks in the few-shot meta-learning stage. What is more, to obtain a more effective global federated universal few-shot model, we respectively proposed Weighted FedL (WFedL) and Client Selection based FedL (CSFedL) strategies. WFedL takes into account the difference between clients performance when building the global model and assigns different weights to different clients. CSFedL considers the malicious participation of clients, and we propose an adaptive client selection strategy to mitigate the impact caused by malicious participation. Extensive federated experiments on CIFAR-10 and CIFAR-100 show the advantage of proposed WFedL, CSFedL and combined Client Selection based WFedL (CSWFedL). We further verify the performance improvement of FedFSL on miniImagenet and propose our overall framework Client Selection based WFedFSL (CSWFedFSL). The best performance of CSWFedFSL is higher than both the few-shot baseline and FedFSL, and CSWFedFSL protects clients data privacy in the few-shot scenario.
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest

Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases including user, image and search based recommendations. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost. While most prior work focuses on building product embeddings from features coming from a single modality, we introduce a transformer-based architecture capable of aggregating information from both text and image modalities and show that it significantly outperforms single modality baselines. We also utilize multi-task learning to make ItemSage optimized for several engagement types, leading to a candidate generation system that is efficient for all of the engagement objectives of the end-to-end recommendation system. Extensive offline experiments are conducted to illustrate the effectiveness of our approach and results from online A/B experiments show substantial gains in key business metrics (up to +7% gross merchandise value/user and +11% click volume).
XDAI: A Tuning-free Framework for Exploiting Pre-trained Language Models in Knowledge Grounded Dialogue Generation

Large-scale pre-trained language models (PLMs) have shown promising advances on various downstream tasks, among which dialogue is one of the most concerned. However, there remain challenges for individual developers to create a knowledge-grounded dialogue system upon such big models because of the expensive cost of collecting the knowledge resources for supporting the system as well as tuning these large models for the task. To tackle these obstacles, we propose XDAI, a knowledge-grounded dialogue system that is equipped with the prompt-aware tuning-free PLM exploitation and supported by the ready-to-use open-domain external knowledge resources plus the easy-to-change domain-specific mechanism. With XDAI, the developers can leverage the PLMs without any fine-tuning cost to quickly create the open-domain dialogue systems as well as easily customize their own domain-specific systems. Extensive experiments including human evaluation, Turing test, and online evaluation have demonstrated the competitive performance of XDAI compared with the state-of-the-art general PLMs and specific PLMs for dialogue. XDAI pilots studies on the exploitation of PLMs and made intriguing findings which could be inspiring for the future research on other PLM-based applications.
Effective Consensus-Based Distributed Auction Scheme for Secure Data Sharing in Internet of Things

In a traditional electronic auction, the centralized auctioneer and decentralized bidders are in an asymmetric structure, where the auctioneer has more ability to decide the auction result. This asymmetric auction structure is not fair to the participants and not suitable for data auctions in the Internet of Things (IoT). The blockchain-based auction system, with participant equality and fairness, is typically symmetrical and particularly suitable for IoT data sharing. However, when applied to IoT data sharing in reality, it faces privacy and efficiency problems. In this context, how to guarantee privacy and break the inherent performance bottleneck of blockchain is still a major challenge. In this paper, a consensus-based distributed auction scheme is proposed for data sharing, which enforces privacy preservation and collusion resistance. A reverse auction-based decentralized data trading model is introduced to solve the trust problem without a centralized auctioneer, where bidders reach consensus on the auction result. Specifically, we devise a differentially private auction mechanism to incentivize data owners to participate in data sharing. An effective hybrid consensus algorithm is constructed among bidders to reach consensus on the auction result with improved security and efficiency. Theoretical analysis shows that the proposed scheme ensures the properties of privacy preservation, incentive compatibility and collusion resistance. Experimental results reveal that the proposed mechanism guarantees the data sharing efficiency and has certain scalability.
Enhancement of DNN-based multilabel classification by grouping labels based on data imbalance and label correlation

Multilabel classification (MLC) is a challenging task in real-world applications, such as project document classification which led us to conduct this research. In the past decade, deep neural networks (DNNs) have been explored in MLC due to their flexibility in dealing with annotated data. However, DNN-based MLC still suffers many problems. Two critical problems are data imbalance and label correlation. These two problems will become more prominent when a training dataset is limited and with a large label set. In this study, special neural network configurations were developed to enhance the performance of DNN-based MLC based on data imbalance and label correlation. The classification accuracy of minority labels and users-preferred labels was increased using customized label groups. The proposed method was evaluated using river restoration project documents and other fifteen datasets. The results show that the proposed method generally increases f1-score for minority labels up to 10%. Adding label dependence into label groups improves the f1-score of user-preferred majority labels up to 5%. The accuracy increase varies in different datasets.
Active contour driven by adaptive‐scale local‐energy signed pressure force function based on bias correction for medical image segmentation

Abstract This paper presents an active contour model driven by an adaptive-scale local-energy signed pressure force (ALSPF) function based on bias correction for segmenting medical images with intensity inhomogeneity. Firstly, a local energy-driven signed pressure force (LESPF) function is designed as the driving force to extract local image features, which can effectively deal with intensity inhomogeneity. Secondly, to avid selecting the neighbourhood size in the LESPF function, a multi-scale adaptive selection schema is put forward to adaptively choose the local window size according to the degree of intensity inhomogeneity. Finally, a novel single-well potential function proved to be a strictly monotonic function is designed to ensure the stable movement of the neighbourhood points in the vicinity of the evolution curve, which can not only avoid the re-initialization process but also enhance. Experiments on synthetic and medical images demonstrate that the proposed model is more robust than the popular ACMs for segmenting images with intensity inhomogeneity. The code is available at the website: https://github.com/HuaxiangLiu/ALSPF