Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Internet of Medical Things

IoMT sensor nodes, Internet of Things (IoT) wearable medical equipment, healthcare facilities, patients, and insurance firms are all increasingly being included in IoMT systems. Therefore, it is difficult to create a blockchain design for such systems, since scalability is among the most important aspects of blockchain technology. This realization prompted us to comprehensively analyze blockchain-based IoMT solutions developed in English between 2017 and 2022. This review incorporates the theoretical underpinnings of a large body of work published in highly regarded academic journals over the past decade, to standardize evaluation methods and fully capture the rapidly developing blockchain space. This study categorizes blockchain-enabled applications across various industries such as information management, privacy, healthcare, business, and supply chains according to a structured, systematic evaluation, and thematic content analysis of the literature that is already identified. The gaps in the literature on the topic have also been highlighted, with a special focus on the restrictions posed by blockchain technology and the knock-on effects that such restrictions have in other fields. Based on these results, several open research questions and potential avenues for further investigation that are likely to be useful to academics and professionals alike are pinpointed.
Credit corruption, financial constraint and corporate innovation: Evidence on China

This paper investigates the casual effects of credit corruption and financial constraints on firms' innovation activities, by using a unique data from the 2012 World Bank micro survey on Chinese firms. The results show that both the credit corruption and financial constraints significantly inhibit corporate innovation, and the financial constraints have more pronounced inhibitory effect on the innovation in firms with credit corruption. In addition, the credit corruption reinforces the inhibitory effect of financial constraints on the innovation in SMEs, private firms, capital-intensive firms, and firms with lower regional financial development.
Aspect-based sentiment analysis via multitask learning for online reviews

Aspect based sentiment analysis(ABSA) aims to identify aspect terms in online reviews and predict their corresponding sentiment polarity. Sentiment analysis poses a challenging fine-grained task. Two typical subtasks are involved: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC). These two subtasks are usually trained discretely, which ignores the connection between ATE and APC. Concretely, we can relate ATE to APC through aspects and train them concurrently. We mainly use the ATE task as an auxiliary task, allowing the APC to focus more on relevant aspects to facilitate aspect polarity classification. In addition, previous studies have shown that utilizing dependency syntax information with a graph neural network (GNN) also contributes to the performance of the APC task. However, most studies directly input sentence dependency relations into graph neural networks without considering the influence of aspects, which do not emphasize the important dependency relationships. To address these issues, we propose a multitask learning model combining APC and ATE tasks that can extract aspect terms as well as classify aspect polarity simultaneously. Moreover, we exploit multihead attention(MHA) to associate dependency sequences with aspect extraction, which not only combines both ATE and APC tasks but also stresses the significant dependency relations, enabling the model to focus more on words closely related to aspects. According to our experiments on three benchmark datasets, we demonstrate that the connection between ATE and APC can be better established by our model, which enhances aspect polarity classification performance significantly. The source code has been released on GitHub https://github.com/winder-source/MTABSA.
Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review

With the rapid development of electronic commerce, the availability of a large amount of information on the products, as well as from other users, make the customers’ decision-making processes more time-consuming. The recommender system has emerged to assist the users choosing suitable products more easily, while companies can precision marketing more effectively. To solve the above-mentioned problems, this study adopted the particle swarm optimization algorithm (PSO) to determine the most suitable similarity of consumer ratings to avoid the problem of data distortion due to data sparsity. Moreover, bidirectional encoder representations from transformers (BERT) were applied to extract the characteristics of consumer feedbacks. Finally, the PSO was employed to determine the appropriate weight matrix and combine the characteristics of different data types. The combination of rating and review data could improve the recommendation performance. In addition, the proposed method was applied on six datasets of Amazon, and it outperformed several existing methods in terms of mean absolute error and mean squared error.
Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation

In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.
Effects of platform’s blockchain strategy on brand manufacturer’s distribution strategy in the presence of counterfeits

This study establishes a theoretical model between one brand manufacturer and one e-commerce platform. The manufacturer would like to open a new online channel through the platform apart from the traditional channel to attract more consumers. Nevertheless, the online channel has the threat of deceptive counterfeits, and the platform can choose to adopt blockchain technology to eliminate the counterfeits. The main results show that in addition to the anti-counterfeiting effect, blockchain also has an incentive effect. That is, the platform can adopt blockchain to incentivize the manufacturer to open the online channel as long as the blockchain’s cost and the traditional channel’s service advantage are not too high. In some cases, both the supply chain and consumers can benefit from blockchain. Besides, the impact of two channels’ different characteristics on blockchain’s value and the case of blockchain adoption with an anti-counterfeiting level are also analyzed.
SFE: A Simple, Fast, and Efficient Feature Selection Algorithm for High-Dimensional Data

In this article, a new feature selection (FS) algorithm, called simple, fast, and efficient (SFE), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: 1) nonselection and 2) selection. It comprises two phases: 1) exploration and 2) exploitation. In the exploration phase, the nonselection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features and changes the status of the features from selected mode to nonselected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results and changes the status of the features from nonselected mode to selected mode. The proposed SFE is successful in FS from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this article proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for FS are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed FS algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.
Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain

Given the issues of low efficiency of agricultural Internet of Things (IoT) data collection and data storage security, this study proposes a fast and reliable storage method for IoT data based on blockchain. Firstly, it performs RC5 encryption for data in the IoT sensor module. Secondly, it aggregates the same batch of collected data in the gateway into a transaction and reconstructs the Merkle ordered tree to verify the data integrity. Finally, it modifies the configuration rules of blockchain to improve the efficiency of blockchain data storage. Compared with experimental results for hash values of blockchain storage data and the stored data itself in the blockchain, the proposed method has significant advantages in data writing, and its efficiency in data reading was nearly 10 times higher than the other methods. At the same time, the method has the advantages of confidentiality, integrity, availability, controllability and non-repudiation of information security. The study can provide a solution for efficient collection and secure storage of agricultural IoT data, and it can provide technical support for realizing decentralized agricultural IoT data collection.
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.
A blockchain-enabled security framework for smart agriculture

Food consumers are more concerned about transparency, accountability, contamination risks, food safety, and food quality. Thus, an overwhelming demand of a traceability solution is required to address the food consumers’ concerns. Blockchain is a promising technique to maintain product traceability in the agricultural food supply chain. The paper proposes a framework which uses the concept of blockchain smart contracts. All the entities engaged in the blockchain-based supply chain network handle the transaction between parties. All transactions are recorded to maintain high transparency and traceability across the supply chain ecosystem. In the event of abuse detection, a group signature mechanism is used. Three metrics, transaction efficiency, system risk reduction, and trust between participants, are considered for testing the system’s overall performance. After implementing the framework, the transaction efficiency is measured in terms of gas cost, average execution time, throughput and latency. These parameters show the satisfactory system performance.
Learning from data with structured missingness

Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness. Gathering big datasets has become an essential component of machine learning in many scientific areas, but it is unavoidable that some data values are missing. An important and growing effect that needs careful attention, especially when heterogeneous data sources are combined, is that of structured missingness, where data values are missing not at random, but with a specific structure.
Multi-party quantum private comparison of size relationship with two third parties based on d-dimensional Bell states

Abstract In this paper, we put forward a multi-party quantum private comparison (MQPC) protocol with two semi-honest third parties (TPs) by adopting <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional Bell states, which can judge the size relationship of private integers from more than two users within one execution of protocol. Each TP is permitted to misbehave on her own but cannot collude with others. In the proposed MQPC protocol, TPs are only required to apply <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional single-particle measurements rather than <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional Bell state measurements. There are no quantum entanglement swapping and unitary operations required in the proposed MQPC protocol. The security analysis validates that the proposed MQPC protocol can resist both the outside attacks and the participant attacks. The proposed MQPC protocol is adaptive for the case that <?CDATA $n$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>n</mml:mi> </mml:math> users want to compare the size relationship of their private integers under the control of two supervisors. Furthermore, the proposed MQPC protocol can be used in the strange user environment, because there are not any communication and pre-shared key between each pair of users.
Federated Learning Attacks and Defenses: A Survey

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down "data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.
Review of Deep Reinforcement Learning

With the continuous development of information technology, machine intelligence has become a hot research issue. Deep learning can effectively extract the characteristic information in the environment, reinforcement learning can propose effective behavior strategies, and the integration of the two to form deep reinforcement learning is an inevitable trend in the field of artificial intelligence research, with the development of a variety of deep reinforcement learning algorithms. Focusing on the development of deep reinforcement learning, this paper summarizes the classification, characteristics and applications of common algorithms for deep reinforcement learning.
Macro-financial implications of central bank digital currencies

The considerable deployment of central bank digital currencies (CBDCs) is imminent, as its interest has attracted the whole world. However, CBDCs faces several political, technological, and legal challenges. We add a few more challenges that have not received much attention and summarize them in the extant literature. We then emphasize a broad version of money to examine its likely impact on financial stability. Moreover, using time series data of three leading economies and replying to past episodes' evidence of financial innovation, the historical behaviour to incorporate the impact of CBDCs, and the velocity of circulation, we scrutinize the hypothetical influence of CBDCs on financial stability and inflation. This study employs McCallum's policy rule based on money growth, which deals with monetary policy. Our simulations indicate that the CBDCs do not produce higher inflation while setting financial stability at risk. This study also suggests the necessary policy implications.
One-stage and lightweight CNN detection approach with attention: Application to WBC detection of microscopic images

White blood cell (WBC) detection in microscopic images is indispensable in medical diagnostics; however, this work, based on manual checking, is time-consuming, labor-intensive, and easily results in errors. Using object detectors for WBCs with deep convolutional neural networks can be regarded as a feasible solution. In this paper, to improve the examination precision and efficiency, a one-stage and lightweight CNN detector with an attention mechanism for detecting microscopic WBC images, and a white blood cell detection vision system are proposed. The method integrates different optimizing strategies to strengthen the feature extraction capability through the combination of an improved residual convolution module, hybrid spatial pyramid pooling module, improved coordinate attention mechanism, efficient intersection over union (EIOU) loss and Mish activation function. Extensive ablation and contrast experiments on the latest public Raabin-WBC dataset verify the effectiveness and robustness of the proposed detector for achieving a better overall detection performance. It is also more efficient than other existing studies for blood cell detection on two additional classic public BCCD and LISC datasets. The novel detection approach is significant and flexible for medical technicians to use for blood cell microscopic examination in clinical practice.
Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model

Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids.