Reviewerly/exHarmony huggingface dataset:
Agricultural Cold Chain Logistics Mode Based on Multi-Mode Blockchain Data Model

Aiming at the problem of massive data storage and transaction authority in the blockchain system in the data sharing transaction scenario, this paper proposes a blockchain system for the controllable sharing transaction of massive data. By designing the blockchain system model of the consortium chain and private chain, reconstructing the data structure of the block, and introducing the InterPlanetary File System (IPFS) technology, we adopt the off-chain storage method to solve the problem of high cost caused by too much data carried by the block. The isolation control of data resource transactions is realized by introducing identity authentication and channel mechanism, which meets the needs of different subjects for confidentiality of transaction data information. From the analysis results of cold chain logistics efficiency, in the five years from 2017 to 2021, the comprehensive cold chain logistics efficiency, pure technology cold chain logistics efficiency, and scale cold chain logistics efficiency of agricultural product cold chain logistics in a certain region have all reached above 0.9. The return to scale remains unchanged, and the cold chain logistics resources have been effectively allocated and reasonably utilized. The value of comprehensive cold chain logistics efficiency, pure technology cold chain logistics efficiency, and scale cold chain logistics efficiency in a certain month is less than 1, which means that the cold chain logistics efficiency is weak or ineffective. This shows that in these years, the infrastructure investment, cold chain technical support, and management level related to the cold chain logistics of agricultural products in a certain region need to be strengthened, and it is necessary to adjust the input-output structure and allocate resources reasonably. From 2018 to 2021, the total factor productivity of agricultural product cold chain logistics in a certain region is greater than 0.85, indicating that the level of total factor productivity is relatively high during this time period, and the increase in the efficiency of technical cold chain logistics is the main reason for the increase in total factor productivity.
A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text

Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance.
Stress and Burnout in EFL Teachers: The Mediator Role of Self-Efficacy

Within the class environment, particularly in the language learning setting, stress is considered to be the most common mental condition educators experience in their work, and due to the effect of stress on teachers, burnout similarly occurs because English as a foreign language (EFL) teachers periodically experience affective trauma while participating and engaging in their careers. To solve the problem, one must consider teacher self-efficacy, a significant construct that can mitigate the probability of burnout by preventing the occurrence of stress at work that also plays a key role in enhancing positive upshots. To emphasize the impact of teachers' stress and burnout on the one hand and the mediator role of teachers' efficacy, on the other hand, this mini-review attempted to study the issue. Finally, some scholastic recommendations are pinpointed.
Synthesis of the Supremal Covert Attacker Against Unknown Supervisors by Using Observations

In this article, we consider the problem of synthesizing the supremal covert damage-reachable attacker, in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors, which are consistent with the given set of observations. There is a gap between the de facto supremality, assuming the model of the supervisor is known, and the supremality that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this article is to reduce the synthesis of the supremal covert damage-reachable attacker, given the model of the plant and the finite set of observations, to the synthesis of the supremal safe supervisor for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
Manifold Enhanced 2-D Fuzzy Subspace Clustering for Image Data

Many fuzzy subspace clustering methods have been proposed for high-dimensional image data with rich structural information. However, since these methods do not fully exploit the subspace information in each cluster, their performance on image clustering is still not promising. In this work, we propose to find soft partitions directly based on the construction of subspaces. For each cluster, we use a bilinear orthogonal subspace to represent it. Then, through the reconstruction error of a sample in the subspace corresponding to a cluster, a new membership measure for the sample to the cluster is established. Furthermore, the graph regularization is imposed on these bilinear subspaces to preserve the local relational or manifold information of the image data in the original space. Altogether, we get a clustering model considering not only the subspace information but also the manifold information in image data. An efficient optimization algorithm is proposed to our model, and its theoretical convergence and time complexity are presented correspondingly. The proposed method is a one-stage clustering model that does not require vectorized image data, thereby reducing the computational burden while maintaining the structural relationship between pixels in the image. Competitive experimental results on benchmark datasets show that our model can converge quickly with strong clustering performance, which confirms the efficiency and superiority of the proposed method compared to other state-of-the-art fuzzy clustering methods.
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching

Code-Switching (CS) is a common linguistic phenomenon in multilingual communities that consists of switching between languages while speaking. This paper presents our investigations on end-to-end speech recognition for Mandarin-English CS speech. We analyze different CS specific issues such as the properties mismatches between languages in a CS language pair, the unpredictable nature of switching points, and the data scarcity problem. We exploit and improve the state-of-the-art end-to-end system by merging nonlinguistic symbols, by integrating language identification using hierarchical softmax, by modeling subword units, by artificially lowering the speaking rate, and by augmenting data using speed perturbed technique and several monolingual datasets to improve the final performance not only on CS speech but also on monolingual benchmarks in order to making the system more applicable on real life settings. Finally, we explore the effect of different language model integration methods on the performance of the proposed model. Our experimental results reveal that all the proposed techniques improve the recognition performance. The best combined system improves the baseline system by up to 35% relatively in terms of mixed error rate and delivers acceptable performance on monolingual benchmarks.
Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of several objects like hair, reflections, air bubbles, and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irregular shapes.An entropy and morphology-based automated mask selection is proposed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study, features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP).When four different moments pull out in six different color spaces like HSV, Lin RGB, YIQ, YCbCr, XYZ, and CIE L*a*b then global information from different colors channels have been combined.Therefore, hybrid fused texture features; such as local, color feature as global, shape features, and Artificial neural network (ANN) as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis, DermQuest, and PH2.The results of our advanced method showed superiority and contrast with the existing state-of-the-art techniques.
An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

Currently, worldwide industries and communities are concerned with building, expanding, and exploring the assets and resources found in the oceans and seas.More precisely, to analyze a stock, archaeology, and surveillance, several cameras are installed underseas to collect videos.However, on the other hand, these large size videos require a lot of time and memory for their processing to extract relevant information.Hence, to automate this manual procedure of video assessment, an accurate and efficient automated system is a greater necessity.From this perspective, we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy (PME) method to first extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore, the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to the field of underwater archaeology, stock assessment, and surveillance.
Learning causal Bayesian networks based on causality analysis for classification

Revealing causal information by analyzing purely observational data, known as causal discovery, has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning. In this paper, heuristic search strategy is applied to explore the causal interpretation in the form of directed acyclic graph (DAG) for classification. While adding directed edges to the DAG, we first introduce the log-likelihood equivalence assertion to make the learned joint probability encoded in BN approximates the true one, then introduce the causal dependence assertion to assess the rationality of the learned causal relationship. We perform a range of experiments on 35 datasets and empirically show that this novel algorithm demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers (e.g. SKDB, WATAN, SLB, and TAODE).
Transformative School Digital Sustainability Model to Improve Teacher Professionalism and Identity in Primary Education

In the transformation of education, it is essential that the teacher conceives their pedagogical practice as a way to strengthen their professional identity and exercise their teaching work in accordance with the trends and challenges of education, showing good preparation, developing research, using technology, and reflecting on their teaching practice. In order to improve the development of Teacher Professional Identity (TPI), the transformative school digital sustainability model was applied. The design was pre-experimental of an explanatory type, and a questionnaire was applied to 31 teachers from a school in Peru. Taking into account the dimensions of teaching performance established in the Framework for Good Teaching Performance (MBDD, from the Spanish Marco de Buen Desempeño Docente), the extent of the development of teaching professionalism and identity was chosen. By applying the model, it was possible to reverse the problem identified in the pre-test, increasing the high level to 67.74%.
Is My Transaction Done Yet? An Empirical Study of Transaction Processing Times in the Ethereum Blockchain Platform

Ethereum is one of the most popular platforms for the development of blockchain-powered applications. These applications are known as ÐApps. When engineering ÐApps, developers need to translate requests captured in the front-end of their application into one or more smart contract transactions. Developers need to pay for these transactions and, the more they pay (i.e., the higher the gas price), the faster the transaction is likely to be processed. Developing cost-effective ÐApps is far from trivial, as developers need to optimize the balance between cost (transaction fees) and user experience (transaction processing times). Online services have been developed to provide transaction issuers (e.g., ÐApp developers) with an estimate of how long transactions will take to be processed given a certain gas price. These estimation services are crucial in the Ethereum domain and several popular wallets such as Metamask rely on them. However, despite their key role, their accuracy has not been empirically investigated so far. In this article, we quantify the transaction processing times in Ethereum, investigate the relationship between processing times and gas prices, and determine the accuracy of state-of-the-practice estimation services. Our results indicate that transactions are processed in a median of 57 seconds and that 90% of the transactions are processed within 8 minutes. We also show that higher gas prices result in faster transaction processing times with diminishing returns. In particular, we observe no practical difference in processing time between expensive and very expensive transactions. With regards to the accuracy of processing time estimation services, we observe that they are equivalent. However, when stratifying transactions by gas prices, we observe that Etherscan’s Gas Tracker is the most accurate estimation service for the very cheap and cheap transactions. EthGasStation’s Gas Price API, in turn, is the most accurate estimation service for regular, expensive, and very expensive transactions. In a post-hoc study, we design a simple linear regression model with only one feature that outperforms the Gas Tracker for very cheap and cheap transactions and that performs as accurately as the EthGasStation model for the remaining categories. Based on our findings, ÐApp developers can make more informed decisions concerning the choice of the gas price of their application-issued transactions.
Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Furthermore, a multiscale Gaussian-difference-based background prior is proposed to reweight the data consistency term, thereby avoiding the loss of defect information in the enhanced image. Last, by using the powerful nonlinear fitting ability of deep neural networks, a deep denoised prior is proposed to replace existing physics priors, effectively reducing the time consumption. Various experiments are carried out on public and private datasets, which are used to compare the defect images and enhanced results in a symmetric way. The experimental results demonstrate that our method is more conducive to downstream visual inspection tasks than other methods.
Internal Similarity Network for Rejoining Oracle Bone Fragment Images

Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.
Continued Fractions and Probability Estimations in Shor’s Algorithm: A Detailed and Self-Contained Treatise

Shor’s algorithm for prime factorization is a hybrid algorithm consisting of a quantum part and a classical part. The main focus of the classical part is a continued fraction analysis. The presentation of this is often short, pointing to text books on number theory. In this contribution, we present the relevant results and proofs from the theory of continued fractions in detail (even in more detail than in text books), filling the gap to allow a complete comprehension of Shor’s algorithm. Similarly, we provide a detailed computation of the estimation of the probability that convergents will provide the period required for determining a prime factor.
Evaluation of classification ability of logistic regression model on <scp>SERS</scp> data of <scp>miRNAs</scp>

Logistic regression (LR) is a supervised multiple linear regression model, which uses linear weighted calculation for input to obtain weight coefficients of model. The surface enhanced Raman spectroscopy (SERS) technology greatly enhances the Raman signal of analyte. LR model was used to analyze the data of seven types of pancreatic cancer-related miRNAs obtained from commercial SERS substrate. The classification ability of the model on such data was observed under the configurations of different key parameters (classification mode, regularization method and loss function optimization way), and the effect of the two types of data formats were also evaluated. The results showed that though LR model used to classify this data did not perform well as expected, miRNA-191 and miRNA-4306 still had high recalls (sensitivity), which laid a theoretical foundation for the purpose of using LR model to identify these two miRNAs to jointly diagnose of pancreatic cancer at miRNA level.
An Empirical Analysis of Tamil Optical Character Recognition

The concept of visual cues in a paperless workspace is based on converting scanned images into machine-readable text. It requires the development of a variety of new applications, such as automated postal systems, banks, institutions, word processing, and library systems, to name a few. Artificial Intelligence (AI) is an area of computer science that focuses on making machines smart. Hand typing, script, and print text recognition are key study subjects because no 100% recognition can be achieved even if the scanned image is accurate. Text, numbers, and images can all be used to write text in a variety of ways. The basics of Tamil text, previous tasks in Tamil text attention, Tamil letter recognition algorithms, and recognition barriers are covered in this study.