Reviewerly/exHarmony huggingface dataset:
A critical review of information asymmetry in the business cycle: How digital ledger technology can transform and sustain the business cycle

Distributed ledger technology (DLT) is a disruptive technology with the potential to reengineer the entire trading cycle by alleviating inefficiencies such as time lags, multiple record keeping, human errors, and transparency common with the traditional trade cycle. This study evaluates the potential benefits of DLT in mitigating information asymmetry in trading relationships and how a DLT model can be deployed to revamp the trading process. We find that information friction results from differences in stakeholder preferences by identifying and categorizing information friction into 4 groups through a review of key studies in leading management journals. This finding aligns with conclusions reached in scientific research that the benefits of DLT prevail in markets with imperfect information. In addition, we illustrate the potential benefits of DLT in mitigating inefficiencies in trading relationships resulting from information asymmetry. The article concludes with a word of caution for potential users to take gradual steps of adoption to keep pace with changing technology so as not to become laggards.
Visual Saliency Modeling with Deep Learning: A Comprehensive Review

Visual saliency models mimic the human visual system to gaze towards fixed pixel positions and capture the most conspicuous regions in the scene. They have proved their efficacy in several computer vision applications. This paper provides a comprehensive review of the recent advances in eye fixation prediction and salient object detection, harnessing deep learning. It also provides an overview on multi-modal saliency prediction that considers audio in dynamic scenes. The underlying network structure and loss function for each model are explored to realise how saliency models work. The survey also investigates the inclusion of specific low-level priors in deep learning-based saliency models. The public datasets and evaluation metrics are succinctly introduced. The paper also makes a discussion on the key issues in saliency modeling along with some open problems and growing research directions in the field.
Automatic Recognition and Extraction of English Verb Types Based on Index Line Clustering

Languages are not uniform and certain words are used differently by speakers of different languages more or less often, or with distinct meanings. In both linguistics and natural language processing (NLP) problems, the classification that groups together verbs and a collection of similar syntactic and semantic features are of great interest. In the modern era of science and technology, NLP technology is developing rapidly. However, the interpretation of index lines still needs to be realized manually. This method takes a long time, especially after entering the era of big data, the number of corpora has increased rapidly and it is normal to have a corpus with hundreds of millions of words. The quantity of text generated every day is increasing intensely and the word index based on search words is as high as tens of thousands of lines, so it is very difficult to analyze index lines manually. Automatic lexical knowledge acquisition is essential for a variety of NLP activities. Particularly knowledge about verbs is critical, which are the major source of relationship information in a sentence. Due to this issue, this study attempts to automatically identify and extract English verbs by index line clustering. Each index behavior can be regarded as microtext automatic clustering to realize the automatic identification and extraction of English verb forms. This study first focuses on the clustering index algorithm including the C-means clustering algorithm and fuzzy C-means clustering algorithm, then describes in detail the automatic recognition and extraction process of English verbs based on index line clustering, and creates a verification set and completes the index line clustering of English verbs. Finally, the effect of index line algorithm is analyzed from two aspects: automatic recognition of English verb types and recall rate. At the same time, the verbs are selected to analyze their types and judge the probability of each type. The experimental results show that the average recognition rate of English verbs in the manual classification is 91.01%, and the average accuracy of automatic recognition and extraction of English verb patterns based on index row clustering is 95.99%.
DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images

With the recent development of deep convolutional neural network (CNN), remote sensing for ship detection methods has achieved enormous progress. However, current methods focus on the whole ships and fail on the component’s detection of a ship. To detect ships from remote-sensing images in a more refined way, we employ the inherent relationship between ships and their critical parts to establish a multilevel structure and propose a novel framework to improve the performance in identifying the multilevel objects. Our framework, named the dual detector network (DD-Net), consists of two carefully designed detectors, one for ships (the ship detector) and the other for their critical parts (the critical part detector), for detecting the critical parts in a coarse-to-fine manner. The ship detector offers detection results of the ship, based on which the critical part detector detects small critical parts inside each ship region. The framework is trained in an end-to-end way by optimizing the multitask loss. Due to the lack of publicly available datasets for critical part detection, we build a new dataset named RS-Ship with 1015 remote-sensing images and 2856 annotations. Experiments on the HRSC2016 dataset and the RS-Ship dataset show that our method performs well in the detection of ships and critical parts.
Blockchain Rental Platform for College Students Driven by Reward and Punishment Mechanism

For those who are employed nowadays, the living environment largely affects the quality of work. In order to access employment opportunities in first-tier cities, renting an apartment has become a hot social issue for the new generation of youth, and therefore the development of the rental market has gradually become an important part of the economic development. In order to ensure the stable growth of China's market economy and a favorable employment environment for the new generation of Chinese talents, it is necessary to help the new youth group with insufficient social experience to avoid falling into the trap of rental agents and to match with suitable housing, this paper proposes a solution based on a blockchain rental platform driven by reward and punishment mechanisms for college students, focusing on the innovation of specific reward and punishment mechanisms, through the design of coin issuance mechanism, workload determination By designing key components such as coin issuance mechanism, workload determination mechanism, and reputation mechanism, combined with the design of personalized platform functional blocks for college students, the functional operation of this platform is envisioned based on smart contracts, which fills the gap of blockchain renting for college students as well as the refinement of reward mechanism of blockchain renting platform. This design can be applied to build a practical scenario of a blockchain rental platform serving college students and promote the process of upgrading and transforming the traditional rental industry using blockchain technology.
The enumeration of finite rings

Let p $p$ be a fixed prime. We show that the number of isomorphism classes of finite rings of order p n $p^n$ is p α $p^\alpha$ , where α = 4 27 n 3 + O ( n 5 / 2 ) $\alpha =\frac{4}{27}n^3+\mathnormal {O}(n^{5/2})$ . This result was stated (with a weaker error term) by Kruse and Price in 1969; a problem with their proof was pointed out by Knopfmacher in 1973. We also show that the number of isomorphism classes of finite commutative rings of order p n $p^n$ is p β $p^\beta$ , where β = 2 27 n 3 + O ( n 5 / 2 ) $\beta =\frac{2}{27}n^3+\mathnormal {O}(n^{5/2})$ . This result was stated (again with a weaker error term) by Poonen in 2008, with a proof that relies on the problematic step in Kruse and Price's argument.
Conducting Sentiment Analysis. Lei Lei and Dinlin Liu

All human languages, whether spoken or written, transmits sentiment, and emotion, which is a major field of linguistic research. Researchers used to examine this issue mainly in terms of semantic prosody, which describes a word that commonly co-occurs with other words that correspond to a specific positive/neutral/negative semantic set. Such studies use context to infer the sentiment or emotion of single words, but the challenge now is determining the sentiment orientation of words, phrases, and even entire texts in documents with millions of words or more. Researchers used to manually tag negative and positive words in texts in early studies, which was time-consuming and labor-intensive. Later, as large-scale corpora and corpus linguistics advanced, data to anyone looking for solid empirical evidence to test, illustrate, and demonstrate linguistic hypotheses and theories. Sentiment analysis is being increasingly integrated with linguistics, particularly corpus-based linguistics, and is gaining popularity in corpus-based language teaching, discourse analysis, translation research, and other domains as a form of natural (NLP).
A feature-based method for tire pattern similarity detection

Innovation and inheritability are the key requirements of tire pattern design. This article call attention to the similarity detection of tire patterns. A similarity detection method based on tire pattern features is proposed. Through in-depth analysis of the pattern structure, the tire pattern features are separated into shape and texture features. Shape features describe the arrangement rules between tire grooves and pattern blocks. On the other hand, texture features define each pattern block’s surface characteristics, groove depth, groove width, and other characteristics. The tire pattern raw data is firstly analyzed through digital image processing technology. Then, shape and texture features were recognized and separated using edge extraction and co-occurrence matrix methods, respectively. The tire pattern photos are then realized, measured, and detected for similarity following the rules of similarity theory. Experiments were designed to fit the framework of the given detection method. The test results prove that the proposed method can detect tire pattern similarity quickly and conveniently.
Trans-CNN: A Sentiment Analysis Model which Focuses on Key Information of Sentence

Most of sentiment analysis methods rely on large-scale pre-trained models, which require a lot of computing resources. Moreover, these models only extract features on the sentence level, which cannot consider the influence of sentence meaning or local keywords on emotional polarity. In order to solve these problems, we use multi-head attention, RNN (recurrent neural network) and CNN (convolutional neural network), by paying attention to the global and local features of each sentence, and enhancing the expression of emotional features. We propose Trans-CNN which is based on multi-head attention, RNN and CNN. Firstly, we get the vector representation of the sentence. Then, with the help of multi-head attention and CNN, we focus on the global and local feature information of sentence. Finally, we use the SoftMax classifier to get the emotional label about the sentence. Experiments show that the proposed method achieves better performance than other models without pre-trained models. The accuracy is 89.88%.
LiFE: Deep Exploration via Linear-Feature Bonus in Continuous Control

Reinforcement Learning (RL) algorithms work well with well-defined rewards, but they fail with sparse/deceptive rewards and require additional exploration strategies. This work introduces a deep exploration method based on the Upper Confidence Bound (UCB) bonus. The proposed method can be plugged into actor-critic algorithms that use deep neural networks as a critic. Based on the conclusion of the regret bound under the linear Markov decision process approximation, we use the feature matrix to calculate the UCB bonus for deep exploration. The proposed method is equivalent to the count-based exploration method in special cases and is suitable for general situations. Our method uses the last d-dimensional feature vector in the critic network and is easy to deploy. We design a simple task, “swim”, to demonstrate the principle of the proposed method to achieve exploration in sparse/deceptive reward environments. Then, we perform an empirical evaluation on sparse/deceptive reward version gym environments and Ackermann robot control tasks. The evaluation results verify that the proposed algorithm can perform effective deep explorations in sparse/deceptive reward tasks.
Using Parametric Test to determine the Significance of Banded patterns in N-dimensional 0-1 dataset

Background: The identification of banded patterns in N-dimensional (ND) 0-1 dataset is one where all the elements in the dimensions are arranged such that the one’s entries are ordered about the center of dimensions. Reordering 0-1 datasets in order to determine banded patterns in data, allows for the identification of interesting pattern that are hidden in the data. The challenge is whether or not the identify banded patterns are significant. Previous work on the significance of banded patterns using parametric test was aimed at 2D and 3D banding algorithms, using a single banding algorithm in each case, which meant no work on the significance of banded patterns using different banding algorithms with different dimensions. Aim: To this end, this paper presents a comparison between ND banding algorithms for 2D and 3D. Method: The approach is to use parametric test on synthetic data, UCI and the real datasets taken from the cattle tracing system (CTS). The ND banding algorithms considered for 2D are:2D banding, barycentric (BC) and 2D sort, and for 3D are: exact-Euclidean, exact-Manhattan variations and the approximate. Results: The experimental results presented shows the significance of banded patterns with p value less than 0.05. However, the post-hoc test result shows a statistically significant difference between 2D banding and BC, 2D banding and 2D sort, exact-Euclidean and exact-Manhattan, exact-Euclidean and the approximate but no significant difference between BC and 2D sort as well as exact-Manhattan and the approximate.
Analysis of the Impact of Particulate Matter on Net Load and Behind-the-Meter PV Decoupling

With the increasing penetration of the photovoltaic (PV) generator, uncertainty surrounding the power system has increased simultaneously. The uncertainty of PV generation output has an impact on the load demand forecast due to the presence of behind-the-meter (BtM) PV generation. As it is hard to assess the amount of BtM PV generation, the load demand pattern can be distorted depending on the solar irradiation level. In several literature works, the influence of the load demand pattern from BtM PV generation is modeled using environmental data sets such as the level of solar irradiation, temperature, and past load demand data. The particulate matter is a severe meteorological event in several countries that can reduce the level of solar irradiation on the surface. The accuracy of the forecast model for PV generation and load demand can be exacerbated if the impact of the particulate matter is not properly considered. In this paper, the impact of particulate matter to load demand patterns is analyzed for the power system with high penetration of BtM PV generation. Actual meteorological data are gathered for the analysis and correlations between parameters are built using Gaussian process regression.
Data Recovery Technology Based on Subspace Clustering

High-dimensional data usually exist asymptotically in low-dimensional space. In this study, we mainly use tensor t-product as a tool to propose new algorithms in data clustering and recovery and verify them on classical data sets. This study defines the “singular values” of tensors, adopts a weighting strategy for the singular values, and proposes a tensor-weighted kernel norm minimization robust principal component analysis method, which is used to restore low-probability low-rank third-order tensor data. Experiments on synthetic data show that in the recovery of strictly low-rank data, the tensor method and weighting strategy can also obtain more accurate recovery when the rank is relatively large, which improves the volume of the rank. The proposed method combines the two and reflects its superiority through the restoration of 500 images under a small probability noise level.
User-Characteristic-Oriented Bilateral Matching between Online Learning Service Demanders and Providers

Under the background of information technology and the Internet era, the matching problem in different application scenarios is becoming increasingly prominent. With respect to the matching problem in knowledge services, enabling users to choose suitable knowledge out of massive information has become an urgent demand to be satisfied. Initiating from interdisciplinary perspective, this paper proposes a matching method for online learning services according to user characteristics, which focuses on the matching of decision making for knowledge service with user relevance and characteristic under social network environment. Firstly, the complex network among users is constructed, and the user group is subcategorized into subgroups, thereby aggregating the subgroup information effectively. Secondly, the weight of the indices that evaluate the matching subject is determined by conducting the best-worst method. Thirdly, considering the difference between the expectation and actual levels of the matching subject, the cumulative prospect theory is adopted to calculate the satisfaction degree of both sides. Aiming at maximizing the satisfaction degree of the subjects, a multi-objective optimization model is established to obtain the optimal matching pairs. Finally, the validity and rationality of the proposed method are verified, offering interdisciplinary perspective and theoretical foundation for knowledge service matching and the education reform of humanities.
Metal(loid) Deportment in Sulfides from the High-Grade Core of the Bingham Canyon Porphyry Cu-Mo-Au Deposit, Utah

Abstract Porphyry deposits supply the bulk of the world’s Cu and Mo and significant amounts of Au, as well as other minor and trace metal(loid)s, including Ag, Re, Te, Pd, Se, Bi, Zn, and Pb. Porphyry deposits are gaining in importance as a source of critical raw materials with the increasing global demand for these commodities. To date, minor and trace metal(loid)s are still commonly recovered as by-products from porphyry ores without prior characterization of their host mineralogy that could inform more efficient processing and improved recoveries. We report a comprehensive metal(loid) deportment study on a complete vein paragenetic series in samples from the northwestern high-grade zone of the Bingham Canyon Cu-Mo-Au porphyry deposit, Utah. The polyphase Bingham stock comprises an early premineralization equigranular monzonite phase that was intruded by a series of five successive, ore-related porphyry intrusions. Veins with hypogene Cu-(Fe) sulfide assemblages from all five porphyry intrusions were characterized for their trace metal(loid) contents by laser ablation-inductively coupled-mass spectrometry (LA-ICP-MS). It was found that bornite and digenite contain elevated Bi, Ag, Te, and Se relative to chalcopyrite, whereas the latter contains elevated concentrations of Co, Ga, and In. A stepwise decline in sulfide abundance occurs over the porphyry intrusion sequence and is more pronounced in digenite and bornite than in chalcopyrite. The related diminishing concentration per rock volume (inventory) of Bi, Ag, Te, and Se in the youngest porphyry dikes could have been caused in part by a geochemical change in the mineralizing fluid supply across successive intrusive-hydrothermal cycles. Element mapping of exsolved digenite within bornite revealed characterstic partitioning of metal(loid)s between bornite and digenite; most notably Ag, but also Te and Au are enriched in digenite relative to enclosing bornite. Bornite domains within these composite grains reveal complex zonation of Sn, In, and Bi, which are attributed to stress-induced diffusion within bornite, resulting from the digenite exsolution process. The selective partitioning of metal(loid)s between bornite and digenite is likely a common feature in many porphyry Cu deposits, given the fundamental mineralogical characteristics of these two sulfides. Our results contribute to an improved understanding of the distribution (from mineral to deposit scale) of critical trace metal(loid)s in porphyry deposits, particularly those containing exsolved digenite. This knowledge can be applied to determine more accurately the value of ore resources, to improve geometallurgical models and by-product recoveries, and to help limit the environmental effects of metal(loid) dispersion.
Evaluation of College Basketball Teaching Based on Deep Learning

Physical education at college is the final step of school physical education, and it is critical to a student's educational experience. There are numerous issues such as the integration of existing teaching resources, combining the characteristics of basketball teaching to improve teaching quality, and the use of basketball as a means of lifelong physical exercise faced by college basketball teachers. To address these challenges, this research attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods. A teacher’s effectiveness as a teacher is examined using numerous variables utilizing the suggested BP neural network assessment model for measuring teaching quality. In addition, the specific implementation of the improved BP neural network algorithm includes the determination of the number of BP neural network layers, the input data preparation method, and the number of neurons per layer. Finally, the simulation experiment evaluates the teacher’s teaching quality using the upgraded BP neural network.