Reviewerly/exHarmony huggingface dataset:
Topics Identification in a Corpus based on Transformers

Classifying a corpus of texts based on a set of classes using Transformers allows to build a model based on the words that contain the information, however, the model uses all the words in the training process and the order in which its found, but, which of these words are directly related to the topic of the texts? This work focuses on proposing a methodology that allows, using a multi-labeled corpus that contains the description of 1200 comics organized in 4 classes, to eliminate information that are not related to the topic, based on the identification of named entities and noun phrases, thereby generating a new corpus with which to train and validate a Transformer, using the macro accuracy measure as an evaluation measure, as a base case the macro accuracy value, obtained of the validation of a Transformer trained with the original data is proposed, demonstrating that when we using data related to the subject matter of the texts, the classification results are improved from 0.733 to 0.992
Detection of Human Behavior Using Swarm Technique and Neural Networks

Artificial neural network plan is a complicated job since its demonstration relies upon the design, the selected task and the knowledge calculation used to prepare the arrangement of synaptic loads. Here, these papers present a technique that consequently plans a neural system utilizing particle swarm advancement. Neural network merges faster and comes into sight throughout the best neural system design. Utilizing PSO for the preparation interaction expects to enhance the outcome of the collection vectors on neural system which thus improves the classification precision to appear at the quality presentation contrasted with the best in group strategy the assessment investigates the performance of convolution neural system with swarm method. The simulation results showed that convolution neural system using swarm technique is capable to attain elevated enactment of accurateness after compared by other algorithm.
Research on Logistics Information Management System Based on Blockchain Perspective

The current problems of information asymmetry, low process transparency and insecure information storage in logistics industry have restricted the development of logistics industry to a certain extent.Drawing on the advantages of blockchain technology in decentralization, data tamper-proof and data confidentiality, the article analyzes the typical application scenarios of blockchain in logistics, proposes a blockchain-based logistics information management framework, and gives a typical logistics blockchain network topology and information management process.
Dynamic Transaction Confirmation Double-chain System Based on Alliance Chain

In recent years, the high performance, strong controllability and partial decentralization of the alliance chain make it have a wide range of application scenarios and development potential.However, restricted by the consensus algorithm, the alliance chain cannot fully meet the needs of practical applications at present.Aiming at the poor stability of the Byzantine Fault Tolerance (PBFT) consensus algorithm, this paper proposes a fast consensus algorithm, which reduces the message size by optimizing the transaction verification process and dynamically adjusting the number of verification nodes.Combined with the reward and punishment system of points, a dynamic transaction confirmation double-chain system is designed.Compared with the alliance chain system implemented by the PBFT algorithm, the system has stronger stability in the case of multiple nodes, and the TPS is increased by at least 21% under the same number of nodes.It is suitable for some nodes with frequent access and poor network.
Research and Implementation of Automatic Composition System Based on ACMN

The use of artificial neural network to compose music is a new computer composition method proposed in recent years.The advantage of this method is that it can process large-scale data in a relatively short time, greatly reducing the preparation work before computer composition and improving composition efficiency.However, there are still many problems that have not made obvious breakthroughs.For example, the yield is low, and a considerable proportion of the generated works have no appreciation value.They are completely random patchwork of notes and chords in the time dimension; the music produced is not pleasant, the melody is too simple and so on.In order to make the songs generated by the composition system more in line with the rules of music theory, this paper proposes a music generation model ACMN (Actor-Critic Music Network) based on the policy gradient method, which is characterized by the combination of reinforcement learning technology and neural network.Experiments show that, compared with the composition model without reinforcement learning technology, the ACMN model has more outstanding performance in terms of repetition rate and pleasantness.
Overall Cognitive Consistency of Complex Combination Form

The work aims to study the formal aesthetic consistency of car body and wheel matching.An "aesthetic matching threshold method" was proposed.Taking the SUV model as an example, the clustering analysis of sample A was carried out by a k-means clustering algorithm to find out the cluster of typical samples in dealing with the cognitive consistency problem of model A and wheel type B. The morphological analysis of part B of the cluster was carried out to find out the typical characteristics of the influential design factors of part B. Based on the corresponding analysis, through the perceptual experiment, the design features were analyzed to find out the AB matching range.Through the experiment, six influential wheel hub design feature ranges were obtained, and the perceptual score range that reflected the SUV model and the hub matching was obtained by the perceptual experiment.In this range, model A and hub B were more harmonious regarding the visual aesthetics of matching.The proposed best aesthetic matching threshold method can be used to study the matching relationship between complicatedly combined products and their matching quality to help designers, businesses or individuals find suitable solutions.
Two-Dimensional Codes Recognition Algorithm Based on Yolov5

In order to realize fast and high-precision recognition of QR codes, a QR code recognition method using YOLOv5 algorithm is designed.The original image of the QR code will be obtained by using the LabelImg annotation tool and data enhancement method to build a data set, and trained by the YOLOv5s model.The experimental results show that the test on the real two-dimensional code can be effectively identified, and the average accuracy rate is about 90%, which can meet the requirements of real-time identification of the two-dimensional code when using an augmented reality device.
Construction of Online and Offline mixed courses for Business English Writing in the New Era of Internet Economy

This paper gives some suggestion on the construction of online and offline mixed courses for business English writing in the new era of network economy.This paper focuses on analying the existing problems of application of mixed courses in Business English Writing and giving some specific suggestion including the constructon of students'online teaching and training practice.In the construction of students'online -learning resources library, this paper suggests that workers should build the electrontic textbook database, optimize online course recording and construct various online question banks, which can be helpful to the teaching of mixed course of bussiness English writing.
Residual Neural Networks for Gemstone Recognition and Classification

For rapidly and accurately identifying and classifying different gemstones, a residual neural network-based gemstone classification and recognition model is presented using the image feature differences of 15 classes of gemstones.The gemstone image set is firstly established, then the image set is expanded using the data enhancement method, and afterwards the sample data set is obtained using the data cleaning method.The images are divided into training and test sets in the ratio of 8:1, and then the training set is trained using a residual neural network (ResNet 50).Finally, the correctness of the network is evaluated.The experimental results show that the average accuracy of this gem classification and identification algorithm reaches 93.46%, and basic engineering applications can be achieved.
Research on the Current Situation, Characteristics and Development Countermeasures of Chinese Rope Skipping Sports——Based on the Analysis of the Results of International and Domestic Rope Skipping Competitions

In recent years, the development of China's rope skipping has ushered in opportunities.Under the organization and promotion of the Social Sports Guidance Center of the State General Administration of Sports and the National Rope Skipping Sports Promotion Center, large-scale rope skipping events such as the World Rope Skipping Championships, Opens, and Finals have been held for many times.The Chinese national rope skipping team has a place in the international rope skipping arena.In the development of Chinese rope skipping and its competitions, the development of this emerging sport also faces a series of problems.Such as: insufficient attention to skipping rope in China, lack of funds for project development, unbalanced development of campus sports, lack of relevant teachers, etc.The above-mentioned problems have affected the development of rope skipping in China to a certain extent.When collecting and sorting out related content about the development of Chinese rope skipping and competitions through the literature data method, it is found that domestic research on rope skipping is relatively weak, especially in competitions.To this end, this article uses the literature method, questionnaire survey method, field research method, expert interview method and combined with the results of international and domestic rope skipping competitions to carry out relevant data analysis.Investigate the status quo of the development of rope skipping in China, sort out the development process of rope skipping, and understand the advantages and disadvantages of its sports.Analyze the gap between China's rope skipping sport and the world's rope skipping powers and the problems highlighted in China's rope skipping competitions.Analyze the reasons that restrict the development of domestic rope skipping, explore targeted adjustment countermeasures, and propose development paths and specific measures for the development of rope skipping in China, so as to jointly promote the healthy and sustainable development of rope skipping in China.
Robust Rotation Estimation From Three Orthogonally-Oriented Cameras

This paper presents the novel ORThogonally-oriented CAMeras (ORTCAM) algorithm that robustly estimates rotation of a set of three orthogonally-oriented cameras undergoing motion. Orthogonally-oriented cameras are any set of divergently-facing cameras where the cameras are oriented 90° to one another. The ORTCAM algorithm robustly estimates rotation using feature correspondences between two views from each camera. The ORTCAM algorithm is shown to both resolve planar and rotation degeneracies and yield rotation estimates superior to those obtained by averaging the rotation estimates from all cameras.
Measuring the Size of Object-Oriented Software Applications

In measuring the software size, feature Points and Function Points are two examples of metric methodology. The Function Point Analysis (FPA) is by far the most popular counting method for software development used in industry. However, when applying the IFPUG standard to count the Object-Oriented software application, the resulting function points are insignificant or inaccurately low. In this paper, we present a methodology, called Object Point Analysis (OPA), for counting the object points for the Object-Oriented software application. The OPA has been used by multi-million-dollar software projects for more than six years. The positive results received from these projects provide a preliminary study on sizing of OO software application. Comparing to the FPA, the OPA not only produces a more accurate size measurement for OO software applications, but also provides a means for sizing automation, which reduces the human errors often found in FPA.
Research on the Teaching Model of College English Listening and Speaking Based on the Output-oriented Approach

Listening and speaking class is an important way to cultivate students' English communicative ability.The use of "Output-oriented Approach" in college English listening and speaking classes embodies the teaching concept of "Integration of Learning and Use", which can help students clarify their learning goals, improve their learning enthusiasm, and enhance teaching effects.Taking the theme of Festivals as an example, this article elaborates on the teaching design of college English listening and speaking classes based on the "Output-oriented Approach".
A Study of School Culture in Secondary Schools of District Swat Khyber Pakhtunkhwa, Pakistan

Schools’ culture is a key indicator for schools’ effectiveness and overall activities of the school. Its influence is much more significant at the level of secondary schools that are having the duty for the provision of quality graduates to institutions of higher education and skilled people to the job market. School culture consists of norms, beliefs, principles, rituals and behaviors which designs the rules of thinking, feelings and action inside an educational organization and ultimately influences school performance. Having the significant role of school culture in the background, the study aimed to investigate the school culture at secondary level. The objectives of the study were; to investigate the schools’ culture at secondary level and recommend strategies for the improvement of school culture in secondary schools. The design of the study was quantitative descriptive survey. The population of the study was 162 male heads of secondary schools in District Swat. The researcher selected a sample of 118 heads through simple random sampling, developed a Likert questionnaire, validated it and pilot tested for the determination of its reliability. The data were gathered through self-administered questionnaires from the respondents and analyzed with mean scores, standard deviation and Chi-square test. The study found a positive school culture with clear chalked out visions and missions, heads of the school took initiatives for achieving the vision of schools, teachers themselves prepared the rules for functioning of the schools and the head and teachers had collegiality for achieving schools’ visions. The administration of the schools were ambitious, friendly and supportive for the students, democratic in decision making, resolved the problems of teachers and students and were competent enough to satisfy the needs of the schools, teachers worked for the academic excellence of their students, regularly assessed students’ academic progress, focused on co-curricular activities and observed positive human relationship. It further forwarded recommendations for the improvement of the school culture at secondary level.
Approximate Pareto Dominance and its Application to Filter Feature Selection

The Pareto dominance has been applied to solve the problem of selecting key features from a multi-label dataset. High-dimensional labels will directly result in the difficulty of forming Pareto dominance. To solve this problem, this study proposes a multi-label feature selection algorithm based on the approximate Pareto dominance (MAPD). It maps the multi-label feature selection to the problem of solving the approximate Pareto dominant solution set. By introducing an approximate parameter, the number of features in the selected feature subset can be effectively reduced and the quality of the feature subset can be improved. To confirm the performance of MAPD, this study uses nine public multi-label datasets to compare MAPD algorithm with other methods in terms of Hamming loss, accuracy, and selected feature size. The results show that MAPD algorithm has higher level of classification accuracy, lower level of Hamming loss and limited number of the selected features.
Branches

A behavioral description of an analog system is constructed as a network of interconnected branches. The constitutive equations of the system component are formulated in terms of branch potential and flow signals. This chapter describes how to declare branches as well as how to access and contribute branch signals.
Leverage Supervised and Self-supervised Pretrain Models for Pathological Survival Analysis via a Simple and Low-cost Joint Representation Tuning

The large-scale pretrained models from terabyte-level (TB) data are now broadly used in feature extraction, model initialization, and transfer learning in pathological image analyses. Most existing studies have focused on developing more powerful pretrained models, which are increasingly unscalable for academic institutes. Very few, if any, studies have investigated how to take advantage of existing, yet heterogeneous, pretrained models for downstream tasks. As an example, our experiments elucidated that self-supervised models (e.g., contrastive learning on the entire The Cancer Genome Atlas (TCGA) dataset) achieved a superior performance compared with supervised models (e.g., ImageNet pretraining) on a classification cohort. Surprisingly, it yielded an inferior performance when it was translated to a cancer prognosis task. Such a phenomenon inspired us to explore how to leverage the already trained supervised and self-supervised models for pathological survival analysis. In this paper, we present a simple and low-cost joint representation tuning (JRT) to aggregate task-agnostic vision representation (supervised ImageNet pretrained models) and pathological specific feature representation (self-supervised TCGA pretrained models) for downstream tasks. Our contribution is in three-fold: (1) we adapt and aggregate classification-based supervised and self-supervised representation to survival prediction via joint representation tuning, (2) comprehensive analyses on prevalent strategies of pretrained models are conducted, (3) the joint representation tuning provides a simple, yet computationally efficient, perspective to leverage large-scale pretrained models for both cancer diagnosis and prognosis. The proposed JRT method improved the c-index from 0.705 to 0.731 on the TCGA brain cancer survival dataset. The feature-direct JRT (f-JRT) method achieved $$60\times $$ training speedup while maintaining 0.707 c-index score.
Single Domain Generalization via Spontaneous Amplitude Spectrum Diversification

Due to the domain shift problem, the deep learning models trained on one domain often fail to generalize well on others. Researchers formulated such a realistic-yet-challenging scenario as a new research line, termed single domain generalization (single-DG), which aims to generalize a model trained on single source domain to multiple target domains. The existing single-DG approaches tried to address the problem by generating diverse samples using extra trainable network modules. However, due to the limited amount of medical data, the extra network parameters are difficult to train. The generated samples are often failed to achieve satisfactory effect for improving model generalization. In this paper, we propose a simple-yet-effective Fourier-based approach, which augments data via spontaneous Amplitude SPECTrum diverSification (ASPECTS), for single domain generalization. Concretely, the proposed approach first converts the image into frequency domain using the Fourier transform, and then spontaneously generates diverse samples by editing the amplitude spectrum using a pool of randomization operations. The proposed approach is established upon the assumption that the high-level semantic information (domain-invariant) is embedded in the phase spectrum of images after Fourier transform, while the amplitude spectrum mainly contains the domain-variant information. We evaluate the proposed ASPECTS approach on both publicly available and private multi-domain datasets. Compared to the existing single-DG approaches, our method is much easier to implement (i.e., without training of extra network modules) and yields the superior improvement.
Mixed Teaching Mode for Online and Offline under COVID-19 Epidemic Situation

This paper carried out a study on the teaching mode of mixed line and offline under the background of post epidemic.The outbreak of New Coronavirus has hit the whole education industry like flood.In order to fight this battle of education, schools at all levels have been actively creating "war time" classes in the air.The purpose is to deeply integrate information technology and education and teaching, build a new effective hybrid teaching model focusing on online live broadcasting and cooperating with other online and offline learning activities, and ensure "online and offline, equal quality and efficiency".Therefore, summarizing and analyzing the prominent problems exposed by online education under the background of the epidemic and putting forward online and offline education integration measures are of great significance for giving play to the boosting role of online education and improving the quality of education in the information society in the post epidemic era.
An Empirical Study on the Application of “Moso Teach” Software in the Teaching of College English Writing

Moso Teach" is a smart teaching platform based on big data, which support the mobile environment education and multiple evaluation of the whole process of students' participation in learning activities.Aiming at the current situation of time-consuming and inefficient traditional feedback in the teaching of College English writing, this paper empirically discusses the application of "Moso Teach" software in College English writing, analyses its effectiveness, and helps to trigger students' interest and autonomous consciousness in writing.Through the analysis of students' attitude and evaluation toward "Moso Teach", this paper gives tentative suggestions on the updating and improvement of the intelligent writing evaluation system represented by "Moso Teach".
Research on Evaluation of Physical Education Teaching in Colleges and Universities Based on the "Evaluation-Feedback" Mechanism

Based on the theoretical perspective of "evaluation-feedback" mechanism, this study investigates the evaluation of physical education courses for college students in general universities, reflects on the current evaluation mechanism, explores how to reform the evaluation of physical education courses, gives full play to the improvement and motivation role of evaluation, and better promotes the implementation of course teaching.
Method for Correcting Low-illumination Images Based on Adaptive Two-dimensional Gamma Function

A two-dimensional gamma function-based color image correction method based on the illumination-reflection model and multi-scale theory is proposed.Firstly, the original image is histogram equalized, then converted to HSV color space, and the light components of the scene are extracted by multi-scale Gaussian function, and then the parameters of the 2D gamma function are adjusted by the distribution characteristics of the light components to construct a 2D gamma function to enhance the image.Finally, the images are fused.The comparison with the classical algorithm shows that the algorithm in this paper can better reduce the effect of uneven illumination on the image and improve the brightness and contrast of the image.