Reviewerly/exHarmony huggingface dataset:
Improved salp swarm algorithm based on gravitational search and multi-leader search strategies

&lt;abstract&gt; &lt;p&gt;The salp swarm algorithm (SSA) will converge prematurely and fall into local optimum when solving complex high-dimensional multimodal optimization tasks. This paper proposes an improved SSA (GMLSSA) based on gravitational search and multi-swarm search strategies. In the gravitational search strategy, using multiple salp individuals to guide the location update of search agents can get rid of the limitation of individual guidance and improve the exploration ability of the algorithm. In the multi-swarm leader strategy, the original population is divided into several independent subgroups to increase population diversity and avoid falling into local optimization. In the experiment, 20 benchmark functions (including the well-known CEC 2014 function) were used to test the performance of the proposed GMLSSA in different dimensions, and the results were compared with the most advanced search algorithm and SSA variants. The experimental results are evaluated through four different analysis methods: numerical, stability, high-dimensional performance, and statistics. These results conclude that GMLSSA has better solution quality, convergence accuracy, and stability. In addition, GMLSSA is used to solve the tension/compression spring design problem (TCSD). The proposed GMLSSA is superior to other competitors in terms of solution quality, convergence accuracy, and stability.&lt;/p&gt; &lt;/abstract&gt;
Text Sentiment Analysis of Hotel Online Reviews

Taking the Intercontinental Shenzhen as an example, this paper presents text analysis of hotel online reviews, including sentiment analysis based on sentiment dictionary and LDA topic analysis. The professional sentiment dictionary formed by expansion experiment improves the accuracy of sentiment analysis of online hotel reviews, which can be effectively applied to various scenarios of hotel online review sentiment analysis. Furthermore, LDA topic analysis is made for positive and negative reviews, in which perplexity index is introduced to determine the appropriate number of topics. Moreover, the output of LDA model is visualized by PyLDAvis. Through LDA topic analysis, the factors that lead to different guest reviews can be identified, which can further make recommendations for hotel operation management.
An adaptive weight texture classification method based on Local Binary Pattern Variance

The extraction of local texture information using the traditional Local Binary Mode (LBP) is limited, and it ignores the representation of global texture information, which leads to an unsatisfactory outcome for the texture classification task. Local Binary Mode (LBP) has been widely used in texture classification. This paper utilizes LBPV to resolve this issue (Local Binary Pattern Variance) and proposes a novel adaptive weight joint multi-scale LBPV2 texture picture classification algorithm. The typical variance weight is replaced by the square of covariance as the cumulative weight of the histogram in this method, and the multi-scale texture information is retrieved using an adaptive weight and multi-scale scheme. Thus, the texture classification performance is further improved. Simulation experiments on the commonly used Outex reference texture database show that the proposed adaptive weight combined with multi-scale LBPV2 can significantly improve the performance of texture classification.
On the Analysis of the Reflexion in Science, in the Russian Philosophy, and the STS Strong Program

This reply draws attention to the importance of taking into account the results of the study of the phenomenon of reflexivity in scientific knowledge, which have been obtained in the domestic philosophical and methodological tradition. We believe that taking this kind of results into account could enrich the analysis of the strong programme in the STS. We touch the origins of reflexive tendencies, the reflexive and non-reflexive in scientific knowledge, personal and transpersonal forms, the types and levels of reflexion in science, as well as denote the mechanisms that allows taking an external position in relation to the subject about which reflexive procedures are carried out (if we mean the interval approach implementation).
Research on Methods of Improving Robustness of Deep Learning Algorithms in Autonomous Driving

One of the main reasons why automatic driving cannot be industrialized today is that the target detection model used cannot have good detection results under various severe weather conditions, that is, the robustness is poor. If a model with good robustness to all possible image damage can be constructed, then interference similar to weather changes will not be a problem. This paper introduces a method of simulating image damage, and proposes a robust detection method to detect the robustness of the algorithm, and proves that two widely used target detection models have serious performance losses for damaged images. Provides a simple data enhancement technology to stylize training images. After training the model using this method, the robustness of the model can be significantly improved.
Episodic Projection Network for Out-of-Distribution Detection in Few-shot Learning

The increasing demands of safety-critical computer vision applications have attracted extensive research on Out-of-Distribution (OOD) detection in recent years. Nevertheless, a large proportion of real-world tasks are in low-data regime, and the gap between meta-learning paradigm and OOD detection mechanism causes low performance in few-shot settings. In order to bridge the gap, we first propose an simple yet effective Episodic Projection Scheme (EPS). EPS is designed to project feature vectors to task-specific feature space for OOD detection, without sacrificing generalization of few-shot models. We then construct a multi-modal representation space for few-shot OOD detection by employing representations of the labels and their synonyms. At last, we put forward a few-shot OOD detection framework named Episodic Projection Network (EPN), which can integrate many kinds of perturbation based OOD algorithms with ease. To verify effectiveness of the proposed scheme, we implement several OOD algorithms into EPN and conduct experiments on two few-shot classification datasets, i.e., Omniglot and mini-ImageNet. Experimental results demonstrate that accuracy has been increased by 5% by integrating the OOD algorithms into the EPN framework.
LayoutLM-Critic: Multimodal Language Model for Text Error Correction of Optical Character Recognition

Recently, many approaches have been proposed to correct grammatical errors. Above them, LM-Critic. (Language model critic) achieved great success. It uses a language model to judge whether a sentence is grammatical and then uses Break-It-Fix-It (BIFI) framework to fix the broken sentence. However, it does not work in the scenario of multimodal, since the text is usually not a complete sentence and the errors are often not grammatical errors. Besides, because of the noise of scanned images, there always be inevitable recognition mistakes even though using the best Optical Character Recognition (OCR) engine. And some of those are intolerable, such as errors in receipt date, total amount, etc. Therefore, it's essential to introduce an error correction system to fix OCR results. Inspired by LayoutLMv2 (Layout Language Model version 2), which introduces a pretraining task to align the text and image, we present LayoutLM-Critic, a critic used to assess how much a sentence matches the bounding box and image for the visually-rich document.
Study on recommendation method of high quality MOOC English teaching resources based on fuzzy clustering

In order to improve the mining and recommendation accuracy of high-quality teaching resources, a new recommendation method of high quality teaching resources is developed according to fuzzy clustering algorithm. Firstly, fuzzy clustering method is used to mine high quality MOOC English teaching resources to improve the effectiveness and accuracy of mining. Secondly, it analyses the main factors affecting learners' learning preferences and constructs a teaching resource recommendation model. Finally, the linear weighting method is used to construct the recommended objective function, and the constraints of the function are set. The recommendation can be realised by solving the function under relevant constraints. The experimental results show that compared with the traditional recommendation methods, the resource mining accuracy of this method is higher, and the recommendation accuracy is higher, which is always maintained at more than 97%.
Image Encryption Algorithm Through Hill Cipher, Shift 128 Cipher, and Logistic Map Using Python

One of the cryptographic approaches is the Hill Cipher. A square key matrix must have an inverse modulo in the Hill Cipher algorithm. The unimodular matrix is one of the special matrices that has an inverse. In the encryption process, a unimodular matrix can be utilized as a key matrix. The goal of this study is to demonstrate a safe digital image data alternative. Symmetric cryptography is the type of encryption utilized. The described approach uses a Logistic Map to construct a unimodular matrix. To begin, we employ an identity matrix to obtain a unimodular matrix. The encryption mechanism in this study is modulo matrix multiplication. Because an incomplete partition may arise as a result of a file, we must apply Shift Cipher 128 encryption in the next step. We utilize Shift Cipher 128 to encrypt the imperfect partition, especially if the size is a large enough prime integer. The results reveal that the photos are well encrypted and difficult for third parties to decrypt. This encryption methods also assures that the encrypted and decrypted file sizes are identical to the original file sizes. As a result, the Hill cipher works well with the combination of these techniques for encrypting digital images.
QLoc: A Realistic Quantum Fingerprint-based Algorithm for Large Scale Localization

Fingerprinting is one of the most commonly used techniques for indoor localization due to their high accuracy in the presence of the wireless channel noise. Nonetheless, they require significant storage overhead and running time. This becomes even more important when scaling such systems to a worldwide scale.Quantum computing can speedup computation of intractable problems that are hard to solve on the traditional classical computers. In this paper, we propose a realistic quantum fingerprint based algorithm that builds on quantum computing concepts to scale. In particular, the proposed fingerprinting algorithm can provide exponential enhancement in both space and running time over the classical fingerprinting techniques. We explain the details of the quantum fingerprint construction, how to encode the fingerprint signal strength vectors in quantum states, and how to calculate the quantum similarity between the online signal strength measurement and the offline ones. In addition, we study the different practical implementation and deployment issues on IBM quantum computing cloud machines.Implementation and evaluation of our algorithm in a real testbed using quantum machines confirms its ability to correctly obtain the estimated location with an exponential enhancement in both time and space compared to the traditional classical fingerprinting techniques. This highlights the promise of our algorithm to scale fingerprinting localization to be used globally.
ABILITY TO ANALYZE VOCAL AND CHORAL WORK AS ONE OF THE PROFESSIONAL COMPETENCIES OF THE FUTURE TEACHER-MUSICIAN

The proceeding considers aspects of preparation of students of art faculties of pedagogical universities for future professional activity. The ability of future music teachers to analyze vocal and choral works in the process of musical activity is emphasized. The development of the ability to carry out music-theoretical, vocal-choral, artistic and pedagogical analysis of vocal-choral work at the present stage of development of music pedagogy becomes important in the formation of professional competencies of future teachers of music. This is because the teacher must be constantly ready to model the educational process during music and practical activities with their students in the classroom. Successful work on the study of vocal and choral works involves preliminary self-acquaintance and a thorough study of the work. The analysis of the choral work includes step-by-step work on all the details of the vocal and choral performance of the work. Taking into account the actualization and use of knowledge, skills and abilities from other humanities and music- theoretical disciplines, the analysis of the work acquires a pedagogical direction. To explore all the elements of vocal- choral sonority and means of musical expression. The analysis needs to be carried out according to a certain plan. The plan should consist of appropriate sections that will help to explore the work in more detail. Sections and subsections are used as needed depending on the characteristics of the work, which has been analyzed. In some sections it is possible to pay more attention to the details that are significant and special for this work, in others - to reduce the information. In the first section it is necessary to characterize the general information about the composition: brief information about the authors and a comparative analysis of literature and music text. In the second section you can analyze the music and music-theoretical analysis of the work: musical form, key, meter, rhythm, tempo, dynamics, texture, interval, harmony and more. The third section should be devoted to vocal-choral analysis: type and type of chorus, ensemble, system, range of parts, breathing, sound, etc. In the fourth section it is necessary to carry out the executive analysis: character and peculiarities of the conductor, gesture, identification of difficulties in the implementation and basic methods of overcoming them in the process of Learning the work. In the article on the example of Ukrainian folk song arranged by V. Ostrovsky «Oh you, the moon…» a complete analysis of the choral work.
Improved Decision Based Filtering algorithm for impulse noise removal in Digital Images

Random valued impulse noise is removed in digital images, and an Improved Decision Based (IDB) filtering technique is proposed. The decision-based filtering algorithm’s function is to detect noisy pixels by making judgments in three different modules. Once the noisy pixel has been found, the noise is removed using a Hybrid Wiener Adaptive Centre Weighted Median filter. Comparing the existing decision tree-based approach, the suggested filter removes substantially more noise. The suggested modified decision tree method produces better outcomes for high-density noise levels of up to 90%, according to both visual and quantitative data.
Weakly Supervised Learning of Instance Segmentation with Confidence Feedback

Only provided with image category supervision, weakly supervised instance segmentation is a challenging yet significant task when required to simultaneously learn object locations and instance segmentation. In this paper, we propose a region-based multibranch network with feed-forward and feedback procedures to estimate image classifiers, object detectors, and mask predictors in an end-to-end manner. In the feed-forward procedure, the instance confidences estimated by the image classification branch are transferred to train object detectors and mask predictors. In the feedback procedure, the instance confidences retrieved from detection outputs and mask confidences obtained from segmentation outputs are employed for classifier enhancement. With iterative feed-forward and feedback procedures, our approach produces a closed-loop multitask learning mechanism that can efficiently correct object localization while generating high-quality instance segmentation. On PASCAL VOC benchmark datasets, our methods greatly improve the performance of weakly supervised object detection (WSOD) and weakly supervised instance segmentation (WSIS).
Spatial-Temporal Wavelet Analysis Video Prediction Based on DifferentialAttention Mechanism

Inspired by the visual process of human, a video prediction algorithm based on spatial-temporal wavelet analysis and differential attention is proposed to solve the problem that it is difficult to accurately predict the details of spatial structure information and the dependence of temporal motion. Firstly, the spatial-temporal wavelet analysis module is used to decompose the video in multiple frequencies, so as to enhance the model’s ability to understand high-frequency details and procedural motion. Then, the differential attention mechanism guides the model to allocate attention resources more efficiently and reasonably, and improves the expression ability of instantaneous motion. Experimental results on the KTH, Cityscapes, BAIR, KITTI, Caltech Pedestrian datasets show the proposed algorithm achieves better results than the existing algorithms in the quantitative evaluation metrics of PSNR, SSIM and LPIPS. Meanwhile, the visualization results also show that the prediction of the proposed algorithm is clearer.
Secret Key Generation using Genetic Algorithm for the Hybrid Blowfish Encryption and Substitution Ciphers

Today security and privacy of user from unauthorized access, corruption, or theft is becoming more crucial requirement due to increased use of internet and communication networks. To protect data effectively, there is a need to know about the different security mechanisms. The proposed work presents a novel approach of cryptography discipline. In this approach data privacy is achieved using genetic algorithm, symmetric encryption and the substitution cipher. Implemented model may be used in the medical fields, military applications, forensic science, e-commerce etc. where there is a great exchange of sensitive data. Key point of the proposed method is the use of genetic algorithm for the key generation. Real time clock values are used to generate the keys. Parallel programming is achieved by having two threads for execution. Blowfish algorithm is executed by one thread and substitution cipher method is executed by another thread. Performance analysis is done by observing file accuracy and execution time of the proposed technique and the traditional blowfish technique. Result shows that blowfish technique with the substitution cipher gives better file accuracy in minimum execution time.
Banking industry instability, governance and development: empirical evidence from Sub-Saharan Africa

This empirical study assesses the effect of instability in the banking industry and quality of governance on development in the sub-region of Sub-Saharan Africa (SSA) using data compiled from 40 countries from 1996 to 2018. Two-step system generalised method of moments (TSS-GMM) model based empirical estimates suggest that instability in the banking sector hinders development in the sub-region all things being equal. Improvement in quality of governance is however found to have positive influence on development among economies in the sub-region. Again, the results of the study suggest that governance quality lessens adverse effect of banking industry instability on development all things being equal. The importance of stability in banking industry and improved governance among economies in the sub-region is thus, affirmed by this study's empirical conclusions.
RELATIONSHIP BETWEEN FINANCIAL INCLUSION, TRADE, AND ECONOMIC GROWTH

This study inspects relation between financial inclusion, trade, and economic growth in twenty-four developing countries namely Algeria, Azerbaijan, Bangladesh, Botswana, Brazil, Colombia, Egypt, Kuwait, Madagascar, Mauritania, Mauritius, Namibia, Nigeria, Moldova, Pakistan, Paraguay, Peru, Rwanda, Tajikistan, Tanzania, Thailand, Uganda, Vanuatu, Zambia from period 2004-2020(both inclusive) by scrutinizing effect of financial inclusion and trade on economic growth with an emphasis on whether financial inclusion catalyzes ‘the influence of trade on economic growth by employing Newey West standard approach, Feasible Generalized Least Square, Pooled Ordinary Least Square regression, Fixed Effect Model and Random Effect Model. Results found a strong, significant, and positive association linking financial inclusion and trade with economic growth. Policy implications are developing countries should frame and execute pro-growth policies to enhance financial inclusion which will boost the impact of trade on growth. Moreover, developing economies should establish more multilateral trade agreements, diversify their export baskets and ensure that trade happens through formal financial institutions Keywords: Financial inclusion index, Trade, Economic growth, FGLS. JEL Codes: E01, F1, G53
Accelerating the Learning of TAMER with Counterfactual Explanations

The capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and interactively. Human-in-the-loop Reinforcement Learning (HRL) combines human feedback and Reinforcement Learning (RL) techniques. State-of-the-art interactive learning techniques suffer from slow learning speed, thus leading to a frustrating experience for the human. We approach this problem by extending the HRL framework TAMER for evaluative feedback with the possibility to enhance human feedback with two different types of counterfactual explanations (action and state based). We experimentally show that our extensions improve the speed of learning.
Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model

Recently, intelligent dialogue systems have shown promise in terms of reducing the load of human customer care agents and decreasing user wait times. In some cases, these systems still cannot understand user intent which leads to the generation of inappropriate responses. Therefore, their inability to handle inappropriate responses has limited their utility in the real world. In this work, we propose a character deep learning model for the detection of chatbot quality of services to handle inappropriate responses by intelligently transferring the dialogue to a human agent. The proposed model has two goals: detect CQoS based on the sentiment score of the utterance using a deep learning model and transferring the user to a live agent when the utterance is inappropriate. The proposed model's effectiveness is evaluated on the dialogue breakdown detection task. The results of the experiment show that our proposed model is effective in achieving the desired goals.
Selection of informative parameters of the performed movements according to the data of the wearable device in biometric authentication

This work is related to the assessment of biometric indicators of human movement. The purpose is using them to user authentication systems, as well as assessing of the health of the musculoskeletal system. As part of the work, the results of determining the informative parameters of the movements performed on the basis of a wearable device are shown. The paper considers statistical, time and frequency parameters, as well as their change in time. The obtained results are necessary for the development of an algorithm for biometric user authentication based on mobile phone data