Reviewerly/exHarmony huggingface dataset:
Exploration of Wavelets for Pre-processing of Speech Signals

Speech signal carries much more information than spoken words and speaker’s information. Noise present in the speech signal is an obstacle in the path of analysing the speech signal precisely. Therefore, speech signal denoising has an important role in the speech signal analysis. In this paper, Wavelets are explored for pre-processing and suppression of noise from the noisy speech signals. The main aim of the proposed methodology involves, the pre-processing of the speech signals by using the combination of Discrete Wavelet Transform (DWT) and hard thresholding and to evaluate the result to find the optimal wavelet family with there optimal order followed by the suitable level of decomposition. This paper involves the exploration of the Wavelet Family and an improved Signal to Noise Ratio (SNR) values of the acquired speech signal which is shown in the result analysis.
Convergence Analysis of the Hessian Estimation Evolution Strategy

The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the (1 + 4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.
Intellectual readiness of future engineers to master the disciplines of the professional curriculum part while online learning

The concept of intellectual readiness for learning, which is considered as the main condition for successful education at a university, is defined. Developed competencies of BSc students in training area 35.03.06 “Agro engineering” were analyzed. This analysis demonstrates the importance of knowledge of the basic mathematical and natural laws for solving typical problems and participating in experimental professional research. The role of the “Electrical Materials”course in the development of students’ general professional and specific professional competencies is revealed. The paper presents the research results identifying the relationship between the mental development level, formed in general education, and the success of mastering the “Electrical materials” course. At the same time, knowledge of scientific concepts, the development of the classification mental operation and the ability to perform mental actions by analogy turned out to be determining. According to the results of the research, five groups of students were identified. They differ in the success of the acquisition of knowledge and proximity to the socio-psychological standard. Quantitative and qualitative indicators of their mental development characteristics were also described. The specificity of the relationships between the parameters of intellectual readiness and success in mastering the discipline is described. It is concluded that it is necessary to stimulate the mental development of students in the process of developing professional competencies, taking into account the specific features of the structure of intellectual abilities, improving methods for assessing intellectual readiness.
Blockchain Applications in Smart Grid Systems

Blockchain has been implemented in several application in smart grid systems, such as data management and cybersecurity in utility grid systems. In addition, blockchain has been deployed in peer-to-peer electricity trading in microgrid systems. Due to the promising Blockchain features, it is considered as a good fit for energy trading. However, Blockchain applications in power grids are in their infancy so there are many research opportunities in this field. This paper reviews the existing applications of Blockchain in power grids and suggests some new opportunities for applying this technology, especially in enhancing the resiliency of power grids.
Predicting Customer Behavior by Analyzing Clickstream Data

Websites, these days, are a huge market for many things. Individuals are straying towards web-based shopping, online promotions, E-commerce, and so forth. It likewise brings about a surplus of unstructured data. This unstructured information, when utilized with logical analytics, can help in comprehending client conduct. Recording and analyzing the behavior of users is a key aspect in improving business profit and its market coverage. A clickstream is essentially the pathway that a client takes through their online excursion. This pathway is stored in the form of log data. Training models using such clickstreams is a very complex process and predictions take a long time. This paper proposes a novel framework for predicting the customer behavior using an ensemble method called Extreme Gradient Boosting. This model predicts the output at a good rate and is easy to understand. The parameters of the model are tuned using Randomized Search CV. Extreme Gradient Boosting utilizes a more formalized paradigm to manage overfitting, thereby providing a superior performance. The accuracy and recall of the model are 85.9% and 91.04% respectively.
Application of the clustering algorithm to the small and micro industrial companies for mapping regions with k-medoids

Abstract The largest segment of national economic actors is the micro and small enterprises (MSEs). The research aims to analyze the number of micro and small companies in Indonesia using data mining techniques to map a cluster. The dataset used consists of three-four records of the central Statistical Bureau (SourceUrl: https:/www.bps.go.id/), composed of the micro and smaller companies in 2017-2019. The k-medoids method is the solution used in cluster mapping. The average number of small and micro enterprises for 2017-2019 is the attribute used. The numbers of clusters were determined with the Davies Bouldin Index method (DBI), where k = 2 is the best value (0.111). In accordance with the results of the cluster (k=2), the label is divided into two (high cluster (clt1) and low group) (clt2). The results of the calculation of k-medoids show that 90% of Indonesia’s area is within the low class. In the high cluster are only Central Java, West Java and East Java. Accuracy, accuracy, reminder and f-measurement parameters are 100% demonstrated in the cluster test results. This mapping can be one of the foundations on which to constantly increase the number of micro and small firms, since the role of MSEs in jobs is extremely important, since it is apparent that MSEs have continued to grow considerably.
Contact-free Airport Borders with Biometrics-on-the-Move

Queuing at airport border controls is one of the bottlenecks in the flow of passengers, which results in a poor travel experience and in serious health risks, like COVID19, due to the concentration of people and contact surfaces [3]. To address this problem, biometrics-on-the-move removes physical barriers for passengers, while preserving security at a maximum level. In this paper we describe a prototype, tested at the Lisbon Airport, demonstrating biometrics-on-the-move technology, where airport passengers are authenticated at a distance without having to pass through physical barriers or contact surfaces. This new setting requires a careful design of the flow of passengers and liveness detection biometrics to allow the robust authentication of passengers and, more importantly, detect fraud attempts in the different points of the process.
Discrepancy Detection in Merkle Tree-Based Hash Aggregation

Hash aggregation is an accepted approach to mitigate the burden of storing substantial amounts of data on a distributed ledger. Merkle trees are used to derive a single hash from data and ensure the integrity of the aggregated individual information. However, to identify a single manipulated datum or a subset of manipulated data, one needs to have access to the entire Merkle tree. This is not a problem if the Merkle tree is stored on the distributed ledger. However, for substantial amounts of hashes, such a tree can become quite large. At some point it is not longer feasible to store the tree on the ledger. Especially, when aggregating large numbers of transactions that occur in a high frequency. In this paper, we discuss four approaches to identify manipulated data in a Merkle tree without the need to persist the entire Merkle tree on the distributed ledger.
Ethical Considerations in User Modeling and Personalization

Ethical considerations are getting increased attention with regards to providing responsible personalization for robots and autonomous systems. This is partly as a result of the currently limited deployment of such systems in human support and interaction settings. The tutorial will give an overview of the most commonly expressed ethical challenges and ways being undertaken to reduce their impact using the findings in an earlier undertaken review supplemented with recent work and initiatives. The tutorial will exemplify the challenges related to privacy, security and safety through several examples from own and others’ work.
Developing and evaluating an academic collocations and phrases search engine for academic writers

Knowledge of collocations is essential for English academic writing. However, there are few academic collocation referencing tools available and there is a pressing need to develop more. In this paper, we will introduce the ACOP (Academic Collocations and Phrases Search Engine), a newly developed corpus-based tool to search large academic corpora. Using the AntCorGen corpus crawling tool, 100,000 articles from 10 academic areas were downloaded from the PLOS One journal database. A multidisciplinary 500-million-word academic corpus was thus created. Our team further developed the ACOP to allow users to search for collocations and phrases in the academic corpus that we compiled. Additionally, this new tool will allow learners to search for not only bigrams but also for three- to five-word strings. Learners can further search using parts of speech. To validate whether the new tool can assist EFL learners in finding appropriate academic collocations, we compared this tool with BYU’s state-of-the-art COCA corpus search tool. The 35 students who participated in the study were randomly divided into two groups. One group used BYU’s COCA tool and the other used the ACOP tool. Both groups were asked to identify suitable collocates for 25 gap-fill questions. Paired t-tests and independent t-tests were used to analyze the data. The results indicated that both groups showed a significant improvement in the posttest when they had access to the collocation retrieval systems. However, statistically, the results showed no significant difference between the two tools, as they were equally useful in helping the students find appropriate collocates. According to the users’ surveys, compared with the COCA group, the participants in the ACOP group found the ACOP easier to use and the interface was clearer and simpler. The Mann-Whitney U tests of the two groups showed significant differences in the aspect of the interface. The preliminary results indicated that the ACOP can assist EFL learners in finding suitable academic collocations and phrases, and the users’ surveys showed positive results.
Research on Hybrid Recommendation Algorithm of Insurance Products Based on Time Weighted Optimization

Traditional collaborative filtering recommendation algorithm has some problems, such as sparse data and cold start, and the purchase of insurance products is easily affected by the characteristics of the insured. Therefore, a hybrid recommendation algorithm based on time weighted optimization is proposed, which combines knowledge graph and collaborative filtering recommendation. Knowledge graph extracts the attributes of users and objects as features, constructs triples for training, and shares potential features by cross compression units, which can fully excavate the potential relationship between different information, and can also extend the user preferences by using multiple relationships to ensure the diversity of recommendations; the timeliness of recommendation is guaranteed by time weighting, and the timeliness of recommendation is further considered The influence of age on the purchase preference of the insured better adapts to the characteristics of insurance products. Through the empirical analysis on the insurance product data set, the experimental data show that the algorithm is better in each evaluation index compared with the existing algorithm.
Blockchain Towards Decentralized Digital Marketing

Blockchain is a decentralized digital technology that is growing and standing out in digital marketing. The potential use of blockchain technology will help affected companies create secure digital records and will also allow secure storage of data. During this chapter, the authors will try to explain the meaning of the concept of blockchain technology, its operating principle, as well as its different types and sectors of applications to make the relationship between blockchain technology and other emerging technologies such as big data, AI, and smart contracts and to review the positive impact of blockchain technology on the e-commerce sector and in particular on customer relationship management.
Criteria and indicators associated with adaptation to the educational process of first-year students at the university

The issue of adaptation of modern first-year students to the educational process at the university is one of the current pedagogical tasks. Successful adaptation significantly affects the quality of received education, the degree of formation of personal and professional qualities, contributes to the development of motivation, self-education, and self-development. The purpose of the research is to substantiate the criteria, indicators, and levels of adaptation of first-year students to the learning process at the university. The material for the study was the domestic scientific sources of studying the peculiarities of the adaptation process of students to educational activities in higher education. Research methods: analysis and generalization of psychological-pedagogical and educational-methodical literature on the research topic. We determine as the main criteria for the adaptation of first-year students to the university, the adaptive potential and professionally important qualities of students, consider these concepts, their structure, and their basic properties. On the basis of the analysis and generalization of the existing indicators of the implementation of the adaptive potential, we formulate the author's indicators for determining the level of its development. The degree of formation of professionally important qualities of students are low, medium, and high levels of development of emotional intelligence, negative communicative attitude, intellectual lability, and stress tolerance.
Multi-resolution Texture Analysis for Osteoporosis Classification

Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs. In this paper, a feature extraction scheme is proposed to analyze different bone X-ray images of two different populations in order to extract a precise and lightweight feature. The proposed scheme uses two efficient variants of Local Binary Patterns (LBP) and combines the information extracted from each level to improve the classification rate. The experimental results prove the efficiency of the proposed method in terms of classification speed and system accuracy.
Attention-based Efficient Lightweight Model for Accurate Real-Time Face Verification on Embedded Device

With the rapid development of face verification technology, the current face verification has reached a high accuracy rate. However, face verification requires large computing resources and complex model parameters, so it is difficult to be applied in real scenes. The face verification system based on embedded applications can well solve the problems of huge model storage space occupation and high computing resource consumption. The existing mainstream face verification models have reached high accuracy, such as VGG16Net. Although there have been studies trying to design lightweight neural networks, such as MobileFaceNet, etc., there are still shortcomings such as complex structure and high resource consumption, so it is only suitable for mobile devices and other embedded devices, and it is difficult to apply to low power consumption and low performance such as embedded systems. In the device. For embedded systems with limited memory and computing power, a more lightweight neural network model is needed. Therefore, this paper conducts two researches on the problems of model storage requiring a large amount of space and high computational resource consumption. First of all, a lightweight face verification network model MobileFaceNet-v3m based on the attention mechanism is designed to solve the problem of model storage space occupation. Our model has a 15.26% smaller space occupation than MobileFaceNet, which successfully reduces the resource consumption of the model. The accuracy rate can still be maintained at a high level-95.47% of face verification accuracy is achieved on LFW; secondly, We use the learning rate optimization method based on efficient warm restart to train the model, the accuracy of the model is improved faster, and the model is successfully transplanted to the embedded platform, which is another step forward to the real scene application in the future.
Multi-View Image Feature Correlation Guided Cost Aggregation For Multi-View Stereo

In this paper, we propose a learning-based multi-view stereo network with the proposed feature correlation aggregation network (FCANet). We notice that the source views used to infer the depth of reference view are quite different, which are reflected in the images. Therefore, the contribution of source views should be different for building cost volume, which depends on the similarity between the source and reference views in our opinion. To this end, we propose FCANet infer the similarity to guide the cost aggregation. In addition, we adopt the strategy to build cost volume and infer depth in coarse to fine. We evaluate the proposed FCA-MVSNet and conduct ablation studies for the proposed FCANet on DTU dataset. The results show that we can significantly outperform the baseline and achieve state-of-the-art results, especially the reconstruction completeness has broken through 0.3mm of mean distance metric. Moreover, the proposed FCANet can significantly improve the reconstruction quality compared with the widely used variance metric.
Anti-leakage Mutable Order Preserving Distribution Encoding

Over the years, a lot of studied have been conducted on Order Preserving Encryption (OPE) schemes because of its possible application to database design. Unfortunately, few schemes have been able to attain verifiable security and leaks the minimum amount of information possible. Until Popa et al. (2013), no OPE scheme achieved Indistinguishability under an Order Chosen Plaintext Attack (IND-OCPA) security. However, even Popa et al.’s. design potentially leaks the distribution of repeated plaintext values based on ciphertexts. This paper presents encryption scheme, Order Preserving Distribution Scheme (OPDS) which is secure and do not leakages the distribution of repeated plaintext value based on ciphertexts. We modify the mOPE algorithm by adding a security layer, we introduce the security protocol Indistinguishability under an Order Chosen Repeated Plaintext Distribution Attack (INDOCRPDA) which is equivalent to Popa IND-OCPA security, and proof that OPDS is secure. Finally, we provide experimental results on the effectiveness of OPDS scheme in practice.
Identification of Lampung Script Using K-Neighbor, Manhattan Distance And Population Matrix Algorithm

Abstract Language is a communication tool that is used as interaction with others. The use of indigenous languages is decreasing and erasing over time. Lampung script is a script that becomes the identity of the province of Lampung. However, only a few native Lampung residents who know the Lampung script and the younger generation often write Lampung in Latin letters because it is considered easier. This study uses Optical Character Recognition to recognize Lampung characters in image images using a smartphone. This study uses the Pixel Population Matrix feature extraction to extract the characteristics of the characters. The distance calculation for each test character and database uses Manhattan Distance and is classified using K-Nearest Neighbor with the value of k is 3. The results of the character recognition process can be translated into Indonesian. Testing this application is done by taking image samples with several conditions. The script image tested is in the form of a screenshot image of printed writing with random fonts, a handwritten photo image, a screenshot image with a combination of several words and a photo image with a smaller size and random slope. The test results according to these conditions obtained an average percentage of success of 91.49%.
Research on Algorithms of Fusion System of Artificial Intelligence University Curriculum Derivatives Based on Internet Big Data

Abstract With the rapid development of artificial intelligence technology and the rapid progress of big data technology, based on the theory of artificial intelligence fusion systems based on the lives of college students, and the use of advanced technology, university courses have also expanded in multiple directions. A reasonable and scientific development model of electronic technology design. Derived by using electronic data as a carrier to optimize the derivative tools of university courses and improve performance in many aspects such as learning and sports. Using the empirical formula of the Big Data Fourier algorithm, the manual verification was verified through the statistical comparison and analysis of the competitive auxiliary review system. The application of intelligent systems in the fusion system of university curriculum derivatives and artificial intelligence algorithms based on Internet big data play an active role in the fusion system of university curriculum derivatives.