Reviewerly/exHarmony huggingface dataset:
CapRadar: Real-time adaptive bandwidth prediction for dynamic wireless networks

With the emergence of 4G/5G cellular networks, mobile Internet is becoming increasingly popular and numerous mobile applications have emerged, which puts higher demands on the accuracy of predicting available bandwidth. However, compared to WiFi and wired networks, the links of 4G/5G cellular networks are highly dynamic, and the unstable nature of their network environments makes the bandwidth trajectories vary. Traditional bandwidth prediction models rarely consider the bandwidth characteristics of various network scenarios, and a single prediction model is difficult to be applied to all scenarios, which makes it challenging to achieve high accuracy of available bandwidth prediction. To solve the above problems, we propose a real-time bandwidth prediction method called CapRadar. The method classifies bandwidth into scenarios and matches the optimal prediction model for each type of scenario, i.e., switches the prediction model in real time according to the changes of the scenario. Specifically, we first extract the statistical characteristics of the bandwidth using statistical method, and based on the extracted characteristics, a SVM classifier is used to detect different network scenarios. After that, the best prediction model is matched for them in the algorithm library based on the scenario classification results. The experimental results show that CapRadar can reduce the root mean square error (RMSE) by about 18.9% and the mean error (MAE) by about 21.5%. For practical applications, we use a pre-trained SVM model for real-time scenario detection, and then we can dynamically switch the prediction model.
A New Approach to Protect Data In-Use at Document Oriented Databases Springer Guidelines for Authors of Proceedings

The NoSQL Databases are often vulnerable face to internal and external attacks. Attackers can easily access to sensitive data using different ways and methods particularly in a critical or strategic environments. Data encryption is considered as an efficient and effective security method for protecting sensitive data in transit and at rest. However, the most databases encrypt data in motion and in storage at rest but they do not cover encrypting data that are in the used memory. In this work, we focus on NoSQL document database by proposing a protection model for sensitive data while it is in-use by the database. Our approach encrypts transparently and efficiently sensitive data before sending it to database. The obtained results show that the proposed model adds a security layer which can be considered as a standard solution against possible attacks on an active database instance.
Predicament and strategy of campus football teaching under the background of artificial intelligence and deep learning

In recent years, the application of artificial intelligence in various fields of education has increasingly become a social hotspot, and people have begun to use the research of artificial intelligence as a means to promote the development of education. Promoting teaching equality and improving the quality of education through AI is an important breakthrough in achieving educational development. Soccer is the number one sport in the world, and generally speaking the level of development of soccer indicates the level of development of sports in that country. This study combines artificial intelligence technology and deep learning methods with school soccer to solve school soccer development problems from a technical perspective, which has certain practical significance, fills the research gap in this field, and promotes the development of this field. Therefore, how to use artificial intelligence and deep learning to develop soccer teaching in schools is of great significance. The article proceeds according to the idea of asking questions to solve problems, this paper firstly explains the research on technologies such as artificial intelligence and deep learning, after that, we deeply investigate the dilemma of school soccer development, through questionnaire method and field interview method we come up with the current situation of low overall satisfaction of school soccer participation, concentration of participation grades and low level of skill learning, for which we propose to improve the underlying data of artificial intelligence for these problems, the Promote the integration of AI and education, and the development of AI in education driven by big data strategies. Finally, the research content of the paper is summarized. This paper is innovative and professional in addressing the dilemmas in education from a technical perspective.
Towards an Educational Planning Information System in Big Data Environment

Educational planning is a key activity for programming and steering projects in the education sector. And with the explosion of educational data captured and stored since the automation of educational management systems, and the desire to make these systems more efficient and effective, it has become a necessity to implement an educational management information system for the Big Data era. This system will handle the gigantic amount of data, and apply more sophisticated techniques for storing and analyzing this data. It will also provide considerable added value for strategic decision making. The proposed system provides a vision on the Big Data framework to be adopted.
Content-Based Image Retrieval Using Octree Quantization Algorithm

Image color representation is among the most important aspects of Content-Based Image Retrieval (CBIR). Indeed, color features are one of the low-level image features commonly used CBIR and Color Histogram (CH) is one of the most widely used techniques for color features extraction in CBIR systems. However, we do believe that we can have other techniques to outperform CH in terms of retrieval precision. In this work, a new color feature descriptor called Color Octree Quantization Descriptor (COQD) combined with color strings coding (CSC), is proposed. It applies to a color image the Octree Color Quantization (OCQ) algorithm and then constructs a color palette of size K which is used to extract a color string from the Octree. Indeed, most nowadays images are treated first using RGB (Red, Green, Blue) color space, thus we test in RGB color space. It is critical to consider how we extract visual features using color, so our proposed method uniformly encodes the resulting image into a color string that we use as a feature. The proposed approach is experimentally validated on Wang datasets containing 1000 natural images, and the showed that the proposed COQD method outperforms the CH descriptor in terms of precision.
Handwriting Recognition in Historical Manuscripts Using a Deep Learning Approach

The ancient manuscripts are considered one of the most important and valuable treasures of the cultural heritage of nations. A great effort has been made in recent decades to digitize and make the contents of these manuscripts available to everyone, but only a very small percentage of these manuscripts are accessible to the public. In this context, we propose in this paper a system for the recognition of handwritten text in ancient manuscripts. The system begins with a pre-processing step which aims to improve the quality and eliminate degradations in the input images. Next, a convolutional neural network (CNN) model is adapted to perform the step of extracting the most relevant features from each text word image. Next, the classification phase is carried out using the recurrent neuron network RNN with long short-term memory (LSTM) block and connectionist temporal classification (CTC) layer, which predicts the sequence of characters that represents the text included in each input word image. The experimentation of the proposed system is performed on the ESPOSALLES dataset, and the performance results show promising recognition rates.
A New Distributed Architecture Based on Reinforcement Learning for Parameter Estimation in Image Processing

This paper comes to solve the problem of parameter tuning in image processing. This task is mostly done manually by users, but the multitude of possible values makes it tedious and time consuming. A distributed reinforcement learning using the Q-learning algorithm combined with Kalman filters is proposed to help users to find the optimal values of a combination of image processing operators. This combination is used to extract an object of interest from an image. The obtained results show how the proposed method behaves well to reach good results.
Adversarial Robustness Enhancement of UAV-Oriented Automatic Image Recognition Based on Deep Ensemble Models

Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances. However, DNN-based visual recognition systems on UAVs show serious vulnerability to adversarial camouflage patterns on targets and well-designed imperceptible perturbations in real-time images, which poses a threat to safety-related applications. Considering a scenario in which a UAV is suffering from adversarial attack, in this paper, we investigate and construct two ensemble approaches with CNN and transformer for both proactive (i.e., generate robust models) and reactive (i.e., adversarial detection) adversarial defense. They are expected to be secure under attack and adapt to the resource-limited environment on UAVs. Specifically, the probability distributions of output layers from base DNN models in the ensemble are combined in the proactive defense, which mainly exploits the weak adversarial transferability between the CNN and transformer. For the reactive defense, we integrate the scoring functions of several adversarial detectors with the hidden features and average the output confidence scores from ResNets and ViTs as a second integration. To verify their effectiveness in the recognition task of remote sensing images, we conduct experiments on both optical and synthetic aperture radar (SAR) datasets. We find that the ensemble model in proactive defense performs as well as three popular counterparts, and both of the ensemble approaches can achieve much more satisfactory results than a single base model/detector, which effectively alleviates adversarial vulnerability without extra re-training. In addition, we establish a one-stop platform for conveniently evaluating adversarial robustness and performing defense on recognition models called AREP-RSIs, which is beneficial for the future research of the remote sensing field.
Logical reduction of relations: From relational databases to Peirce’s reduction thesis

Abstract We study logical reduction (factorization) of relations into relations of lower arity by Boolean or relative products that come from applying conjunctions and existential quantifiers to predicates, i.e. by primitive positive formulas of predicate calculus. Our algebraic framework unifies natural joins and data dependencies of database theory and relational algebra of clone theory with the bond algebra of C.S. Peirce. We also offer new constructions of reductions, systematically study irreducible relations and reductions to them and introduce a new characteristic of relations, ternarity, that measures their ‘complexity of relating’ and allows to refine reduction results. In particular, we refine Peirce’s controversial reduction thesis, and show that reducibility behaviour is dramatically different on finite and infinite domains.
Speech Synthesis in the “Mother Tongue”: Designing, Training, and Evaluating a Text-to-Speech System for Yiddish

Abstract Although few linguistic corpora are available in Yiddish, there are numerous sources of so-called “found data” that can be adapted for language research, pedagogy, and resource development. We describe the steps taken to create the first speech synthesis (text-to-speech) program in Yiddish. A state-of-the-art TTS model, FastSpeech 2, was trained on a hand-corrected data set consisting of literary texts paired with audio narrations by native speakers of the Polish and Lithuanian dialects. A quantitative evaluation by listeners found that the system produced speech that was both intelligible and natural-sounding. To demonstrate the system’s applications for language pedagogy, we offer a qualitative evaluation of Yiddish phonological features that are present or absent in a sample of synthesized recordings. We hope that the success of speech synthesis in Yiddish will inspire future projects to enable technological support for other minority languages in which transcribed recordings are available.
Data Cleaning in Machine Learning: Improving Real Life Decisions and Challenges

Data in real world is generally impure, it may contain outliers, incomplete, duplicated or obsolete values. Unclean data can direct to false conclusions and incorrect decisions. Thus it is crucial to clean the data efficiently. It is extensively known that the data quality affects machine learning (ML) model performances, data scientists spend considerable time on data cleaning before model training. During this work we shed light on some of the most important aspects of data cleaning efforts. We focused on a very important problem which is outlier detection. Regarding outlier detection we classified its approaches into three categories: statistics-based, distance-based, and Model-Based techniques. We discussed the previous techniques also mentioning the different sub-techniques of each, likewise the advantages and disadvantages of every technique. We also showed how contextual outlier identification and subspace outlier detection techniques may be used to overcome the “curse of dimensionality” in highdimensional outlier detection. The next task was to benchmark the major algorithms of the outlier detection and selecting the best algorithms depending on the dataset. Finally we proposed a new algorithms based on mainly two techniques k-Means clustering and Isolation forest in order to improve results of the outlier detection algorithms.
Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The growing evidence shows that app-based contact tracing can reduce the spread of COVID-19 if a certain proportion of the population uses the apps. However, the risk of privacy breaches that comes with such apps has long been a public concern which may hinder the uptake of the apps. In this paper, the authors attempt to find a solution to complete the spatiotemporal intersection computation without exposing the infected patient location and the user location to one another. The authors implement the solution in the WeChat applet to aid the local health center. This study conducts experiments for six scenarios to justify the applicability of the applet. Experiment results indicate that the applet is a promising non-pharmaceutical tool for curbing the spread of COVID-19.
Management Efficiency Analysis of Insurance Industry

최근 국민들이 체감하는 경기침체가 지속되고 이로인한 우리나라 경제의 저성장 또는 경기침체는 보험산업의 성장성 및 수익성을 저해할 뿐만 아니라 보험계약 해지 증가로 인한 경영의 어려움이 직면하고 있는 실정이다. 이에 본 연구는 국내 보험업의 경영효율성 측정을 위해 재무데이터를 통해 DEA를 분석하였고 더 나아가 분석결과를 통한 벤치마킹을 할 수 있는 참조기업을 제시하였다. 그리고 보험업의 효율적인 운영을 위한 개선비율과 규모수익성을 제시하였다. 분석에 포함된 보험업은 총 12개 기업이며 선행연구를 통해 도출된 투입변수는 종업원수, 자본금, 대출채권이며, 산출변수로는 매출액, 영업이익, 당기순이익을 선정하였다. 분석기법으로는 DEA를 적용하였으며, BCC-I 모형과 CCR-I 모형을 이용하여 투입 및 산출변수의 수치를 통하여 효율성을 분석하였다. 최종적으로 BCC 효율성 결과에서는 총 5개 보험업이 효율성이 1로 나타났으며, CCR효율성의 결과 총 4개 보험업으로 나타났다. 규모수익성은 규모수익불변으로 분석된 기업이 9개 기업, 규모수익체감으로 분석된 기업이 3개 기업으로 확인되었다. Recently, the economic recession that everyone can feel is continuing. The low growth or economic recession of the Korean economy not only hinders the growth and profitability of the insurance industry, but also faces management difficulties due to an increase in insurance contract cancellations. Accordingly, the management efficiency of the domestic insurance industry was analyzed by applying DEA to financial data, and a reference company that can be benchmarked through the analysis results was presented. And the improvement ratio for the efficient operation of the insurance industry was presented, and the profitability of scale was presented. A total of 12 insurance companies were included in the analysis, and the number of employees, capital stock, and loan receivables were selected as input variables derived from previous research, and sales, operating profit, and net income were selected as output variables. DEA was applied as an analysis technique, and efficiency was analyzed by checking the values of input and output variables through the BCC-I and CCR-I models. Finally, in the BCC efficiency results, a total of 5 insurance companies showed an efficiency of 1, and a total of 4 insurance companies showed a result of CCR efficiency. As for profitability of scale, 9 companies were analyzed with constant returns to scale, and 3 companies were analyzed with diminishing returns to scale.
A Pairing-Free Certificateless Searchable Public Key Encryption Scheme for Industrial Internet of Things

The Industrial Internet of Things (IIoT) collects a large amount of data through various types of sensors and intelligently processes this data using cloud computing, which is flexible, efficient, and cost-effective. Since IIoT data is stored on the cloud service provider’s server, the data must be encrypted to protect the user’s privacy. However, the encrypted data faces the search problem, which is usually solved by Public Key Encryption with Keyword Search (PEKS). In addition, most existing PEKS schemes are vulnerable to Inside Keyword Guessing Attacks (IKGA). Recently, some certificateless public key authenticated encryption with keyword search (CLPEKS) schemes have been proposed, which not only avoid the problems of certificate management and key escrow but can also resist IKGA. However, most of them rely on the expensive bilinear pairing. To overcome these problems, in this paper we propose a pairing-free CLPEKS scheme. The security of the proposed scheme is proved in the random oracle model. The analysis results show that the proposed scheme has better overall performance in terms of computational cost, communication cost and security properties.
Fashion Image Classification Using Convolutional Neural Network-VGG16 and eXtreme Gradient Boosting Classifier

Deep learning models have been gaining importance in recent years and are mostly used in various computer vision applications. Because of its ability to extract complex features from input images, deep learning constitutes an efficient tool for performing image recognition and classification. Apparel classification is considered important field of research of computer vision that has been explored and investigated. This work proposes an architecture based on CNN-VGG16 as a trainable feature extractor and XGBoost as a classifier. The input image is preprocessed and then extracted by CNN-VGG16 to produce features that XGBoost uses to produce a result. The proposed model was evaluated on the fashion MNIST dataset. The results obtained show that our proposed method performs better than other methods described in the literature with the same dataset.
A resampling-based method to evaluate NLI models

Abstract The recent progress of deep learning techniques has produced models capable of achieving high scores on traditional Natural Language Inference (NLI) datasets. To understand the generalization limits of these powerful models, an increasing number of adversarial evaluation schemes have appeared. These works use a similar evaluation method: they construct a new NLI test set based on sentences with known logic and semantic properties (the adversarial set), train a model on a benchmark NLI dataset, and evaluate it in the new set. Poor performance on the adversarial set is identified as a model limitation. The problem with this evaluation procedure is that it may only indicate a sampling problem. A machine learning model can perform poorly on a new test set because the text patterns presented in the adversarial set are not well represented in the training sample. To address this problem, we present a new evaluation method, the Invariance under Equivalence test (IE test). The IE test trains a model with sufficient adversarial examples and checks the model’s performance on two equivalent datasets. As a case study, we apply the IE test to the state-of-the-art NLI models using synonym substitution as the form of adversarial examples. The experiment shows that, despite their high predictive power, these models usually produce different inference outputs for equivalent inputs, and, more importantly, this deficiency cannot be solved by adding adversarial observations in the training data.
Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer can be controlled with early detection. Early discovery helps to manage a lot of cases and lower the death rate. On breast cancer, numerous studies have been conducted. Machine learning is the method that is utilized in research the most frequently. There have been a lot of earlier machine learning-based studies. Decision trees, KNN, SVM, naive bays, and other machine learning algorithms perform better in their respective fields. However, a newly created method is now being utilized to categorize breast cancer. Deep learning is a recently developed method. The limitations of machine learning are solved through deep learning. Convolution neural networks, recurrent neural networks, deep belief networks, and other deep learning techniques are frequently utilized in data science. Deep learning algorithms perform better than machine learning algorithms. The best aspects of the images are extracted. CNN is employed in our study to categorize the photos. Basically, CNN is the most widely used technique to categorize images, on which our research is based.
Optimal sampling-based path planning for mobile cable-driven parallel robots in highly constrained environment

Abstract Mobile cable-driven parallel robots (MCDPRs) is a novel concept of cable-driven parallel robots (CDPRs) developed by mounting several mobile bases to discrete the conventional fixed frame. However, the additional mobile bases introduce more degree-of-freedom (DoF), thereby causing the kinematic redundancy. Moreover, mobile bases are susceptible to disturbances causing inconsistent performance. Hence, path planning of MCDPRs becomes a challenging issue due to various internal and external constraints involved. In this article, an optimization-based path planning method is proposed for MCDPRs in highly constrained environments with considering kinematic stability. The proposed approach quickly generates feasible paths for coupled mobile bases by using the developed multi-agent rapidly exploring random tree (MA-RRT). In this process, the tree can share information through the heuristics method to optimize the path, and the adaptive sampling strategy is thus proposed to increase the tree growth efficiency by self-adjusting sampling space. Moreover, the developed dynamic control checking method (DCC) is integrated with MA-RRT to smooth the path and the kinodynamic constraints of mobile bases can be satisfied. To generate the path of the end-effector, two performance metrics are designed considering the kinematic and stability of the MCDPR. Based on the performance metrics, the grid-based search method is developed to generate the path for the end-effector. Finally, the convincing performance of the proposed method is revealed through the dynamic simulation software (CoppeliaSim) and real-world experiments based on a self-built MCDPR prototype.