Reviewerly/exHarmony huggingface dataset:
Diffraction-based self-calibration method for defocused speckle imaging

Defocused speckle imaging (DSI) is an optical technique where a diffuse object surface is illuminated by a laser source, and the scattered interference speckle field is sampled by a defocused camera. The recorded image contains a speckle pattern that characterizes the surface; if the object displaces or rotates, the speckles move within the camera view. Non-contact surface motion measurements are thus possible by simply tracking the speckle movements. The observed speckle motion magnitude scales proportional to the object distance, which makes DSI attractive for remote measurement applications. However, because DSI measurement sensitivity depends on the specific geometric arrangement, object range and orientation must be known to properly scale the measured speckle motions. In field conditions, manual extraction of the geometric parameters is challenging and time-consuming due to large distances and harsh conditions. To solve the scaling problem, a diffraction-based self-calibration method is proposed here to extract the geometric parameters from the captured speckle patterns directly with no additional sensors. The solution utilizes multi-longitudinal mode laser illumination combined with speckle pattern diffraction analysis. A laser with a multi-wavelength spectrum creates several copies of partially overlapping speckle patterns that propagate at slightly different directions. The related spatial speckle offset measured on the image sensor depends on the surface angle and scales proportional to the sampling distance. Therefore, all calibration parameters can be explicitly determined from a pair of speckle patterns recorded at axially separated focal planes. The speckle offset equations are derived from the general diffraction equation using simple geometric treatment. The speckle pattern dependence on the laser source spectrum is experimentally studied, and the proposed self-calibration method is demonstrated. The diffraction analysis can extract 500–1000 mm sampling distances at a 1.7% accuracy and determine 15–45˚ oblique surface angles at a 0.7˚ accuracy.
Algorithm for Different-Time Image Alignment on the Base of Mutual Information

The stochastic algorithm for multi-time image alignment based on Tsallis mutual information is proposed. The algorithm assumes determining a set of fragments on the reference image, estimating the deformation parameters of these fragments using a local alignment model, filtering unreliable parameter estimates, finding the parameters of the global alignment model by deformations of fragments.
Non-minimum tensor rank Gabidulin codes

The tensor rank of some Gabidulin codes of small dimension is investigated. In particular, we determine the tensor rank of any rank metric code equivalent to an 8-dimensional Fq-linear generalized Gabidulin code in Fq4×4. This shows that such a code is never minimum tensor rank. In this way, we detect the first infinite family of Gabidulin codes which are not minimum tensor rank.
The Fully Self-Checking Structures Synthesis Features of Concurrent Checking Systems Implemented on the Boolean Complement Method Basis to the Constant-Weight “2-out-of-4” Code

The concurrent checking system structural diagram properties implemented by the Boolean method to the constant-weight “2-out-of-4” code is analyzed. The conditions for the implementation of fully self-checking structures are established. These conditions are stringent and practically feasible in most cases when controlling multi-output discrete devices. In this case, it will be necessary to implement the control logic block in the control system in the form of a circuit with independent outputs. An example of the outputs controllable groups detection is shown. Completely self-checking structures will be obtained by implementing concurrent checking subsystems using this method.
Matching Distributions Algorithms Based on the Earth Mover’s Distance for Ordinal Quantification

The goal of quantification learning is to induce models capable of accurately predicting the class distribution for new bags of unseen examples. These models only return the prevalence of each class in the bag because prediction of individual examples is irrelevant in these tasks. A prototypical application of ordinal quantification is to predict the proportion of opinions that fall into each category from one to five stars. Ordinal quantification has hardly been studied in the literature, and in fact, only one approach has been proposed so far. This article presents a comprehensive study of ordinal quantification, analyzing the applicability of the most important algorithms devised for multiclass quantification and proposing three new methods that are based on matching distributions using Earth mover’s distance (EMD). Empirical experiments compare 14 algorithms on synthetic and benchmark data. To statistically analyze the obtained results, we further introduce an EMD-based scoring function. The main conclusion is that methods using a criterion somehow related to EMD, including two of our proposals, obtain significantly better results.
Effective Breast Cancer Classification Using SDNN Based E-Health Care Services Framework

In the modern environment, many health care issues are raised day by day. Detecting the early stage of breast cancer may lead to prevention. In this paper, we proposed the breast cancer classification of the E-health care services framework with the Internet of Medical Things by utilizing the support value-based Deep Neural Network (SDNN) classification. Initially, the input cytology images are taken from the local health care center, and then attain the process of pre-processing and filtering technique is used the noise removed image goes to the feature extraction process includes entropy, Geometrical features, Textural features. After that segmentation is done using Histo-sigmoid fuzzy clustering. Finally, it attains the classification process of the proposed SDNN support value-based deep neural network. SDNN classifier classifies the breast cancer images as normal or abnormal compared with the existing approaches. The proposed method accuracy is 97.4 % which is better than other state - of - the art methods.
A new static path planning strategy for drones

Unmanned Aerial Vehicles (UAVs) have been progressively used in environmental monitoring and disaster sensing. The drones fly over a set of waypoints in the area to achieve their surveillance task. Despite their potential advantages, UAVs have several drawbacks, including a limited energy supply and a short operational duration which make all attempts at planning ineffective. In this paper, we propose a novel UAV path planning for the monitoring task that considerably reduces energy consumption and minimizes the number of turns with minimum overlapping. In addition, the proposed path gives the whole area the same level of importance. The proposed path was compared with four static paths: back and forth, spiral, LMAT, and Zamboni. Results show that the proposed path allows a better coverage with less energy consumption than the state‐of‐the‐art methods.
Hybrid Approach to Enhance Security and Friendliness of Visual Secret Sharing Scheme

In Block based Progressive Visual Secret Sharing (BPVSS) scheme, secret image will be recovered block by block from noise-like and meaningful shares. It is found that only meaningful shares are user-friendly. But the meaningful shares generated through BPVSS techniques show poor visual quality so it is suspicious to intruder while transmission over internet. Thus, this paper proposes a meaningful share generating strategy based on BPVSS and LSB-based steganography to solve the above-mentioned problem with less processing power and time. The proposed system delivers superior standard performance parameter readings and has various advantages, including (1) it is suited for grayscale as well as color secret images, (2) the contrast of the recovered image is the same as the original secret image, and (3) computational power and time required to recover secret image is very less.
Coalition of metaheuristics through parallel computing for solving unconstrained continuous optimization problems

Purpose The purpose of this study is to develop a new parallel metaheuristic algorithm for solving unconstrained continuous optimization problems. Design/methodology/approach The proposed method brings several metaheuristic algorithms together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment. The proposed approach runs different single solution based metaheuristic algorithms in parallel and employs WSAR (which is a recently developed and proposed swarm intelligence based optimizer) as controller. Findings The proposed approach is tested against the latest well-known unconstrained continuous optimization problems (CEC2020). The obtained results are compared with some other optimization algorithms. The results of the comparison prove the efficiency of the proposed method. Originality/value This study aims to combine different metaheuristic algorithms in order to provide a satisfactory performance on solving the optimization problems by benefiting their diverse characteristics. In addition, the run time is shortened by parallel execution. The proposed approach can be applied to any type of optimization problems by its problem-independent structure.
Research on the Model of Balanced Development of Compulsory Education and Difficult Problems of Rural Education Based on a Deep Learning Algorithm

The equity of CE is an important issue in the whole educational cause. Its level has a significant impact on China’s overall quality and economic and social progress. The BDCE is the basic realization of educational equity in China. It also promotes the sustained and healthy growth of economy and society and provides a strong guarantee for building a well-off society in an all-round way. In this paper, the PageRank algorithm of deep learning is used to collect data related to rural education and deeply analyze the problems existing in rural education, providing data support for the development of rural education. Firstly, this paper introduces the BDCE, expounds the problems and reasons in the progress of rural education, and studies the application of data analysis. Then, this paper uses the method of questionnaire to investigate and analyze the difficult problems and current situation of rural education. Finally, the result is that the quality balance of students needs to be further improved. Although the resources of rural education are unbalanced, more than 70% of teachers have bachelor’s degree or above, and there is no significant difference among schools. It can be seen that highly educated teachers have become the norm in the stage of CE. Teachers are generally satisfied and recognized with the teaching process. In rural school education, the degree of dissatisfaction with the school curriculum and course selection system is greater than the degree of satisfaction. This shows that there is a lack of diverse and rich teaching courses in rural CE.
Comparative Study on Fall Detection using Machine Learning Approaches

Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%.
Gaming QoE Assessment

The growth of multimedia industry increased the competition over delivering a better service to the users. Therefore, it is essential for service providers to evaluate the quality of multimedia services. This chapter provides a short overview about quality assessment on the multimedia and cloud gaming services. The aim of the chapter is to allow the reader to understand the decision made in the design of subjective experiments, as well as to provide a general understanding of the motivation behind the work.
Collaborative Multi-Chain Architecture for Data Transmission across Homogeneous Blockchain

Blockchain technology is a system for storing data in an immutable way that makes it hard to manipulate or trick the system. However, Blockchain scalability remains a crucial challenge that is encountering the issues of low efficiency with low transaction throughput. This paper proposes an innovative mechanism for data transmission across homogeneous blockchain systems called Collaborative Multi-Chain Architecture (CMCA). We use a sharding mechanism combined with the inter blockchain communication model (IBCM) for routing management and data transmission to make the entire computation and scalability much easier to manage. The architecture includes a cross-chain transaction protocol to ensure transaction transmission effectively. Finally, the result conducted accelerates the execution of transactions and increases blockchain throughput.
A Data Mining Algorithm for Accessing Research Literature in Electronic Databases

Searching and retrieving relevant research materials from electronic databases are difficult for many students and early career researchers. Many researchers have abandoned beneficial research projects because they believe that related literature is unavailable to ground their work. This chapter serves as a guide to students, professionals, and internet users on how to pull information from electronic databases easily. The chapter begins by clarifying the concept of electronic databases, the evolution of electronic databases, and the processes involved in indexing scholarly works in an electronic database. The advantages and disadvantages associated with the use of electronic databases are also discussed. The chapter describes how electronic database search works, with insights into some poor practices. The concept of Boolean operators and how they can be used to easily mine desired contents from electronic databases are discussed. The knowledge and use of Boolean operators might become unavoidable in enabling researchers to locate relevant materials for their projects.
A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.
A research on the role of women in economic development in the BRICS countries

Women account for almost half of the world population, however, their contribution to the economic development is rather limited due to significant gender imparity manifested in different aspects: from participation in decision-making and employment to incomes and property. The paper aims to explore and assess the potential of women empowerment to the benefits of economic growth in the BRICS countries. Methodologically, the paper relies on the theory of gender order and the interdisciplinary approach to the research of gender inequality. The study applies methods of systematisation, content analysis, and comparison of time series. By analysing a range of international organisations’ reports, the paper provides insight in the state of affairs with gender inequality in the BRICS countries looking at its key characteristics in the economic, educational, social, and political spheres. Using the World Bank data for 2000–2021, the paper examines women’s empowerment according to the dimensions accounted for in the UN Gender Inequality Index: empowerment, health, and labour market. The findings indicate that the BRICS countries are far behind in terms of delivering real justice to women’s empowerment. Among the five countries China and Russia seem to be putting the strongest efforts to reduce gender gap, Brazil demonstrates good dynamics, but has a long way to go, while South Africa and India confront many deep-rooted problems that strongly thwart their progress. The theoretical and practical significance of the study lies in the consolidation of the data on the problem of gender imparity as well as their interpretation in the context of supporting gender equal societies in the BRICS countries to promote the global spatial economic development.
Imbalanced classification with tpg genetic programming

Recent research advances on Tangled Program Graphs (TPGs) have demonstrated that Genetic Programming (GP) can be used to build accurate classifiers. However, this performance has been tested on balanced classification problems while most of the real world classification problems are imbalanced, with both over-represented classes and rare classes.
Converting high resolution multi-lingual printed document images in to editable text using image processing and artificial intelligence

The optical character recognition technique is used to convert information, mainly printed or handwritten text in paper materials, into an electronic format that the computers can edit. According to the literature, there are few competent OCR systems for recognizing multilingual characters in the form of Sinhala and English characters together. The lack of an appropriate technology to recognize multilingual text still remains as a problem that the current research community must address, and it has been designated as the key problem for this study. The main goal of this research is to develop a multilingual character recognition system that uses character image geometry features and Artificial Neural Networks to recognize printed Sinhala and English scripts together. It is intended that the solution would be improved to cover three Sri Lanka’s most commonly spoken languages, with the addition of Tamil as a later upgrade. The primary technologies for this study were character geometry features and Artificial Neural Networks. At the moment almost an 85% of success rate has been achieved with a database containing around 800 images, which are divided into 46 characters (20 Sinhala and 26 English), and each character is represented in 20 different forms of character images. Recognition of text from printed bi-lingual documents is experimented by extracting individual character data from such printed text documents and feeding them to the system.
Feature selection using improved multiobjective and opposition-based competitive binary gray wolf optimizer for facial expression recognition

Facial expression recognition (FER) in computer vision is a very daunting computational task because of high-intra class variations. Researchers have developed methods for extracting features from images that apply conventional and deep learning approaches. When conventional approaches are used in FER system, the size of extracted features is very large, which may adversely affect the performance of the classifier. Therefore, in the process of FER, feature selection is an essential phase. We propose an improvement on competitive binary gray wolf optimizer in discrete search space named as Improved MOCBGWO, and it is utilized to perform feature selection within wrapper-based setup. Next, we applied the SVM and K nearest neighbors classifiers to selected features for investigating the performance of the proposed system through two publicly available standard datasets: CK+ and JAFFE. We compared the performance of the proposed improvement with binary gray wolf optimizer, binary moth flame optimizer, binary particle swarm optimizer, and competitive binary GWO for evaluating its efficacy. The experimental results reveal that our proposed improvement boosts the recognition accuracy along with reduction in the size of feature vector in a superior way as compared to other methods.
Effective Free-Driving Region Detection for Mobile Robots by Uncertainty Estimation Using RGB-D Data

Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation based on what they learned in the learning phase. On the other hand, existing techniques often have low performance when confronted with complex situations since unfamiliar objects are not included in the training dataset. Additionally, the use of a large amount of labeled data is generally essential for training deep neural networks to achieve good performance, which is time-consuming and labor-intensive. Thus, this paper presents a solution to these issues by proposing a self-supervised learning method for the drivable areas and road anomaly segmentation. First, we propose the Automatic Generating Segmentation Label (AGSL) framework, which is an efficient system automatically generating segmentation labels for drivable areas and road anomalies by finding dissimilarities between the input and resynthesized image and localizing obstacles in the disparity map. Then, we train RGB-D datasets with a semantic segmentation network using self-generated ground truth labels derived from our method (AGSL labels) to get the pre-trained model. The results showed that our AGSL achieved high performance in labeling evaluation, and the pre-trained model also obtains certain confidence in real-time segmentation application on mobile robots.
Identity-Referenced Deepfake Detection with Contrastive Learning

With current advancements in deep learning technology, it is becoming easier to create high-quality face forgery videos, causing concerns about the misuse of deepfake technology. In recent years, research on deepfake detection has become a popular topic. Many detection methods have been proposed, most of which focus on exploiting image artifacts or frequency domain features for detection. In this work, we propose using real images of the same identity as a reference to improve detection performance. Specifically, a real image of the same identity is used as a reference image and input into the model together with the image to be tested to learn the distinguishable identity representation, which is achieved by contrastive learning. Our method achieves superior performance on both FaceForensics++ and Celeb-DF with relatively little training data, and also achieves very competitive results on cross-manipulation and cross-dataset evaluations, demonstrating the effectiveness of our solution.