Reviewerly/exHarmony huggingface dataset:
Divergent applications of Blockchain Security: A Survey

The way people communicate and share information is changed with the use of the internet. Almost everything is digitalized from communication, payments, shopping, and learning. Due to which a massive amount of data is generated and stored on centralized servers making a single point of failure and a single point to attack. Through this paper, we discuss and analyze how blockchain is not only used for cryptocurrency transactions but can also provide DLT (decentralized ledger technology) and security services in other fields. The way blockchain technology finds its applications in emerging fields like IoT, autonomous vehicles, smart toll, energy trading, games, decentralized video streaming, their future research directions, and limitations are discussed by us in this paper.
Maximizing the Generative Performance of Echo State Networks Using the Particle Swarm Optimization

This work reveals the hidden potential of two Echo State Networks (ESNs) in time-series generation tasks. The first system is a typical ESN with all-to-all connection weights. The latter possess sparse reservoir's connectivity and a limited number of input-output connections, or an Economy ESN (EcoESN). The standard Particle Swarm Optimization (PSO) is adopted to adjust the connection weight between the input and the reservoir. Two experiments regard the variation of the PSO training, and the rate of the input-output connection is conducted. The results confirm that optimizing the input connection of the ESN can boost the generative performance of the two networks. However, the EcoESN gains better benefit from the optimization and can surpass the fully connected in most of the tests. Furthermore, EcoESN with a low input-output rate of 0.1 can outperform the use of higher ones. This finding could shed insight into the construction of a lightweight and accurate generative model.
Model Predictive Sample-based Motion Planning for Unmanned Aircraft Systems

This paper presents an innovative kinodynamic motion planning algorithm for Unmanned Aircraft Systems, called MP-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">#</sup> . MP-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">#</sup> leverages the idea of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">#</sup> and the Model Predictive Control strategy to solve a motion planning problem under differential constraints. Similar to RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">#</sup> , the algorithm explores the map by constructing an asymptotically optimal graph. Each time the graph is extended with a new vertex, a forward simulation is performed with a Model Predictive Control to evaluate the motion between two adjacent vertices and compute the trajectory in the state space and the control space. As result, the MP-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">#</sup> algorithm generates a feasible trajectory for the UAS satisfying dynamic constraints. Preliminary simulation results corroborate the proposed approach, in which the computed trajectory is executed by a simulated drone controlled with the PX4 autopilot.
Kinetic Gas Molecule Optimization (KGMO)-Based Speckle Noise Reduction in Ultrasound Images

Due to various advantages such as low cost and complexity, ultrasound imaging (UI) is popularly used in medical diagnosis. Although it suffers from various imperfections, such as the presence of background tissue, acquisition noise, ambient noise and body structure such as body fat, the breathing and movement user interface, it is commonly used in medical diagnostics. Therefore, noise reduction is important because a variety of sounds are created to limit the effectiveness of the diagnosis of medical images. Hence, reduction of noise is significant in biomedical image diagnostics, as different kinds of noise limit the performance of processing. For ages, researchers have been proposing several techniques that were used to remove the noise from the image. This paper presents a method for eliminating spot noise in ultrasound images using kinetic gas molecule optimization (KGMO) and Bayesian structure. In this work, KGMO is used to optimize the window size of the image patches, and the Bayesian network is used to de-noise the patches. The proposed method is evaluated with conventional de-noising techniques and found higher performance based on various image de-noising evaluation techniques.
IGAN: Intrusion Detection Using Anomaly-Based Generative Adversarial Network

Shah, Jui Das, ManiklalWe present an architecture, an Anomaly-based Generative Adversarial Network (IGAN), that detects malicious strings with decent accuracy. The proposed IGAN is comprised of an encoder, a decoder, and a discriminator. The encoder and decoder form the generative unit, trying to reconstruct the input and map that input and output to a latent space variable. IGAN exploits this latent space together with the adversarial training with the discriminator to enhance the learning of the normal distribution. The discriminator used is as a classifier as well as a feature extractor. To identify an anomaly or a normal instance, the anomaly score is calculated after passing it to the trained model. We have performed a detailed analysis of the existing and the proposed architecture using reliable metrics such as AUC score, Precision, Recall, and F_score. The experimental results of the proposed IGAN outperform other existing models in detecting anomalies with high accuracy.
Higher Education (HE) Across the Continent. How HE Is Addressed by Region

Industry 4.0 and digital transformation are hot topics in Latin America related to higher education. Some authors explore such topics from different points of view. However, we need to understand the context surrounding higher education in Latin America to weigh the challenges and opportunities these hot topics face in our region. Accordingly, in this chapter, we propose a knowledge representation of the context of higher education in Latin America based on the so-called pre-conceptual schemas. We aim to provide a framework for understanding how to implement the solutions proposed in the remainder of this book, related to the different fields of Industry 4.0 and digital transformation. We start by performing a state-of-the-art review of the context of higher education in Latin America. Then, we identify the main concepts and relationships addressed in the resulting papers to unify terminology about this context. Finally, we develop the pre-conceptual schema, and we discuss findings based on such knowledge representation. Moreover, the pre-conceptual schema presented in this chapter can be used to identify interconnections and relationships among the diversity of solutions and cases proposed in this book.
Content-Based Image Retrieval Techniques: A Survey

Abstract In these eras, digital day’s cell smartphones and cameras are gaining a titanic reputation, resulting in the speedy growth of virtual images that are available throughout the internet. With an exponential boom within the image sizes, databases in different media lie an extremely good project for massive scale image search. Image retrieval from databases or the internet wishes an unsuccessful and effective technique because of the explosive increase of digital snapshots. Image recovery is known to be a comprehensive research area, particularly in content-based image retrieval (CBIR). CBIR retrieves comparable photographs from a huge photograph database primarily based on photo features, which has been totally lively study vicinity recently. The content materials that may be derived from images which include color, shade, texture, shape, are used for retrieving an image from the database. This paper will provide a survey and discuss the contemporary literature of various sorts of image retrieval (IR) structures and comparisons among them.
Gender-Invariant Face Representation Learning and Data Augmentation for Kinship Verification

Different from conventional face recognition, the gender discrepancy between parent and child is an inevitable issue for kinship verification. Father and daughter, or mother and son, may have different facial features due to gender differences, which renders kinship verification difficult. In view of this, this paper proposes a gender-invariant feature extraction and image-to-image translation network (Gender-FEIT) that learns a gender invariant face representation and produces the transgendered images simultaneously. In Gender-FEIT, the male (female) face is first projected to a feature representation through an encoder, then the representation is transformed into a female (male) face through the specific generator. A gender discriminator is imposed on the encoder, forcing to learn a gender invariant representation in an adversarial way. This representation preserves the high-level personal information of the input face but removes gender information, which is applicable to cross-gender kinship verification. Moreover, the competition between generators and image discriminators encourages to generate realistic-looking faces that can enlarge kinship datasets. This novel data augmentation method significantly improves the performance of kinship verification. Experimental results demonstrate the effectiveness of our method on two most widely used kinship databases.
Quality Monitoring and Measuring for Internet of Services

In the Internet of Services (IoS), service platforms need to integrate services from multiple domains and various service providers to fulfill user requirements. Considering services and service solutions (composed of services) in IoS are complicated, a big challenge is how to design and implement a system to effectively monitor, measure and evaluate the quality of the services and solutions. For this challenge, we introduce a framework and develop a platform for service providers to establish a VQC (value, quality, capability) index hierarchy and efficiently obtain running indices of services and solutions in IoS. The main idea is to divide the indices into three types: value, quality, and capability. Then, service providers can establish a dynamic hierarchy that defines the calculation relationship between indices as well as binds the indices to corresponding services and solutions by this platform so that the system can monitor services and solutions in run-time. Real-time data would be collected and measured. The design and implementation of the platform supporting the above process are introduced in detail. Finally, a case study is proposed to demonstrate the framework and system.
NMF Based System for Speaker Identification

Automatic identification of speakers from text-independent information is a task required in a broad base of applications. Gaussian Mixture Models are a state-of-the-art solution to the task. We apply this method to a text-independent speech dataset, and present a novel method using Nonnegative Matrix Factorization and sparseness constraints. Results show that our method scores on par with the state of the art, even without optimization, while also providing architecture-related advantages. We provide a comparison of the results for the two methods.
Comparative Analysis for Grey Relation Estimation Models of Soil Organic Matter based on Hyperspectral Data

Abstract Rapid and accurate acquisition of farmland soil organic matter content is of great significance for the rapid monitoring soil fertility and the development of precision agriculture. Based on the hyperspectral data and organic matter content data of 74 soil samples in Zhangqiu District, Jinan City, Shandong Province, first transform soil spectral reflectance by the logarithm of the first-order differential, square, reciprocal, logarithm and square root, the estimation factors were selected according to the principle of maximum relation. Second, the hyperspectral estimation of soil organic matter was carried out by using six models such as grey close relation degree, and the estimation results of different methods were combined and analyzed. The results show that the combined grey relation model can effectively improve the estimation accuracy, and the average relative error of 16 test samples is 8.931%. Studies have shown that using the combined model of grey relation degree to estimate soil organic matter content with hyperspectral data is valid.
Evaluating the Accuracy and User Experience of a Gesture-Based Infrared Remote Control in Smart Homes

To enhance user experience while satisfying basic expectations and needs is the most important goal in the design of assistive technical devices. As a contribution, the user experience with the SmartPointer, a novel hand-held gesture-based remote control for everyday use in the living environment, is being explored in comprehensive user tests. The concept and design of the SmartPointer exploits the user’s familiarity with TV remotes, flashlights or laser pointers. The buttonless device emits both an infrared (IR) and a visible (VIS) laser beam and is designed to be universally and consistently used for a large variety of devices and appliances in private homes out of arm's reach. In the paper, the results of three user studies regarding recognition rates and usability issues are summarized. Study One was a mixed-method study in the pre-implementation stage with 20 older adults, gathering the expectations towards a gesture-based remote control and exploring simple, quasi-intuitive controlling gestures. In Study Two, the acceptance and usability of a prototype of the SmartPointer remote control was verified and compared with a group of 29 users from the target group, exploring 8 most frequently used gestures from Study One. In Study Three, comprehensive gesture-recognition tests with an updated version of the remote were carried out with a group of 11 younger adults in various light conditions, postures and distances to the operated device. All three studies confirm the feasibility of the underlying principle, the usability and satisfaction among the participants and the robustness of the technical solution along with a high success rate of the recognition algorithm.
Interpolation functions of interval type-2 fuzzy systems

The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system.
Speech Steganalysis Based on Multi-classifier Combination

Aiming at the problems of low detection rate and high feature dimension in the current steganalysis method based on support vector machine (SVM), a speech steganalysis scheme based on multi-classifier combination is proposed. The steganalysis features are first input into the classifier set, and the first type of prediction results are obtained. Then, the output of the first set of classifiers is taken as the input of the other set of classifiers to get another result.Finally, the two results are processed by the fusion algorithm to obtain a detection result.Based on the feature of pitch delay second-order difference, the proposed steganalysis scheme is evaluated comprehensively, and compared with the existing steganalysis method based on SVM.Experimental results show that this method has better performance than existing steganalysis methods based on SVM.
Hierarchical Extraction of Skeleton Structures from Discrete Buildings

Map generalization is a process of hierarchically reorganizing features whereby the global shape of the original datasets can be transferred in different scales. We propose a stroke and centrality-based method to hierarchically extract the skeleton structures from buildings aiming to support generalization. Firstly, the strokes are generated from refined proximity graph network. Next, by regarding the strokes as dual graph, three centrality indices are calculated for each stroke whereby an integrated factor is created to measure the importance level of the strokes. Finally, the hierarchical skeleton structures are extracted based on the stroke importance levels through different selection ratios. By classifying the buildings into different categories, different generalization operators are selected considering their characteristics. The experimental results demonstrate that the extracted hierarchical skeleton structures can represent the global shape of the entire region. Through this support, the global and local patterns of the original buildings can be both preserved.
Age Estimation from the Age Period by Using Triplet Network

This paper proposes an age estimation method from the age period using Triplet Network. Age estimation is still an active research topic in machine learning, and it can be formulated as a regression problem. Usually, a specific age value to each of the training face images is assigned as a correct label, and the model to estimate the age value of an unknown face image is trained from the training samples. In this paper, we consider the age estimation problem from the age period in which only the label of each of the training samples is the age period, such as teens or twenties. In this setting, the model has to interpolate the age values from the age period based on the similarity between the samples in the same age period. To achieve this functionality, we use Triplet Network to capture the age relationship between the face images. Then the age of each image is estimated by the linear regression. The effectiveness of the proposed approach is experimentally confirmed by using MegaAge-Asian, UTKFace, and MegaAge.
A comparative study of batch ensemble for multi-object tracking approximations in embedded vision

We present a series of adaptations in low probability distributions scenarios to detect and track multiple moving objects of interest. We investigate the benefits of the linearization of the loss trajectory<sup>1</sup> in training neural networks, mainly addressing the lack of auto-differentiation in MOTA<sup>2</sup> evaluations, and observe what characteristics can support parallelism<sup>3</sup> and differential computation and to what extent these observations contributes to our objectives. Using benchmarks from DeepMOT<sup>4</sup> and CenterNet<sup>,5</sup> we highlight the use of sparsemax activations by mounting a finite number of independent, asynchronous detectors to augment performance and gain from compounded accuracy.∗ Empirical results show optimistic gains when applying parallelization on low-powered, low-latency embedded systems in cases where automatic differentiation is available.
Enabling women's nutrition knowledge through empowerment: the role of ICTs

The study aims to identify the extent of rural farm women's use of information and communication technologies (ICTs) and explore the relationship between ICT use, empowerment, and nutrition knowledge. A total of 184 women were interviewed out of 3005 farm families from three unions of Kalapara upazila under Patuakhali district of Bangladesh using a structured interview schedule. Data were analyzed using Partial Least Squares (PLS)-based Structural Equation Modeling (SEM). This study discovered that the increase in respondents' ICT skills and level of ICT use is positively related to respondents' empowerment. Such empowerment is associated with an increase in their nutrition knowledge as well. This study guides how to promote more ICT-based solutions for rural households.
Resilient uniformity: applying resiliency in masking

Threshold Implementations are known countermeasures defending against side-channel attacks via the use of masking techniques. While sufficient properties are known to defend against first-order side-channel attacks, it is not known how to achieve higher-order security. This work generalizes the Threshold Implementation notion of uniformity and proves it achieves second-order protection. The notion is applied to create a second-order masking of the Present cipher with a low randomness cost.
Automated Design of Classification Algorithms

Data classification provides effective solutions to various real-world problems in areas such as disease diagnosis, network intrusion detection, and financial forecasting, among others. Classification algorithms such as induction algorithms, e.g., ID3, and genetic programming are used to produce classifiers. The design of these classification algorithms is time-consuming, requiring many person hours, and is an optimization problem. This chapter examines the automated design of genetic programming as a classification algorithm. The study compares the performance of genetic algorithms and grammatical evolution in automating the design of genetic programming for classifier induction. The performance of the classifiers produced by automated design is compared to that produced by manually designed genetic programming algorithms for binary and multi-class classification in various areas including network intrusion detection and financial forecasting. The automated design required less design time and produced classifiers that performed better than the manually designed classifiers. Grammatical evolution was found to produce better-performing classifiers for binary classification and genetic algorithms for multi-class classification.
Instance Segmentation Based Background Reference Frame Generation for Surveillance Video Coding

Efficient intelligent analysis and video compression are critical modules in terms of the advanced surveillance system. However, existing solutions always deal each task with independent strategies, leading to low-efficiency of the surveillance system. In this paper, we propose to handle these two tasks in a hybrid manner. In particular, a hybrid surveillance processing scheme towards efficient analysis and compression is presented, where the extracted semantic information can not only be utilized in intelligent analysis tasks, but also used to improve compression efficiency by facilitating the background reference frame (BRF) generation. Moreover, we propose to remove background redundancy of surveillance video by introducing the high quality BRF, where motion metric and semantic metric work in a complementary way to ensure the accurate detection of background blocks. Experimental results manifest considerable advantages of the proposed BRF. When the proposed BRF is integrated into reference picture set (RPS), 3% coding gains are obtained compared with state-of-the-art method.