Reviewerly/exHarmony huggingface dataset:
MiNB: Minority Sensitive Naïve Bayesian Algorithm for Multi-Class Classification of Unbalanced Data

The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms. The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events. Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems. To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting. In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling. We compared our proposed model with the data sampling technique and cost-sensitive techniques. We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean. The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification. Results shows reduction in misclassification rate and improve predictive performance for the minority class.
Artificial Intelligence Signal Processing Technology in Civil Aviation Electronic Information Engineering

With the continuous advancement of science and technology, intelligent signal processing technology has been widely used in aviation systems, and has achieved a large degree of development. At the same time, civil aviation electronic information engineering is an emerging discipline, which involves electronic technology, signals and systems, and the principles and applications of digital signal processing technology. It converts these complex and huge abstracted primitive databases into digital formats through computer network systems. In this way, it enables people to make better use of existing resources to solve some problems, and makes it possible to intelligently identify and analyze relevant signals, thereby promoting the development of civil aviation electronic informatization. This paper adopts experimental analysis method and data analysis method, which is intended to combine artificial intelligence signal processing technology to explore its application in civil aviation electronic information engineering. According to the experimental results, as the target distance increases, the ranging error gradually increases. In general, the short-range measurement of the test system is more accurate and can meet the needs of accurate ranging.
Bistatic Radar Tracking With Significantly Improved Bistatic Range Accuracy

Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a nonellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. The recently developed conversion of the bistatic radar measurements into Cartesian coordinates enables to maintain consistency by using a converted measurement Kalman filter. However, such a filter suffers from a poor bistatic range accuracy, limiting the multitarget tracking performance in a dense environment of targets or clutter. A solution is suggested by using a filter in the measurement coordinate system and converting its results into Cartesian coordinates. Consistent Cartesian estimation is obtained together with significantly improved filtered bistatic range accuracy. The latter is important in data association, resulting in a measurement-to-track association gates that are over an order of magnitude smaller compared to the Cartesian filters. It is shown that if the data association is performed in sensor coordinates, the (Cartesian) volume of the contact lens association region is over 20 times smaller than if it is performed in Cartesian coordinates because the ellipsoid in the latter case is extremely conservative.
Adapting gaze-transition entropy analysis to compare participants’ problem solving approaches for chemistry word problems

A method is adapted for calculating two measures of entropy for gaze transitions to summarize and statistically compare eye-tracking data. A review of related eye-tracking studies sets the context for this approach. We argue that entropy analysis captures rich data that allows for robust statistical comparisons and can be used for more subtle distinctions between groups of individuals, expanding the scope and potential for eye-tracking applications and complementing other analysis methods. Results from two chemistry education studies help to illuminate this argument and areas for further research. The first experiment compared the viewing patterns of twenty-five undergraduate students and seven instructors across word problems of general chemistry topics. The second experiment compared viewing patterns for eighteen undergraduate students divided into three intervention groups with a pre- and post-test of five problems involving periodic trends. Entropy analysis of the data from these two experiments revealed significant differences between types of questions and groups of participants that complement both visualization techniques like heat maps and quantitative analysis methods like fixation counts. Finally, we suggest several considerations for other science education researchers to standardize entropy analyses including normalizing entropy terms, choosing between collapsed sequences or transitions within areas of interest, and noting if fixations in blank spaces are included in the analysis. These results and discussion help to make this powerful analysis technique more accessible and valuable for eye-tracking work in the field of science education research.
<i>q</i>-R<i>é</i>nyi Kernel Functioned Kalman Filter for Land Vehicle Navigation

In this brief, a q-Rényi kernel functioned Kalman filter (qRKFKF) is proposed based on the q-Rényi kernel function which is to provide better flexibility and performance in non-Gaussian environment. The concrete realization of the proposed qRKFKF are created and analyzed in detail, and its performance is presented and discussed. Three examples are carried out to verify the performance of the proposed qRKFKF for land vehicle navigation via numerical simulations: large outliers in non-Gaussian noise, alpha-stable noise and diverse noise distributions that combines Gaussian and Laplace noises. Compared with Kalman filter (KF), Maximum Correntropy Kalman Filter (MCKF) and minimum error entropy KF (MEE-KF), the qRKFKF is superior to others for combating non-Gaussian noises.
Herd Behavior and its Effect on the Stock Market: An Economic Perspective

This study aims to shed light on the concept of herd behavior and its impact on the financial and stock markets, on which some studies were conducted.Herding means following the decisions of others rather than having a unique or different decision taken by a certain group.In this regard, herd behavior falls into two main disciplines: psychological and economic.The current study presents the economic point of view regarding herd behavior, its impact on the stock markets, and how it can cause changes in stock prices by either going up or down; its effect during the financial crisis, and the role it may play during the creation of economic bubbles.The study shows that herd behavior has an apparent impact on the status of the stock markets and the values of prices.Deeper research is recommended to be conducted concerning aspects such as the impact of culture, the region, the country, etc., on this type of economic behavior.
An Effective Analysis of Cryptography and Importance of Implementing Cryptography in Fuzzy Graph Theory

The study intends to give the essential importance of fugitive graphic hypothesis concepts and applications of dominations in fugitive graphs to distinct real conditions in cryptographic areas. Due to numerous uses in PCs and communication, biomedicine, science and atomic material, interpersonal organizations, natural sciences and different regions, this is an outstanding growth. In this work we provide the ideas of maximum products for fuzzy graph structures, regular furious graphic structures and provide examples and characteristics for these notions. We also give the degree and full degree of a vertex in the maximum product of fuzzy graphical structures and use cryptography to explain several of their features. In addition, we construct a flowchart to demonstrate the general method for applying the fused graph structure, in respect of the cryptography implementation in fuzzy graphs. Graph theory concepts may be applied in various fields of mathematics. A fuzzy set with an interval is an extension of the idea of a fuzzy set. In comparison to fuzzy models, interval-rated fuzzy models provide the system better precision, flexibility and compatibility. In this article, we present in fluffy graph the idea of encryption to secure fluffy computations.
A Dynamic Reputation–based Consensus Mechanism for Blockchain

In recent years, Blockchain is gaining prominence as a hot topic in academic research. However, the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance. Although Proof-of-Authority (PoA) consensus mechanism, as a lightweight consensus mechanism, is more efficient than traditional Proof-of-Work (PoW) and Proof-of-Stake (PoS), it suffers from the problem of centralization. To this end, on account of analyzing the shortcomings of existing consensus mechanisms, this paper proposes a dynamic reputation-based consensus mechanism for blockchain. This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node, which can monitor the behavior of validators in case that validators with excessive power cause harm to the blockchain network. At the same time, the reputation evaluation algorithm is also introduced to select nodes with high reputation to become validators in the network, thus increasing the cost of malicious behavior. In each consensus cycle, validators and monitoring nodes are dynamically updated according to the reputation value. Through security analysis, it is demonstrated that the scheme can resist the attacks of malicious nodes in the blockchain network. By simulation experiments and analysis of the scheme, the result verifies that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the time of consensus to guarantee the security of the system.
External Wrench Estimation for UAVs Based on Variational Bayesian Unscented Kalman Filter

External wrench estimation has become a necessary part in many emerging applications of the Unmanned Aerial Vehicles (UAVs) such as aerial contact tasks, tactile mapping and human-UAV interaction, etc. Since measurement noises of sensors may be unknown or time-varying, this letter proposes a novel external wrench estimator based on variational Bayesian unscented Kalman Filter (VBUKF). The VB algorithm is combined into an UKF based estimator for estimating the external wrench and unknown measurement noise covariance simultaneously. Simulations and actual experiments are conducted to verify the proposed method. The results demonstrate that the VBUKF based estimator is robust to unknown and changing measurement noise. It has a good precision of external wrench estimation, which is consistent with the force sensor.
Learning Gaussian Hidden Markov Models From Aggregate Data

We consider system identification (learning) problems for Gaussian hidden Markov models (GHMMs). We propose an algorithm to tackle the cases where the data is recorded in aggregate (collective) form generated by a large population of individuals following a certain dynamics. Our parameter learning algorithm is built upon the expectation-maximization algorithm with a novel expectation step proposed recently known as the collective Gaussian forward-backward algorithm. The proposed learning algorithm generalizes the traditional Baum-Welch learning algorithm for GHMMs as it naturally reduces to the latter in case of individual observations.
An Analytical Fault Diagnosis Method: Component Relation and Event Relationship Description Matrices

There are large amounts of status, switch, and protection information in ac transmission and distribution networks. Due to the complex relationship of information and high overlap of recognition logic, it is difficult to find the exact homeomorphic mapping between a fault and information. This article proposes an analytical fault diagnosis method by the use of a matrixed relationship description and operation to build a certain mapping between the fault and certain related information.
Generating Very Large RNS Bases

Residue Number Systems (RNS) are proven to be effective in speeding up computations involving additions and products. For these representations, there exists efficient modular reduction algorithms that can be used in the context of arithmetic over finite fields or modulo large numbers, especially when used in the context of cryptographic engineering. Their independence allows random draws of bases, which also makes it possible to protect against side-channel attacks, or even to detect them using redundancy. These systems are easily scalable, however the existence of large bases for some specific uses remains a difficult question. In this article, we present four techniques to extract RNS bases from specific sets of integers, giving better performance and flexibility to previous works in the litterature. While our techniques do not allow to solve efficiently every possible case, we provide techniques to provably and efficiently find the largest possible available RNS bases in several cases, improving the state-of-the-art on various works of the recent literature.
A Lightweight Power Side-Channel Attack Protection Technique With Minimized Overheads Using On-Demand Current Equalizer

A lightweight on-demand current equalizer is proposed in this brief to enhance the resistance of the encryption engine to power side-channel attack (PSCA) with a minimized performance-power-area (PPA) overhead. The proposed on-demand current equalizer is operated based on the real-time activity of the encryption engine to minimize the corresponding current signature leakage, while enhancing the supply quality by reducing the voltage droop. Therefore, an encryption engine employing the proposed PSCA protection technique has a low power overhead without suffering from any performance degradation. In addition, an embedded randomization method is proposed to further strengthen the resistance of the encryption engine to PSCA while maintaining the power supply quality. A prototype chip that integrates the proposed design, a 128-bit advanced encryption standard (AES) engine, and a digital low-dropout voltage regulator (DLDO) was fabricated in 180-nm CMOS technology. Measurement results show that the proposed design achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$234{\times }$ </tex-math></inline-formula> minimum traces to disclose (MTD) improvements in correlation power analysis (CPA), with no performance overhead, 21.17% power overhead, and 32.88% area overhead, respectively.
Comparative Analysis of Structured Pruning and Unstructured Pruning

In recent years, neural networks have achieved great success promising superior in various fields of artificial intelligence, including image recognition, target detection, natural language processing, and data analysis. However, the training and inference of neural networks require a large number of parameters and calculations, which hinders the deployment of embedded devices with limited resources. Therefore, using neural network pruning to reduce memory overhead and increase inference speed has become a hot topic. In this paper, the pruning algorithm is analyzed and concluded from two aspects: structured pruning and unstructured pruning. Moreover, the effects of structured pruning and unstructured pruning algorithms after running different data sets in different neural network models have been comparatively analyzed. Furthermore, we have proposed certain suggestions to the future development of neural network pruning based on the experimental results.
Higher Education and Dilemmas of Deinstitutionalization

The pandemic has made the mass remote delivery of higher education more plausible as a general direction for growth in the long-term. Choosing between this general direction and the status quo introduces various ethical dilemmas having to do with the basic aims and values of higher education. A move to remote learning as the institutional norm may set back some of these aims and values while advancing others. However, we also argue that complicating the assessment of these dilemmas is epistemic uncertainty about the traditional (i.e., late-twentieth-century) institutional context and what makes that context educationally worthwhile. Addressing these dilemmas requires a better understanding of what an educational institution is as well as those respects in which it adds value to society’s educational projects.
A Progressive Bayesian Filtering Framework for Nonlinear Systems With Heavy-Tailed Noises

This article studies the Bayesian filtering problem for nonlinear systems with heavy-tailed noises. Because of the nonlinearity and heavy tail characteristics, the Gaussian distribution or particle sets may fail to express the posterior probability density distribution; thus, the progressive Bayesian filtering framework is proposed. With the filtering framework, the measurement update is divided into several steps, and the intermediate posterior distributions are chosen as the importance proposal distributions to improve the approximation of posterior probability density distributions. Moreover, termination conditions for the progressive measurement update are also proposed to improve the robustness of the progressive Bayesian filter against outliers. Finally, a simulation example is exploited to illustrate the effectiveness and superiority of the proposed filtering framework.
Introducing Trigger Evolutionary Graph and Event Segment for Event Prediction

Event chain is composed of a sequence of events which are closely related. Event prediction models aim to predict the most possible following event given the existing chain. Previous approaches based on event pairs or the whole chain may ignore much structural and semantic information. Current datasets for event prediction, naturally, can be used for supervised learning. Chains are either from document-level procedural action flow, or from news sequences under the same column. This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus, and adopts standard multiple choice narrative cloze task evaluation. The topic analysis method is used to extract event chains from the news corpus. Based on trigger-guided structural relations in the event chains, we construct trigger evolutionary graph, and trigger representations are learned via Graph Convolution Neural Network. Then there are features of two levels for each event, namely text level semantic feature and trigger level structural feature. We learn the features of event segments split according to event subjects by attention mechanism, and integrate relevance between the candidate event and each event segment. The most possible following event is selected by the relevance. Experimental results on the real-world news corpus demonstrate the effectiveness of the proposed model.
<i>Shadows</i>: Blockchain Virtualization for Interoperable Computations in IIoT Environments

In this work, we propose <i>Shadows</i>, a virtual blockchain (VC) for achieving parallel consensus and efficient management of data in industries by utilizing BC. Typically, industrial processes involve heterogeneous activities which require real-time consensus, managed execution, isolation, data sharing, accelerated computation, and efficient utilization of various computational resources such as CPU, RAM, and storage. Achieving these in real-time using a single conventional blockchain (BC) leads to the exertion of computational power. To achieve resource-efficient real-time consensus, we virtualize the nodes of the BC network and create different BC for various activities. Further, to virtualize BC and provide better access to data, we propose smart contracts liable for providing a unified view of a single BC, dynamically creating BCs, allocating resources to these, and making communication between the same. Through lab-scale experiments, we demonstrate that <i>Shadows</i> is capable of utilizing the resources efficiently and achieving real-time consensus. In particular, <i>Shadows</i> uses 18&#x0025; CPU and 92&#x0025; memory while reducing consensus time by 56&#x0025;, compared to a single conventional BC. <i>Shadows</i> also accesses the data efficiently by utilizing smart contracts and dynamically balances the load by migrating the virtual nodes. Further, <i>Shadows</i> reduces the number of migrations to make the balance system by 67&#x0025;.
Using Artificial Jellyfish Algorithm with Transformation Technique for Secure Video Watermarking Embedding Process

Digital watermarking is seen as a potential technology for addressing a broad class of challenges in a digital era. In addition to copyright protection, other potential applications of digital watermarking include content authentication, distribution tracing, and tamper recovery. Nowadays, the majority of data are transferred in the form of digital photos or videos, which are relatively easy for infringements of exclusive rights to fake and then share for profit. As a result, digital watermarking emerged as a potential way to mention these infringements on exclusive rights. In this paper, we proposed the efficient and secure watermarking technique by using the Discrete Wavelet Transforms (DWT) technique and Singular Value Decomposition (SVD), these are used for decomposition and swapping of original images. To enhance the DWT and SVD performance, the co-coefficients are optimally selected by using an Artificial Jellyfish Search Algorithm (AJS). In the embedding process, initially, the original videos are divided into several video frames, and then applied the proposed method process to the embedded watermarking process. The proposed model performs the encryption process by using the Elliptic-Curve Cryptography (ECC) technique. The main two themes as first swapped the text images with an original image and also embedded the greyscale image with a swapped image. The watermarked image is compressed by using H.265 video encoding algorithm to reduce the size of the video, which is most helpful for transmission