Reviewerly/exHarmony huggingface dataset:
An Environment Adaptive Detection Algorithm for Substation Equipment Inspection

Substation is the hub of the transmission and transformation network. It is necessary to conduct regular inspections of the important equipment in the substation, and if possible, it is appropriate to conduct continuous monitoring. The HD video inspection system built in the substation can replace the traditional manual on-site inspection work. However, the video inspection system captures a large number of photos to ensure full coverage of equipment appearance, and the average number of photos for a single inspection of a 110kV substation is about 400, which need to be checked manually one by one to confirm whether there is any abnormality in equipment appearance. To solve the above problems, this paper designs an automatic image detection algorithm, which can replace the manual checking of inspection photos and determine whether the status of the equipment in the photos has changed. The algorithm is improved on the basis of the traditional image recognition algorithm, frame difference method, and proposes an adaptive detection algorithm, which can adapt to different lighting conditions at substation sites and make correct judgments even under poor photo quality conditions, greatly improving the detection efficiency and detection accuracy.
Research on UAV Target Tracking and Localization Algorithm Based on Computer Filtering Algorithm

Target tracking and positioning of UAV is an important part of UAV intelligence. Its main purpose is to achieve accurate tracking of target position, speed and attitude, and timely acquisition of target motion information in the process of movement. Based on the analysis and research of UAV trajectory and flight path characteristics, the traditional UAV motion parameter estimation method has many problems and shortcomings, and a UAV motion parameter estimation algorithm based on multi-sensor information fusion technology is established. This method realizes the estimation of target position and speed by the tracking system based on multi-source sensor information fusion technology. It can effectively solve the problems such as low accuracy of traditional inertial navigation system and difficult to achieve accurate target positioning in complex environment, which can improve the target positioning accuracy and lay the foundation for the intelligent control of UAV.
Secure Blockchain-based Storage for Electricity Marketing Data

As the number of electricity customers grows and the application of electricity marketing data continues to develop, the traditional centralized data storage model can no longer meet the storage needs of electricity marketing data. To address this issue, this paper proposes a distributed storage mechanism for electricity marketing data based on blockchain technology. This storage mechanism is supported by blockchain technology and combines a distributed storage architecture to provide a high reliability and safety electricity data storage solution. This storage mechanism supports the step-by-step encryption and verification of processes such as electricity data on-chain and electricity data transmission, further ensuring the security of electricity data storage.
A Study of Visual Question Answering Techniques Based on Collaborative Multi-Head Attention

In visual question answering task, the dominant approach recently has been to use a unified model for pre- training and fine tuning it. This unified model typically uses a transformer to fuse image and text information. In order to optimize the performance of the model on visual question answering task, this paper proposes a transformer architecture based on a collaborative multi-head attention mechanism to address the key/value projection redundancy problem in the multi-head attention mechanism of the transformer. In addition, this paper uses the Swin transformer model as the image feature extractor to extract multi-scale image information. Validation experiments are conducted on the VQA v2 dataset in this paper, and the experimental results show that applying the collaborative multi-head attention approach and the Swin transformer backbone to the visual question answering model can effectively improve the correct rate of the visual question answering task.
Techniques for Resource-Efficient, Lightweight Cryptography in IoT Devices for Smart Environment

The Internet of Things (IoT) is the result of the expansion of modern technology, which has led to the development of numerous connected devices that are able to communicate with one another and collect vast amounts of data. Consequently, there is a substantial demand for enhanced IoT security. Cryptography is employed for network security for a variety of reasons, including authentication, privacy, data integrity, and robust control. Existing cryptographic methods may not be compatible with certain IoT configurations and protocols. This is because IoT devices have limited access to resources. As a direct result, cryptographers have proposed a large number of condensed cryptography protocols and algorithms. In this paper, we compare the modern lightweight cryptography protocols that are currently in use. Based on the findings of this study, we have utilized the PRESENT block cipher and compared its performance to that of numerous other lightweight cryptography algorithms.
Factors that Influence Major Choice of Pedagogy of National and Foreign Languages Students at Santa Elena University, Ecuador

This study aims to determine the variables that affect the Pedagogy of National and Foreign Language Majors at Santa Elena University in Ecuador and what inspires young students to begin and complete this major. Quantitative research is used in this study. A survey in Google Forms was used to gather data, and multiple-choice questions included 100 students selected randomly from the UPSE Pre-university courses. They are members of both locations at the Villamil Playas branch and the UPSE headquarters. This study shows that most students are intrinsically driven to become English teachers because they enjoy the language. However, some of them were under family pressure. A sizable portion is determined to earn the major despite all odds. The students must complete a rigorous academic program, achieve CEF proficiency in English at level B2, and work on an educational project based on conducting research at various local institutions as part of their development as major students. Additionally, they get to work on a study case pulled from the classroom where they practice for their pre-professional jobs. To conclude this work, it is necessary to emphasize that while there are many alternatives based on careers like technology and environment, being an English teacher is among the students' choices in Santa Elena Province.
Role of situational pedagogics in English teaching in the context of soft computing and fuzzy sets

This paper improves the current situation of English language teaching from a technological perspective. Fuzzy that is a method of having local information to project the whole. Soft computing is a kind of optimization relative to traditional should computing, emphasizing the uncertainty and imprecision of things, and with the development of modern society, soft computing can solve many problems in production practice. Scenario teaching method is an important asset to enrich classroom content and mobilize students’ learning enthusiasm. Situational teaching is the sum use of preparation before class to improvisation in the classroom. It has been proved that using scenario teaching in the process of English teaching can improve students’ English ability. However, this traditional situational teaching method has problems such as incomplete evaluation index and poor teaching effect. To solve this problem, we constructed a new situational teaching method and a fuzzy evaluation system based on fuzzy sets and soft computing technology, and verified through experiments that both the new situational teaching method and the fuzzy evaluation system have good effects. At present, the popularity of situational English teaching is low, the overall level of English teaching is not high, fill-in-the-blank teaching is predominant, spoken English is weak, the cost of situational teaching is high, and teachers’ willingness to implement it is low. To improve these problems, based on the research results above we propose the following four recommendations to improve the quality of contextualized teaching, solve the problems of contextualized teaching, and reduce teachers’ pressure to prepare lessons. Finally, we make recommendations based on the current situation of the development of scenario-based teaching. (1) Build situational teaching in many ways. (2) Improve the quality of teachers’ English. (3) Develop individualized improvement programs for students. (4) Promote the reform of teaching tools.
Optimization technique for optimal location selection based on medical image watermarking on healthcare system

The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal.
A discrete equilibrium optimization algorithm for breast cancer diagnosis

Illness diagnosis is the essential step in designating a treatment. Nowadays, Technological advancements in medical equipment can produce many features to describe breast cancer disease with more comprehensive and discriminant data. Based on the patient’s medical data, several data-driven models are proposed for breast cancer diagnosis using learning techniques such as naive Bayes, neural networks, and SVM. However, the models generated are hardly understandable, so doctors cannot interpret them. This work aims to study breast cancer diagnosis using the associative classification technique. It generates interpretable diagnosis models. In this work, an associative classification approach for breast cancer diagnosis based on the Discrete Equilibrium Optimization Algorithm (DEOA) named Discrete Equilibrium Optimization Algorithm for Associative Classification (DEOA-AC) is proposed. DEOA-AC aims to generate accurate and interpretable diagnosis rules directly from datasets. Firstly, all features in the dataset that contains continuous values are discretized. Secondly, for each class, a new dataset is created from the original dataset and contains only the chosen class’s instances. Finally, the new proposed DEOA is called for each new dataset to generate an optimal rule set. The DEOA-AC approach is evaluated on five well-known and recently used breast cancer datasets and compared with two recently proposed and three classical breast cancer diagnosis algorithms. The comparison results show that the proposed approach can generate more accurate and interpretable diagnosis models for breast cancer than other algorithms.
Exploiting the implicit independence assumption for learning directed graphical models

Bayesian network classifiers (BNCs) provide a sound formalism for representing probabilistic knowledge and reasoning with uncertainty. Explicit independence assumptions can effectively and efficiently reduce the size of the search space for solving the NP-complete problem of structure learning. Strong conditional dependencies, when added to the network topology of BNC, can relax the independence assumptions, whereas the weak ones may result in biased estimates of conditional probability and degradation in generalization performance. In this paper, we propose an extension to the k-dependence Bayesian classifier (KDB) that achieves the bias/variance trade-off by verifying the rationality of implicit independence assumptions implicated. The informational and probabilistic dependency relationships represented in the learned robust topologies will be more appropriate for fitting labeled and unlabeled data, respectively. The comprehensive experimental results on 40 UCI datasets show that our proposed algorithm achieves competitive classification performance when compared to state-of-the-art BNC learners and their efficient variants in terms of zero-one loss, root mean square error (RMSE), bias and variance.
Click fraud prediction by stacking algorithm

Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.
Feature evolvable learning with image streams

Feature Evolvable Stream Learning (FESL) has received extensive attentions during the past few years where old features could vanish and new features could appear when learning with streaming data. Existing FESL algorithms are mainly designed for simple datasets with low-dimension features, nevertheless they are ineffective to deal with complex streams such as image sequences. Such crux lies in two facts: (1) the shallow model, which is supported to be feasible for the low-dimension streams, fails to reveal the complex nonlinear patterns of images, and (2) the linear mapping used to recover the vanished features from the new ones is inadequate to reconstruct the old features of image streams. In response, this paper explores a new online learning paradigm: Feature Evolvable Learning with Image Streams (FELIS) which attempts to make the online learners less restrictive and more applicable. In particular, we present a novel ensemble residual network (ERN), in which the prediction is weighted combination of classifiers learnt by the feature representations from several residual blocks, such that the learning is able to start with a shallow network that enjoys fast convergence, and then gradually switch to a deeper model when more data has been received to learn more complex hypotheses. Moreover, we amend the first residual block of ERN as an autoencoder, and then proposed a latent representation mapping (LRM) approach to exploit the relationship between the previous and current feature space of the image streams via minimizing the discrepancy of the latent representations from the two different feature spaces. We carried out experiments on both virtual and real scenarios over large-scale images, and the experimental results demonstrate the effectiveness of the proposed method.
Book Review: Sanz et al. Elements and Mineral Resources; Springer: Cham, Switzerland, 2022; ISBN 978-3-030-85888-9

Mineral resources remain essential to contemporary society and determine the important patterns of its sustainable development [...]
Academic Adaptation to Remote Higher Education Questionnaire: Adaptation and Psychometric Study with Universities

Abstract Due to the need to understand the academic adaptation in the temporary transition from the in-person to the virtual modality, the general objective of this research is to adapt the Academic Adaptation to Higher Education Questionnaire for remote teaching. The specific objectives are to investigate their evidence of content validity and internal structure-based validity and reliability estimates. The questionnaire was adapted for remote teaching in the first stage of the research. The questionnaire was, then, evaluated by three judges and 12 students, whose results evidenced the content validity. Subsequently, 319 students answered the questionnaire. With confirmatory factor analysis, a plausible model was identified, containing five factors (Institutional, Social, Study, Personal-Emotional, and Career Planning), indicating validity based on the internal structure. Additionally, the questionnaire showed adequate accuracy indices (ω &gt;.85). The practical implications are traced, and we also suggest for the questionnaire to be adjusted for the hybrid teaching modality.
Adaptive Algorithm of the Wheelchair-Verticalizer User Interface System

Purpose of reseach. Development of a scheme and adaptive algorithm for the operation of an intelligent humanmachine system for the user interface of a wheelchair-verticalizer, taking into account the physiological characteristics of the operator, the wheeled platform and the external environment. Tasks . The study of the physiological characteristics of human-machine interaction in people with damage to the musculoskeletal system. Design development and mathematical description of the joystick as a component of a human-machine system. Development of adaptive algorithm and software for the wheelchair user interface system. Methods . Using the method of finite-state machine to describe the algorithm of motion modes switches. The use of polynomial functions in order to obtain smooth laws for changing the setpoints for the device drives. The use of nonlinear sensitivity coefficients of the joystick handle to provide adaptive modes of movement of the wheelchairverticalizer. Results . In the course of the study, the modes of movement of a wheelchair-verticalizer were developed and described. The design scheme and principle of operation of the joystick are presented and described. The modes of operation of the human-machine system was described mathematically and graphically. The adaptive algorithm of the user interface system of a wheelchair-verticalizer proposed in the paper interpret joystick inclinations into setpoints signals for wheelchair drive controllers. The use of non-linear sensitivity coefficients of the joystick handle to provide adaptive modes of operation of the wheelchair, taking into account the features of the hands movement of people with impaired functions of the musculoskeletal system, the movement of the wheelchair and the state of environment, is described. Conclusion . The adaptive algorithms of the human-machine system and the modes of movement of the upright wheelchair developed within the framework of the work make it possible to increase the safety and smoothness of movement through the use of non-linear sensitivity coefficients of the joystick handle and smooth laws of setting influences obtained on the basis of polynomial functions.
A Novel Framework for Privacy Enabled Healthcare Recommender Systems

The proliferation in the research of recommender systems especially in the healthcare domain raised up several problems particularly security and privacy of patients’ medical data. Majority of the existing work depends on third parties that manage healthcare systems. Access of the PII, i.e., “personally identifiable information,” of the patients to the health-managing third parties is a sincere issue. Although various suggestions to these privacy violations have been made, the present solutions lack in addressing the primary issues. In our paper, we have proposed a two-tier framework to discuss the same. Firstly, the patient’s identity is anonymized, and then a homomorphic encryption mechanism is used to randomize data of patients. The main purpose of the given framework is twofold. On one hand, it is independent of the third-party involvement; on the other side it also improves privacy by consolidating two-layer architecture for the patients’ data security. The present work adds the privacy mechanism in the recommendation research and also projected that the recommended solution can help as a standard for the protection of patients’ privacy and integrity.
Pemanfaatan Teknologi Informasi dan Komunikasi (TIK) di Perguruan Tinggi Telah Melahirkan Inovasi Dosen Pendidikan

This paper uses a theoretical approach to the conceptual study of educational innovation in the field of Higher Education and the use of Information and Communication Technology, as a fundamental support to achieve it. It is intended to present the idea of how the University is a producer of knowledge in a new culture and paradigm in which ICT generates a process of continuous transformation and improvement. Similarly, some educational technologies such as mobile phones and other mobile devices are in the classroom and can be a key element for learning. You should know what to do with them. That's the challenge that seats teachers around innovation. Special interest in technological innovations is manifested as the prerogative of relevance in the digital age. There is a certain consensus within the scientific community that is consulted, when understanding the educational innovations associated with the technologies of the digital age. It is important to value innovation as a procedural category that defines incremental or radical transformations to overcome, complement or enhance an object, process or phenomenon, which can be social, cultural, technical, productive, economic or environmental.