Reviewerly/exHarmony huggingface dataset:
MK-SCE:A Novel Multi-Kernel Based Self-adapt Concept Drift Ensemble Learning

Under the category imbalance in real-world applications, the assumption of data independent identical distribution is invalid, therefore, traditional machine learning and pattern recognition methods based on this assumption are not applicable. In this paper, the MK-CSE concept drift self-adapt algorithm is proposed to solve this problem. First, the linear optimal kernel combination is introduced to measure data distribution. Then, with dynamic cost sensitive feature space construction, the discriminant features under dynamically imbalanced conditions are obtained. And the misclassification cost and distribution difference factor are added into the model integrity strategy. Finally, the correctness and effectiveness of the proposed MK-CSE algorithm on conventional concept drift data and imbalanced concept drift data are verified, compared with 6 state-of-art algorithms.
Algorithm of Gaussian Sum Based Cubature Kalman Filter for Non-Gaussian Systems

To improve the performance of the cubature kalman filter (CKF) in nonlinear non-Gaussian filtering system, the Gaussian sum cubature kalman filter (GSCKF) is proposed. A formulation of the CKF for the nonlinear system with non-Gaussian process noise and non-Gaussian measurement noise is presented. The GSCKF uses the Gaussian sum theory to divide the non-Gaussian system into several Gaussian sub-systems. And each subsystem is conducted the filtering process by the CKF. A target tracking problem with non-Gaussian noise is used as a simulation application to compare the filtering performance of the GSCKF. The simulation results show that the GSCKF outperforms the unscented Kalman filter (UKF) and CKF under non-Gaussian conditions. It proves that for the nonlinear systems with non-Gaussian noise, the filtering performance of the GSCKF is significantly improved.
Advances in the application of machine learning methods in mineral prospectivity mapping

This paper reviews the development of mineral prospectivity mapping at home and abroad, and conducts statistical comparative analysis of relevant foreign literature in the past decade.It shows that machine learning methods have become a hot topic in the field of mineral prospectivity mapping, and have played an active role in the following three aspects: ① extraction and mining of hidden and unrecognizable mineralization information in complex data; ② association and transformation of ore-forming anomaly information; ③ fusion, prediction and discovery of ore-forming anomaly information from multi-source geological data.Firstly, the application effects of major machine learning algorithms and models, such as logistic regression, artificial neural networks, random forests, and support vector machines, in mineral prospectivity mapping are reviewed.Secondly, it discusses the main problems in the application process, such as sample selection, misclassification cost, uncertainty evaluation, and model performance evaluation, as well as the current solutions.Finally, it is proposed that quantitative prediction of mineral resources based on big data and machine learning is an important trend in the future.
Persepsi mahasiswa Program Studi Ilmu Perpustakaan dan Informasi Islam IAIN Tulungagung terhadap pemanfaatan telematika dan penerapan e-learning sebagai penunjang kegiatan belajar mengajar di masa pandemi Covid-19

The use of telecommunication, media, and informatics technologies in teaching and learning activities has now been implemented, yet it has not been optimally carried out. Covid-19 pandemic has forced teaching and learning activities to use online system also known as e-learning where online lectures are remotely carried out using telematics. Current e-learning practices are considered to be merely providing lecture materials and paying less attention to the characteristics of its users. Moreover, in reality every e-learning user or student has different abilities in how to manage information. The method used in this research was quantitative with literature review, and the sample in this study were students of Library and Islamic Information Science from IAIN Tulungagung. The problems investigated in this study were (1) How students perceive the implementation of e-learning, and (2) What obstacles occurred during the e-learning process. The result of this research is that through e-learning, the study program of Library and Islamic Information Science at IAIN Tulungaggung can minimize crowds in campus environment. However, in practice, there were still many students who were not prepared to optimally use telematics media and e-learning. Students require good adaptations to be able to undergo e-learning because it will affect the implementation of an effective learning process. There were several obstacles faced by students, particularly related to internet data and networks as well as the ability to understand the delivered materials. Thus, students' perception of e-learning becomes very essential since it may influence their willingness and attitude in performing and utilizing online learning. In this case, lecturers also have an important role in providing interactive and fascinating learning materials in order to improve students' motivation and enthusiasm so that they may actively participate in virtual learning.
Field Robot Environment Sensing Technology Based on TensorRT

The inference speed of complex deep learning networks on embedded platforms of mobile robots is low, and it is difficult to meet actual application requirements, especially in complex environments such as the wild. This experiment out motion blur processing on the data set to improve the robustness, by using NVIDIA inference accelerator TensorRT to optimize the operation, the computational efficiency of the model is improved, and the inference acceleration of the deep learning model on the mobile quadruped robot platform is realized. The experimental results show that, on the test data set, the method achieves 91.67% mAP of 640 × 640 model on the embedded platform Nvidia Jetson Xavier NX. The reasoning speed is about 2.5 times faster than before, reaching 35 FPS, which provides support for the real-time application of mobile robot environment sensing ability in the field.
Feed-in Tariff (FiT) Costs and Potential Benefits, a Framework for Policy Analysis - The Case of Iran

The large gap between conventional and renewable electricity prices imposes a high cost to the government to support electricity from renewable energy sources (RES-E) by Feed-in Tariff (FiT) in most fossil fuel-rich countries in the Middle East. Despite the cost of FiT, the potential benefits of renewable energy development, including emission reduction, and avoiding consumption of natural gas and water in power plants have been identified. Indeed, avoiding natural gas combustion in power plants by RES-E deployment can be exported or utilized for downstream development. This study aimed at assessing the cost and potential benefits of RES-E deployment in the history of renewable energy policy in Iran (2009-2019) using benefit-cost analysis. FiT policy benefit-cost ratio was found to be 0.197, indicating all renewable energy potential benefits can offset only 19.7 percent of FiT cost which is a poor coverage for renewable energy development. Alongside FiT policy, electricity subsidy removal is an important determinant of renewable energy development in Iran which causes great price gap between electricity market price and FiT price. Average electricity price gap is 9.8 Cents/kWh in the period 2009-2019 and the benefit-cost ratio could increase to 0.38 by 50% decrease in price gap to 4.9 Cents/KWh.
Novel Multiple-Image Encryption Scheme Based on Coherent Beam Combining and Equal Modulus Decomposition

In our research, we propose a novel asymmetric multiple-image encryption method using a conjugate Dammann grating (CDG), which is based on the coherent beam combining (CBC) principle. The phase generated by the Dammann grating (DG) beam splitting system is processed and added to the image to be encrypted, and then, the ciphertexts and keys are generated by equal modulus decomposition (EMD). Decryption is to combine the beams through the CDG and collect the combined images in the far field. The proposed encryption scheme is flexible and thus extendable. CDG structure parameters, such as one period length of CDG, can be used as encryption key for the increase of the complexity. The Fresnel diffraction distance can also be used as an encryption key. The power of the combined beam is stronger than that of the single beam system, which is convenient for long-distance transmission and also easy to detect. Simulation results show that the proposed method is effective and efficient for asymmetric multiple-image encryption. Sensitivity analysis of CDG alignment has also been performed showing the robustness of the system. The influence of occlusion attack and noise attack on decryption are also discussed, which proves the stability of the system.
A Neural Machine Translation Model Based on Sequence to Dependency

A supervised self-attention network is proposed to introduce the dependency structure into the Transformer model. This scheme is mainly proposed based on the characteristics of the self-attention mechanism in Transformer, which converts the dependency syntax tree into two equivalent adjacency matrices, and then uses the adjacency matrix to supervise the self-attention network in the Transformer's encoder. So that Transformer learns how to model the dependency structure of the source language. Although this scheme is simple, the effect is remarkable. In particular, there is no need to modify the network structure, as long as two additional loss functions are added in the process of training Transformer. In the decoding process, there is no need to use an external syntactic analyzer to perform syntactic analysis on the source language, but it can automatically construct the dependent syntactic structure of the source language. Experiments demonstrate that the quality of the constructed dependency syntactic structure is also good.
A Semi-Supervised Bayesian Anomaly Detection Technique for Diagnosing Faults in Industrial IoT Systems

The Industry 4.0 paradigm has changed the way industrial systems with hundreds of sensor-actuator enabled devices, including industrial internet of things (IIoT), cooperate and communicate with the physical and human worlds. Given the intricacy, the diagnostics of such systems is extremely important. While anomaly detection is a valid approach to avoid unplanned maintenance or even complete breakdown, its effective realization in IIoT requires the design and implementation of frameworks for efficient monitoring, data collection, and analysis. Most of the existing anomaly detection techniques provide only a diagnosis of the fault without taking into account the uncertainty. Moreover, the lack of ground truth data (which is a typical problem in the industrial context), make their implementation even more challenging. This paper proposes an anomaly detection technique built on top of an industrial framework for the data collection and monitoring. Specifically, we address the lack of labeled data by designing a semi-supervised anomaly detection algorithm that exploits Bayesian Gaussian Mixtures to assess the working condition of the plant while measuring the uncertainty during the diagnosis process and we implement the proposed framework on a real-life IIoT testbed, namely a scale replica assembly plant. Experimental results demonstrate that our anomaly detection algorithm is able to detect the plant working conditions with 99.8% of accuracy, and the semi-supervised approach performs better than a supervised one.
Multi-view Stereo Network with Attention Thin Volume

We propose an efficient multi-view stereo (MVS) network for inferring depth value from multiple RGB images. Recent studies use the cost volume to encode the matching correspondence between different views, but this structure can still be optimized from the perspective of image features. First of all, to fully aggregate the dominant interrelationship from input images, we introduce a self-attention mechanism to our feature extractor, which can accurately model long-range dependencies between adjacent pixels. Secondly, to unify the extracted feature maps into the MVS problem, we further design an efficient feature-wise loss function, which constrains the corresponding feature vectors more spatially distinctive during training. The robustness and accuracy of the reconstructed point cloud are improved by enhancing the reliability of correspondence matches. Finally, to reduce the extra memory burden caused by the above methods, we follow the coarse to fine strategy. The group-wise correlation and uncertainty estimates are combined to construct a lightweight cost volume. This can improve the efficiency and generalization performance of the network while ensuring the reconstruction effect. We further combine the previous steps to get what we called attention thin volume. Quantitative and qualitative experiments are presented to demonstrate the performance of our model.
Reading Skills of Elementary Pupils as Basis for an Intervention Plan: The Case of Don E. Sero Elementary School, Philippines

Every pupil who starts primary school learns to read during his/her ordinary developmental period and makes sense of the text. However, even if some pupils learn to read, they have difficulty reading fluently and experiencing problems in understanding what they read. This study aimed to determine the reading skills of Grade II pupils at Don E. Sero Elementary School with an end to propose an intervention, innovation, and strategy. The study is a descriptive survey utilizing a researcher-made questionnaire administered to 56 pupils who are non-readers, using purposive sampling, employing frequency counts, percentage, and mean. Findings revealed that reading skills in terms of phonemic awareness were high, which has helped understand how pupils acquire the ability to read the text in which their future achievement in reading is greatly enhanced. Reading comprehension is revealed to be high as pupils comprehend what they are reading, their effectiveness and pleasure in reading increase. Thus, non-readers must be adequately guided to learn in any school subject and to pass high-stakes tests. However, it is suggested that there is a need to propose an intervention, innovation, and strategy to sustain their reading performance especially understanding profound Filipino texts.
Optical convolutional neural network based on an amplitude modulation SLM and a 4-level phase plate

In this paper, we propose a method to realize complex amplitude modulation with a 4-level phase plate and an amplitude-modulated SLM. We implement this complex amplitude modulator system in classical 4f system and use this system to realize highly precise convolution to verify its feasibility. The optical convolution accuracy is higher than the phase-only modulator or the amplitude-only modulator method. We then incorporate this system with a one-layer CNN as its convolutional layer to accomplish a handwriting recognition inference, the image classification accuracy is also comparable to all-electrical result with the same framework.
sklvq : Scikit Learning Vector Quantization

The sklvq package is an open-source Python implementation of a set of learning vector quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ, GMLVQ, and LGMLVQ algorithms, sklvq is distinctive by putting emphasis on its modular and customizable design. Not only resulting in a feature-rich implementation for users but enabling easy extensions of the algorithms for researchers. The theory behind this design is described in this paper. To facilitate adoptions and inspire future contributions, sklvq is publicly available on Github (under the BSD license) and can be installed through the Python package index (PyPI). Next to being well-covered by automated testing to ensure code quality, it is accompanied by detailed online documentation. The documentation covers usage examples and provides an in-depth API including theory and scientific references.
Haze and Smoke Removal for Visualization of Multispectral Images: A DNN Physics Aware Architecture

Remote sensing multispectral images are extensively used by applications in various fields. The degradation generated by haze or smoke negatively influences the visual analysis of the represented scene. In this paper, a deep neural network based method is proposed to address the visualization improvement of hazy and smoky images. The method is able to entirely exploit the information contained by all spectral bands, especially by the SWIR bands, which are usually not contaminated by haze or smoke. A dimensionality reduction of the spectral signatures or angular signatures is rapidly obtained by using a stacked autoencoders (SAE) trained based on contaminated images only. The latent characteristics obtained by the encoder are mapped to the R - G - B channels for visualization. The haze and smoke removal results of several Sentinel 2 scenes present an increased contrast and show the haze hidden areas from the initial natural color images.
Project- and research-based learning as a technology for developing master students’ methodological culture development

In accordance with the requirements of the Federal State Educational Standard of Higher Education ++, the graduates of the master’s programme on “Teacher education” must be ready to solve research problems in their professional field. This implies that the students develop an appropriate methodological culture through their involvement in independent project and research activities. However, the analysis of the current state of practice in training master students in the field of education demonstrates that it is still dominated by teaching technologies that focus on performing and reproduction activities of students, contributing to developing the habit of receiving ready-to-use knowledge. As a result, the contradiction between the objective need for the formation of a methodological culture among students on the one hand, and inadequate development in many students in the field of “Teacher education”, on the other, is aggravated. The research aim was to show and substantiate the didactic conditions for project- and researchbased learning as a technology aimed at the efficient development of master students’ methodological culture. Research methods were theoretical (analysis, synthesis, comparison, generalization and etc.), empirical (educational experiment, testing in order to assess the level of research competence formation), and mathematical methods of data processing. Results: The process of master students' methodological culture development is based on a search for information and implementation of didactic activities and conditions that serve as a motivation tool for students to be a part of research activities. The course on “Methodology and methods of educational research” becomes a platform for methodological culture development among master students on the “Teacher education” programme. The efficiency of students’ methodological culture development is increased if: (a) the structure and content of their methodological culture are designed on the basis of convergence of conventional and research-based approaches and (b) the project-and research-based teaching technology is implemented through a short-term (limited by the course duration) projects and a number of variable components (a combination of individual and team forms of independent activities),“inverted” or “blended” learning techniques; interdisciplinary, problematic and interactive training; reflection at the end of each class; exams in the form of a project defense, etc.). Impact: The results will be useful for development of educational and methodological support for research-oriented teaching in the process of theoretical and practical training of future bachelors, masters, graduate students, as well as in the system of additional and vocational education.
Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).
Detection of Spambot using Random Forest Algorithm

Spam bot detection is an automated computer program has beendesigned to mini human behaviors in the sending and spreading of spam. Spambot usually create a fraud account that can be used to send spam. In the existing work, the spambot is identified by using a social fingerprinting technique and also the Spambot account is detected by the Longest Common Subsequences (LCS) algorithm. The similarities between both genuine account and Spambot account groups of users are characterized by the digital DNA. In the LCS algorithm, the spambot detection is not verified in the efficient manner. To overcome this problem in the proposed work is going to identify the Spambot detection using Random forest classifier technique instead of LCS method from the twitter account. In addition, it will also enhance the accuracy of the classification by using Latent Semantic Analysis(LSA) and also it will detect and blocks the spambot accounts.