Reviewerly/exHarmony huggingface dataset:
Design and Simulation of Monitoring Current, Voltage, and Temperature of 60WP Solar Panel Using Solar Tracker, Passive Cooling, and Reflector

Fossil energy such as oil, geothermal, and coal are sources of electrical energy that dominate electrical energy in Indonesia. If this continues, it will cause the energy extinct. The breakthrough of renewable energy development in Indonesia is expected to be an alternative energy source to replace the energy that has been used, which more efficient and environmentally friendly, one of which is solar panels. However, although it can be used as an alternative, it cannot be denied that the technology offered has disadvantages due to the many conditions that can affect the amount of solar panel output power such as irradiance, temperature, environmental conditions, etc. This paper aims to provide a new idea about an effective and efficient system to minimize the shortage of solar panels. This paper presents the design of several methods to increase the output power of solar panels such as solar tracker, passive cooling, and reflector. This paper also presents simulation of solar tracker, temperatur, voltage, and current sensors. From the design results, it can be concluded that among some of the proposed design methods will be obtained by combining methods that maximize the acceptance of light by solar panels without any temperature increase.
Active Learning Based Audio Tampering Detection

Audio authentication is the primary task in an audio forensics scenario in which audio tampering detection is one of the objectives. In this paper, we offer a fresh approach to audio tampering detection using supervised learning and active learning methods. The present techniques are based on supervised learning, and they require a massive amount of labeled data for classification. There is very little availability of standard data. The paper provides a comparative study of supervised and active learning approaches. The work uses unlabeled dataset for classification which is the primary focus in any active learning method. The proposed work uses less than 1-sec audio files for copy and move tampering. Result gives 92.78 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy for supervised learning using stft whereas for active learning it gives 87.38%. Active learning reduces the cost of annotation as we do not have to label all the data.
All up to You: Controllable Video Captioning with a Masked Scene Graph

Controllable video captioning is generating video descriptions following designated control signals. However, most controllable video captioning models focus exclusively on contents of interest or descriptive syntax. In this paper, we propose to guide the video caption generation with a Masked Scene Graph (MSG). Formally, given a video and a MSG, which not only contains semantic contents nodes, but also implies the syntactic form in the graph structure. The MSG can be constructed manually or be modified from the original scene graph of a sampled frame, due to the motion information is hard to be captured by the frame scene graph, so we mask the relationship node to obtain a MSG. From the MSG, we propose a MSG encoder and adopt a masked autoregressive decoding algorithm, which is able to recognize semantics and syntax information of the graph structure. Extensive experiments demonstrate that our framework can achieve better performance and controllability than several strong baselines on MSVD and MSR-VTT benchmarks.
4 Adopting a New Mestiza Consciousness in the University: A Southern Approach to Academic Activism in the 21st Century

Abstract: Universities are presently focused on neoliberal competition, commodification, consumerism and corporatisation. Such processes have devalued knowledge and impoverished the university’s commitment to social and epistemic justice, cultural democracy and inclusion. In this article, I seek to explore why academic activism, both within and outside of the academy, matters more than ever in the 21st century. Focusing on the BlackLivesMatter movement and the imperatives it creates to take seriously the significant theoretical resources offered by Southern philosophical approaches to activism, I outline how Anzaldúa’s concept of a new mestiza consciousness and the three stages of political action she proposes that new mestizas can enact can be applied to academic activism in the present. I argue that adopting these three types of new mestiza political activities might allow us to reconceptualise academic activism and reignite ideas about the university as offering important spaces for resistance, rebellion and political rejuvenation.
Super-resolution reconstruction of underwater sonar image based on multi-scale feature fusion

Because the underwater environment is too complex, underwater sonar images have many problems, such as low resolution, blurred details, speckle noise and so on. Therefore, it is urgent to improve the quality of underwater sonar images. To solve this problem, we propose the generative adversarial network based on octave convolution and channel attention mechanism (OCTGAN), which is a multi-scale feature fusion super-resolution reconstruction method suitable for underwater sonar images. OCTGAN consists of two parts, namely a generator and a discriminator. The generator is a network after the fusion of Octave convolution and channel attention mechanism. Octave convolution divides the feature map into a low-frequency part and a high-frequency part. The information of the feature map is updated and exchanged before fusion, and then recalibration is performed through the channel attention mechanism module. OCTGAN can learn more high-frequency details in the image and reconstruct super-resolution images with clearer texture details. Experimental results show that compared with the existing super-resolution methods, this method has better image reconstruction effects.
BFO-ACO roaming path planning based on multi-constraint scenarios

In recent years, when the path planning based on ant colony algorithm is used to find the optimal path under multiple constraints, it is easy to fall into the local optimal solution and lead to slow convergence. Under the constraints of path length, number of effective scenic spots, path smoothness and path obstacle distance, this paper constructs a fitness function model to evaluate the quality of roaming path. A hybrid algorithm of bacterial foraging optimization and ant colony optimization (BFO-ACO) is proposed by using the taboo table optimization strategy to solve the deadlock problem in the traditional ant colony algorithm, which improves the path diversity in the early stage of the algorithm. The replication and dispersion mechanism of bacterial foraging algorithm is introduced to improve the convergence speed as well as jump out of the local optimum. Experimental results show that the BFO-ACO algorithm obtains a higher-quality roaming path with the fewer iterations under multi-constrained environments, which provides the foundation for designing roaming path.
Incremental Unsupervised Domain Adaptation Through Optimal Transport

In this paper, we address the problem of unsupervised domain adaptation where we ask to infer a low target risk classifier, while labeled data are only available from the source domain. Our proposed approach, called DA-OTP, aims to learn a gradual subspace alignment of the source and target domains through Supervised Locality Preserving Projection, so that projected data in the joint low-dimensional latent subspace can be domain-invariant and easily separable. However, this objective can be rather challenging to achieve because of the absence of labeled data in the target domain. To overcome this conundrum, we use an incremental label propagation technique based on optimal transport, which performs selective pseudo-labeling in the target domain. The selected pseudo-labeled target samples are then combined with labeled source samples to learn in a self-training fashion a robust classifier after the incremental subspace alignment. Experiments show the competitiveness of the proposed approach across contemporary state-of-the-art methods over a range of domain adaptation problems. We make our code publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at: https://github.com/DA-OTP/DA-OTP
Camera Auto-calibration from the Steiner Conic of the Fundamental Matrix

This paper addresses the problem of camera auto-calibration from the fundamental matrix under general motion. The fundamental matrix can be decomposed into a symmetric part (a Steiner conic) and a skew-symmetric part (a fixed point), which we find useful for fully calibrating camera parameters. We first obtain a fixed line from the image of the symmetric, skew-symmetric parts of the fundamental matrix and the image of the absolute conic. Then the properties of this fixed line are presented and proved, from which new constraints on general eigenvectors between the Steiner conic and the image of the absolute conic are derived. We thus propose a method to fully calibrate the camera. First, the three camera intrinsic parameters, i.e., the two focal lengths and the skew, can be solved from our new constraints on the imaged absolute conic obtained from at least three images. On this basis, we can initialize and then iteratively restore the optimal pair of projection centers of the Steiner conic, thereby obtaining the corresponding vanishing lines and images of circular points. Finally, all five camera parameters are fully calibrated using images of circular points obtained from at least three images. Experimental results on synthetic and real data demonstrate that our method achieves state-of-the-art performance in terms of accuracy.
Collusion-Tolerant Data Aggregation Method for Smart Grid

In smart grid, smart meters record real-time customers’ electricity consumption and transmit it to the control center. Leakage of customers’ real-time power usage data will impose severe risk on consumers’ privacy. To Protect users’ privacy, researchers observe that what the control center really needs for providing services is not raw data but aggregate values of customers’ real-time usage data. On this basis, many privacy-preserving data aggregation schemes are proposed for smart grid. However, these schemes pay little attention to the problem of collusion. In these schemes, edge nodes store the ciphertexts of customers’ raw usage data, and the control center has the decryption key. If edge nodes collude with the control center, customers’ usage data will still be leaked. To tackle this problem, a collusion-tolerant and privacy-preserving data aggregation scheme for smart grid is proposed in this paper. By leveraging homomorphic encryption and threshold scheme, the proposed scheme supports typical aggregate operations in smart grid, while keeping the privacy of customers and defending the collusions from edge nodes and the control center in practice. Security analysis and performance evaluation demonstrates that the proposed scheme protects the privacy of customers’ usage data effectively even under collusions in practice.
Confidence-Aware Active Feedback for Interactive Instance Search

Online relevance feedback (RF) is widely utilized in instance search (INS) tasks to further refine imperfect ranking results, but it often has low interaction efficiency. The active learning (AL) technique addresses this problem by selecting valuable feedback candidates. However, mainstream AL methods require an initial labeled set for a cold start and are often computationally complex to solve. Therefore, they cannot fully satisfy the requirements for online RF in interactive INS tasks. To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks. Inspired by the explicit difficulty modeling scheme in self-paced learning, CAAF utilizes a pairwise manifold ranking loss to evaluate the ranking confidence of each unlabeled sample. The ranking confidence improves not only the interaction efficiency by indicating valuable feedback candidates but also the ranking quality by modulating the diffusion weights in manifold ranking. In addition, we design two acceleration strategies, an approximate optimization scheme and a top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> search scheme, to reduce the computational complexity of CAAF. Extensive experiments on both image INS tasks and video INS tasks searching for buildings, landscapes, persons, and human behaviors demonstrate the effectiveness of the proposed method. Notably, in the real-world, large-scale video INS task of NIST TRECVID 2021, CAAF uses 25% fewer feedback samples to achieve a performance that is nearly equivalent to the champion solution. Moreover, with the same number of feedback samples, CAAF's mAP is 51.9%, significantly surpassing the champion solution by 5.9%. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nercms-mmap/caaf</uri> .
A hybrid approach for improving data classification based on PCA and enhanced ELM

The efficient and effective process of extracting the useful information from high-dimensional data is worth studying. High-dimensional data is big and complex and that it becomes difficult process and classify. Dimensionality reduction (DR) is important and the key method to address these problems. This paper presents a hybrid approach for data classification constituted from the combination of principal component analysis (PCA) and enhanced extreme learning machine (EELM). The proposed approach has two basic components. Firstly, PCA as a linear data reduction, is implemented to reduce the number of dimensions by removing irrelevant attributes to speed up the classification method and to minimise the complexity of computation. Secondly, EELM is performed by modifying the activation function of single hidden layer feedforward neural network (SLFN) for the perfect distribution of categories. The proposed approach depends on a static determination of the reduced number of principal components. The proposed approach is applied on several datasets and its effectiveness is supported by the different experiments performed. For more reliability, the proposed approach is compared with two of the previous works, which used PCA and ELM in data analysis.
Multi-Agents Interaction Approach based on Graph Network and Reinforcement Learning

Due to the development of artificial intelligence technology, the research of unmanned equipment has gained great progress. However, how to make multi-agents in unmanned clusters to be reasonable for interaction is the key problem to be solved at present. And the characteristics of unmanned clusters can be derived from a variety of work modes. At the same time these mode are inseparable from the interaction and collaboration mechanism. Graph reinforcement learning is a new algorithm that combines graph neural network and reinforcement learning. This paper is based on graph reinforcement learning to solve the problem of interaction between various nodes in unmanned clusters. And the approach provides the technical guidance for the future development of unmanned cluster technology.
PROSPECTS FOR THE USE OF DIGITAL TECHNOLOGIES IN INCREASING THE EDUCATIONAL MOTIVATION OF STUDENTS

The article outlines the need for using digital technologies in the development of learning motivation. The concepts of «digitalization» and «digital technologies» are analyzed; the main digital tools used in pedagogical practice are listed; own experience of using digital tools is given; the fact that digital technologies are an effective tool for increas­ ing the level of students’ motivation is substantiated. It is concluded that the high level of learning motivation con­ tributes to the formation of a student’s worldview, to the intensification of the educational process and, as a result, to the formation of a personality that meets the requirements of modern society.
Two-photon gates tolerant to the photons’ temporal wave shapes and joint correlations

Propagating photons are always present in form of pulses. Correlated and distinguishable photons typically break the gate action. Here we show a possibility of gates, independent on the photonic waveshapes and correlations between the photons.
Deep Learning Image Steganalysis Method Fused with CBAM

Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average.
Ebonyi State: The Unconstitutionality of the Ebonyi State Cybercrimes (Prohibition) Law No. 12 of 2021: Review of Pascal Etu v. The Executive Governor of Ebonyi State &amp; 4 others

Article Ebonyi State: The Unconstitutionality of the Ebonyi State Cybercrimes (Prohibition) Law No. 12 of 2021: Review of Pascal Etu v. The Executive Governor of Ebonyi State & 4 others was published on October 1, 2022 in the journal Computer Law Review International (volume 23, issue 5).
Cut-controller: An Effective Framework for Chinese Text Classification

Text classification task is a basic task in Natural Language Processing, which can provide comprehensive information for other downstream tasks. Chinese text classification is very challenging in text classification tasks, which needs word segmentation compared to other tasks such as English text classification, and researchers usually need a large number of data to get promising results. However, the word segmentation always gets wrong results because of ambiguity, and the data are always insufficient due to the large labeling efforts. Addressing these cases, in this paper, we propose an effective framework named Cut-controller for Chinese text classification. We first design a novel data augmentation method to generate more data. We split the sentences according to their length, and segment text sequences by applying a window scanning according to the input, which can maintain the information discarded by other word segmentation methods. We also introduce a novel word segmentation algorithm, which combines Inverse Document Frequency into ambiguous words selection to measure the probability of one word. It can choose more reasonable results compared to the common word cut algorithm, and get a further performance improvement. In experiments, we achieve better classification performance than baseline on two public datasets, and we can obviously see the differences between the traditional dictionary-based word segmentation method and our method.
Design and implementation of shoes arrangement robot system for unknown environment

In recent years, the robot and artificial intelligence technology have developed rapidly, but there still remains a challenging problem to realize the contact operation of robot in home environment. In order to make the robot arrange shoes automatically, we design an autonomous shoes arrangement robot system based on the 3D vision. In this system, the instance segmentation network and minimum enclosing rectangle are used to recognize the shoes and their orientation, and the grasping pose and placing pose of robot are estimated accurately by means of the point cloud information of depth camera. In addition, the convolution neural network and cosine similarity are adopted to match with a pair of shoes. Afterwards, we evaluate the accuracy of shoe orientation recognition and shoe matching in the system, and then carry out a real robot arrangement experiment. The result show that this method could assure 96.2% accuracy of shoe orientation recognition, and the matching accuracy of shoes increases from 62.6% to 87.4% when the VGG16 network is added to the shoes matching algorithm. In conclusion, this method can accurately recognize the shoes and their orientation, and then match with a pair of shoes, meanwhile, improves the stability of the robot shoes arrangement.
Forecasting Performances Comparison of Univariate Time Series Models with Land Price Index

The Purpose of this paper is to compare different models’ forecasting performance of ARIMA, ARIMA-GARCH, ARIMA-RS models in Korean land market covering the period from the first quarter of 1987 to the first quarter of 2022. I carry out in-sample and out-of-sample forecasting power to Korean land price index. For the comparison of forecast powers, I calculate root mean squared forecast errors and mean absoute forecast errors for each forecasting horizon of the second quarter of 2021 to the first quarter of 2022. I test the statistical signification of the predictive comparison results using DM test. The empirical results are as follows: First, the forecasting power of the ARIMA(1,1,0)-GARCH model is the best in all horzions in- and out-of-sample. Second, within the sample, the forecasting power of the ARIMA(1,0) model is relatively better than that of the ARIMA(2,1,0) model, but outside the sample, it is the opposite. In addition, within the sample, the forecasting power of the ARIMA(3,1,0)-RS model is relatively better than that of the ARIMA(1,0)-RS model, but it is contrary to the sample. Third, it is found that the predictive power of the regime-switching model(RSM) is significantly decreased in all predictive horzions in- and out-of-sample. In order to stabilize the real estate market, it is necessary to consider the ARIMA(1,0)-GARCH model as an alternative to the prediction model.
Report on Investigation of Convergence Media in E’xi —Limit of Growth and Journalist Cultivation

Convergence; Limit; Journalist
Research and Implementation of CPI Analysis and Prediction Based on SVR

With the continuous improvement of living standards, people gradually changed their consumption concepts and began to consume a lot. If you want to grasp the consumption situation, you need to introduce the CPI (Consumer Price Index), that is, the consumer price index. In order to better analyze the change trend of CPI, this paper firstly obtains the original data about CPI on the Internet; then, in order to clarify the distribution of the data, a simple visual analysis of the data is carried out; Using the algorithm model to model the CPI data of the three parts of the country, cities and rural areas, through training, the corresponding accuracy scores of 0.8821, 0.8854, and 0.8700 were finally obtained; finally, it was used to predict new data. After the actual simulation test, the model can simplify the analysis method for the relevant personnel to analyze the relevant consumption situation, which is convenient for the relevant personnel to use, and can reduce a lot of repetitive work.