Reviewerly/exHarmony huggingface dataset:
Assessment of CO2 Reduction Potential of Indoor Plants Using Artificial Neural Network in Classrooms

Carbon dioxide gas (CO2) is one of the critical factors used to measure indoor air quality that affects the well-being of school building occupants daily. Therefore, efforts to reduce the indoor-CO2 amounts have been made by adding indoor plants to absorb the CO2. The critical knowledge is to understand the factors affecting the rate of CO2 adsorption. This research aims to study the relationship between indoor CO2 reduction using trees and environments. First, a flowerpot with snake plants is placed in a room of 24.5 m2 for the data collection of the temperature, the relative humidity, light intensity, and the amount of CO2 using sensors. Then, the data were used to create a forecast model using the Artificial Neural Network (ANN) technique, which its accuracy was 99.64%. The results showed that the snake plants could reduce 2.13% of the indoor CO2. The suitable environment for plant photosynthesis is a temperature of 25 to 30°C and relative humidity of 40% at a light intensity of 200 Lux. The results can be used as data in the design of rooms in educational institutions to effectively increase the air quality in response to building occupants' health.
Technological Innovations to Ensure Confidence in the Digital World

The digital world is increasing humanity’s ability to acquire, produce, distribute, and consume information at unprecedented levels, raising questions about the origin, reliability, and use. This introduction is related to the special issue on Technological Innovations to Ensure Confidence in the Digital World, which will bring together relevant papers about technological innovations to ensure confidence in the digital world. The papers included in the special issue focus on trust issues in the adoption of digital innovations, such as the Internet of Things, Big Data, cloud computing, and digital platforms, as well as on information distribution processing in the digital age and how it affects financial market prices.
On sets of terms having a given intersection type

Working in a variant of the intersection type assignment system of Coppo, Dezani-Ciancaglini and Venneri [1981], we prove several facts about sets of terms having a given intersection type. Our main result is that every strongly normalizing term M admits a *uniqueness typing*, which is a pair $(\Gamma,A)$ such that 1) $\Gamma \vdash M : A$ 2) $\Gamma \vdash N : A \Longrightarrow M =_{\beta\eta} N$ We also discuss several presentations of intersection type algebras, and the corresponding choices of type assignment rules. Moreover, we show that the set of closed terms with a given type is uniformly separable, and, if infinite, forms an adequate numeral system. The proof of this fact uses an internal version of the B\"ohm-out technique, adapted to terms of a given intersection type.
Unsupervised Learning for Large Scale Data

Recent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.
Load Similarity based Virtual Machine Placement in Cloud Computing Environment

The emerging cloud applications run on physical machines in cloud environments in the form of virtual machines. Therefore, the placement strategy of virtual machines greatly affects the performance and efficiency of data centers. To solve the virtual machine placement problem in the scheduling process of the data center, this paper presents a virtual machine placement algorithm based on load similarity, by taking into account the load balance and physical resource utilization of the data center. Our algorithm manages an ideal map from virtual machines to physical machines by using the load similarity to determine the placement strategy that is the closest to the ideal case. The experimental results show that our method outperforms existing solutions by 10%-15% in load balance and resource utilization.
An Approach to Mining Scholars' Research Interests based on Academic Papers and Citation Networks

In the study of scholars, the research interests of scholars have strong personalized features, which can well reflect the interest preferences of scholars in specific research fields to serve the tasks of user profiling and academic recommendation of scholars. Research interest mining aims to mine topics that can be used to describe scholars' research interests directly from the text. The existing methods lack the comprehensive use of semantic information of academic papers and academic network information. In this paper, we construct a research interest mining method for scholars based on the text of their papers and citation networks. On the one hand, the LSI model and Doc2Vec model are used for text representation of scholars' papers, which can effectively extract the topics of papers and mine the semantic information in the text; on the other hand, the MinHash algorithm is used to reduce the complexity of citation network similarity calculation. Finally, the integration method is used for fusion of interest labels to obtain the final scholars' research interest labels. The experimental results demonstrate that the integrated approach can get higher accuracy rates.
Perhitungan Jumlah Eksponen Relasi Picture Pada Barisan Generator Modul Homotopi Pada Presentasi Grup 〈x,y |xyx=yxy〉 KE 〈a,b |a^2,b^3 〉

This paper discuss about the number of exponents relation for generator of second homotopy module for two group presentation to . From group presentation we get picture can be operation picture so that we get picture for From sequence generators we can get number of exponents relation is 0,1,2,3.
Global convergence of rank-one PGD approximations by alternate minimization

Low-rank tensor approximations of solutions to high dimensional partial differential problems have shown their great relevance among the most used numerical methods in recent years, both in terms of accuracy and time computation.The central point of these methods is the computation of an optimal low-rank tensor to enrich, in a progressive way, the obtained tensorial approximation.For minimization problems, this point can be performed through the classical alternate minimization method.However, the transition to the tensorial framework breaks the linearity and convexity of the considered problems and their associated functionals, which impacts the convergence of the alternate minimization sequences.In the literature, only local convergence results and global convergence results, under some restrictive hypotheses, are available.In the following work, we give an unconditional convergence result of the alternate minimization scheme to compute the optimal low-rank tensor, for multi-dimensional variational linear elliptic equations.Also, we provide an adequate choice of the initialization as well as a relevant stopping criterion in the alternating minimization process.
Students’ Experiences About Entering Higher Education During Pandemic

Autumn 2020 was an unexpected situation for many new university students in Finland as they suffered lack of orientation activities as well as both formal and informal learning experiences on campus.Instead of social events, peer group mentoring and study guidance, they entered university online.Theoretical background.The conceptual framework of the study consists of the engagement and belongingness during studies.In addition, the first-year experience as footing for the academic educational path creates the framework of the empirical study.Method.The study features a student survey carried out in the autumn of 2020 at a midsized university in Finland.A total of 803 first-year students (35% response rate) answered questions about their experience of the first months at the university, the online learning experience of the first courses and the support for learning, and they shared their feelings about belonging to the university and groups as well as concerning loneliness at the beginning of the studies.Results.The main results show that there were contradictory experiences among the students during the pandemic.30% said that the COVID-19 pandemic did not hamper the beginning of their studies but 60% suffered from the pandemic's circumstances.There were no differences between faculties or disciplines.Some of the students responded that the distance learning went smoothly for them.In contrast, some students felt it was disappointing.Because of the COVID-19 pandemic, the beginning of their studies was hampered for several reasons.The critical issues of starting university studies online without campus experience and the consequences for the development of a supporting transition are discussed.
Bio-driven visual saliency detection with color factor

Most visual saliency computing methods build models based on the content of an image without considering the colorized effects. Biologically, human attention can be significantly influenced by color. This study firstly investigates the sole contribution of colors in visual saliency and then proposes a bio-driven saliency detection method with a color factor. To study the color saliency despite the contents, an eye-tracking dataset containing color images and gray-scale images of the same content is proposed, collected from 18 subjects. The CIELab color space was selected to conduct extensive analysis to identify the contribution of colors in guiding visual attention. Based on the observations that some particular colors and combinations of color blocks can attract much attention than others, the influence of colors on visual saliency is represented computationally. Incorporating the color factor, a novel saliency detection model is proposed to model the human color perception prioritization, and a deep neural network model is proposed for eye fixation prediction. Experiments validate that the proposed bio-driven saliency detection models make substantial improvements in finding informative content, and they benefit the detection of salient objects which are close to human visual attention in natural scenes.
IMPLEMENTATION OF IDENTITY BASED REMOTE DATA INTEGRITY CHECKING SCHEME FOR CLOUD STORAGE

–In this paper, we executing Identity based (ID-based) far off remote data integrity checking (RDIC) convention by utilizing key-homomorphic cryptographic crude to decrease the framework intricacy and the expense for laying out and dealing with the public key verification structure in PKI based RDIC plans. We formalize ID-based RDIC and its security model including protection from a noxious cloud server and zero information security against an outsider verifier. The proposed ID-based RDIC convention releases no data of the put away information to the verifier during the RDIC cycle. The new development is demonstrated secure against the vindictive server in the nonexclusive gathering model and accomplishes zero information protection against a verifier. Index Terms – Cloud storage, data integrity, privacy preserving, identity-based cryptography.
Towards Practical Quantum Repeaters Using Room-Temperature Vapor Technology

We present our work on the development of a bichromatic photon source based on warm atomic vapors. This entanglement source is capable of directly interfacing with telecom networks as well as atomic quantum memories.
Design of Adaptive Iterative Reconstruction Method for Music Curriculum Integration and Reconstruction

Today, when music education generally attaches great importance to the construction of the subject curriculum, it is of practical significance to increase the research and implementation of curriculum integration and reconstruction. Curriculum integration and reconstruction not only provide a scientific concept guide for the formation of a good educational joint force in music education but also play an important role in the educational reform that focuses on human harmony and subsequent development. For the integration and reconstruction of music courses, this paper evaluates the teaching quality after integration and reconstruction through the neural network and then iterates the method of integration and reconstruction, so as to achieve the optimal effect. This paper introduces the development status of curriculum integration and reconstruction at home and abroad and provides a theoretical basis for the construction of the corresponding index system in the following sections. The related principles of Convolutional Neural Network (CNN) and Generalized Regression Neural Network (GRNN) are introduced, and an education quality evaluation system for music courses is constructed. We constructed a custom data set and introduced the design ideas and the specific parameter settings of the CNN-GRNN model. In addition, the CNN-GRNN model and the CNN-BP (CNN backpropagation) model are compared and analyzed in terms of the quality assessment accuracy of music courses. The results show that the CNN-GRNN model proposed in this paper outperforms the other methods. The mean squared error (MSE) of the model using one-dimensional convolution is lower than that of the CNN-GRNN model using two-dimensional convolution. As compared to CNN-BP model, the CNN-GRNN model outputs results that are closer to the expert evaluation results and the error is small.
TEACHER LIBRARIAN CAPABILITY IN CLASS MANAGEMENT OF THE LEARNING PROCESS IN INTEGRATED MADRASAH IBTIDAIYAH

&lt;span lang="EN-US"&gt;The purpose of this study is to determine the librarian teacher's ability to manage the learning process in the classroom. This type of research is qualitative in the sense that it generates descriptive data in the form of written and oral words based on the behavior of the people observed. The researcher employs a purposive sampling technique to gather the necessary information. and the principal. The results showed that the fifth grade teacher at MI Terpadu Almadinah's classroom management ability in the learning process was quite good, as in the application of classroom management principles such as warmth and enthusiasm, challenge, variety, flexibility, emphasis on positive things, and inculcation of discipline. While designing the learning process and the physical environment of the classroom, it is possible to create a positive learning climate; however, there are still obstacles in terms of using media methods and completeness in the classroom.&lt;/span&gt;
Two-photon quantum interference between separate NV centres in the telecom-band

We demonstrate interference of photons emitted by remote, spectrally distinct NV-centers. Quantum frequency conversion at the nodes brings the photons to the same wavelength in the telecom L-band, compatible with entanglement generation at metropolitan scale.
29th Reconfigurable Architectures Workshop (RAW 2022)

This book presents the proceedings of the 29th Reconfigurable Architectures Workshop (RAW 2022) held in Lyon in May 2022. RAW 2022 is associated with the 36th Annual International Parallel & Distributed Processing Symposium (IPDPS 2022) and is sponsored by the IEEE Computer Society's Technical Committee on Parallel Processing. The workshop is one of the major meetings for researchers to present ideas, results, and ongoing research on both theoretical and practical advances in reconfigurable computing.
Significance of excitation source sequence information for Speaker Verification

Automatic speaker verification (ASV) system is used to verify the speaker’s identity. Over the years, many spectral and excitation source features have successfully contributed to improving the performance of the ASV. In previous works, it has been shown that there is complementary information in the excitation sources. Due to the variation in the physiological structure across speakers, the generated excitation varies from speaker to speaker. In this work, we have performed a comparative study to analyze the extent of speaker signatures in each excitation source. The excitation sources over which comparison is made are epoch, weighted epoch, Hilbert envelope of linear prediction (LP) residual, residual phase, and Gaussian noise. The comparison with the Gaussian noise sequence shows that excitation sources considered noisy sequences have better speaker-specific information than the noise. Pearson correlation coefficient, structural similarity index measure (SSIM), and equal error rate (EER) were used as the metrics to evaluate the performances. The ASV performance is tested on TIMIT and IITG-MV datasets.
Recommendation System based on User Trust and Ratings

Recommendation systems aim at providing the user with large information that will be user-friendly.They are techniques based on the individual's contribution in rating the items.The main principle of recommendation systems is that it is useful for user's sharing the same interests.Furthermore, collaborative filtering is a widely used technique for creating recommender systems, and it has been successfully applied in many programs.However, collaborative filtering faces multiple issues that affect the recommended accuracy, including data sparsity and cold start, which is caused by the lack of the user's feedback.To address these issues, a new method called "GlotMF" has been suggested to enhance the collaborative filtering method of recommendation accuracy.Trust-based social networks are also used by modelling the user's preferences and using different user's situations.The experimental results based on real data sets show that the proposed method performs better result compared to trust-based recommendation approaches, in terms of prediction accuracy.
Effect of weak randomness flaws on security evaluation of practical quantum key distribution with distinguishable decoy states

Quantum key distribution provides an unconditional secure key sharing method in theory, but the imperfect factors of practical devices will bring security vulnerabilities. In this paper, we characterize the imperfections of the sender and analyze the possible attack strategies of Eve. Firstly, we present a quantized model for distinguishability of decoy states caused by intensity modulation. Besides, considering that Eve may control the preparation of states through hidden variables, we evaluate the security of preparation in practical quantum key distribution (QKD) scheme based on the weak-randomness model. Finally, we analyze the influence of the distinguishability of decoy state to secure key rate, for Eve may conduct the beam splitting attack and control the channel attenuation of different parts. Through the simulation, it can be seen that the secure key rate is sensitive to the distinguishability of decoy state and weak randomness, especially when Eve can control the channel attenuation.
Blind Image Deblurring using GLCM and ElasticNet Regularization

The well-known source of digital degradation is camera shake, photos under dim light and a handheld camera etc. Extensive research has taken place over the last decade in the field of retrieving a latent image from burry input; most of them work quite well, but very often incur to blur in edges.This paper has been proposed a new deblurring method in which the high-frequency layer is extracted from the blurred image using a 2D Haar wavelet transform in the luminance channel, then from the highfrequency layer, rich edge region is extracted using GLCM and sliding window concepts after the canny edge detection process.Finally, the extracted rich edge region is used to estimate the blur kernel using the elastic net regularization of singular value.Here regularization is used to avoid over-fitting of the data and reduces the blurring effects of the image.Experimental result demonstrates that the proposed deblurring algorithm achieves the better results on natural images which are evaluated using the parameter such as PSNR and SSIM.
Social sentiment analyzer for online post of the COVID-19 Pandemic based on Neural network

In order to explore the emotions of people during this pandemic by using the Baidu Post Bar, we propose a neural network to classify text based on emotions. The data for training the proposed model and performing experiments is obtained from various online forums. The results of the proposed model are compared with other models presented in literature. The experimental results show that the proposed CNN_BiLSTM model has good results in classifying the data samples and it satisfies the requirements of emotion classification of forum Chinese texts. The proposed method accurately classifies the emotion of each sentence and provides necessary technical support for subsequent emotion analysis.
An Ensemble Classification Algorithm of Micro-Blog Sentiment Based on Feature Selection and Differential Evolution

In the face of massive texts, dimensionality reduction algorithm and efficient classification model have become the key steps for sentiment classification of microblogs. X2 statistics and TF-IDF statistics are commonly used dimension reduction methods. When applied to micro-blog sentiment analysis, traditional X2 statistics do not consider the probability of a certain sentiment word in a micro-blog text. TF-IDF weight measure ignores the synonyms in the text of micro-blog. Therefore, this paper proposes a NewChi-TF-IDF feature selection method combining form and semantics. In the classification stage, the generalization performance of single classifier is low. To enhance the generalization performance of Weibo sentiment classification, based on the existing ensemble strategy, differential evolution algorithm is introduced to assign different excitation functions to multiple weak classifiers to train the optimal weight distribution. Thus, the problem that the weight of weak classifier is difficult to determine is solved. Experimental results show that NewChi-TF-IDF feature selection method reduces the dimension, and the generalization ability of the proposed algorithm is enhanced, and the average F-score of the proposed algorithm is improved to a higher degree than that of Ada-All and Vote-All classifiers.