Reviewerly/exHarmony huggingface dataset:
Statistical Evaluation of Classification Models for Various Data Repositories

Exploitation of massive amount of multidimensional data of numerous diversities from heterogeneous sources is emerging. Segments can be expanded by the effective use of the data and simultaneously the warehoused data furnishes significant eventualities for strengthening cardinal decision making. Cutting edge intelligence and efficacious approaches and methods are essential to produce a model by means of various types of data. Machine learning is described as constructing a model for handling unfathomable accumulated or streaming data which is difficult to be handled by conventional data processing techniques. The prerequisite for machine learning is also prompted based on preprocessing, analysis, demonstration and prediction on diverse categories of data. The experimentation has been performed on Mushrooms dataset and Census dataset by carrying out machine learning algorithms. It has been proved that the ensemble algorithm shows better performance on the intended datasets in terms of various performance measures.
An Elucidative Review on the Current Status and Prospects of Eye Tracking in Spectroscopy

This study examined how the measurement of eye activity is done using various technologies. Eye tracking (ET) is dominant and prominent. It analyzes the gazing direction of people and measures the entire functioning of the eye. In this paper, we will come to know about various technologies for eye tracking and can able to choose the best approach. Irrespective of technology, as the eye is the most affected part, we can employ this technique in all fields. The main motto of this is to demonstrate a full-fledged review of diverse topics and techniques used in eye tracking. We will be seeing some interesting techniques like skin electrodes, contact lenses, head-mounted and remote systems, and the approach behind them. Most importantly we will learn what is pupil center corneal reflection technique (PCCR). ET gives numerous application which relates to the interaction between humans and computers. This paper also incorporates various elements which took part in the selection of a particular eye-tracking method.
Effect of the Addition of Porcelain Tile Polishing Residue in Ceramic Masses for Sanitary Ware

Currently, there is a severe shortage of good-quality feldspar deposits. On the other hand, porcelain tile polishing residue has been widely recognized as an alternative raw material for the ceramic industry. Thus, this research evaluated the effect of the partial replacement of feldspar by the porcelain polishing residue (PPR) in formulations for sanitary stoneware. It was replaced from 2% to 20% of the total mass of feldspar by the residue. The raw materials were characterized by X-ray fluorescence, X-ray diffraction, and particle size determination. Then, they were mixed in a wet ball mill, and the rheological properties of the suspensions were analyzed. Samples of the test were produced by slip casting and sintered at 1160 °C, 1180 °C, and 1200 °C. Rheological characteristics were not affected when up to 20% of feldspar was replaced by residue. Bodies with high amounts of PPR had flexural strength and water absorption values similar to those of bodies without PPR in formulations. Furthermore, apparent porosity values lower than the reference mass were obtained with the addition of 6% of the residue in the formulation.
Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.
A Self-Tuning Version for the Possibilistic Fuzzy $c-\text{means}$ Clustering Algorithm

This paper presents an alternative version of the possibilistic fuzzy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c-\mathbf{means}$</tex> (PFCM) algorithm, which can tune automatically its possibilistic penalty terms and uses less parameters as the original PFCM. The proposed method incorporates some cluster size controlling variables into the objective function of PFCM, and with their help it can dynamically modify the penalty terms during the alternating optimization of the objective function. The proposed method is evaluated in comparison with the original PFCM using the IRIS data set and synthetic data. Numerical experiments show that the proposed method can produce fine partitions, and is stable in a wide range of its parameters.
Robust structured light 3D measurement method based on polarization-encoded projection patterns

Fringe projection profilometry (FPP) is widely used in 3D vision measurement because of its high robustness and measurement accuracy. In the case of HDR objects, due to the problem of surface reflectivity, the obtained image will be overexposed. This will cause the sinusoidality of the fringes projected on the surface of the object in the acquired image to be interfered, resulting in a phase error in the calculated wrapped phase. Therefore, a polarization-encoded sinusoidal structured light is proposed to enhance the sinusoidality of the fringe. The phase information contained in the polarized sinusoidal structured light fringe is only related to the polarization state, not to the light intensity. A polarization coding assisted structured light measurement strategy (PASM) is proposed. This method uses polarization coding assisted polarization phase-shifting fringes for phase unwrapping. The angle of the linear polarizer is set to zero in this method, and it does not require rotating the polarizer. It only needs a single exposure to improve the fringe quality and obtain a more stable unwrapping phase. The experimental results show that the obtained polarization fringes have better sinusoidality, and the phase unwrapping can be more accurate. The reconstructed 3D point cloud also does not appear missing and has better accuracy. It is a reliable method for vision measurement of HDR objects.
Addressing data scarcity in optical matrix multiplier modeling using transfer learning

We present and experimentally evaluate the use of transfer learning to address experimental data scarcity when training neural network (NN) models for Mach–Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pretraining the model using synthetic data generated from a less accurate analytical model and fine-tuning it with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve &lt;1 dB root-mean-square error on the 3×3 matrix weights implemented by a photonic chip while using only 25% of the available data.
Determining the Foreign Currencies Affecting the Bitcoin.

Abstract In the present paper, the Granger causality test is used to study the causality relationships between Bitcoin and some of the most highly traded currencies, including euro, Japanese yen, British pound, Chinese yuan, and Indian rupee. To this purpose, the daily exchange rates of Bitcoin and the selected currencies to USD between 2014 and 2018 were used. Different from findings in existing literature, our study shows that there are no Granger causalities between Bitcoin and Euro, Japanese yen, British pound, and Indian rupee. A Granger causality is found in the direction from the Chinese yuan to Bitcoin.
Semi-2DCAE: a semi-supervision 2D-CNN AutoEncoder model for feature representation and classification of encrypted traffic

Traffic classification is essential in network-related areas such as network management, monitoring, and security. As the proportion of encrypted internet traffic rises, the accuracy of port-based and DPI-based traffic classification methods has declined. The methods based on machine learning and deep learning have effectively improved the accuracy of traffic classification, but they still suffer from inadequate extraction of traffic structure features and poor feature representativeness. This article proposes a model called Semi-supervision 2-Dimensional Convolution AutoEncoder (Semi-2DCAE). The model extracts the spatial structure features in the original network traffic by 2-dimensional convolution neural network (2D-CNN) and uses the autoencoder structure to downscale the data so that different traffic features are represented as spectral lines in different intervals of a one-dimensional standard coordinate system, which we call FlowSpectrum. In this article, the PRuLe activation function is added to the model to ensure the stability of the training process. We use the ISCX-VPN2016 dataset to test the classification effect of FlowSpectrum model. The experimental results show that the proposed model can characterize the encrypted traffic features in a one-dimensional coordinate system and classify Non-VPN encrypted traffic with an accuracy of up to 99.2%, which is about 7% better than the state-of-the-art solution, and VPN encrypted traffic with an accuracy of 98.3%, which is about 2% better than the state-of-the-art solution.
Smart Cities Mission in the Face of COVID: Scope and Scale of ‘Smart’ COVID Responses in India

COVID has expedited and expanded the already precarious smart city development in India with the multitude of 'smart' COVID responses-broadly known as CovTech-introduced since the early days of the global pandemic. This paper offers an analysis of the scope and scale of smart responses to COVID in the first 20 cities prioritized for smart city implementation-as part of the Smart Cities Mission in India. The analysis unravels the diversity within the smart COVID response, as 125 COVID applications, 14 COVID-War-Rooms, and numerous examples of smart public place initiatives are discussed. The findings include a typology of COVID applications and shed light on the operations of COVID-War-Rooms throughout the nation. The learnings point toward a mostly top-down approach to smart COVID response. Yet, early evidence indicates the existence of an alternative subaltern smart COVID response to provide bottom-up support to the most vulnerable groups, filling the gaps in the top-down approach. More research is required to thoroughly understand the scope and scale of the subaltern smart response to COVID.
The Impacts of Bossware on Business Management: An Analysis of Benefits and Challenges

The novel coronavirus pneumonia has forced many people to work from home. In the home-based office scenario, ensuring work efficiency and results and staying on track with business progress is a big challenge for enterprise management. Therefore, increasingly, companies use bossware to monitor employees working status at home through the software, hoping to urge employees to work efficiently, but the result could be better. This paper expounds on the current situation of companies using bossware in recent years and analyzes the advantages and disadvantages of using bossware in enterprise management from multiple perspectives. At the same time, it puts forward suggestions on how to rationalize the management of employees under special circumstances, hoping that bossware can play its due role.
Research hotspots and development trends of international learning cycle model:Bibliometric analysis based on CiteSpace

The Learning Cycle Model (LCM) is an inquiry-based teaching strategy and curriculum model that has been researched and practiced for almost half a century and has achieved good educational results in science education, especially in primary and secondary schools. This study collected LCM educational research data to better understand the recent progress of LCM educational research. It used the bibliometric analysis software CiteSpace for the first time to sort out the overall development of LCM. Compared to other literature review methods, we obtained a more comprehensive picture of the current research hotspots and future research trends in LCM research. First, we searched 498 articles from the Web of Science core collection between 2000 and May 16, 2023. The trajectory of LCM research was identified by analyzing publication trends, authors, countries/regions, and research institutions. Secondly, we obtained the keyword co-occurrence map and clustering map through CiteSpace's built-in algorithm to analyze the main areas and research frontiers of this study. Meanwhile, the development trend is shown by co-keyword burst detection. After analysis, this study found that the current research hotspot findings focus on conceptual learning, validity research, and integration with different teaching processes. The research frontiers are mainly related to teacher professional development and research on the impact of LCM on learning outcomes. Finally, this study discusses future research directions, including research on the impact of LCM on 21st century skills, comparison with other modes of inquiry teaching and learning, and its use in engineering education, to promote better development of LCM.
Efficacy of real-time computer-aided detection of colorectal adenoma in a randomized trial in routine

Aims Artificial intelligence has recently been proposed to improve polyp detection. The aim of this study was to evaluate the impact of real-time computer-aided detection (CADe) system on adenoma detection rate (ADR) in routine colonoscopy.
Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO&lt;sub&gt;x&lt;/sub&gt; emission concentration

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.
Improving Generative Adversarial Networks for Patch-Based Unpaired Image-to-Image Translation

Deep learning models for image segmentation achieve high-quality results, but need large amounts of training data. Training data is primarily annotated manually, which is time-consuming and often not feasible for large-scale 2D and 3D images. Manual annotation can be reduced using synthetic training data generated by generative adversarial networks that perform unpaired image-to-image translation. As of now, large images need to be processed patch-wise during inference, resulting in local artifacts in border regions after merging the individual patches. To reduce these artifacts, we propose a new method that integrates overlapping patches into the training process. We incorporated our method into CycleGAN and tested it on our new 2D tiling strategy benchmark dataset. The results show that the artifacts are reduced by 85% compared to state-of-the-art weighted tiling. Additionally, we demonstrate transferability to real-world 3D biological image data, receiving a high-quality synthetic dataset. Increasing the quality of synthetic training datasets can reduce manual annotation, increase the quality of model output, and can help develop and evaluate deep learning models.
Metadata matters: evaluating the quality of Electronic Theses and Dissertations (ETDs) descriptions in Malaysian institutional repositories

This study investigates the quality of metadata records in institutional repositories for Electronic Theses and Dissertations (ETDs) in three Malaysian research universities. The study employs metrics derived from Bruce and Hillmann (2004) to evaluate the completeness, accuracy, and consistency of 1138 metadata records from the three institutional repositories, and utilizes quantitative content analysis to identify variants of Dublin Core metadata elements used to describe ETDs. The study uncovers a range of issues with metadata completeness, accuracy, and consistency, emphasizing the need for standardized systems to ensure quality metadata. By highlighting the importance of quality metadata for ETD collections, the study provides valuable insights for academic libraries in designing and describing ETD collections. Overall, this study sheds light on the current metadata practices and elements used in ETD collections, and underscores the significance of effective metadata management for the dissemination and discoverability of scholarly works.
Improved image forgery localization method from a statistical perspective

Powerful image editing software makes the process of image manipulation easy, which increases security risks. Therefore, it is urgent to locate the tampered region to uncover the processing history. However, previous research has mainly focused on feature extraction, with little discussion on classifiers for classifying original and tampered regions. We improve the splicing forgery localization method from a statistical perspective. The refined color filter array feature provides sufficient data for statistical analysis, and the geometric mean is used to eliminate anomalous data. Subsequently, a classifier that combines the expectation–maximization algorithm and Bayesian theory is proposed to binarize the original and tampered regions. The two steps of feature extraction and feature classification are associated from a statistical perspective, which ultimately improve the performance of the method effectively. Extensive experimental results demonstrate that the refined feature used for classification has several advantages, and the proposed classifier is appropriate for handling complex image manipulation across different statistical distributions. The proposed method outperforms the reference methods in both the Columbia and Korus datasets.
Thoughts on constructing non-academic education and training system for higher vocational colleges with industry characteristics

Non-degree education occupies a relatively important role in vocational education, but at present, non-degree education in the field of higher education mainly focuses on research from the perspective of undergraduate colleges, and less from the perspective of higher vocational colleges. As the main force of vocational education, vocational colleges and universities occupy the advantages of the industry and profession, and should give full play to their advantages to play an important role in the development of vocational education. Therefore, from the perspective of industry-characteristic vocational colleges, this paper analyzes the connotation, significance and difficulties faced by non-degree education in vocational colleges, and puts forward the strategy of building a non-academic education and training system of "two platforms + one alliance" industry-characteristic vocational colleges, which provides ideas for non-academic education in vocational colleges.
Research on Cultivating Cultural Confidence of College Students from the Perspective of Digital Transformation

Strengthening the cultivation of cultural confidence among college students is an inevitable requirement for the current socialist modernization construction. To effectively cultivate the cultural confidence of college students at the current stage, it is necessary to combine modern information technology, especially big data technology, increase the dissemination path of excellent culture, expand the breadth of cultural dissemination, and enable more and more college students to receive positive influence from excellent traditional culture, truly establishing cultural confidence. Cultural confidence is an important measure for building a strong cultural nation. In today's digital transformation, it provides a large amount of data resources for researchers in the field of ideology and politics, and also provides better development opportunities for the cultivation of cultural confidence among college students.
Improving institutional visibility and reputation in the Open Science era

Open Science calls for open access to research results and for more openness and greater transparency in the research process as well. But many institutions lack comprehensive tools to effectively communicate the latter: showcase their rich legacy, available resources, full range of achievements and potential to the wider world.This presentation will cover several tools that are designed to increase the global visibility and reputation of your institution by demonstrating research activities, output, expertise and impact of individual authors and groups. Making this available to the public and professional academic or corporate users facilitates future collaborations.But connecting and managing all the relevant data scattered across multitute of sources on a scale of even mid-size university without a system is impractical, if not impossible. I will focus on the publicfacing side of CRIS/RIM systems and repositories. Additionally I will touch on research data sharing and monitoring tools which is a fast growing topic for a number of institutions.