Reviewerly/exHarmony huggingface dataset:
A new hybrid approach based on genetic algorithm and support vector machine methods for hyperparameter optimization in synthetic minority over-sampling technique (SMOTE)

&lt;abstract&gt; &lt;p&gt;The crucial problem when applying classification algorithms is unequal classes. An imbalanced dataset problem means, particularly in a two-class dataset, that the group variable of one class is comparatively more dominant than the group variable of the other class. The issue stems from the fact that the majority class dominates the minority class. The synthetic minority over-sampling technique (SMOTE) has been developed to deal with the classification of imbalanced datasets. SMOTE algorithm increases the number of samples by interpolating between the clustered minority samples. The SMOTE algorithm has three critical parameters, "k", "perc.over", and "perc.under". "perc.over" and "perc.under" hyperparameters allow determining the minority and majority class ratios. The "k" parameter is the number of nearest neighbors used to create new minority class instances. Finding the best parameter value in the SMOTE algorithm is complicated. A hybridized version of genetic algorithm (GA) and support vector machine (SVM) approaches was suggested to address this issue for selecting SMOTE algorithm parameters. Three scenarios were created. Scenario 1 shows the evaluation of support vector machine SVM) results without using the SMOTE algorithm. Scenario 2 shows that the SVM was used after applying SMOTE algorithm without the GA algorithm. In the third scenario, the results were analyzed using the SVM algorithm after selecting the SMOTE algorithm's optimization method. This study used two imbalanced datasets, drug use and simulation data. After, the results were compared with model performance metrics. When the model performance metrics results are examined, the results of the third scenario reach the highest performance. As a result of this study, it has been shown that a genetic algorithm can optimize class ratios and k hyperparameters to improve the performance of the SMOTE algorithm.&lt;/p&gt; &lt;/abstract&gt;
Learning Rich Information for Quad Bayer Remosaicing and Denoising

In this paper, we propose a DNNs-based solution to jointly remosaic and denoise the camera raw data in Quad Bayer pattern. The traditional remosaic problem can be viewed as an interpolation process that converts the Quad Bayer pattern to a normal CFA pattern, such as the RGGB one. However, this process becomes more challenging when the input Quad Bayer data is noisy. In addition, the limited amount of data available for this task is not sufficient to train neural networks. To address these issues, we view the remosaic problem as a bayer reconstruction problem and use an image restoration model to remove noises while remosaicing the Quad Bayer data implicitly. To make full use of the color information, we propose a two-stage training strategy. The first stage uses the ground-truth RGGB Bayer map to supervise the reconstruction process, and the second stage leverages the provided Image Signal Processor (ISP) to generate the RGB images from our reconstructed bayers. With the use of color information in the second stage, the quality of reconstructed bayers is further improved. Moreover, we propose a data pre-processing method including data augmentation and bayer rearrangement. The experimental results show it can significantly benefit the network training. Our solution achieves the best KLD score with one order of magnitude lead, and overall ranks the second in Quad Joint Remosaic and Denoise @ MIPI-challenge.
Enhanced Coarse-to-Fine Network for Image Restoration from Under-Display Cameras

New sensors and imaging systems are the indispensable foundation for mobile intelligent photography and imaging. Nowadays, under-display cameras (UDCs) demonstrate practical applicability in smartphones, laptops, tablets, and other scenarios. However, the images captured by UDCs suffer from complex image degradation issues, such as flare, haze, blur, and noise. To solve the above issues, we present an Enhanced Coarse-to-Fine Network (ECFNet) to effectively restore the UDC images, which takes the multi-scale images as the input and gradually generates multi-scale results from coarse to fine. We design two enhanced core components, i.e., Enhanced Residual Dense Block (ERDB) and multi-scale Cross-Gating Fusion Module (CGFM), in the ECFNet, and we further introduce progressive training and model ensemble strategies to enhance the results. Experimental results show superior performance against the existing state-of-the-art methods both qualitatively and visually, and our ECFNet achieves the best performance in terms of all the evaluation metrics in the MIPI-challenge 2022 Under-display Camera Image Restoration track. Our source codes are available at this repository .
Learning an Efficient Multimodal Depth Completion Model

With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems. First, the fusion of multimodal information requires more network modules to process different modalities. But the application scenarios of sparse ToF measurements usually demand lightweight structure and low computational cost. Second, fusing sparse and noisy depth data with dense pixel-wise RGB data may introduce artifacts. In this paper, a light but efficient depth completion network is proposed, which consists of a two-branch global and local depth prediction module and a funnel convolutional spatial propagation network. The two-branch structure extracts and fuses cross-modal features with lightweight backbones. The improved spatial propagation module can refine the completed depth map gradually. Furthermore, corrected gradient loss is presented for the depth completion problem. Experimental results demonstrate the proposed method can outperform some state-of-the-art methods with a lightweight architecture. The proposed method also wins the championship in the MIPI2022 RGB+TOF depth completion challenge.
One-Shot Learning for Human Affordance Detection

The diversity of action possibilities offered by an environment, a.k.a affordances, cannot be addressed in a scalable manner simply from object categories or semantics, which are limitless. To this end, we present a one-shot learning approach that trains on one or a handful of human-scene interaction samples. Then, given a previously unseen scene, we can predict human affordances and generate the associated articulated 3D bodies. Our experiments show that our approach generates physically plausible interactions that are perceived as more natural in 60–70% of the comparisons with other methods.
McEliece-Type Encryption Based on Gabidulin Codes with No Hidden Structure

This paper presents a new McEliece-type public key encryption scheme based on Gabidulin codes, which uses linearized transformations to disguise the private key. When endowing this scheme with the partial cyclic structure, we obtain a public key of the form $$GM^{-1}$$ , where G is a partial circulant generator matrix of Gabidulin code and M as well as $$M^{-1}$$ is a circulant matrix of large rank weight. Another difference from Loidreau’s proposal at PQCrypto 2017 is that both G and M are publicly known. Recovering the private key is equivalent to deriving from M a linearized transformation and two circulant matrices of small rank weight. This scheme is shown to resist some well-known distinguisher-based attacks, such as the Overbeck attack and Coggia-Couvreur attack, and also has a very small public key size. For instance, 2592 bytes are enough for our proposal to achieve the security of 256 bits, which is around 400 times smaller than Classic McEliece that has been selected into the fourth round of the NIST Post-Quantum Cryptography (PQC) standardization process.
Fast approximation of the top‐k items in data streams using FPGAs

Two methods are presented for finding the top-k items in data streams using Field Programmable Gate Arrays (FPGAs). These methods deploy two variants of a novel accelerator architecture capable of extracting an approximate list of the topmost frequently occurring items in a single pass over the input stream without the need for random access. The first variant of the accelerator implements the well-known Probabilistic sampling algorithm by mapping its main processing stages to a hardware architecture consisting of two custom systolic arrays. The proposed architecture retains all the properties of this algorithm, which works even if the stream size is unknown at run time. The architecture shows better scalability compared to other architectures that are based on other stream algorithms. In addition, experimental results on both synthetic and real datasets, when implementing the accelerator on an Intel Arria 10 GX 1150 FPGA device, showed very good accuracy and significant throughput gains compared to the existing software and hardware-accelerated solutions. The second variant of the accelerator is specifically tailored for applications requiring higher accuracy, provided that the size of the stream is known at run time. This variant takes advantage of the embedded memory resources in an FPGA to implement a sketch-based filter that precedes the main systolic array in the accelerator's pipeline. This filter enhances the accuracy of the accelerator by pre-processing the stream to remove much of the insignificant items, allowing the accelerator to process a significantly smaller filtered stream.
Training Simulator for Manual Lathe Operation Using Motion Capture – Addition of Teaching Function and Evaluation of Training Effectiveness –

Numerous training simulators have been developed using virtual reality (VR) owing to their various advantages. Systems for training machine operations with physical movements face differences in the operational feel between actual and virtual machines. Moreover, virtual training is problematic in safety education because trainees in safe virtual environments can exhibit unsafe behavior in reality. To solve these problems, a previous study developed a virtual reality (VR) system to train a lathe operation with mixed reality using a motion capture system. This study included a function to teach the procedure and safety precautions for straight turning operations using a lathe. To evaluate the training effectiveness of this system, an experiment was conducted to compare learning using a video. Testees were divided into a simulator group, who learned with the system, and a video group, who learned with the video material. Work on the actual lathe by each testee after learning, was evaluated. Consequently, the actual work by the testees who used this system had fewer errors and shorter standstill times in which they attempted to recollect the next phase task. Although the number of trainees was small, this relationship had a statistical advantage. In the actual work, all the testees in the video group entered the danger area; however, only half of the testees in the simulator group entered the danger area. Therefore, a trainee using a simulator can remember the work process more reliably and accurately and perform it safely. Moreover, trainees who have undergone training several times should be able to perform actual work without making operational errors or engaging in unsafe behaviors.
An Interdisciplinary Project as a Means of Developing Digital Skills

The article gives insights into developing students’ individual abilities to use information and communication technologies in certain contexts using project technology. Its application is not only conducive to developing students’ critical thinking, but also teaches them to work in a team and to find creative approaches to solving problems, inter alia by using digital tools. The article illustrates how economic majors improved their digital skills by participating in a project referred to as “Breaking News”. The students participating in the project used Google Sites to create a web-site on business and economic news coverages. The project was interdisciplinary since it integrated three components: economics, the English language and digital literacy. At the initial stage of the project the participants formed groups and familiarized themselves with the digital tools. Then each student had to prepare a presentation in English on a recent event, make a glossary of the key terms used in the upcoming presentation and then give a talk in class. At the final stage, the site was discussed in class. A final poll was aimed at assessing the extent to which the students’ digital skills had improved by the end of the project. The poll illustrated that 100% of the participants improved their digital skills; 96% of the students felt that working on the project motivated them to study English, and 76% of the respondents claimed to have improved their knowledge of economics. Thus, similar interdisciplinary projects can be included in the course of teaching English for Special Purposes.
The Experience of Forming Multilingual Competences in Universities in Russia and Turkey

The Russian and Turkish systems of higher education are similar in structure, both have three main levels: bachelor’s, master’s, and postgraduate. The duration of education is the same: 4–6 years. The higher education system in Russia and Turkey includes universities, academies, and high schools. Russian and Turkish universities are very popular among foreign students. However, Russia and Turkey are traditionally characterized by a rather poor level of foreign language skills. Accordingly, the insufficient level of foreign language proficiency may be one of the obstacles to inclusion in the multilingual new reality. The aim of this article is to analyze the capabilities of universities in Turkey and Russia to form multilingual competences of students. Based on the analysis of the experience in the formation of multilingual competences of students in two universities - in Russia and Turkey - the authors have concluded that it is necessary to replicate the best practices of the two universities in the formation of multilingual competences of students and teachers.
SOCluster - Towards Answering Unanswered Questions on Stack Overflow via Answered Questions

Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising, which is observed in various similar community Question & Answering platforms (Q&A) such as Yahoo, Quora and so on. To address this issue, these communities have explored clustering mechanisms to answer unanswered questions using other answered questions in the same cluster, which could also improve the response time for new questions. It is here, we propose SOCluster, an approach and a tool to cluster SO questions using a graph-based clustering approach. We selected four datasets of 10k, 20k, 30k & 40k SO questions without code-snippets or images involved, and performed clustering on them. We have done a preliminary evaluation of our tool by analyzing the resultant clusters using the commonly used metrics of Silhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We performed clustering for 8 different threshold similarity values and analyzed the intriguing trends reflected by the output clusters through the three evaluation metrics. At 90% threshold similarity, it shows the best improvement for the three evaluation metrics on all four datasets. We further manually assessed the content in the clusters to confirm the similarity of elements in clusters. This revealed clusters to correspond to topics such as mouse over effect, speed optimisation, how to perform ‘some’ action in JavaScript, and so on. The source code and tool are available for download on Github at: https://github.com/rishalab/SOCluster, and the demo can be found here: https://youtu.be/Ewm-M_rg_x8.
Optimization of College Teachers’ Performance Management Using Artificial Intelligence Technology

This work aims to adapt to the coming of a knowledge economy society and promote the improvement of China’s higher education system. It is necessary to establish a new management mechanism of college teachers’ performance evaluation to strengthen the quality of college teachers and improve the level of education and scientific research. Performance appraisal can be used to monitor the teaching staff scientifically and effectively to continuously improve and develop the college teacher system in China. This work first investigates the characteristics of performance evaluation of worldwide colleges, analyzes the development status of performance evaluation, and constructs a new performance evaluation index system through data and interviews. Then, based on the radial basis function neural network in artificial intelligence technology, a fine evaluation model of Chinese college teachers’ performance is established. Network training is adopted to analyze the previous performance evaluation to ensure that the final weight is obtained to minimize the sum of previous evaluation errors. Then, the index data of 61 teachers’ educational performance evaluation of X college from 2016 to 2021 are used for analysis and verification. The experimental results show that only 9.9% of the teachers in X college have excellent performance evaluation results, 29.5% of the teachers have medium evaluation results, and the statistical excellent rate is only 26.3%. Finally, the corresponding improvement suggestions and countermeasures are given for the low excellent rate of colleges.
“Smartphone as an educational tool” the perception of dental faculty members of all the dental colleges of Khyber Pakhtunkhwa - Pakistan

Abstract Background With the increasing advancement in the field of information technology, it’s about time we realize that our future will be shaped by this field. With more and more people using smartphones, we need to adapt them to the medical field. Already many advancements in medical field are done thanks to the advancement of computer science. But we need to implement this into our teaching and learning as well. Almost all students and faculty members use smartphones in one way or another if we can utilize the smartphone to enhance the learning opportunities for our medical students, it would greatly benefit them. But before the implementation, we need to find out if our faculty is willing to adopt this technology. The objective of this study is to find out what are the perceptions of dental faculty members about using a smartphone as a teaching tool. Methodology A validated questionnaire was distributed among the faculty members of all the dental colleges of KPK. The questionnaire had 2 sections. First one contains information regarding the demographics. The second one had questions related to the faculty members’ perception regarding using a smartphone as a teaching tool. Results The results of our study showed that the faculty (Mean 2.08) had positive perceptions regarding using a smartphone as a teaching tool. Conclusion Most of the Dental Faculty members of KPK agree that smartphone can be used as a teaching tool, and it can have better outcomes if proper applications and teaching strategies are used.
The Finnic Languages

This chapter is an overview of Finnic languages. It contains a general typological profile and phonological features of Finnic languages. The morphology section covers the properties of nominals (number, case, possession), verbs (tense, mood, aspect, person), and other parts of speech. The syntactic section includes information, for example, about basic clause types, possession, comparison, argument indexing and noun phrase flagging, negation, complex sentences (relative, complement, supporting), and questions. Each point is illustrated with authentic glossed, segmented, and translated examples from corpora. The chapter also contains a section on word-formation and information about the lexicon. The chapter concludes with a list of references.
A saturation-value histogram equalization model for color image enhancement

In this paper, we propose and develop a novel variational histogram equalization framework for color image enhancement. The main idea is to propose a variational model containing an energy functional which leads to adjust the pixel values of an input image so that the resulting histogram of saturation and value components can be redistributed uniformly. In the proposed model, we make use of a mean brightness constraint term to guarantee the preservation of mean brightness information. The saturation-value total variation is incorporated for color correction and noise elimination in order to improve the image quality. Theoretically, the existence of the minimizer of the variational model and the convergence of the proposed algorithm are analyzed. Experimental results are shown to demonstrate the feasibility and effectiveness of the proposed model.
Experimental Demonstration of LLO Continuous-Variable Quantum Key Distribution With Polarization Loss Compensation

The compensation process for the state of polarization (SOP) from quantum signal plays an important role in the practical implementation of continuous-variable quantum key distribution (CV-QKD). In contrast to the previous scheme using an expensive digital dynamic polarization controller, we choose a cheaper manual polarization controller interfaced with a digital algorithm based on Kalman filter to achieve compensation of the polarization loss under the condition that the SOP of the fiber is relatively stable. This paper also enumerates the details of other digital signal processing method to achieve final secret key rate. And a pilot-sequential Gaussian modulated coherent state (GMCS) continuous-variable quantum key distribution system with a local local oscillator (LLO) is experimentally implemented based on the analysis of excess noise. Finally, the experimental results show that the method of polarization loss compensation takes advantages in maintaining the stability of the final secret key rate. And a secret key rate of 110 kbps is achieved within 20 km optical fiber (with 4 dB loss) under the finite-size block of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1 \times {10^{6}}$</tex-math></inline-formula> during a more extended time in the laboratory.
Solar-Cast: Solar Power Generation Prediction from Weather Forecasts using Machine Learning

The rapid growth of solar generation technology has become a boon in the energy sector. Smart grids have replaced the conventional Grids due to upcoming various distributed energy sources feeding the grid. The correct estimation of solar intensity according to geographical features will help in determining the capacity of smart Grids. In real-time, most smart grids are compelled to change their renewable energy production process according to the real-time availability of energy resources like wind and solar during the day. Thus, to assuage this problem, the possibility is investigated by using readily available weather data on the NSRDB website to predict solar forecasts 48 hours ahead in the future by using various Machine Learning(ML) algorithms. In this paper, a new day-night model has been designed to limit the uncertainty of solar power generation and reduce the dependability of power grids on non-renewable energy sources like fossil fuels. Further, to improve the solar forecasting prediction, multiple weather observations were taken from preceding time intervals to establish a new data set in linear regression and its subtypes (Ridge and Lasso regression).