Reviewerly/exHarmony huggingface dataset:
Efficient List Decoding Applied to $$\mathrm{ECC}^2$$

$$\mathrm{ECC}^2$$ is an public key encryption system based on elliptic code. It can resist known attacks based on the special structures of algebraic geometric code. However, the computational overhead of decryption of $$\mathrm{ECC}^2$$ is unsatisfactory, because the list decoding algorithm occupies a major part of the computational overhead of decryption of $$\mathrm{ECC}^2$$ . Therefore, we propose our module basis reduction interpolation of list decoding for elliptic code to speed up the decryption of $$\mathrm{ECC}^2$$ . The algorithm we proposed is based on the theory of Gr $$\ddot{\mathrm{o}}$$ bner basis of modules. By implementing our proposed algorithm combined with $$\mathrm{ECC}^2$$ , it shows that the proposed algorithm performs better than the list decoding algorithms used in $$\mathrm{ECC}^2$$ .
A Hybrid Application Model to Improve the Accuracy by Using Naive Bayes Algorithm

In this paper, we mainly focus on improving the accuracy in the joining rate of values. The dataset comprises of one lakh records with different features as input and further performed data cleaning using data profile report for handling the missing values and unnecessary data. After performing pre-processing and cleaning the data, an effective model is built using five different algorithms based on which we propose the implementation-based predictive model with more than 75% accuracy. The selection process is performed by achieving maximum conversion rate over selective targeted clients by denoting the maximum probability of each lead that enrolees in the university through consultancy with possible discounts or contacts with high potential leads. The training set is used to train the classifier, and test data is used to generate a confusion matrix by calculating the accuracy. Several techniques can be used to solve this problem. The proposed Naive Bayes algorithm has achieved an accuracy of approximately 93.5% on the testing data, which is greater than decision tree accuracy.
Design and Implementation of Statistics and Analysis System for Volleyball Match

With the increasingly fierce competition in modern sports competition, the position and role of coaches in on-the-spot command are becoming more and more important. There are many factors in the on-the-spot command of coaches, such as the psychological adjustment of athletes, the grasp of competition rhythm, the flexible use of techniques and tactics, etc. The main problems of sports computing research in the field of volleyball competition are: the data collection can not meet the requirements of real-time, at the same time, for the analysis of massive data, it is difficult to select the appropriate data mining algorithm and establish a suitable model for analysis. With the increasing influence of volleyball, it has become a consensus to use computer technology and other high-tech means to improve the training and competition level of athletes.
Asynchronous Double-Frame-Exposure Binocular-Camera With Pixel-Level Pipeline Architecture for High-Speed Motion Tracking

The synchronous double-frame exposure (S-DFE), also called frame-straddling, has been widely applied for commercial video-based velocity measurements to provide high-speed velocimetry-flow images. Nevertheless, no low-cost, high-speed cameras offer the video quality that researchers, scientists, and industry demand. This brief presents an asynchronous DFE (A-DFE) method with a binocular camera for motion estimation, which captures double frames with the scalable time interval of the dual generic cameras. This time interval can significantly improve the upper limit of displacements of the motion, which is commonly solved by an expensive high-speed camera. A dedicated hardware architecture is designed to implement the perspective transformation and Wallis filter to eliminate the two cameras’ position mismatch and brightness variance. We build a high-speed Lucas-Kanade optical flow estimation system with A-DFE on the XILINX Spartan-7 FPGA platform with MT9V034 CMOS image sensors. The shortest time interval can reach 1.06 ns, which means a significant improvement of the upper limit of the measurement velocity. Except for the image sensor, the compact, fully parallel architecture can attain energy efficiency with 3.23 nJ/pixel on a low-cost FPGA device.
Improving Mixed Data Imputation Methods Using Generative Adversarial Networks

Missing values are common in real-world datasets and pose a significant challenge to the performance of statistical and machine learning models. Generally, missing values are imputed using statistical methods, such as the mean, median, mode, or machine learning approaches. These approaches are limited to either numerical or categorical data. Imputation in mixed datasets that contain both numerical and categorical attributes is challenging and has received little attention. Machine learning-based imputation algorithms usually require a large amount of training data. However, obtaining such data is difficult. Furthermore, no considerate work has been conducted in the literature that focuses on the effects of the training and testing size with an increasing amount of missing data. To address this gap, we propose that increasing the amount of training data will improve the imputation performance. We first used generative adversarial network (GAN) methods to increase the amount of training data. We considered two state-ofthe-art GANs (tabular and conditional tabular) to add synthetic samples using observed data with different synthetic sample ratios. We then used three state-of-the-art imputation models that can handle mixed data: MissForest, multivariate imputation by chained equations, and denoising auto encoder (DAE). We propose a robust experimental setup on four publicly available datasets with different training-testing data divisions that have increasing missingness ratios. Extensive experimental results show that incorporating synthetic samples with training data achieves better performance compared to the baseline methods for mixed data imputation in both categorical and numerical variables, especially for large missingness ratios.
Empirical Analysis of Pair Programming Using Bloom's Taxonomy and Programmer Rankers Algorithm to Improve the Software Metrics in Agile Development

Collaborative programming is a co-operative effort of 2 teams to n-teams to share knowledge, synergize and produce better code. Pairing, Swarming and Mobbing are the standard of agile technology which are adapted by many organizations. In this paper, software development is carried out using Pair Programming(PP) in a medium sized organization developing mobile applications using android is presented. The first method uses the Programmers Competency Matrix (PCM) based on Blooms Taxonomy to assess the skill of the programmers. Since, the pairs are chosen randomly in the PCM method, a novel algorithm is proposed to pair the programmers by Programmer Ranking Algorithm (PRA). The two proposed methods are evaluated in an organization and the results are validated. The results prove that PP definitely improves the software development process than when it is developed by individual programmers. The PRA methodology outperforms the PCM because the PRA chooses the pair wisely using the programming skills of the programmer.
ChefAI Text to Instructional Visualization Using Amazon Web Services

Food industry is one of the most widespread and rapidly growing one with scope for video content generation and analysis via social media. There is an immense rise in the popularity of videos based on food items preparation, as people watching these videos feel attached and involved in the process. Watching someone cook in person as a third person viewer is not as intuitive as watching a pair of hands cooking from the viewer’s perspective. Therefore, in this work, we capitalize on Tasty Video datasets, a collection of 2511 recipes, in order to train a model on temporal dynamics for video-captioning and video-generation tasks. It is proposed to have a system to take in the recipe text and pass it through a transformer-based network to produce the cooking video. It is intended to qualitatively demonstrate the capability to generate plausible videos conditioning on the text as input. Moreover, quantitative experiments will be performed to validate the models using various metrics and human validation. Amazon Web services are the cloud environment to be used for data ingestion, preprocessing, computation, and deployment of the constructed model.
Research on Online Teaching by Using Public Physical Teaching Network Teaching System

Network teaching is the application of advanced computer technology and Internet technology to the field of education, and the transmission of text, graphics, sound, animation and video information through a variety of media technologies, which integrates the real world and virtual reality, and has educational resource sharing, with features such as automatic teaching management, flexible teaching time and space. Carry out network teaching of public physical education, integrating the dual advantages of online and offline teaching, to meet the needs of students' learning subjectivity and pertinence. In order to meet the needs of the information age in public physical education, the paper designs the public physical education network teaching system based on the. NET platform, and proposes specific application strategies to help public physical education reform and comprehensively promote the development of public physical education online teaching.
Innovation of Online Teaching Mode Based on Mobile Internet Technology

In the current rapid development of mobile Internet technology (MIT), human beings have created and experienced an ever-expanding new society. Online teaching is an important part of socialization, and it is inevitable. This article aims to study the innovation of online teaching model based on mobile Internet technology. This article mainly studies the meaning of MIT, the online teaching process based on mobile Internet technology, the related theories of MIT support vector machines and the core operation of vector support engines. We will introduce the advantages of mobile Internet technology. The experimental results of this article show that MIT has strong potential and market prospects. And through the satisfaction survey and analysis of MIT, it is concluded that 89% of people think that the security of MIT is very high.
Neural Network: A Way to Know Consumer Satisfaction During Voice Call

Consumer satisfaction is a major factor to grow a business. And if this thing is said about the telecom industry then it becomes even more important. According to Google research, 88% of people still prefer to receive phone calls because it is easier to speak than to write something. In our country, mobile consumer dissatisfaction is increasing as consumer connections are growing fast, due to lack of voice quality during ongoing calls while we are moving towards 5G and 6G. Many academia, researchers and industries are working on this but till now this issue has not been solved. In this research paper, an approach is given to judge the level of customer satisfaction during voice calls. On the basis of few LTE parameters obtained from the nearest cell phone tower, the proposed model will be able to predict whether or not a voice call was satisfactory. The objective of this paper is to measure consumer satisfaction by applying an artificial neural network which is widely referred to for automatic feature extraction, classification and prediction.
Hybrid Optimization and Deep Learning for Detecting Fraud Transactions in the Bank

This paper, the efficient and effective fraud detection technique, termed SpiHWO-based Deep RNNtechnique is developed. At first, the data transformation is performed for transforming the data using Yeo-Johnson transformation. After that, the effective features are selected based on wrapper method where the best features are selected for further processing. Then, by using selected features, fraud detection is executed based on Deep RNN classifier, which is trained by developed SpiHWO technique. The proposed SpiHWO algorithm is newly developed by combining the SMO algorithm and HWO algorithm. Furthermore, the performance of developed method is computed using performance metrics, like sensitivity, specificity and also accuracy. The developed method achieved improved performance with respect to accuracy of 0.951, sensitivity of 0.985 and specificity of 0.792.
Granular Property Law: Recalibrating Optimal Standardization of Property Rights in the Internet of Things

The numerus clausus principle, which limits both the number and content of property rights, has been explained by law and economics scholars as a device for reducing information costs. From this perspective, a more complex system of property rights can only be achieved at the price of less legal certainty or, in economic terms, higher information costs. However, advances of information technology could change this equation and make it possible to recalibrate the optimal standardization of property rights leading to a more granular property law. Starting from this premise, this article explores whether the emerging scholarship on “personalized law” could provide new perspectives on the future of the numerus clausus. In particular, it is examined to what extent standardization of information about idiosyncratic property rights could be an alternative to standardization of property rights in the Internet of Things.This is an early draft to be discussed at the Modern Studies in Property Law Conference (Oxford, 29-31 March 2022) which further elaborates some ideas from a paper to be published in the Liber Amicorum for Christian von Bar (forthcoming in May 2022).
Precise and scalable evaluation on the robustness metric of neural networks as classifiers

It is well known that the minimum adversarial distortion associated with a specific sample x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> reflects the local robustness of neural networks. However, it is intractable to solve the optimization problem related to the minimum adversarial distortion for general neural network cause it is non-convex. Works have studied the lower bound or upper bound of the minimum adversarial distortion to give a robustness metrics for neural networks, such as CLEVER score and CW attack. In this paper, we provide a formal robustness guarantee to transform the robustness analysis into two sub-problems: (1), compute out the maximum of the objective function g <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ); (2), generate a sequence {r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> } s.t. limn→∞ r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> is the minimum adversarial distortion for targeted attack. Based on this transformation, we propose an efficient and effective algorithm to directly estimate the instance-specific minimum adversarial distortion on the norm of the input manipulation required to change the classifier decision. Experimental results on two data-sets, MNIST and CIFAR, show that our measure of robustness is between the lower bound given by CLEVER and the upper bound by CW, indicating our approach gives a precise measure of the robustness of neural network. In addition, the powerful mathematical technique Extreme Value Estimation enables our algorithm computationally feasible for large neural networks. To the best of our knowledge, our algorithm is the first attack-independent approach to directly evaluate the minimum adversarial distortion as a robustness metric for neural network.
Inception-Based CNN for Low-Light Image Enhancement

With any elevated camera, a poorly lit region can provide low-light pictures. It is challenging to test a single approach to enhance a photo in the context of many kinds of noise. These low-light images are improved in this paper using an Inception-based Convolutional Neural Network (CNN). This model uses Inception model for efficient computation and produces better results than previous approaches like MSR-net, LIME, etc. This design can improve even the most distorted low-light images while offering the best results. Salt and Pepper, Gaussian, and Poisons noises are used to degrade images and test the model’s performance using PSNR as a loss function. The results of evaluation of our model to current solutions based on NIQE/Entropy characteristics show that this approach outperforms there. It can also be beneficial in many essential applications, including medical imaging (where low-light images or scanning might be harmful), undersea imaging, monitoring, and surveillance (where images play a significant role in decision-making).
Drifting by Intention – Four Epistemic traditions in Constructive Design Research

It is assumed that to appreciate a knowledge contribution in research-through-design, we all agree on what the act of designing is and should deliver in research. However, just from a glance at contributions in an HCI context, this is far from the case. The course is based on the book: Drifting by intention – four epistemic traditions in constructive design research authored by the instructors. It unpacks different ways of knowing in practice-based design and provides operational models and hands-on exercises applied on participants cases to help plan and articulate the contribution of design in each participant's individual research project.
Towards a Blockchain-Based System for Research Data Publishing and Verification

The quantity of research data produced is increasing over time and digital recording and storage enable quick and easy editing of these data. This paper explores questionable research practices and the requirement for transparency and trust in the capture, management and publication of research data. A system is envisaged that utilizes a permissioned blockchain to record and store research data against certificated projects, proposing suitable data structures and consensus mechanism. The proposed system allows for data to be gathered and added to the blockchain in such a way that a researcher can provide their peers with a higher level of confidence in the authenticity of the data.
Scenario Forecasting of University Development Based on a Combined Cognitive Model

A new approach to solving the problem of predicting the performance of a university as a weakly structured system based on an aggregated model, including an ensemble of gray fuzzy cognitive maps (FCM), is proposed. A combination of cognitive modeling, interval mathematics, and causal algebra was used to construct the gray FCM. Aggregation of gray FCM models gives a synergistic effect, in which the disadvantages of some models are compensated by the advantages of other models. The use of the interval approach in describing the relationships between the concepts made it possible to reduce the uncertainty of expert assessments of the strength of the relationship between the concepts of the cognitive map, as well as to increase the efficiency of the forecasting process. The model based on the ensemble of gray FCMs provided an increase in the reliability of the solution using several options for formalizing the knowledge and experience of experts. The novelty of the approach is determined by the use in the construction of a fuzzy cognitive map of special constructions in the form of interval estimates to assess the strength of connections between concepts, providing an adequate cognitive model, as well as the use of a predictive ensemble of gray FCMs, based on which a more accurate result was obtained in comparison with a single FCM and existing ones. Models of classic FCM. The proposed approach provided, under the given constraints, obtaining the most acceptable scenario for planning the increment of the basic indicators of the university's activity to target values by identifying the latent factors affecting them and calculating the aggregate of impulse influences on latent factors to ensure the achievement of the goal.
Path Planning and Formation Control of Multi-target Using Virtual Spring Model

Due to its significant importance in many aspects, formation flight of swarm unmanned aerial vehicles (UAVs) has drawn a lot of attentions from scholars and engineers. This paper represents an optimal path planning scheme of multi-target system in a leader-follower strategy. The proposed method utilizes virtual springs structure to maintain the inter-distance and to avoid collision. After establishing the dynamical model of four high speed targets in horizonal plane, a numerical study is performed to demonstrate the performance of the formation flight system. The results verified the feasibility and validity of our proposed method in the path planning program of multi-target.
Semantic Learning for Facial Action Unit Detection

This article proposes semantic embedding for image transformers (SEiTs) to explore semantic features of facial morphology in the action unit (AU) detection task. The conventional approaches typically rely on external information (e.g., facial landmarks) to obtain the location of facial components, whereas the SEiT can learn morphological features intrinsically from the face image. The pre-training task, namely semantic masked facial image modeling (SMFIM), aims to actively obtain facial morphological information. The pixels of the input facial image are randomly erased with semantic masks (e.g., nose, eyes, eyebrows, mouth, and lip). The embedding model tries to predict the presence of facial components for the input image that can learn semantic representations of the face simultaneously. The learned semantic embeddings are fed to transformer blocks, which enable global interaction between semantic elements. The SEiT integrates facial morphological information and global interaction characters, appropriate for AU detection. The experiments are conducted on the Binghamton-Pittsburgh 4D (BP4D) dataset and Denver intensity of spontaneous facial action (DISFA) dataset, and the results demonstrate the effectiveness of the proposed SEiT.
Subjective Freedom of Speech: Why Do Citizens Think They Cannot Speak Freely?

We provide the first systematic research into the origins of subjective freedom of speech in Germany. Relying on the GLES 2021 Cross-Section Pre-Election Survey which includes a newly designed survey item on subjective freedom of speech, we evaluate a whole range of plausible candidate hypotheses. First, we contribute to cumulative research by testing the explanatory factors in Gibson (1993) – citizens’ social class, their political involvement and political preferences as well as their personality dispositions – for the German case. Second, we move beyond the state of the art and test three new hypotheses that reflect more recent political developments and arguments in the free speech debate: the role of social media, increasing political and social polarization and the rise of populism. Importantly, all hypothesis tests reported in this paper have been pre-registered prior to data collection. Our results reveal that three explanatory factors are significantly, consistently, and substantively related to subjective free speech in Germany: political preferences, populist attitudes, and identification with the AfD.