Reviewerly/exHarmony huggingface dataset:
Being Somehow Without (Possibly) Being Something

Abstract Contingentists—who hold that it is contingent what there is—are divided on the claim that having a property or standing in a relation requires being something. This claim can be formulated as a natural schematic principle of higher-order modal logic. On this formulation, I argue that contingentists who are also higher-order contingentists—and so hold that it is contingent what propositions, properties and relations there are—should reject the claim. Moreover, I argue that given higher-order contingentism, having a property or standing in a relation does not even require possibly being something.
Effect of Peer Tutoring-based Software Instructional Design: Focusing on Computational Thinking and Learning Experience

Objectives This study aimed to reveal the effect of peer tutoring-based software instructional design on enhancing learners' computational thinking and learning experiences. Methods To this end, peer tutoring-based software classes were designed for 23 elementary school students participating in after-school classes, pre- and post-computational thinking skills were measured using Bebras Task, and a paired-sample t-test was conducted. In addition, the learners' learning experiences were derived by qualitatively analyzing the coding plans and reflection journals written by the students. Results As a result of the t-test, it was confirmed that the software class based on peer tutoring was helpful in enhancing the learner's computational thinking ability. As a result of qualitative analysis, the learner's positive learning experience was ‘improved task performance speed through division of labor and overcome difficulties through collaboration’, ‘active opinion presentation and efforts to overcome disagreement’, ‘consideration for peers’, ‘value experience of co-writing a coding plan’, and ‘repeated challenge to verify hypotheses’. Then ‘Unconditional acceptance of opinions and avoidance of conflict’, ‘problems of fairness in task weighting’, and ‘dissatisfaction with partners’ were confirmed as negative learning experiences. Conclusions This study was able to confirm that learners' cooperative interactions, such as active communication between peers and structured problem-solving exploration in software classes, affect learners cognitively and affectively. Based on this, it was confirmed that peer tutoring-based software is meaningful in suggesting important factors to be considered in designing classes.
Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality.
A case study on migrating large digital collections into a consolidated repository

On July 1, 2020, the University of South Florida underwent a consolidation to merge three independently accredited branch campuses under a single umbrella. The Libraries responded to this change by consolidating their institutional repositories, which included a combination of theses, dissertations, faculty publications, open access journals, faculty publications, and digital collections. To make the most of this shared platform, administration determined that the Tampa campus would also merge their independent Digital Collections into the shared repository. Beginning in spring 2021, a team from the Tampa campus undertook the task of migrating over 65,000 digital objects across platforms into a newly consolidated institutional repository comprised of both digital scholarship and collections from multiple campuses. In addition to the technical improvements, holistic review of metadata structures, and data cleanup, the review opened the door to implement conscious editing initiatives to improve the language and discoverability of collections representing diverse populations. With the scale of the contents, it was not feasible to manually migrate items one at a time, so a batch method was devised. The original repository included item level metadata via METS/MODS XML. A script was created to scrape, concatenate, and transform the metadata into the Digital Commons batch upload XML standard. Accounting for unique metadata schemas throughout the repository, the batch process needed to be updated and revised on a collection-by-collection basis. This case study discusses the project planning, management and implementation of the migration.
Echo-Enhanced Embodied Visual Navigation

Visual navigation involves a movable robotic agent striving to reach a point goal (target location) using vision sensory input. While navigation with ideal visibility has seen plenty of success, it becomes challenging in suboptimal visual conditions like poor illumination, where traditional approaches suffer from severe performance degradation. We propose E3VN (echo-enhanced embodied visual navigation) to effectively perceive the surroundings even under poor visibility to mitigate this problem. This is made possible by adopting an echoer that actively perceives the environment via auditory signals. E3VN models the robot agent as playing a cooperative Markov game with that echoer. The action policies of robot and echoer are jointly optimized to maximize the reward in a two-stream actor-critic architecture. During optimization, the reward is also adaptively decomposed into the robot and echoer parts. Our experiments and ablation studies show that E3VN is consistently effective and robust in point goal navigation tasks, especially under nonideal visibility.
3D reconstruction of light-field images based on spatiotemporal correlation super-resolution

In this paper, we make full advantage of the information correlation of subaperture images and propose a new super-resolution (SR) reconstruction method based on spatiotemporal correlation to achieve SR reconstruction for light-field images. Meanwhile, the offset compensation method based on optical flow and spatial transformer network is designed to realize accurate compensation between adjacent light-field subaperture images. After that, the obtained light-field images with high resolution are combined with the self-designed system based on phase similarity and SR reconstruction to realize accurate 3D reconstruction of a structured light field. Finally, experimental results demonstrate the validity of the proposed method to perform accurate 3D reconstruction of light-field images from the SR data. Generally, our method makes full use of the redundant information between different subaperture images, hides the upsampling process in the convolution, provides more sufficient information, and reduces time-consuming procedures, which is more efficient to realize the accurate 3D reconstruction of light-field images.
MGM beyond the birthday bound

In this work we study the security of Russian authenticated encryption with associated data mode that is known as MGM. We examine the mode properties under the condition that we have $$\mathcal {O}\left( 2^{n/2} \right) $$ queries, where n is the state size of the used block cipher. Two attacks that are based on birthday paradox are proposed. One of these attacks does not reuse nonse and allows you to generate a message with the correct authentication code without knowing the secret key. It should be noted that the number of protected information on one key with MGM mode does not exceed $$2^{n/2}$$ bits.
A Novel Membership Function Definition for Fuzzy Classification

In this paper, a novel membership function is defined for fuzzy sets using a supervised learning approach. Firstly, the training dataset is separated using the previously defined polyhedral conic functions in a supervised learning approach. Then obtained polyhedral conic functions are used for defining a new membership function. After that, a new fuzzy classification algorithm is formed to classify fuzzy sets with a similar structure. The algorithm with all suggested methods is implemented on real-world datasets, and the performance values are compared with the state of art classification algorithms.
Research on application of BBMOPSO algorithm in building energy efficiency design

In order to meet the goals of minimum building energy consumption and high user comfort, PSO algorithm is applied to multi-objective optimization; Aiming at the problems of PSO with many control parameters and easy to fall into local optimum, a BBPSO algorithm with few control parameters is designed, and an adaptive factor is introduced to improve the convergence speed of particles. Through testing and application practice in architectural design time, the results show that on different test sets, BBMOPSO-A has the least number of iterations and the smallest convergence value of 0.009, which has excellent convergence performance; BBMOPSO-A in the optimal solution overlay graph-A has the lowest energy consumption and the least discomfort time; in the HV value curves of building designs in different regions, the BBMOPSO-A curve performs the best compared to the comparison algorithm, and with the increase of variable parameters, the optimization of the algorithm better performance. In practical applications, BBMOPSO-A can better take into account the relationship between building energy consumption and comfort, achieve coordinated optimization among multiple objectives.
Adaptive fractional differential algorithm for image edge enhancement and texture preserve using fuzzy sets

Abstract This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself.
A non-linear source-filter based vocoder with prosody control

Speech signal reconstruction from its compact acoustic representation is a challenging task. Although the acoustic representations obtained from the speech processing systems (like Text-to-speech synthesis, speech enhancement, etc.) are highly accurate, the performance of the vocoder affects the naturalness of the synthesized speech. Conventional vocoders are based on the linear source-filter model of the human speech production mechanism. But, we can’t incorporate them in training an end-to-end model, and they are vulnerable to the estimated acoustic representations. Neural vocoders like WaveNet can be incorporated in training end-to-end models. But the complexity and the inference time are pretty high and do not have provision to control the prosody. In this paper, we propose a neural network based compact non-linear vocoder with prosody control using the source-filter model of the human speech production mechanism. We can effectively control the prosody of the synthesized speech by controlling the prosodic parameters like fundamental frequency $(f_{0})$ without affecting the naturalness of the speech. The model achieves a better performance with a mean opinion score (MOS) of 4.09, with a much lower real-time factor and model complexity.
Adversarial Robustness via Class Partitioning

Classifiers based on Deep Neural Networks (DNNs) are vulnerable to adversarial attacks. To protect DNNs from adversarial attacks, recent research suggests the use of a diverse ensemble of models and combining their outputs. While this provides defence under adversarial input, the total number of parameters in the ensemble is significantly higher when compared to a single network. In this work, we propose a new method for designing and training model ensembles that provides equivalent protection with much fewer parameters. The main idea is to partition the classes into groups and train a shared part in every neural network of the ensemble to separate the classes but cluster groups together by using a carefully designed loss function. The rest of the neural network is trained to classify smaller sets of training classes containing one class from each group. The training classes are varied for different models to achieve diversity. Evaluations with the MNIST and CIFAR10 data sets confirm the effectiveness of our approach when compared with other existing approaches.
Effective WBC Segmentation Using Hybrid Loss

The performance of computer-aided blood related disease detection depends on accuracy of white blood cell segmentation. The segmentation task is very challenging mainly due to data imbalance problem. To address the problem of data imbalance a hybrid loss is proposed which focus more on pixels which are mispredicted and less in number. The hybrid loss achieves a good trade off between recall and precision by reducing the counts of false positive and false negative instances. The hybrid loss helped in achieving F1-score of 0.936, 0.868, and 0.943 using UNet, attention based UNet, and attention based UNet having the residual block, respectively.
Machine Learning Decoder for 5G NR PUCCH Format 0

5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected neural networks to classify the received samples based on the uplink control information content embedded within them. The trained neural network, tested on real-time wireless captures, shows significant improvement in accuracy over conventional DFT-based decoders, even at low SNR. The obtained accuracy results also demonstrate conformance with 3GPP requirements.
On Secret Sharing Schemes from Nonlinear Codes

In a secret sharing scheme, a secret is divided into shares and then distributed among participants. The legitimate sets of participants that can recover the secret is called the access structure of the secret sharing scheme. The existing constructions of secret sharing schemes are primarily based on linear error correcting codes. These schemes are however susceptible to “Tompa-Woll attack”. Recently, secret sharing schemes have been designed based on nonlinear error correcting codes to resist “Tompa-Woll attack”. In this paper, we determine the access structure of several nonlinear codes and examine its performance against “Tompa-Woll attack”. We explained that access structure based on nonlinear code is obtained from it’s formally dual code. We also explain the idea of finding the access structure of secret sharing scheme of shortened and punctured code from the access structure of secret sharing scheme of their parental code.
Robust Manipulation Parameter Estimation Scheme for Post-JPEG Compressed Images using CNN

The importance of estimating the manipulation parameters in forged images has increased to grasp the life cycle of the image and gives forensic clues. Although several techniques have been put forth to do this, they are limited to a single alteration in the uncompressed scenario. However, estimation is difficult for multiple manipulations because a new image modification is produced every time. Therefore, it is necessary to repeat the procedure of creating a mathematical framework to determine a parameter estimation. To get around this, we create a deep residual framework that can estimate multiple manipulation parameters in a post-JPEG compressed image with achievable arbitrary precision. Specifically, the framework goes through the steps of noise residual extraction, feature extraction, and classification. To begin, the front-end detector is greatly augmented by the noise residual extraction stage, which adds three residual blocks with skip connections to generate noise residuals by reducing visual features and boosting alteration clues. Further, characteristics of the deep tampering clues are then obtained using a cross-feature learning technique and procured to fully connected layers for classification. To improve forged parameter estimates in the real world, we conduct experiments on three modifications and then apply several quality factors to the results. To top it all off, the proposed MPeR-Net outperforms state-of-the-art methodologies in the most crucial instance of lossy post-JPEG compression.
A heterogeneous stacking ensemble-based security framework for detecting phishing attacks

This paper proposes a heterogeneous stacking ensemble-based security framework comprising three different base-level classifiers, and a single meta-level classifier for detecting phishing attacks. The proposed framework initially extracts 24 different URL based and 20 distinct web-page based feature from a given URL corpus. These features are then used to create feature vectors for training the base-level classifiers of the proposed framework. The trained base-level classifiers are then evaluated on the unseen feature vectors from the test set. The output vectors produced by the trained base-level classifiers are then combined into a dataframe object, and used for training and evaluating a fully connected neural network(FCNN) based meta-level classifier. The trained meta-level classifier makes the final prediction about whether the web-pages corresponding to given URLs are benign or malicious(phishing). Experimental results on two benchmark datasets namely, PhishTank & UNB datasets show that the proposed framework achieves an accuracy of 99% on the binary class PhishTank & UNB datasets, while it achieves an accuracy of 98% on multiclass UNB dataset. The proposed framework is also shown to outperform other similar phishing attack detection frameworks proposed in the literature.
A Random-key Based Second-level Encryption for Reversible Data Hiding in Encrypted Images

Reversible data hiding is an area explored widely in recent days due to its scope of applications in secure message transmission by embedding it in images. The existing RDH schemes in the encrypted image are lagging in terms of embedding rate. In this paper, we propose a new RDH scheme in encrypted images that will ensure the embedding rate without compromising the bit error rate or image recovery. In the proposed scheme, the encrypted image pixels will be classified into black-and-white pixels based on a checkerboard pattern. During the data hiding phase, the sender will select 8 unique random integer sequences S, that form the data keys and whose values are within the range 0 to 255. Data hiding is performed by performing bit-XOR with the white pixels in an image block (size B $\times$ B pixels) using one integer sequence from the 8 keys. Each key is correlated to a 3-bit sequence from the secret message. The receiver must have the data keys and the decryption key to extract the hidden message and recover the original image. A smoothness measure between adjacent pixels is defined and used for data extraction and image recovery. The experimental results show that the proposed scheme performs well on the standard image data set (USC-SIPI).
Rate Adaptation For Low Latency Real-Time Video Streaming

We study video rate control as a means to minimize latency for a real-time video stream while ensuring good video quality. When the channel capacity changes, we react by changing the video rate appropriately such that latency does not degrade significantly and, at the same time, capacity is not severely underutilized. We setup the underlying control problem as a Markov Decision Process and obtain two different policies using a greedy approach and the Stochastic Gradient Descent (SGD) method. We compare the two policies by implementing variants of them over a testbed comprising FFmpeg video codec transmitting over WiFi and wired networks. Our policies require estimates of bottleneck channel capacity and queue size at the video source. We devise a network-agnostic technique to estimate these parameters. We evaluate the performance of the two policies first in a controlled setting over a wired network and then in an uncontrolled setting over WiFi 802.11n network. For a 30 fps video, we conclude that when the capacity is fluctuating over short intervals of time, which is the case in real wireless networks, though the greedy approach achieves slightly higher capacity utilization, but SGD performs significantly better in achieving lower latency while still maintaining a good video bit rate.