Reviewerly/exHarmony huggingface dataset:
A new quantum-safe multivariate polynomial public key digital signature algorithm

Abstract We propose a new quantum-safe digital signature algorithm called Multivariate Polynomial Public Key Digital Signature (MPPK/DS). The core of the algorithm is based on the modular arithmetic property that for a given element g, greater than equal to two, in a prime Galois field GF(p) and two multivariate polynomials P and Q, if P is equal to Q modulo p-1, then g to the power of P is equal to g to the power of Q modulo p. MPPK/DS is designed to withstand the key-only, chosen-message, and known-message attacks. Most importantly, making secret the element g disfavors quantum computers’ capability to solve the discrete logarithm problem. The security of the MPPK/DS algorithm stems from choosing a prime p associated with the field GF(p), such that p is a sum of a product of an odd prime number q multiplied with a power x of two and one. Given such a choice of a prime, choosing even coefficients of the publicly available polynomials makes it hard to find any private information modulo p-1. Moreover, it makes it exponentially hard to lift the solutions found modulo q to the ring of integers modulo p-1 by properly arranging x and q. However, finding private information modulo the components q and power x of two is an NP-hard problem since it involves solving multivariate equations over the chosen finite field. The time complexity of searching a private key from a public key or signatures is exponential over GF(p). The time complexity of perpetrating a spoofing attack is also exponential for a field GF(p). MPPK/DS can achieve all three NIST security levels with optimized choices of multivariate polynomials and the generalized safe prime p.
Denoising Pretraining for Semantic Segmentation

Semantic segmentation labels are expensive and time consuming to acquire. To improve label efficiency of semantic segmentation models, we revisit denoising autoencoders and study the use of a denoising objective for pretraining UNets. We pretrain a Transformer-based UNet as a denoising autoencoder, followed by fine-tuning on semantic segmentation using few labeled examples. Denoising pretraining outperforms training from random initialization, and even supervised ImageNet-21K pretraining of the encoder when the number of labeled images is small. A key advantage of denoising pretraining over supervised pretraining of the backbone is the ability to pretrain the decoder, which would otherwise be randomly initialized. We thus propose a novel Decoder Denoising Pretraining (DDeP) method, in which we initialize the encoder using supervised learning and pretrain only the decoder using the denoising objective. Despite its simplicity, DDeP achieves state-of-the-art results on label-efficient semantic segmentation, offering considerable gains on the Cityscapes, Pascal Context, and ADE20K datasets.
Aligning digital literacy and student academic success: Lessons learned from COVID-19 pandemic

In this paper the alignment of computer and digital literacy as well as student academic success were examined. Lack of adequate functional digital literacy training and the unreadiness of higher education institutions for the impact of random shocks such as COVID-19 pandemic has gravely affected teaching and learning. The purpose of this paper is to make the case for preparedness of students to meet the needs of technology jobs by mandating and aligning digital literacy and student success. As COVID-19 Pandemic was spreading through the communities of the United States, institutions of higher education transitioned to fully online teaching and learning. Prior to the pandemic, fully online education was secondary to face-to-face format. Only about 20% of classes were fully online while 80% were face-to-face. Digital literacy was given a token treatment as a percentage of the entire curriculum and relegated to only the departments of computer information systems and computer sciences. Faculty, students, staff, families, and communities were not trained for the intensity of fully online education as the only format. Many of them never heard of most of the digital literacy tools. Both faculty and students were forced to learn the use of computers and digital literacy tools to survive the spring 2020 semester. Low-income families were left to the operational schedules of local libraries, many of whom did not have internet presence. The institutions provided limited training for faculty and students to meet the urgency of the time. Faculty and students were forced to purchase expensive tools and hardware to withstand the intensive demand of teaching and learning. Many students were overwhelmed by the pressure of the new way of learning; some dropped out of school while many performed very poorly in their examinations which negatively impacted their overall grade point averages. One year later in spring 2021, the student success outcomes have barely changed. At the same time, technology is advancing despite the raging COVID pandemic. Millions of technology-enabled jobs remain unfilled while millions of university graduates are unemployed. There continues to be a mismatch between current job requirements in the industries and graduating students’ skills. This paper discusses the indispensable value of building computer and digital literacy training into all undergraduate curriculums. We argue for mandated computer and digital literacy exit skills assessment test for all graduating students irrespective of their discipline. We also make the case for increased institutional investment in faculty training in computer and digital literacy readiness. There are a number of remedies suggested to address the speed of advancement in technology, faculty and student functional mastery of basic computer and digital literacy skills. We conclude that all institutions must be proactive rather than reactive to systemic shocks by preparing students for academic success and technological readiness for today’s job markets
A survey of transformer-based multimodal pre-trained modals

With the broad industrialization of Artificial Intelligence(AI), we observe a large fraction of real-world AI applications are multimodal in nature in terms of relevant data and ways of interaction. Pre-trained big models have been proven as the most effective framework for joint modeling of multi-modality data. This paper provides a thorough account of the opportunities and challenges of Transformer-based multimodal pre-trained model (PTM) in various domains. We begin by reviewing the representative tasks of multimodal AI applications, ranging from vision-text and audio-text fusion to more complex tasks such as document layout understanding. We particularly address the new multi-modal research domain of document layout understanding. We further analyze and compare the state-of-the-art Transformer-based multimodal PTMs from multiple aspects, including downstream applications, datasets, input feature embedding, and model architectures. In conclusion, we summarize the key challenges of this field and suggest several future research directions.
Metaverse-based virtual reality experience and endurance performance in sports economy: Mediating role of mental health and performance anxiety

Metaverse sports arena is gaining popularity globally that empowers virtual reality sporting experience through digital avatars. The main objective of the current study is to explore the impact of the Metaverse-based virtual reality sporting experience on the endurance performance of young Chinese athletes, with the mediating role of their mental health condition and performance anxiety. The study's participants mainly included Chinese athletes, especially the sample group is an accurate depiction of young athletes using a convenience sampling approach. SEM-AMOS statistical software was used for the analysis and validation of the proposed relationships. The study findings statistically validate that mental health and performance anxiety fully mediate the direct associations between virtual reality sporting experiences and the endurance performance of young Chinese athletes. Interestingly, the mental health condition of the young Chinese athletes imposes a greater impact on their endurance performance, in contrast to the adverse effects of their performance anxiety. The outcomes of the present research guide young athletes on the opportunities to enhance their virtual reality sporting abilities and boost their endurance performance. Policymakers can also build systems to dissolve physical and geographical barriers, reduce performance anxiety, and sustain mental health in virtual reality sporting events through the metaverse.
ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

Abstract Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin &amp; eosin (H&amp;E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&amp;E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&amp;E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&amp;E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&amp;E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.
A critical professional ethical analysis of Non-Fungible Tokens (NFTs)

Non-Fungible Tokens (NFTs) have quickly become an important part of the blockchain economy, theoretically representing ownership of a digital asset registered on a public blockchain such as Ethereum. While several applications of this technology exist, the key underlying factor in NFTs’ success is in their potential for investment – buying, selling, and trading the digital assets such as artwork or video game items using cryptocurrency. The rise and mid-2022 crash of NFT and associated crypto markets have shown the volatility of the sector, and questions have been raised around the sustainability, environmental impact, and exploitative practices within this space – and whether there are, in fact, any possible socially responsible use cases for NFTs. This paper aims to fill a gap in the literature surrounding NFTs, primarily through a thorough ethical analysis of the technology and its implementation, deployment, and sustainability. To do this, it uses the Association of Computing Machinery's Code of Ethics and Professional Conduct as a framework for analysis and, following this analysis, makes some recommendations for those wishing to investigate and/or implement NFTs in an ethically responsible manner. The key message is that unless there is absolutely no other way to solve a problem other than using NFTs, then they should not be implemented, as there is currently no ethical use case or means of implementation of NFTs.
Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection

Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from existing work, we have a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the training set distribution which can be leveraged for reliable OOD detection. Our empirical study on validating this hypothesis, which measures the model activation's mean for OOD and in-distribution (ID) minibatches, surprisingly finds that activation means of OOD mini-batches consistently deviate more from those of the training data. In addition, training data's activation means can be computed offline efficiently or retrieved from batch normalization layers as a ‘free lunch’. Based upon this observation, we propose a novel metric called Neural Mean Discrepancy (NMD), which compares neural means of the input examples and training data. Leveraging the simplicity of NMD, we propose an efficient OOD detector that computes neural means by a standard forward pass followed by a lightweight classifier. Extensive experiments show that NMD outperforms state-of-the-art OOD approaches across multiple datasets and model architectures in terms of both detection accuracy and computational cost.
A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions

Natural language could be a common method for individuals to specific social relationships or self-evaluation (i.e., assured or unconfident). Hence, it is important to contemplate customized linguistics in collective decisions that involve linguistic variables, because the same linguistic term set may have totally different individual numerical scales. This research develops a consensus model taking into consideration the personalized individual self-confidence and trust semantics in dynamic social network group decision-making scenarios. Firstly, an optimization model driven by best additive consistency is proposed to derive the personalized self-confidence and trust semantics of individuals. Subsequently, a trust aggregation technique supporting the shortest path is outlined for weight distribution. The proposed technique not only considers the personalized individual trust linguistics but can also effectively decrease the risk of information distortion caused by trust decay. Moreover, a consensus feedback strategy is developed that combines opinion adjustment and dynamic trust interaction to enhance the potency of consensus. Totally different from most existing consensus models supported static trust network, the bestowed consensus feedback mechanism considers the inverse driving result of opinion adjustment on trust interaction within the consensus reaching processes. Finally, numerical examples and comparisons are provided to justify the practicability of the proposed model.
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark. Code and models are available at github.com/dvlab-research/VoxelNeXt.
Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment

Abstract This paper has given a fresh approach of hybridization of invasive weed optimization (IWO) with improved particle swarm optimization (IPSO) to obtain the optimal path for each robot in multi-robots system in a non-stationary environment. The main emphasis of the algorithm is to lessen the path length and time taken by all robots to arrive at the respective destined target in the cluttered environment. Each robot takes independent decisions of their own to evaluate the next positions from their current position in the global map using proposed hybrid IWO-IPSO. The proposed approach embedded the spatial dispersion method of IWO with motion mechanism of IPSO called IWO-IPSO. The main aim of the hybridization is to maintain a good balance between intensification and diversification and avoid local minima value because of adopting co-evolutionary techniques to update the velocity and particle positions with IPSO simultaneously. The effectiveness of the algorithm has been validated through simulation and real platform. Finally, the experimental and simulation results of IWO-IPSO for multi-robot path planning are weighed against with results obtained by IPSO, IWO in a analogous environment to make sure the integrity of the algorithm is maintained. The results obtained from simulation on top of Khepera environment justifies that, the proposed IWO-IPSO performs superiorly than IPSO and IWO with respect to arrival time, energy utilization during travel of path, optimal path length and deviation of path.
Evaluation of noise in measurements with speckle shearography

Speckle shearography has the advantage of allowing non-contact, full-field and high resolution measurements. Nevertheless, due to its nature and to its sensitivity to ambient perturbations, the measurements may present high levels of noise. The evaluation of the experimental noise is needed in order to assess the degree of accuracy of the measurements. This assessment is not straightforward since the measurements will involve resolving simultaneously the phase discontinuities and the phase fluctuations produced by the noise which are 2π wrapped. The phase discontinuities can be easily removed if the phase map is smooth and the phase jumps are not greater than 2π rad. However, this procedure cannot be directly applied for resolving the random phase fluctuations produced by the noise. In this work, a method is proposed to efficiently evaluate the noise in measurements with speckle shearography whenever it is in the range [−π, π]. The limits of the proposed method are assessed by applying it to phase maps with added noise, which are obtained through the numerical simulation of speckle shearography. The results show a very good evaluation of noise with low and moderate amplitudes. Finally, the proposed method is used to evaluate the experimental noise in rotation fields of a free-free aluminum beam measured with speckle shearography.
A Universal Optical Flow Based Real-Time Low-Latency Omnidirectional Stereo Video System

Omnidirectional stereoscopic video (ODSV) is a key element of creating an immersive experience for virtual reality that has attracted extensive interest while presenting many technical challenges. Two such key challenges are real-time, low-latency high-quality seamless video stitching from multiple cameras, and faithful reconstruction of 3-D information. Even though various attempts have been made to achieve different combinations of real-time, low-latency, automation, and high output resolution in stereoscopic panoramic video communication, achieving these characteristics simultaneously remains a challenge to be tackled. In this paper, we present a universally applicable and practical end-to-end system based on a novel real-time optical flow algorithm to produce high-quality real-time ODSV with reconstructed 3-D depth information at low latency. Through a configurable process, various camera systems can be calibrated and seamlessly stitched together using the proposed system. The stitched 3-D panoramic video is encoded with a standard compliant video encoder that is optimized for panoramic video. Thanks to various optimizations introduced in this paper, the proposed system is capable of producing real-time ODSV of ultra High definition resolution with a glass to glass latency of 2.2 s using a desktop computer with a single Nvidia graphic card. Experiments show that the proposed system achieves an encoding performance superior to existing open-source HEVC implementations and an optical flow estimation performance better than the Facebook algorithm while running two orders of magnitudes faster.
Direct Uncertainty Prediction for Medical Second Opinions

The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. In medicine, this often corresponds to doctor disagreements on a patient diagnosis. In this work, we show that machine learning models can be trained to give uncertainty scores to data instances that might result in high expert disagreements. In particular, they can identify patient cases that would benefit most from a medical second opinion. Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score. We show this both with a theoretical result, and on extensive evaluations on a large scale medical imaging application.
An Improved Integer Transform Combining with an Irregular Block Partition

After conducting deep research on all existing reversible data hiding (RDH) methods based on Alattar’s integer transform, we discover that the frequently-used method in obtaining the difference value list of an image block may lead to high embedding distortion. To this end, we propose an improved Alattar’s transform-based-RDH-method. Firstly, the irregular block partition method which makes full use of high correlation between two neighboring pixels is proposed to increase the embedding performance. Specifically, each image block is composed of a center pixel and several pixels surrounding this center pixel. Thus, the difference value list is created by using the center pixel to predict each pixel surrounding it. Since the center pixel is highly related to each pixel surrounding it, a sharp difference value histogram is generated. Secondly, the mean value of an image block in Alattar’s integer transform has embedding invariance property, and therefore, it can be used for increasing the estimation performance of a block’s local complexity. Finally, two-layer embedding is combined into our scheme in order to optimize the embedding performance. Experimental results show that our method is effective.