Reviewerly/exHarmony huggingface dataset:
Two New Families of Quadratic APN Functions

In this paper, we present two new families of APN functions. The first family is in bivariate form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\big (x^{3}+xy^{2}+ y^{3}+xy, x^{5}+x^{4}y+y^{5}+xy+x^{2}y^{2} \big)\,\,\vphantom {_{\int _{\int }}}$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{m}}^{2}$ </tex-math></inline-formula> . It is obtained by adding certain terms of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sum _{i}(a_{i}x^{2^{i}}y^{2^{i}},b_{i}x^{2^{i}}y^{2^{i}})$ </tex-math></inline-formula> to a family of APN functions recently proposed by Gölo&gcaron;lu. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vphantom {_{\int _{\int }}}$ </tex-math></inline-formula> second family has the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L(z)^{2^{m}+1}+vz^{2^{m}+1}$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{{2^{3m}}}$ </tex-math></inline-formula> , which generalizes a family of APN functions by Bracken et al. from 2011. By calculating the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> -rank of the constructed APN functions over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{8}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{9}}$ </tex-math></inline-formula> , we demonstrate that the two families are CCZ-inequivalent to all known families. In addition, the two new families cover two known sporadic APN instances over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{8}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{9}}$ </tex-math></inline-formula> , which were found by Edel and Pott in 2009 and by Beierle and Leander in 2021, respectively.
Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm

Breast cancer is a common and major health concern in women across the world. Early prognosis can ensure better and cost-effective treatment thus reducing the mortality rate. In recent years researchers are proposing different models for the early prediction of breast cancer. In this paper computer-aided diagnosis (CAD) using breast ultrasound images has been proposed for multi-classification that is to classify tumors into benign, malignant, or normal. We propose a hybrid approach by combining a pre-trained Convolutional neural network (CNN) with optimization and machine learning for tumor diagnosis. The CNN pre-trained model ResNet-50 is used in this study for feature extraction, binary gray wolf optimization (BGWO) for feature selection, and classification Support vector machine (SVM). We examine two different approaches: a Transfer Learning (TL) approach and a machine learning approach and we assess their performance with and without BGWO for feature selection. There was a total of 780 images in the BUSI dataset, with 437 benign, 210 malignant, and 133 normal images. The classification accuracy and the Area under the receiver operating characteristics (AUROC) of the proposed method were 84.9% and 0.97. The computation time taken is also reduced with feature selection to 4.41 seconds from 18.275 seconds (without feature selection). Finally, when tested on the BUSI dataset, the results show that the feature selection method improves overall performance.
A Stable Adaptive Adversarial Network With Exponential Adversarial Strategy for Bearing Fault Diagnosis

Given an industry’s development, fault diagnosis has received significant attention. Owing to complex and changeable working conditions and lacking marked fault data for fault diagnosis, domain adaptation has become a new solution. However, a variable operating environment will cause changes in data distributions, which complicates domain adaptation. Resultantly, a challenge exists in measuring a data distribution and combining it with domain adaptation. Thus, this article proposes a method of dynamically adapting a marginal distribution and a conditional distribution, including a new adaptive factor, which can use distance metrics and exponential functions to stably adapt to different data distributions in source and target domains. By dynamically adjusting the importance of the marginal and conditional distributions in a model, the proposed model can achieve excellent diagnostic results. Compared with a fixed-scale model without an adaptive factor that only has high diagnostic results for some working conditions, the proposed model has stable and accurate diagnosis results, whether it is facing different speeds and different loads. In addition, experiments are conducted to verify the effectiveness and usability of the proposed model.
Aggregating Fuzzy Sentiments with Customized QoS Parameters for Cloud Provider Selection Using Fuzzy Best Worst and Fuzzy TOPSIS

Consumers often get confused to select the best cloud providers from the huge marketplace. The hesitancy of consumers further escalates when multiple service providers offer the same type and quality of services. To deal with such an uncertainty, the decision-makers always combine multiple factors to make an informed choice. Sentiment mining is one of the key parameters to determine the service quality and get an insight into the business. It assists service providers in precisely deduce consumer's emotions regarding the product. The analysis helps providers fine-tune the product based on consumer's sentiment and accommodates the consumer's request to find an optimal service provider. Several existing literature for cloud service selection mainly focuses on the Quality of Service (QoS) of the offered services. However, very few of them have considered the user experience of a consumer in the decision-making process. Moreover, there is minimal literature that amalgamates Quality of Experience (QoE) with customized Quality of Service (QoS) requirements to decide on a complex framework. The paper addresses the issue by aggregating consumer’s sentiments with customized QoS parameters to choose an optimal service provider. The paper uses the fuzzy Best Worst Method (BWM) to determine the weights of selection criteria and use the fuzzy TOPSIS to handle the uncertain linguistic preference. Analysis results demonstrate the applicability and effectiveness of the framework.
A Noninteractive Multireplica Provable Data Possession Scheme Based on Smart Contract

With the explosive growth of data, cloud storage has become a widely used storage method. To protect the integrity and availability of data in cloud storage systems, multireplica provable data possession has gradually become a research hotspot. This paper uses smart contracts to replace traditional third-party auditor (TPA) and proposes a noninteractive multireplica provable data possession scheme based on smart contracts, making the verification process public, immutable, traceable, and able to be carried out periodically and automatically. This paper introduces the concept of noninteractivity to reduce the transaction fees caused by the frequent operation of blockchain in the verification process. By stipulating payment rules in the smart contract, we can ensure the fairness of all parties. Finally, we give the correctness proof of the scheme and the security proof in the random oracle model, comparing it with other schemes and verifying the practicability of our scheme through experiments.
Deterministic secure quantum communication with double-encoded single photons

Quantum communication is an important branch of quantum technology. It can safely transmit private information between legitimate parties and its unconditional security is guaranteed by quantum physics. So far, deterministic secure quantum communication without entanglement usually transmits single photons in two-way quantum channels. We propose a deterministic secure quantum communication proposal, and it requires a one-way quantum channel and a classical channel. In our protocol, a sender encodes logical bits by using two conjugate bases consisting of the polarization and time-bin degrees of freedom of a photon and transmits it to a receiver over a quantum channel. Upon receiving this photon, the receiver measures it randomly in two bases and can decode the bit deterministically with the help of the sender. Any attack from eavesdroppers will be detected by the legitimate parties. Furthermore, this protocol can be implemented with linear-optic elements and single-photon detectors.
Fake It Till You Make It: Data Augmentation Using Generative Adversarial Networks for All the Crypto You Need on Small Devices

Deep learning-based side-channel analysis performance heavily depends on the dataset size and the number of instances in each target class. Both small and imbalanced datasets might lead to unsuccessful side-channel attacks. The attack performance can be improved by generating traces synthetically from the obtained data instances instead of collecting them from the target device, but this is a cumbersome and challenging task. We propose a novel data augmentation approach based on conditional Generative Adversarial Networks (cGAN) and Siamese networks, enhancing the attack capability. We also present a quantitative comparative deep learning-based side-channel analysis between a real raw signal leakage dataset and an artificially augmented leakage dataset. The analysis is performed on the leakage datasets for both symmetric and public-key cryptographic implementations. We investigate non-convergent networks’ effect on the generation of fake leakage signals using two cGAN based deep learning models. The analysis shows that the proposed data augmentation model results in a well-converged network that generates realistic leakage traces, which can be used to mount deep learning-based side-channel analysis successfully even when the dataset available from the device is not optimal. Our results show that the datasets enhanced with “faked” leakage traces are breakable (while not without augmentation), which might change how we perform deep learning-based side-channel analysis.
Pseudo Segmentation for Semantic Information-Aware Stereo Matching

Stereo matching plays an important role in computer vision and robotics. Though substantial progress has been made on deep learning-based algorithms, the inherent semantic information within the ground truth of the training data for stereo matching has not been well explored. In this letter, we propose to use a pseudo segmentation sub-network to extract additional semantic information. More specifically, we divide the disparity label into groups and let each group correspond to a class for pseudo segmentation. To assist stereo matching with the semantic information obtained from pseudo segmentation, we inject the feature maps at the end of the pseudo segmentation sub-network into the cost volume that is used to infer the pixel-level disparity. To validate the effectiveness of the proposed approach, we select PSMNet (Chang and Chen, 2018)and GwcNet (Guo <i>et al.</i>, 2019) as baselines and enhance them with the pseudo segmentation sub-network. Comprehensive experiments are carried out on the Scene Flow, KITTI 2015, and KITTI 2012 datasets, and the results show that our proposed method can improve the performance notably.
Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features

Topic classification systems on spoken documents usually consist of two modules: an automatic speech recognition (ASR) module to convert speech into text and a text topic classification (TTC) module to predict the topic class from the decoded text. In this paper, instead of using the ASR transcripts, the fusion of deep acoustic and linguistic features is used for topic classification on spoken documents. More specifically, a conventional CTC-based acoustic model (AM) using phonemes as output units is first trained, and the outputs of the layer before the linear phoneme classifier in the trained AM are used as the deep acoustic features of spoken documents. Furthermore, these deep acoustic features are fed to a phoneme-to-word (P2W) module to obtain deep linguistic features. Finally, a local multi-head attention module is proposed to fuse these two types of deep features for topic classification. Experiments conducted on a subset selected from Switchboard corpus show that our proposed framework outperforms the conventional <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ASR}+\text{TTC}$</tex> systems and achieves a 3.13% improvement in ACC.
Dynamic evaluation of college English writing ability based on AI technology

Abstract To accurately evaluate and improve college students’ English writing ability, this article proposes a dynamic evaluation method of college English writing ability based on artificial intelligence technology. First, a dynamic evaluation model of college English writing ability is constructed. Second, the index system of English writing dynamic evaluation model is established. Based on this, the dynamic evaluation of college English writing ability is realized. The experimental results show that the design method in this paper can effectively realize the dynamic evaluation of the writing process. After the application of the design method, the number of students interested in writing has increased by 37.8%, and the enthusiasm of students to participate in writing has been improved, with a view to providing some help to improve students’ English writing ability through this research.
Secure Multi-Party Quantum Summation Based on Quantum Homomorphic Encryption

Secure multi-party computation has been playing a fundamental role in terms of classical cryptography.Quantum homomorphic encryption (QHE) could compute the encrypted data without decryption.At present, most protocols use a semi-honest third party (TP) to protect participants' secrets.We use a quantum homomorphic encryption scheme instead of TP to protect the privacy of parties.Based on quantum homomorphic encryption, a secure multi-party quantum summation scheme is proposed in which N participants can delegate a server with strong quantum computing power to assist computation.By delegating the computation and key update processes to a server and a semi-honest key center, participants encrypt their private information data using Pauli operators to get the sum.Besides, the server can design and optimize the summation lines itself, and the correct results can be obtained even if the secret information is negative.The correctness analysis showed that the participants could correctly obtain the results of the calculation.The security analysis proves the scheme is resistant to both outside attack and participant's attack, and is secure against collusive attack by up to N-2 participants.From the theoretical point of view, our protocol can extend to other secure multi-party computing problems.
Electromagnetic Analysis of Integrated On-Chip Sensing Loop for Side-Channel and Fault-Injection Attack Detection

Securing crypto-cores is becoming increasingly difficult with the advent of electro-magnetic (EM) side-channel analysis (EMSCA) and fault injection attacks (FIAs). This letter presents an Ansys high-frequency structure simulator (HFSS)-based simulation framework for EM analysis of an integrated on-chip sensor for detecting EMSCA and FIA and validates the efficacy of an on-chip higher metal layer loop-based zero area-overhead sensor using a custom-built 65-nm CMOS IC. A simple technique for incoming H-probe detection is also presented by measuring the absolute average of the induced voltage for every encryption.
Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection

This paper focuses on the Unsupervised Salient Object Detection (USOD) issue. We come up with a two-stage Activation-to-Saliency (A2S) framework that effectively excavates saliency cues to train a robust saliency detector. It is worth noting that our method does not require any manual annotation in the whole process. In the first stage, we transform an unsupervisedly pre-trained network to aggregate multi-level features into a single activation map, where an Adaptive Decision Boundary (ADB) is proposed to assist the training of the transformed network. Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels. In the second stage, a self-rectification learning strategy is developed to train a saliency detector and refine the pseudo labels online. In addition, we construct a lightweight saliency detector using two Residual Attention Modules (RAMs) to learn robust saliency information. Extensive experiments on several SOD benchmarks prove that our framework reports significant performance compared with existing USOD methods. Moreover, training our framework on 3,000 images consumes about 1 hour, which is over 10 times faster than previous state-of-the-art methods. Code has been published at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/moothes/A2S-USOD</uri> .
Augmented and virtual reality technologies in education for sustainable development: An expert-based technology assessment

Digitalization in the field of education for sustainable development (ESD) has gained attention in the last decade. In particular, technologies such as augmented reality (AR) and virtual reality (VR) offer new ways to deliver educational content on sustainable development by simulating real-world experiences and using immersive and interactive formats for learning. Using an explorative qualitative research approach, the benefits of AR and VR technologies in the context of ESD are assessed. The results of a first expert panel show that AR and VR technologies are particularly suitable for sustainability topics when an understanding of action and the transfer of knowledge and values are to be promoted among learners.
Agriculture 4.0: Bioinformationalism and Postdigital Hybrid Assemblages

Agriculture is undergoing a new technology revolution with new technologies viewed as being needed for sustainable and resilient food systems. These technologies include robotics, sensors, Big Data, Internet of Things, artificial intelligence, machine learning, blockchain, ubiquitous connectivity, gene editing, cellular agriculture nanotechnology, and 3D food printing. The implementation of these technologies is leading to the fourth agricultural revolution or Agriculture 4.0. This chapter offers an answer to two questions. Firstly, how can we understand new agricultural technologies from a postdigital hybrid assemblage perspective? Secondly, can the concept of postdigital hybrid assemblages help us understand the impact of the biodigital in the agricultural sector? In answering these questions, the chapter illustrates the complexity of Agriculture 4.0. These new agricultural technologies are characterised by confident expectations of progress, invoking ideas of sustainability and increased food security. Whilst these new technologies will no doubt achieve these goals, there are implications which are revealed when examining these technologies through the lens of postdigital hybrid assemblages. Looking at agriculture through the lens of the postdigital hybrid assemblage, enables us to see how digital and genetic technologies can be full of promise, yet at the same time provide dystopian futures in agriculture.
Pre-trained vs. Random Weights for Calculating Fréchet Inception Distance in Medical Imaging

Fréchet Inception Distance (FID) is an evaluation metric for assessing the quality of images synthesized by generative models. Conventionally this involves using an Inception v3 convolutional neural network that has been pretrained to classify everyday color images with the ImageNet dataset. The final classification section of this network is omitted, leaving an efficient feature extractor that outputs an encoded representation of each input image in the form of a 2048 element vector. Difference or similarity between samples of images can then be compared by measuring the distance between the distributions of their corresponding feature representations. Researchers have raised concerns about the utility of FID for evaluating unorthodox images (e.g. medical images) that are unlike those used for model training; suggesting that randomly initialized convolutional neural networks may be more appropriate. The aim of this study was to compare pre-trained and random approaches for evaluating medical images. Robustness to synthetic image distortions (Gaussian noise, blurring, swirl, and impulse noise) and different image types (Xray, CT, fundus, and everyday images) was addressed. Feature representations were converted into two-dimensional space and visualized using t-distributed stochastic neighbor embedding (tSNE) and principal component analysis (PCA). Normalized FID between image classes was substantially larger and more consistent for the pre-trained model. Overall, this suggests that the pre-trained model is preferable to the randomly initialized model for evaluating medical images.
Symbiotic joint operation of quantum and classical coherent communications

We report successful joint operation of quantum and classical communications with shared hardware. Leveraging information learned from the classical DSP, low-noise quantum communications (0.009 SNU at 15 km) compatible with 15 Mbit/s QKD is demonstrated.
Biodigital Becoming

Rapid developments in the field of biotechnology raise important ethical and practical questions which require immediate attention. We face now not only the possibility of being able to manufacture life itself directly from digital sources, but also the danger of biological information being easily accessed for subversive ends by hacking into the storage of data. Most responses so far depend upon traditional understandings of human reason and autonomy which assume that sound judgement and appropriate regulation and governance can prevent any potential harmful impacts of these. It will be argued that this is not the case and that new and expanded interpretations of both are now required. These will acknowledge that reason itself is subject to a process of evolution and can take into account both the embodied and embedded nature of human reasoning and also the elements of becoming that are characteristic of this new understanding. Becoming is that which the present is about to be, so opening up the possibilities of intervening in biodigital developments at an earlier stage than that of formal regulation by governments or scientific bodies. Thus a different philosophical conceptuality can provide a framework better adapted to respond to these new challenges.