Reviewerly/exHarmony huggingface dataset:
On Polynomial Functions Modulo $$p^e$$ and Faster Bootstrapping for Homomorphic Encryption

In this paper, we perform a systematic study of functions $$f: \mathbb {Z}_{p^e} \rightarrow \mathbb {Z}_{p^e}$$ and categorize those functions that can be represented by a polynomial with integer coefficients. More specifically, we cover the following properties: necessary and sufficient conditions for the existence of an integer polynomial representation; computation of such a representation; and the complete set of equivalent polynomials that represent a given function. As an application, we use the newly developed theory to speed up bootstrapping for the BGV and BFV homomorphic encryption schemes. The crucial ingredient underlying our improvements is the existence of null polynomials, i.e. non-zero polynomials that evaluate to zero in every point. We exploit the rich algebraic structure of these null polynomials to find better representations of the digit extraction function, which is the main bottleneck in bootstrapping. As such, we obtain sparse polynomials that have 50% fewer coefficients than the original ones. In addition, we propose a new method to decompose digit extraction as a series of polynomial evaluations. This lowers the time complexity from $$\mathcal {O}(\sqrt{pe})$$ to $$\mathcal {O}(\sqrt{p}\root ^4 \of {e})$$ for digit extraction modulo $$p^e$$ , at the cost of a slight increase in multiplicative depth. Overall, our implementation in HElib shows a significant speedup of a factor up to 2.6 over the state-of-the-art.
Finding the Impossible: Automated Search for Full Impossible-Differential, Zero-Correlation, and Integral Attacks

Impossible differential (ID), zero-correlation (ZC), and integral attacks are a family of important attacks on block ciphers. For example, the impossible differential attack was the first cryptanalytic attack on 7 rounds of AES. Evaluating the security of block ciphers against these attacks is very important but also challenging: Finding these attacks usually implies a combinatorial optimization problem involving many parameters and constraints that is very hard to solve using manual approaches. Automated solvers, such as Constraint Programming (CP) solvers, can help the cryptanalyst to find suitable attacks. However, previous CP-based methods focus on finding only the ID, ZC, and integral distinguishers, often only in a limited search space. Notably, none can be extended to a unified optimization problem for finding full attacks, including efficient key-recovery steps. In this paper, we present a new CP-based method to search for ID, ZC, and integral distinguishers and extend it to a unified constraint optimization problem for finding full ID, ZC, and integral attacks. To show the effectiveness and usefulness of our method, we applied it to several block ciphers, including SKINNY, CRAFT, SKINNYe-v2, and SKINNYee. For the ISO standard block cipher SKINNY, we significantly improve all existing ID, ZC, and integral attacks. In particular, we improve the integral attacks on SKINNY-n-3n and SKINNY-n-2n by 3 and 2 rounds, respectively, obtaining the best cryptanalytic results on these variants in the single-key setting. We improve the ZC attack on SKINNY-n-n (SKINNY-n-2n) by 2 (resp. 1) rounds. We also improve the ID attacks on all variants of SKINNY. Particularly, we improve the time complexity of the best previous single-tweakey (related-tweakey) ID attack on SKINNY-128-256 (resp. SKINNY-128-384) by a factor of $$2^{22.57}$$ (resp. $$2^{15.39}$$ ). On CRAFT, we propose a 21-round (20-round) ID (resp. ZC) attack, which improves the best previous single-tweakey attack by 2 (resp. 1) rounds. Using our new model, we also provide several practical integral distinguishers for reduced-round SKINNY, CRAFT, and Deoxys-BC. Our method is generic and applicable to other strongly aligned block ciphers.
Systematic literature review: Trend analysis on the design of lightweight block cipher

Lightweight block ciphers have become a standard for security protections on IoT devices. Advanced technology is required to secure the data, thus encryption is the method that can provide information security. From previous studies, comparisons of lightweight algorithms in various literature focus on their performance and implementation. However, a lack of analysis has been done on the relationship between the algorithm components and their security strength. This information is crucial for developers in designing secure algorithms. In this paper, a comprehensive systematic literature review on 101 existing lightweight algorithms is presented. This review focuses on the security aspect of lightweight algorithms that cover the identification of secure design components based on substitution and permutation. Security analysis and the evolution of lightweight algorithms are also presented. This research includes the results and discussions to observe the selections of substitution and permutation functions to analyse their impact on the security strength. Recommendations from the developer's insight on methods and considerations for designing an algorithm are also presented. Findings from the research indicate that various techniques can be used to develop a secure algorithm. Most importantly, an algorithm must be provided with confusion and diffusion properties in the design to ensure sufficient security.
ProUDA: Progressive unsupervised data augmentation for semi-Supervised 3D object detection on point cloud

Unsupervised data augmentation (UDA) is a simple and general semi-supervised learning (SSL) framework. However, for the task of semi-supervised 3D object detection (SSOD-3D), due to the impact of object occlusion and point cloud resolution, the quality of pseudo labels is uncertain so that the performance of UDA is limited. In this paper, We propose an efficient and novel SSL framework progressive unsupervised data augmentation (ProUDA). At first, to minimize the overfitting risk of the inaccurate pseudo labels, we sort the unlabeled samples by prediction complexity, and present a progressive consistency loss to adjust the usage ratio of the unlabeled samples. After that, we employ a strategy of iteration check to select the best learning result using a few but representative validation dataset annotated from the unlabeled samples. It ensures the safe model weight updating. Extensive experiments are conducted on both the public indoor and outdoor 3D object detection datasets. Results demonstrate that ProUDA has better 3D average precision than UDA and the proposed method benefits to 3D object detector training.
Threshold and Multi-signature Schemes from Linear Hash Functions

This paper gives new constructions of two-round multi-signa-tures and threshold signatures for which security relies solely on either the hardness of the (plain) discrete logarithm problem or the hardness of RSA, in addition to assuming random oracles. Their signing protocol is partially non-interactive, i.e., the first round of the signing protocol is independent of the message being signed. We obtain our constructions by generalizing the most efficient discrete-logarithm based schemes, $$\textsf{MuSig2}$$ (Nick, Ruffing, and Seurin, CRYPTO '21) and $$\textsf{FROST}$$ (Komlo and Goldberg, SAC '20), to work with suitably defined linear hash functions. While the original schemes rely on the stronger and more controversial one-more discrete logarithm assumption, we show that suitable instantiations of the hash functions enable security to be based on either the plain discrete logarithm assumption or on RSA. The signatures produced by our schemes are equivalent to those obtained from Okamoto's identification schemes (CRYPTO '92). More abstractly, our results suggest a general framework to transform schemes secure under OMDL into ones secure under the plain DL assumption and, with some restrictions, under RSA.
Towards Data and Model Interoperability for Industrial Extended Reality in Manufacturing

Abstract Extended reality (XR) technologies have realized significant value for design, manufacturing, and sustainment processes. However, industrial XR, or XR implemented within industrial applications, suffers from scalability and flexibility challenges due to fundamental gaps with interoperability between data, models, and workflows. Though there has been a number of recent efforts to improve the interoperability of industrial XR technologies, progress has been hindered by an innate separation between the domain-specific models (e.g., manufacturing execution data, material specifications, and product manufacturing information) with XR (often-standard) processes (e.g., multiscale spatial representations and data formats optimized for run-time presentation). In this paper, we elaborate on promising research directions and opportunities around which the manufacturing and visualization academic community can rally. To establish such research directions, we (1) conducted a meta-review on well-established state-of-the-art review articles that have already presented in-depth surveys on application areas for industrial XR, such as maintenance, assembly, and inspection and (2) mapped those findings to publicly published priorities from across the US Department of Defense. We hope that our presented research agenda will spur interdisciplinary work across academic silos, i.e., manufacturing and visualization communities, and engages either community within work groups led by the other, e.g., within standards development organizations.
Smartphone-based human activity recognition using lightweight multiheaded temporal convolutional network

Sensor-based human activity recognition (HAR) has drawn extensive attention from the research community due to its potential applications in various domains, including interactive gaming, activity monitoring, healthcare, etc. Although plentiful approaches (i.e., handcrafted feature-based and deep learning methods) have been proposed throughout the years, there are still several challenges in developing an efficient and effective HAR system. For instance, handcrafted feature-based methods rely on manual feature engineering by experts and require time-consuming feature selection methods. Conversely, deep learning methods can automatically capture salient features without domain experts. However, some deep learning methods, especially Convolutional Neural Networks (CNN), cannot extract temporal features effectively, which are significant to motion analysis. Unlike CNN, recurrent models are exceptional at capturing temporal characteristics, but these models contain gigantic model parameters, requiring tremendous computation. This may limit the deployment of such models, especially to low-spec or embedded devices. Hence, this paper proposes a lightweight deep learning model, Lightweight Multiheaded TCN (Light-MHTCN), for human activity recognition. Light-MHTCN extracts the multiscale features of the inertial sensor signals through the parallelly organised Convolutional Heads to capture richer information. Further, integrating dilated causal convolutions and residual connections preserves longer-term dependency, which can boost the overall model performance. The performance of Light-MHTCN is assessed on three popular smartphone-based HAR databases: UCI HAR, WISDM V1 and UniMiB SHAR. With only ∼0.21 million parameters, our lightweight model is able to achieve state-of-the-art performance with recognition accuracies of 96.47%, 99.98% and 98.63% on these databases, respectively.
Students’ Perception of Metaverses for Online Learning in Higher Education: Hype or Hope?

Online learning environments have been widely adopted, particularly during the COVID-19 pandemic. Today, their usage is envisioned for multiple teaching scenarios, including international courses involving students from different locations. However, as e-learning frameworks lack many advantages of physical interaction, diverse technologies to enhance the learning experience in this regard, including the use of metaverses, are currently in demand. In this study, we implemented an educational metaverse platform for engineering courses at four different European Universities from Poland, Portugal, Germany, and Spain, then had it quantitatively evaluated by students from these higher education institutions. The obtained results illustrate positive student perceptions of its ease of use, satisfaction and fun, and immersion, although their opinions with respect to learning experience and usefulness are less clear. The students seem to agree that interactivity remains insufficient in comparison with face-to-face teaching. For this reason, we provide an examination other related works and finalize this article by suggesting future research directions in the field.
Intelligent Eye-Controlled Electric Wheelchair Based on Estimating Visual Intentions Using One-Dimensional Convolutional Neural Network and Long Short-Term Memory

When an electric wheelchair is operated using gaze motion, eye movements such as checking the environment and observing objects are also incorrectly recognized as input operations. This phenomenon is called the "Midas touch problem", and classifying visual intentions is extremely important. In this paper, we develop a deep learning model that estimates the user's visual intention in real time and an electric wheelchair control system that combines intention estimation and the gaze dwell time method. The proposed model consists of a 1DCNN-LSTM that estimates visual intention from feature vectors of 10 variables, such as eye movement, head movement, and distance to the fixation point. The evaluation experiments classifying four types of visual intentions show that the proposed model has the highest accuracy compared to other models. In addition, the results of the driving experiments of the electric wheelchair implementing the proposed model show that the user's efforts to operate the wheelchair are reduced and that the operability of the wheelchair is improved compared to the traditional method. From these results, we concluded that visual intentions could be more accurately estimated by learning time series patterns from eye and head movement data.
Comparison of path planning methods for robot navigation in simulated agricultural environments

Path planning is a research topic that is still being studied for the area of mobile robotics. However, path-planning algorithms for mobile robot applications depend strongly on the environment and its complexity. In this work, we implemented three different path-planning algorithms for a simulated agricultural process. The selected algorithms are Breadth First search (BFS), Depth first search (DFS), and A*. We compare and evaluate such algorithms by using different accuracy metrics. The results demonstrate that the A* path planning method outperforms the other methods considering processing time, travel time, distance, and battery consumption.
A comparison of Generative Adversarial Networks for automated prostate cancer detection on T2-weighted MRI

Generative Adversarial Networks (GANs) have shown potential in medical imaging. In this study, several previously developed GANs were investigated for prostate cancer (PCa) detection on T2-weighted (T2W) magnetic resonance images (MRI). T2W MRI from an in-house collected dataset (N=961) were used to train, validate, and test an automated computer-aided detection (CAD) pipeline. The open-access PROSTATEx training dataset (N=199) was used as an external test set. The CAD pipeline consisted of normalization, prostate segmentation, quality control, prostate gland cropping, and a GAN model. Six GANs (f-AnoGAN, HealthyGAN, StarGAN, StarGAN-v2, Fixed-Point-GAN and DeScarGAN) were evaluated for PCa detection on the patient-level using the area under the receiver operating characteristic curve (AUC). The best performing GAN (validation set) was trained with five different initializations and evaluated on the internal and external test sets to assess its robustness. Fixed-Point-GAN performed best (validation, AUC 0.76) and was selected for further assessment. The highest performance on the internal and external test sets were an AUC of 0.73 (95% CI: 0.68-0.79) and 0.77 (95% CI: 0.70-0.83), respectively. The average AUCs ± standard deviation across all runs corresponded to 0.71 ± 0.01 and 0.71 ± 0.04, respectively. Fixed-Point-GAN was identified as a promising GAN for the detection of PCa on T2W MRI. This model needs to be further investigated and trained on a larger dataset of multiparametric or biparametric MR images to assess its full potential as a support tool for radiologists.
A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development

As the topic of sustainable development continues to prominence in global affairs, the case for renewable energy has never been stronger. To be regarded as a perfect alternative to conventional (non-renewable) energy sources in many climes, renewable energy, such as solar and wind, shows promise when considering concepts like grid parity. A significant number of studies have been devoted to understanding the concept. However, only a few studies have committed themselves to analysing the research activity carried out on it. This paper will present a bibliometric and empirical review of worldwide grid parity, energy transition, and electricity cost research. To situate the progress in this research area, a detailed search of Scopus was used to identify and situate research development in the field from 1965 until 2021. Using the data extracted from Scopus and VOSviewer for analysis, we explore different aspects of the publications, such as the volume, growth rate, and coverage of published documents, the most influential research papers and journals in this research area, and the most studied research themes in recent years. We also discuss Governmental policies in developed and developing economies that have accelerated the attainment of Grid parity in certain countries. Also, an empirical review of top-down, bottom-up, and artificial neural network approaches to evaluating grid parity was conducted. The study revealed a steady increase in the research articles focused on grid parity, energy transition, and electricity cost research from 2006. The geographic distribution of the publications shows that most of the publications on the subject originated from the USA, Germany, China, United Kingdom, and Spain, raking in 42.2% of the publications. Also, the top 7 authors with the highest document count from Scopus are from Finland, which coincidentally is one of the countries making significant progress in Grid parity attainment. Of the total document count from Scopus, only 0.02% are papers published from African Countries. Could this reluctance to publish research findings on energy transition be one of the reasons for the slow progression of sustainable energy for all in Africa? Therefore, it is imperative now more than ever for more research focusing on the attainment of grid parity, energy transition, and electricity costs for developing countries to be brought to the fore. This article provides a review of state-of-the-art research on the attainment of grid parity and energy transition with a focus on the Levelized Cost of Electricity (LCOE) models of renewable energy sources.
Research of Different Neural Network Architectures for Audio and Video Denoising

Recently, neural networks have attracted considerable attention due to their better denoising quality compared to the previously used frequency-time filters for audio denoising and algorithmic methods for visual denoising. In this paper different neural network architectures used for denoising from audio and video files are discussed. The architectures of two neural networks RNNoise and PoCoNet are compared to audio denoising. The main source of visual noise in video is Gaussian noise, which occurs during digital imaging of real objects, such as sensor noise caused by poor light or high temperature, and electronic noise. Two solutions to neural network architectures are compared to remove Gaussian noise from video. In the first approach, each frame is processed as a separate image. For this approach, the DnCNN and Restormer neural networks have been considered. In the second approach, frames are considered as a sequence of images over time. In this approach, FastDVDnet and PaCNet neural networks have been considered. The aim is to compare different neural networks architectures for removing audio and video noise from public room camera recordings.
Multi-Slice Privacy-Aware Traffic Forecasting at RAN Level: A Scalable Federated-Learning Approach

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services.Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP).To cope with an ever-changing traffic demand, MVNOs seek to preallocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions.Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data).To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models.The results obtained by training the models on a realworld dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy.To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks.These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.
On workflow scheduling for latency-sensitive edge computing applications

With the rapid proliferation of edge computing-based solutions, many edge computing applications use the cloud for data processing and analysis. However, latency-sensitive applications have low latency requirements and can be very bandwidth hungry, so processing their collected data through cloud servers is not efficient and cost-effective. For example, traffic management and health condition monitoring applications, require real-time data processing at the network edge to respond immediately to unexpected events. These applications are typically executed as workflows with dependent tasks that require careful scheduling to allocate the appropriate resources to the tasks so that the execution is done in a way that satisfies the users' target functions. In this work, we evaluate some traditional scheduling heuristics for the execution of workflow tasks in an edge-computing scenario. Our goal is to compare their performance in terms of execution time and cost and show that these heuristics, previously used in scheduling in the context of cloud environments, can also be used in edge computing scenarios. The results show that the MinMin and PSO scheduling algorithms offer the best results with regard to execution time and cost.