Reviewerly/exHarmony huggingface dataset:
Strongly Anonymous Ratcheted Key Exchange

Anonymity is an (abstract) security goal that is especially important to threatened user groups. Therefore, widely deployed communication protocols implement various measures to hide different types of information (i.e., metadata) about their users. Before actually defining anonymity, we consider an attack vector about which targeted user groups can feel concerned: continuous, temporary exposure of their secrets. Examples for this attack vector include intentionally planted viruses on victims’ devices, as well as physical access when their users are detained. Inspired by Signal’s Double-Ratchet Algorithm, Ratcheted (or Continuous) Key Exchange (RKE) is a novel class of protocols that increase confidentiality and authenticity guarantees against temporary exposure of user secrets. For this, an RKE regularly renews user secrets such that the damage due to past and future exposures is minimized; this is called Post-Compromise Security and Forward-Secrecy, respectively. With this work, we are the first to leverage the strength of RKE for achieving strong anonymity guarantees under temporary exposure of user secrets. We extend existing definitions for RKE to capture attacks that interrelate ciphertexts, seen on the network, with secrets, exposed from users’ devices. Although, at first glance, strong authenticity (and confidentiality) conflicts with strong anonymity, our anonymity definition is as strong as possible without diminishing other goals. We build strongly anonymity-, authenticity-, and confidentiality-preserving RKE and, along the way, develop new tools with applicability beyond our specific use-case: Updatable and Randomizable Signatures as well as Updatable and Randomizable Public Key Encryption. For both new primitives, we build efficient constructions.
Enhancing Differential-Neural Cryptanalysis

In CRYPTO 2019, Gohr shows that well-trained neural networks can perform cryptanalytic distinguishing tasks superior to traditional differential distinguishers. Moreover, applying an unorthodox key guessing strategy, an 11-round key-recovery attack on a modern block cipher Speck32/64 improves upon the published state-of-the-art result. This calls into the next questions. To what extent is the advantage of machine learning (ML) over traditional methods, and whether the advantage generally exists in the cryptanalysis of modern ciphers? To answer the first question, we devised ML-based key-recovery attacks on more extended round-reduced Speck32/64. We achieved an improved 12-round and the first practical 13-round attacks. The essential for the new results is enhancing a classical component in the ML-based attacks, that is, the neutral bits. To answer the second question, we produced various neural distinguishers on round-reduced Simon32/64 and provided comparisons with their pure differential-based counterparts.
Potential obstacles to adopting augmented reality (AR) technologies as pedagogical tools to support students learning in higher education

Many universities have adopted AR to enhance teaching and learning practices, but it has faced some obstacles. There is a lack of research on the obstacles of implications of AR in Saudi universities. This study aims to identify the extent to which faculty members agree with adopting AR as a pedagogical tool and whether there are statistically significant differences between academic perceptions of the AR potential obstacles and their faculties and gender. A mixing method approach was applied to understand the study problem. A total of 851 faculty members completed a survey, then 20 were interviewed. Statistical analyses were used to assess the survey data, and thematic analysis was used to evaluate the interview data. The finding shows that most faculty members agreed with the obstacles limiting AR technologies' adoption in teaching and learning practice in higher education. Lacking AR training courses was the most significant obstacle that faculty members agreed upon, with limited available resources and a lack of technical team support. The interview explores why these obstacles have existed. This study offers further empirical support for previous studies. The contribution is theoretical and practical, as well as recommendations to help higher education implement technology in its educational environment.
Authentication for cloud computing system through smartcard

The platform has wide consequence on information technology systems was cloud computing. Outflow can be reduced significantly from pay-per- use method. Society shifting from personal software and hardware system towards this platform which offers high flexibility. Main threat for cloud computing is safekeeping. Securing their system against external competitors by using secured connections and firewalls is done by most of those benefactors of the cloud. When as soon as that figures are send to outer party, the information secrecy will convert as main problematic, beyond that problematic of which those illegal users access that resource of cloud server can steal data of legal users and those legal users access that illegal server. For the security of users privacy, when the authorised cloud users access that service sources, they needs to verify the cloud server, and cloud server needs to identify those users login requests to make ensure the users are legal users. In this system we are proposing the virtual smart card ID produce with clutter techniques Which is used to prevent the fake cloud servers an also protect that cloud data from many hackers.
Deep learning-based small object detection: A survey

&lt;abstract&gt; &lt;p&gt;Small object detection (SOD) is significant for many real-world applications, including criminal investigation, autonomous driving and remote sensing images. SOD has been one of the most challenging tasks in computer vision due to its low resolution and noise representation. With the development of deep learning, it has been introduced to boost the performance of SOD. In this paper, focusing on the difficulties of SOD, we analyze the deep learning-based SOD research papers from four perspectives, including boosting the resolution of input features, scale-aware training, incorporating contextual information and data augmentation. We also review the literature on crucial SOD tasks, including small face detection, small pedestrian detection and aerial image object detection. In addition, we conduct a thorough performance evaluation of generic SOD algorithms and methods for crucial SOD tasks on four well-known small object datasets. Our experimental results show that network configuring to boost the resolution of input features can enable significant performance gains on WIDER FACE and Tiny Person. Finally, several potential directions for future research in the area of SOD are provided.&lt;/p&gt; &lt;/abstract&gt;
Diagnostics of Professional Deficits and Competencies of General Education Teachers: Cluster Analysis

The study examines a new perspective for the use of big data processing technologies that is associated with the possibility of assessing professional competencies of a teacher by means of assessing the learning results of students and contributes to the creation of prerequisites for eliminating professional deficiencies of teachers with the help of new specially designed educational programs. The aim of the study is to develop and test such a technology that makes it possible to analyze the results of assessing procedures of the quality of schoolchildren's education and establish the connection between these results and the level of teachers' professionalism in order to plan further activity to eliminate professional deficiencies and develop teachers' competencies. To implement such a technology, an algorithm has been developed for assessing professionalism and level of teachers' competencies, making possible to assess educational results of students. The technology is based on a cluster approach to data analysis, the methods of clustering k-means and hierarchical clustering have been chosen. In the study, for primary clustering, the learning results of students (Tomsk Oblast) in the national Unified State Exam within the period from 2015 to 2019 were analyzed (subjects: Russian, Mathematics (profile level), Physics, Social Studies). The number of clusters was selected by the selection method, n = 15. The next stage of the study analyzed the results of students within each cluster. The stage made possible to identify groups of “difficult” assignments that 30% or more of schoolchildren could not cope with (subjects: Russian and Mathematics (profile level)). As a result, the assignments that caused the greatest difficulty for schoolchildren were fixed, and thus deficiencies in their knowledge and skills were identified. It was not possible to assess “difficult” assignments in Physics and Social Studies in the framework of the study, since a small number of students had chosen these subjects to pass in the exam. The comparative analysis demonstrated that, within the same cluster, schoolchildren had similar difficulties in doing the same assignments, which that allows us to focus on appropriate teachers' competencies and on subject and methodology deficiencies, and to conduct further activity on assessing the professional competence of teachers focused narrowly. In addition, the study allows us to outline ways of solving the problem of overcoming professional deficits of teachers. Thus, it becomes possible to specifically develop additional professional training programs (advanced/refresher training) for teachers of the same cluster, the curricula of the programs containing topics and learning paths that will improve the educational results of their students, and thereby improve the quality of secondary education in Tomsk Oblast.
CLAD: A realistic Continual Learning benchmark for Autonomous Driving

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently released large-scale dataset that concerns autonomous driving related problems. First, we review and discuss existing continual learning benchmarks, how they are related, and show that most are extreme cases of continual learning. To this end, we survey the benchmarks used in continual learning papers at three highly ranked computer vision conferences. Next, we introduce CLAD-C, an online classification benchmark realised through a chronological data stream that poses both class and domain incremental challenges; and CLAD-D, a domain incremental continual object detection benchmark. We examine the inherent difficulties and challenges posed by the benchmark, through a survey of the techniques and methods used by the top-3 participants in a CLAD-challenge workshop at ICCV 2021. We conclude with possible pathways to improve the current continual learning state of the art, and which directions we deem promising for future research.
Exploring SAT for Cryptanalysis: (Quantum) Collision Attacks Against 6-Round SHA-3

In this work, we focus on collision attacks against instances of SHA-3 hash family in both classical and quantum settings. Since the 5-round collision attacks on SHA3-256 and other variants proposed by Guo et al. at JoC 2020, no other essential progress has been published. With a thorough investigation, we identify that the challenges of extending such collision attacks on SHA-3 to more rounds lie in the inefficiency of differential trail search. To overcome this obstacle, we develop a SAT-based automatic search toolkit. The tool is used in multiple intermediate steps of the collision attacks and exhibits surprisingly high efficiency in differential trail search and other optimization problems encountered in the process. As a result, we present the first 6-round classical collision attack on SHAKE128 with time complexity $$2^{123.5}$$ , which also forms a quantum collision attack with quantum time , and the first 6-round quantum collision attack on SHA3-224 and SHA3-256 with quantum time and , where S represents the hardware resources of the quantum computer. The fact that classical collision attacks do not apply to 6-round SHA3-224 and SHA3-256 shows the higher coverage of quantum collision attacks, which is consistent with that on SHA-2 observed by Hosoyamada and Sasaki at CRYPTO 2021.
Improved Straight-Line Extraction in the Random Oracle Model with Applications to Signature Aggregation

The goal of this paper is to improve the efficiency and applicability of straightline extraction techniques in the random oracle model. Straightline extraction in the random oracle model refers to the existence of an extractor, which given the random oracle queries made by a prover $$P^*(x)$$ on some theorem x, is able to produce a witness w for x with roughly the same probability that $$P^*$$ produces a verifying proof. This notion applies to both zero-knowledge protocols and verifiable computation where the goal is compressing a proof. Pass (CRYPTO ’03) first showed how to achieve this property for NP using a cut-and-choose technique which incurred a $$\lambda ^2$$ -bit overhead in communication where $$\lambda $$ is a security parameter. Fischlin (CRYPTO ’05) presented a more efficient technique based on “proofs of work” that sheds this $$\lambda ^2$$ cost, but only applies to a limited class of Sigma Protocols with a “quasi-unique response” property, which for example, does not necessarily include the standard OR composition for Sigma protocols. With Schnorr/EdDSA signature aggregation as a motivating application, we develop new techniques to improve the computation cost of straight-line extractable proofs. Our improvements to the state of the art range from 70 $$\times $$ –200 $$\times $$ for the best compression parameters. This is due to a uniquely suited polynomial evaluation algorithm, and the insight that a proof-of-work that relies on multicollisions and the birthday paradox is faster to solve than inverting a fixed target. Our collision based proof-of-work more generally improves the Prover’s random oracle query complexity when applied in the NIZK setting as well. In addition to reducing the query complexity of Fischlin’s Prover, for a special class of Sigma protocols we can for the first time closely match a new lower bound we present. Finally we extend Fischlin’s technique so that it applies to a more general class of strongly-sound Sigma protocols, which includes the OR composition. We achieve this by carefully randomizing Fischlin’s technique—we show that its current deterministic nature prevents its application to certain multi-witness languages.
Probabilistic buckling of imperfect hemispherical shells containing a distribution of defects

The buckling of spherical shells is plagued by a strong sensitivity to imperfections. Traditionally, imperfect shells tend to be characterized empirically by the knockdown factor, the ratio between the measured buckling strength and the corresponding classic prediction for a perfect shell. Recently, it has been demonstrated that the knockdown factor of a shell containing a single imperfection can be predicted when there is detailed a priori knowledge of the defect geometry. Still, addressing the analogous problem for a shell containing many defects remains an open question. Here, we use finite element simulations, which we validate against precision experiments, to investigate hemispherical shells containing a well-defined distribution of imperfections. Our goal is to characterize the resulting knockdown factor statistics. First, we study the buckling of shells containing only two defects, uncovering non-trivial regimes of interactions that echo existing findings for cylindrical shells. Then, we construct statistical ensembles of imperfect shells, whose defect amplitudes are sampled from a lognormal distribution. We find that a 3-parameter Weibull distribution is an excellent description for the measured statistics of knockdown factors, suggesting that shell buckling can be regarded as an extreme-value statistics phenomenon. This article is part of the theme issue ‘Probing and dynamics of shock sensitive shells’.
Anomaly Detection Requires Better Representations

Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position paper, we first explain how self-supervised representations can be easily used to achieve state-of-the-art performance in commonly reported anomaly detection benchmarks. We then argue that tackling the next generation of anomaly detection tasks requires new technical and conceptual improvements in representation learning.
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning

While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity. In this paper, we present a novel MicroISP model designed specifically for edge devices, taking into account their computational and memory limitations. The proposed solution is capable of processing up to 32MP photos on recent smartphones using the standard mobile ML libraries and requiring less than 1 s to perform the inference, while for FullHD images it achieves real-time performance. The architecture of the model is flexible, allowing to adjust its complexity to devices of different computational power. To evaluate the performance of the model, we collected a novel Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The experiments demonstrated that, despite its compact size, the MicroISP model is able to provide comparable or better visual results than the traditional mobile ISP systems, while outperforming the previously proposed efficient deep learning based solutions. Finally, this model is also compatible with the latest mobile AI accelerators, achieving good runtime and low power consumption o n smartphone NPUs and APUs. The code, dataset and pre-trained models are available on the project website: https://people.ee.ethz.ch/~ihnatova/microisp.html .
STr-GCN: Dual Spatial Graph Convolutional Network and Transformer Graph Encoder for 3D Hand Gesture Recognition

Skeleton-based hand gesture recognition is a challenging task that sparked a lot of attention in recent years, especially with the rise of Graph Neural Networks. In this paper, we propose a new deep learning architecture for hand gesture recognition using 3D hand skeleton data and we call STr-GCN. It decouples the spatial and temporal learning of the gesture by leveraging Graph Convolutional Networks (GCN) and Transformers. The key idea is to combine two powerful networks: a Spatial Graph Convolutional Network unit that understands intra-frame interactions to extract powerful features from different hand joints and a Transformer Graph Encoder which is based on a Temporal Self-Attention module to incorporate inter-frame correlations. We evaluate the performance of our method on three benchmarks: the SHREC'17 Track dataset, Briareo dataset and the First Person Hand Action dataset. The experiments show the efficiency of our approach, which achieves or outperforms the state of the art. The code to reproduce our results is available in this link.
The META-Learning Project: Design and Evaluation of an Experiential-Learning Intervention in the Metaverse for Soft Skills Improvement

Cyberpsychology, Behavior, and Social NetworkingVol. 26, No. 3 CyberEuropeThe META-Learning Project: Design and Evaluation of an Experiential-Learning Intervention in the Metaverse for Soft Skills ImprovementCompiled by: Sabrina Bartolotta, Andrea Gaggioli, and Giuseppe RivaCompiled by: Sabrina BartolottaAddress correspondence to: Sabrina Bartolotta, Research Center in Communication Psychology, Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy E-mail Address: [email protected]Experience Lab., Department of Psychology, Catholic University of Milan, Milan, Italy.Search for more papers by this author, Andrea GaggioliAddress correspondence to: Prof. Andrea Gaggioli, Research Center in Communication Psychology, Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy E-mail Address: [email protected]Research Center in Communication Psychology, Department of Psychology, Catholic University of Milan, Milan, Italy.Applied Technology for Neuro-Psychology Lab., IRCCS Istituto Auxologico Italiano, Milan, Italy.Search for more papers by this author, and Giuseppe RivaAddress correspondence to: Prof. Giuseppe Riva, Humane Technology Lab., Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy E-mail Address: [email protected]Applied Technology for Neuro-Psychology Lab., IRCCS Istituto Auxologico Italiano, Milan, Italy.Humane Technology Lab., Catholic University of Milan, Milan, Italy.Search for more papers by this authorPublished Online:6 Mar 2023https://doi.org/10.1089/cyber.2023.29268.ceuAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 26Issue 3Mar 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Sabrina Bartolotta, Andrea Gaggioli, and Giuseppe Riva.The META-Learning Project: Design and Evaluation of an Experiential-Learning Intervention in the Metaverse for Soft Skills Improvement.Cyberpsychology, Behavior, and Social Networking.Mar 2023.221-224.http://doi.org/10.1089/cyber.2023.29268.ceuPublished in Volume: 26 Issue 3: March 6, 2023Online Ahead of Print:February 23, 2023PDF download