Reviewerly/exHarmony huggingface dataset:
Moving a Step of ChaCha in Syncopated Rhythm

The stream cipher ChaCha is one of the most widely used ciphers in the real world, such as in TLS, SSH and so on. In this paper, we study the security of ChaCha via differential cryptanalysis based on probabilistic neutrality bits (PNBs). We introduce the syncopation technique for the PNB-based approximation in the backward direction, which significantly amplifies its correlation by utilizing the property of ARX structure. In virtue of this technique, we present a new and efficient method for finding a good set of PNBs. A refined framework of key-recovery attack is then formalized for round-reduced ChaCha. The new techniques allow us to break 7.5 rounds of ChaCha without the last XOR and rotation, as well as to bring faster attacks on 6 rounds and 7 rounds of ChaCha.
$$\mathsf {CSI\text {-}Otter}$$: Isogeny-Based (Partially) Blind Signatures from the Class Group Action with a Twist

In this paper, we construct the first provably-secure isogeny-based (partially) blind signature scheme. While at a high level the scheme resembles the Schnorr blind signature, our work does not directly follow from that construction, since isogenies do not offer as rich an algebraic structure. Specifically, our protocol does not fit into the linear identification protocol abstraction introduced by Hauck, Kiltz, and Loss (EUROCYRPT'19), which was used to generically construct Schnorr-like blind signatures based on modules such as classical groups and lattices. Consequently, our scheme does not seem susceptible to the recent efficient ROS attack exploiting the linear nature of the underlying mathematical tool. In more detail, our blind signature exploits the quadratic twist of an elliptic curve in an essential way to endow isogenies with a strictly richer structure than abstract group actions (but still more restrictive than modules). The basic scheme has public key size 128 B and signature size 8 KB under the CSIDH-512 parameter sets—these are the smallest among all provably secure post-quantum secure blind signatures. Relying on a new ring variant of the group action inverse problem ( $$\textsf{rGAIP}$$ ), we can halve the signature size to 4 KB while increasing the public key size to 512 B. We provide preliminary cryptanalysis of $${\textsf{rGAIP}} $$ and show that for certain parameter settings, it is essentially as secure as the standard $$\textsf{GAIP}$$ . Finally, we show a novel way to turn our blind signature into a partially blind signature, where we deviate from prior methods since they require hashing into the set of public keys while hiding the corresponding secret key—constructing such a hash function in the isogeny setting remains an open problem.
New Design Techniques for Efficient Arithmetization-Oriented Hash Functions: $$\texttt{Anemoi}$$ Permutations and $$\texttt{Jive}$$ Compression Mode

Advanced cryptographic protocols such as Zero-knowledge (ZK) proofs of knowledge, widely used in cryptocurrency applications such as Zcash, Monero, Filecoin, Tezos, Topos, demand new cryptographic hash functions that are efficient not only over the binary field $$\mathbb {F}_2$$ , but also over large fields of prime characteristic $$\mathbb {F}_p$$ . This need has been acknowledged by the wider community and new so-called Arithmetization-Oriented (AO) hash functions have been proposed, e.g. MiMC-Hash, Rescue–Prime, Poseidon, Reinforced Concrete and Griffin to name a few. In this paper we propose Anemoi: a new family of ZK-friendly permutations, that can be used to construct efficient hash functions and compression functions. The main features of these algorithms are that 1) they are designed to be efficient within multiple proof systems (e.g. Groth16, Plonk, etc.), 2) they contain dedicated functions optimised for specific applications (namely Merkle tree hashing and general purpose hashing), 3) they have highly competitive performance e.g. about a factor of 2 improvement over Poseidon and Rescue–Prime in terms of R1CS constraints, a 21%–35% Plonk constraint reduction over a highly optimized Poseidon implementation, as well as competitive native performance, running between two and three times faster than Rescue–Prime, depending on the field size. On the theoretical side, Anemoi pushes further the frontier in understanding the design principles that are truly entailed by arithmetization-orientation. In particular, we identify and exploit a previously unknown relationship between CCZ-equivalence and arithmetization-orientation. In addition, we propose two new standalone components that can be easily reused in new designs. One is a new S-box called Flystel, based on the well-studied butterfly structure, and the second is $$\textsf{Jive}_{}$$ – a new mode of operation, inspired by the "Latin dance" symmetric algorithms (Salsa, ChaCha and derivatives). Our design is a conservative one: it uses a very classical Substitution-Permutation Network structure, and our detailed analysis of algebraic attacks highlights can be of independent interest.
A Readiness Model and Factors Influencing Blockchain Adoption in Malaysia’s Software Sector: A Survey Study

The technology of Blockchain may open up new potential for innovation and distinction. It can enable the software sector to develop more safe and transparent systems that can function in an environment without trust. The adoption rate still needs to be higher despite the potential advantages; the relatively low adoption rate may be attributable to issues such as a lack of awareness, the difficulty of adoption, and ambiguity surrounding legal and regulatory frameworks. Considering technical, organizational, and environmental aspects, this study aims to determine the primary factors impacting the readiness of software firms to adopt Blockchain technology. The research on adopting Blockchain technology in the Malaysian software sector is limited. Using a quantitative method, the researchers used structural equation modeling to analyze 251 survey responses from the Malaysian software sector. In light of the findings, eight hypotheses were considered significant, and one hypothesis was rejected. At the same time, the R2 indicated that all these variables explained 71% of the dependent variable’s variance, which is considered substantial. Overall, it makes it easier for firms in the software sector to use Blockchain technology, which would increase the overall competitiveness of Malaysia’s software sector in the international market.
Digital Image Forensic Analyzer to Detect AI-generated Fake Images

In recent years, the widespread use of smartphones and social media has led to a surge in the amount of digital content available. However, this increase in the use of digital images has also led to a rise in the use of techniques to alter image contents. Therefore, it is essential for both the image forensics field and the general public to be able to differentiate between genuine or authentic images and manipulated or fake imagery. Deep learning has made it easier to create unreal images, which underscores the need to establish a more robust platform to detect real from fake imagery. However, in the image forensics field, researchers often develop very complicated deep learning architectures to train the model. This training process is expensive, and the model size is often huge, which limits the usability of the model. This research focuses on the realism of state-of-the-art image manipulations and how difficult it is to detect them automatically or by humans. We built a machine learning model called G-JOB GAN, based on Generative Adversarial Networks (GAN), that can generate state-of-the-art, realistic-looking images with improved resolution and quality. Our model can detect a realistically generated image with an accuracy of 95.7%. Our near future aim is to implement a system that can detect fake images with a probability of odds of 1- P, where P is the chance of identical fingerprints. To achieve this objective, we have implemented and evaluated various GAN architectures such as Style GAN, Pro GAN, and the Original GAN.
Event-Driven Fabric Blockchain - ROS 2 Interface: Towards Secure and Auditable Teleoperation of Mobile Robots

The integration of blockchain technology in robotic systems has been met by the community with a combination of hype and skepticism. The current literature shows that there is indeed potential for more secure and trustable distributed robotic systems. However, it is still unclear in what aspects of robotics beyond high-level decision making can blockchain technology be indeed usable. This paper explores the limits of a permissioned blockchain framework, Hyperledger Fabric, for teleoperation. Remote operation of mobile robots can benefit from the auditability and security properties of a blockchain. We study the potential benefits and the main limitations of such an approach. We introduce a new design and implementation for a event-driven Fabric-ROS 2 bridge that is able to maintain lower latencies at higher network loads than previous solutions. We also show this opens the door to more realistic use cases and applications. Our experiments with small aerial robots show latencies in the hundreds of milliseconds and the ability to remotely control both a single and multi-robot system. We analyze the main trade-offs and limitations for real-world near real-time remote teleoperation.
Static and Dynamic Collision Avoidance for Autonomous Robot Navigation in Diverse Scenarios Based on Deep Reinforcement Learning

This paper proposes an efficient robot training method to navigate environments with static and dynamic obstacles and reach their goal autonomously using deep rein-forcement learning algorithms. Previous methods have focused on specific scenarios, such as crowded environments. However, in practice, the variety of scenarios with static and dynamic obstacles tends to make the real robotic system fail or be restricted. In this work, the training is performed in six scenarios per episode, whereas traditional methods only consider one scenario. Several neural networks are trained and compared based on the following metrics: success rate, collision rate, uncomfortable rate, travel time, and average travel distance. In addition, we conducted Gazebo simulations using ROS and experimental tests across four scenarios to demonstrate that our approach has a better performance compared to previous studies. The results show a greatly enhanced robot's ability to act in various situations, as shown in the following link: https://youtu.be/mvkMFZjlaQo
Richness and dynamics: how to improve virtual reality tourism adoption with virtual social clues

Purpose Tourism via virtual reality (VR) technology has become an interesting option for consumers to “travel.” The best approaches to optimizing the VR tourism environment, improving the interactive experience of tourists and encouraging tourists to adopt VR are not yet fully understood. This study explores the willingness of tourists to adopt VR tourism from the dual aspects, richness and dynamics, of virtual social cues. Design/methodology/approach To examine the effects of richness (multiple vs. few cues) and dynamic (changeable vs. static cues) on consumers' willingness to adopt VR tourism, three virtual tourism scenes were designed and presented by head-mounted displays. The data were collected for participants in the VR laboratory and tested by ANOVA and partial least squares–structural equation modeling. Findings Virtual social cues can generate mental imagery through interactivity, vividness and parasocial interactions, thus increasing the consumer's likelihood of adopting VR tourism. It was also found that imagination moderates mental imagery and adoption intention. When the consumer's imagination is stronger, their mental imagery stimulates a stronger willingness to adopt VR tourism. Originality/value The authors innovatively utilize concepts of parasocial interaction and mental imagery and discuss the various influences and mediation mechanisms of social cue characteristics on consumers' adoption of VR tourism. The conclusions may provide new insights for VR tourism managers and tourism scholars.
Start-up sustainability: does blockchain adoption drives sustainability in start-ups? A systematic literature reviews

Purpose The purpose of this study is to provide a literature review of the past literature on the use of blockchain for attaining sustainability in start-ups, covering major characteristics of current findings, limitations and prospective study possibilities. Design/methodology/approach Papers that included the term “Blockchain,” “Sustainability” and “Start-ups” were considered for the analysis. Biblioshiny package under the R programming tool was considered for a detailed paper analysis. Findings Numerous sectors are quickly adopting blockchain technology, which has enormous potential to remove middlemen and boost sustainability in start-ups. Research limitations/implications This research is limited to 147 papers from the Scopus database through 2022. Practical implications This paper stresses the crucial part that blockchain technology has played in igniting a conversation about innovation and technology. This research will aid in advancing the researcher’s knowledge of blockchain technology in start-ups. Social implications Blockchain technology will give transparency and sustainable product or services to consumers of the start-ups. Originality/value The novelty of this research is based on its presentation of an organized and thorough literature evaluation, which defines the current state of the art concerning the use of blockchain for sustainability in start-ups. To create a sustainable start-up, a thorough study of how the information gained in respect of its implementation might be spread into the start-ups is presented.
SGEM: stochastic gradient with energy and momentum

In this paper, we propose SGEM, stochastic gradient with energy and momentum, to solve a class of general non-convex stochastic optimization problems, based on the AEGD method introduced in AEGD (adaptive gradient descent with energy) Liu and Tian (Numerical Algebra, Control and Optimization, 2023). SGEM incorporates both energy and momentum so as to inherit their dual advantages. We show that SGEM features an unconditional energy stability property and provide a positive lower threshold for the energy variable. We further derive energy-dependent convergence rates in the general non-convex stochastic setting, as well as a regret bound in the online convex setting. Our experimental results show that SGEM converges faster than AEGD and generalizes better or at least as well as SGDM in training some deep neural networks.
Dynamic graph neural network-based fraud detectors against collaborative fraudsters

Telecom fraud detection is a challenging task since the fact that fraudulent behaviors are hidden in the vast amount of telecom records. More concerning, the ongoing coronavirus pandemic (COVID-19) accelerated the use of mobile internet, providing more criminal opportunities for fraudsters. However, current telecom fraud detection mostly focuses on individual sequences representation, rarely noticing the collaboration of fraudsters, making it exhibit unsatisfactory performance in the face of gang crimes. To address this problem, we propose to extract collaborative networks from user call logs with an emphasis on unveiling collaborative fraud. We employ eight months of telecom datasets in China with 6,106 users and 5.0 million call logs between 1.25 million telephone recipients. Through our study, we find that the social structure of fraudsters evolute rapidly while the normal users remain stable relatively. In addition, we find that mining collaborative fraud strategies help to detect fraudsters with less distinct fraud characteristics. To this end, we propose a novel model named COllaborative-REsistant Dynamic Graph Neural Network (CORE-DGNN), to enhance the dynamic GNN aggregation process. Specifically, we first use co-recipients to obtain the collaborative network under each time slice. Then, we design a multi-frequency graph neural network to adaptively aggregate the features of node neighbors at different frequencies to address the problem of heterophily in collaborative networks. Finally, a self-attentive temporal convolutional network is designed to aggregate node embedding features across multiple time spans. Comprehensive experiments on two real-world telecom fraud datasets show that our approach outperforms several state-of-the-art algorithms.
Partial model averaging in Federated Learning: Performance guarantees and benefits

Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple clients and periodically averages the model across all the clients. This periodic model averaging potentially causes a significant model discrepancy across the clients making the global loss converge slowly. While recent advanced optimization methods tackle the issue focused on non-IID settings, there still exists the model discrepancy issue due to the underlying periodic model averaging. We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning. The partial averaging encourages the local models to stay close to each other on parameter space, and it enables to more effectively minimize the global loss. We extensively evaluate the performance of the partial averaging strategy using CIFAR-10/100 and FEMNIST benchmarks. Given a fixed number of training iterations and a large number of clients (128), the partial averaging achieves up to 2.2% higher accuracy than the periodic full averaging.
Factors affecting augmented reality head-mounted device performance in real OR

Over the last years, interest and efforts to implement augmented reality (AR) in orthopedic surgery through head-mounted devices (HMD) have increased. However, the majority of experiments were preclinical and within a controlled laboratory environment. The operating room (OR) is a more challenging environment with various confounding factors potentially affecting the performance of an AR-HMD. The aim of this study was to assess the performance of an AR-HMD in a real-life OR setting.An established AR application using the HoloLens 2 HMD was tested in an OR and in a laboratory by two users. The accuracy of the hologram overlay, the time to complete the trial, the number of rejected registration attempts, the delay in live overlay of the hologram, and the number of completely failed runs were recorded. Further, different OR setting parameters (light condition, setting up partitions, movement of personnel, and anchor placement) were modified and compared.Time for full registration was higher with 48 s (IQR 24 s) in the OR versus 33 s (IQR 10 s) in the laboratory setting (p < 0.001). The other investigated parameters didn't differ significantly if an optimal OR setting was used. Within the OR, the strongest influence on performance of the AR-HMD was different light conditions with direct light illumination on the situs being the least favorable.AR-HMDs are affected by different OR setups. Standardization measures for better AR-HMD performance include avoiding direct light illumination on the situs, setting up partitions, and minimizing the movement of personnel.
A two-step deep learning method for 3DCT-2DUS kidney registration during breathing

Abstract This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D–2D CNN-based registration network. The feature network has handcrafted texture feature layers to reduce the semantic gap. The registration network is an encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with a retrospective dataset cum training data generation strategy and then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite applications. The experiment was performed on 132 U/S sequences, 39 multiple-phase CT and 210 public single-phase CT images, and 25 pairs of CT and U/S sequences. This resulted in a mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. Datasets with small transformations resulted in MCDs of 0.82 and 1.02 mm, respectively. Large transformations resulted in MCDs of 1.10 and 1.28 mm, respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategies.
Artificial intelligence to support publishing and peer review: A summary and review

Abstract Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation.
Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion

Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices.