Reviewerly/exHarmony huggingface dataset:
Certificateless ring signature from NTRU lattice for electronic voting

Number theory research unit (NTRU) lattice has small storage space, high execution efficiency and anti-quantum security. To obtain the quantum immunity and high efficiency in electronic voting, we devise certificateless ring signature from NTRU lattice (NTRUL-CLRS) for electronic voting in this article. Security of NTRUL-CLRS relies on the small integer solution on ring (R-SIS) assumption. Rejection sampling technique is used to generate the ring signature and so the signature speed is greatly improved. NTRUL-CLRS has the quantum immunity, unconditional anonymity and unforgeability; it is suitable for application in electronic voting system because it simplifies the electronic voting process and reduces the electronic voting cost.
Designing attribute-based verifiable data storage and retrieval scheme in cloud computing environment

The cloud computing technology is a novel storage and computing paradigm that enables individuals and organizations to store data, share data with intended group of users and retrieve data when require. It greatly improves peoples' data storage and sharing, and data retrieval capabilities by providing flexible, less expensive and quality services. For data security and privacy concerns, secure and authenticated data storage, fine-grained access control of encrypted data, secure search for the outsourced data and search results verification are of critical importance. However, achieving the aforementioned functionalities simultaneously is quite challenging. In this paper, for the first time, we propose a secure lightweight Attribute-Based verifiable Data Storage and data Retrieval Scheme (ABDSRS) for cloud environments that attains the following features: (i) lightweight design, (ii) provably secure, (iii) fine-grained data access control, (iv) data owner (DO) anonymity, (v) data and DO authenticity, (vi) keyword policy search over encrypted data, (vii) keyword privacy, and (viii) search results verification. ABDSRS employs attribute-based online-offline mechanism in which only authorized DOs can anonymously upload data to the cloud. And, a data user (DU) can search over encrypted data using keyword policy. ABDSRS enables a DU to verify the correctness of the search results (i.e., the correctness of the operations performed by the cloud) without interacting with any authority. ABDSRS is lightweight in the sense that the heavy computations are offloaded either to the cloud or to offline phase, while only lightweight operations are executed at the DU device. We formalize more general security definitions of ABDSRS by considering various possible adversarial capabilities and present rigorous security analysis. We also conduct experiments to evaluate ABDSRS's performance.
Special Issue: Feature Papers to Celebrate the Inaugural Issue of Standards

Standards are a set of guidelines or criteria used to ensure consistency, quality, safety, and compatibility in products, services, and processes [...]
Scalable set of reversible parity gates for integer factorization

Abstract Classical microprocessors operate on irreversible gates, that, when combined with , half-adder and full-adder operations, execute complex tasks such as multiplication of integers. We introduce parity versions of all components of a multiplication circuit. The parity gates are reversible quantum gates based on the recently introduced parity transformation and build on ground-space encoding of the corresponding gate logic. Using a quantum optimization heuristic, e.g., an adiabatic quantum computing protocol, allows one to quantum mechanically reverse the process of multiplication and thus factor integers, which has applications in cryptography. Our parity approach builds on nearest-neighbor constraints equipped with local fields, able to encode the logic of a binary multiplication circuit in a modular and scalable way.
National Payment Switches and the Power of Cognitive Computing against Fintech Fraud

National Payment Switches (NPSs) and International Payment Switches (IPSs), including major players such as SWIFT, Mastercard, and CHIPS, have become vital to the financial infrastructure, facilitating secure and efficient transactions among local financial institutions. Nonetheless, the growing adoption of digital payments has heightened the risk of financial fraud. Consequently, NPSs, under the direct ownership of Central Banks (CBs), are increasingly adopting advanced technologies, such as cognitive computing, to bolster their fraud detection capabilities in their respective countries. This article delves into the role of cognitive computing in detecting financial fraud within NPSs. It examines the advantages of cognitive computing in recognising patterns of fraudulent behaviour and analysing vast amounts of data. Additionally, the study highlights the importance of focusing on how cognitive computing can augment traditional fraud detection methods, such as rule-based systems and data analytics. Nineteen real-world cases from eighteen countries are analysed, exploring the cognitive computing tools employed by NPSs to identify fraudulent transactions. The challenges and limitations of implementing cognitive computing in fraud detection and potential solutions to address these issues are identified. The primary assumption that cognitive computing is crucial for detecting financial fraud in NPSs is substantiated. Its ability to analyse large datasets and pinpoint patterns of fraudulent behaviour proves invaluable for financial institutions seeking to protect themselves against financial fraud in a progressively digital world. The conclusions drawn from the overview of the cases aim to identify best practices, potentially trigger new benchmarking standards, and facilitate the development of integrated cross-border solutions to combat financial fraud on a global scale effectively. The purpose of this research is to examine the role of cognitive computing in detecting financial fraud within NPSs, identify its advantages, challenges and limitations, and provide real-world case examples.
Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory

The paper presents the results of subjective and objective quality assessments of H.264-, H.265-, and VP9-encoded video. Most of the literature is devoted to subjective quality assessment in well-defined laboratory circumstances. However, the end users usually watch the films in their home environments, which may be different from the conditions recommended for laboratory measurements. This may cause significant differences in the quality assessment scores. Thus, the aim of the research is to show the impact of environmental conditions on the video quality perceived by the user. The subjective assessment was made in two different environments: in the laboratory and in users’ homes, where people often watch movies on their laptops. The video signal was assessed by young viewers who were not experts in the field of quality assessment. The tests were performed taking into account different image resolutions and different bit rates. The research showed strong correlations between the obtained results and the coding bit rates used, and revealed a significant difference between the quality scores obtained in the laboratory and at home. As a conclusion, it must be underlined that the laboratory tests are necessary for comparative purposes, while the assessment of the video quality experienced by end users should be performed under circumstances that are as close as possible to the user’s home environment.
AR Long-Term Tracking Combining Multi-Attention and Template Updating

Aiming at the problem that the augmented reality system is susceptible to complex scenes and easily leads to the failure of tracking registration, a long-term augmented reality tracking algorithm combining multi-attention and template updating is proposed. Firstly, we improved the ResNet-50 network to extract richer semantic features instead of AlexNet. Secondly, the attention-based feature fusion network effectively fuses the template and search area features through a combination of dual self-attention and cross attention. Dual self-attention effectively enhances the information in the context, whereas cross attention adaptively enhanced the features of both self-attention branches. Thirdly, the ORB feature-matching algorithm is utilized to match the template and search image features, with the template updated if more than 150 matching feature points are found. Lastly, the anchor frameless mechanism is adopted in the classification and regression network, resulting in a significant reduction in the number of parameters. The results of experiments conducted on various public datasets demonstrate the algorithm’s high success rate and accuracy, as well as its robustness in complex environments.
Optimized Algorithms and Hardware Implementation of Median Filter for Image Processing

Abstract Image processing algorithms are essential for clarifying the image and improving the ability to recognize distinct characteristics of the image. The field of digital image processing is widespread in several research and technology applications. In many of these applications, the existence of impulsive noise in the obtained images is one of the most frequent problems. The median filter is a strong method to remove the impulsive noise; it effectively eliminates salt and pepper noise from the image. The main target of this paper is to investigate efficient median filter units to be connected to a general-purpose processor (GPP) for FPGA-based embedded systems. The paper exposes three novel techniques, two of them specially for median filtering techniques and the third one is used to get the maximum number of any 9 elements array. The proposed algorithms are inspired by the Median Of Median (MOM) algorithm. The first two techniques are tested for filtering $$3 \times 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>×</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> image windows and optimized for producing the expected result in high accuracy, short time, and reduced number of comparisons. The last technique is tested for a 9 elements array for extracting the maximum number in same high efficiency manner. Furthermore, the three proposed techniques are implemented leveraging the advantage of the parallel processing and the FPGA flexible resources to satisfy the real-time processing constraints. A comparison between the first two proposed filtering units and their counterparts in the literature is included. The comparison reveals the superiority of the first technique in terms of accuracy with fewer comparators than previously published techniques. Besides, the paper illustrates how the concept beyond the proposed techniques can be used to perform the maximum pooling for convolution neural networks.
A Two-Stage Method for Ultra-Short-Term PV Power Forecasting Based on Data-Driven

To promote the real-time dispatching of a power grid and balanced decision-making of power producers, accuracy and real-time forecasting are two main problems that need to be solved in ultra-short-term photovoltaic (PV) forecasting. Focusing on the problems of slow model training speed and low forecasting accuracy due to the redundancy of training data samples and insufficient long periodic capture of data in complex weather, this paper proposes a two-stage method for ultra-short-term PV power forecasting based on data-driven. In the meteorological analysis stage, the generation power samples similar to the forecast day were extracted by inputting daily meteorological features and using maximal information coefficient (MIC) weighted grey correlation degree to form the corresponding forecast data set. In the power forecasting stage, the temporal convolutional used network (TCN) extracts local features to maintain the sequence of extracted features. Then the bidirectional gating unit (BiGRU) combined with the Skip connection strategy was used to fully learn the long and the short time sequence of photovoltaic sequences, and the attention mechanism was used to pay adaptive attention to the more important historical states. This study experimented on the measured data from a photovoltaic power station in Southeastern China. The experimental results show that this method is effective in photovoltaic power short-term forecasts. In addition, compared with some traditional forecasting models, this method has smaller forecasting errors and higher robustness in the time scales of 15 minutes, 30 minutes, 45 minutes, and 60 minutes.
A Robust Security Scheme Based on Enhanced Symmetric Algorithm for MQTT in the Internet of Things

Message Queuing Telemetry Transport (MQTT) is expected to be the de facto messaging IoT standard.Therefore, MQTT must achieve efficient security.Nevertheless, the most significant drawback of the MQTT is its lack of protection mechanisms.Meanwhile, the existing approaches have added processing overhead to the devices and are still vulnerable to various attacks.Therefore, this research work presented an integrated scheme known as the Robust Security Scheme (RSS) to protect the MQTT against any exploitations that might result in sophisticated cyberattacks.The proposed RSS employs two cryptosystems:(1) a dynamic variant of the Advanced Encryption Standard (D-AES) and (2) Key-Policy Attribute-Based Encryption (KP-ABE).RSS introduces a new design architecture of the symmetric AES algorithm to encrypt the MQTT payload called D-AES.Additionally, the second part of the proposed hybrid cryptosystem is KP-ABE, which is utilized to cipher the private key of the proposed D-AES to avoid the computation overhead of bilinear maps.The performance of the proposed RSS is measured in terms of processing time and traffic overhead.Additionally, the security aspects are evaluated in terms of balance, avalanche effect, and hamming distance and compared to the existing works in a testbed environment.Results revealed that the proposed D-AES is more promising with improvements than the standard AES algorithm.The proposed scheme achieves polymorphism while maintaining interoperability.RSS exhibited improvements over the standard AES algorithm by 8.75%, 10.45%, and 6.81% in terms of balance, avalanche effect, and hamming distance, respectively.
Universal Image Embedding: Retaining and Expanding Knowledge With Multi-Domain Fine-Tuning

The overall purpose of this study is to propose a novel fine-tuning method for the CLIP architecture that enables the retention of pre-existing knowledge from large datasets and the creation of a domain-agnostic image encoder for universal image embedding, addressing the challenge of transferring knowledge from source to target tasks using deep learning models. The basic design of the study involves applying the proposed method directly (without fine-tuning) to a wide range of instance retrieval and recognition tasks to evaluate its effectiveness. The study’s major findings indicate that the proposed method significantly enhances performance on unseen domains without requiring separate fine-tuning for each domain. The authors’ success in the Google Universal Image Embedding competition, where they were awarded a Gold medal out of 1200 teams, inspired their proposed method. These results have significant implications for real-life applications where multiple domains are common. In conclusion, the study offers a practical solution for transfer learning that addresses the challenges of dealing with multiple domains and advances deep learning, potentially inspiring further research in this area and driving progress in the field.
Privacy-Preserving and Security in SDN-Based IoT: A Survey

In recent years, the use of Software Defined Networking (SDN) has increased due to various network management requirements.Using SDN in computer network applications has brought several benefits to users, including lower operational costs, better hardware management, flexibility, and centralized network deployment.On the other hand, the Internet of Things (IoT) is another rapidly growing technology.Distributed and dynamic infrastructures are two critical characteristics of IoT.These characteristics lead to some challenges while using SDN in IoT in terms of security and privacy.In this paper, we address security and privacy issues and solutions for SDN-based IoT systems.We analyze the techniques used for defense in previous works to achieve an acceptable level of security and privacy protection in SDN-based IoT systems.In the data plane, SDN-based IoT papers have considered hashing and encryption techniques, in the control plane, certificate authority and access control have been analyzed, and in the application plane, attack detection, and authentication have been discussed.We also provide a statistical analysis of the existing work.This analysis shows that researchers have focused on certain areas more than others in recent years.The final analysis also highlights issues that previous researchers have ignored.
Mechanism of supply chain coordination based on price discount with privacy protection in one-supplier-one-buyer system

It is of great economic significance to optimize the total cost and improve the performance of the supply chain. In this paper, we assume that the market demand is random, and the seller and the buyer share information and make decisions together. We analyze the optimal joint order quantity under probabilistic demand and design the quantity discount model and profit distribution mechanism. Under a certain quantity discount mechanism and profit distribution strategy, both the seller and the buyer can reduce costs. The quantity discount model and profit distribution mechanism designed require supply chain members to share information. In order to protect the privacy of members and improve the willingness of supply chain members to share information, we designed a privacy protection joint ordering policy protocol and privacy protection quantity discount policy based on Secure multiparty computation technology. Then, the joint ordering strategy, the privacy-preserving joint ordering strategy, and quantity discount protocol are numerically simulated. The numerical simulation results show that the privacy-preserving quantity discount coordination mechanism designed by us can reduce the cost of supply chain members to varying degrees and effectively protect the shared information of supply chain members. This work is helpful to the research of cost optimization of the system in complex supply chain systems.
A Research on Teachers’ Views about the Metaverse Platform and Its Usage in Education

This study was conducted to determine primary and secondary teachers' views of the concept of "metaverse". In line with the aim of the study, a questionnaire consisting of closed and open-ended questions about the metaverse platform and its applications in the field of education was applied to 122 teachers. The results showed that approximately 65% of the teachers stated that they knew the concept of the metaverse and its applications. In addition, approximately 55% of the teachers stated that they first heard about the metaverse concept from social media, and 45% stated that they were most aware of metaverse applications in the field of play. Most teachers (69.7%) answered no about the benefits of the metaverse platform in education. Approximately 95% of the teachers stated that they wanted seminars and workshops on metaverse applications in education and wanted these studies to be mostly aimed at introducing the metaverse platform and its applications in the educational environment. Based on the study results, introductory pre-service and in-service studies can be conducted for teachers regarding the metaverse and its applications in the field of education and its possible benefits.
Bayesian optimization for demographic inference

Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent technique for optimizing expensive black box functions. Comparing to the existing widely used genetic algorithm solution, we demonstrate new pipeline's superiority in the limited time budget setting with four and five populations, when using the log-likelihoods provided by the moments tool.
End-to-end weakly-supervised single-stage multiple 3D hand mesh reconstruction from a single RGB image

In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from a single 2D image. Previous studies either focus on single hand reconstruction or solve this problem in a multi-stage way. Moreover, the conventional two-stage pipeline firstly detects hand areas, and then estimates 3D hand pose from each cropped patch. To reduce the computational redundancy in preprocessing and feature extraction, for the first time, we propose a concise but efficient single-stage pipeline for multi-hand reconstruction. Specifically, we design a multi-head auto-encoder structure, where each head network shares the same feature map and outputs the hand center, pose and texture, respectively. Besides, we adopt a weakly-supervised scheme to alleviate the burden of expensive 3D real-world data annotations. To this end, we propose a series of losses optimized by a stage-wise training scheme, where a multi-hand dataset with 2D annotations is generated based on the publicly available single hand datasets. In order to further improve the accuracy of the weakly supervised model, we adopt several feature consistency constraints in both single and multiple hand settings. Specifically, the keypoints of each hand estimated from local features should be consistent with the re-projected points predicted from global features. Extensive experiments on public benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our method outperforms the state-of-the-art model-based methods in both weakly-supervised and fully-supervised manners. The code and models are available at https://github.com/zijinxuxu/SMHR.
Efficient multi-stage network with pixel-wise degradation prediction for real-time motion deblurring

Motion deblurring is an indispensable task in perceptual systems because motion blur can seriously influence the visual effect and the quality of subsequent perception tasks. In practical application, efficiency and effect of motion deblurring are both important. However, among the existing methods, there is no design that can meet the real-time and visual quality requirements at the same time. Thus, an efficient motion deblurring network is proposed by leveraging multi-stage pixel-wise degradation prediction. Specifically, some lightweight modules are designed to accelerate processing while attention and multi-scale mechanism are introduced to maintain quality. In addition, a pixel-wise degradation prediction module and a spatial-channel compensation module are further employed to improve the deblurring quality, such as the distortion of moving objects in the restoration images. Extensive experimental results show that the proposed network can achieve the same PSNR level as the SOTA lightweight deblurring methods and is far faster (5.3 times for DMPHN, 6.7 times for IFI-RNN). Therefore, the proposed design achieves a balance between quality and speed compared with the existing methods.