Reviewerly/exHarmony huggingface dataset:
Secure and efficient data transmission based on quantum dialogue with hyperentangled states in cloud office

Quantum cloud computing facilitates the rapid processing of vast amounts of data and provides robust network services. To enhance the efficiency and security of data transmission in cloud computing, this study proposes a quantum cloud office model and presents a novel quantum dialogue protocol particularly designed for this model. The proposed protocol enables authenticated communication among office correspondents. Leveraging the unique properties of multiple quantum degrees of freedom, this protocol expands the channel capacity and enhances transmission efficiency. Compared to previous measurement-device-independent quantum dialogue (MDI-QD) protocols, this protocol not only further improves transmission efficiency but also enables mutual authentication between correspondents, effectively defending against the risk of man-in-the-middle attacks. The utilization of a cross-Kerr medium for Bell measurements offers a 50% increase in efficiency compared to linear optical equipment. This novel protocol is tailored to the data transmission process within the cloud office model, thereby enhancing the communication efficiency and reinforcing secure data transmission.
A high-accuracy phishing website detection method based on machine learning

The rapid development of e-commerce, e-banking, and social networks has made phishing attack detection one of the most critical technologies in all cyber security systems. To improve the efficiency of anti-phishing techniques, we present an improved predictive model based on machine learning. The proposed method uses six different algorithms; Logistic Regression, K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). The experiments are based on a public dataset of 58,000 legitimate websites and 30,647 phishing ones, including 112 attributes for each sample. Our evaluations in the feature selection process show that after balancing the dataset and dropping constant features, a noticeable improvement can be achieved. We conducted our evaluation found on eight major unique scenarios. The experimental results of our phishing websites detection (PWD) method indicate remarkable performances in which each algorithm reached an accuracy of more than 93%, and the XGBoost classifier outperforms others with 99.2% overall accuracy, 99.1% precision, 99.4% recall, and 99.1% specificity. In addition, the study achieved optimal run-time of about 1500 ms for the XGBoost algorithm without dimension reduction while using Principal Component Analysis (PCA) reduces it down to just 869 ms. As a result, the proposed approach would be practical in both offline and real-time applications.
Formal Modeling and Verification of ERC Smart Contracts: Application to NFT

Blockchain-based applications are basically built on smart contracts, which are widely different in regards of the encoded logic and the used standards. When talking about Ethereum standards, ERC-721 is a well-known standard interface developed for Non-Fungible Tokens. Even though it is standard-based contracts that are more and more exploited, prior work on smart contracts verification mostly investigates efforts in regards of specific vulnerabilities. To address this gap, this paper introduces a formal modeling and verification approach for Ethereum smart contracts including the standard-based ones. We propose a model checking framework that, according to a Solidity smart contract provided as an input, uses ERC guidelines as a standard template to extract the related security properties. Another added benefit of our proposal consists on modeling ERC contracts using the extended finite state machine formalism. As a proof of concept, we illustrate our model checking approach through an NFT contract.
Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN

Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN "BCF-Lym-Detector" for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed "BCF-Lym-Detector" generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed "BCF-Lym-Detector" show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique's generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists' assistance.
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data

Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model.
Self-Sovereign Identity in Semi-Permissioned Blockchain Networks Leveraging Ethereum and Hyperledger Fabric

Patients often have their healthcare data stored in centralized systems, leading to challenges when reconciling or consolidating their data across providers due to centralized databases that store patient identities. The challenges disrupt the flow of patient care where time is sensitive for both patients and providers. Decentralized technologies have enabled a new identity model–Self-Sovereign Identity (SSI)–that grants individuals the right to freely control, access, and share their own data. This work proposes a system that achieves SSI in a semi-permissioned blockchain network using an open protocol as the certificate of authority and several guidelines for securely handling transactions in the network. Open protocols like Keccak can grant access to a permission-based network such as Hyperledger Fabric. The network architecture ensures data security and privacy through mechanisms of multi-signature transactions and guidelines for storing transactions locally, making this architecture ideal for privacy-centered use cases, such as healthcare data-sharing applications. The ultimate goal is to give patients full control over their identity and other data derived from their identity within a semi-permissioned network.
A Perceptual Quality Assessment Exploration for AIGC Images

AI Generated Content (AIGC) has gained widespread attention with the increasing efficiency of deep learning in content creation. AIGC, created with the assistance of artificial intelligence technology, includes various forms of content, among which the AI-generated images (AGIs) have brought significant impact to society and have been applied to various fields such as entertainment, education, social media, etc. However, due to hardware limitations and technical proficiency, the quality of AIGC images (AGIs) varies, necessitating refinement and filtering before practical use. Consequently, there is an urgent need for developing objective models to assess the quality of AGIs. Unfortunately, no research has been carried out to investigate the perceptual quality assessment for AGIs specifically. Therefore, in this paper, we first discuss the major evaluation aspects such as technical issues, AI artifacts, unnaturalness, discrepancy, and aesthetics for AGI quality assessment. Then we present the first perceptual AGI quality assessment database, AGIQA-1K, which consists of 1,080 AGIs generated from diffusion models. A well-organized subjective experiment is followed to collect the quality labels of the AGIs. Finally, we conduct a benchmark experiment to evaluate the performance of current image quality assessment (IQA) models. The database is released on https://github.com/lcysyzxdxc/AGIQA-1k-Database.
A State-Separating Proof for Yao's Garbling Scheme

Secure multiparty computation enables mutually distrusting parties to compute a public function of their secret inputs.One of the main approaches for designing MPC protocols are garbled circuits whose core component is usually referred to as a garbling scheme.In this work, we revisit the security of Yao's garbling scheme and provide a modular security proof which composes the security of multiple layer garblings to prove security of the full circuit garbling.We perform our security proof in the style of state-separating proofs (ASIACRYPT 2018).
Towards a Privacy-Preserving Distributed Cloud Service for Preprocessing Very Large Medical Images

Digitized histopathology glass slides, known as Whole Slide Images (WSIs), are often several gigapixels large and contain sensitive metadata information, which makes distributed processing unfeasible. Moreover, artifacts in WSIs may result in unreliable predictions when directly applied by Deep Learning (DL) algorithms. Therefore, preprocessing WSIs is beneficial, e.g., eliminating privacy-sensitive information, splitting a gigapixel medical image into tiles, and removing the diagnostically irrelevant areas. This work proposes a cloud service to parallelize the preprocessing pipeline for large medical images. The data and model parallelization will not only boost the end-to-end processing efficiency for histological tasks but also secure the reconstruction of WSI by randomly distributing tiles across processing nodes. Furthermore, the initial steps of the pipeline will be integrated into the Jupyter-based Virtual Research Environment (VRE) to enable image owners to configure and automate the execution process based on resource allocation.
Performance Analysis of Scaling NoSQL vs SQL: A Comparative Study of MongoDB, Cassandra, and PostgreSQL

This study aims to investigate state-of-the-art methods for scalable database management, focusing on cost-effectiveness. The study compares two commonly used NoSQL databases, MongoDB, and Cassandra DB, with a traditional relational database management system, PostgreSQL. The comparison is made through performance evaluations conducted on multiple clusters encompassing cloud and on-premises environments. The evaluations include database operations such as insertion, selection, and deletion. The study results indicate that each database management system is optimized for particular use cases. The choice must be based on a comprehensive assessment of multiple factors, not limited to scalability and performance. The novelty of this study lies in its scalability comparison of three popular database management systems and its emphasis on cost-effectiveness while evaluating performance in different environments.
Go-Chemist! App and Its Impact on Students’ Attitudes Toward Chemistry

The current study aims to explore the impact of using the Go-Chemist! app on students’ attitudes toward chemistry on colligative properties. This pretest-posttest quasi-experimental design study involved 71 twelfth-grade students in Jakarta, Indonesia. One class was randomly assigned to a treatment group which was taught using a mobile application and another class was assigned to a comparison group which was taught using e-books. The Attitude Toward Chemistry Lessons Scale (ATCLS) was administered before and after instruction to both groups. To analyze the data, we employed independent and paired sample t-tests. The results indicated that after the intervention, the gap in attitude scores between the two groups was statistically significant in favor of the intervention group. Students in the treatment group also showed a statistically significant improvement in average scores from pre- to post-administration with a large effect size. This reflected the use of the Go-Chemist! app effectively increases students’ positive attitudes toward chemistry lessons on the topic of colligative properties.
Underground hyperspectral outcrop scanning for automated mine‐face mapping: The lithium deposit of Zinnwald/Cínovec

Abstract The inherent complexity of underground mining requires highly selective ore extraction and adaptive mine planning. Repeated geological face mapping and reinterpretation throughout mine life is therefore routine in underground mines. Hyperspectral imaging (HSI) has successfully been applied to enhance geological mapping in surface mining environments, but remains a largely unexplored opportunity in underground operations due to challenges associated with illumination, wet surfaces and data corrections. In this study, we propose a workflow that paves the way for the operational use of HSI in active underground mines. In a laboratory set‐up, we evaluated different hyperspectral sensors and lighting set‐ups as well as the effect of surface moisture. We then acquired hyperspectral data in an underground mine of the Zinnwald/Cínovec Sn‐W‐Li greisen‐type deposit in Germany. These data were corrected for illumination effects, back‐projected into three dimensions and then used to map mineral abundance and estimate Li content across the mine face. We validated the results with handheld laser‐induced breakdown spectroscopy. Despite remaining challenges, we hope this study will help establish hyperspectral sensors in the extractive industry as a means to increase the volume and efficiency of raw material supply, advance digitalisation, and reduce the environmental footprint and other risks associated with underground mining.
A Novel Administration Model for Managing and Organising the Heterogeneous Information Security Policy Field

Information security policy (ISP) plays a crucial role in maintaining the availability, confidentiality, and integrity of sensitive data. However, it is of high complexity and heterogeneity due to the variety and redundancy of security policy practices and complexity of organisational systems. Various and duplicate ISP models and frameworks have been offered in the literature. The duplicate security policy practices, procedures, and processes in the existing models have made ISP disorganised, unstructured, and unclear to organisational users. As a result, there is still a need for a standardised and integrated model to make it simpler to share, manage, and reuse ISP practices amongst the organisations. The main objective of this study is to construct a metamodel to unify, organise, and structure ISP practices. By identifying, recognising, extracting, and combining the common information security policy practices from various ISP models in a built ISP metamodel called ISPM, we seek to make it simple for users and field specialists to derive/instantiate security policy models for their organisations. The development and validation process of the ISPM is based on the common security frameworks such as ISO 27001 frameworks. The developed ISPM consists of 19 common security practices: organisation, risk management, access control policy, edit, review, compliance, business management, backup and recovery, incident response, SETA program, security awareness, security training, security education, email security policy, cloud security policy, network security policy, website security policy, physical security policy, and privacy security policy. Each common security practice consists of several operations and attributes. The performance of the developed ISPM was compared to that of other models to evaluate its completeness and logicalness. Using ISO 27001 as a framework, the findings confirmed the comprehensiveness of ISPM. Therefore, it can contribute to organisations’ security by helping them to develop their own security policy models.
CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments

In complex environments, path planning for mobile robots faces challenges such as insensitivity to the environment, low efficiency, and poor path quality with the rapidly-exploring random tree (RRT) algorithm. We propose a novel algorithm, the complex environments rapidly-exploring random tree (CERRT), to address these issues. The CERRT algorithm builds upon the RRT approach and incorporates two key components: a pre-allocated extension node method and a vertex death mechanism. These enhancements aim to improve vertex utilization and overcome the problem of becoming trapped in concave regions, a limitation of traditional algorithms. Additionally, the CERRT algorithm integrates environment awareness at collision points, enabling rapid identification and navigation through narrow passages using local simple sampling techniques. We also introduce the bidirectional shrinking optimization strategy (BSOS) based on the pruning optimization strategy (POS) to further enhance the quality of path solutions. Extensive simulations demonstrate that the CERRT algorithm outperforms the RRT and RRV algorithms in various complex environments, such as mazes and narrow passages. It exhibits shorter running times and generates higher-quality paths, making it a promising approach for mobile robot path planning in challenging environments.
An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.
Local look-up table upsampling for accelerating image processing

Abstract The resolution of cameras is increasing, and speedup of various image processing is required to accompany this increase. A simple way of acceleration is processing the image at low resolution and then upsampling the result. Moreover, when we can use an additional high-resolution image as guidance formation for upsampling, we can upsample the image processing results more accurately. We propose an approach to accelerate various image processing by downsampling and joint upsampling. This paper utilizes per-pixel look-up tables (LUTs), named local LUT, which are given a low-resolution input image and output pair. Subsequently, we upsample the local LUT. We can then generate a high-resolution image only by referring to its local LUT. In our experimental results, we evaluated the proposed method on several image processing filters and applications: iterative bilateral filtering, $$\ell _0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> smoothing, local Laplacian filtering, inpainting, and haze removing. The proposed method accelerates image processing with sufficient approximation accuracy, and the proposed outperforms the conventional approaches in the trade-off between accuracy and efficiency. Our code is available at https://fukushimalab.github.io/LLF/ .
Video Blockchain: A Decentralized Approach for Secure and Sustainable Networks with Distributed Video Footage from Vehicle-Mounted Cameras in Smart Cities

In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance data for observation, thereby enhancing overall situational awareness. We utilize the decentralized nature of blockchain to implement a vehicle-based surveillance system across a smart city. To ensure reliability, the integration of two cryptographic functions, hashing and signing, with the blockchain is employed. This integration ensures secure and tamper-proof solutions for the existing intelligent surveillance system. In this paper, our primary focus is on combining blockchain technology to achieve sustainable and robust smart solutions for intelligent vehicular distributed video networks while eliminating the need for third-party intermediaries. Through extensive experiments and analysis, we demonstrate the effectiveness and feasibility of our proposed video blockchain approach. The results indicate that this innovative framework provides enhanced security, privacy, and scalability for intelligent vehicular distributed networks in smart cities, paving the way for a connected and efficient urban environment.
Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation

Abstract Collecting large datasets for investigations into human locomotion is an expensive and labor-intensive process. Methods for 3D human pose estimation in the wild are becoming increasingly accurate and could soon be sufficient to assist with the collection of datasets for analysis into running kinematics from TV broadcast data. In the domain of biomechanical research, small differences in 3D angles play an important role. More precisely, the error margins of the data collection process need to be smaller than the expected variation between athletes. In this work, we propose a method to infer the global geometry of track and field stadium recordings using lane demarcations. By projecting estimated 3D skeletons back into the image using this global geometry, we show that current state-of-the-art 3D human pose estimation methods are not (yet) accurate enough to be used in kinematics research.