Reviewerly/exHarmony huggingface dataset:
Verifiable and Redactable Blockchains With Fully Editing Operations

Blockchain technology has been highly praised for its immutability, attracting considerable attention in the academic and industrial community. However, since permanent storage in blockchains would result in copyright disputes and harmful information spreads, it is desired to equip blockchains with mutability nowadays for legal and moral duty restrictions. A redactable blockchain is a variation, which enables the editing of block objects without affecting other blocks in the blockchain. Most existing redactable blockchains can only support editing operations such as modification and deletion, rather than insertion. However, insertion is necessary for some scenarios, especially when patching codes are needed for smart contracts and mistaken deletions occur. In addition, we argue that the verifiability of blockchains is also significantly important, especially for the redactable ones, in which one block may have multiple versions. Hence, an efficient mechanism is required to invalidate history versions and incent distributed adoptions of editing operations. In this paper, we propose a verifiable and redactable blockchain with fully editing operations for the first time. One distinguishable property is the simultaneous achievement of fully editability of block objects and verifiability of blockchain state, with acceptable extra cost. Specifically, we construct the redactable blockchain based on a double trapdoor chameleon hash family, enabling computationally efficient and key-exposure resistant block editing. Additionally, we combine trapdoorless universal accumulators and the largest sequence number principle to make the blockchain state verifiable. Furthermore, we present a comprehensive analysis and extensive experiments to demonstrate the security and feasibility of the proposed redactable blockchain.
A Study on the Application of TensorFlow Compression Techniques to Human Activity Recognition

In the human activity recognition (HAR) application domain, the use of deep learning (DL) algorithms for feature extractions and training purposes delivers significant performance improvements with respect to the use of traditional machine learning (ML) algorithms.However, this comes at the expense of more complex and demanding models, making harder their deployment on constrained devices traditionally involved in the HAR process.The efficiency of DL deployment is thus yet to be explored.We thoroughly investigated the application of TensorFlow Lite simple conversion, dynamic, and full integer quantization compression techniques.We applied those techniques not only to convolutional neural networks (CNNs), but also to long short-term memory (LSTM) networks, and a combined version of CNN and LSTM.We also considered two use case scenarios, namely cascading compression and stand-alone compression mode.This paper reports the feasibility of deploying deep networks onto an ESP32 device, and how TensorFlow compression techniques impact classification accuracy, energy consumption, and inference latency.Results show that in the cascading case, it is not possible to carry out the performance characterization.Whereas in the stand-alone case, dynamic quantization is recommended because yields a negligible loss of accuracy.In terms of power efficiency, both dynamic and full integer quantization provide high energy saving with respect to the uncompressed models: between 31% and 37% for CNN networks, and up to 45% for LSTM networks.In terms of inference latency, dynamic and full integer quantization provide comparable performance.
FM-Unet: Biomedical image segmentation based on feedback mechanism Unet

With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
FP‐BFT: A fast pipeline Byzantine consensus algorithm

Abstract The Byzantine Fault Tolerant (BFT) consensus algorithms have been widely applied in the blockchain systems because of their fault tolerance capability to determine system consistency in the presence of malicious nodes. However, the BFT consensus algorithms are confronted with low efficiency and scalability problems caused by multiple rounds of handshake communication. In this paper, a pipeline‐based Fast Pipeline Byzantine Fault Tolerance consensus algorithm (FP‐BFT) is proposed, which adopts a non‐leader pipeline framework to process different rounds of transactions in parallel. By means of randomly selecting 2 f +1 nodes to form a committee for one round of transactions, consensus agreement can be reached within the committee via nodes broadcasting and voting. Committee nodes participating in the consensus are chosen by chance to avoid the monopoly of which becomes the block producer. Consensus efficiency and the system throughput can be significantly improved with the pipeline framework. Comparison experiments are conducted to verify the superiority of the FP‐BFT algorithm, and the theoretical proof is given to guarantee the Byzantine fault‐tolerant security. Experimental results show that FP‐BFT has improved the consensus efficiency by decreasing communication overhead to make it better applied both in public blockchain and consortium blockchain systems.
Exploring the Application of Artificial Intelligence in Foreign Language Teaching: Challenges and Future Development

With the rapid development of artificial intelligence (AI) technology, it is increasingly being utilized in the field of education, particularly in foreign language teaching. The concept of education meta-universe is also gaining popularity, and the emergence of ChatGPT has revolutionized traditional foreign language teaching methods. AI technologies such as speech recognition, machine translation, and natural language processing have proven to be effective tools to help students improve their foreign language learning outcomes. However, the application of these technologies also brings some challenges and problems. This paper aims to discuss the impact of AI on foreign language teaching, analyze its advantages and disadvantages, explore its future development trend, and provide suggestions for educators to make better use of AI technologies to assist foreign language teaching.
Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.
Geo-hazards assessment of the new-found industrial communities: an example from the 10th of Ramadan Industrial Region, Egypt

Abstract The impacts of Geo-hazard events are main obstacles to the use of land in numerous arid and semi-arid provinces of the World. Furthermore, the various activities of industrial communities, such as Egypt's 10th Ramadan Industrial Region (10th RIR), are the most dangerous examples of waste's impact on soil, surface water, and groundwater. The current study uses image processing and GIS tools to identify, delineate, monitor, and assess some of the environmental hazards caused by both human and natural activities in the 10th RIR. The findings revealed that there are rapid changes in land cover, which could be attributed to both anthropogenic and natural activities. Furthermore, the results revealed that waterlogging, salt-affected soils, water pollution, and flash floods were the most serious environmental threats to the 10th RIR. The obtained geo-hazard map showed that the 10th RIR has high flash flood hazards zones that are geospatially distributed in the southern parts of the Wadi Gafra basin and the western parts of 10th of Ramadan City. Furthermore, salinized soil zones are viewed as geospatially restricted zone in the eastern portion of 10th of Ramadan City and/or near the oxidation ponds. Additionally, vast areas of highly contaminated groundwater exist beneath industrial, urban, and oxidation pond zones. The current work provides decision-makers with numerous mitigation measures and recommendations required to minimize the impacts of geo-environmental hazards endangering the 10th RIR.
Security Threats to 5G Networks for Social Robots in Public Spaces: A Survey

This paper surveys security threats to 5G-enabled wireless access networks for social robots in public spaces (SRPS).The use of social robots (SR) in public areas requires specific Quality of Service (QoS) planning to meet its unique requirements.Its 5G threat landscape entails more than cybersecurity threats that most previous studies focus on.This study examines the 5G wireless RAN for SRPS from three perspectives: SR and wireless access points, the ad hoc network link between SR and user devices, and threats to SR and users' communication equipment.The paper analyses the security threats to confidentiality, integrity, availability, authentication, authorisation, and privacy from the SRPS security objectives perspective.We begin with an overview of SRPS use cases and access network requirements, followed by 5G security standards, requirements, and the need for a more representative threat landscape for SRPS.The findings confirm that the RAN of SRPS is most vulnerable to physical, side-channel, intrusion, injection, manipulation, and natural and malicious threats.The paper presents existing mitigation to the identified attacks and recommends including physical level security (PLS) and post-quantum cryptography in the early design of SRPS.The insights from this survey will provide valuable risk assessment and management input to researchers, industrial practitioners, policymakers, and other stakeholders of SRPS.
Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO.
Do system quality and information quality affect job performance? The mediation role of users’ perceptions

This study aims to analyze the influence of the system quality and information quality from the Correctional Database System (CDS) on the job performance of correctional Technical Support Officers (TSO) in Sumatra, either directly or through the mediating role of perceived ease of use and perceived usefulness. This study tested 118 correctional officers selected by the purposive sampling technique. A quantitative approach is used with Partial Least Square-Structural Equation Modeling analysis technique. It was found that the direct influence of the system and information quality on job performance is insignificant. Nonetheless, it shows that perceived ease of use and perceived usefulness fully mediate system quality and information quality on job performance. The findings demonstrate that perceived ease of use and perceived usefulness are important factors that motivate individual performance when supported by high-quality information systems. This study provides an overview of the use of a mandatory system for public organizations and the effects on user performance. It can be used as a basis for correctional institutions to strengthen and maintain a positive perception of CDS among their officers, such as through mentoring programs.
A multi-label text message classification method designed for applications in call/contact centre systems

This paper presents a system for multi-label classification of text data processed in Call/Contact Centre (CC) systems. The solution presented herein constitutes a significant innovation and an advantage in relation to the solutions used so far in CC systems, as the contents can be automatically routed directly to even several agents with different competences (depending on the number of classes recognised in the message). The proposed approach combines a set of vectorisation methods, dimensionality reduction methods, and a classifier based on artificial neural networks. Analyses were performed using data from real databases of a large commercial CC system and data extracted from the publicly available Stackoverflow database to evaluate the effectiveness of the developed classification method. The proposed approach was compared with the existing text data classification methods. The method enables classification of text messages belonging to one or more classes and can be used to automatically route contents to agents with appropriate competences.
Automation for digital forensics: Towards a definition for the community

Automation is crucial for managing the increasing volume of digital evidence. However, the absence of a clear foundation comprising a definition, classification, and common terminology has led to a fragmented landscape where diverse interpretations of automation exist. This resembles the wild west: some consider keyword searches or file carving as automation while others do not. We, therefore, reviewed automation literature (in the domain of digital forensics and other domains), performed three practitioner interviews, and discussed the topic with domain experts from academia. On this basis, we propose a definition and then showcase several considerations concerning automation for digital forensics, e.g., what we classify as no/basic automation or full automation (autonomous). We conclude that it requires these foundational discussions to promote and progress the discipline through a common understanding.
COME: Clip-OCR and Master ObjEct for text image captioning

Text image captioning aims to understand the scene text in images for generating image captions. The key challenge of this task is to accurately and comprehensively understand the OCR tokens of scene text. Due to the dual modal of visual and textual features of scene text, expressing the multimodal semantic features of OCR tokens accurately is a challenging task. Additionally, since scene text cannot exist independently of specific objects and is always associated with its surroundings, establishing a scene graph centered around OCR tokens is also an important approach to understand its relationship with other objects in the image. In this paper, we propose a novel model named Clip-OCR and Master ObjEct (dubbed as COME) for text image captioning. First, we introduce a CLIP-OCR module to enhance the multimodal representation of OCR tokens. We separate the OCR representation into visual and textual items and narrow the similarity by contrastive learning. With the assistance of the CLIP-OCR module, we realize correlation alignment between different modes. Next, we propose the concept of master object for each OCR text and purify the OCR-oriented scene graph with it. The master object is defined as the object to which the OCR is attached, which bridges the semantic relationship between the OCR tokens and the image. We consider the master object as a proxy that connects OCR tokens and other regions in the image. By exploring the master object for each OCR token, we build a purified scene graph based on the master object and then enrich the visual embedding by the Graph Convolution Network (GCN). Furthermore, we cluster the OCR tokens and append the hierarchical information on the input embedding to provide a complete representation. Experiments on the TextCaps validation set and test set demonstrate the effectiveness of the proposed framework.
A post-quantum blockchain notary scheme for cross-blockchain exchange

Due to its decentralized nature, blockchain serves as a foundational platform for smart transportation, enabling peer-to-peer communication. However, exchanging data between different blockchain applications often poses a complex challenge known as cross-blockchain exchange. To address potential vulnerabilities and tackle challenges related to cross-blockchain exchange, we introduce post-quantum techniques that utilize secure blockchain notary schemes. First, we present a multi-signature method employing multivariate cryptographic primitives, offering security in the face of quantum computer attacks. Second, building upon our initial technique, we develop a post-quantum notary scheme to enhance the security of cross-blockchain exchanges. Finally, utilizing the post-quantum notary scheme, we propose a post-quantum cross-blockchain exchange model. We implement this scheme on the blockchain using Python and analyze the experimental results of the cross-blockchain exchange model. The integration of our blockchain design into intelligent transportation systems offers several advantages, including improved security, transparency, and efficiency.
Towards an Automated Framework for Realizing Quantum Computing Solutions

Quantum computing is fast evolving as a technology due to recent advances in hardware, software, as well as the development of promising applications. To use this technology for solving specific problems, a suitable quantum algorithm has to be determined, the problem has to be encoded in a form suitable for the chosen algorithm, it has to be executed, and the result has to be decoded. To date, each of these tedious and error-prone steps is conducted in a mostly manual fashion. This creates a high entry barrier for using quantum computing—especially for users with little to no expertise in that domain. In this work, we envision a framework that aims to lower this entry barrier by allowing users to employ quantum computing solutions in an automatic fashion. To this end, interfaces as similar as possible to classical solvers are provided, while the quantum steps of the workflow are shielded from the user as much as possible by a fully automated backend. To demonstrate the feasibility and usability of such a framework, we provide proof-of-concept implementations for two different classes of problems which are publicly available on GitHub (https://github.com/cda-tum/MQTProblemSolver) as part of the Munich Quantum Toolkit (MQT). By this, this work provides the foundation for a low-threshold approach realizing quantum computing solutions with no or only moderate expertise in this technology.
A novel secure authentication protocol for eHealth records in cloud with a new key generation method and minimized key exchange

In wake of covid19, many countries are shifting their paper-based health record management from manual processes to digital ones. The major benefit of digital health record is that data can be easily shared. As health data is sensitive, more security is to be provided to gain the trust of stakeholders. In this paper, a novel secure authentication protocol is planned for digitalizing personal health record that will be used by the user. While transacting data, a key is used to secure it. Many protocols used elliptic curve cryptography. In this proposed protocol, at an initial stage, an asymmetric and quantum-resistant crypto-algorithm, Kyber is used. In further stages, symmetric crypto-algorithm, Advanced Encryption Standard in Galois/Counter mode (AES-GCM) is used to secure transferred data. For every session, a new key is generated for secure transactions. The more interesting fact in this protocol is that transactions are secured without exchanging actual key and also minimized the key exchange. This protocol not only verified the authenticity of user but also checked rightful citizenship of user. This protocol is analyzed for various security traits using ProVerif tool and provided better results relating to security provisioning, cost of storage, and computation as opposed to related protocols.