Reviewerly/exHarmony huggingface dataset:
An Anonymous Authentication Scheme with Selective Linkability and Threshold Traceability

In order to protect the user's privacy identity, authentication requires anonymous authentication. Anonymous authentication is divided into unconditional anonymous authentication and traceable anonymous authentication. Unconditional anonymous authentication can verify that the user belongs to an anonymous set, but the user's true identity cannot be obtained. However, in some applications, it is necessary to trace the true identity of the user. Therefore, a traceable anonymous authentication scheme is proposed. In order to prevent random tracing, the proposed scheme uses threshold joint tracing. When the identity of the authenticator needs to be traced, the threshold number of members can jointly trace the identity of the authenticator. In some special network applications such as anonymous electronic voting, in order to prevent repeated authentications and repeated elections, it is necessary to verify whether the two authentication signatures are signed by the same user without revealing the true identity of the user. Therefore, the proposed anonymous authentication scheme should have selective linkability. In order to achieve linkable authentication, the linkable tag is embedded by linkable ring signature. Compared with similar schemes through the simulation experiments, the implementation time of the proposed scheme is slightly better than other schemes.
Photonic Reservoir Computing Based on Reflective Semiconductor Optical Amplifier

We propose a time-delayed photonic reservoir computing structure utilizing a reflective semiconductor optical amplifier. Simulation results indicate that improved time series prediction performance can be achieved, compared to the traditional reservoir computing system.
Modeling Control and Forecasting Nonlinear Systems Based on Grey Signal Theory

Based on this article, a fuzzy NN (neural network) based on the EBA (evolved bat algorithm) was developed to devise adaptive control with gray signal prediction to provide asymptomatic stability and increased driving comfort. The method is used to assess plant nonlinearity and to perform structural tracking of the signal. The set of Gray’s differential equations is applied to Gray’s model (GM) (n, h), which has been an active system model. In the model, n is the order of the Gray’s differential equation and h is the number of variables considered. In this paper, a GM(2.1) has been utilised to achieve advanced nonlinear motion of a system, allowing the controller to demonstrate the efficiency and stability of the whole system in a Lyapunov-like expression. The controller design standard for a MEW (mechanical elastic wheel) is presented, creating a realistic framework in mathematical for practical engineering applications.
Cost-efficient protocol upgrading for quantum key distribution over metro optical networks

This paper proposes a cost-efficient protocol upgrading algorithm for quantum key distribution over metro optical networks, reducing the number of channels added for QKD protocol upgrading by 17.6% compared to the benchmark.
Automatic Detection of API Access Control Vulnerabilities in Decentralized Web3 Applications

Web3 is a blockchain-powered web evolution. In many situations, Web3 smart contracts require data from outside their applications (off-chain data) via APIs to function as designed. Existing APIs in Web3 facing the most common and critical risks originate through access control vulnerabilities (i.e., Broken Object Level Authorization, Broken Function Level Authorization, and Broken Authentication). Such vulnerabilities during runtime cannot be spotted during the development and testing phases of a Web3 application that integrates APIs. Continuous monitoring is the key to proactive hunting access control attacks, which are not attainable through manual monitoring. In this paper, we design a real-time automated security monitoring approach named the access behavior learning (ABL) model for early detection and prevention of access control attacks before they could cause any damage. In two steps, the ABL approach predicts an attacker's access behavior in response to environmental behavior. First, it verifies the API providers and oracle by defining authentication schemes using OpenAPI Specification (OAS) standard to identify the API endpoints to endorse authenticity. In addition, it validates the oracle-level authentication security schemes for approving authentication. Second, it scans metadata for the current access record and compares it with the previous access records, such as location, application id, and API key, to form a baseline that determines authentication. Using this baseline, ABL determines legitimate application access based on both factors for identifying its authentication. ABL approach retains API security by designing an efficient correlation to enable complex off-chain computation by predicting API access attacks. The ABL approach is evaluated against different Web3 applications with varying levels of access control vulnerabilities where applied for early attack detection and prevention. Compared to traditional manual detection processes, the ABL approach offers early automated detection and prevention of attacks during runtime, which results in enhanced security measures and reduces the risk of potential threats.
Building a Cross-Chain Identity: A Self-Sovereign Identity-Based Framework

There is a lack of effort in standardizing how users can connect their identities belonging to different blockchains and how to transfer on-chain data associated with each identity between ledgers. The absence of a general solution for managing a cross-chain identity can cause fragmentation, leading to the possibility of realizing applications that are more prone to security bugs. In this paper, we propose a framework to build a cross-chain identity based on the principles and standards of a new identity management paradigm, i.e., Self-Sovereign Identity (SSI). Promoting the standardization of applications running on the blockchain enhances their security and encourages the adoption of this technology in an increasing number of application contexts. According to the SSI paradigm, users create and update their identities in a blockchain without intermediaries. Following this principle, our framework does not require intermediaries to transfer identity-associated data from a source blockchain to a target blockchain. We realize that using a blockchain relay, an interoperability solution for storing and validating the headers of the source blockchain on the target blockchain. In our paper, we provide a new relay, the StateRelay, reducing the final usage costs for users willing to realize a cross-chain identity. We will show this by comparing its costs, in terms of transaction fees, with the other relay solutions in the literature.
Analyzing Geospatial Distribution in Blockchains

Blockchains are decentralized; are they genuinely? We analyze blockchain decentralization's often-overlooked but quantifiable dimension: geospatial distribution of transaction processing. Blockchains bring with them the potential for geospatially distributed transaction processing. They enable validators from geospatially distant locations to partake in consensus protocols; we refer to them as minority validators. Based on our observations, in practice, most validators are often geographically concentrated in close proximity. Furthermore, we observed that minority validators tend not to meet the performance requirements, often misidentified as crash failures. Consequently, they are subject to punishment by jailing (removal from the validator set) and/or slashing (penalty in native tokens). Our emulations, under controlled conditions, demonstrate the same results, raising serious concerns about the potential for the geospatial centralization of validators. To address this, we developed a solution that easily integrates with consensus protocols, and we demonstrated its effectiveness.
UBIC-A Blockchain-Less Cryptocurrency

In this paper we propose UBIC, a novel blockchain-less architecture that preserves the main advantages of classic cryptocurrencies while avoiding their pitfalls. The proposed construction is general-that is, UBIC can be adopted at par with any other cryptocurrency-though UBIC also satisfies the requirements to support state-sponsored financial services, like Universal Basic Income and Central Bank Digital Currency. Indeed, UBIC stands for Universal Basic Income Coin, to highlight one of its most straightforward use-cases. One of the key features of UBIC is that every user participating in the protocol gets fair and equal access to the rewards, regardless of the available resources, e.g., computational power or financial stake. Moreover, by leveraging standard cryptographic techniques, such as homomorphic encryption and verifiable random functions, UBIC ensures full user privacy and trust in the network, while enjoying a highly scalable architecture. Our experimental results confirm the feasibility of the proposed architecture and demonstrate that UBIC is very efficient in terms of transaction verification time. To the best of our knowledge, this is the first blockchain-less cryptocurrency proposal. Other than being interesting on its own, and being particularly fit to support UBI and Central Bank Digital Currency, the architectural solutions and the technical choices discussed in this contribution have the potential to generate high impact and further research in the field.
Using Django Framework and DLT for Drug Supply Chain Management

This paper presents a general-purpose approach for the drug supply chain management, by proposing a DLT-based methodology to facilitate and make more efficient the development of such applications. For specific domains, such as drug management and traceability, a system based on Django Python framework, on Ethereum blockchain, and on web3.py library has been developed that can be customized for most real supply chains, automatically generating the specific applications (database schema, smart contracts, apps). A case study about a simple drug traceability system for a producer to hospital wards is described, to show how this approach works.
DIDAPPER: A Practical and Auditable On-Chain Identity Service for Decentralized Applications

The popularity of decentralized applications brings increasing on-chain audit concerns. Since on-chain accounts do not link to real-world identities, decentralized applications have been wildly used for illegal activities like money laundering. Decentralized identity (DID) is one of the most promising solutions to guarantee on-chain auditability in a privacy-preserving way. However, the existing DID systems suffer from two limitations, making them rarely adopted in on-chain decentralized applications. First, most previous systems only provide partial auditability, meaning that their regulators cannot trace users' real-world identities after users' malicious behaviors occur. Second, current solutions are usually isolated systems, causing a lack of interoperability with on-chain decentralized applications. To address the problems, we propose Didapper, a practical and auditable on-chain identity service with both privacy and auditability for decentralized applications. We introduce group signatures to provide anonymous and traceable credentials for on-chain users and decentralized applications. In order to meet the efficiency and flexibility requirements, we design credential structure and workflow mostly compatible with W3C standards (a DID recommendation that enables verifiable, decentralized digital identity), and realize credential verification service with updatable policies. We implement Didapper and evaluate it on Ethereum, and the results show that the performance of Didapper is acceptable in practice.
Performance Evaluation of Self-Sovereign Identity Use Cases

Self-sovereign identity (SSI) enables the creation of user-centric applications where the user has complete control over his data. This research evaluates the performance of SSI-based applications; to do this, we implemented healthcare use cases using Hyperledger Indy and Hyperledger Aries frameworks, deployed it in a cloud environment, and executed their empirical evaluation. The results indicate that the bottleneck is the CPU; a simple setup can support up to 80 concurrent users without relevant errors and 120 simultaneous users before system degradation. Finally, we discuss the system bottlenecks and possible optimization techniques.
PENINGKATAN KEMAMPUAN TEKNOLOGI DIGITAL SISWA MELALUI APLIKASI CONSTRUCT 3: GAMES FOR EDUCATION

Currently, Games for Education is an interesting topic for students in Vocational School, especially in this digital era. The training in games-making conducted by the Universitas Teknokrat Indonesia social service team gives a significant impact to develop digital competencies, creativity, and collaboration; furthermore, it is relevant to the needs of the nowadays digital job market. First, this training in this case gives the students a chance to have such a digital technology, which is relevant to the job market. It can increase their creativity in producing interesting and original digital products. Then, this training also leads the students to be able to work in a team for collaboration. In the process of game making, the students should work together to design, test, and develop the games. It thus builds up students’ ability for collaborating, communicating, and solving problems. This ability is also crucial in the digital job market, especially for project teams. Related to the job market, this training can also create a career path in game industries, graphic design, animation, and multimedia. In closing, the training gives experiences and competencies for the students in game-making that are useful for their careers; furthermore, it can help the students to master digital technology, increase their creativities, develop their collaborative ability, and to prepare themselves for the challenges of the fast- digital industry development.
Reducing Reconfiguration Time in Hybrid Optical-Electrical Datacenter Networks

We study how to reduce the reconfiguration time in hybrid optical-electrical Datacenter Networks (DCNs). With a layer of Optical Circuit Switches (OCSes), hybrid optical-electrical DCNs could reconfigure their logical topologies to better match the on-going traffic patterns, but the reconfiguration time could directly affect the benefits of reconfigurability. The reconfiguration time consists of the topology solver running time and the network convergence time after triggering reconfiguration. However, existing topology solvers either incur high algorithmic complexity or fail to minimize the reconfiguration overhead.
Training ChatGPT-like Models with In-network Computation

ChatGPT shows the enormous potential of large language models (LLMs). These models can easily reach the size of billions of parameters and create training difficulties for the majority. We propose a paradigm to train LLMs using distributed in-network computation on routers. Our preliminary result shows that our design allows LLMs to be trained at a reasonable learning rate without demanding extensive GPU resources.
Host Efficient Networking Stack Utilizing NIC DRAM

The growth in host resource and network speed is not synchronized, and the status quo of this imbalance from the network speed of 100 ∼ Gbps makes the host resource the bottleneck. We categorize existing body of work to reduce the host burden into the following three approaches: (1) to eliminate payload copy (zero-copy), (2) to utilize special-purpose hardware for payload copy, and (3) to offload protocol to NIC. Each approach, however, has drawbacks. (1) Most zero-copy methods require application modification. Furthermore, the application must ensure its buffer is not modified until network I/O is complete. (2) Copy elimination through special-purpose hardware still uses host memory, consuming considerable memory bandwidth. (3) The protocol offloaded to NIC has limited flexibility.
ChatIoT: Zero-code Generation of Trigger-action Based IoT Programs with ChatGPT

Trigger-Action Program (TAP) is a popular and significant form of Internet of Things (IoT) applications, commonly utilized in smart homes. Existing works either just perform actions based on commands or require human intervention to generate TAPs. With the emergence of Large Language Models (LLMs), it becomes possible for users to create IoT TAPs in zero-code manner using natural language. Thus, we propose ChatIoT, which employs LLMs to process natural language in chats and realizes the zero-code generation of TAPs for existing devices.
An Investigation into the Security of Register Allocation with Spilling and Splitting

Compiler optimization can be functionally correct but not secure. Register allocation (RA) is an essential optimization performed by a compiler. This paper analyzes the security threat of RA concerning information flow. We define the relative security between two programs with respect to information flow. According to our definition of relative security, we show that RA is secure when there is no splitting and spilling into memory. We also show that register allocation with splitting is also secure based on our attack model. Then, we show that RA can lead to information leaks during spilling as it introduces new leaks through memory. Further, our experimental results on various benchmarks show that RA in LLVM is actually leaky. To address this vulnerability, we propose a secure RA approach in LLVM that mitigates the risk of new leaks during spilling. Our experimental evaluation on various benchmarks shows the effectiveness of our proposed approach.
Application Profiling Using Register-Instruction Hardware Performance Counters

Kleptographic attacks are a type of security threat that involve weakening a cryptographic implementation in order to extract sensitive information from a computer system. These attacks can be particularly harmful when they target cryptographic keys or other security-critical information. Since software-based defenses are not robust, to address these threats, prior studies have explored the use of trusted hardware-based solutions, involving tailor-made Hardware Performance Counters (HPCs). However, these tailor-made HPCs lack the fine-grained characterization necessary to correctly differentiate between individual applications. As a result, a large number of HPCs are required to monitor the application, which incurs high overhead on the system. To this end, we propose the development of Register-Instruction Hardware Performance Counters (RIHPCs), a bespoke set of special-purpose registers designed to characterize applications, and thus detect Kleptographic attacks, with low granularity and low performance overhead. To assess the performance of RIHPCs against Kleptographic attacks, we profile NIST’s Post Quantum Cryptographic Key Encapsulation Mechanism (PQC-KEM) algorithms. Our results show that RIHPC traces can distinguish between PQC algorithms with an accuracy of over 99%, while furnishing up to 67% reduction in performance overhead in comparison to tailor-made HPCs.
Efficient and Structural Gradient Compression with Principal Component Analysis for Distributed Training

Distributed machine learning is a promising machine learning approach for academia and industry. It can generate a machine learning model for dispersed training data via iterative training in a distributed fashion. To speed up the training process of distributed machine learning, it is essential to reduce the communication load among training nodes. In this paper, we propose a layer-wise gradient compression scheme based on principal component analysis and error accumulation. The key of our solution is to consider the gradient characteristics and architecture of neural networks by taking advantage of the compression ability enabled by PCA and the feedback ability enabled by error accumulation. The preliminary results on image classification task show that our scheme achieves good performance and reduces 97% of the gradient transmission.
Peningkatan Penggunaan Social Learning Network Bagi Siswa-Siswi SMP Negeri 2 Syamtalira Aron Kab. Aceh Utara

Today the development of the internet is increasing rapidly and has penetrated into several fields, including the field of education. Learning media also develops and creates a Social Learning Network. Edmodo is an online learning medium that combines several Learning Management Systems and Social Network features into a Social Learning Network specifically made for educational purposes. To obtain an effective learning process, teachers and students must have innovative and interesting learning media, this is what is experienced by SMP Negeri 2 Syamtalira Aron North Aceh Regency. Teachers of SMP Negeri 2 Syamtalira Aron North Aceh Regency have carried out training on increasing the use of Edmodo learning media, but students still have not used social network-based learning media. This is the background for the service team to provide training on "Increasing the Use of Social Learning Network for Students of SMP Negeri 2 Syamtalira Aron North Aceh Regency ". This activity was held at the Syamtalira Aron 2 Public Middle School Computer Lab using lecture methods and hands-on practice. The participants of this training were 30 people, all of whom were students of SMP 2 Syamtalira Aron North Aceh Regency. The activity lasts for one day with a duration of four hours. Through this activity, it is hoped that the participant's skills and understanding will increase regarding the use of the Edmodo learning social network
Research on Edge Computing Access Platform Architecture and components

This paper is introducing the research on edge computing access platform architecture, components, the key techniques as well as the implementation, it is to show that this architecture could help to access devices through different protocols especially in the IIoT scenarios.
Optimization Design of Concurrency Control Algorithm for Main Memory Database Based on Machine Learning

Effective real-time network measurement provides a basis for generating routing optimization strategies, so that the network can sense congestion. The existing out of band network telemetry technology will transmit additional probes to measure the network status, which will inevitably lead to the "observer" effect, leading to inaccurate measurement information. In addition, it is difficult to describe the relationship between complex network states and routing optimization strategies with accurate mathematical models. Let some neurons stop working temporarily, reduce the interaction between neurons, implicitly delete neurons in the network, and prevent some functional synergies to alleviate excessive matching. The algorithm selects temporarily discarded neurons with random probability, while the optimization algorithm uses Ising model in machine learning data to identify neurons with low link energy, and temporarily discards these neurons in training and reasoning. This algorithm makes the model more general and effectively alleviates the over fitting problem of network training. Its purpose is to select key features closely related to the learning purpose or eliminate irrelevant features to improve the performance of the model. Feature sparse methods can be divided into two categories. And in machine learning, the use of control algorithms can effectively improve the development of machine learning, and in machine learning, the proportion of optimization using control algorithms is very large. This paper puts forward many new ideas, hoping to improve the use of machine learning control algorithm.