Reviewerly/exHarmony huggingface dataset:
Jolt: Recovering TLS Signing Keys via Rowhammer Faults

Digital Signature Schemes such as DSA, ECDSA, and RSA are widely deployed to protect the integrity of security protocols such as TLS, SSH, and IPSec. In TLS, for instance, RSA and (EC)DSA are used to sign the state of the agreed upon protocol parameters during the handshake phase. Naturally, RSA and (EC)DSA implementations have become the target of numerous attacks, including powerful side-channel attacks. Hence, cryptographic libraries were patched repeatedly over the years.Here we introduce Jolt, a novel attack targeting signature scheme implementations. Our attack exploits faulty signatures gained by injecting faults during signature generation. By using the signature verification primitive, we correct faulty signatures and, in the process deduce bits of the secret signing key. Compared to recent attacks that exploit single bit biases in the nonce that require 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">45</sup> signatures, our attack requires less than a thousand faulty signatures for a 256-bit (EC)DSA. The performance improvement is due to the fact that our attack targets the secret signing key, which does not change across signing sessions. We show that the proposed attack also works on Schnorr and RSA signatures with minor modifications.We demonstrate the viability of Jolt by running experiments targeting TLS handshakes in common cryptographic libraries such as WolfSSL, OpenSSL, Microsoft SymCrypt, LibreSSL, and Amazon s2n. On our target platform, the online phase takes less than 2 hours to recover 192 bits of a 256-bit ECDSA key, which is sufficient for full key recovery. We note that while RSA signatures are protected in popular cryptographic libraries, OpenSSL remains vulnerable to double fault injection. We have also reviewed their Federal Information Processing Standard (FIPS) hardened versions which are slightly less efficient but still vulnerable to our attack. We found that (EC)DSA signatures remain largely unprotected against software-only faults, posing a threat to real-life deployments such as TLS, and potentially other security protocols such as SSH and IPSec. This highlights the need for a thorough review and implementation of faults checking in security protocol implementations.
Xray-Net: Self-supervised pixel stretching approach to improve low-contrast medical imaging

Medical imaging is crucial for monitoring the progression of a diagnosis. However, poor-quality images, particularly radiographs which are commonly used for their affordability, can lack sufficient visual information and lead to lengthy and inaccurate diagnoses, reducing clinicians' ability to examine and decreasing the accuracy of their assumptions. To address this issue, this paper proposes an Xray-Net approach that utilizes self-supervised pixel-contrast stretching to improve low-contrast images. The approach consists of three main phases: (1) Pre-processing, which involves obtaining X-ray contrast-based classification through the measurement of average brightness, (2) Development of a conventional neural network model, and (3) Contrast-adaptive stretching, where the stretching control coefficients are predicted adaptively based on input image information. The proposed approach was evaluated using quantitative and qualitative experiments on three X-ray databases, and six different quality measures were considered. The results obtained by Xray-Net demonstrate superior low-contrast enhancement performance compared to several state-of-the-art methods.
Contribution-Based Cooperative Co-Evolution With Adaptive Population Diversity for Large-Scale Global Optimization [Research Frontier]

Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations’ evolution. In this paper, an adaptive method for enhancing population diversity is embedded in a contribution-based CC. In CC, there are two kinds of subpopulation: the convergent or stagnant subpopulations and the non-convergent and non-stagnant subpopulations. A method is proposed in the paper to evaluate the convergent and stagnant subpopulations’ contributions to improving the best overall objective value, which is different from the contribution evaluation on the non-convergent and non-stagnant subpopulations. In each co-evolutionary cycle, the new CC adaptively determines to select a subpopulation, which can make a greater contribution to improving the best overall objective value, between the above two kinds of subpopulation to undergo evolution. When a convergent or stagnant subpopulation is selected to undergo evolution, the subpopulation is re-diversified to enhance its global search capability. Our experimental results and analysis suggest that the new CC algorithm can improve the performance of CC and serves as a competitive solver for large-scale optimization problems.
User-Perceived Privacy in Blockchain

This paper studies users’ privacy perceptions of UTXO-based blockchains such as Bitcoin. It elaborates – based on interviews and questionnaires – on a mental model of employing privacy-preserving techniques for blockchain transactions. Furthermore, it evaluates users’ awareness of blockchain privacy issues and examines their preferences towards existing privacy-enhancing solutions, i.e., add-on techniques to Bitcoin versus built-in techniques in privacy coins. Using Bitcoin as an example, we shed light on existing discrepancies between users’ privacy perceptions and preferences as well as current implementations.
Designing a reliable and cost-effective Internet of Medical Things (IoMT) topology to minimize the maintenance and deployment cost

The Internet of Things (IoT) can now be used to automate healthcare facilities and make patient data available for use at any time and from any location via the Internet. Healthcare-related data is now shared and accessed via the host-based Internet paradigm. Latency, mobility, and security issues are all exacerbated by its location-dependent nature. For the present host-based Internet paradigm, which is already in place, NDN has been promoted as the next Internet paradigm. The new species, unfortunately, lacks a stable healthcare system. A lightweight certificate less (CLC) signature is used to build an NDN-IoMT framework in this paper. We employ the Hyper elliptic Curve Cryptosystem (HCC) since it is cheaper than the Elliptic Curve Cryptosystem, which provides higher security with a smaller key (ECC). In addition, we use AVISPA to verify the proposed scheme's safety. In order to determine the most cost-effective solution, we look at existing certificate less signature methods. Results reveal that our proposed method utilizes very little network resources. Finally, we put the architecture into action on NDN-IoMT.
Towards Smart Contract-Based Verification of Anonymous Credentials

Smart contracts often need to verify identity-related information of their users. However, such information is typically confidential, and its verification requires access to off-chain resources. Given the isolation and privacy limitations of blockchain technologies, this presents a problem for on-chain verification. In this paper, we show how CL-signature-based anonymous credentials can be verified in smart contracts using the example of Hyperledger Indy, a decentralized credential management platform, and Ethereum, a smart contract-enabled blockchain. Therefore, we first outline how smart contract-based verification can be integrated in the Hyperledger Indy credential management routine and, then, provide a technical evaluation based on a proof-of-concept implementation of CL-signature verification on Ethereum. While our results demonstrate technical feasibility of smart contract-based verification of anonymous credentials, they also reveal technical barriers for its real-world usage.
Drivers of Bitcoin Energy Use and Emissions

The global Bitcoin mining industry has grown to a size where its overall energy consumption is frequently compared to that of entire countries. Indeed, as of 30 April 2022, following the successful migration of the entire Chinese mining industry after its expulsion from China in mid-2021, Bitcoin uses approximately 247.0 TWh of primary energy per year, slightly less than the entire nation of New Zealand, the 63 $$^{rd}$$ ranked nation by total energy consumption. To understand what drives Bitcoin’s energy use and emissions however, one must understand four key concepts: how Bitcoin works and incentivises its miners, the nature of competition in the mining industry, the nature of mining hardware and innovation, and importantly, international energy and electricity markets and the differences between them. This paper will provide a thorough explanation of these concepts, as well as provide commentary on Bitcoin’s current state, and what the Bitcoin Mining Industry may potentially look like towards the end of the decade.
Integrated Power Plant and Bitcoin Mining Economics

Bitcoin mining can be a difficult process to understand much less model. It is confounded by currency exchange rates and jurisdictional taxes along with rapid historical technological development. We present a framework for simplifying the economic modeling process for bitcoin. To account for historical and potential future technological changes, we model trends in bitcoin miner performance showing that further gains in efficiency have plateaued. Then, we derive the marginal value of bitcoin treating the process of the block reward in a quantum mechanical framework and show that the measure of bitcoin’s value is the energy used to mine it. Finally, we use the energy value of bitcoin to model the dollar price of bitcoin with a power law relationship over bitcoin’s entire price history.
Not so Immutable: Upgradeability of Smart Contracts on Ethereum

A smart contract that is deployed to a blockchain system like Ethereum is, under reasonable circumstances, expected to be immutable and tamper-proof. This is both a feature (promoting integrity and transparency) and a bug (preventing security patches and feature updates). Modern smart contracts use software tricks to enable upgradeability, raising the research questions of how upgradeability is achieved and who is authorized to make changes. In this paper, we summarize and evaluate six upgradeability patterns. We develop a measurement framework for finding how many upgradeable contracts are on Ethereum that use certain prominent upgrade patters. We find 1.4 million proxy contracts which 8,225 of them are unique upgradeable proxy contracts. We also measure how they implement access control over their upgradeability: about 50% are controlled by a single Externally Owned Address (EOA), and about 14% are controlled by multi-signature wallets in which a limited number of persons can change the whole logic of the contract.
Adaptive mobile cloud computing on college physical training education based on virtual reality

Mobile Cloud Computing (MCC) allows the e-learning platform to provide teaching materials, exchange knowledge and share learning experiences. The MCC process gives various instructions, and the usage of electrical bio-signals in Alternative and Augmented Communication plays a significant role. The physical education system faces significant challenges in training physically challenged people for Para Olympics. The difficulties are resolved by applying the Virtual Reality Assisted Convolution Neural Network (VRA-CNN) framework for effective Augmentative Communication. The VRA-CNN process uses several biosensors to read various person's body parameters according to different simulated conditions using Virtual Reality Technology (VR). Further, augmentative Communication provides an interactive environment in real-time for physically challenged people. Simulation analysis shows that the introduced VRA-CNN can improve players' confidence and sports knowledge with improved SNR, response time, and less error rate. The VRA-CNN has been validated based on the optimization parameter, and real-time performance outperforms traditional approaches.
Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output

The efficiency, return on investment, and output of Solar power plants are all significantly impacted by the development of Digital Twin technology. Digital Twining of the Solar PV system will help to improve the predictability of the power output, gather the necessary information for energy forecasting (predict ahead the power output to balance supply and demand) and power system default detection. The main objective of this study is to develop a Digital Twin Solar PV system. The technical approach used to develop a Solar PV Digital Twin includes: (1) real time data collection of power output from Solar PV system using smart sensors, (2) development, calibration, and validation of a Solar PV system model using MATLAB (3) development and testing the accuracy of LSTM-based forecasting model to predict ahead (15 mins) the Solar PV electrical power output using historical data. The results show that the Solar PV Digital Twin compares well with the targeted data from the real system (data from sensors of the physical Solar PV system). The accuracy of the models was assessed using the coefficient of correlation (R). The results show that the Solar PV Digital Twin simulation and forecasting models have R-values of 0.99893 and 0.99427, respectively. To help an asset owner or operator to visualize the performance of Digital Twin, a dashboard application was developed using MATLAB App Designer. The dashboard shows current, historical, and continuously forecasts future energy consumption and production to balance supply and demand. The development of Digital Twin technology, artificial intelligence and machine learning predictive tools for monitoring, forecasting and maintenance of Solar PV plants are keys to reach the Net Zero Energy Building goals. Additionally, to ensure the reproducibility of the research, the irradiance measurements data was published in a GitHub repository.
Keep it simple: random oversampling for imbalanced data

The issue of imbalanced data affects a wide range of applications. Despite a plethora of sophisticated sampling techniques for dealing with imbalanced data, the simple random oversampling (ROS) method remains a robust alternative. The goal of this paper is to compare the performance of ROS to the more advanced sampling algorithms. To this end, we conduct numerical experiments on multi-label data. The results of the experiments reveal that ROS outperforms several advanced sampling algorithms. Given the computational efficiency of ROS and its robust accuracy, we believe that it provides a good option for dealing with imbalanced data.
ESC-PAN: An Efficient CNN Architecture for Image Super-Resolution

Deep Learning models, based on Convolutional Neural Network (CNN) architecture, have proven to be useful and effective in many image processing tasks, and have recently been shown to be effective for image Super-Resolution (SR). Common trends in SR improve the quality of the reconstructed image by increasing the depth and complexity of the CNN model. While this approach produces superior performance in objective image quality metrics (IQA), such as Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index, having the number of parameters in the order of millions sacrifices the practicality of model deployment. This is especially true for applications that require real-time processing, such as online conferencing. In this paper, a CNN-based SR model architecture that integrates an attention mechanism while maintaining low complexity is proposed. The number of parameters of the model is reduced by adopting depthwise-separable convolution (DSC) throughout the model. Multiply-accumulate operations (MACs) are reduced by adopting a late upsampling scheme to operate only on low-dimensional features maps. Experimental results show that the proposed model architecture has better performance in terms of objective IQA metrics, such as PSNR and SSIM, and subjective IQA. This improved performance is achieved at a reduced complexity. We also showcase the scalability of the proposed CNN architecture by increasing the model complexity slightly to gain better desired performance.
Deep learning based video-related super-resolution technique: a survey

Abstract： Video-related super-resolution (VSR) technique can be focused on high-resolution video profiling and restora• tion to optimize its low-resolution version-derived quality.It has been developing intensively in relevant to such domains like satellite remote sensing detection， video surveillance， medical imaging， and low-involved electronics.To reconstruct high-resolution frames， conventional video-relevant super-resolution methods can be used to estimate potential motion sta• tus and blur kernel parameters， which are challenged for multiscene hetegerneity.Due to the quick response ability of fully integrating video spatio-temporal information of real and natural textures， the emerging deep learning based video superresolution algorithms have been developing dramatically.We review and analyze current situation of deep learning based video super-resolution systematically and literately.First， popular YCbCr datasets are introduced like YUV25， YUV21， ultra video group(UVG)， and the RGB datasets are involved in as well， such as video 4 (Vid4)， realistic and dynamic scenes (REDS)， Vimeo90K.The profile information of each dataset is summarized， including its name， year of publica• tion， number of videos， frame number， and resolution.Furthermore， key parameters of the video super-resolution algo• 中图法分类号： TP391
Blockchain-Based Secure Voting Mechanism Underlying 5G Network: A Smart Contract Approach

Purpose: With the advancement and modernization of Information and Communication Technology, electronic voting (e-voting) has been adopted across the globe for conducting a secure and efficient voting procedure. However, e-voting can be easily exposed to various malicious attacks such as denial-of-service, malware, insider, compromised credentials, etc., that can risk the voter’s privacy and affect the final voting results. Thus, we have considered the decentralized and immutable blockchain technology for completing the voting procedure securely. Nevertheless, many researchers have considered blockchain to mitigate the security and privacy issues of the voting procedure, but they ignored the aspect of cost-efficiency, latency, and response time while voting. Method: The idea of the proposed mechanism is to deploy and implement the smart contract in the Remix Integrated Development Environment (IDE) involving number of voters electing the candidate with various functionalities. Moreover, we considered the amalgamation of blockchain and IPFS to overcome the data storage cost issues of blockchain for voters and candidates communicating through 5G wireless network. The 5G network, with its low latency, high availability, and high reliability facilitates high data rate and reliable communication between voters and candidates to complete the voting procedure efficiently. Results: The deployed smart contract of the proposed voting mechanism highlight various functionalities required for electing the candidate by the voters. Moreover, the deployed smart contract is evaluated and analyzed considering various performance metrics such as gas consumption analysis for smart contract functions and number of voters, cost analysis for smart contract functions, bit error rate, and storage cost comparison with the traditional scheme. We have also verified the security of the proposed voting mechanism using Echidna security tool which shows the election of candidate without any threat. Conclusion: Finally, we have presented a blockchain-based voting mechanism in which IPFS and 5G is employed to avail the cost-efficient, reliable, and secure candidate election by the voters. Moreover, the voters and candidates can communicate with lower response time and high reliability using 5G network so that smart contract consisting of all the functionalities related to the candidate election can be executed efficiently in the Remix IDE.
K-VIL: Keypoints-Based Visual Imitation Learning

Visual imitation learning provides efficient and intuitive solutions for robotic systems to acquire novel manipulation skills. However, simultaneously learning geometric task constraints and control policies from visual inputs alone remains a challenging problem. In this paper, we propose an approach for keypoint-based visual imitation (K-VIL) that automatically extracts sparse, object-centric, and embodiment-independent task representations from a small number of human demonstration videos. The task representation is composed of keypoint-based geometric constraints on principal manifolds, their associated local frames, and the movement primitives that are then needed for the task execution. Our approach is capable of extracting such task representations from a single demonstration video, and of incrementally updating them when new demonstrations become available. To reproduce manipulation skills using the learned set of prioritized geometric constraints in novel scenes, we introduce a novel keypoint-based admittance controller. We evaluate our approach in several real-world applications, showcasing its ability to deal with cluttered scenes, viewpoint mismatch, new instances of categorical objects, and large object pose and shape variations, as well as its efficiency and robustness in both one-shot and few-shot imitation learning settings. Videos and source code are available at https://sites.google.com/view/k-vil.
Chatbots in Academic Advising: Evaluating the Acceptance and Effects of Chatbots in German Student-University Communication

In order to attract new students, German universities must provide quick and easy access to relevant information. A chatbot can help increase the efficiency in academic advising for prospective students. In this study we evaluate the acceptance and effects of chatbots in German student-university communication. We conducted a qualitative UX-Study with the chatbot prototype of Offenburg University of Applied Sciences (HSO), in order to determine which features are particularly relevant and which requirements are made by the users. The results show that acceptance increases if the chatbot offers quick and adequate assistance, furthermore, our participants preferred an informal communication style and valued friendly and helpful personality traits for chatbots.
Measuring the Effectiveness of U.S. Government Security Awareness Programs: A Mixed-Methods Study

The goal of organizational security awareness programs is to positively influence employee security behaviors. However, organizations may struggle to determine program effectiveness, often relying on training policy compliance metrics (e.g., training completion rates) rather than measuring actual impact. Few studies have begun to discover approaches and challenges to measuring security awareness program effectiveness within compliance-focused sectors such as the United States (U.S.) government. To address this gap, we conducted a mixed-methods research study that leveraged both focus group and survey methodologies centered on U.S. Government organizations. We discovered that organizations do indeed place emphasis on compliance metrics and are challenged in determining other ways to gauge success. Our results can inform guidance and other initiatives to aid organizations in measuring the effectiveness of their security awareness programs.