Reviewerly/exHarmony huggingface dataset:
Efficient Homomorphic Convolution for Secure Deep Learning Inference

To mitigate the ever-increasing privacy concerns of model inference, intensive efforts have been put to develop cryptograph-based private deep learning inference, that preserves the confidentiality of the submitted query and its inference result. However, privacy is not free but expensive as the secure computation over the ciphertext domain is time-consuming for both linear and non-linear layers, especially the homomorphic operations. To boost efficiency, a novel optimization is proposed for the evaluation of homomorphic convolutions, which is the most computation-intensive component throughout the entire inference processing. In specific, our approach involves the following critical designs. First, the Winograd fast convolution algorithm is applied to minimize the number of multiplications in convolutions. Second, we fuse this algorithm with the SIMD-enabled additive homomorphic encryption to expedite homomorphic convolution evaluation. Third, the sparsity of the model parameters is explored to further compress the computational cost brought by homomorphic encryption. In addition, it is non-trivial to extend the original Winograd algorithm to accommodate convolution operations, when kernels are larger than 3 × 3 and strides are greater than 1. We conquer this technical challenge and enable the applicability of the proposed optimizations for general convolution parameter configurations. In terms of performance, our scheme outperforms state-of-the-art secure inference methods, demonstrating a 2× reduction in the number of multiplications for convolution evaluation and a 30% improvement in end-to-end latency.
VPASS: Voice Privacy Assistant System for Monitoring In-home Voice Commands

Voice assistant systems (VAS), such as Google Assistant or Amazon Alexa, provide convenient means for users to interact verbally with online services. VAS is particularly important for users with severe health conditions or motor skills impairment. At the same time, voice commands may contain highly-sensitive information about individuals. Therefore, sharing such data with service providers must be done in a carefully controlled and transparent manner in order to prevent privacy breaches. One important challenge is identifying which voice commands contain sensitive information. Different individuals are likely to have distinct interpretations of what is sensitive and what must be kept private, depending on gender, age, cultural background, etc. Furthermore, even for the same individual, the context in which a command is issued can result in significantly different sensitivity perceptions. We introduce a framework named VPASS that supports the management of personalized privacy requirements for VAS systems. Specifically, we propose mechanisms to quantify two key aspects: the amount of information disclosure and the level of privacy sensitivity that each voice command has. Our mechanisms employ deep transfer learning techniques for processing voice commands and can accurately detect privacy-sensitive commands based on an individual’s prior history of VAS interaction. Finally, VPASS generates monthly reports or immediate privacy alerts based on the privacy policies pre-defined by users.
An Efficient Federated Learning Framework for Privacy-Preserving Data Aggregation in IoT

With the development of Internet of Things (IoT) technology, smart mobile devices are widely used in daily life. The service providers always extensively collect data from users for training machine learning models in order to improve their accurate services. This also raises users’ concerns about data privacy and security. Federated learning, as an extension of centralized machine learning, allows several users working together to train a machine learning model on their own devices without sending their data to the centralized servers. However, existing research suggests that local models also contain privacy related to the users’ data. Unfortunately, the current privacy-preserving secure aggregation methods have either poor accuracy or high computational and communication costs in training process which can not afford by the IoT devices. In this work, we propose a federated learning framework supporting privacy-preserving data aggregation against external and internal attackers with lower computational and communication costs, which is suitable for the weak IoT devices. The scheme is also supporting aggregation with fault tolerance and dynamic user set even if a part of users leave the system in the training. Detailed security analysis and extensive experiments using a real dataset confirm the efficacy and efficiency of the proposed schemes.
PFONet: A Progressive Feedback Optimization Network for Lightweight Single Image Dehazing

Image dehazing is an effective means to enhance the quality of images captured in foggy or hazy weather conditions. However, existing image dehazing methods are either ineffective in dealing with complex haze scenes, or incurring too much computation. To overcome these deficiencies, we propose a progressive feedback optimization network (PFONet) which is lightweight yet effective for image dehazing. The PFONet consists of a multi-stream dehazing module and a progressive feedback module. The progressive feedback module feeds the output dehazed image back to the intermedia features extracted by the network, thus enabling the network to gradually reconstruct a complex degraded image. Considering both the effectiveness and efficiency of the network, we also design a lightweight hybrid residual dense block serving as the basic feature extraction module of the proposed PFONet. Extensive experimental results are presented to demonstrate that the proposed model outperforms its state-of-the-art single-image dehazing competitors for both synthetic and real-world images.
EGRC-Net: Embedding-Induced Graph Refinement Clustering Network

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology structure of the embedding space. In order to address this issue, we propose a novel clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net), which effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation. Subsequently, we utilize the local geometric structure within the feature embedding space to construct an adjacency matrix for the graph. This adjacency matrix is dynamically fused with the initial one using our proposed fusion architecture. To train the network in an unsupervised manner, we minimize the Jeffreys divergence between multiple derived distributions. Additionally, we introduce an improved approximate personalized propagation of neural predictions to replace the standard graph convolution network, enabling EGRC-Net to scale effectively. Through extensive experiments conducted on nine widely-used benchmark datasets, we demonstrate that our proposed methods consistently outperform several state-of-the-art approaches. Notably, EGRC-Net achieves an improvement of more than 11.99% in Adjusted Rand Index (ARI) over the best baseline on the DBLP dataset. Furthermore, our scalable approach exhibits a 10.73% gain in ARI while reducing memory usage by 33.73% and decreasing running time by 19.71%. The code for EGRC-Net will be made publicly available at https://github.com/ZhihaoPENG-CityU/EGRC-Net.
A DEA And Machine Learning Based Platform for Investigating Success of Initial Coin Offerings (ICOs)

Initial coin offerings (ICOs) have become a popular way for startups to raise capital. However, the ICO market is also highly volatile and risky, with many ICOs failing. In this paper, we use a combination of data envelopment analysis (DEA) and machine learning to identify successful ICOs and the factors that are associated with their success.First, we use DEA to identify a set of efficient ICOs. Once we have identified a set of efficient ICOs, we use machine learning to identify the factors that are most associated with their success. Our results show that DEA and machine learning can be used to identify successful ICOs and the factors that are associated with their success. We find that the most important factors associated with ICO success are existence of a legal entity backing up the coin, social media activities on reddit, availability of roadmap, being in finance industry, and a triable product.
A Categorical Approach to Synthetic Chemistry

We introduce a mathematical framework for retrosynthetic analysis, an important research method in synthetic chemistry. Our approach represents molecules and their interaction using string diagrams in layered props – a recently introduced categorical model for partial explanations in scientific reasoning. Such principled approach allows one to model features currently not available in automated retrosynthesis tools, such as chirality, reaction environment and protection-deprotection steps.
Robustness in Metric Spaces over Continuous Quantales and the Hausdorff-Smyth Monad

Generalized metric spaces are obtained by weakening the requirements (e.g., symmetry) on the distance function and by allowing it to take values in structures (e.g., quantales) that are more general than the set of non-negative real numbers. Quantale-valued metric spaces have gained prominence due to their use in quantitative reasoning on programs/systems, and for defining various notions of behavioral metrics. We investigate imprecision and robustness in the framework of quantale-valued metric spaces, when the quantale is continuous. In particular, we study the relation between the robust topology, which captures robustness of analyses, and the Hausdorff-Smyth hemi-metric. To this end, we define a preorder-enriched monad $$\textsf{P}_S$$ , called the Hausdorff-Smyth monad, and when Q is a continuous quantale and X is a Q-metric space, we relate the topology induced by the metric on $$\textsf{P}_S(X)$$ with the robust topology on the powerset $$\textsf{P}(X)$$ defined in terms of the metric on X.
Modular Soundness Checking of Feature Model Evolution Plans

A software product line (SPL) is a family of closely related software systems which capitalizes on the variability and reusability of the software products and can be formalised by a feature model. Feature model evolution plans (FMEP) capture the current SPL as well as the planned evolution of the SPL to ensure successful long-term development. As business requirements often change, FMEPs should support intermediate update. This modification may cause paradoxes in an FMEP, e.g., a node left without a parent, making the plan impossible to realise. Current tools exist to validate FMEPs, but require analysing the entire plan even when a modification affects only small parts of it. Hence, there is a need for a method that detects such paradoxes in a more efficient way. In this paper, we present an interval based feature model (IBFM), a representation for FMEPs, that allows local reasoning to validates only the parts of the plan affected by changes. We define operations for updating the FMEPs and the preconditions under which they preserve soundness, i.e., absence of paradoxes, and show the correctness of the method.
Artificial Intelligence and evolving issues under US copyright and patent law

The proliferation of AI tools in the arts, commercial design industries, and other endeavours has raised core questions regarding who or what actually supplied the alleged creative or inventive elements, if any, to the AI system’s output. In both US copyright and patent law the question focuses on a case-by-case analysis as to how much of the final product evidences human ‘authorship’ or invention. Also, creativity as well as infringement, can be located in various phases of the AI system’s creation, ingestion of training materials, management, and operation – including its output, whether affected prior to the output or after it. Issues such as liability for selecting ingestion materials or target data, as well as the potential inadvertent triggering of patent law’s bar date through use of specific AI systems, have also come to the forefront of AI’s potential to secure, forfeit, or impact claimed proprietary rights in AI-assisted creative and inventive activities. Several alternative intellectual property and unfair competition approaches that can supplement or supplant copyright and patent law principles also come into play as users of AI seek to protect the products of their efforts.
Active Learning—Review

In the previous chapter, we discussed different Active Learning (AL) scenarios, classical query strategies, and examples of when to deploy them.
Secure Hydrogen Production Analysis and Prediction Based on Blockchain Service Framework for Intelligent Power Management System

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems.
Limits of fault tolerance on resource-constrained quantum circuits for classical problems

Existing lower bounds on redundancy in fault-tolerant quantum circuits are applicable when both the input and the intended output are quantum states. These bounds may not necessarily hold, however, when the intended outputs are classical bits, as in Shor's or Grover's algorithms. Here we show that, indeed, noise thresholds obtained from existing bounds do not apply to a simple fault-tolerant implementation of the quantum phase estimation algorithm where the output quantum state suffers from noise before the measurement. Then we obtain a nonasymptotic lower bound on the minimum required redundancy for fault-tolerant quantum circuits with classical inputs and outputs. Further, recent results show that, due to physical resource constraints in quantum circuits, increasing redundancy can increase noise, which in turn may render many fault-tolerance schemes useless. So it is of both practical and theoretical interest to characterize the effect of resource constraints on the fundamental limits of fault-tolerant quantum circuits. As an application of our lower bound, we characterize the fundamental limit of fault-tolerant quantum circuits with classical inputs and outputs under resource-constraint-induced noise models.
Social media trolls as faux third-party agents of image repair: China’s disinformation campaign and statecraft in the Daryl Morey affair

This study examines trolls affiliated with the People’s Republic of China (PRC) that targeted conversations around former Houston Rockets’ General Manager Daryl Morey, who tweeted support for Hong Kong protests and apologized in October 2019. Researchers coded 5,800 tweets to inform a machine-learning approach examining a corpus of over 163,000 tweets. Findings suggested that a PRC disinformation campaign was organized as a faux third-party image repair effort that sought to force an apology, deprive Morey of support, discourage future image threats, and shape public understanding. This effort used provocation, bolstering, barnacle, redirection, and attack the accuser. While apologies are generally held to be restorative in image repair and statecraft literature, Morey’s apology was a turning point that rallied opposition to the PRC and support for democracy and Morey. Implications include insight into the prevalence, strategies, and efficacy of PRC trolls, as well as recommendations for social media practitioners.
Synchronous Agents, Verification, and Blame—A Deontic View

A question we can ask of multi-agent systems is whether the agents’ collective interaction satisfies particular goals or specifications, which can be either individual or collective. When a collaborative goal is not reached, or a specification is violated, a pertinent question is whether any agent is to blame. This paper considers a two-agent synchronous setting and a formal language to specify when agents’ collaboration is required. We take a deontic approach and use obligations, permissions, and prohibitions to capture notions of non-interference between agents. We also handle reparations, allowing violations to be corrected or compensated. We give trace semantics to our logic, and use it to define blame assignment for violations. We give an automaton construction for the logic, which we use as the base for model checking and blame analysis. We also further provide quantitative semantics that is able to compare different interactions in terms of the required reparations.
Composing Diverse Ensembles of Convolutional Neural Networks by Penalization

Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members’ diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions.
The Property Law of Crypto Tokens

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Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time.
Fine-Grained Modeling of ROP Vulnerability Exploitation Process under Stack Overflow Based on Petri Nets

Software vulnerability discovery is currently a hot topic, and buffer overflow remains a prevalent security vulnerability. One of the key issues in vulnerability discovery and analysis is how to quickly analyze buffer overflow vulnerabilities and select critical exploitation paths. Existing modeling methods for vulnerability exploitation cannot accurately reflect the fine-grained execution process of stack overflow exploitation paths. This paper, based on the discussion of buffer overflow exploitation techniques, proposes a fine-grained modeling and analysis method based on Petri nets for the selection and execution of exploitation processes, specifically focusing on the return-oriented programming in stack overflow. Through qualitative analysis, we compared the simulated time of the software with the execution time of existing exploitation tools, achieving timeout-based simulation experiments. We validated the model’s effectiveness using symbolic execution and dynamic analysis techniques. The results indicate that this model performs well for vulnerable programs with Position Independent Executable (PIE) protection enabled and has an advantage in selecting exploitation paths, enabling timeout-based simulation. This method provides a reference for rapidly constructing exploitation implementations.