Reviewerly/exHarmony huggingface dataset:
Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.
A methodology for evaluating digital contact tracing apps based on the COVID-19 experience

Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future.
Annotating, Tracking, and Protecting Cryptographic Secrets with CryptoMPK

Protecting confidential data against memory disclosure attacks is crucial to many critical applications, especially those involve cryptographic operations. However, it is neither easy to identify involved cryptographic confidential data in a program nor to implement a fine-grained and yet efficient protection. Existing defensive techniques face many shortcomings such as coarse-grained protection or exorbitant overhead. As a result, real world crypto applications seldom applied this kind of protection in practice.To make the protection of cryptographic confidential data practical, we design and implement CRYPTOMPK, a source code analysis and transformation system to implement a domain-based memory isolation. CRYPTOMPK first automatically tracks and labels all sensitive memory buffers and operations in source code with a context-sensitive, crypto-aware information flow analysis. Then it partitions the source code into crypto and non-crypto domains with a context-dependent privilege switch instrumentation. By further utilizing Intel Memory Protection Keys (MPK), CRYPTOMPK generates executables with efficient domain switching, protecting them against typical memory disclosure vulnerabilities such as arbitrary memory read. In particular, by using CRYPTOMPK, a large number of intermediate memory buffers that have been previously ignored before are well protected, and thus the security risks are reduced significantly. We leveraged CRYPTOMPK to protect prevalent applications such as Apache and Nginx with widely used crypto libraries (e.g., OpenSSL, LibSodium). CRYPTOMPK only needs several minutes to analyze each of these complex cryptographic programs and incurs at most 9.53% performance overhead for the protected programs.
Spook.js: Attacking Chrome Strict Site Isolation via Speculative Execution

The discovery of the Spectre attack in 2018 has sent shockwaves through the computer industry, affecting processor vendors, OS providers, programming language developers, and more. Because web browsers execute untrusted code while potentially accessing sensitive information, they were considered prime targets for attacks and underwent significant changes to protect users from speculative execution attacks. In particular, the Google Chrome browser adopted the strict site isolation policy that prevents leakage by ensuring that content from different domains is not shared in the same address space. The perceived level of risk that Spectre poses to web browsers stands in stark contrast with the paucity of published demonstrations of the attack. Before mid-March 2021, there was no public proof-of-concept demonstrating leakage of information that is otherwise inaccessible to an attacker. Moreover, Google’s leaky.page, the only current proof-of-concept that can read such information, is severely restricted to only a subset of the address space and does not perform cross-website accesses. In this paper, we demonstrate that the absence of published attacks does not indicate that the risk is mitigated. We present Spook.js, a JavaScript-based Spectre attack that can read from the entire address space of the attacking webpage. We further investigate the implementation of strict site isolation in Chrome, and demonstrate limitations that allow Spook.js to read sensitive information from other webpages. We further show that Spectre adversely affects the security model of extensions in Chrome, demonstrating leaks of usernames and passwords from the LastPass password manager. Finally, we show that the problem also affects other Chromium-based browsers, such as Microsoft Edge and Brave.
Private Approximate Nearest Neighbor Search with Sublinear Communication

Nearest neighbor search is a fundamental building-block for a wide range of applications. A privacy-preserving protocol for nearest neighbor search involves a set of clients who send queries to a remote database. Each client retrieves the nearest neighbor(s) to its query in the database without revealing any information about the query. To ensure database privacy, clients must learn as little as possible beyond the query answer, even if behaving maliciously by deviating from protocol. Existing protocols for private nearest neighbor search require heavy cryptographic tools, resulting in high computational and bandwidth overheads. In this paper, we present the first lightweight protocol for private nearest neighbor search. Our protocol is instantiated using two non-colluding servers, each holding a replica of the database. Our design supports an arbitrary number of clients simultaneously querying the database through the two servers. Each query consists of a single round of communication between the client and the two servers. No communication is required between the servers to answer queries. If at least one of the servers is non-colluding, we ensure that (1) no information is revealed on the client’s query, (2) the total communication between the client and the servers is sublinear in the database size, and (3) each query answer only leaks a small and bounded amount of information about the database to the client, even if the client is malicious. We implement our protocol and report its performance on real-world data. Our construction requires between 10 and 20 seconds of query latency over large databases of 10M feature vectors. Client overhead remained under 10ms of processing time per query and less than 10MB of communication.
Graphics Peeping Unit: Exploiting EM Side-Channel Information of GPUs to Eavesdrop on Your Neighbors

As the popularity of graphics processing units (GPUs) grows rapidly in recent years, it becomes very critical to study and understand the security implications imposed by them. In this paper, we show that modern GPUs can “broadcast” sensitive information over the air to make a number of attacks practical. Specifically, we present a new electromagnetic (EM) side-channel vulnerability that we have discovered in many GPUs of both NVIDIA and AMD. We show that this vulnerability can be exploited to mount realistic attacks through two case studies, which are website fingerprinting and keystroke timing inference attacks. Our investigation recognizes the commonly used dynamic voltage and frequency scaling (DVFS) feature in GPU as the root cause of this vulnerability. Nevertheless, we also show that simply disabling DVFS may not be an effective countermeasure since it will introduce another highly exploitable EM side-channel vulnerability. To the best of our knowledge, this is the first work that studies realistic physical side-channel attacks on non-shared GPUs at a distance.
Divide-and-Conquer Predictor for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) aims to detect the objects and their pairwise predicates in an image. Existing SGG methods mainly fulfil the challenging predicate prediction task that involves severe long-tailed data distribution with a single classifier. However, we argue that this may be enough to differentiate predicates that present obvious differences (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$on$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$near$ </tex-math></inline-formula> ), but not sufficient to distinguish similar predicates that only have subtle differences (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$on$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$standing~on$ </tex-math></inline-formula> ). Towards this end, we divide the predicate prediction into a few sub-tasks with a Divide-and-Conquer Predictor (DC-Predictor). Specifically, we first develop an offline pattern-predicate correlation mining algorithm to discover the similar predicates that share the same object interaction pattern. Based on that, we devise a general pattern classifier and a set of specific predicate classifiers for DC-Predictor. The former works on recognizing the pattern of a given object pair and routing it to the corresponding specific predicate classifier, while the latter aims to differentiate similar predicates in each specific pattern. In addition, we introduce the Bayesian Personalized Ranking loss in each specific predicate classifier to enhance the pairwise differentiation between head predicates and their similar ones. Experiments on VG150 and GQA datasets show the superiority of our model over state-of-the-art methods.
Attention-Based Fully Convolutional DenseNet for Earthquake Detection

We propose a novel deep learning method using an attention-based fully convolutional dense network (FCDNet) for automatic earthquake detection. The FCDNet consists of encoder-decoder parts with skip connections, where each encode-decoder block contains a block of densely connected layers to enhance the feature learning capability. The spatial attention mechanism is added within the FCDNet to assign greater attention to useful features and hence improve the accuracy of earthquake detection. The time-frequency representations of three-component seismograms produced by the Stockwell transform are used for better extracting the hidden data features. The attention-based FCDNet extracts the time-frequency features needed for distinguishing the seismic signal from the background noise. We evaluate the performance of the proposed method using a Mediterranean dataset. The attention-based FCDNet is trained using 90% of the Mediterranean dataset and tested using the remaining 10%. Accordingly, the training and testing accuracies are 97.71% and 97.02%, respectively. The intersection over union (IoU), precision, recall, and F1-score of the attention-based FCDNet are 93.80%, 99.72%, 99.55%, and 99.64%, respectively. Moreover, to evaluate the generalization ability of the trained model, we utilize 100,000 seismic waveforms recorded in different seismic regions from the global STanford EArthquake Dataset (STEAD) dataset for testing, which shows robust performance. We also apply the attention-based FCDNet to the Japanese seismic data and compare the performance to the CRED and SCALODEEP methods. The attention-based FCDNet outperforms the benchmark methods and achieves a higher detection accuracy of 99.46%. The attention-based FCDNet is additionally evaluated using one-day continuous seismic data recording a seismic swarm that occurred in the Helike region. As a result, the attention-based FCDNet recognizes 135 earthquakes and raises 15 false alarms with a detection accuracy of 90.06%.
SIoVChain: Time-Lock Contract Based Privacy-Preserving Data Sharing in SIoV

Social Internet of Vehicles (SIoV) is an emerging technology in the smart city environment, enabling smart vehicles to form social groups and exchange data among themselves. SIoV facilitates many applications aiming to improve driving safety and traffic monitoring by sharing data among vehicles. It ensures a safe and comfortable drive. However, privacy, data confidentiality, and data integrity are the major challenges during multi-hop data transfer that must be addressed for the wide adoption of SIoV. The existing solutions do not provide anonymity and consume more network resources. To address these issues, we propose SIoVChain, a time-lock contract-based privacy-preserving data sharing scheme with incentives for SIoV. It does not only enable data sharing between vehicles anonymously but incentivizes them also. In addition, the proposed framework imposes a penalty anonymously if a malicious vehicle disseminates false information. SIoVChain is shown to be secure against stealing processing fee attack while preserving sender-receiver privacy and path privacy. Universal Composability (UC) framework is used to verify user privacy. The feasibility and efficiency of the scheme are also demonstrated.
An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing

The Blockchain has been given great attention in recent literature among emerging technologies in software architectures. More specifically, when verifiable transactions between untrusted parties are concerned in a safe and reliable environment, its peculiar decentralized and tamper-proof structure makes it suitable for a vast class of business domains, such as Cloud Manufacturing, which is a new paradigm in the industry based on cloud technologies. However, the stiffness of existing solutions, that are unable to provide and implement heterogeneous services in a Cloud environment, emphasizes the need of a standard framework to overcome this limit and improve collaboration. Firstly, this paper introduces a Blockchain based platform designed with Smart Contracts for improving digital processes in a manufacturing environment. The primary contribution is the integration of two popular cloud technologies within the Blockchain: Docker, a scalable platform to run applications in lightweight environments, and Cloud Storage. Each process available in the platform requires input files and produces output files by using cloud storage as a repository and it is delivered by the owner as a self-contained Docker image, whose digest is safely stored in the chain. Secondly, with the purpose of selecting the fastest node for each new process instance required by consumers, we introduce a task assignment problem based on a deep learning approach and past metrics. The proposed platform is applied to a real-world industrial case study regarding ophthalmic lenses manufacturing and the optimization of lens surface calculation.
Privacy-Preserving Online Medical Prediagnosis Training Model Based on Soft-Margin SVM

Online medical prediagnosis systems have already shown great achievement in providing the guidance of healthcare services with lower time and cost. Achieving a high-precision medical primary diagnosis system faces many severe challenges on the privacy of individual health information, the distributed storage of medical data and the diversity of the disease. In this paper, we propose an efficient and privacy-preserving framework for obtaining a pre-clinical guide model, which allows an authorized data analysis center to train a disease classifier using a combination of medical data gathered from different entities. Our proposed scheme is based on soft-margin support vector machine (SVM) which takes Taylor polynomial of exponential-loss as penalty. Our scheme achieves the following advantages: the trained model can tolerate some abnormal samples therefore has higher generalization ability, and the training process can constraint the inefficient operations in the encrypted domain thus leads to the availability of partial homomorphic encryption system. Lately, we prove that the proposed scheme achieves the goal of medical prediagnosis system construction and data without privacy leakage to data analysis center and model parameters without exposure to data providers, as well as demonstrating its utility and efficiency using real-world medical datasets.
Federated Markov Logic Network for indoor activity recognition in Internet of Things

Indoor activity recognition is essential in numerous Internet of Things (IoT) applications. As one of the widely used methods in this domain, Markov Logic Network (MLN) can simultaneously use activity knowledge and data by unifying probability and logic. The “cloud computing” model has recently been adopted to concentrate the activity data and activity knowledge in a central node for processing in indoor activity recognition by using MLN, which may lead to the data leakage of the clients. Therefore, to further alleviate client data privacy issues when building an indoor activity recognition model by training MLN, this paper proposes a Federated Markov Logic Network (FMLN) framework for indoor activity recognition. We designed different scenarios to investigate the FMLN framework, including statistical heterogeneity, the number ofvarious clients, and various network environments. The experimental results show that the FMLN framework effectively detects indoor activity.
Strong Noise-Tolerance Deep Learning Network for Automatic Microseismic Events Classification

Identifying useful microseismic events is one of the key steps in monitoring tunnel rockbursts. Here, we propose a strong noise-tolerance deep learning (SNTDL) network for the automatic classification of noisy microseismic events. The training set, validation set, and test set of the SNTDL network consist of 27,989 unfiltered microseismic recordings. First, to comprehensively characterize the microseismic events, we extract 10 weakly correlated features of the microseismic recordings as the input of the SNTDL network. Then, the skip connection and concatenation structure are added to this network, which can further enhances its generalization ability. Additionally, the SNTDL, AlexNet, Inception, Visual Geometry Group, and ResNet are compared using the synthetic microseismic recordings with different signal-noise ratios. The results demonstrate that the SNTDL network has a higher accuracy and stronger noise-tolerance capability than the other approaches. Application to a dataset collected from a different construction environment confirms that the SNTDL network can still achieve an accurate classification result, which further verifies that the proposed network has a reliable generalization performance.
Introducing Chaos and Chaos Based Encryption Applications to University Students - Case Report of a Seminar

During the fall semester of 2021-2022 academic year, a seminar series on ’Chaotic Cryptography’ was delivered at the Department of Computer Aided Design (CAD), St. Petersburg Electrotechnical University ’LETI’, Russia. The seminar aimed at introducing students to state of the art research topics that connect chaos theory to various applications relating to encryption and security. This work constitutes a case report on this seminar. First, the seminar syllabus is presented. Then, future goals are discussed for annual reruns of the seminar.
Robust Scene Text Detection for Partially Annotated Training Data

This article analyzed the impact of training data containing un-annotated text instances, i.e., partial annotation in scene text detection, and proposed a text region refinement approach to address it. Scene text detection is a problem that has attracted the attention of the research community for decades. Impressive results have been obtained for fully supervised scene text detection with recent deep learning approaches. These approaches, however, need a vast amount of completely labeled datasets, and the creation of such datasets is a challenging and time-consuming task. Research literature lacks the analysis of the partial annotation of training data for scene text detection. We have found that the performance of the generic scene text detection method drops significantly due to the partial annotation of training data. We have proposed a text region refinement method that provides robustness against the partially annotated training data in scene text detection. The proposed method works as a two-tier scheme. Text-probable regions are obtained in the first tier by applying hybrid loss that generates pseudo-labels to refine text regions in the second-tier during training. Extensive experiments have been conducted on a dataset generated from ICDAR 2015 by dropping the annotations with various drop rates and on a publicly available SVT dataset. The proposed method exhibits a significant improvement over the baseline and existing approaches for the partially annotated training data.
Identifying patient-specific root causes of disease

Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many significant variables with inconsequential effect sizes. A few highly predictive root causes may nevertheless generate disease within each patient. In this paper, we define patient-specific root causes as variables subject to exogenous "shocks" which go on to perturb an otherwise healthy system and induce disease. In other words, the variables are associated with the exogenous errors of a structural equation model (SEM), and these errors predict a downstream diagnostic label. We quantify predictivity using sample-specific Shapley values. This derivation allows us to develop a fast algorithm called Root Causal Inference for identifying patient-specific root causes by extracting the error terms of a linear SEM and then computing the Shapley value associated with each error. Experiments highlight considerable improvements in accuracy because the method uncovers root causes that may have large effect sizes at the individual level but clinically insignificant effect sizes at the group level. An R implementation is available at github.com/ericstrobl/RCI.