Reviewerly/exHarmony huggingface dataset:
Strong tracking square-root modified sliding-window variational adaptive Kalman filtering with unknown noise covariance matrices

The Kalman filter’s performance deteriorates in the existence of slowly time-varying and unknown measurement and process noise covariances. A simplified strong tracking square-root modified sliding window variational adaptive Kalman filter is proposed for the aforementioned challenges in this paper. A modified slidingwindow variational adaptive Kalman filtering is designed in the proposed algorithm capable of correcting and smoothing the previous states in accordance with the latter states and reducing the number of backward iterations to improve the filtering accuracy and computational efficiency of the algorithm. The multiple fading factors have been constructed to correct the one-step predicted error covariance matrix. Moreover, the square root decomposition approach is developed for decomposing the error covariance matrices to eliminate numerical rounding errors. The simulation results demonstrate that the proposed algorithm exhibits superior tracking capacity of the one-step predicted error covariance matrix and filtering accuracy compared with the existing filters.
Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems

As one of the most popular modeling methods, data-driven methods would fail easily when the online data are outside the scope of training data. To tackle this problem, this paper proposed a novel concept named as the rejection ability of data-driven models. Its main idea is rejecting the unreliable predictions based on the threshold which can be either calculated from out-of-distribution detection model or the confidence score. To compare with traditional data-driven models, three evaluation metrics are derived from the rejection table to quantify model performance. The usefulness of rejection method is validated through the fault detection and diagnosis problem of chillers. The ASHARE RP-1043 dataset is adopted to conduct the data experiments where the in-distribution performance and out-of-distribution performance of three task models, including RF, ET and ANN, and their combinations with four rejection methods, including AE-MSE, AE-MAE, Entropy and Max probability, are comprehensively investigated. The experimental results indicate that the model performance improves for a large margin with the introduction of rejection method. The optimal combination is ET-Entropy model, where its threshold selection is discussed for different application scenarios. The proposed rejection methods might provide a solid foundation for the real-world application of data-driven models.
Toward practical defense against traffic analysis attacks on encrypted DNS traffic

The primary goal of the DNS-over-HTTPS (DoH) protocol is to address users' privacy concerns regarding on-path adversaries, including local ISPs, who observe DNS traffic to learn web browsing activities even when the web traffic is not observable. To achieve this goal, in DoH protocol, DNS traffic between a DNS client and its local DNS resolver is channeled through an encrypted HTTPS tunnel. However, as shown in previous studies, adversaries can still infer users' web browsing activities from encrypted DoH traffic through machine-learning-based traffic analysis (TA) attacks. These attacks rely on unencryptable features in the DNS footprint of a website, including query/response lengths and counts and delays between subsequent queries (timing), to identify which website is being visited. To defend against such TA attacks, existing DoH clients and resolvers pad DNS queries and responses with null bytes to specific block sizes before encrypting them, as recommended in RFC 8467. However, as shown by prior research and our current work, this padding alone is not effective in defeating TA attacks, and even with this padding, attackers can still achieve over 95% accuracy. In this paper, we propose a novel client-side obfuscation approach to defeat TA attacks on the DoH footprint of websites. Using a combination of (1) compression-aware padding, (2) fake query injection, and (3) random delaying of queries, our approach obfuscates query/response lengths, counts, and timings. Our approach does not require changes to existing protocols and is incrementally deployed on client machines as a local proxy. Using a robust classifier with a comprehensive set of over 150 features derived from recently proposed traffic analysis attacks on encrypted DNS traffic and experimenting with the Cloudflare and Google resolvers and 200 target websites, we show that while with the existing padding-only obfuscation, the TA attacker can achieve over 95.58% accuracy, our obfuscation algorithm can degrade this accuracy to below 9% with reasonable overhead.
Multiple individual guided differential evolution with time varying and feedback information-based control parameters

Differential evolution (DE) is a simple and efficient metaheuristic algorithm for solving global optimization problems. It is used widely in various fields due to its concise structure and strong search ability. In this study, an extended version of the DE named multiple individual guided differential evolution (MGDE) is proposed. The MGDE is distinguished by introducing a novel mutation strategy based on multiple guiding individuals of the DE population to manage the diversity and convergence. The base vector of the mutation strategy is defined as a center of guiding individuals and the difference vectors are assigned to perform a search towards one of the top fitted and top diversified individuals available in the population. The control parameters of the DE are adjusted in a way to provide a suitable transition from exploration to exploitation and to utilize the information of recent success history of evolution. The performance of the proposed MGDE is evaluated on three different benchmark sets including IEEE CEC2014, IEEE CEC2017, and IEEE CEC2011 of real-world problems. Different performance metrics such as average and standard deviation of fitness, ranking of algorithms, Wilcoxon signed-rank test, and convergence analysis are used to analyze and compare the results of the MGDE with several other metaheuristic algorithms. Comparison attest that the proposed MGDE algorithm is highly competitive with the other metaheuristic algorithms.
Polarization dehazing method based on separating and iterative optimizing airlight from the frequency domain for different concentrations of haze

Polarization technology has been widely used in imaging through a scattering medium. However, the existing polarization dehazing methods are unstable because they require manual selections of polarization correction parameters. In addition, most of them only focus on the spatial domain without utilizing the frequency domain features, so their dehazing performances are insufficient. In this paper, we propose a polarization dehazing method based on separating and iterative optimizing airlight from the frequency domain. By separating the low-frequency sub-bands of polarization images and refining them as the airlight at three states, we calculated the Stokes parameters of airlight and obtained the preliminary dehazed image. We also propose an iterative optimization approach between the high-frequency sub-band of the dehazed image and airlight to effectively improve the dehazing performance. As a by-product, we introduce our real-world polarization datasets collected in different concentrations of haze. Both the qualitative and quantitative experiments show that our method is effective and robust in different concentrations of haze.
LoneNeuron: A Highly-Effective Feature-Domain Neural Trojan Using Invisible and Polymorphic Watermarks

The wide adoption of deep neural networks (DNNs) in real-world applications raises increasing security concerns. Neural Trojans embedded in pre-trained neural networks are a harmful attack against the DNN model supply chain. They generate false outputs when certain stealthy triggers appear in the inputs. While data-poisoning attacks have been well studied in the literature, code-poisoning and model-poisoning backdoors only start to attract attention until recently. We present a novel model-poisoning neural Trojan, namely LoneNeuron, which responds to feature-domain patterns that transform into invisible, sample-specific, and polymorphic pixel-domain watermarks. With high attack specificity, LoneNeuron achieves a 100% attack success rate, while not affecting the main task performance. With LoneNeuron's unique watermark polymorphism property, the same feature-domain trigger is resolved to multiple watermarks in the pixel domain, which further improves watermark randomness, stealthiness, and resistance against Trojan detection. Extensive experiments show that LoneNeuron could escape state-of-the-art Trojan detectors. LoneNeuron~is also the first effective backdoor attack against vision transformers (ViTs).
Efficient Secure Three-Party Sorting with Applications to Data Analysis and Heavy Hitters

We present a three-party sorting protocol secure against passive and active adversaries in the honest majority setting. The protocol can be easily combined with other secure protocols which work on shared data, and thus enable different data analysis tasks, such as private set intersection of shared data, deduplication, and the identification of heavy hitters. The new protocol computes a stable sort. It is based on radix sort and is asymptotically better than previous secure sorting protocols. It improves on previous radix sort protocols by not having to shuffle the entire length of the items after each comparison step.
A fault detection model for edge computing security using imbalanced classification

The edge computing-based Internet of Thing (IoT) has been growing drastically by taking advantage of edge computing which provides great assistance for IoT and mobile devices to complete sophisticated tasks. However, the rapid development leads to the neglect of security threats in edge computing platforms and their enabled applications, which has been one of the main limitations in the smart cities. In this article, we propose a fault and attack detection model for edge computing-based IoT systems to ensure the security of edge computing. Since the risk and fault cases are very imbalanced compared to normal cases, this paper proposes a novel fault detection algorithm by using the imbalance classification technique. Utilizing deep learning techniques, the proposed algorithm overcomes data overlapping problems occurring in many traditional oversampling methods and achieves outstanding performance. With this novel imbalance classification algorithm, the proposed security prediction model achieves pretty good performance on real-world edge computing applications.
Vehicle Re-identification method based on Swin-Transformer network

Vehicle Re-identification is to find out the exact vehicle captured by other cameras given a vehicle image. In natural traffic surveillance systems, vehicle re-identification can play a role in target vehicles localization, supervision, and criminal investigation. While current convolutional neural network-based approaches have been very successful, most studies consider learning representations from individual images, ignoring potential interactions between them. The Swin Transformer method currently shows excellent advantages in image classification. It can handle features at different scales like convolutional neural networks and make elements more closely related to each other, enhancing the learning of robust features and improving feature robustness and discrimination compared to convolutional neural nets. Therefore, this paper adopts the Swin Transformer method for vehicle re-identification and conducts experimental validation on two large vehicle datasets, VeRi-776, and VehicleID. The experimental results show that the recognition effect of this method is significantly better than existing vehicle re-identification methods, such as VAM, FDA-Net, TransReID, etc.
Decoding silent speech from high-density surface electromyographic data using transformer

Recent silent speech recognition (SSR) studies based on surface electromyography (sEMG) have been conducted by classifying a finite number of words or phrases without sufficient understanding of temporally semantic information compared to sequential decoding at a fine-grained syllable or phoneme level. This paper presents a syllable-level sequential decoding method using a transformer model for sEMG-based SSR. The proposed method consists of a transformer model and a language model. The input sEMG data was first translated into a sequence of syllable-level decisions by the transformer model. Then, these sequential syllable-level decisions were tuned as a final syllable sequence to approximate natural language through the language model. To verify the effectiveness of the proposed method, experiment data were recorded using two high-density electrode arrays with 64 channels from a total of eight subjects during subvocally reading a corpus of 33 Chinese phrases generated from a dictionary of 82 syllables. The proposed method achieved the lowest character error rate of 5.14 ± 3.28 % and the highest phrase recognition accuracy of 96.37 ± 2.06 %, and it significantly outperformed other common methods for sEMG-based SSR. These findings demonstrated the feasibility and usability of the proposed method for practical SSR applications.
Transformer-Based Maneuvering Target Tracking

When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center-max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network.
Tunable up-conversion single-photon detector at telecom wavelengths

Abstract Up-conversion single-photon detectors (UCSPD) are based on sum-frequency generation of the telecom band single-photons to near-infrared wavelengths at which efficient and low-noise silicon single-photon detectors are available. Moreover, because of high dynamic range of silicon single-photon detectors, UCSPD is suitable for high-speed quantum communication. UCSPDs reported to date, however, have a very narrow fixed window of detectable wavelengths, severely limiting their applications in wavelength-multiplexed quantum networks. In this work, we report a tunable UCSPD module that covers the complete telecom C band, making it suitable for quantum communication networks based on sharing wavelength-multiplexed entangled photons.
Re-examining regular expressions with backreferences

Most modern regular expression matching libraries (one of the rare exceptions being Google's RE2) allow backreferences, operations which bind a substring to a variable, allowing it to be matched again verbatim. However, both real-world implementations and definitions in the literature use different syntactic restrictions and have differences in the semantics of the matching of backreferences. Our aim is to compare these various flavors by considering the classes of formal languages that each can describe, establishing, as a result, a hierarchy of language classes. Beyond the hierarchy itself, some complexity results are given, and as part of the effort on comparing language classes new pumping lemmas are established, old classes are extended to new ones, and several incidental results on the nature of these language classes are given.
ROAST: Robust Asynchronous Schnorr Threshold Signatures

Bitcoin and other cryptocurrencies have recently introduced support for Schnorr signatures whose cleaner algebraic structure, as compared to ECDSA, allows for simpler and more practical constructions of highly demanded ''t-of-n'' threshold signatures. However, existing Schnorr threshold signature schemes still fall short of the needs of real-world applications due to their assumption that the network is synchronous and due to their lack of robustness, i.e., the guarantee that t honest signers are able to obtain a valid signature even in the presence of other malicious signers who try to disrupt the protocol. This hinders the adoption of threshold signatures in the cryptocurrency ecosystem, e.g., in second-layer protocols built on top of cryptocurrencies.
Investigating the strengths and weaknesses of online education during <scp>COVID</scp>‐19 pandemic from the perspective of professors and students of medical universities and proposing solutions: A qualitative study

This study aimed to explore the strengths and weaknesses of e-learning during the COVID-19 pandemic from the perspective of its primary stakeholders, namely professors and students, and to provide practical solutions. Design is a qualitative study. We enrolled 22 faculty members and 58 students purposively. Research data were collected through a data collection checklist and via email and continued until the data were saturated. The qualitative content analyses were the basis of analysis in this study. Strengths were presented in 6 themes and 26 subthemes, weaknesses in 5 themes and 23 subthemes, and solutions were presented in 5 themes and 20 subthemes. Save money, time and energy; use modern software and educational technologies; and the ability to individualize education were among the strengths of e-learning. The most important weaknesses related to e-learning include infrastructure difficulties, problems related to the ability of professors and students to use educational systems. The most beneficial solutions offered included improving and upgrading the e-learning infrastructure, empowering professors and students to use educational systems. We concluded that using online teaching has many strengths as well as some weaknesses. Identifying these strengths and weaknesses can help policymakers plan better.
Selective MPC

Key-value data is a naturally occurring data type that has not been thoroughly investigated in the local trust model. Existing local differentially private (LDP) solutions for computing statistics over key-value data suffer from the inherent accuracy limitations of each user adding their own noise. Multi-party computation (MPC) maintains better accuracy than LDP and similarly does not require a trusted central party. However, naively applying MPC to key-value data results in prohibitively expensive computation costs. In this work, we present selective multi-party computation, a novel approach to distributed computation that leverages DP leakage to efficiently and accurately compute statistics over key-value data. By providing each party with a view of a random subset of the data, we can capture subtractive noise. We prove that our protocol satisfies pure DP and is provably secure in the combined DP/MPC model. Our empirical evaluation demonstrates that we can compute statistics over 10,000 keys in 20 seconds and can scale up to 30 servers while obtaining results for a single key in under a second.
Being digital to being vulnerable: does digital transformation allure a data breach?

Purpose While every firm is striving to embrace digital transformation (DT) to form new differentiating business capabilities, there are dark sides to such initiatives, and it is essential to acknowledge, identify and address them. The purpose of this paper is to identify and emperically demonstrate the impact of such darksides of DT. While a firm's DT effort may have many dark sides, the authors identify data breaches as the most critical one and focus on proving their impact since it can inflict significant damage to the firm. Design/methodology/approach Through the lens of paradox theory, the authors argue that the DT efforts of a firm will lead to increased risk and severity of data breaches. The authors developed a one-of-a-kind longitudinal data set by combining data from multiple sources, including 3604 brands over a 10-year period, and employed a DT performance scorecard to evaluate a firm's DT effort across four key digital selling touchpoints: site, mobile, digital marketing and social media. Findings The findings of this study show that a firm's DT efforts pertaining to its mobile and digital marketing platforms significantly increase the likelihood and severity of a data breach event indicating that these two channels are most vulnerable and need heightened attention from firms. Furthermore, the findings suggest that the negative repercussions of some DT initiatives may be minimized as the firm becomes more innovative. The findings can help firms re-strategize their DT efforts by promoting security and also encouraging a balanced communication strategy. Originality/value This research is one of the first to identify, recognize and empirically illustrate the downsides of a DT effort that is otherwise thought to provide only benefits.
Temporal Dropout for Weakly Supervised Action Localization

Weakly supervised action localization is a challenging problem in video understanding and action recognition. Existing models usually formulate the training process as direct classification using video-level supervision. They tend to only locate the most discriminative parts of action instances and produce temporally incomplete detection results. A natural solution for this problem, the adversarial erasing strategy, is to remove such parts from training so that models can attend to complementary parts. Previous works do it in an offline and heuristic way. They adopt a multi-stage pipeline, where discriminative regions are determined and erased under the guidance of detection results from last stage. Such a pipeline can be both ineffective and inefficient, possibly hindering the overall performance. On the contrary, we combine adversarial erasing with dropout mechanism and propose a Temporal Dropout Module that learns where to remove in a data-driven and online manner. This plug-and-play module is trained without iterative stages, which not only simplifies the pipeline but also makes the regularization during training easier and more adaptive. Experiments show that the proposed method outperforms previous erasing-based methods by a large margin. More importantly, it achieves universal improvement when plugged into various direct classification methods and obtains state-of-the-art performance.