Reviewerly/exHarmony huggingface dataset:
EyeSyn: Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition

Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition.To achieve state-ofthe-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers.However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect.To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset.Taking gazebased museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct patterns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from different measurement setups and subject heterogeneity.Moreover, in the few-shot learning scenario, EyeSyn can be readily incorporated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training. CCS CONCEPTS• Human-centered
Robust-FusionNet: Deep Multimodal Sensor Fusion for 3-D Object Detection Under Severe Weather Conditions

The LiDAR point cloud data and camera images are distorted to a different degree under various severe weather conditions. Due to this, the traditional single modal object detection methods are unable to use the complementary information between different sensors. Consequently, these algorithms are unable to address various issues caused by severe weather conditions. Recently, the multimodal data fusion methods are applied to the road object detection under severe weather conditions. However, the multimodal algorithms suffer from low data alignment accuracy and the inability to suppress the changes in exposure under severe weather conditions. In this work, we propose a new multimodal sensor fusion object detection network. The proposed network effectively overcomes the shortcomings caused by the camera and LiDAR distortions in severe weather conditions and achieves robust environment perception. We propose: 1) point-wise aligned data fusion method based on K-means++ clustering to improve the accuracy of data alignment; 2) implicit feature pyramid network (i-FPN) to fuse the image features for suppressing the distortions caused by the changes in exposure; 3) hybrid attention mechanism (HAM) to deal with the fusion features and improve the adaptability towards different working conditions. We perform experiments using the ONCE and KITTI dataset. The experimental results and analysis show that the proposed method effectively improves the performance of multimodal deep fusion network under both clear and severe weather conditions.
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published “Automotive Ethernet Intrusion Dataset show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.
Koopman wavefunctions and classical states in hybrid quantum–classical dynamics

We deal with the reversible dynamics of coupled quantum and classical systems. Based on a recent proposal by the authors, we exploit the theory of hybrid quantum–classical wavefunctions to devise a closure model for the coupled dynamics in which both the quantum density matrix and the classical Liouville distribution retain their initial positive sign. In this way, the evolution allows identifying a classical and a quantum state in interaction at all times, thereby addressing a series of stringent consistency requirements. After combining Koopman's Hilbert-space method in classical mechanics with van Hove's unitary representations in prequantum theory, the closure model is made available by the variational structure underlying a suitable wavefunction factorization. Also, we use Poisson reduction by symmetry to show that the hybrid model possesses a noncanonical Poisson structure that does not seem to have appeared before. As an example, this structure is specialized to the case of quantum two-level systems.
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models

Large pretrained language models (LMs) have become the central building block of many NLP applications.Training these models requires ever more computational resources and most of the existing models are trained on English text only.It is exceedingly expensive to train these models in other languages.To alleviate this problem, we introduce a novel method -called WECHSEL -to efficiently and effectively transfer pretrained LMs to new languages.WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword.The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language.We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili).We also study the benefits of our method on very low-resource languages.WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort.Our method makes training large language models for new languages more accessible and less damaging to the environment.We make our code and models publicly available.
Blockchain-Based Privacy-Preserving Positioning Data Sharing for IoT-Enabled Maritime Transportation Systems

Data-driven applications play an important role in modern-time maritime transportation systems, for instance in facilitating decision-making relating to communication and safety. One example application is position data sharing between vessels within the maritime Internet of Things (IoT)-enabled context. When designing such applications, we need to also consider how to ensure data accuracy as well as privacy in a large scale deployment. In this paper, we demonstrate the potential of using blockchain to facilitate privacy-preserving data sharing. Specifically, we develop a zero-knowledge proof-based scheme to protect vessel identities while allowing data sharing, and a commitment-based approach to ensure relationship-related privacy in data trading between participants. Our security and performance evaluations demonstrate the utility of the proposed approach.
When is a DAO Decentralized?

While previously a nascent theoretical construct, decentralized autonomous organizations have grown rapidly in recent years. DAOs typically emerge around the management of decentralized financial applications and thus benefit from the rapid growth of innovation in this sector. In response, global regulators increasingly voice the intent to regulate these activities. This may impose an excessive compliance burden on DAOs, unless they are deemed sufficiently decentralized to be regulated. Yet, decentralization is an abstract concept with scarce legal precedence. We investigate dimensions of decentralization through thematic analysis, combining extant literature with a series of expert interviews. We propose a definition of 'sufficient decentralization' and present a general framework for the assessment of decentralization. We derive five dimensions for the assessment of decentralization in DAOs: Token-weighted voting, Infrastructure, Governance, Escalation and Reputation. We present a discretionary sample application of the framework and five propositions on the future regulation and supervision of DAOs. We contribute new practical insights on the topic of compliance and decentralized organizations to the growing discourse on the application of blockchain technology in information systems and management disciplines
Researcher degrees of freedom in statistical software contribute to unreliable results: A comparison of nonparametric analyses conducted in SPSS, SAS, Stata, and R

Researcher degrees of freedom can affect the results of hypothesis tests and consequently, the conclusions drawn from the data. Previous research has documented variability in accuracy, speed, and documentation of output across various statistical software packages. In the current investigation, we conducted Pearson’s chi-square test of independence, Spearman’s rank-ordered correlation, Kruskal–Wallis one-way analysis of variance, Wilcoxon Mann–Whitney U rank-sum tests, and Wilcoxon signed-rank tests, along with estimates of skewness and kurtosis, on large, medium, and small samples of real and simulated data in SPSS, SAS, Stata, and R and compared the results with those obtained through hand calculation using the raw computational formulas. Multiple inconsistencies were found in the results produced between statistical packages due to algorithmic variation, computational error, and statistical output. The most notable inconsistencies were due to algorithmic variations in the computation of Pearson’s chi-square test conducted on 2 × 2 tables, where differences in p-values reported by different software packages ranged from .005 to .162, largely as a function of sample size. We discuss how such inconsistencies may influence the conclusions drawn from the results of statistical analyses depending on the statistical software used, and we urge researchers to analyze their data across multiple packages to check for inconsistencies and report details regarding the statistical procedure used for data analysis.
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computational complexity of deep learning models, most embedded HAR systems are based on simple and not-so-accurate classic machine learning algorithms. This work bridges the gap between on-device HAR and deep learning, proposing a set of efficient one-dimensional Convolutional Neural Networks (CNNs) that can be deployed on general purpose microcontrollers (MCUs). Our CNNs are obtained combining hyper-parameters optimization with sub-byte and mixed-precision quantization, to find good trade-offs between classification results and memory occupation. Moreover, we also leverage adaptive inference as an orthogonal optimization to tune the inference complexity at runtime based on the processed input, hence producing a more flexible HAR system. With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) we are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency, and energy consumption; (ii) thanks to adaptive inference, we can derive &gt;20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3× in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, while reducing the memory occupation by more than 100×. The few methods that obtain better performance (both shallow and deep) are not compatible with MCU deployment; (iv) all our CNNs are compatible with real-time on-device HAR, achieving an inference latency that ranges between 9 μs and 16 ms. Their memory occupation varies in 0.05–23.17 kB, and their energy consumption in 0.05 and 61.59 μJ, allowing years of continuous operation on a small battery supply.
Adversarial scratches: Deployable attacks to CNN classifiers

A growing body of work has shown that deep neural networks are susceptible to adversarial examples. These take the form of small perturbations applied to the model's input which lead to incorrect predictions. Unfortunately, most literature focuses on visually imperceivable perturbations to be applied to digital images that often are, by design, impossible to be deployed to physical targets. We present Adversarial Scratches: a novel L0 black-box attack, which takes the form of scratches in images, and which possesses much greater deployability than other state-of-the-art attacks. Adversarial Scratches leverage B\'ezier Curves to reduce the dimension of the search space and possibly constrain the attack to a specific location. We test Adversarial Scratches in several scenarios, including a publicly available API and images of traffic signs. Results show that, often, our attack achieves higher fooling rate than other deployable state-of-the-art methods, while requiring significantly fewer queries and modifying very few pixels.
Convolutional Fine-Grained Classification With Self-Supervised Target Relation Regularization

Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such target coding schemes are less flexible to model inter-class correlation and are sensitive to sparse and imbalanced data distribution as well. In light of this, this paper introduces a novel target coding scheme -- dynamic target relation graphs (DTRG), which, as an auxiliary feature regularization, is a self-generated structural output to be mapped from input images. Specifically, online computation of class-level feature centers is designed to generate cross-category distance in the representation space, which can thus be depicted by a dynamic graph in a non-parametric manner. Explicitly minimizing intra-class feature variations anchored on those class-level centers can encourage learning of discriminative features. Moreover, owing to exploiting inter-class dependency, the proposed target graphs can alleviate data sparsity and imbalanceness in representation learning. Inspired by recent success of the mixup style data augmentation, this paper introduces randomness into soft construction of dynamic target relation graphs to further explore relation diversity of target classes. Experimental results can demonstrate the effectiveness of our method on a number of diverse benchmarks of multiple visual classification tasks, especially achieving the state-of-the-art performance on popular fine-grained object benchmarks and superior robustness against sparse and imbalanced data. Source codes are made publicly available at https://github.com/AkonLau/DTRG.
Constructing All Qutrit Controlled Clifford+T gates in Clifford+T

For a number of useful quantum circuits, qudit constructions have been found which reduce resource requirements compared to the best known or best possible qubit construction. However, many of the necessary qutrit gates in these constructions have never been explicitly and efficiently constructed in a fault-tolerant manner. We show how to exactly and unitarily construct any qutrit multiple-controlled Clifford+T unitary using just Clifford+T gates and without using ancillae. The T-count to do so is polynomial in the number of controls k, scaling as $$O(k^{3.585})$$ . With our results we can construct ancilla-free Clifford+T implementations of multiple-controlled T gates as well as all versions of the qutrit multiple-controlled Toffoli, while the analogous results for qubits are impossible. As an application of our results, we provide a procedure to implement any ternary classical reversible function on n trits as an ancilla-free qutrit unitary using $$O(3^n n^{3.585})$$ T gates.
Leakage-Resilient Certificate-Based Authenticated Key Exchange Protocol

Certificate-based public key cryptography (CB-PKC) removes the problem of certificate management in traditional public key systems and avoids the key escrow problem in identity-based public key systems.In the past, many authenticated key exchange (AKE) protocols based on CB-PKC systems, called CB-AKE, were proposed to be applied to secure communications between two remote participants.However, these existing CB-AKE protocols become insecure since attackers could compute and obtain the whole secret key from some partial leaked information of the secret key by side channel attacks.In this paper, our goal is to propose the f irst CB-AKE protocol with the property to resist side channel attacks, called leakage-resilient CB-AKE (LR-CB-AKE).The proposed LR-CB-AKE protocol is formally proven to be secure in the generic bilinear group (GBG) model under the discrete logarithm (DL) and computational Diffie-Hellman (CDH) assumptions.
A novel method of data compression using ROI for biomedical 2D images

In the era of modern medical imaging communal, huge volume of medical image data are being acquired and need to be transmitted and archived which necessitate the development of efficient image compression techniques on both 2D and 3D images. The compression of medical images is an essential process to support remote healthcare services. The medical diagnostics through these services require more accurate information from an image. As the property of inverse proportionality between the compression rate and quality of the image takes place in any kind of compression method, there is a need to sacrifice any one of those credentials (Quality or Compression Rate). With this context, Region of Interest (ROI) codecs are emerging and reduces this proportionality that yields more compression rate without compromising the quality. In this paper, presents an ROI based near lossless image compression method that incorporates the Set Partitioning in Hierarchical Trees (SPIHT) and Vector Quantization coding for medical images.
Privacy, accuracy, and model fairness trade-offs in federated learning

As applications of machine learning become increasingly widespread, the need to ensure model accuracy and fairness while protecting the privacy of user data becomes more pronounced. On this note, this paper introduces a federated learning training model, which allow clients to simultaneously learn their model and update the associated parameters on a centralized server. In our approach, we seek to achieve an acceptable trade-off between privacy, accuracy, and model fairness by using differential privacy (DP), which also helps to minimize privacy risks by protecting the presence of a specific sample in the training data. Machine learning models can, however, exhibit unintended behaviors, such as unfairness, which result in groups with certain sensitive characteristics (e.g., gender) receiving different patterns of outcomes. Hence, we discuss the fairness and privacy effect of local DP and global DP when applied to federated learning by designing a fair and privacy quantification mechanism. In doing so, we can achieve an acceptable trade-off between accuracy, privacy, and model fairness. We quantify the level of fairness based on the constraints of three definitions of fairness, including demographic parity, equal odds, and equality of opportunity. Finally, findings from our extensive experiments conducted on three real-world datasets with class imbalance demonstrate the positive effect of local and global DP on fairness. Our study also shows that privacy can come at the cost of fairness, as stricter privacy can intensify discrimination. Hence, we posit that careful parameter selection can potentially help achieve a more effective trade-off between utility, bias, and privacy.
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL module (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on end-to-end optimization. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.
OES-Fed: a federated learning framework in vehicular network based on noise data filtering

The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.