Reviewerly/exHarmony huggingface dataset:
Accelerometer time series augmentation through externally driving a non-linear dynamical system

Coupled non-linear dynamical systems have gained attention not only as a ubiquitous occurrence in natural and artificial scenarios, but also as a basis for atypical computation paradigms. This paper introduces an approach to time series data augmentation involving driving a single low-dimensional entity, namely the Rössler system, with a physically-recorded sensor signal, and leveraging its responses to enhance the performance of a conventional classifier. A representative internet of things application in agriculture, namely cattle behavior recognition using a triaxial accelerometer, is investigated via a publicly-available dataset. Numerical simulations and experiments with an analog electronic circuit reveal that diversified responses to the external input are attainable, and the additional time series obtained from the driven system enhance the behavior classification accuracy. The advantage, down to the combined effects of its dynamical response and a static non-linearity transforming the driving signal, is appreciable both when using a small multi-layer perceptron network operating on elementary features and, albeit to a lesser extent, when feeding the time series directly to a convolutional neural network. One possibility is that the driven system translates non-linear dynamical features into linear signal properties that can be more easily extracted. Some considerations about the engineering implementation using either analog hardware or programmable logic on an edge device are given.
Measuring and mitigating language model biases in abusive language detection

Warning: This paper contains abusive samples that may cause discomfort to readers. Abusive language on social media reinforces prejudice against an individual or a specific group of people, which greatly hampers freedom of expression. With the rise of large-scale pre-trained language models, classification based on pre-trained language models has gradually become a paradigm for automatic abusive language detection. However, the effect of stereotypes inherent in language models on the detection of abusive language remains unknown, although this may further reinforce biases against the minorities. To this end, in this paper, we use multiple metrics to measure the presence of bias in language models and analyze the impact of these inherent biases in automatic abusive language detection. On the basis of this quantitative analysis, we propose two different debiasing strategies, token debiasing and sentence debiasing, which are jointly applied to reduce the bias of language models in abusive language detection without degrading the classification performance. Specifically, for the token debiasing strategy, we reduce the discrimination of the language model against protected attribute terms of a certain group by random probability estimation. For the sentence debiasing strategy, we replace protected attribute terms and augment the original text by counterfactual augmentation to obtain debiased samples, and use the consistency regularization between the original data and the augmented samples to eliminate the bias at the sentence level of the language model. The experimental results confirm that our method can not only reduce the bias of the language model in the abusive language detection task, but also effectively improve the performance of abusive language detection.
PVF-DectNet: Multi-modal 3D detection network based on Perspective-Voxel fusion

The detection of small objects such as pedestrians still poses challenges to the LiDAR-based 3D object detection due to the sparseness and disorder of point clouds. Conversely, images from cameras can provide rich semantic information, which makes these small-sized objects easy to be detected. To take use of the advantages of both devices to achieve better 3D object detection, research on the fusion of LiDAR and camera information is now being conducted. The existing fusion methods between point clouds and image are normally weighed more on the point clouds. Hence the semantic information of images is not fully utilized. We propose a new fusion method named PVFusion to try to fuse more image features. We first divide each point into a separate perspective voxel and project the voxel onto the image feature maps. Then the semantic feature of the perspective voxel is fused with the geometric feature of the point. A 3D object detection model (PVF-DectNet) is designed using PVFusion. During training we employ the ground truth paste (GT-Paste) data augmentation and solve the occlusion problem caused by newly added object. The KITTI validation set is used to validate the PVF-DectNet, which shows 3.6% AP improvement over the other feature fusion methods in pedestrian detection. On the KITTI test set, the PVF-DectNet outperforms the other multi-modal SOTA methods by 2.2% AP in pedestrian detection. And PVFusion shows better detection performance for sparse point clouds than PointFusion in both car and pedestrian categories. As for 32 beams LiDAR scene, there are 4.2% AP increment in moderate difficulty car category and 5.2% mAP improvement in pedestrian category.
ML-CapsNet meets VB-DI-D: A novel distortion-tolerant baseline for perturbed object recognition

Suffering from spatiotemporal-varying perturbations (e.g., overexposure, jitter, and motion), the gathered images frequently undergo visual distortions (e.g., shear, defocus blur, affine transformation, and speckle noise). Due to the lack of effective information carriers, prior works cannot extract sufficient original representations of instances from corrupted images, thus fail to extrapolate to various geometric transformations. This paper proposes a Distortion-Tolerant Capsule Network (DT-CapsNet) to realize object detection whilst resisting visual distortions. It first learns the distribution of capsule encoding vectors as a new information carrier by casting a feature extractor dubbed Multi-lane Capsule Network (ML-CapsNet). This model consists of three independent encoder lanes and runs under the support of modified Segment-By-Segment Dynamic Routing Agreement (SBS-DRA). Then the invariant dimension detection and elimination, descriptor generation, and correspondence establishment are conducted on the learned vector distributions by casting a adaptive algorithm dubbed Vector-Based Deformation-Invariant Descriptor (VB-DI-D). Finally, a reliable soft matching with relaxation margins between the patterns of original standard instances and those of disturbed instances. Quantitative and ablation verifications demonstrate that DT-CapsNet can deliver competitive perturbed object detection performance among state-of-the-arts, i.e., achieves the highest testing accuracy (90.86% versus the second highest score 90.53%) on hand-crafted wheat dataset, and achieves the highest average testing accuracy (91.18% versus the second highest score 91.15%) on three public benchmarks (Stanford Cars, Stanford Dogs, CUB-200-2011). The results evidence that DT-CapsNet indeed improves the invariance against numerous encountered geometric distortions.
An edge map-guided acceleration strategy for multi-scale weighted nuclear norm minimization-based image denoising

By exploring the nonlocal self-similarity of images, weighted nuclear norm minimization (WNNM) based image denoising algorithm has achieved competitive performance. Multi-scale patches-based image denoising using weighted nuclear norm minimization (MS-WNNM) is one of the follow-up researches, which further improves WNNM by considering cross-scale self-similarity in block-matching. However, the better performance is achieved at the price of spending more time in a larger search space. In this paper, we propose an edge map-guided strategy to accelerate MS-WNNM based image denoising. Inspired by the fact that patches belonging to the structure part and patches belonging to the texture part follow different repetitive patterns, we propose a prior that the patches containing and surrounding the edges carry most of the structural and textural information of the image. The proposed prior is explained from an information-theoretic perspective. Based on this prior, an edge map-guided acceleration strategy is designed, where redundant patches are trimmed off and the informative patches are retained. Block-matching in the pruned search space, which still has patch diversity, can significantly reduce time without much loss of quality. As a result, comparable denoising can be achieved in less runtime. The experimental results demonstrate the quality and efficiency of the proposed method.
Low-light robust face super resolution via morphological transformation based locality-constrained representation

A new low-light robust face super-resolution (SR) model via morphological transformation-based locality-constrained representation (MTLcR) is proposed to improve the robustness of the current SR algorithms against low-light problems. The proposed model first tries to detect the amount of low-light effects present in the input images with the help of dictionary images of the same domain. Further, the same amount of effect is applied to low-resolution (LR) dictionary images with the purpose of minimizing the reconstruction error while representing the input image as a weighted linear combination of LR dictionary images. In addition to this, morphological transformation is introduced in the objective function to further improve the reconstruction weights. The simulation results on a publicly available data set and some real-world images suggest the better reconstruction capability of the proposed model under low-light scenario as compared to other state-of-the-art works.
Investigation of gesture recognition based on optical fiber Bragg grating sensors

This paper discusses the investigation of optical fiber Bragg grating (FBG) sensors in gesture recognition. We proposed a new attempt by embedding the FBG into polyimide films, then the FBG sensors are arranged on the upper surface of the muscle along the normal direction of the muscle to recognize gestures through detection of muscle activity. A series of experiments were conducted by the selected eight healthy subjects, to quantitatively studied and comprehensively evaluated the performances of this method. Composite experimental systems with multiple gestures, multiple samples and multiple cycles were established for the investigation of the reproducibility and further verification of feasibility. The results show that the FBG sensors has good reproducibility and no chirp effect of the reflection spectrum profile during gesture recognition. The proposed method can effectively detect six gestures with quick responses. Moreover, this method also has the potential to distinguish muscle deformation with different physiological characteristics.
Mitigate the classification ambiguity via localization-classification sequence in object detection

In anchor-based detectors, the confidence scores and label-assignment results for the classification task are determined by the unrefined anchors rather than the final-refined boxes, which causes classification ambiguity due to the lack of correlation between the classification and localization tasks. In this paper, we investigate the classification ambiguity thoroughly via extensive experiments, and present the localization-classification sequence detector (LCSDet) that performs localization and classification in order, bridging the gap between them. To achieve this, the refinement-aware (RA) classification branch and RA assignment are proposed in LCSDet. In inference, the RA classification branch rectifies the feature misalignment and directly classifies the refined anchors. During training, the RA assignment tackles the training instability, narrows the location-quality gap and assigns the refined anchors to ground-truth objects. Comprehensive experiments indicate that the LCSDet can effectively mitigate the classification ambiguity and achieve stable improvement across different baselines.
Two-dimensional Gaussian hierarchical priority fuzzy modeling for interval-valued data

In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS) is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of the average value of interval-valued data in each dimension to generate two-dimensional gaussian membership functions of two-level rules. The two levels of rules are associated by calculating the activation strength of the second-level rules to the first-level rules and setting the connection threshold. The regularized least squares method is used to optimize the consequents of the second-level rules. The two-dimensional gaussian membership function designed in this paper is used to model the antecedents of interval-valued data, solving the correlation problem between the left and right values of interval-valued data. The effectiveness of TGHPFS is validated using real-world datasets, and the proposed method is compared with other latest methods to show the superiority of TGHPFS.
Artifact-Based Domain Generalization of Skin Lesion Models

Deep Learning failure cases are abundant, particularly in the medical area. Recent studies in out-of-distribution generalization have advanced considerably on well-controlled synthetic datasets, but they do not represent medical imaging contexts. We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context. First, we partition the data into levels of increasingly higher biased training and test sets for better generalization assessment. Then, we create environments based on skin lesion artifacts to enable domain generalization methods. Finally, after robust training, we perform a test-time debiasing procedure, reducing spurious features in inference images. Our experiments show our pipeline improves performance metrics in biased cases, and avoids artifacts when using explanation methods. Still, when evaluating such models in out-of-distribution data, they did not prefer clinically-meaningful features. Instead, performance only improved in test sets that present similar artifacts from training, suggesting models learned to ignore the known set of artifacts. Our results raise a concern that debiasing models towards a single aspect may not be enough for fair skin lesion analysis.
Blockchain-Based Cyber Threat Intelligence Sharing Using Proof-of-Quality Consensus

Cyber threat intelligence (CTI) is contextualised knowledge, built on information that is collected, processed, analysed, and disseminated to the right audience, in order to comprehend a malicious threat actor’s motivation, goals, objectives, targets, and attack behaviours. The CTI value increases by the ability to be shared, consumed, and actioned timely, by the right stakeholders, based always on quality standards and parameters, which boost the cyber security community to understand how adversaries act and to counter the constantly emerging sophisticated cyber threats. In this article, along with the identification of research gaps, after a comparison between existing research studies in the similar scope of CTI evaluation and sharing mechanisms, we propose a blockchain-based cyber threat intelligence system architecture, which collects, evaluates, stores, and shares CTI, enabling tamper-proof data and exclusion of untrustworthy evaluation peers, while evaluating, at the same time, the quality of CTI Feeds against a defined set of quality standards. The evaluation of the data is performed utilising a reputation and trust-based mechanism for selecting validators, who further rate the CTI feeds using quality-based CTI parameters, while the consensus for preserving the fairness of the results and their final storage is performed via the recently introduced proof-of-quality (PoQ) consensus algorithm. The data, which are stored in the proposed ledger, constitute a reliable, distributed, and secure repository of CTI Feeds and contain their objective evaluation, as well as the performance of the validators who participated in each evaluation, while these data can be further used for assessing the reputation of CTI Sources. Finally, in order to assess the proposed system’s reliability, integrity, and tolerance against malicious activities, the model is subject to a theoretical analysis using a probabilistic simulation, taking into account various aspects and features of the integrated mechanisms. The results show that the tolerance against malicious validators is acceptable, even when the ratio between legitimately vs. maliciously behaving validators is 1 : 50.
New Approaches on Parameter Estimation of the Gamma Distribution

This paper discusses new approaches to parameter estimation of gamma distribution based on representative points. In the first part, the existence and uniqueness of gamma mean squared error representative points (MSE-RPs) are discussed theoretically. In the second part, by comparing three types of representative points, we show that gamma MSE-RPs perform well in parameter estimation and simulation. The last part proposes a new Harrel–Davis sample standardization technique. Simulation studies reveal that the standardized samples can be used to improve estimation performance or generate MSE-RPs. In addition, a real data analysis illustrates that the proposed technique yields efficient estimates for gamma parameters.
Steering-enhanced quantum metrology using superpositions of noisy phase shifts

Quantum steering is an important correlation in quantum information theory. A recent work [Nat. Commun. 12, 2410 (2021)] showed that quantum steering is also useful for quantum metrology. Here, we extend the exploration of steering-enhanced quantum metrology from single noiseless phase shifts to superpositions of noisy phase shifts. As concrete examples, we consider a control system that manipulates a target system to pass through a superposition of either dephased or depolarized phase shifts channels. We show that using such superpositions of noisy phase shifts can suppress the effects of noise and improve metrology. Furthermore, we also implemented proof-of-principle experiments for a superposition of dephased phase shifts on the IBM quantum experience, demonstrating a clear improvement on metrology.
Anomaly detection using a model-agnostic meta-learning-based variational auto-encoder for facility management

In a smart building, various types of sensor data are generated and employed to control devices within the building. Detecting anomalies in these sensor data is critical for effective facility management because it can prevent device malfunction or failure. Although deep learning-based methods have been used to detect anomalies, effective model construction is challenging because abnormal data tend to be rare under real-world conditions. In image classification and load forecasting, model-agnostic meta-learning (MAML) has recently been proposed to alleviate this problem using common knowledge learned from various tasks. This paper proposes an MAML-based unsupervised anomaly detection method called MAVAE for time-series sensor data. The proposed method uses a variational auto-encoder (VAE) as an anomaly detection model and adapts the model to a new target task with few unlabeled anomaly data via MAML. To our knowledge, this is the first study to train a VAE using MAML with time-series data. Extensive experiments on public data reveals that the proposed method outperforms existing anomaly detection methods, achieving an average improvement in prediction performance of 45% compared to state-of-the-art methods. The robustness of the proposed method is also demonstrated by evaluating its performance with a different number of samples for MAML. Our code is available at github.com/17011813/MAVAE.
Texture enhanced underwater image restoration via Laplacian regularization

Underwater images are usually degraded by color distortion, blur, and low contrast due to the fact that the light is inevitably absorbed and scattered when traveling through water. The captured images with poor quality may greatly limit their applications. To address these problems, we propose a new Laplacian variation model based on underwater image formation model and the information derived from the transmission map and background light. Technically, a novel fidelity term is designed to constrain the radiance scene, and a divergence-based regularization is applied to strengthen the structure and texture details. Moreover, the brightness-aware blending algorithm and quad-tree subdivision scheme are integrated into our variational framework to perform the transmission map and background light estimation. Accordingly, we provide a fast-iterative algorithm based on the alternating direction method of multipliers to solve the optimization problem and accelerate its convergence speed. Experimental results demonstrate that the proposed method achieves outstanding performance on dehazing, detail preserving, and texture enhancement for improving underwater image quality. Extensive qualitative and quantitative comparisons with several state-of-the-art methods also validate the superiority of our proposed method.
An Improved Corpus-Based NLP Method for Facilitating Keyword Extraction: An Example of the COVID-19 Vaccine Hesitancy Corpus

In the current COVID-19 post-pandemic era, COVID-19 vaccine hesitancy is hindering the herd immunity generated by widespread vaccination. It is critical to identify the factors that may cause COVID-19 vaccine hesitancy, enabling the relevant authorities to propose appropriate interventions for mitigating such a phenomenon. Keyword extraction, a sub-field of natural language processing (NLP) applications, plays a vital role in modern medical informatics. When traditional corpus-based NLP methods are used to conduct keyword extraction, they only consider a word’s log-likelihood value to determine whether it is a keyword, which leaves room for concerns about the efficiency and accuracy of this keyword extraction technique. These concerns include the fact that the method is unable to (1) optimize the keyword list by the machine-based approach, (2) effectively evaluate the keyword’s importance level, and (3) integrate the variables to conduct data clustering. Thus, to address the aforementioned issues, this study integrated a machine-based word removal technique, the i10-index, and the importance–performance analysis (IPA) technique to develop an improved corpus-based NLP method for facilitating keyword extraction. The top 200 most-cited Science Citation Index (SCI) research articles discussing COVID-19 vaccine hesitancy were adopted as the target corpus for verification. The results showed that the keywords of Quadrant I (n = 98) reached the highest lexical coverage (9.81%), indicating that the proposed method successfully identified and extracted the most important keywords from the target corpus, thus achieving more domain-oriented and accurate keyword extraction results.
Cybercriminal networks in the UK and Beyond: Network structure, criminal cooperation and external interactions

Abstract This article examines the network structure, criminal cooperation, and external interactions of cybercriminal networks. Its contribution is empirical and inductive. The core of this study involved carrying out 10 case analyses on closed cybercrime investigations – all with financial motivations on the part of the offenders - in the UK and beyond. Each analysis involved investigator interview and access to unpublished law enforcement files. The comparison of these cases resulted in a wide range of findings on these cybercriminal networks, including: a common division between the scam/attack components and the money components; the presence of offline/local elements; a broad, and sometimes blurred, spectrum of cybercriminal behaviour and organisation. An overarching theme across the cases that we observe is that cybercriminal business models are relatively stable.