Reviewerly/exHarmony huggingface dataset:
UAS Cyber Security Hazards Analysis and Approach to Qualitative Assessment

In this paper, some cybersecurity vulnerabilities of modern UASs were considered. Vulnerabilities of communication, navigation, control, and surveillance equipment of modern UASs were analyzed. The cyber threats analysis and assessment algorithm were presented. The proposed algorithm is focusing on possible intruders’ portrait that can be important when qualitative risk assessment. The approach to assess the UASs cyber hazards connected with considered vulnerabilities and threats was made. It was stressed that the particular assessment highly depends on the properly defined mission, potential intruders, and even social and political situation. A short overview of the recommendations to mitigate risk connected with cyber hazards was done.
Effects of virtual learning environments: A scoping review of literature

The purpose of this scoping review is to isolate and investigate the existing data and research that identifies if the synchronous face-to-face visual presence of a teacher in a virtual learning environment (VLE) is a significant factor in a student's ability to maintain good mental health. While the present research on this explicit interaction among VLE implementation and student mental health is limited, the material suggests a framework for strong utilization of VLEs. Overall, our research has shown that authentic, high quality VLEs are ones that have as their primary focus the communication between students and their teachers and between students and their peers. This communication is best generated through synchronous connections where there exists the ability to convey the student's immediate needs in real-time. Our research results and discussion will outline how a team approach that brings together teachers, students, administration, counsellors, mental health support staff, instructional designers, and ICT specialists is necessary to create a genuinely enriching VLE where both learning and social-emotional needs can be met. The authors present a case for further study in order to reveal the nature of the interaction among VLEs and student mental health.
EPS-Ledger: Blockchain Hyperledger Sawtooth-Enabled Distributed Power Systems Chain of Operation and Control Node Privacy and Security

A distributed power system operation and control node privacy and security are attractive research questions that deliver electrical energy systems to the participating stakeholders without being physically connected to the grid system. The increased use of renewable energy in the power grid environment creates serious issues, for example, connectivity, transmission, distribution, control, balancing, and monitoring volatility on both sides. This poses extreme challenges to tackle the entire bidirectional power flow throughout the system. To build distributed monitoring and a secure control operation of node transactions in the real-time system that can manage and execute power exchanging and utilizing, balancing, and maintaining energy power failure. This paper proposed a blockchain Hyperledger Sawtooth enabling a novel and secure distributed energy transmission node in the EPS-ledger network architecture with a robust renewable power infiltration. The paper focuses on a cyber-physical power grid control and monitoring system of renewable energy and protects this distributed network transaction on the blockchain and stores a transparent digital ledger of power. The Hyperledger Sawtooth-enabled architecture allows stakeholders to exchange information related to power operations and control monitoring in a private ledger network architecture and investigate the different activities, preserved in the interplanetary file systems. Furthermore, we design, create, and deploy digital contracts of the cyber–physical energy monitoring system, which allows interaction between participating stakeholders and registration and presents the overall working operations of the proposed architecture through a sequence diagram. The proposed solution delivers integrity, confidentiality, transparency, availability, and control access of the distribution of the power system and maintains an immutable operations and control monitoring ledger by secure blockchain technology.
Dynamic subspace dual-graph regularized multi-label feature selection

In multi-label learning, feature selection is a topical issue for addressing high-dimension data. However, most of existing methods adopt imperfect labels to perform feature selection. Although some graph-based multi-label feature selection methods are proposed to deal with the problem, they adopt the fixed graph Laplacian matrix so that the performances of these models are under-performing. To this end, this paper proposes a Dynamic Subspace dual-graph regularized Multi-label Feature Selection method named DSMFS. DSMFS decomposes the original label space into a low-dimensional subspace, and then both the dynamic label-level subspace graph and the feature-level graph are used to obtain a high-quality label subspace to conduct feature selection process. Seven state-of-the-art methods are compared to the proposed method on twelve multi-label benchmark data sets in the experiments. Experimental results demonstrate the superiority of DSMFS.
Fooling LiDAR Perception via Adversarial Trajectory Perturbation

LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicles are sending LiDAR point clouds to deep networks for perception and planning, could the motion compensation consequently become a wide-open backdoor in those networks, due to both the adversarial vulnerability of deep learning and GPS-based vehicle trajectory estimation that is susceptible to wireless spoofing? We demonstrate such possibilities for the first time: instead of directly attacking point cloud coordinates which requires tampering with the raw LiDAR readings, only adversarial spoofing of a self-driving car’s trajectory with small perturbations is enough to make safety-critical objects undetectable or detected with incorrect positions. Moreover, polynomial trajectory perturbation is developed to achieve a temporally-smooth and highly-imperceptible attack. Extensive experiments on 3D object detection have shown that such attacks not only lower the performance of the state-of-the-art detectors effectively, but also transfer to other detectors, raising a red flag for the community. The code is available on https://ai4ce.github.io/FLAT/.
Aptenodytes Forsteri Optimization: Algorithm and applications

This paper proposes a new naturally inspired swarm intelligence algorithm called the Aptenodytes Forsteri Optimization Algorithm (AFO). The main inspiration is the emperor penguin's warm-hugging behaviour. When looking for a suitable location, emperor penguins need to sense the change of temperature, consider the location of other penguins, move closer to the centre of the penguin population, minimize their energy loss, and refer to their memory. These five strategies are evolved into five update modes of variables. According to the characteristics of these update modes in the exploration and exploitation stage, adaptive adjustment methods are designed to mix the five ways. The effectiveness of the proposed AFO is checked, through a comparison with other nature-inspired algorithms, on shifted classical benchmark problems and CEC2017 benchmark problems. Moreover, four engineering problems are utilized to estimate the effectiveness of AFO in optimizing constrained problems. The experimental results show that AFO has the best performance in most problems, which can be considered an excellent and competitive algorithm. In addition, as for the problem of partial algorithm update bias towards the origin, particular experiments and metrics are designed to examine the impact of such update methods on the generality of the algorithm. Source codes of AFO are publicly available at https://github.com/TwilightArchonYz/A-new-Nature-inspired-optimization-algorithm-AFO.
Personalized and Invertible Face De-identification by Disentangled Identity Information Manipulation

The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues. De-identification is regarded as an effective tool for visual privacy protection with the process of concealing or replacing identity information. Most of the existing de-identification methods suffer from some limitations since they mainly focus on the protection process and are usually non-reversible. In this paper, we propose a personalized and invertible de-identification method based on the deep generative model, where the main idea is introducing a user-specific password and an adjustable parameter to control the direction and degree of identity variation. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both face de-identification and recovery.
An Evidence of Distributed Trust in Blockchain-Based Sustainable Food Supply Chain

It is necessary to identify what factors affect distributed trust and validate their effect on distributed trust and user satisfaction in an area of the food supply chain for sustainable business. The purpose of the present study is to examine determinants of distributed trust in the blockchain-based food supply chain and test seven hypotheses derived from the structural equation model integrating distributed trust, its three determinants, and user satisfaction. Transparency, traceability, and security are suggested as three determinants of distributed trust along the blockchain-based food supply chain. Data were collected from users of Chinese firms employing blockchain-based food supply chains to validate the research model and test the seven hypotheses. The present study contributes to clarifying the significance of distributed trust and suggesting evidence of its role in the food supply chain. The present study discussed trust-free systems based on blockchain technology related to sustainability through the findings.
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

Self-supervised monocular depth estimation has achieved impressive performance on outdoor datasets. Its performance however degrades notably in indoor environments because of the lack of textures. Without rich textures, the photometric consistency is too weak to train a good depth network. Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network. Specifically, we adopt two extra supervisory signals for self-supervised training: 1) the Manhattan normal constraint and 2) the co-planar constraint. The Manhattan normal constraint enforces the major surfaces (the floor, ceiling, and walls) to be aligned with dominant directions. The co-planar constraint states that the 3D points be well fitted by a plane if they are located within the same planar region. To generate the supervisory signals, we adopt two components to classify the major surface normal into dominant directions and detect the planar regions on the fly during training. As the predicted depth becomes more accurate after more training epochs, the supervisory signals also improve and in turn feedback to obtain a better depth model. Through extensive experiments on indoor benchmark datasets, the results show that our network outperforms the state-of-the-art methods. The source code is available at https://github.com/SJTU-ViSYS/StructDepth.
Semi-supervised Meta-learning with Disentanglement for Domain-Generalised Medical Image Segmentation

Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between source and unseen domains. Recently, gradient-based meta-learning approaches where the training data are split into meta-train and meta-test sets to simulate and handle the domain shifts during training have shown improved generalisation performance. However, the current fully supervised meta-learning approaches are not scalable for medical image segmentation, where large effort is required to create pixel-wise annotations. Meanwhile, in a low data regime, the simulated domain shifts may not approximate the true domain shifts well across source and unseen domains. To address this problem, we propose a novel semi-supervised meta-learning framework with disentanglement. We explicitly model the representations related to domain shifts. Disentangling the representations and combining them to reconstruct the input image allows unlabeled data to be used to better approximate the true domain shifts for meta-learning. Hence, the model can achieve better generalisation performance, especially when there is a limited amount of labeled data. Experiments show that the proposed method is robust on different segmentation tasks and achieves state-of-the-art generalisation performance on two public benchmarks. Code is publicly available at: https://github.com/vios-s/DGNet.
Interactive Prototype Learning for Egocentric Action Recognition

Egocentric video recognition is a challenging task that requires to identify both the actor’s motion and the active object that the actor interacts with. Recognizing the active object is particularly hard due to the cluttered background with distracting objects, the frequent field of view changes, severe occlusion, etc. To improve the active object classification, most existing methods use object detectors or human gaze information, which are computationally expensive or require labor-intensive annotations. To avoid these additional costs, we propose an end-to-end Interactive Prototype Learning (IPL) framework to learn better active object representations by leveraging the motion cues from the actor. First, we introduce a set of verb prototypes to disentangle active object features from distracting object features. Each prototype corresponds to a primary motion pattern of an egocentric action, offering a distinctive supervision signal for active object feature learning. Second, we design two interactive operations to enable the extraction of active object features, i.e., noun-to-verb assignment and verb-to-noun selection. These operations are parameter-efficient and can learn judicious location-aware features on top of 3D CNN backbones. We demonstrate that the IPL framework can generalize to different backbones and outperform the state-of-the-art on three large-scale egocentric video datasets, i.e., EPIC-KITCHENS-55, EPIC-KITCHENS-100 and EGTEA.
Vision Transformer Hashing for Image Retrieval

Recently, Transformer has emerged as a new architecture in deep learning by utilizing self-attention without convolution. Transformer is also extended to Vision Transformer (ViT) for the visual recognition with a promising performance on ImageNet. In this paper, we propose a Vision Transformer Hashing (VTS) for image retrieval. We utilize the pre-trained ViT on ImageNet as the backbone network and add the hashing head. The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks with their objective functions. We perform the extensive experiments on CIFAR10, ImageNet, NUS-Wide, and COCO datasets. The proposed VTS based image retrieval outperforms the recent state-of-the-art hashing techniques with a significant margin. We also find the proposed VTS model as the backbone network is better than the existing networks, such as AlexNet and ResNet. The code is released at https://github.com/shivram1987/VisionTransformerHashing.
How feelings of unpleasantness develop during the progression of motion sickness symptoms

To mitigate motion sickness in self-driving cars and virtual reality, one should be able to quantify its progression unambiguously. Self-report rating scales either focus on general feelings of unpleasantness or specific symptomatology. Although one generally feels worse as symptoms progress, there is anecdotal evidence suggesting a non-monotonic relationship between unpleasantness and symptomatology. This implies that individuals could (temporarily) feel better as symptoms progress, which could trouble an unambiguous measurement of motion sickness progression. Here we explicitly investigated the temporal development of both unpleasantness and symptomatology using subjective reports, as well as their mutual dependence using psychophysical scaling techniques. We found symptoms to manifest in a fixed order, while unpleasantness increased non-monotonically. Later manifesting symptoms were generally judged as more unpleasant, except for a reduction at the onset of nausea, which corresponded to feeling better. Although we cannot explicate the origin of this reduction, its existence is of importance to the quantification of motion sickness. Specifically, the reduction at nausea onset implies that rating how bad someone feels does not give you an answer to the question of how close someone is to the point of vomiting. We conclude that unpleasantness can unambiguously be inferred from symptomatology, but an ambiguity exists when inferring symptomatology from unpleasantness. These results speak in favor of rating symptomatology when prioritizing an unambiguous quantification of motion sickness progression.
A novel sensitivity-based method for feature selection

Abstract Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Note that the existing perturbation techniques may lead to inaccurate feature ranking due to their sensitivity to perturbation parameters. This study proposes a novel approach that involves the perturbation of input features using a complex-step. The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. While the results obtained for the classification task indicated that the proposed method outperformed other feature ranking methods, in the case of the regression task, it was found to perform more or less similar to that of other feature ranking methods.
LA-ICP-MS trace element analysis of pyrite from the Dafang gold deposit, South China: Implications for ore genesis

• The LA-ICP-MS analysis of pyrite indicates the gold occurring as Au 1+ , and the silver in Ag-bearing sulfosalt inclusion. • Pyrite trace element compositions can reflect physicochemical conditions of the hydrothermal fluids . • Combined geological and mineralogical evidences could confirm the genetic type of gold deposit. The Dafang is a hydrothermal gold deposit (6 t Au with an average grade of 1.8 g/t) in periphery of the Sn-Cu metallogenic system in Nanling Range in south China. This deposit locates in the contact zone between late Jurassic granodiorite porphyry and late Paleozoic limestone as well as the fractures of them. The mineralization comprises three stages, pyrite + arsenopyrite ± chalcopyrite + quartz (I), pyrite + arsenopyrite + gold + calaverite + sulfosalt ± sphalerite ± galena + quartz (II), and pyrite + arsenopyrite + quartz + calcite (III). Pyrite is the dominant gold-bearing mineral, which can be classified into three generations (Py1 to Py3), corresponding to three ore stages (Stage I to Stage III), respectively. Different stage pyrites show textural homogeneity in both reflected light and backscattered electron images. In addition, the LA-ICP-MS results show that all pyrites have relatively low Au concentrations (0.17 ppm for Py1, 0.48 ppm for Py2 and 0.58 ppm for Py3), and trace element mapping for Py1 demonstrate oscillatory zoning patterns for Au, As, Sb, Te, Co, and Ni. Compared to Py2 and Py3, Py1 has higher Co, Ni, Cu, Pb, Zn, Ag, and Te contents, but lower Au and Tl contents. Gold contents for all stage pyrite plot well below the solubility limit for gold in arsenian pyrite, indicating that invisible gold occurs as Au 1+ in the pyrite lattice. Silver occurs mainly as Ag-bearing sulfosalt inclusion or galena micro-inclusion in pyrite, while Pb and Zn as galena and sphalerite micro-inclusions in pyrite. Trace element mapping yields that Au-rich zone coincides well with As rich zones, demonstrating that the occurrence of As in pyrite promotes the enrichment of lattice gold. From Stage I to Stage III, the decrease of Co and Ni contents indicates the decrease of the ore-fluid temperature, and the change of Se and Te contents suggests the f O 2 increased from Stage I to Stage II and then decreased to Stage III. The Co and Ni contents suggest that all studied pyrite is dominantly of hydrothermal origin. Combined geological and mineralogical evidences indicate that the Dafang gold deposit is an epithermal Au deposit.
Unshuffling Data for Improved Generalization in Visual Question Answering

Generalization beyond the training distribution is a core challenge in machine learning. The common practice of mixing and shuffling examples when training neural networks may not be optimal in this regard. We show that partitioning the data into well-chosen, non-i.i.d. subsets treated as multiple training environments can guide the learning of models with better out-of-distribution generalization. We describe a training procedure to capture the patterns that are stable across environments while discarding spurious ones. The method makes a step beyond correlation-based learning: the choice of the partitioning allows injecting information about the task that cannot be otherwise recovered from the joint distribution of the training data.We demonstrate multiple use cases with the task of visual question answering, which is notorious for dataset biases. We obtain significant improvements on VQA-CP, using environments built from prior knowledge, existing meta data, or unsupervised clustering. We also get improvements on GQA using annotations of "equivalent questions", and on multi-dataset training (VQA v2 / Visual Genome) by treating them as distinct environments.
Multi-source unsupervised domain adaptation for object detection

Domain adaptation for object detection has been extensively studied in recent years. Most existing approaches focus on single-source unsupervised domain adaptive object detection. However, a more practical scenario is that the labeled source data is collected from multiple domains with different feature distributions. The conventional approaches do not work very well since multiple domain gaps exist. We propose a Multi-source domain Knowledge Transfer (MKT) method to handle this situation. First, the low-level features from multiple domains are aligned by learning a shallow feature extraction network. Then, the high-level features from each pair of source and target domains are aligned by the followed multi-branch network. After that, we perform two parts of information fusion: (1) We train a detection network shared by all branches based on the transferability of each source sample feature. The transferability of a source sample feature means the indistinguishable degree to the target domain sample features. (2) For using our model, the target sample features output by the multi-branch network are fused based on the average transferability of each domain. Moreover, we leverage both image-level and instance-level attention to promote positive cross-domain transfer and suppress negative transfer. Our main contributions are the two-stage feature alignments and information fusion. Extensive experimental results on various transfer scenarios show that our method achieves the state-of-the-art performance.