Reviewerly/exHarmony huggingface dataset:
A Set of Recommendations for Assessing Human–Machine Parity in Language Translation

The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a number of empirical investigations. We reassess Hassan et al.'s 2018 investigation into Chinese to English news translation, showing that the finding of human–machine parity was owed to weaknesses in the evaluation design—which is currently considered best practice in the field. We show that the professional human translations contained significantly fewer errors, and that perceived quality in human evaluation depends on the choice of raters, the availability of linguistic context, and the creation of reference translations. Our results call for revisiting current best practices to assess strong machine translation systems in general and human–machine parity in particular, for which we offer a set of recommendations based on our empirical findings.&#x0D;
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action-Gesture Recognition

Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and environmental robustness. However, previous methods heavily rely on designing hand-crafted traversal rules or graph topologies to draw dependencies between the joints, which are limited in performance and generalizability. In this work, we present a novel decoupled spatial-temporal attention network (DSTA-Net) for skeleton-based action recognition. It involves solely the attention blocks, allowing for modeling spatial-temporal dependencies between joints without the requirement of knowing their positions or mutual connections. Specifically, to meet the specific requirements of the skeletal data, three techniques are proposed for building attention blocks, namely, spatial-temporal attention decoupling, decoupled position encoding and spatial global regularization. Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions. To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.
An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation

As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., “other”) to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A <inline-formula><tex-math notation="LaTeX">${^2}$</tex-math></inline-formula> -GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph convolution network to characterize the complicated interactions among <inline-formula><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula> users, items, attributes <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> . Furthermore, to learn the node representation, we adopt the message-passing strategy to aggregate the messages passed from the other directly linked types of nodes (e.g., a user or an attribute). Towards this end, we are capable of incorporating associate attributes to strengthen the user and item representation learning, and thus naturally solve the attribute missing problem. Given that for different users, the attributes of an item have different influence on their preference to this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information. Extensive experiments have been conducted on several publicly accessible datasets to justify our model, demonstrating that our model outperforms several state-of-the-art methods and demonstrate the effectiveness of our attention method.
MuSig-DN: Schnorr Multi-Signatures with Verifiably Deterministic Nonces

MuSig is a multi-signature scheme for Schnorr signatures, which supports key aggregation and is secure in the plain public key model. Standard derandomization techniques for discrete logarithm-based signatures such as RFC~6979, which make the signing procedure immune to catastrophic failures in the randomness generation, are not applicable to multi-signatures as an attacker could trick an honest user into producing two different partial signatures with the same randomness, which would reveal the user's secret key.
Objective and subjective responses to motion sickness: the group and the individual

We investigated and modeled the temporal evolution of motion sickness in a highly dynamic sickening drive. Slalom maneuvers were performed in a passenger vehicle, resulting in lateral accelerations of 0.4 g at 0.2 Hz, to which participants were subjected as passengers for up to 30 min. Subjective motion sickness was recorded throughout the sickening drive using the MISC scale. In addition, physiological and postural responses were evaluated by recording head roll, galvanic skin response (GSR) and electrocardiography (ECG). Experiment 1 compared external vision (normal view through front and side car windows) to internal vision (obscured view through front and side windows). Experiment 2 tested hypersensitivity with a second exposure a few minutes after the first drive and tested repeatability of individuals' sickness responses by measuring these two exposures three times in three successive sessions. An adapted form of Oman's model of nausea was used to quantify sickness development, repeatability, and motion sickness hypersensitivity at an individual level. Internal vision was more sickening compared to external vision with a higher mean MISC (4.2 vs. 2.3), a higher MISC rate (0.59 vs. 0.10 min-1) and more dropouts (66% vs. 33%) for whom the experiment was terminated due to reaching a MISC level of 7 (moderate nausea). The adapted Oman model successfully captured the development of sickness, with a mean model error, including the decay during rest and hypersensitivity upon further exposure, of 11.3%. Importantly, we note that knowledge of an individuals' previous motion sickness response to sickening stimuli increases individual modeling accuracy by a factor of 2 when compared to group-based modeling, indicating individual repeatability. Head roll did not vary significantly with motion sickness. ECG varied slightly with motion sickness and time. GSR clearly varied with motion sickness, where the tonic and phasic GSR increased 42.5% and 90%, respectively, above baseline at high MISC levels, but GSR also increased in time independent of motion sickness, accompanied with substantial scatter.
Input-driven bifurcations and information processing capacity in spintronics reservoirs

The authors find two types of input-driven bifurcation in the spin-torque oscillator, and they reveal the information processing capacity as a reservoir computing changes according to these bifurcations.
Promoting female economic inclusion for tax performance in Sub-Saharan Africa

This study explores whether female economic inclusion enhances tax performance in a sample of 48 countries in Sub-Saharan Africa from 2000 to 2018. The study’s empirical evidence is based on the generalized method of moments in order to account for endogeneity concerns. Three tax performance measurements are used, notably, total taxes revenue excluding social contributions, reported tax revenue derived from natural resources sources, and total non-resource tax revenue. Three female inclusion indicators are used, namely, female employment in industry, female labour force participation, and female employment. The following empirical evidences are documented; (i) There is a negative net effect from the enhancement of female employment in the industry on the total tax revenue. (ii) There is a positive net effect of female employment in the industry on the non-resource taxes. An extended threshold analysis is performed to establish the critical masses that could further influence tax performance positively. The following thresholds are established. (i) a minimum of 15.35 “employment in industry, female (% of female employment)” for the total tax revenue and (ii) a maximum of 23.75 “employment in industry, female (% of female employment)” for the non-resource tax revenue. These critical masses are crucial for sustainable development because, below or beyond these thresholds, policymakers should complement the female economic inclusion with other economic measures designed to improve tax performance in Sub-Saharan Africa.
Representative Graph Neural Network

Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy. Instead of propagating the messages from all positions, our RepGraph layer computes the response of one node merely with a few representative nodes. The locations of representative nodes come from a learned spatial offset matrix. The RepGraph layer is flexible to integrate into many visual architectures and combine with other operations. With the application of semantic segmentation, without any bells and whistles, our RepGraph network can compete or perform favourably against the state-of-the-art methods on three challenging benchmarks: ADE20K, Cityscapes, and PASCAL-Context datasets. In the task of object detection, our RepGraph layer can also improve the performance on the COCO dataset compared to the non-local operation. Code is available at https://git.io/RepGraph .
CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains are transferable, which cripples domain-wise transfer with untransferable knowledge; 2) they fail to narrow category-wise distribution shift due to category-agnostic feature alignment. To address above challenges, we develop a new Critical Semantic-Consistent Learning (CSCL) model, which mitigates the discrepancy of both domain-wise and category-wise distributions. Specifically, a critical transfer based adversarial framework is designed to highlight transferable domain-wise knowledge while neglecting untransferable knowledge. Transferability-critic guides transferability-quantizer to maximize positive transfer gain under reinforcement learning manner, although negative transfer of untransferable knowledge occurs. Meanwhile, with the help of confidence-guided pseudo labels generator of target samples, a symmetric soft divergence loss is presented to explore inter-class relationships and facilitate category-wise distribution alignment. Experiments on several datasets demonstrate the superiority of our model.
Rethinking Image Deraining via Rain Streaks and Vapors

Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light. While advanced models are proposed for image restoration (i.e., background image generation), they regard rain streaks with the same properties as background rather than transmission medium. As vapors (i.e., rain streaks accumulation or fog-like rain) are conveyed in the transmission map to model the veiling effect, the fusion of rain streaks and vapors do not naturally reflect the rain image formation. In this work, we reformulate rain streaks as transmission medium together with vapors to model rain imaging. We propose an encoder-decoder CNN named as SNet to learn the transmission map of rain streaks. As rain streaks appear with various shapes and directions, we use ShuffleNet units within SNet to capture their anisotropic representations. As vapors are brought by rain streaks, we propose a VNet containing spatial pyramid pooling (SSP) to predict the transmission map of vapors in multi-scales based on that of rain streaks. Meanwhile, we use an encoder CNN named ANet to estimate atmosphere light. The SNet, VNet, and ANet are jointly trained to predict transmission maps and atmosphere light for rain image restoration. Extensive experiments on the benchmark datasets demonstrate the effectiveness of the proposed visual model to predict rain streaks and vapors. The proposed deraining method performs favorably against state-of-the-art deraining approaches.
Novelty-based Intrusion Detection of Sensor Attacks on Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have proven to be a useful technology in numerous industries including industrial control systems surveillance, law enforcement, and military operations. Due to their heavy reliance on wireless protocols and hostile operating environments, UAVs face a large threat landscape. As attacks against UAVs increase, an intelligent intrusion detection system (IDS) is needed to aid the UAV in identifying attacks. The UAV domain presents unique challenges for intelligent IDS development, such as the variety of sensors, communication protocols, UAV platforms, control configurations, and dataset availability. In this paper, we propose a novelty-based approach to intrusion detection in UAVs by using one-class classifiers. One-class classifiers require only non-anomalous data to exist in the training set. This allows for the use of flight logs as training data, which are created by most UAVs during flight by default. Principal Component Analysis is applied to sensor logs for dimensionality reduction, and one-class classifier models are generated per sensor. A number of one-class classifiers are selected: One-Class Support Vector Machine, Autoencoder Neural Network, and Local Outlier Factor. The pre-processing, feature selection, training, and tuning of the selected algorithms is discussed. GPS spoofing is used throughout the paper as a common example of an external sensor-based attack. This approach shows to be effective across multiple UAV platforms with platform-specific F1 scores up to 99.56% and 99.73% for benign and malicious sensor readings respectively.
Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset

Credit card fraud is a serious and growing problem. It is becoming more challenging with an issues of highly imbalanced class. In the field of data mining, prediction or typically known as data classification problem involved detecting events. Uncommon events are hard to identify on account of their inconsistency and casualness, however, misclassifying rare events can result in heavy costs. Thus, to overcome this issues, it is suggested by few researches to overcome at the stage of pre-processing itself. One of the pre-processing methods available is sampling methods. In sampling methods, oversampling and undersampling is the most widely used techniques in imbalanced data. This paper try to investigate the performance of classification model when combining the method of oversamplings and undersampling in detecting the fraud cases from the fraud detection dataset. Few oversampling techniques is selected to combine with random undersampling techniques to undergo the process and the performance of the model is evaluate using random forest classifier and performance measure selected that is suitable for imbalanced class problem. The results obtained then compared with previous literature. From the results, the combination of oversampling and undersampling techniques gives a better precision, recall and F1-Measure value in average of 0.80%.
Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection

This paper focus on analyzing graph connection of human joints for skeleton based video anomaly detection, which is more effective and efficient than those image-level reconstruction based or prediction based methods that may be affected by complex background. Specifically, we propose a spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection. In other words, we build a normal graph describing graph connection of joints in normal data, where joints of abnormal events will be outliers of this graph. To our knowledge, this is the first work to apply graph convolutional networks on skeleton-based video anomaly detection. Experiments show that our proposed normal graph achieves the-state-of-art performance, compared to those image-level reconstruction-based or prediction-based methods, as well as RNN based methods upon joints.
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) is a challenging topic that has promising prospects in many realistic scenarios. Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem. However, training the gate is usually challenging due to the lack of data in the unseen domain. To resolve this problem, in this paper, we propose a boundary based Out-of-Distribution (OOD) classifier which classifies the unseen and seen domains by only using seen samples for training. First, we learn a shared latent space on a unit hyper-sphere where the latent distributions of visual features and semantic attributes are aligned class-wisely. Then we find the boundary and the center of the manifold for each class. By leveraging the class centers and boundaries, the unseen samples can be separated from the seen samples. After that, we use two experts to classify the seen and unseen samples separately. We extensively validate our approach on five popular benchmark datasets including AWA1, AWA2, CUB, FLO and SUN. The experimental results show that our approach surpasses state-of-the-art approaches by a significant margin.
Towards Generalization Across Depth for Monocular 3D Object Detection

While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind. This work advances the state of the art by introducing MoVi-3D, a novel, single-stage deep architecture for monocular 3D object detection. MoVi-3D builds upon a novel approach which leverages geometrical information to generate, both at training and test time, virtual views where the object appearance is normalized with respect to distance. These virtually generated views facilitate the detection task as they significantly reduce the visual appearance variability associated to objects placed at different distances from the camera. As a consequence, the deep model is relieved from learning depth-specific representations and its complexity can be significantly reduced. In particular, in this work we show that, thanks to our virtual views generation process, a lightweight, single-stage architecture suffices to set new state-of-the-art results on the popular KITTI3D benchmark.
WITHDRAWN: An approach to minimize the energy consumption during blockchain transaction

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Night-to-Day: Online Image-to-Image Translation for Object Detection Within Autonomous Driving by Night

Object detectors are central to autonomous driving and are widely used in driver assistance systems. Object detectors are trained on a finite amount of data within a specific domain, hampering detection performance when applying object detectors to samples from other domains during inference, an effect known as domain gap. Domain gap is a concern for data-driven applications, evoking repetitive retraining of networks when the applications unfold into other domains. With object detectors that have been trained on day images only, a domain gap can be observed in object detection by night. Training object detectors on night images is critical because of the enormous effort required to generate an adequate amount of diversely labeled data, and existing data sets often tend to overfit specific domain characteristics. For the first time, this work proposes adapting domains by online image-to-image translation to expand an object detector's domain of operation. The domain gap is decreased without additional labeling effort and without having to retrain the object detector while unfolding into the target domain. The approach follows the concept of domain adaptation, shifting the target domain samples into the domain knownto the object detector (source domain). Firstly, the UNIT network is trained for domain adaptation and subsequently cast into an online domain adaptation module, which narrows down the domain gap. Domain adaptation capabilities are evaluated qualitatively by displaying translated samples and visualizing the domain shift through the 2D tSNE algorithm. We quantitatively benchmark the domain adaptation's influence on a state-of-the-art object detector, and on a retrained object detector, for mean average precision, mean recall, and the resulting F1-score. Our approach achieves an F1 score improvement of 5.27 % within object detection by night when applying online domain adaptation. The evaluation is executed on the BDD100K benchmark data set.