Reviewerly/exHarmony huggingface dataset:
Towards Natural Language Interfaces for Data Visualization: A Survey

Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than having to worry about how to operate visualization tools on the interface. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed in academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query interpretation, data transformation, visual mapping, view transformation, human interaction, dialogue management, and presentation. Finally, we also shed light on several promising directions for future work in the V-NLI community.
Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.
Offline signature verification using deep neural network with application to computer vision

Biometric technologies, such as handwritten signature verification, are extremely useful for identifying individuals inside an organization or finance department. The improvement of picture categorization using deep learning (DL) neural networks has offered an opportunity to exhibit computer vision in contemporary research applications by applying image processing approaches. Manual signature verification is inefficient, error-prone, time-consuming, and inconvenient; therefore, it is critical to create an automatic signature verification recognition system. This research offers an automatic recognition method based on DL that makes use of the Grupo de Procesado Digital de Seales. The biggest publicly accessible handwritten signature dataset, the synthetic signature dataset, was used to classify the signatures of 100 people, each of whom possessed 24 genuine signatures and 30 forged signatures. An inception V3 transfer learning (TL) model is proposed by hyper-tuning different layers from the middle of its architecture and this model is fine-tuned by adding layers, such as flatten, dense (1024), dropout (0.5), and dense (1). The suggested model was tested against six well-known pre-trained TL convolutional neural network models: VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, and EfficientNet. The suggested model surpasses the pre-trained models. Precision, sensitivity, and F1-score are likewise outperformed by the model, with the values of 88%, 88%, and 87%, respectively. The accuracy of the pre-trained models was evaluated as 80%, 81%, 77%, 73%, 71%, and 74%, respectively. The suggested fine-tuned inception V3 gives the highest accurate classifications, distinguishing between genuine and forged signatures with an accuracy of 88%. This study will aid researchers in developing more effective CNN-based models for offline signature verification with application to computer vision.
Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos

Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from a sequence of frames is a challenging task due to cluttered background, different viewpoints, low resolution, and partial occlusion. Current CNN-based techniques use large-scale computational classifiers along with convolutional operators having local receptive fields, limiting their performance to capture long-range temporal information. Therefore, in this work, we introduce a convolution-free approach for accurate HAR, which overcomes the above-mentioned problems and accurately encodes relative spatial information. In the proposed framework, the frame-level features are extracted via pretrained Vision Transformer; next, these features are passed to multilayer long short-term memory to capture the long-range dependencies of the actions in the surveillance videos. To validate the performance of the proposed framework, we carried out extensive experiments on UCF50 and HMDB51 benchmark HAR datasets and improved accuracy by 0.944% and 1.414%, respectively, when compared to state-of-the-art deep models.
An improved YOLOv5 method for large objects detection with multi-scale feature cross-layer fusion network

SSD and YOLOv5 are the one-stage object detector representative algorithms. An improved one-stage object detector based on the YOLOv5 method is proposed in this paper, named Multi-scale Feature Cross-layer Fusion Network (M-FCFN). Firstly, we extract shallow features and deep features from the PANet structure for cross-layer fusion and obtain a feature scale different from 80 × 80, 40 × 40, and 20 × 20 as output. Then, according to the single shot multi-box detector, we propose the different scale features which are obtained by cross-layer fusion for dimension reduction and use it as another output for prediction. Therefore, two completely different feature scales are added as the output. Features of different scales are necessary for detecting objects of different sizes, which can increase the probability of object detection and significantly improve detection accuracy. Finally, aiming at the Autoanchor mechanism proposed by YOLOv5, we propose an EIOU k-means calculation. We have compared the four model structures of S , M , L , and X of YOLOv5 respectively. The problem of missed and false detections for large objects is improved which has better detection results. The experimental results show that our methods achieve 89.1% and 67.8% mAP @0.5 on the PASCAL VOC and MS COCO datasets. Compared with the YOLOv5_S, our methods improve by 4.4% and 1.4% mAP @ [0.5:0.95] on the PASCAL VOC and MS COCO datasets. Compared with the four models of YOLOv5, our methods have better detection accuracy for large objects. It should be more attention that our method on the large-scale mAP @ [0.5:0.95] is 5.4% higher than YOLOv5_S on the MS COCO datasets. • We proposed Multi-scale Feature Cross-layer Fusion Network (M-FCFN). • Two completely different feature scales are added as the output. • We propose an EIOU k-means Autoanchor calculation. • The problem of missed and false detections for large objects is improved. • Our method on the large-scale mAP @[0.5:0.95] is 5.4% higher than YOLOv5_S.
Strategy for improving the football teaching quality by AI and metaverse-empowered in mobile internet environment

The metaverse has influenced the development of science and technology since it was proposed. While artificial intelligence (AI) is still one of the important technologies to solve problems in metaverse. The success of AI in various fields and the development of information technology make the integration of sports industry and AI an inevitable trend. Currently, the traditional physical education is experiencing qualitative changes, and the demand of integrating AI and metaverse into physical education is becoming more and more obvious. Football teaching is an important part of physical education, and Virtual Reality (VR) has the characteristics of immersion, interaction and imagination, which can build virtual and realistic football teaching process. In this study, through 360-degree panoramic VR football teaching videos empowered by the metaverse and K-means algorithm based on machine learning under AI, we study the strategies for improving the quality of football teaching in the mobile Internet environment. Therefore, we propose K-means based optimized 360-degree panoramic VR football teaching video delivery strategy. In addition, we conduct simulation experiments under content delivery networks simulator, and the simulation results show that the proposed strategy is superior to the baselines in terms of proxy server hit ratio, byte hit ratio, mean response time and students quality of experience. Moreover, through the 360-degree panoramic VR football teaching videos learning, students can intuitively analyze the actions and improve the teaching quality. The reconstruction of football teaching environment is beneficial to promote the combination of football teaching and smart learning.
Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition

Skeleton sequences are lightweight and compact and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3-D joint coordinates as spatial–temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance. The use of first-and second-order features, that is, joint and bone representations, has led to high accuracy. Nonetheless, many models are still confused by actions that have similar motion trajectories. To address these issues, we propose fusing higher-order features in the form of angular encoding (AGE) into modern architectures to robustly capture the relationships between joints and body parts. This simple fusion with popular spatial–temporal graph neural networks achieves new state-of-the-art accuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced run time. Our source code is publicly available at: https://github.com/ZhenyueQin/Angular-Skeleton-Encoding.
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks’ properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field.
Deep learning modelling techniques: current progress, applications, advantages, and challenges

Abstract Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world.
Opacity Enforcement Using Nondeterministic Publicly Known Edit Functions

This note investigates enforcement of opacity by nondeterministic edit functions. The edit functions alter the system's output by inserting fictitious events or erasing observed events. The edit decisions are randomly made while not known by the outside environment a priori. There is an intruder characterized as a passive observer with malicious goals to infer the secrets of the system. We require that opacity be enforced when the intruder may or may not know the implementation of edit functions. This requirement is termed as private and public safety. We also restrict the operation of edit functions by defining edit constraints. Then, the opacity enforcement problem is transformed to a three-player game among the edit function, the environment, and a dummy player, which helps to determine edit decisions. A game structure called the all edit structure (AES) is introduced to characterize the interaction among those players. It embeds all privately safe edit functions and may also embed publicly safe edit functions. Based on the AES, we present an algorithm that provably synthesizes nondeterministic edit functions that satisfy both private safety and public safety.
VideoFlow: A Flow-Based Generative Model for Video

Generative models that can model and predict sequences of future events can,in principle, learn to capture complex real-world phenomena, such as physicalinteractions. In particular, learning predictive models of videos offers anespecially appealing mechanism to enable a rich understanding of the physicalworld: videos of real-world interactions are plentiful and readily available,and a model that can predict future video frames can not only capture usefulrepresentations of the world, but can be useful in its own right, for problemssuch as model-based robotic control. However, a central challenge in videoprediction is that the future is highly uncertain: a sequence of pastobservations of events can imply many possible futures. Although a number ofrecent works have studied probabilistic models that can represent uncertainfutures, such models are either extremely expensive computationally (as in thecase of pixel-level autoregressive models), or do not directly optimize thelikelihood of the data. In this work, we propose a model for video predictionbased on normalizing flows, which allows for direct optimization of the datalikelihood, and produces high-quality stochastic predictions. To our knowledge,our work is the first to propose multi-frame video prediction with normalizingflows. We describe an approach for modeling the latent space dynamics, anddemonstrate that flow-based generative models offer a viable and competitiveapproach to generative modeling of video.
Multiscale Natural Scene Statistical Analysis for No-Reference Quality Evaluation of DIBR-Synthesized Views

This paper proposes to blindly evaluate the quality of images synthesized via a depth image-based rendering (DIBR) procedure. As a significant branch of virtual reality (VR), superior DIBR techniques provide free viewpoints in many real applications, including remote surveillance and education; however, limited efforts have been made to measure the performance of DIBR techniques, or equivalently the quality of DIBR-synthesized views, especially in the condition when references are unavailable. To achieve this aim, we develop a novel blind image quality assessment (IQA) method via multiscale natural scene statistical analysis (MNSS). The design principle of our proposed MNSS metric is based on two new natural scene statistics (NSS) models specific to the DBIR-synthesized IQA. First, the DIBR-introduced geometric distortions damage the local self-similarity characteristic of natural images, and the damage degrees of self-similarity present particular variations at different scales. Systematically combining the measurements of the variations mentioned above can gauge the naturalness of the input image and thus indirectly reflect the quality changes of images generated using different DIBR methods. Second, it was found that the degradations in main structures of natural images at different scales remain almost the same, whereas the statistical regularity is destroyed in the DIBR-synthesized views. Estimating the deviation of degradations in main structures at different scales between one DIBR-synthesized image and the statistical model, which is constructed based on a large number of natural images, can quantify how a DIBR method damages the main structures and thus infer the image quality. Via trials, the two NSS-based features extracted above can well predict the quality of DIBR-synthesized images. Further, the two features come from distinct points of view, and we hence integrate them via a straightforward multiplication to derive the proposed blind MNSS metric, which achieves better performance than each component and state-of-the-art quality methods.
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-tosequence (Seq2Seq) models for this problem.The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system.In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.
Discriminative and Correlative Partial Multi-Label Learning

In partial label learning (PML), each instance is associated with a candidate label set that contains multiple relevant labels and other false positive labels. The most challenging issue for the PML is that the training procedure is prone to be affected by the labeling noise. We observe that state-of-the-art PML methods are either powerless to disambiguate the correct labels from the candidate labels or incapable of extracting the label correlations sufficiently. To fill this gap, a two-stage DiscRiminative and correlAtive partial Multi-label leArning (DRAMA) algorithm is presented in this work. In the first stage, a confidence value is learned for each label by utilizing the feature manifold, which indicates how likely a label is correct. In the second stage, a gradient boosting model is induced to fit the label confidences. Specifically, to explore the label correlations, we augment the feature space by the previously elicited labels on each boosting round. Extensive experiments on various real-world datasets clearly validate the superiority of our proposed method.
Accurate and robust eye center localization via fully convolutional networks

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.