Reviewerly/exHarmony huggingface dataset:
Analysis of simultaneous inpainting and geometric separation based on sparse decomposition

Natural images are often the superposition of various parts of different geometric characteristics. For instance, an image might be a mixture of cartoon and texture structures. In addition, images are often given with missing data. In this paper, we develop a method for simultaneously decomposing an image to its two underlying parts and inpainting the missing data. Our separation–inpainting method is based on an [Formula: see text] minimization approach, using two dictionaries, each sparsifying one of the image parts but not the other. We introduce a comprehensive convergence analysis of our method, in a general setting, utilizing the concepts of joint concentration, clustered sparsity, and cluster coherence. As the main application of our theory, we consider the problem of separating and inpainting an image to a cartoon and texture parts.
Paragraph Level Multi-Perspective Context Modeling for Question Generation

Proper understanding of paragraph is essential for question generation task since the semantic interaction is complicated among sentences. How to integrate long text paragraph information into question generation is still a challenge. In this research, we proposed a multi-perspective paragraph context modeling mechanism, which firstly encodes the contextualized representation of input paragraph, and then utilize multi-head self-attention and Rezero network to further enhance paragraph-level feature extraction and context modeling. Finally, attention-based decoder with copy mechanism generates question according to encoded hidden states. Experimental study on widely used SQuAD dataset has shown the proposed method’s potential.
Variational Parameter Learning in Sequential State-Space Model Via Particle Filtering

Parameter learning of the state-space model (SSM) plays a significant role in the modelling of time-series data and dynamical systems. However, the closed-form inference of the parameter posterior is often limited by sequential construction and non-linearity of the SSMs, which has led to the development of sampling-based algorithms such as particle Markov chain Monte Carlo (PMCMC). We present a novel algorithm, the particle filter variational inference (PF-VI) algorithm, which achieves closed-form learning of SSM parameters while tractably inferring the non-linear sequential states. We apply the algorithm to a popular non-linear SSM example and compare its performance against two competing PMCMC algorithms.
Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem

These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, while students are taking classes on a web-based basis, the problem arises that concentration on the lectures is significantly reduced. Therefore, in order to solve these problems, in this paper, we propose a level-wise learning algorithm based on the difficulty level of the test problem, and we present the simulation results. In order to improve this problem, in this paper, we propose an automatic music recommendation algorithm based on fuzzy reasoning that can improve the level of learning and lecture concentration, and we show our results on developing a web-based, smart e-learning software. As a result of computer simulation, it was proved that the learning test method, considering by level the difficulty of the test and the incorrect answer rate, was more effective than the existing test method, judged the student’s grades fairly, and improved the risk of unfairly failing the test by 30%.
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 × 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. We believe that AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.
Semantic Object Segmentation in Cultural Sites using Real and Synthetic Data

We consider the problem of object segmentation in cultural sites. Since collecting and labeling large datasets of real images is challenging, we investigate whether the use of synthetic images can be useful to achieve good segmentation performance on real data. To perform the study, we collected a new dataset comprising both real and synthetic images of 24 artworks in a cultural site. The synthetic images have been automatically generated from the 3D model of the considered cultural site using a tool developed for that purpose. Real and synthetic images have been labeled for the task of semantic segmentation of artworks. We compare three different approaches to perform object segmentation exploiting real and synthetic data. The experimental results point out that the use of synthetic data helps to improve the performances of segmentation algorithms when tested on real images. Satisfactory performance is achieved exploiting semantic segmentation together with image-to-image translation and including a small amount of real data during training. To encourage research on the topic, we publicly release the proposed dataset at the following url:https://iplab.dmi.unict.it/EGO-CH-OBJ-SEG/.
Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics

This research is a review and analysis paper that offers a transdisciplinary, methodological, and strategic vision for soft computing development towards a wider favorable impact in data analytics. Strategies are defined, explained, and illustrated by examples. The paper also shows how these strategies are expressed in three dimensions of an ambitious actions plan. They are all integrated into a master strategy called wide knowledge discovery, which offers a way towards the augmented analytics paradigm. Some contributions of this work are defining what kind of mathematical elements should be introduced into soft computing towards a better impact on the area of data analytics, offering orientation towards building new mathematical elements, and defining why and how they can be introduced.
Learn to Speak Like A Native: AI-powered Chatbot Simulating Natural Conversation for Language Tutoring

Abstract In a globalizing world, the demand for acquiring a new language is expanding at a fast pace. However, language learners who have little opportunity to use the targeted language in their daily life are unable to increase their familiarity with the language given that contextual conversation is proven helpful in language acquisition. The current study dedicates to address this problem by proposing a language learning chatbot with which one can practice in a simulated setting. The chatbot is based on the deep neural network and is trained by MultiWOZ and personaChat collected in previous works. One chatbot structure is proposed to optimize the performance of the DNN chatbot. In comparison with the conventional chatbot, the new structure features a higher accuracy rate of generating appropriate utterance and suffers less from overfitting. Both machine and human evaluation show that the chatbot is able to produce transactional conversations that cover multiple domains of daily life conversations and incorporates a large vocabulary base as well as diverse sentence structures, which are beneficial for students.
Towards Explainable and Trustworthy Autonomous Physical Systems

The safe deployment of autonomous physical systems in real-world scenarios requires them to be explainable and trustworthy, especially in critical domains. In contrast with 'black-box' systems, explainable and trustworthy autonomous physical systems will lend themselves to easy assessments by system designers and regulators. This promises to pave ways for easy improvements that can lead to enhanced performance, and as well, increased public trust. In this one-day virtual workshop, we aim to gather a globally distributed group of researchers and practitioners to discuss the opportunities and social challenges in the design, implementation, and deployment of explainable and trustworthy autonomous physical systems, especially in a post-pandemic era. Interactions will be fostered through panel discussions and a series of spotlight talks. To ensure lasting impact of the workshop, we will conduct a pre-workshop survey which will examine the public perception of the trustworthiness of autonomous physical systems. Further, we will publish a summary report providing details about the survey as well as the identified challenges resulting from the workshop's panel discussions.
Causeworks Collaboration: Simultaneous Causal Model Construction and Analysis

Military planners use “Operational Design” (OD) methods to develop an understanding of systems and relationships in complex operational environments. Here, we present Causeworks, a visual analytics application for OD teams to collaboratively build causal models of environments and use analytics to understand and find solutions to affect them. Collaborative causal modelling can help teams craft better plans, but there are unique challenges in developing synchronous collaboration tools for building and using causal models. Collaboration systems typically organize information around varying degrees of synchronization between data “values” and user “views.” Our contribution is in extending this collaboration framework to include analytics as layers that are by nature derived from the data values but utilized and displayed temporarily as private views. We describe how Causeworks overlays analytics inputs and outputs over a shared causal model to flexibly support multiple modeling tasks simultaneously in a collaborative environment with minimal state management burden on users.
Improving robustness for pose estimation via stable heatmap regression

Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images. In view of this problem, a stable heatmap regression method is proposed to alleviate network vulnerability to small perturbations. We utilize the correlation between different rows and columns in a heatmap to alleviate the multi-peaks problem, and design a highly differentiated heatmap regression to make a keypoint discriminative from surrounding points. A maximum stability training loss is used to simplify the optimization difficulty when minimizing the prediction gap of two similar images. The proposed method achieves a significant advance in robustness over state-of-the-art approaches on four benchmark datasets and maintains high performance.
Blockchain

IoT is not the only buzzword proliferating in the technological world nowadays; blockchain, the underlying tech behind Bitcoin cryptocurrency, is also becoming more ubiquitous and mainstream in the world market. Blockchain and IoT are the perfect pair and seem to flourish together as there is an immense need of data security for the enormous data produced by IoT sensors. The integration of two technologies going hand-in-hand can make IoT objects more reliable and secure in the network. Many companies will exploit the integration of blockchain-based IoT systems in the near future as blockchain is more commendable. In the blockchain-based IoT systems, nodes deployed in the blockchain technology are more or less likely the devices connected in the IoT systems network. Blockchain-enabled IoT systems ease business processes and enhance transparency for improved customer experiences.
Ensemble noisy label detection on MNIST

In this paper machine learning methods are studied for classification data containing some misleading items.We use ensembles of known noise correction methods for preprocessing the training set.Preprocessing can be either relabeling or deleting items detected to have noisy labels.After preprocessing, usual convolutional networks are applied to the data.With preprocessing, the performance of very accurate convolutional networks can be further improved.
Program Comment Generation Through Distributed Representation Construction Using Parse Tree Information

Comments in the source code of a program are important for understanding that program. Understanding the logical flow and the overall procedure of programs is especially important as the next step for beginners learning the grammar of programming languages, and it is inferred that appropriate comments on the source code can support it. In this study, automatic generation of comments by neural networks that learned distributed representation from the parse tree information of the source code is proposed.
The Future of Synthetic Solar Irradiance

This chapter starts by reflecting on the learning outcomes from the book. The rest of the chapter is dedicated to the possible next steps for the field of synthetic solar irradiance. Previous applications of synthetic solar irradiance are discussed highlighting areas for improvement. Future potential applications are suggested, as well as defining what would be the best implementation of any new synthetic solar irradiance model. Next, the gaps in the existing synthetic solar irradiance methodologies are explored, focusing on whether there is a need for new techniques and if there are promising new approaches, as well as commenting on any scientific questions that remain unanswered. Last, we describe what the future of synthetic solar irradiance validation might look like. We investigate why we validate, and under what terms the structure of a synthetic solar irradiance validation should take. The ideal validation measurement setup is discussed as well as commenting on potential disadvantages that can skew a validation.
JUMPS: Joints Upsampling Method for Pose Sequences

Human Pose Estimation is a low-level task useful for surveillance, human action recognition, and scene understanding at large. It also offers promising perspectives for the animation of synthetic characters. For all these applications, and especially the latter, estimating the positions of many joints is desirable for improved performance and realism. To this purpose, we propose a novel method called JUMPS for increasing the number of joints in 2D pose estimates and recovering occluded or missing joints. We believe this is the first attempt to address the issue. We build on a deep generative model that combines a Generative Adversarial Network (GAN) and an encoder. The GAN learns the distribution of high-resolution human pose sequences, the encoder maps the input low-resolution sequences to its latent space. Inpainting is obtained by computing the latent representation whose decoding by the GAN generator optimally matches the joints locations at the input. Post-processing a 2D pose sequence using our method provides a richer representation of the character motion. We show experimentally that the localization accuracy of the additional joints is on average on par with the original pose estimates.
Object Detection Using Dual Graph Network

Most object detection methods focus only on the local information near the region proposal and ignore the object's global semantic relation and local spatial relation information, resulting in limited performance. To capture and explore these important relations, we propose a detection method based on a graph convolutional network (GCN). Two independent relation graph networks are used to obtain the global semantic information of the object in labels and the local spatial information in images. Semantic relation networks can implicitly acquire global knowledge, and by constructing a directed graph on the dataset, each node is represented by the word embedding of labels and then sent to the GCN to obtain high-level semantic representation. The spatial relation network encodes the relation by the positional relation module and the visual connection module, and enriches the object features through local key information from objects. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the dual-graph network explores more advanced feature information, giving the detector the ability to obtain key relations in labels and region proposals. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that key relation information significantly improve the performance of detection with better ability to detect small objects and reasonable boduning box. The results on COCO dataset demonstrate our method obtains around 32.3% improvement on AP in terms of small objects.
SQLIFIX: Learning Based Approach to Fix SQL Injection Vulnerabilities in Source Code

SQL Injection attack is one of the oldest yet effective attacks for web applications. Even in 2020, applications are vulnerable to SQL Injection attacks. The developers are sup-posed to take precautions such as parameterizing SQL queries, escaping special characters, etc. However, developers, especially inexperienced ones, often fail to comply with such guidelines. There are quite a few SQL Injection detection tools to expose any unattended SQL Injection vulnerability in source code. However, to the best of our knowledge, very few works have been done to suggest a fix of these vulnerabilities in the source code. We have developed a learning-based approach that prepares abstraction of SQL Injection vulnerable codes from training dataset and clusters them using hierarchical clustering. The test samples are matched with a cluster of similar samples and a fix suggestion is generated. We have developed a manually validated training and test dataset from real-world projects of Java and PHP to evaluate our language-agnostic approach. The results establish the superiority of our technique over comparable techniques. The code and dataset are released publicly to encourage reproduction.
A Detection Method of Unsafe Behavior in Substation Based on Deep Learning

During the operation of the substation, the irregular and unsafe operation of the staff will bring safety hazards to the stable operation of the power grid, threaten the safety of the staff, and may cause catastrophic consequences. Therefore, it is very important to detect and identify the behavior of substation workers. Based on this, this paper mainly studies the detection and recognition methods of two unsafe behaviors of substation workers not wearing safety helmets and smoking in the workplace. The deep learning method is used in this paper to improve the deep learning capabilities of the learning deeply supervised object detectors (DSOD) from scratch detector, so that it can detect the two unsafe behaviors mentioned above while considering the same detection object. Traditional recognition methods are easy to misjudge pictures and videos, and the detection method based on deep learning proposed in this paper has the advantage of accurate and reliable recognition effect compared with traditional recognition methods.
A Novel Mini Jet Engine Powered Unmanned Aerial Vehicle: Modeling and Control

Significant progress has been made in recent years on personal air vehicles (PAVs), which offer independent and autonomous urban transportation. On-demand parcel delivery drones and heavy-lift drones are gaining serious attention. Although various designs for these systems have been put forward, they still have not reached sufficient maturity. The current systems provide somehow satisfactory operation, but many of these systems are limited in payload capacity and flight duration, and not suitable for future operations. In this paper, we propose a novel thrust system that uses multiple mini jet engines. Unlike electric motors, the jet engine thrust cannot vary rapidly. This led us to design and develop a thrust vectoring system for each jet engine. This proposed system has the potential to enable drones to carry more payload and achieve longer flight times. This paper discusses the design and modeling of the system as well as the stabilization algorithms that satisfactorily stabilize the proposed system. We presented that due to motor lag, thrust variations cannot stabilize the vehicle. We showed that the use of a thrust vectoring mechanism with LQR-based controller can overcome the effects of motor lag and stabilize the vehicle, successfully.
Text Baseline Recognition Using a Recurrent Convolutional Neural Network

The detection of baselines of text is a necessary preprocessing step for many modern methods of automatic handwriting recognition. In this work, we present a two-stage system for the automatic detection of text baselines of handwritten text. In a first step, we perform pixel-wise segmentation on the document image to classify pixels as baselines, start points, end points and background. This segmentation is then used to extract the start points of lines. Starting from these points we extract the baseline using a recurrent convolutional neural network that directly outputs the baseline coordinates. This method allows the direct extraction of baseline coordinates as the output of a neural network without the use of any post-processing steps. We evaluate the model on the cBAD dataset from the ICDAR 2019 competition on baseline detection.
The Application of Artificial Intelligence in Emotion Recognition

Through the collection and analysis of relevant literature, the relevant knowledge of emotional recognition, the available fields and their shortcomings are briefly described, and the future direction of the development of emotion recognition technology is predicted, which can provide reference for the research in this field.