Reviewerly/exHarmony huggingface dataset:
A Cloud Architecture for Monitoring and Controlling Viral COVID-19

The novel coronavirus disease or COVID-19 is a highly infectious disease and it has infected approximately 150 million people across the world and 20 million people in India. The governments, the hospitals, and even the people themselves are working hard to control the pandemic since March 2020. Various technologies have been used to control the pandemic and Cloud Computing has come to light to be an appropriate solution as it provides better availability, less costs, and security. This research paper presents an efficient solution for monitoring and controlling the viral disease using the Cloud platform. The proposed Cloud architecture predicts and classifies the infected and non-infected people and thereafter works on monitoring the patients based on the severity level of their disease. The paper also focuses on controlling the disease using contact tracing of infected patients using Social Network Analysis (SNA). A dataset containing contract tracing information for the city of Bangalore was used to test on the proposed architecture. The system outperforms by providing an accuracy of 92% and also resource utilization of 81% was observed on the Amazon EC2 cloud platform. The paper's core idea is the usage of SNA graphs for contact tracing and hence, calculating Outbreak Role Index that indicates how much an infected person has spread the disease among his or her contacts. The architecture proposed can be very beneficial for the government and healthcare departments, to effectively analyze and prevent the COVID-19 outbreak.
Anomaly Detection in Low-Cost Sensors in Agricultural Applications Based on Time Series with Seasonal Variation

Recent technological advances have contributed to the increase in the amount of data generated and collected today. However, to successfully extract and make use of the information they condense, it is necessary to improve the quality of the data by detecting those items that disagree or deviate abnormally from the rest. For this reason, automatic anomaly detection is widely studied in data-based applications. In this work, a methodology for unsupervised anomaly detection is proposed to improve the reliability of measurements taken by a low-cost sensor network deployed in an agricultural environment. The developed methodology is based on the conversion of the anomaly detection problem in a time series into an outlier identification problem based on a point cloud. In this way, it is possible to solve the original problem using already-known techniques to solve the derived problem. Specifically, the k nearest neighbors method has been used with metrics adapted to this methodology and automatically inferring the hyperparameter k. In order to evaluate in a controlled scenario the proposed methodology, a low-cost wireless sensor network has been developed as a testbed. The results obtained are promising; area under the curve, recall, and fall-out are around 0.9, 0.8, and 0.15, respectively.
Foundations

Regardless of the current state of your developer documentation, whether you’re starting from scratch, rebuilding or improving your existing documentation, or fine-tuning a well-functioning documentation site, you can only expect good results if you build on solid foundations. This chapter emphasizes the crucial role user research plays in the discovery phase of building a developer documentation site. It introduces user research practices and processes to help you better understand your users, their goals, behaviors, and documentation needs.
Noisy and Unbalanced Multimodal Document Classification: Textbook Exercises as a Use Case

In order to foster inclusive education, automatic systems that can adapt textbooks to make them accessible to children with Developmental Coordination Disorder (DCD) are necessary. In this context, we propose a task to classify exercises according to their DCD adaptation type. We introduce a challenging exercise dataset extracted from French textbooks, with two major difficulties: limited and unbalanced, noisy data. To set a baseline on the dataset, we use state-of-the-art models combined through early and late fusion techniques to take advantage of text and vision/layout modalities. Our approach achieves an overall accuracy of 0.802. However, the experiments show the difficulty of the task, especially for minority classes, where the accuracy drops to 0.583.
Ultra-short-term Photovoltaic Power Prediction Based on STCN Model

It is crucial to ensure the stability and security of the power system by accurately predicting photovoltaic power. Furthermore, the spatial and temporal dependence of regional photovoltaic power stations enhances the accuracy of ultra-short-term power predictions. In this paper, we propose applying a hybrid depth learning model to the irregularly distributed PV sites in the region, namely, the Spatio-temporal causal convolution network (STCN). It utilizes the advantages of a classical convolutional neural network to extract spatial features and a causal convolutional neural network to extract temporal features to predict ultra-short-term solar power generation. Based on the photovoltaic power data provided by the National Renewable Energy Laboratory (NREL), we compare the performance of the proposed model to that of other models. Experimental results indicate that the STCN model can improve the accuracy of prediction.
Educational Digitalization Enables Learners to Achieve Personalized Learning Path: Driven by Four Dimensions

With the deep integration of digital technology and education, online learning has become an important learning approach, and the development of online learning makes personalized learning paths particularly important. Personalized learning has four major needs: precise learning resources, differentiated learning methods, hierarchical learning content, and diversified learning evaluation. The Digital transformation of education has subverted the traditional teaching environment and has a profound impact on four aspects: learning resources, learning methods, learning content and learning evaluation: the national smart education platform gathers high-quality educational resources, personalized learning platform supports the reform of learning methods, digital technology promotes the hierarchy of learning content, and big data application mining promotes the improvement of education evaluation. On the basis of the analysis of personalized learning needs, this research strengthens the understanding and analysis of the constituent elements of education digitalization and personalized learning support, and puts forward path suggestions on the four aspects of learning resources, learning methods, learning content, and learning evaluation for enabling personalized learning of learners in China's education Digital transformation.
An Advanced Computational Model for Data Classification Incorporating Blockchain and Clustering Techniques

This paper proposes an advanced computational model which innovatively combines blockchain and clustering techniques, with a case study in the field of data classification for teacher evaluation. The model was trained and tested with data from private higher education institutions, and the results indicate that after 30 training rounds, it outperforms traditional statistical models and standalone clustering models in terms of classification accuracy. Additionally, the model ensures a more balanced distribution of the classification levels, providing robust and reliable results. While there exist certain limitations in the training process and practical application, this study signifies the potential and efficiency of integrating blockchain and clustering methods in data classification tasks. It offers a novel perspective into the computational possibilities of merging these techniques, hinting at potential applications in various fields requiring data classification, including but not limited to educational technology.
Comparison of fuzzy search algorithms based on Damerau-Levenshtein automata on large data

The object of research is fuzzy search algorithms based on Damerau-Levenshtein automata and Levenshtein automata. The paper examines and compares solutions based on finite state machines for efficient and fast finding of words and lines with a given editing distance in large text data using the concept of fuzzy search. Fuzzy search algorithms allow finding significantly more relevant results than standard explicit search algorithms. However, such algorithms usually have a higher asymptotic complexity and, accordingly, work much longer. Fuzzy text search using Damerau-Levenshtein distance allows taking into account common errors that the user may have made in the search term, namely: character substitution, extra character, missing character, and reordering of characters. To use a finite automaton, it is necessary to first construct it for a specific input word and edit distance, and then perform a search on that automaton, discarding words that the automaton will not accept. Therefore, when choosing an algorithm, both phases should be taken into account. This is because building a machine can take a long time. To speed up one of the machines, SIMD instructions were used, which gave a speedup of 1-10% depending on the number of search words, the length of the search word and the editing distance. The obtained results can be useful for use in various industries where it is necessary to quickly and efficiently perform fuzzy search in large volumes of data, for example, in search engines or in autocorrection of errors.
Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images

Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications.&#x0D; Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results.&#x0D; Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore.&#x0D; Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested.&#x0D; Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.
Intrinsic Weaknesses of IDSs to Malicious Adversarial Attacks and Their Mitigation

Intrusion Detection Systems (IDS) are essential tools to protect network security from malicious traffic. IDS have recently made significant advancements in their detection capabilities through deep learning algorithms compared to conventional approaches. However, these algorithms are vulnerable to meta-attacks, also known as adversarial evasion attacks, which are attacks that improve already existing attacks, specifically their ability to evade detection. Deep learning-based IDS, in particular, are particularly susceptible to adversarial evasion attacks that use Generative Adversarial Networks (GAN). Nonetheless, well-known strategies have been proposed to cope with this threat. However, these countermeasures lack robustness and predictability, and their performance can be either remarkable or poor. Such robustness issues have been identified even without adversarial evasion attacks, and mitigation strategies have been provided. This paper identifies and formalizes threats to the robustness of IDSs against adversarial evasion attacks. These threats are enabled by flaws in the dataset’s structure and content rather than its representativeness. In addition, we propose a method for enhancing the performance of adversarial training by directing it to focus on the best evasion candidates samples within a dataset. We find that GAN adversarial attack evasion capabilities are significantly reduced when our method is used to strengthen the IDS.
Intractability of Optimal Multi-Agent Pathfinding on Directed Graphs

In Multi-Agent Pathfinding (MAPF) problems, multiple agents move simultaneously to reach their individual destinations without colliding with each other. The computational complexity of the problem has been extensively studied for undirected graphs over the past decades. However, plan existence for Directed MAPF (diMAPF) was only recently studied and was shown to be in PSPACE as well as NP-hard. In this paper, we study the optimization versions (on makespan and on travel distance of agents) of diMAPF problems and show that they remain NP-hard even when various important non-trivial restrictions are imposed (e.g., when considering the problem on directed, acyclic, and planar graphs where the vertex-degrees are bounded). We have also provide membership results, thus presenting the first set of NP-completeness results for various optimal diMAPF variants.
Turn on the Right Track: Weakly Supervised Video Moment Retrieval with Self-Improving Query Reconstruction

Existing weakly-supervised temporal sentence grounding methods typically regard query reconstruction as the pretext task in place of the absent temporal supervision. However, their approaches suffer from two flaws, i.e. insignificant reconstruction and discrepancy in alignment. Insignificant reconstruction indicates the randomly masked words may not be discriminative enough to distinguish the target event from unrelated events in the video. Discrepancy in alignment indicates the incorrect partial alignment built by query reconstruction task. The flaws undermine the reliability of current reconstruction-based methods. To this end, we propose a novel Self-improving Query ReconstrucTion (SQRT) framework for weakly-supervised temporal sentence grounding. To deal with insignificant reconstruction, we devise a key words mining strategy to determine the important words for language grounding. To attain better moment-query alignment, we introduce inter-sample contrast to tackle the partial alignment built by query reconstruction. The self-improving framework utilizes query reconstruction for language grounding and alleviates the discrepancy in alignment, thus turning on the right track. Experiments on two popular datasets show that SQRT achieves state-of-the-art performance on Charades-STA and comparable performance to the state-of-the-art on ActivityNet Captions.
Grafting Fine-Tuning and Reinforcement Learning for Empathetic Emotion Elicitation in Dialog Generation

For human-like dialogue systems, it is significant to inject the empathetic ability or elicit the opposite’s positive emotions, while existing studies mostly only focus on either of the above two research lines. In this work, we propose a novel and grafted task named Empathetic Emotion Elicitation Dialog to make a dialog system able to possess both aspects of ability simultaneously. We do not train an empathetic dialog system and an emotion elicitation dialog system separately and then simply concatenate the responses generated by these two systems, which will cause illogical and repetitive responses. Instead, we propose a unified solution: (1) To generate empathetic responses and emotion elicitation responses within the same semantic space, we design a unified framework. (2) The unified framework has three stages which first retrieve the empathetic and emotion elicitation exemplars as external knowledge, then fine-tune the emotion/action prediction on a pre-trained language model to enhance the empathetic ability, and finally model the user feedback by reinforcement learning to enhance the emotion elicitation ability. Experiments show that our method outperforms the baselines in the response generation quality and simultaneously empathizes with the user and elicits their positive emotions.
TreeFlow: Going Beyond Tree-Based Parametric Probabilistic Regression

The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we introduce TreeFlow, the tree-based approach that combines the benefits of using tree ensembles with the capabilities of modeling flexible probability distributions using normalizing flows. The main idea of the solution is to use a tree-based model as a feature extractor and combine it with a conditional variant of normalizing flow. Consequently, our approach is capable of modeling complex distributions for the regression outputs. We evaluate the proposed method on challenging regression benchmarks with varying volume, feature characteristics, and target dimensionality. We obtain the SOTA results for both probabilistic and deterministic metrics on datasets with multi-modal target distributions and competitive results on unimodal ones compared to tree-based regression baselines.
DTBS: Dual-Teacher Bi-Directional Self-Training for Domain Adaptation in Nighttime Semantic Segmentation

Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-condition domains. Self-training (ST) is a paradigm in UDA, where a momentum teacher is utilized for pseudo-label prediction, but a confirmation bias issue exists. Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift. To mitigate this issue, we propose to alleviate domain gap by incrementally considering style influence and illumination change. Therefore, we introduce a one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth knowledge transfer and feedback. Based on two teacher models, we present a novel pipeline to respectively decouple style and illumination shift. In addition, we propose a new Re-weight exponential moving average (EMA) to merge the knowledge of style and illumination factors, and provide feedback to the student model. In this way, our method can be embedded in other UDA methods to enhance their performance. For example, the Cityscapes to ACDC night task yielded 53.8 mIoU (%), which corresponds to an improvement of +5% over the previous state-of-the-art. The code is available at https://github.com/hf618/DTBS.
Reasoning Guided by a Manual: Context-Aware Image Captioning with Novel Objects

Novel object captioning task aims at describing objects that are absent from training data. Due to the scarcity of novel objects, it’s challenging to find a way to utilize external data to improve model’s reasoning ability. While previously designed methods all follow a deep learning approach, we boost novel object captioning by incorporating reasoning with traditional deep learning framework. We design a manual from dictionaries that provides our model with sufficient and accurate external information on novel objects. We propose Manual-guided Context-aware Novel Object Captioning model (MC-NOC) that utilizes image and caption context to generate novel object captions. It contains a Manual-Guided Novel Object Reasoning module to reason about novel objects based on other objects of the given image and a Caption Reconstruction module to incorporate novel objects into generated captions according to caption context. We validate MC-NOC with state-of-the-art performance on the challenging Held-out COCO and Nocaps dataset, leading their leaderboard. In particular, we improved the CIDER metric by 6.4 points on the held-out coco dataset. Comprehensive experiments demonstrate our model’s reasoning capability and the quality of generated captions.
Region-Specific Prototype Customization for Weakly Supervised Semantic Segmentation

It is well known that weakly supervised semantic segmentation requires only image-level labels for training, which greatly reduces the annotation cost. In recent years, prototype-based approaches, which prove to substantially improve the segmentation performance, have been favored by a wide range of researchers. However, we are surprised to find that there are semantic gaps between different regions within the same object, hindering the optimization of prototypes, so the traditional prototypes can not adequately represent the entire object. Therefore, we propose region-specific prototypes to adaptively describe the regions themselves, which alleviate the effect of semantic gap by separately obtaining prototypes for different regions of an object. In addition, to obtain more representative region-specific prototypes, a plug-and-play Spatially Fused Attention Module is proposed for combining the spatial correlation and the scale correlation of hierarchical features. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014, and the results show that our method achieves state-of-the-art performance using only image-level labels.
H2T-FAST: Head-to-Tail Feature Augmentation by Style Transfer for Long-Tailed Recognition

Deep learning algorithms perform poorly on long-tailed datasets because there is insufficient data in the tail classes to recover its original distribution, resulting in an under-representation of the tail classes in the model. In this work, we propose H2T-FAST, a Head-to-Tail Feature Augmentation method by Style Transfer to improve the performance of the tail. H2T-FAST has the following advantages: (1) It is a fast and universal method that acts on the feature space and so, it can be applied to different backbone networks as well as easily integrated into various imbalanced algorithms with stable performance gains; and (2) it is used only in the training phase and therefore, imposes no additional burden on the deep neural network in the testing phase. In particular, we firstly and randomly select the same number of head samples as the tail ones in each training mini batch. Secondly, the style of the head is transferred to the tail to generate new tail data containing the head style, as a way to increase the number of the tail and get better feature representations. We test our methods on several benchmark vision tasks with state-of-the-art performances.
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for model training. In addition, data-efficient reinforcement learning requires the construction of strong inductive biases, which are ignored in the current MARL approaches. Inspired by the symmetry phenomenon in multi-agent systems, this paper proposes a framework for exploiting prior knowledge by integrating data augmentation and a well-designed consistency loss into the existing MARL methods. In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms. Experimental tests on multiple challenging tasks demonstrate the effectiveness of the proposed framework. Moreover, the proposed framework is applied to a physical multi-robot testbed to show its superiority.