Reviewerly/exHarmony huggingface dataset:
Image Impulse Noise Removal using Cascaded Filtering based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks

Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for "salt-and-pepper" (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.
Handwritten Text Recognition and Conversion Using Convolutional Neural Network (CNN) Based Deep Learning Model

Recognition of handwritten documents has been an attractive area of research for the past few years. It is always intended to convert a piece of handwritten information into digital text for sharing or saving without typing the information manually. The proposed model takes a picture of a handwritten text as input and converts it into digital text. The Convolutional Neural Network (CNN) is used to study the features of similar objects from multiple image samples and to classify them. Since the text is sequential data, Long Short Term Memory (LSTM), an extension of Recurrent Neural Networks (RNN) with a longer memory is used. To deal with different placements of the text in the image, Connectionist Temporal Classification (CTC) loss is employed. The IAM Handwriting Database containing handwriting samples from over 600 writers and images of over 100,000 words is used for training. After training for multiple epochs, the model registered 94% accuracy and a loss of 0.147 on training data and 85% accuracy and a loss of 1.105 on validation data.
Human Factors and their Influence on Software Development Teams - A Tertiary Study

Background: Software organizations increasingly need developers with high skills for social interactions. Managers, leaders, and academics need to know the human factors influencing the individuals, the development team, and the software project activities. Despite the increasing number of secondary studies about human factors in Software Engineering (SE) and in Agile Software Development (ASD), there is no study synthesizing which human factors influence software development without a specific perspective from SE or human factor thematic. Objective: We aim to summarise human factors and their influence on SE development teams and ASD teams. Method: We executed a tertiary study. We used thematic analysis to examine the resulting data. Results: We found 29 systematic literature reviews and systematic mapping studies addressing the human perspective in SE teams. We identified 101 human factors and 79 influences grouped in 4 categories (Team member, Team, Project, and Organization). Also, we identified 71 human factors and 60 influences on ASD. The most investigated human factors are Communication, Collaboration, Knowledge, and Motivation. Conclusions: The identified human factors and their influences can be considered most significant by software organizations, researchers, and academics in SE practices. Based on our results, practitioners might propose activities that enhance human capital behaviors that influence individual motivation, agile mindset, team climate, software quality, or agile transition in traditional organizations.
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning

Social distancing, an essential public health measure to limit the spread of contagious diseases, has gained significant attention since the outbreak of the COVID-19 pandemic. In this work, the problem of visual social distancing compliance assessment in busy public areas, with wide field-of-view cameras, is considered. A dataset of crowd scenes with people annotations under a bird’s eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed. A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated. BEV-Net combines detection of head and feet locations, camera pose estimation, a differentiable homography module to map image into BEV coordinates, and geometric reasoning to produce a BEV map of the people locations in the scene. Experiments on complex crowded scenes demonstrate the power of the approach and show superior performance over baselines derived from methods in the literature. Applications of interest for public health decision makers are finally discussed. Datasets, code and pretrained models are publicly available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Decentralized robust evolving cloud-based controller for two input two output systems

The fuzzy logic controller (FLC) makes it possible to control a system using IF-THEN rules through human intellect. It tackles parameter uncertainty using imprecise reasoning. The fuzzy logic controller is usually tuned using offline methods. An online evolving adaptation of fuzzy controller design is a recent trend in fuzzy rule-based systems. The robust evolving cloud-based controller (RECCo) is one such controller implemented for single-input-single-output (SISO) systems. The membership functions and consequent rules are automatically updated in real time based on the input data. In this paper, a decentralized robust evolving cloud-based controller (DRECCo) is proposed for two-input-two-output (TITO) systems. It consists of two independent loops with RECCos having a nonparametric premise facet and an adaptive proportional-integral-derivative (PID) model consequent facet. The effectiveness of the proposed method is validated for the benchmark interacting two-tank process (ITTP) and quadruple-tank process (QTP) by simulation and in real time. The results indicate that with the information of loop pairing and the forward-acting/reverse-acting nature of the process, the proposed controller can adapt itself to ensure set-point tracking and disturbance rejection.
On the Subtle Nature of a Simple Logic of the Hide and Seek Game

We discuss a simple logic to describe one of our favourite games from childhood, hide and seek, and show how a simple addition of an equality constant to describe the winning condition of the seeker makes our logic undecidable. There are certain decidable fragments of first-order logic which behave in a similar fashion and we add a new modal variant to that class of logics. We also discuss the relative expressive power of the proposed logic in comparison to the standard modal counterparts.
Impressions2Font: Generating Fonts by Specifying Impressions

Various fonts give us various impressions, which are often represented by words. This paper proposes Impressions2Font (Imp2Font) that generates font images with specific impressions. Imp2Font is an extended version of conditional generative adversarial networks (GANs). More precisely, Imp2Font accepts an arbitrary number of impression words as the condition to generate the font images. These impression words are converted into a soft-constraint vector by an impression embedding module built on a word embedding technique. Qualitative and quantitative evaluations prove that Imp2Font generates font images with higher quality than comparative methods by providing multiple impression words or even unlearned words.
A Trajectory Prediction Algorithm for HFVs Based on LSTM

Aiming at the trajectory prediction problem of hypersonic flight vehicles skip trajectory, a prediction model of trajectory reachable area based on LSTM is proposed. According to the periodic jump characteristics of the skip trajectory, firstly, the trajectory is expressed as a time-dependent approximate expression consisting of a linear attenuation term and a sine term of amplitude attenuation. Then the dynamic characteristics of the parameters in the approximate expression are analyzed and the range of the parameters is calculated. Finally, according to the parameter range identified by the LSTM network and the trajectory expression, the final predicted trajectory reachable region is calculated. Due to the consideration of the prediction error caused by the drastic change of altitude and density in the jump trajectory, the trajectory accessible region prediction algorithm based on LSTM has more advantages than the previous trajectory prediction algorithm. The simulation results show that the trajectory reachable region prediction algorithm based on LSTM requires less prior knowledge and significantly improves the accuracy of trajectory prediction.
Influence of outliers in MOBA games winner prediction

This work regards about predicting the result of a match in Multiplayer Online Battle Arena team games. This type of games is characterized by very complex mechanics, which makes it difficult to predict the final result of the game based on its course. This prediction is based on the achievements of the teams participating in the game - usually complete data on the course of the match is available. Until now, the work in this field has not focused on capturing atypical games from the data set. For example -a team loses, although during the match its achievements exceeded that of the opposing team. The aim of this study is to examine the influence of atypical games on the quality of prediction process. The conducted research has shown that atypical games have a negative impact on the effectiveness of winner prognosis, and their removal demonstrates a beneficial effect on the prediction quality. The result of the work provokes further research in order to increase the effectiveness of predictions for matches with an unusual course.
A distributed traffic classification model based on Federated Learning

Traffic classification is the basis of protocol and application design for high performance networks, and the guarantee of network operation management, network traffic scheduling and network development planning. There are some problems in current traffic classification methods, such as difficulty in obtaining and labeling actual traffic data, legal risk and so on. In order to solve these problems, we propose a traffic classification model generation method based on federated learning.This work verifies the method to get the traffic classification model by collecting and processing the traffic at the user terminal and training it based on Federated learning under the premise of protecting the privacy security of users. Experiments show that the accuracy of the traffic classification model based on federated learning reaches 96.4%.
Evaluation of Anonymization Tools for Health Data

It is hard to overestimate the value of data in modern healthcare practice. On the one hand, we have an opportunity to use the data for health care quality improvement, and, on the other hand, we have privacy challenges. Considering the fact that we are dealing with sensitive personal data, privacy rights must be protected. Data anonymization is a technique to use healthcare data while preserving privacy.Anonymization is a complex process that requires technical skills, an understanding of privacy requirements, and understanding health data’s nature. Numerous anonymization tools are available to automate the anonymization process. Selecting among existing anonymization tools is challenging due to the necessity of collecting, comparing and analysing information on all tools separately.This research aims to provide a systematic assessment of structured health data anonymization open-access tools and make the selection process easier. This paper describes the tools’ advantages and disadvantages and reports whether these tools are sufficient to anonymize health data. In our understanding, based on the presented research, the tool selection process can speed up to 5 times because instead of installing and testing anonymization tools by themselves and collecting all relevant information, one can focus on the most suitable tools based on criteria given in this paper.This work aims to help readers understand the advantages and disadvantages of free anonymization tools and improve, simplify, and speed up the selection process.
Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images Using Structure-constrained Super-Resolution Network

Medical images routinely acquired in clinical facilities are mostly low resolution (LR), in consideration of acquisition time and efficiency. This renders challenging for clinical diagnosis of hippocampal sclerosis where additional sequences for hippocampus need to be acquired. In contrast, high-resolution (HR) images provide more detailed information for disease investigation. Recently, image super-resolution (SR) methods were proposed to reconstruct HR images from LR inputs. However, current SR methods generally use simulated LR images and intensity constraints, which limit their applications in clinical practice. To solve this problem, we utilized real paired LR and HR images and trained a Structure-Constrained Super Resolution (SCSR) network. First, we proposed a single image super-resolution framework where mixed loss functions were introduced to enhance the reconstruction of brain tissue boundaries besides intensity constraints; Second, since the structure hippocampus is relatively small, we further proposed a weight map to enhance the reconstruction of subcortical regions. Experimental results using 642 real paired cases showed that the proposed method outperformed the the-state-of-the-art methods in terms of image quality with a PSNR of 27.0405 and an SSIM of 0.9958. Also, experiments using Radiomics features extracted from hippocampus on SR images obtained through the proposed method achieved the best accuracy of 95% for differentiating subjects with left and right hippocampal sclerosis from normal controls. The proposed method shows its potential for disease screening using clinical routine images.
Alleviating Data Imbalance Issue with Perturbed Input During Inference

Intelligent diagnosis is often biased toward common diseases due to data imbalance between common and rare diseases. Such bias may still exist even after applying re-balancing strategies during model training. To further alleviate the bias, we propose a novel method which works not in the training but in the inference phase. For any test input data, based on the difference between the temperature-tuned classifier output and a target probability distribution derived from the inverse frequency of different diseases, the input data can be slightly perturbed in a way similar to adversarial learning. The classifier prediction for the perturbed input would become less biased toward common diseases compared to that for the original one. The proposed inference-phase method can be naturally combined with any training-phase re-balancing strategies. Extensive evaluations on three different medical image classification tasks and three classifier backbones support that our method consistently improves the performance of the classifier which even has been trained by any re-balancing strategy. The performance improvement is substantial particularly on minority classes, confirming the effectiveness of the proposed method in alleviating the classifier bias toward dominant classes.
Real-time recognition of team behaviors by multisensory graph-embedded robot learning

Awareness of team behaviors (e.g., individual activities and team intents) plays a critical role in human–robot teaming. Autonomous robots need to be aware of the overall intent of the team they are collaborating with in order to effectively aid their human peers or augment the team’s capabilities. Team intents encode the goal of the team, which cannot be simply identified from a collection of individual activities. Instead, teammate relationships must also be encoded for team intent recognition. In this article, we introduce a novel representation learning approach to recognizing team intent awareness in real-time, based upon both individual human activities and the relationship between human peers in the team. Our approach formulates the task of robot learning for team intent recognition as a joint regularized optimization problem, which encodes individual activities as latent variables and represents teammate relationships through graph embedding. In addition, we design a new algorithm to efficiently solve the formulated regularized optimization problem, which possesses a theoretical guarantee to converge to the optimal solution. To evaluate our approach’s performance on team intent recognition, we test our approach on a public benchmark group activity dataset and a multisensory underground search and rescue team behavior dataset newly collected from robots in an underground environment, as well as perform a proof-of-concept case study on a physical robot. The experimental results have demonstrated both the superior accuracy of our proposed approach and its suitability for real-time applications on mobile robots.
Learning to Remove Refractive Distortions from Underwater Images

The fluctuation of the water surface causes refractive distortions that severely downgrade the image of an underwater scene. Here, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. We first use a physically constrained convolutional network to estimate the distortion map from the refracted image. We then use a generative adversarial network guided by the distortion map to restore the sharp distortion-free image. Since the distortion map indicates correspondences between the distorted image and the distortion-free one, it guides the network to make better predictions. We evaluate our network on several real and synthetic underwater image datasets and show that it out-performs the state-of-the-art algorithms, especially in presence of large distortions. We also show results of complex scenarios, including outdoor swimming pool images captured by drone and indoor aquarium images taken by cellphone camera.
Where Are We in Semantic Concept Extraction for Spoken Language Understanding?

Spoken language understanding (SLU) topic has seen a lot of progress these last three years, with the emergence of end-to-end neural approaches. Spoken language understanding refers to natural language processing tasks related to semantic extraction from speech signal, like named entity recognition from speech or slot filling task in a context of human-machine dialogue. Classically, SLU tasks were processed through a cascade approach that consists in applying, firstly, an automatic speech recognition process, followed by a natural language processing module applied to the automatic transcriptions. These three last years, end-to-end neural approaches, based on deep neural networks, have been proposed in order to directly extract the semantics from speech signal, by using a single neural model. More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing. In this paper, we present a brief overview of the recent advances on the French MEDIA benchmark dataset for SLU, with or without the use of additional data. We also present our last results that significantly outperform the current state-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for the last state-of-the-art system presented this year.
Do Different Deep Metric Learning Losses Lead to Similar Learned Features?

Recent studies have shown that many deep metric learning loss functions perform very similarly under the same experimental conditions. One potential reason for this unexpected result is that all losses let the network focus on similar image regions or properties. In this paper, we investigate this by conducting a two-step analysis to extract and compare the learned visual features of the same model architecture trained with different loss functions: First, we compare the learned features on the pixel level by correlating saliency maps of the same input images. Second, we compare the clustering of embeddings for several image properties, e.g. object color or illumination. To provide independent control over these properties, photo-realistic 3D car renders similar to images in the Cars196 dataset are generated. In our analysis, we compare 14 pretrained models from a recent study and find that, even though all models perform similarly, different loss functions can guide the model to learn different features. We especially find differences between classification and ranking based losses. Our analysis also shows that some seemingly irrelevant properties can have significant influence on the resulting embedding. We encourage researchers from the deep metric learning community to use our methods to get insights into the features learned by their proposed methods.
Uniform Lyndon Interpolation for Basic Non-normal Modal Logics

In this paper, a proof-theoretic method to prove uniform Lyndon interpolation for non-normal modal logics is introduced and applied to show that the logics \(\mathsf {E}\), \(\mathsf {M}\), \(\mathsf {MC}\), \(\mathsf {EN}\), \(\mathsf {MN}\) have that property. In particular, these logics have uniform interpolation. Although for some of them the latter is known, the fact that they have uniform Lyndon interpolation is new. Also, the proof-theoretic proofs of these facts are new, as well as the constructive way to explicitly compute the interpolants that they provide. It is also shown that the non-normal modal logics \(\mathsf {EC}\) and \(\mathsf {ECN}\) do not have Craig interpolation, and whence no uniform (Lyndon) interpolation.
Playing First-Person Perspective Games with Deep Reinforcement Learning Using the State-of-the-Art Game-AI Research Platforms

Computer games have become one of the most interesting and dynamic research areas of artificial intelligence as computer games are the best testbeds to evaluate and test the theoretical ideas in artificial intelligence before applying them in real-world. The enhancement in computing power, advancement in machine learning particularly deep reinforcement learning, and the evolution of neural networks are allowing the autonomous game agents to perform substantially well that often leave behind human beings by using only the screen raw pixels for making their actions or decisions. In this chapter, we use Deep reinforcement learning in the form of Deep Q-learning under its two variants Deep Q-Network (DQN) and Deep Recurrent Q-Learning Network (DRQN) to control agents in playing the two famous computer games i.e. Doom and Minecraft. We present how to build an implementation of a testbed for such state-of-the-art methods using the ViZDoom, Gym-Minecraft and Microsoft's Malmo platforms. Initially, we present our results on a simplified game scenario(s) from Doom in predicting the enemy positions (game features) with the difference in the performance of the DQN and DRQN in both fully observable Markovian decision process (FOMDP) and partially observable Markovian decision process (POMDP) and claim that the DQN performs better at predicting the enemy positions. Finally, we present results on another game scenario(s) from Minecraft to test and confirm the performance of DRQN in POMDP where unlike other existing works, our proposed architectures outperform the built-in AI agents and human players in predicting the game features with enhanced accuracy.