Reviewerly/exHarmony huggingface dataset:
Conditional Encoder-Based Adaptive Deep Image Compression with Classification-Driven Semantic Awareness

This paper proposes a new algorithm for adaptive deep image compression (DIC) that can compress images for different purposes or contexts at different rates. The algorithm can compress images with semantic awareness, which means classification-related semantic features are better protected in lossy image compression. It builds on the existing conditional encoder-based DIC method and adds two features: a model-based rate-distortion-classification-perception (RDCP) framework to control the trade-off between rate and performance for different contexts, and a mechanism to generate coding conditions based on image complexity and semantic importance. The algorithm outperforms the QMAP2021 benchmark on the ImageNet dataset. Over the tested rate range, it improves the classification accuracy by 11% and the perceptual quality by 12.4%, 32%, and 1.3% on average for NIQE, LPIPS, and FSIM metrics, respectively.
On the Effect of Adversarial Training Against Invariance-based Adversarial Examples

Adversarial examples are carefully crafted attack points that are supposed to fool machine learning classifiers. In the last years, the field of adversarial machine learning, especially the study of perturbation-based adversarial examples, in which a perturbation that is not perceptible for humans is added to the images, has been studied extensively. Adversarial training can be used to achieve robustness against such inputs. Another type of adversarial examples are invariance-based adversarial examples, where the images are semantically modified such that the predicted class of the model does not change, but the class that is determined by humans does. How to ensure robustness against this type of adversarial examples has not been explored yet. This work addresses the impact of adversarial training with invariance-based adversarial examples on a convolutional neural network (CNN).
Enhancing BERT Performance with Contextual Valence Shifters for Panic Detection in COVID-19 Tweets

Panic phenomenon is one of the main challenges in the current pandemic time. In this work, we aim to explore the approaches to detect the panic-related COVID-19 tweets. Aligned to this, we propose an unsupervised clustering approach considering negation cues as an extracted feature input to the pre-trained model. This task cannot be done by simply applying state-of-the-art transformer models, since we observed that they occasionally fail in handling negations. Hence, we propose to utilize features based on Contextual Valence Shifters (CVS) along with the pre-trained BERT embeddings. We evaluate and compare the approaches in an unsupervised setup, using standard clustering metrics on a large set of COVID-19 tweets. The obtained results show that CVS effectively facilitates negation handling (positive/negative tweet discrimination).
Graph Based Pattern Classification for NLU and Slot Filling: Approach and Analysis

In Natural Language Understanding, semantic parsing refers to the task of extracting meaningful words from text, with slot filling being a special case. At the same time, syntactic parsing deals with the identification of syntactic structure in the text. In this paper, we analyze to which extent syntactic structure can be used for semantic parsing. For this purpose, we represent the syntactic structure of sentences from an annotated database as graphs, incrementally store a large number of graph prototypes in a knowledge base, and approach the slot filling problem as a graph matching problem. We analyze how this approach scales with the number of prototypes and show that it provides a lightweight framework that can be efficiently used to support semantic parsing tasks.
An Enterprise Data Privacy Governance Model: Security-Centric Multi-Model Data Anonymization

The increasing need for data privacy and the rising complexity of data environments necessitate robust data anonymization techniques to safeguard personal and sensitive information. A multi-model approach to data anonymization can strike an optimal balance between privacy protection and data utility, integrating techniques such as data masking, differential privacy, machine learning algorithms, blockchain technology, and data encryption. This article introduces a Security-Centric Enterprise Data Anonymization Governance Model, a structured framework for managing data privacy across healthcare, finance, and government industries. The model ensures adherence to best practices and compliance with legal and regulatory requirements. The article addresses challenges in implementing data anonymization techniques, including maintaining data utility and preventing re-identification, by advocating for a multi-model approach that combines various technologies and methods. We suggest that by adopting this holistic approach, organizations can enhance their data protection measures and foster a culture of data privacy.
Managing the Unexpected and Unforeseen in Educational Situations: Emergency as New Normal

The paper starts from the reflections that educational research has been able to perform collecting data and analyzing emergency didactic situations activated during the pandemics to understand how educational contexts would have become at the end of the world health crisis. The starting idea is the following: at this point it seems impossible to get back to a longed-for normality or to a supposed balance remembered as the code of the world – not just the educational one – before March 2020. Emergency is the new reality, as Covid-19, more than an isolated event, is the epiphenomenon of a not overcome ecologic and social crisis and therefore harbinger of a situation characterized by the sequencing and overlapping of several emergencies, with different granularity and scalarity that have to be managed though in their being previously unknown, unforeseen, and unexpected. In this situation the culture of one’s own professional culture seems impaired. How can research and education offer useful tools to teachers and educators to understand and manage the new complexity? Keywords: Emergency, learning ecosystem, vicariance, learning design, teachers’ professionalisation.
Bird pecking damage risk assessment of UHV transmission line composite insulators based on deep learning

Abstract Here, a method for assessing the risk of bird pecking damage of composite insulators in ultra high voltage (UHV) lines is proposed using electric field (E‐field) simulation and deep learning. The distribution of E‐field on composite insulators is analysed via numerical simulation for different damage locations and damage sizes. Then, using the defective images of composite insulator strings captured in real inspection environments, the corresponding annotation image data set is constructed according to the finite element calculation results of E‐field under different damage conditions, and through the application of the YOLOX deep learning neural network, the risk assessment of the damage caused by bird pecking of insulators for UHV lines is conducted. Furthermore, to prevent overfitting of YOLOX with small‐scale images, transfer learning, as well as data enhancement, are applied to the YOLOX training process. According to the results, the mean average precision (mAP) of the model is 0.79, indicating that it is capable of high accuracy recognition, provides guidance for operation and maintenance personnel.
Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds

This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements makes the temporal up-sampling of point clouds possible. The only requirement of the system is a camera with a higher frame rate than the LIDAR equipped to the same vehicle, which is usually provided. The pipeline first utilizes optical flow estimations from the available camera frames. Next, optical expansion is used to upgrade it to 3D scene flow. Following that, ground plane fitting is made on the previous LIDAR point cloud. Finally, the estimated scene flow is applied to the previously measured object points to generate the new point cloud. The framework’s efficiency is proved as state-of-the-art performance is achieved on the KITTI dataset.
A Deep Regression Approach for Human Activity Recognition Under Partial Occlusion

In real-life scenarios, Human Activity Recognition (HAR) from video data is prone to occlusion of one or more body parts of the human subjects involved. Although it is common sense that the recognition of the majority of activities strongly depends on the motion of some body parts, which when occluded compromise the performance of recognition approaches, this problem is often underestimated in contemporary research works. Currently, training and evaluation is based on datasets that have been shot under laboratory (ideal) conditions, i.e. without any kind of occlusion. In this work, we propose an approach for HAR in the presence of partial occlusion, in cases wherein up to two body parts are involved. We assume that human motion is modeled using a set of 3D skeletal joints and also that occluded body parts remain occluded during the whole duration of the activity. We solve this problem using regression, performed by a novel deep Convolutional Recurrent Neural Network (CRNN). Specifically, given a partially occluded skeleton, we attempt to reconstruct the missing information regarding the motion of its occluded part(s). We evaluate our approach using four publicly available human motion datasets. Our experimental results indicate a significant increase of performance, when compared to baseline approaches, wherein networks that have been trained using only nonoccluded or both occluded and nonoccluded samples are evaluated using occluded samples. To the best of our knowledge, this is the first research work that formulates and copes with the problem of HAR under occlusion as a regression task.
E-Accounting System as a Service (E-ASaaS): A Conceptual Overview

In this paper we present an innovative concept of a public information system based on several digital technologies and approaches for outsourcing accounting as a public service. Accounting is one of the fundamental business functions of any business entity, that is highly regulated in all the economic systems. Over the recent decades accounting function in business organizations has evolved due to dramatic changes and advancements in information and communication technologies. These technologies-initiated changes in form of digital documentation, online or cloud accounting and similar alternatives to traditional organization of accounting. The goal of this paper is to draw attention to available technologies that may provide even more drastic innovation in organizing accounting function of business entities in a common administrative space such as counties, or entire countries and nation-wide economics. Here we propose a model of a public access E-accounting system that employs the functionality of cloud-based services, data pod concepts and blockchain technologies in order to automate processes of bookkeeping but also reporting on the side of business entities, while at the same time provide autonomous control and audit as defined by law and competent institutions in various segments of supervision (i.e. tax supervision, statistics reporting, etc).
Relationship between preference for the existence of teacher images and learning effects in online learning for junior high school students

This research focuses on online learning, which has rapidly become widespread in recent years, and shows the results of examining the ideal way of showing teacher images in online learning content. The purpose of this study is to clarify the impact of teacher images on the learning effect. We showed the knowledge retention rate verification results up to one month after the quiz with 53 junior high school students. We conducted five quizzes with multiple choices as pseudo-online learning content without interactivity as an experimental method. We also divided the participants into two groups, one group provided content with a teacher image on the screen, and the other was not. As an index of the learning effect, we adopted the percentage of entirely correct answers in the analysis, and comparisons were made at three different timings (1) during the experiment, (2) immediately after the experiment, and (3) one month later. The entirely correct answer means the correct answer after understanding the reason for the correct explanation of the quiz. As a result, there was no clear relationship between the presence or absence of teacher images and the learning effect. It indicates that displaying the teacher image does not directly affect the knowledge retention rate. However, on the other hand, we found that the knowledge retention continued even after one month when the preference for the existence of the teacher image and the actual display matched. Furthermore, even when the preference for the presence of the teacher image and the display matched, the knowledge retention rate after one month was significantly higher in the group with the teacher image display than in the group without the presence. These results suggested that the learning effect may be seen in the matching between the preference for the existence of the teacher image and the actual presented content.
Semantic‐guided fusion for multiple object tracking and RGB‐T tracking

Abstract The attention mechanism has produced impressive results in object tracking, but for a good trade‐off between performance and efficiency, CNN‐based approaches still dominate, owing to quadratic complexity of attention. Here, the SGF module is proposed, an efficient feature fusion block for effective object tracking with reduced linear complexity of attention. The proposed method fuses feature with attention in a coarse‐to‐fine manner. In the low‐resolution semantic branch, the top K regions with highest attention scores are selected; in the high‐resolution detail branch, attention is only calculated within regions corresponding to the top K regions. Thus, the features from the high‐resolution branch can be efficiently fused under the guidance of low‐resolution branch. Experiments on RGB and RGB‐T datasets with reformed FairMOT and MDNet+RGBT trackers demonstrated the effectiveness of the proposed method.
Improving the Effectiveness and Efficiency of Web-Based Search Tasks for Policy Workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands.
Log-Normal Distribution Modelling with Quantised Data

A continuous distribution can be a helpful way to describe natural phenomena, e.g. service times of repairable systems and human tissue growth can be modelled using a log-normal distribution. When a dataset is modified by rounding or truncation, data may become distorted enough not to fit its original distribution due to the quantisation errors. Attempting to fit statistical distributions can even result in convergence errors while fitting if, for example, values are rounded to zero. We explore the effects of quantisation when distribution fitting. We provide a framework to identify impacts of quantisation and different techniques to adjust quantised data and mitigate convergence errors. We specifically look at a log-normal distribution and demonstrate that adding a relative constant based on decimal length can eradicate convergence errors and weaken quantisation.
Differentially Private Timeseries Forecasts for Networked Control

We analyze a cost-minimization problem in which the controller relies on an imperfect timeseries forecast. Forecasting models generate imperfect forecasts because they use anonymization noise to protect input data privacy. However, this noise increases the control cost. We consider a scenario where the controller pays forecasting models incentives to reduce the noise and combines the forecasts into one. The controller then uses the forecast to make control decisions. Thus, forecasting models face a trade-off between accepting incentives and protecting privacy. We propose an approach to allocate economic incentives and minimize costs. We solve a biconvex optimization problem on linear quadratic regulators and compare our approach to a uniform incentive allocation scheme. The resulting solution reduces control costs by 2.5 and 2.7 times for the synthetic timeseries and the Uber demand forecast, respectively.
Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality

This study aims to conduct a systematic literature review on machine learning methods used in virtual reality (VR) and augmented reality (AR). The method proposed in the SLR consists of three main phases: planning, conducting, and reporting. Researchers used data obtained through Google searches sourced from IEEE Xplore, Springer, Science Direct, Scopus, and Web of Science. The application of inclusion and exclusion criteria is used to select documents. The findings from this study consist of countries involved in machine learning research in the VR and AR fields, machine learning methods, technology used, and work sectors that use machine learning in the VR and AR fields. The results also show that when the machine learning method is used in VR and AR applications, the main advantages are high efficiency and algorithm precision. Moreover, this research observes that machine learning is the most widely applied artificial intelligence scientific technique in VR and AR applications. The results of this systematic literature review show that the combination of machine learning and VR and AR methods contributes to trends, opportunities, and applications in the fields of engineering, arts, education, industry, medicine, tourism, and technology
Types, functions and mechanisms of robot-assisted intervention for fall prevention: A systematic scoping review

Any individual may experience accidental falls, particularly older adults. Although robots can prevent falls, knowledge of their fall-preventive use is limited. To explore the types, functions, and mechanisms of robot-assisted intervention for fall prevention. A systematic scoping review of global literature published from inception to January 2022 was conducted according to Arksey and O'Malley's five-step framework. Nine electronic databases, namely, PubMed, Embase, CINAHL, IEEE Xplore, the Cochrane Library, Scopus, Web of Science, PsycINFO, and ProQuest, were searched. Seventy-one articles were found with developmental (n=63), pilot (n=4), survey (n=3), and proof-of-concept (n=1) designs across 14 countries. Six types of robot-assisted intervention were found, namely cane robots, walkers, wearables, prosthetics, exoskeletons, rollators, and other miscellaneous. Five main functions were observed including (i) detection of user fall, (ii) estimation of user state, (iii) estimation of user motion, (iv) estimation of user intentional direction, and (v) detection of user balance loss. Two categories of mechanisms of robots were found. The first category was executing initiation of incipient fall prevention such as modeling, measurement of user–robot distance, estimation of center of gravity, estimation and detection of user state, estimation of user intentional direction, and measurement of angle. The second category was achieving actualization of incipient fall prevention such as adjust optimal posture, automated braking, physical support, provision of assistive force, reposition, and control of bending angle. Existing literature regarding robot-assisted intervention for fall prevention is in its infancy. Therefore, future research is required to assess its feasibility and effectiveness.
Discovering Multiple Algorithm Configurations

Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples which represent typical deployment conditions. Automatic tuning of these settings is traditionally known as algorithm configuration. In this work, we extend algorithm configuration to automatically discover multiple modes in the tuning dataset. Unlike prior work, these configuration modes represent multiple dataset instances and are detected automatically during the course of optimization. We propose three methods for mode discovery: a post hoc method, a multistage method, and an online algorithm using a multi-armed bandit. Our results characterize these methods on synthetic test functions and in multiple robotics application domains: stereoscopic depth estimation, differentiable rendering, motion planning, and visual odometry. We show the clear benefits of detecting multiple modes in algorithm configuration space.
ViNL: Visual Navigation and Locomotion Over Obstacles

We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in its path (e.g., shoes, toys, cables), similar to how humans and pets lift their feet over objects as they walk. ViNL consists of: (1) a visual navigation policy that outputs linear and angular velocity commands that guides the robot to a goal coordinate in unfamiliar indoor environments; and (2) a visual locomotion policy that controls the robot's joints to avoid step-ping on obstacles while following provided velocity commands. Both the policies are entirely ‘model-free’, i.e. sensors-to-actions neural networks trained end-to-end. The two are trained independently in two entirely different simulators and then seamlessly co-deployed by feeding the velocity commands from the navigator to the locomotor, entirely ‘zero-shot’ (without any co-training). While prior works have developed learning methods for visual navigation or visual locomotion, to the best of our knowledge, this is the first fully learned approach that leverages vision to accomplish both (1) intelligent navigation in new environments, and (2) intelligent visual locomotion that aims to traverse cluttered environments without disrupting obstacles. On the task of navigation to distant goals in unknown environments, ViNL using just egocentric vision significantly outperforms prior work on robust locomotion using privileged terrain maps (+32.8% success and -4.42 collisions per meter). Additionally, we ablate our locomotion policy to show that each aspect of our approach helps reduce obstacle collisions. Videos and code at http://www.joannetruong.com/projects/vinl.html.