Reviewerly/exHarmony huggingface dataset:
Image Quality Assessment for Endoscopy Applications

Assessment of image quality parameters in medical applications is crucial to produce high quality images that would significantly improve diagnoses and therapies. Solutions available in the market to assess the image quality provide experimental setups, standard test charts, and illumination setups. Parameters like sharpness, geometric distortion, and dynamic range require separate test charts and therefore can only be measured one at a time. In this paper, a single test chart to measure most of the image quality parameters has been described. A single image of this test chart could provide assessment of all the parameters considered. The size of the test chart could be customized according to the endoscopy application. An experimental setup was also designed in-house. This approach helped in developing a comprehensive and inexpensive assessment technique complying with the International Organization of Standardization (ISO) standards. Currently, the algorithms work with still images and could be extended to assess how the measured parameters would vary on a live video stream.
DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

Analysis of degraded ancient documents is challenging due to the severity and combination of degradation present in a single image. Ancient documents also suffer from additional noise during the digitalization process, particularly when digitalization is done using low-specification devices and/or under poor illumination conditions. The noises over the degraded ancient documents certainly cause a troublesome document analysis. In this paper, we propose a new noise-robust convolutional neural network (CNN) architecture for degradation classification of noisy ancient documents, which is called a degradation classification network (DCNet). DCNet was constructed based on the ResNet101, MobileNetV2, and ShuffleNet architectures. Furthermore, we propose a new self-transition layer following DCNet. We trained the DCNet using (1) noise-free document images and (2) heavy-noise (zero mean Gaussian noise (ZMGN) and speckle) document images. Then, we tested the resulted models with document images containing different levels of ZMGN and speckle noise. We compared our results to three CNN benchmarking architectures, namely MobileNet, ShuffleNet, and ResNet101. In general, the proposed architecture performed better than MobileNet, ShuffleNet, ResNet101, and conventional machine learning (support vector machine and random forest), particularly for documents with heavy noise.
Framework for Coherent Formulation and Implementation of ICT Policies for Sustainable Development at the Bottom of the Pyramid Economy in Nigeria

Developing countries such as Nigeria adapt ICT policies formulated by UN agencies and other intergovernmental organizations to coordinate how they use ICT to reach the sustainable development goals. Different challenges usually impede ICT policy formulation and implementation at the bottom of the pyramid economy because the UN and its agencies deal mostly with national governments. Existing studies claim that factors such as geographical structure, political-administrative structure, weak institutions, and corruption are the main factors that impede the formulation and implementation of ICT policies for sustainable development at the bottom of the pyramid economy. The chapter, however, proposes that differences in the internalized views of those involved in the formulation and implementation of ICT policies and those operating at the bottom of the pyramid economy are the primary challenges to ICT policy formulation and implementation at the bottom of the pyramid economy. The chapter argues that problems connected to geographical structure, political-administrative structure, weak institutions, and corruption result from differences in the internalized views of those that formulate and implement ICT policies and those that operate at the bottom of the pyramid economy in Nigeria. The chapter draws from the extant literature and observation of experiences in Nigeria. It presents a useful framework that proposes how to align the internalized views of those that formulate and implement ICT policies and those that operate at the bottom of the pyramid economy. The framework will promote the coherent formulation and implementation of ICT policies at the bottom of the pyramid economy.
Image Based Information Hiding with One-dimensional Chaotic Systems and Particle Swarm Optimization

Information hiding is a technology that has been used extensively for the hiding of crucial information within digital media. Image based information hiding chooses digital images as the media to contain the hidden information. This paper proposes a new approach that can hide information into a color image with improved security. The hiding is performed in two phases. The first phase encrypts the information that needs to be hidden with a number of one-dimensional logistic systems. The second phase of the approach divides the encrypted information into blocks with equal size and embeds the blocks within the least significant bits of the R, G and B components of a number of selected pixels in the image. The locations of the selected pixels are determined with a number of one-dimensional logistic mappings. To achieve the optimal security of hiding, the difference between a cover image and its stego image is minimized with a particle swarm optimization approach. Experimental results show that the proposed approach can hide information into a color image with improved security and thus may have practical applications in tasks that require image-based information hiding.
General convergence result for continuous-discrete feedback particle filter

In this paper, we shall discuss the convergence of the continuous-discrete feedback particle filter (FPF) proposed in Yang et al. (2014 Yang, T., Blom, H. A., & Mehta, P. G. (2014). The continuous-discrete time feedback particle filter. In 2014 American Control Conference (pp. 648–653). IEEE. [Google Scholar]). The FPF is an interacting system of N particles where the interaction is designed such that the empirical distribution of the particles approximates the posterior distribution by an innovation error-based feedback control structure. Under some assumptions, it is proved that, for a class of functions ϕ and ∀p≥2, the estimate of ϕ(Xtn) by FPF converges to its optimal estimate E[ϕ(Xtn)|Ftn] in Lp sense, as the number of particles goes to infinity and the numerical approximation error of computing the control input U goes to zero. Furthermore, the bound of the estimation error is also delicately analyzed.
HDR-Like Image Generation to Mitigate Adverse Wound Illumination Using Deep Bi-directional Retinex and Exposure Fusion

Periodic assessment is necessary to evaluate the healing prog-ress of chronic wounds. Image analyses using computer vision algorithms have recently emerged as a viable alternative that has been demonstrated by prior work. However, the performance of such image analysis methods degrade on captured in adverse illumination, which is common in many indoor environments. To mitigate these lighting problems, High Dynamic Range (HDR) image enhancement techniques can be used to mitigate over- and under-exposure issues and preserve the details of scenes captured in non-ideal illumination. In this paper, we address over- and under-exposure simultaneously using a deep learning-based bi-directional illumination enhancement network that is able to generate over- and under-exposed images that are then fused into a final image with enhanced illumination. In rigorous evaluations using metrics including structure similarity, peak signal-noise ratio and changes in segmentation accuracy, our proposed method outperformed the state-of-the-art (SSIM scores $$0.76\pm 0.04$$ / $$0.69\pm 0.08$$ on bright/dark images, PSNR scores $$28.60\pm 0.70$$ on dark images and DSC scores $$0.76\pm 0.09$$ / $$0.74\pm 0.09$$ on bright/dark images).
Methods and Challenges in Social Bots Detection: A Systematic Review

Social bots are automated users who make use of social networks to produce content and interact with network users, in order to mimic or attempt to alter user behaviors, with the purposes, among others, of spreading spam and malicious content, violate users privacy, or mislead information in order to influence financial markets or electoral disputes, causing numerous losses. Detecting these bots is a major challenge since, as detection mechanisms evolve, their hiding properties are also enhanced to avoid such mechanisms, either by more sophisticated strategies for emulating real users or by organizing groups of bots in sophisticated networks with the same purpose (botnets). This paper presents a survey about social bot detection approaches, considering the techniques used, the set of characteristics considered for the classification as well as the target of identification (individual or botnets). The main open points identified as well as possible advances in research in the area are also discussed.
Efficient Generic Arithmetic for KKW

Katz et al., CCS 2018 (KKW) is a popular and efficient MPC-in-the-head non-interactive ZKP (NIZK) scheme, which is the technical core of the post-quantum signature scheme Picnic, currently considered for standardization by NIST. The KKW approach simultaneously is concretely efficient, even on commodity hardware, and does not rely on trusted setup. Importantly, the approach scales linearly in the circuit size with low constants with respect to proof generation time, proof verification time, proof size, and RAM consumption. However, KKW works with Boolean circuits only and hence incurs significant cost for circuits that include arithmetic operations. In this work, we extend KKW with a suite of efficient arithmetic operations over arbitrary rings and Boolean conversions. Rings $$\mathbb {Z}_{2^k}$$ are important for NIZK as they naturally match the basic operations of modern programs and CPUs. In particular, we: – present a suitable ring representation consistent with KKW, – construct efficient conversion operators that translate between arithmetic and Boolean representations, and – demonstrate how to efficiently operate over the arithmetic representation, including a vector dot product of length-n vectors with cost equal to that of a single multiplication. These improvements substantially improve KKW for circuits with arithmetic. As one example, we can multiply $$100 \times 100$$ square matrices of 32 bit number using $$3200\times $$ smaller proof size than standard KKW ( $$100\times $$ improvement from our dot product construction and $$32\times $$ from moving to an arithmetic representation). We discuss in detail proof size and resource consumption and argue the practicality of running large proofs on commodity hardware.
A Simulated Environment for Traversability Estimation Experiments in Field Robotics Applications

We present an environment for simulated experiments in field robotics, and especially in experiments on estimating the traversability of foliage and other objects that appear as obstacles but that can be overcome by the robot without circumventing them. The simulated environment is developed in the Unity real-time development platform, integrated with the ROS middleware. In the preliminary experiments presented here, we demonstrate that our environment is able to simulate the sensory input needed in order to train supervised traversability estimation models.
The Influence of Modern Information Technology on College English Teaching Under the Background of Educational Information

The new wave of technology gives impetus to the change of educational forms. Educational Informatization has become the theme of reform and development in China’s higher education. The COVID-19 at the beginning of 2020 has become catalyst that further pushes forward the integration of information technology and course teaching. In China, confronted with the serious epidemic situation in the first half of 2020, schools of all levels provided completely online teaching. This paper is intended to discuss how to effectively integrate information technology into college English teaching on the basis of previous attempts, especially the past year’s online teaching or blended learning.
A Study on VR Training of Baseball Athletes

As virtual reality (VR) technologies continue to mature and with VR headsets becoming widely available on the consumer market, more people are using VR for gaming, entertainment and skill training. It is inevitable that VR simulations have permeated sports training as a tool to enhance athletic performance. The present study is part of an ongoing program at Purdue University where short VR modules are routinely used by the coaching and sports medicine staff to train baseball players. For the present study, three VR simulations were developed to train a player’s ability to recognize ball colors, type of ball trajectories, and strike vs. ball. Twenty-four baseball players took part in the study where half served as the control group and the other half received 12 sessions of VR training. The participants also completed two tasks before and after the main experiment. Although no significant difference was found between the pre- and post-tests, the participants did respond positively to a survey and found the VR training fun and useful for training their eyes. Future work will continue to assess the efficacy of VR training with Purdue baseball team players.
Evaluation of a virtual learning object with augmented reality technology for teaching of the computer parts

Abstract This article refers to the evaluation of the Virtual Learning Object (OVA) Computer AR, OVA with augmented reality technology to facilitate the teaching-learning of the concepts of computer maintenance, related to the internal parts of the computer, with the sixth grade students of the COLMERCEDES educational institution. The methodological design with a quantitative and descriptive approach involved the application of the OVA quality assessment instrument called LORI (Learning Object Review Instrument), under OVA quality assessment dimensions and criteria, which determine that it is a quality digital teaching resource. The quantitative descriptive analysis focuses on the assessments of students and teachers when exploring the tool and its contents, aspects such as motivation, usability, accessibility, among others, are measured, so that the relevance of designing and using interactive virtual objects with augmented reality technology for teaching and learning the basic concepts related to the internal parts of the computer is concluded. Among the results obtained were positive, it was evident that the OVA designed met the quality criteria to obtain a high assessment by the population under study, concluding that it was appropriate to design an OVA with augmented reality to generate contributions to the teaching and learning processes on concepts related to the internal parts of the computer. The data in the dimensions of evaluation with a low tendency indicate that it should be complemented with a contextualized setting, so that it can be adapted to the characteristics and needs of the students.
Extended Object Tracking assisted Adaptive Multi-Hypothesis Clustering for Radar in Autonomous Driving Domain

This paper presents a new adaptive multi-hypothesis clustering method for extended objects on radar data. The proposed method provides several clustering hypotheses per object for a given measurement set efficiently by ordering the data set similar to the HDBSCAN and extracting clusters from the ordered data set with the help of prior knowledge obtained from Extended Object Tracking (EOT) and fusion. The performance of the proposed method is tested on a manually labeled real-world data set. The dependency on accurate prior knowledge is reduced compared to previously introduced adaptive clustering methods.
Experimental Method of Assessment of Ergonomics and Accessibility of Public Facilities

This article presents an original method of assessment of a building space in view of its ergonomics and accessibility. The method draws upon the universal, inclusive and empathic design concepts. Research to define new methods of space assessment is significant in view of the world population aging, which phenomenon entails the revision of the users’ spatial needs. The research is carried out for different types of users, defined as research personas, assigned with specific characteristics, such as age, fitness, physical performance, etc. The researcher, engaging in the role play, acts as the building user. The scenarios of users’ behaviour in the space of a given building are developed based on the ethnographic methods, customer journey maps, interviews and statistical analyses. In order to evaluate the users’ spatial experience, the techniques, including but not limited to, path tracing, user experience mapping, emotional maps and questionnaires are used.
Corporate Portal of a Technopark as a Unified Communication Platform

The paper is concerned with justification of the need for creating a corporate portal of a technopark as a unified communication platform for the knowledge transfer between science, education, and business, which is a new stage in developing their integration and increasing the operating efficiency. The authors performed an analysis of tasks and functions of corporate portals used by Russian organizations. Features of technoparks were identified which served as a basis for the determination of the goal of creation and the key functions of the portal of a technopark. According to the goal of creating a portal of a technopark, which consists in enhancing integration through the knowledge transfer between its residents, the authors identified the following key functions of the portal: developing and maintaining a culture focused on exchange of knowledge, networking, learning.Based on the monitoring of modern products in the software market for the development of web portals and their user experience, basic tools were proposed that are required for the performance of the key functions. One of the most complex tasks that should be solved in order to turn the technopark into an innovative ecosystem and ensure the knowledge transfer between its residents, is the formation of special organizational culture, and primarily identification of its key values. According to the authors, that developing and maintaining a culture focused on exchange of knowledge, by means of a corporate portal of a technopark, should be based on the following key values: cooperation, openness, trust, team spirit, and creativity.Developed proposals for the creation of a corporate portal for technoparks, including the key functions, basic tools, the structure of the project for the development and implementation are notable for their novel nature, are of practical importance and can be used in Russian technoparks.
An indicator of inefficient visualizations: the challenge of transparency during the COVID-19 pandemic in Brazil

The COVID-19 epidemic requires clear and reliable information to guide the population. Visualization is a powerful tool to contribute to the understanding of this data. However, just divulging this resource is not enough to guarantee this understanding. It is important to support users in analyzing this data, making this process easier and more transparent, especially for users with little (or no) literacy. In this work, we define an inefficient graphics indicator, that is, with the potential to be misinterpreted or difficult to understand, according to the basic guidelines of the data visualization area. These guidelines were selected through a literature review, forming a repository of practices that guide good visualization design and provide the indicator assessment items. This proposal can be applied inis necessary because we do not perceive the existence of standards, norms or quality indicators for data visualization that assist in the creation of this artifact efficiently or evaluate the existing ones for improvement, both manually and automatically. This approach can be applied in the most diverse scenarios, being initially in Brazil during the dissemination of COVID-19, analyzing the official visualizations and highlighting the failures of several epidemiological perspectives on the pandemic. Through a study with users where volunteers analyzed the official data and our recommendations, we demonstrated that the indicator is effective in detecting and helping to understand the visualization. We highlight the alert for the need for greater care in the creation of graphics by the government so as not to compromise the understanding of the citizens who use them.
Multi-Machine Gaussian Topic Modeling for Predictive Maintenance

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.