Reviewerly/exHarmony huggingface dataset:
Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS

The existence of non-uniformity in infrared detector output images is a widespread problem that significantly degrades image quality. Existing scene-based non-uniformity correction algorithms typically struggle to balance strong non-uniformity correction with scene adaptability. To address this issue, we propose a novel scene-based algorithm that leverages the frequency characteristics of the non-uniformity, combine and improve single-frame stripe removal, multi-scale statistics, and least mean square (LMS) methods. Following the “coarse-to-fine” correction process, the coarse correction stage introduces an adaptive progressive correction strategy based on Laplacian pyramids. By improving 1-D guided filtering and high-pass filtering to shape high-frequency sub-bands, non-uniformity can be well separated from the scene, effectively suppressing ghosting. In the fine correction stage, we optimize the expected image estimation and spatio-temporal adaptive learning rates based on guided filtering LMS method. To validate the efficacy of our algorithm, we conduct extensive simulation and real experiments, demonstrating its adaptability to various scene conditions and its effectiveness in correcting strong non-uniformity.
Diagnosis of COVID-19 disease based on a modified convolutional neural network on the example of lung X-rays

As a result of the study, a machine learning model based on a modified convolutional neural network was developed to diagnose COVID-19 lesions from lung X-rays. Due to the improvement of the network architecture, it was possible to obtain a classification accuracy of 91%. The developed model can be used in the field of health care to assist medical staff in the analysis of X-rays, which will reduce the likelihood of medical error.
PECULIARITIES OF PROFESSIONAL TRAINING OF FUTURE FOREIGN LANGUAGE TEACHERS FOR WORK IN INCLUSIVE CLASSROOMS

The reform of education in Ukraine has revealed a problem that the general public tried not to mention: the adaptation of children with physical disabilities to society. In the pre-reform period, there was no question of their studying in a general secondary education institution with their peers. Persons with disabilities were offered placement in specialised boarding schools, where they felt isolated from society. Instead, in international practice, there was a system of education for such children in general secondary education institutions, the so-called inclusion. While the issue of tolerance towards children with disabilities on the part of students was resolved fairly quickly, the problem of organising the work of teachers in inclusive classes proved to be much more difficult. Many teachers, including foreign language teachers, were not ready to work in such classes. The article “Features of professional training of future foreign language teachers for work in inclusive classrooms” explores the issue of preparing foreign language teachers to work with students with special needs in inclusive classrooms. The article considers the theoretical aspect of developing skills and abilities of foreign language teachers, considering the needs of students with various developmental characteristics. The author of the study highlights the main approaches and teaching methods that can be applied to work with such students. The article also discusses the issue of organizing a differentiated approach to foreign language teaching, considering the individual needs of each student. The author concludes that foreign language teachers should have not only knowledge of the language, but also knowledge and skills in working with students with special needs, which allows for creating a comfortable and effective learning environment for all students. This article may be useful for foreign language teachers, future teachers, and anyone interested in the issue of inclusive education and teacher preparation.
The Past and Present of Computer-Augmented Hypothesis Generation

Don Swanson’s career started on a path familiar to many who read this blog. A BA in Physics from CalTech in 1945. A Ph.D. in Theoretical Physics from UC Berkeley in 1952. A decade working in scientific labs.
How Recent Innovation in Jiu-jitsu Helps Us Think About OpenAI’s Future

Check out my guest post for the ChinaTalk Substack on recent innovation in the sport of jiu-jitsu and how it can help us think about OpenAI’s future.
DPIS: Blockchain Makes Personal Information Safer

This paper provides an in-depth analysis of the current state of personal information storage and usage, as well as the existing issues in this domain. It proposes an architecture for building a decentralized, self-organizing, and scalable personal information service system. By improving the organization of the blockchain and constructing a circular access structure, the storage and usage of personal information leverage blockchain technology and cryptographic techniques to enhance the system’s availability and reliability. The article elaborates on the various components of the system and their operational mechanisms, as well as the process for personal information query and authorization for use. The implementation and benchmarking results show the proposed architecture has significant performance advantage comparison with other main stream blockchain for safe date exchange. The proposed architecture balances the use of personal information with security protection, offering a standardized, secure, and convenient approach for personal information usage.
ABA Learning via ASP

Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples.We propose a novel method for implementing ABA Learning using Answer Set Programming as a way to help guide Rote Learning and generalisation in ABA Learning.
Communication and Computation Efficient Federated Learning for Internet of Vehicles with a Constrained Latency

Considering the privacy and security issues in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internet of Vehicles</i> (IoV), wireless <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> (FL) can be adopted to facilitate various emerging vehicular applications. However, wireless FL would suffer from a large learning latency due to the limitation of bandwidth and computing power as well as the unreliable communication caused by vehicle mobility. To cope with these challenges, a new structure is designed in this paper to facilitate the implementation of FL for IoV. First, we apply the gradient compression and mini-batch federated stochastic gradient descent to reduce the local gradient transmission and computation. Then, with theoretical analysis of the convergence rate and the learning latency, the learning performance can be improved by maximizing the convergence rate under a constrained latency. Accordingly, an optimization problem is formulated to jointly optimize compression ratio, batch size, and spectrum allocation. To solve this problem, an iterative algorithm is developed by problem decomposition. From the results, compression ratio and batch size should be adjusted according to the channel state information and computing power of the road side units to boost the learning efficiency at the cost of slight degradation on the learning accuracy. The superiority of the proposed algorithm is finally demonstrated through extensive simulations.
Semantic Data Sourcing for 6G Edge Intelligence

As a new function of 6G networks, edge intelligence refers to the ubiquitous deployment of machine learning and artificial intelligence (AI) algorithms at the network edge to empower many emerging applications ranging from sensing to auto-pilot. To support relevant use cases, including sensing, edge learning, and edge inference, all require transmission of high-dimensional data or AI models over the air. To overcome the bottleneck, we propose a novel framework of SEMantic DAta Sourcing (SEMDAS) for locating semantically matched data sources to efficiently enable edge-intelligence operations. The comprehensive framework comprises new architecture, protocol, semantic matching techniques, and design principles for task-oriented wireless techniques. As the key component of SEMDAS, we discuss a set of machine learning based semantic matching techniques targeting different edge-intelligence use cases. Moreover, for designing task-oriented wireless techniques, we discuss different trade-offs in SEMDAS systems, propose the new concept of joint semantics-and-channel matching, and point to a number of research opportunities. The SEMDAS framework not only overcomes the said communication bottleneck but also addresses other networking issues including long-distance transmission, sparse connectivity, high-speed mobility, link disruptions, and security. Re-identification experimental results on the CUHK-03 dataset are presented to demonstrate the performance gain of SEMDAS.
Deep Fake Video Detection Based on Enhanced Capsule Network with Golden Ratio

The fact that deepfake video content can be produced in a very realistic way causes serious social problems. Therefore, it is important to detect such fake content. In most of the studies in the literature, a certain number of random frames of the videos are selected and used in the detection processes. However, all frames are not equally important. In this study, which frames of the videos will be used are determined with the help of the golden ratio information on the face and classification is made with the capsule network. Frames selected using the golden ratio information constitute examples showing the feature differences that will enable real/fake distinction for detection tasks. The use of these examples in the training and testing stages of the used capsule network model greatly increases the detection performance. Experimental results were obtained for the Celeb-DF and DFDC-P databases. Better results were obtained in both Celeb-DF and DFDC-P databases compared to studies using the same capsule network.
Investigation of Approaches in the Literature and Potential Problems for Audio Spoofing Detection

Automatic speaker verification (ASV) systems are widely used in industry. Audio spoofing attacks, whose success has increased with the rapid developments in deep learning methods in recent years, threaten the reliability of ASV systems. There are many studies in the literature in order to take precautions against this threat. These studies mostly use data from the ASVspoof competition, which includes different types of attacks. Although ASVspoof includes different types of attacks, such as text-to-speech, audio conversion, and playback, results can be misleading because training and test data are single-sourced and the data is unbalanced. In our study, instead of trying to create new state-of-the-art methods, we examined the way the problem was handled and relaibility of results from literature. In this paper, the contributions to the literature can be summarized as: showing that it is possible to detect different types of attacks with a single model, combining models that give successful results in different attack types with hybrid architectures can increase performance, the effect of the reliable class in the training data on the generalization performance, and the data enrichment methods can significantly increase the performance.
Beyond Traditional Neural Networks: Toward adding Reasoning and Learning Capabilities through Computational Logic Techniques

Deep Learning (DL) models have become popular for solving complex problems, but they have limitations such as the need for high-quality training data, lack of transparency, and robustness issues.Neuro-Symbolic AI has emerged as a promising approach combining the strengths of neural networks and symbolic reasoning.Symbolic Knowledge Injection (SKI) techniques are a popular method to incorporate symbolic knowledge into sub-symbolic systems.This work proposes solutions to improve the knowledge injection process and integrate elements of ML and logic into multi-agent systems (MAS).
Performance Comparison of Vision Transformer-Based Models in Medical Image Classification

In recent years, convolutional neural networks have shown significant success and are frequently used in medical image analysis applications. However, the convolution process in convolutional neural networks limits learning of long-term pixel dependencies in the local receptive field. Inspired by the success of transformer architectures in encoding long-term dependencies and learning more efficient feature representation in natural language processing, publicly available color fundus retina, skin lesion, chest X-ray, and breast histology images are classified using Vision Transformer (ViT), Data-Efficient Transformer (DeiT), Swin Transformer, and Pyramid Vision Transformer v2 (PVTv2) models and their classification performances are compared in this study. The results show that the highest accuracy values are obtained with the DeiT model at 96.5% in the chest X-ray dataset, the PVTv2 model at 91.6% in the breast histology dataset, the PVTv2 model at 91.3% in the retina fundus dataset, and the Swin model at 91.0% in the skin lesion dataset.
How QDR Meets Federal Criteria for Data Repositories

How QDR Meets Federal Criteria for Data Repositories Two US federal government agencies recently published exciting, forward-looking guidance on data sharing for researchers receiving US federal funding.
PID parameter tables after ITAE to control overshooting systems found with AI algorithm

Embedded controllers are used in many applications. It often happens that these do not offer any possibilities to set the controller parameters, because they are capsulated to outside. The closed-loop systems are then stable, but the systems are often weakly damped and their step responses exhibit overshoot. In the following, a method is described, how such embedded controlled systems can be controlled via a superimposed feedback loop using PID controllers in such a way that the system deviation of the step response is minimized according to the ITAE method. A method from artificial intelligence, hill climbing, is used for this purpose. For the control of such systems, the paper essentially provides PID controller parameters, which are usable for many weakly damped systems. The parameters are verified in a practical application with a speed control of a motor with inertial mass.
Just look away: Could attention allocation to scene grammar violations during unrelated object searches be modulated by individual differences in language experience?

Disengaging attention from task-irrelevant information is not only a process crucial to navigating the world around us but may also be a skill that bilinguals are particularly well-practiced at as they are constantly juggling information from multiple languages. The current study investigates the extent to which individual differences in language experience are associated with increased object search efficiency in scenes containing mismatches between objects and scene context. Efficient search in real-world scenes relies on a set of rules (“scene grammar”), which support object localization and identification (e.g., knowing that a spatula goes neither in a toaster nor a bathroom). Violations to these rules may impair processing as viewers need to resolve ambiguity resulting from the unexpected element of the scene and have been shown to modulate ongoing eye-movements, even when they are irrelevant to the task at hand. Twenty-one young adults performed a visual search task on scenes containing scene grammar violations while their eye movements were recorded. Targets were either present or absent from the scene, but never part of the violation. Additionally, participants responded to a language background questionnaire. Our preliminary analysis revealed significant interactions between whether the target was present or absent from the scene and second language proficiency on dwell time and number of fixations, with higher second language proficiency being associated with shorter dwell times and fewer fixations to the scene grammar violation when the target was absent. Of note, no such interaction was observed on time to first fixation of the violation, suggesting that these differences might only arise during later stages of search. Taken together, these preliminary results suggest that knowledge of a second language may be associated with more effective disengagement of attention from distracting violations that are irrelevant to the search task.
Investigating the effects of a virtual reality vs. screen-based testing setup on incidental memory after visual search through scenes

Many experiments investigating visual search are performed on a computer screen under highly controlled settings. However, the generalizability of the investigated search mechanisms to the real world is unclear. Here, we tested whether the formation of incidental object memories during visual search follows similar patterns in virtual reality (VR) and on a computer screen. We present two studies that were identical in the administered tasks, but differed in the setup they were tested in. In both experiments, participants searched for ten out of 20 objects in indoor scenes, either in full illumination or with constrained visual input via a controller-contingent 8-degree window (flashlight condition). After the search task, participants’ incidental memory was tested with two surprise tasks: an object recognition and a location memory task (scene rebuilding). Critically, the first experiment took place in an immersive virtual environment, while the second experiment displayed the same interactive 3D environment on a computer screen. In VR, we found that participants relied more on memory when searching through scenes with a flashlight: Search times decreased more strongly over trials and non-targets were recognized better than ones from illuminated scenes. Both results indicate stronger memorization of objects when only limited visual input was available. Additionally, participants were able to reallocate target and non-target objects from the flashlight condition just as accurately as from illuminated scenes, despite never having seen them in full view, indicating the formation of a similarly holistic scene representation in both conditions. In contrast, when administered on a computer screen, preliminary analyses suggest no benefit for object recognition in the flashlight condition, and a detrimental effect on location memory. Our results indicate that visual search on a computer screen elicits weaker scene memory than in VR. Sensorimotor signals engaged in the search process might play a role for strengthening scene memory.
Are attentional templates based on physical feature values or perceptual interpretations?

Visual attention is thought to select task-relevant objects using attentional templates tuned toward relevant features. When we look for ripe raspberries, templates are tuned to red. But what information are these guidance templates tuned toward: perceptual interpretations or physical values? Are they tuned to physical red (wavelength roughly 620-750 nm) or to what appears red in the current context due to color constancy mechanisms? We addressed this question using a variation of the feature distribution learning paradigm (FDL; Chetverikov et al., 2016). Our observers (N=15) searched for an odd-one-out colored diamond among 47 distractors and reported which of the four corners of the target diamond had a notch (4AFC). Importantly, observers performed mini-blocks of visual searches within displays as seen through different color filters. Each mini-block consisted of three to four learning trials and one test trial. Throughout the learning trials, the target, distractors, and color filter were constant, promoting the formation of a specific attentional template. Importantly, on test trials, we manipulated context via color filters and/or the physical values of the stimuli to introduce a physical change or a perceptual change in target and distractor colors. The results showed that after the physical change (when perceptual values stayed the same), there were little or no differences in RT and error rates between the prime and test trials. Conversely, RT and error rates increased dramatically after the perceptual change. This means that perceptual, not physical changes affect the attentional selection and that attentional templates are formed according to perceptual interpretations, not physical values. This supports the idea that attentional guidance templates operate at higher levels of the visual hierarchy where perceptual interpretation has been resolved.
Context Matters: Understanding the Effect of Usage Contexts on Users’ Modality Selection in Multimodal Systems

AbstractUsers often select different modalities in multimodal systems based on context and function. This study aimed to investigate the impact of context on users’ modality selection in a multimodal system. A modality selection task was presented through the developed multimodal system, where the task’s difficulty was controlled based on the number of syllables and touches. The experiments were examined in four usage contexts (baseline, watching, reading, and driving), and the selected modality, physical and mental effort, and satisfaction were measured. A regression model for predicting modality selection was established using binomial logistic regression. The results showed that voice usage increased in the order of baseline, watching, reading, and driving contexts. It was revealed that these changes were attributable to differences in physical and mental interaction efforts of modalities according to contexts. Our finding provides valuable insights into users’ modality selection in different contexts, which could inform the design of more efficient multimodal systems.Keywords: Multimodalmodality selectionusage contexttouch interactionvoice modality Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMin Chul ChaMin Chul Cha is a Postdoctoral Researcher in Industrial Engineering at Yonsei University, Seoul, Korea. He received his PhD from Yonsei University in 2023. His research interests include multimodal interface, voice user interaction, automotive user interface, and smart devices.Yong Gu JiYong Gu Ji is a Professor in the Department of Industrial Engineering at Yonsei University, where he directs the Interaction Design Laboratory. He received his PhD in Human Factors/HCI from Purdue University. His research interests include usability/UX in smart devices and self-driving vehicles.