Reviewerly/exHarmony huggingface dataset:
Feature Variance Loss: A Loss Function for Semantic Segmentation using Feature Space Distance

Semantic segmentation is a task that classifies all pixels in an image. In terms of the classification task, the segmentation network uses convolutional neural network(CNN) to extract the feature vectors of all pixels and infer which class they are. Pixels that correspond to the same class from the perspective of contrastive learning should have similar feature vectors extracted. Using the assumption that similar feature vectors should be extracted in the same class, we propose a Feature Variation(FV) loss function that induces the corresponding feature vectors of the same class to have similar values. This FV loss induces features extracted from the same class to be located closely in the feature embedding space. The proposed FV loss can be applied model-agnostic by adding it to the existing segmentation network. As demonstrated in the paper, the proposed FV loss improves the performance of the segmentation by inducing the feature vector of the same class to be closer.
Cognitive modeling using multivariate multiscale entropy analysis of EEG: entropy changes according to auditory inputs and the level of attention

Multivariate multiscale entropy (MMSE) is one measure to measure the amount of information in a random signal. The amount of information contained in electroencephalogram (EEG) signals that vary depending on auditory input and state was measured using MMSE in this study. As a result, the MMSE value rises not only when rehearsal of a given sentence is performed, but also when a non-semantic sentence is given, and appropriate noise is mixed in with the input sentence. As a result of these findings, this study proposes a method for quantitatively analyzing various cognitive models.
A Multi-branch Network with Internal Feature Fusion for Road Risk Detection

To detect and locate road objects precisely is crucial for the routing and navigation of autonomous vehicles and robots. A deep neural network can be used for this purpose but the inference cost is usually too high for real-time processing with low-cost devices. How to guide the network to attend risky areas plays an essential role in cost reduction. This paper proposes a novel multi-branch network that selectively extracts pre-specified features and detects road risks in corresponding regions of interest through different branch networks. The proposed network consists of one common block to obtain global features and several branches to detect road risks in particular interested regions. Experimental results show that our method can reduce the inference cost significantly while preserving accuracy.
Procedural Content Generation using Reinforcement Learning and Entropy Measure as Feedback

In this work, we investigate how we can approach procedural content generation with reinforcement learning and mixed-initiative design. A second question discussed here is how we can use metrics to evaluate the diversity of the generated level. Our proposal has as its main hypothesis to use scenario models, provided by an expert human level designer specialist, for the reinforcement learning agents in order to generate new scenarios. The levels provided by the specialist are separated into segments or blocks that are used to compose the new scenario structures. Also, a new reward function based on the use of entropy was proposed to measure the diversity of the generated scenarios. Initially, we trained our model for three different 2D Dungeon crawlers game environments. We analyzed our results through the value of the entropy, and it shows that our approach can generate wide levels with a diversity of segments.
AI life safety image classification by using CNN architecture

A user of life-safety service can upload safety related image by using his smartphone. Until now, when uploading an image related to life safety, the user had to manually input the description of the image by himself. To eliminate this process and simply upload a life safety image, the life safety prevention service platform uses AI to analyze the image uploaded by the user and automatically analyze and process what kind of risk the image represents. By using AI classification technology, the convenience of safety reporting can be improved more user friendly.
DRLeague: a Novel 3D Environment for Training Reinforcement Learning Agents

The development of autonomous agents performing unique interactions that resemble human-like behavior is currently driven by Deep Reinforcement Learning (DRL) techniques combined with complex virtual environments. These constitute an active field of research that is fueled by environments usually inspired or borrowed from video games. Although works in the area commonly do not make use of trending 3D games, these games are interesting testbeds for more complex and compelling behaviors, as they tend to explore more variables than their predecessors. This paper introduces DRLeague, a novel DRL environment, proposed to be open-source, and easily customizable, which supports mechanics for 3D games inspired by the popular “car football” game Rocket League. Besides the typical gameplay, we implemented four challenging minigames based on the mechanics from this title with advanced physics simulation and fine-grained car control: penalty shoot, multiplayer penalty shoot, barrier kick, and aerial shoot, each of these requiring more complex skills than the previous ones. Finally, we provide solid baseline experimental results showing the learning progress of agents using Unity's ML-Agents toolkit, evidencing DRLeague as a suitable testbed in the application of machine learning techniques.
Exploring Deep Reinforcement Learning for Battling in Collectible Card Games

Collectible card games (CCGs), such as Magic: the Gathering and Hearthstone, are a challenging domain where game-playing AI arguably has not yet reached human-level performance. We propose a deep reinforcement learning approach to battling in CCGs, using Legends of Code and Magic, a CCG designed for AI research, as a testbed. To do so, we formulate the battles as a Markov decision process, train agents to solve it, and evaluate them against two existing agents of different skill levels. Contrasting with the current state-of-the-art, our resulting agents act fast and can play many battles per second, despite their limited performance. We identify limitations and discuss several promising directions for improvement.
Fast and energy-efficient object detection and tracking for de-identification in video

In modern life, camera modules are available everywhere, including smartphones and home appliances so efficient de-identification methods are continuously required to protect personal information. In this paper, we propose object detection and tracking method that operates within the decoding process in video for fast de-identification. We reduce computational complexity by partially detecting personal information such as faces and vehicle license plates in GoPs and tracking locations using object displacement features. The proposed method confirms the reduction in computation and power consumption through experiments.
Contrastive Learning Aided Single Image Deblurring

Fast and high-quality image deblurring is essential for real-time applications on edge devices. Reduction of the solution space is important for the ill-posed deblurring task. Different from most existing deblurring networks that pull together a blurry image and a sharp image in the latent space, this paper proposes contrastive learning aided deblurring that also pushes a deblurred image apart from a blurry image for training the existing MIMO-UNet. The contrastive loss combined with the multi-scale content loss and frequency reconstruction loss helps the deblurring network pull together the anchor and positive pair while pushes the anchor apart from the negative pair. The proposed progressive negative sample scheme gradually increases the lower bound of the quality of negative samples to keep the contrastive loss decreasing. For the GoPro dataset, the proposed scheme improves the quality of MIMO-UNet while keep the low inference time (0.392 second on GPU 1080Ti) and number of parameters (6.8 M) of the network unchanged.
Text-Driven In-The-Wild Car Image Manipulation Using Object Localization and Optimized Encoder Inversion

Notwithstanding remarkable advancements in text-prompted image manipulation tasks, there are limited or no application cases of the models on real-world car images. This is in part due to the less consistent structure of car images relative to human faces. We propose a new framework combining an object localization method and an encoder-based network optimized for input images to overcome the limitations and achieve higher reconstructive ability and editability in the text-driven image manipulation task on unseen car images. We first utilize YOLOv5 for object localization. The cropped images are then inverted to latent representations through HyperStyle. Finally, we leverage the power of StyleCLIP to enable text-driven image manipulation on in-the-wild car images.
A Digital Sign Language Recognition based on a 3D-CNN System with an Attention Mechanism

Sign language recognition makes communication between hearing impaired people and people more convenient. The rapid development of deep learning has brought new opportunities for sign language recognition. This paper proposes a 3D-CNN with the attention mechanism for real-time digital sign language recognition. The background noise in the gesture data extraction process is removed, and the introduction of the attention mechanism enables the model to enhance the feature expression from the angle information. The experimental results show that our system performs real-time recognition of digital sign language well, and the accuracy rate reaches 98.49%.
Data Scanning Methods for Quantum-Classical Interface in Quanvolutional Neural Networks

Quantum machine learning has recently drawn a lot of attention of the research community. Particularly, hybrid quantum-classical architectures have been introduced to take advantage of advances in both classical computing and quantum computing. However, most existing quantum circuits are of the 1D type. For 2D contents, such as images, to be represented in quantum states, they must be converted to an 1D array. In this paper, we introduce some data scanning methods for 2D-to-D1 mapping. These methods are investigated in the context of quanvolutional neural networks. Through experiments, it is found that the proposed scanning methods provide significant gains compared to the default raster scan.
Joint Test Action Group Data Acquisition for Cyber-Physical System Security

It is crucial to secure the cyber-physical systems that automate the industrial processes essential to modern life. A hardware monitor utilizing the JTAG interface supported by most microprocessors could provide an immutable security monitor for industrial controllers. This paper explores the viability of using a commercial off-the-shelf debugger to dynamically extract memory and register data from industrial controllers to detect cyber attacks. We conducted five experiments using the Lauterbach PowerDe-bug Pro and the Schweitzer Engineering Laboratories RTAC-3505 real-time automation controller to determine access speed for register and memory information. The results show the fastest average downtime for register acquisition to be 8.178ms, with the fastest average downtime for processor pausing being 8.087ms. The fastest average time for reading 1KB of memory information was 24.798ms. The need to pause the microprocessor before data collection is the most significant performance-limiting factor. Based on these results, we conclude that commercial debuggers are not designed for high-speed data collection and are unsuitable for realtime cyber-attack detection. In the future, a custom tool may be designed to optimize data collection, reduce cost, and minimize the performance impact to the target device.
Calibration and Definition of a System for Automated Ship Length Measurement

This paper describes the system for automated ship length measurement. The methodology of reconstructing a 3D scene using a stereovision system with all necessary steps is presented. The location of the system at the entrance to the marina has been proposed. The process of stereovision calibration is explained, and results of calibration are presented. The image rectification process and the calculation of disparity were performed. The disparity is used to reconstruct the 3D scene and dimensions of the ship. Examples of this process are presented. The stereo vision system will be used for text recognition and ship length measurement on low power embedded systems.
Cost Aggregation with Transformers for Sparse Correspondence

In this work, we introduce a novel network, namely SuperCATs, which aims to find a correspondence field between visually similar images. SuperCATs stands on the shoulder of the recently proposed matching networks, SuperGlue and CATs, taking the merits of both for constructing an integrative framework. Specifically, given keypoints and corresponding descriptors, we first apply attentional aggregation consisting of self- and cross-graph neural network to obtain feature descriptors. Subsequently, we construct a cost volume using the descriptors, which then undergoes a tranformer aggregator for cost aggregation. With this approach, we manage to replace the hand-crafted module based on solving an optimal transport problem initially included in SuperGlue with a transformer well known for its global receptive fields, making our approach more robust to severe deformations. We conduct experiments to demonstrate the effectiveness of the proposed method, and show that the proposed model is on par with SuperGlue for both indoor and outdoor scenes.
3D Synthetic Image Training and Testing Data for Autonomous Driving

In this paper, we propose generating training and testing images for autonomous driving using the CARLA simulator. Several experiments have been performed to prove the effectiveness of training on synthetic data, including those on mixed datasets including both data generated from CARLA and real-world data. The result of such experiments showed that synthetic data generated using the CARLA simulator was effective as training and testing data for on-road object detection.
Supervised and Unsupervised Machine Learning Approaches on Class Imbalanced Data

Huge amounts of data are stored digitally every day. This data has various characteristics. Class imbalance is one of the characteristics that has the effect of machine learning algorithms performance and this problem is receiving attention among academia and industry. Class imbalance occurs when the number of instances in one class is significantly different than the number of instances in the other class (in binary classification). In this paper, we are combining supervised and unsupervised machine learning approaches on one imbalanced dataset from a publicly available repository. Unsupervised machine learning approach of cluster analysis is applied on the most significant variables discovered by sensitivity analysis on predictive models developed by decision tree. Our results indicated a hybrid approach of decision tree and cluster analysis as a promising tool to work with imbalanced data.
Design and Architecture of an Open Multiplayer Exergaming Platform called Move2Play

Exergaming has been shown to increase the interest in regular physical activity. However, the available options usually involve acquiring specialized hardware, may be tied to a single game, and are generally designed for single player. A gym environment, for example, could benefit from not having to acquire adhoc and expensive hardware for each user. In this scenario, we propose Move2Play, an exergaming platform that relies on commodity hardware - a computer, a screen, and one smartphone per player - to offer single and multiplayer game sessions. In addition, the platform is extensible to new games, which third parties can produce. Also, four toy exergames were developed for demonstration. We believe that Move2Play can foster the practice of physical activities and contribute to promoting health and well-being.
Visual Quality Inspection of Rotors and Stators

Automated visual quality inspection using machine vision is now widely used to reduce wastage and ensure better quality of products. Numerous algorithms using computer vision were made to solve specific issues in the industry. Each of these issues is unique in some way, and so are their solutions as well. We describe a system for visual quality inspection that we developed for a client who produces some mechanical parts for automobile industry. System uses classical computer vision algorithms to achieve sub-millimeter level fault detection tolerance.
Spatial and Temporal Complexity Analysis of 4K Drone Footage

Video encoding nowadays mostly revolves around optimizing and modifying existing encoders. Popular encoders such as H.264 and H.265 have open source implementations, and researchers oftentimes modify only certain parts of the encoder to obtain desired results. In this work, several 4K raw drone videos will be analyzed in regard to spatial and temporal complexity, which could be used for encoder optimization. In the last few years, drones have gained in popularity and their use cases have expanded. Drone racing and filmmaking evolved from being only hobbies, and today there are professional drone racers and filmmakers. Hence, drone video transmission systems must keep pace with industrial demand. It is known that scene complexity affects encoding quality. Specifically, spatial information (SI) and temporal information (TI) can be calculated in various ways and depending on SI and TI score, video bitrate must be increased/decreased in order to sustain the desired quality level.
Bio-inspired wrapper-based feature selection: does the choice of metric matter?

Applications of bio-inspired algorithms to feature selection offering promising performance can be frequently encountered in the literature. Serving as wrappers, their application essentially boils down to the selection of a classifier and an appropriate metric for evaluating model performance. Although the choice of classifier was investigated to a viable extent, this is, however, not the case for the choice of metric and its impact on performance. Nevertheless, the utilisation of a wide variety of metrics is clearly apparent, albeit to different proportions. This raises the question of whether some metrics might be a better choice than others for bio-inspired wrapper-based methods. This paper sheds some light on this matter by comparing different metrics from different perspectives, like correlation, general performance and subset sizes. Unexpected results were obtained, to say the least, apart from some minor exceptions. Generally, no single metric appears to have an edge over the others. Yet, further investigation is certainly warranted.
Artificial Intelligence in Breast Imaging: A Special Focus on Advances in Digital Mammography &amp; Digital Breast Tomosynthesis

Breast cancer accounts for 30% of female cancers and is the second leading cause of cancerrelated deaths in women. The rate is rising at 0.4% per year. Early detection is crucial to improve treatment efficacy and overall survival of women diagnosed with breast cancer. Digital Mammography and Digital Breast Tomosynthesis have widely demonstrated their role as a screening tool. However, screening mammography is limited by radiologist's experience, unnecessarily high recalls, overdiagnosis, overtreatment and, in the case of Digital Breast Tomosynthesis, long reporting time. This is compounded by an increasing shortage of manpower and resources issue, especially among breast imaging specialists. Recent advances in image analysis with the use of artificial intelligence (AI) in breast imaging have the potential to overcome some of these needs and address the clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. This article focuses on the most important clinical implication and future application of AI in the field of digital mammography and digital breast tomosynthesis, providing the readers with a comprehensive overview of AI impact in cancer detection, diagnosis, reduction of workload and breast cancer risk stratification.
Cyber Deception, the Ultimate Piece of a Defensive Strategy - Proof of Concept

Asymmetries between attacker and defender approaches are made more significant. Defenders are in a perpetual course to protect their assets, data and network complying with regulations and standards while attackers can win the battle by breaching one critical asset with few resources. What about inverting this dominance or at least making both players equally competent? Security by Deception is a proactive strategy that deliberately introduces misinformation or misleading functionalities into existing systems to trick and redirect adversaries to a fake environment in ways that render attacks ineffective and allow collecting intelligence. Today, organizations are still reluctant to implement cyber Deception as there is little consensus on what Deception actually is and how to deploy it to prevent adversaries from accessing organization's critical assets. We provide an overview of the cyber Deception technology and posit a methodology followed by a Proof of Concept showing how to successfully integrate a Deception strategy in an active production environment. We use two leading solutions of the Deception market, which have fundamentally different approaches. Tests are run by a Red team and analysis is done with respect to predefined criteria to assess the opportunity to deploy Deception as a defensive strategy on the defender's environment, with the platform that best suits its ecosystem. Recommendations and lessons learned are provided for future expansion.
Semi-Supervised Learning using Sequential Data for Mobile Applications

Learning on mobile devices has various constraints unlike workstations or learning servers. The entire training dataset is not defined before training network since the dataset should be collected with online. Thus, it is not a problem of learning a well-classified dataset, but a problem of learning using the input of a real camera. In addition, the camera input has the characteristics of unlabeled data, so it is difficult to apply the traditional supervised learning technique. In this paper, we propose a method for using semi-supervised learning on a mobile device with sequential input. In order to verify the proposed method, we make our own dataset for classifying 5 human faces with sequential input. In the experimental section, we verified that the proposed method effectively selects images from a continuous data set and prevents overfitting.