Reviewerly/exHarmony huggingface dataset:
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space

This paper introduces a novel deep-learning approach for human-to-robot motion retargeting, enabling robots to mimic human poses accurately. Contrary to prior deep-learning-based works, our method does not require paired human-to-robot data, which facilitates its translation to new robots. First, we construct a shared latent space between humans and robots via adaptive contrastive learning that takes advantage of a proposed cross-domain similarity metric between the human and robot poses. Additionally, we propose a consistency term to build a common latent space that captures the similarity of the poses with precision while allowing direct robot motion control from the latent space. For instance, we can generate in-between motion through simple linear interpolation between two projected human poses. We conduct a comprehensive evaluation of robot control from diverse modalities (i.e., texts, RGB videos, and key poses), which facilitates robot control for non-expert users. Our model outperforms existing works regarding human-to-robot retargeting in terms of efficiency and precision. Finally, we implemented our method in a real robot with self-collision avoidance through a whole-body controller to showcase the effectiveness of our approach.
From RGB images to Dynamic Movement Primitives for planar tasks

DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also features relating to the overall structure and layout of the scene. To make DMP applicable to a wider range of tasks and further automate their use, we design a framework combining deep residual networks with DMP, that can encapsulate different motion patterns of a planar task, provided through human demonstrations on the RGB image plane. We can then automatically infer from new raw RGB visual input the appropriate DMP parameters, i.e. the weights that determine the motion pattern and the initial/target positions. We compare our method against another SoA method for inferring DMP from images and carry out experimental validations in two different planar tasks.
Optimal State Estimation for DNN Visual Servoing Systems with Detection Loss

The introduction of deep learning techniques, such as object detection in visual servoing systems, has produced more sophisticated robotic systems capable of working in unknown environments. However, the interaction between the object detection network and the controller, when detection loss occurs, has received little attention. In this paper, we investigate a way of mitigating the effect of detection loss in VSNN systems. In our approach detection losses are modeled as a Bernoulli random variable and integrated into the state space model of the dynamic system. To mitigate the effect of detection loss, we propose a variation of a Kalman filter, that artificially inflates the measurement noise covariance when detection loss occurs. The Kalman filter was implemented on a 6DOF robotic manipulator with an eye-in-hand configuration with YOLOv5 as the object detection network. The results show, that the proposed Kalman filter decreases the effects of detection losses and significantly improves performance compared to having a standard Kalman filter, and not having a state estimator at all. The benefit of our approach is especially noticeable when detection loss occurs frequently and for relatively long periods of time.
Analyzing Insider Threats and Human Factors in Healthcare 5.0

The healthcare industry has shifted towards Healthcare 5.0, emphasizing electronic and remote services. However, this has also increased the risk of data breaches, often caused by human errors rather than technology flaws. This paper highlights the importance of addressing human-related factors to balance technological advancements and data security in healthcare. It specifically examines insider threats as a significant human factor affecting data security and discusses various types of insiders and related security threats. The paper also explores preventive strategies based on human factor analysis to enhance security measures for protecting sensitive patient information within healthcare organizations.
Reservoir Computing for Dispersion Compensation in IMDD Transmission

The digital reservoir computing for chromatic dispersion compensation is compared with traditional FFE and DFE equalizers in the IMDD transmission system for the first time, showing its more than 1-dB penalty of receiver sensitivity.
Conditioning Latent-Space Clusters for Real-World Anomaly Classification

Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles. In this work, we focus on high-resolution camera data from urban scenes that include anomalies of various types and sizes. Based on a Variational Autoencoder, we condition its latent space to classify samples as either normal data or anomalies. In order to emphasize especially small anomalies, we perform experiments where we provide the VAE with a discrepancy map as an additional input, evaluating its impact on the detection performance. Our method separates normal data and anomalies into isolated clusters while still reconstructing high-quality images, leading to meaningful latent representations.
ForestMonkey: Toolkit for Reasoning with AI-Based Defect Detection and Classification Models

Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have gained popularity recently, enabling users to explain the predictions or decision processes of AI models. This paper introduces Forest Monkey (FM), a toolkit designed to reason the outputs of any AI-based defect detection and/or classification model with data explainability. Implemented as a Python package, FM takes input in the form of dataset folder paths (including original images, ground truth labels, and predicted labels) and provides a set of charts and a text file to illustrate the reasoning results and suggest possible improvements. The FM toolkit consists of processes such as feature extraction from predictions to reasoning targets, feature extraction from images to defect characteristics, and a decision tree-based AI-Reasoner. Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets. Lastly, a tutorial is provided to guide users in performing reasoning tasks using the FM toolkit.
Efficient Learning of Fast Inverse Kinematics with Collision Avoidance

Fast inverse kinematics (IK) is a central component in robotic motion planning. For complex robots, IK methods are often based on root search and nonlinear optimization algorithms. These algorithms can be massively sped up using a neural network to predict a good initial guess, which can then be refined in a few numerical iterations. Besides previous work on learning-based IK, we present a learning approach for the fundamentally more complex problem of IK with collision avoidance. We do this in diverse and previously unseen environments. From a detailed analysis of the IK learning problem, we derive a network and unsupervised learning architecture that removes the need for a sample data generation step. Using the trained network's prediction as an initial guess for a two-stage Jacobian-based solver allows for fast and accurate computation of the collision-free IK. For the humanoid robot, Agile Justin (19 DoF), the collision-free IK is solved in less than 10 ms (on a single CPU core) and with an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \times 10^{-4}\mathrm{m}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 10^{-3}$</tex> rad based on a high-resolution world model generated from the robot's integrated 3D sensor. Our method massively outperforms a random multi-start baseline in a benchmark with the 19 DoF humanoid and challenging 3D environments. It requires ten times less training time than a supervised training method while achieving comparable results.
Machine Learning Technology to Recognize American Sign Language Alphabet

Recent technological advancements have facilitated the development of a variety of tools and software designed to enhance the quality of life for individuals with hearing impairments. In this research paper, a comprehensive investigation was conducted utilizing three distinct machine learning models to interpret hand gestures representing the American Sign Language (ASL) alphabet. The study employed the Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) models, all trained and tested using a dataset comprising 87,000 images of ASL alphabet hand gestures converted into grayscale pixels. Multiple experiments were performed, involving adjustments to the models' architectural parameters to achieve the highest possible recognition accuracy. The experimental results were exceptional; the Random Forest model achieved an outstanding accuracy rate of 99.55%, the highest among all models. The SVM model achieved an accuracy rate of 99.29%, while the KNN model reached an accuracy rate of 98.69 %.
Text Description to Facial Sketch Generation using GANs

Text-to-face generation is an exciting area of research focused on generating human faces based on textual descriptions, presenting unique challenges that have primarily been explored in academia. The advancement of generative adversarial networks (GANs) has played a crucial role in implementing text-to-face generation. This study aims to develop a framework that allows users to express their desired human face characteristics through textual descriptions, with the tool generating lifelike human face images in response. The approach utilizes a Deep Fusion generative adversarial network (DFGAN) architecture, incorporating deep convolutional neural networks (CNNs), trained on the CelebA dataset, which provides a diverse collection of labeled face images for training. The two model's performance are evaluated using the Fréchet Inception Distance (FID) score. DFGAN has achieved a FID score of 100 after evaluating 15k images, surpassing the results obtained by the DCGAN model.
Efficient Neuroevolution Using Island Repopulation and Simplex Hyperparameter Optimization

Recent studies have shown that the performance of evolutionary neural architecture search (i.e., neuroevolution) algorithms can be significantly improved by the use of island based strategies which periodically experience extinction and repopulation events. Further, it has been shown that the simplex hyperparameter optimization (SHO) method can also improve neuroevolution (NE) performance by optimizing neural network training hyperparameters while the NE algorithm also trains and designs neural networks. This work provides an extensive examination of combining island repopulation events with five different island-based variations of SHO. These methods are evaluated for the evolution of recurrent neural networks for the challenging problem of multivariate time series forecasting on two real world datasets. We show with statistical significance that adding repopulation to the SHO variants in almost every case improves performance, and for those that does there is no statistical difference. In addition, we find that one variant in particular, multi-island, random island best genome (MIRIB) performs the best across all experiment types.
One-Class Classification for Intrusion Detection on Vehicular Networks

Controller Area Network bus systems within ve-hicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely on there being a sufficient representation of attack data, which may not be available due to there either not being enough data present to adequately represent its distribution or the distribution itself is too diverse in nature for there to be a sufficient representation of it. With the use of one-class classification methods, this issue can be mitigated as only normal data is required to train a model for the detection of anomalous instances. Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks. In this paper, we investigate the performance of various state-of-the-art one-class classification methods for detecting injection attacks on Controller Area Network bus traffic. We investigate the effectiveness of these techniques on attacks launched on Controller Area Network buses from two different vehicles during normal operation and while being attacked. We observe that the Subspace Support Vector Data Description method outperformed all other tested methods with a Gmean of about 85%.
Domain-Specific Large Language Model Finetuning using a Model Assistant for Financial Text Summarization

The financial market and public opinion are cor-related. This means that changes in the financial market can result in changes to public opinion and changes to public opinion can result in changes to the financial market. Accordingly, it is essential for understanding and interacting with the financial market to gather text content from online sources and process it. As a result of the rapid growth of social media and other online sources, we have seen an exponential rise in data, particularly textual data, in recent years. It can be difficult for a person to read, let alone process, the massive volumes of data generated every day. This indicates that we need automated methods for processing textual data and extracting useful information. Automated text summarization is a method of shortening huge amounts of text without losing essential information. Transformers, which can efficiently manage and analyze textual data, are state-of-the-art text summarization models. However, developing such an automated text summarization model specialized in a domain (e.g. finance) can be challenging since we lack necessary domain-specific summarization datasets. In this work, we propose a pipeline for fully automating the finetuning of a text summarization model in a specific domain, namely cryptocurrency domain, without the involvement of human annotators. To this end, we introduce a novel method for self-improvement of text summarization models which relies on a model assistant which encodes domain knowledge, enabling finetuning text summarization models in specific domains in which we lack specific-domain summarization datasets. The proposed method is evaluated on a cryptocurrency-related text summarization problem and three well-known Large Language Models (LLMs) used for text summarization.
Decision Support Component for the Localized Epidemiological Modelling of COVID-19

This study develops a decision support system for localized epidemiological modelling of infectious disease spread. We propose a Bayesian network topology for performing inference supplementary to an epidemiological simulation framework and a cohesive integration of this decision support system with the framework. The Bayesian network topology is structured with data defined as inputs, outputs, or derived features within the simulation framework. All features are mo-tivated by their clinical relevance and utility for administrative policy guidance. Edges in the final network are quantitatively assessed using structural equation modelling to ensure strong causal connections. Various inference scenarios are demon-strated to provide proof of concept for real-world application and validation in future directions. The outcomes of this project contribute to a larger body of work for infectious disease risk mitigation and emergency management in generalized environments.
Observing Bag Gain in JPEG Batch Steganography

The bag gain relates to a gain in security due to spreading payload among multiple covers when the steganographer maintains a positive communication rate. This gain is maximal for a certain optimal bag size, which depends on the embedding method, payload spreading strategy, communication rate, and the cover source. Originally discovered and analyzed in the spatial domain, in this paper we study this phenomenon for JPEG images across quality factors. Our experiments and theoretical analysis indicate that the bag gain is more pronounced for senders that assign payloads based on image content more aggressively and maintain a fixed payload per bag in terms of bits per DCT rather than per non-zero AC DCT. We also observe a larger bag gain for higher JPEG qualities.
Towards Summarization of Aggregated Multimedia Verticals Web Search Results

The web is experiencing exponential growth of multimedia content. Due to the enormity of multimedia information, web users employ web search engines as a gateway to access multimedia information. The existing state-of-the-art web search provides access to multimedia content by aggregating media-specific content called verticals. However, these verticals are disjoint and provide multimedia search results in separately ranked linear lists. Moreover, the traditional search results presentation in ranked linear lists poses several challenges to user interaction. Users with exploratory needs have to perform several clicks and scrolls through extensive lists to explore their desired information, hindering efficient interaction with the retrieved information. We propose a generic multimedia verticals summarization framework that aggregates and summarizes the disjoint verticals in conceptually coherent multimedia documents to facilitate the exploration of ranked linear search results. Our proposed framework allows users to perform minimum clicks and scrolls to explore aggregated multimedia search results, hence improving the user search result exploration experience.
Audio Spectrogram Transformer for Synthetic Speech Detection via Speech Formant Analysis

In this paper, we address the challenge of synthetic speech detection, which has become increasingly important due to the latest advancements in text-to-speech and voice conversion technologies. We propose a novel multi-task neural network architecture, designed to be interpretable and specifically tailored for audio signals. The architecture includes a feature bottle-neck, used to autoencode the input spectrogram, predict the fundamental frequency (f0) trajectory, and classify the speech as synthetic or natural. Hence, the synthesis detection can be considered a byproduct of attending to the energy distribution among vocal formants, providing a clear understanding of which characteristics of the input signal influence the final outcome. Our evaluation on the ASVspoof 2019 LA partition indicates better performance than the current state of the art, with an AUC score of 0.900.
Impact of System Initial Values on Multivariate LSTM Neural Network Performance: A Finding from a Control Process Perspective

Nowadays, artificial intelligence (AI) has made significant contributions to the identification and modeling of physical systems across a wide range of scientific disciplines. However, integrating domain knowledge into an AI model has always been imperative in order to ensure a more accurate representation and capture all the pertinent system information. In this context, the initial value of a physical system can have a significant impact on the dynamic behavior of that system, particularly from a control design perspective, given its effect on transient behavior and system stability. The present study examines how the system's initial values can affect the output forecasting of the system by employing a Long-Short- Term Memory Recurrent Neural Network (LSTM-RNN). In this regard, a novel architecture design and data acquisition framework have been presented with the aim of providing a precise AI model to address the challenges related to the control design. The inclusion of the initial value as an input to the LSTM architecture is intended to exhibit how it potentially impacts the network's ability to capture long-term dependencies and make accurate predictions. Based on the study findings, considering the initial values of the system to improve the effectiveness of LSTM-RNN, ultimately leads to superior accuracy and predictive capabilities network.
Optimization of Large-Scale Solar Panels Distribution Using Genetic Algorithm

Renewable energy is rapidly growing, and solar power systems are a significant contributor. By 2050, the UAE aims to generate 50% of electric energy from carbon-free sources such as solar photovoltaic (PV). PV panel system's maximum output power varies over time, depending on external elements such as the sun's radiation, the surrounding temperature, dust, wind, etc., which is highly associated with the panel positioning. As of now, there is no well-defined methodology for determining the optimal distribution and positioning of large-scale solar panels to maximize energy output. To tackle this issue, our research investigates the impact of solar panel positioning and distribution on energy efficiency using a Genetic Algorithm (GA). GA is an optimization technique inspired by the principles of natural selection and genetics, known for its effectiveness in solving intricate problems across various domains. We anticipate that this study will contribute to the optimization of distribution and positioning in large-scale solar power plants, ultimately enhancing their performance.
Comparing Behaviour Tree and Hierarchical Task Network Planning Methods for their Impact on Player Experience

The AI in games has a large impact on the player's experience, but the large variety of available AI implementation methods makes it difficult to determine which one(s) to use in any particular project, and the differences in their impact on players are mostly unstudied. This paper presents a compar-ative study to analyse the effects of Behaviour Tree AI and Hierarchical Task Network Planning AI on players experiences. The study participants (players) were given two prototypes of a third-person shooter game, each utilising different AIs, to play and give feedback on. According to the results obtained, players did not notice any major differences between the two prototypes, leading us to believe that the Behaviour Tree AI may be a better solution in most cases, as it is easier to implement.
Weight Binary Fish School Search Algorithm for Feature Selection

This study proposes a multimodal approach to the Improved version of Binary Fish School Search (IBFSS) algorithm by incorporating aspects of the Weight Based Fish School Search (wFSS) algorithm to address the attribute selection problem. The proposed model, named Weight Binary Fish School Search (wBFSS), was evaluated on three benchmark datasets, consistently delivering the best solutions in most of the runs. Additionally, two variations of the new wBFSS model were tested to understand the impact on the algorithm's performance by adding a fitness function evaluation before executing the Collective Instinctive Movement.