Reviewerly/exHarmony huggingface dataset:
MultiRuler: A Multi-Dimensional Resource Modeling Method for Embedded Intelligent Systems of Autonomous Driving

With the development of vehicular technology, autonomous driving has experienced explosive growth, making it an important and popular research field. In autonomous driving scenarios, autonomous driving tasks are often neural network-based intelligent tasks with different levels of complexity. These intelligent tasks usually have different requirements for hardware resources such as computational performance, memory capacity and power consumption. Additionally, the evaluation becomes even more challenging due to the different system architectures and resource configurations of hardware devices from different manufacturers. It is crucial to accurately estimate the resource requirements of different intelligent tasks on different hardware platforms in advance.To address this issue, this paper proposes a multi-dimensional resource modeling method based on deep learning techniques and constructs an evaluation model to predict the consumption of three resources: computing performance, memory usage and power consumption.Experiments demonstrate that for the performance, memory and power metrics, the evaluation model built using this method for predicting seven mainstream neural networks achieves average error rates of 6.78%, 8.86% and 7.89% respectively on the NVIDIA Jetson AGX Xavier platform, and the average error rates for the same metrics are 8.05%, 9.25% and 3.67% respectively on the Cambricon MLU220 platform.
Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning

In practical applications, federated learning (FL) suffers from slow convergence rate and inferior performance resulting from the statistical heterogeneity of distributed data. Personalized FL (pFL) has been proposed to overcome this problem. However, existing pFL approaches mainly focus on measuring differences between entire model dimensions across clients, ignore the layer-wise differences in convolutional neural networks (CNNs), which may lead to inaccurate personalization. Additionally, two potential threats in FL are that malicious clients may attempt to poison the entire federation by tampering with local labels, and the model information uploaded by clients makes them vulnerable to inference attacks. To tackle these issues, (1) we propose a novel pFL approach in which clients minimize local classification errors and align the local and global prototypes for data from the class that is shared with other clients. This method adopts layer-wise collaborative training to achieve more granular personalization and converts local prototypes to the frequency domain to prevent source data leakage; (2) To prevent the FL model from misclassifying certain test samples as expected by poisoners, we design a robust aggregation method to ensure that benign clients who provide trustworthy model predictions for its local data are weighted far more heavily in the aggregation process than malicious clients. Experiments show that our scheme, especially in the data heterogeneity situation, can produce robust performance and more stable convergence while preserving privacy.
Machine Learning Assisted Statistical Variation Analysis of Voltage Gated Spin Orbit Torque Magnetic Tunnel Junction

In the semiconductor industry, spintronic technology has gained significant attention in the new era due to its compatibility with CMOS design. Among the promising spintronic devices, the voltage-gated spin-orbit torque magnetic tunnel junction (VGSOT-MTJ) stands out, offering the potential to overcome limitations found in other spintronic devices like spin-transfer torque (STT) and spin-orbit torque (SOT) magnetic tunnel junctions. However, the performance and reliability of VGSOT-MTJ can be influenced by variations in critical device parameters. This work investigates the variability of device characteristics in VGSOT-MTJ and proposes an innovative framework assisted by machine learning (ML) to streamline the technological pathfinding process. The proposed framework leverages three regression models based on machine learning techniques: K-nearest neighbors (KNN), random forest, and neural network regressors. Each model’s effectiveness is assessed by evaluating metrics such as mean error, R2 score, root mean square error (RMSE), and inference time. It is observed that the random forest regressor outperforms neural network regressor and K-nearest regressor model in terms of mean error, R2 score, and RMSE. Our ML assisted approach provided a more accurate and efficient analysis, with R2 scores of 0.99. Furthermore, significant improvement in prediction time is observed as compared to the SPICE simulation time.
Image denoising in fluorescence microscopy using feature based gradient reconstruction

PurposeThe utility of fluorescence microscopy imaging comes with the challenge of low resolution acquisitions, which severely limits information extraction and quantitative analysis. Image denoising is a technique that aims to remove noise from microscopy acquisitions by taking into account prior statistics of the corrupting noise. In this work, we propose an image denoising technique for fluorescence microscopy imaging.ApproachThe proposed technique is based on the principle of multifractal feature extraction from a noisy sample followed by a reconstruction technique from these features. It is observed that by following a proper hierarchical classification procedure, meaningful features can be extracted from a noisy image. A denoised image is then estimated from this sparse feature set through proper formulation of an optimization problem.ResultsExperiments are performed on both synthetic image databases as well as on real fluorescence microscopy data. Superior denoising results, in comparison to multiple comparing techniques, validate the potential of the proposed approach.ConclusionThe proposed method gives superior denoising results for low resolution fluorescence microscopy image acquisitions and can be used for post processing of data by biologists.
Fast reconstruction of programmable interferometers with intensity-only measurements

Programmable linear optical interferometers are promising for classical and quantum applications. Their integrated design makes it possible to create more scalable and stable devices. To use them in practice, one has to reconstruct the whole device model taking the manufacturing errors into account. The inability to address individual interferometer elements complicates the reconstruction problem. A naive approach is to train the model via some complex optimization procedure. A faster optimization-free algorithm has been recently proposed [Opt. Express 31, 16729 (2023)]. However, it requires the full transfer matrix tomography while a more practical setup measures only the fields intensities at the interferometer output. In this paper, we propose the modification of the fast algorithm, which uses additional set of interferometer configurations in order to reconstruct the model in the case of intensity-only measurements. We show that it performs slightly worse than the original fast algorithm but it is more practical and still does not require intensive numerical optimization.
Enhancing Image Segmentation Performance with MRAM based Processing-in-Memory Architecture

Image segmentation is essential for several computer vision applications, including object detection, autonomous driving, and medical imaging. Traditional image segmentation algorithms suffer due to computational inefficiency and memory bandwidth problems, limiting their real-time performance and accuracy. This work focuses on addressing these issues by enhancing image segmentation performance by leveraging a Processing-in-Memory (PIM) architecture based on Magnetic Random-Access Memory (MRAM) technology. The proposed approach optimizes data flow and memory access by integrating processing units into the MRAM memory array, resulting in better efficiency and real-time image segmentation capabilities. The proposed work introduces an MRAM-based PIM architecture to accelerate matrix multiplication operations in segmentation architectures. The study aims to assess and compare the effectiveness of the MRAMs for PIM based architecture for implementing segmentation models by analysing their performance. The computationally intensive segmentation models such as UNet demonstrate a competitive edge in generating images with high correlation to ground truth images. PIM architecture with Voltage Gated Spin-Orbit Torque (VGSOT) has demonstrated substantial reduction in terms of power, and latency by 99.97%, 35.34% respectively as compared to architectures using Phase Change Memory (PCM) crossbars in implementing segmentation inference operations.
A Deep Learning-Based Study on Multiple Object Tracking of Small Targets with Application to Mice Tracking

The presence of mice in enclosed indoor environments such as kitchens, warehouses, and storage rooms can reflect the cleanliness and hygiene conditions. Identifying and tracking mice in real-world indoor settings is crucial for monitoring the hygiene conditions. To address the aforementioned issues, we propose an improved SCBAM-YOLO detection model based on YOLOv5, combined with the DeepSORT tracking model and overall model enhancements. This model extracts both the appearance and motion features of mice, enabling real-time tracking. The results demonstrate that the SCBAM-YOLO achieves an mAP (mean Average Precision) value of 96.8% in object detection. For object tracking, the adoption of the OSNet model based on receptive fields enhances generalization and multi-scale features, leading to an improvement in IDF1 (ID F1-score) to 88.3% compared to the original model, with a 75% reduction in ID switch occurrences. The proposed approach provides technical references for real-time tracking of small targets moving in practical environments.
Improving The Information and Communication Competence of Higher Education Teachers in The Process of Distance Education

This article examines the development of information and communication competence of university teachers in the environment of distance learning support. In connection with the ever-increasing requirements for the level of training of specialists in higher professional education, the need to change the very paradigm of the educational process, its conceptual model from “education for life” to “education throughout life” has become obvious. The paradigm shift determines the main trends in the development of modern education, namely: continuity, mass character, fundamentality, individualization and humanization, orientation towards a competency-based approach. The use of new technologies currently makes it possible to organize the educational process at a qualitatively new level and to significantly expand the forms and methods of its organization. One of these forms is distance learning. Distance support of the educational process involves the use of tools and methods inherent in distance learning in the traditional educational process of a university. The purpose of the study is to develop and analyze a system for developing information and communication competence of teachers when using tools and methods of distance learning support.
AI-Enhanced Digital Creativity Design: Content-Style Alignment for Image Stylization

This paper presents an AI(Artificial Intelligence)-powered method for enhancing digital creative design through image stylization. To achieve this, we introduce the Content-Style Alignment Module (CSAM), which includes the Dual-Stream Content-Style Processing Block (DS-CSPB), Content-Style Matching Attention Block (CS-MAB), and Content-Style Space-Aware Interpolation Block (CS-SAIB). DS-CSPB removes style information from content descriptors using whitening transformation while preserving semantic structures. CS-MAB reorganizes each content descriptor with its most relevant style descriptor, ensuring optimal style adaptation for content semantics. CS-SAIB aligns content and style descriptors in the same space, enabling diverse semantic distributions in content images to match various style patterns. Moreover, we introduce the Multifaceted Optimization Loss (MOL). This loss comprises multiple components: The relaxed Earth Mover Distance (rEMD) loss enhances color and texture distributions on content images. The Moment Matching (MM) loss reduces visual artifacts caused by cosine distance. The differentiable Color Histogram (CH) loss efficiently addresses color blending issues, preserving image naturalness. The content loss ensures no significant deformation or distortion during stylization. The reconstruction loss constrains all encoder-decoder features to the VGG feature space, maintaining shared spaces between content and style descriptors. We conducted extensive comparative and ablation experiments, which demonstrated superior performance in image stylization, resulting in high-quality stylized images. Additionally, we provide a comprehensive review of current research in image stylization, effectively bridging the gap in this area.
Semantic Scene Difference Detection in Daily Life Patroling by Mobile Robots Using Pre-Trained Large-Scale Vision-Language Model

It is important for daily life support robots to detect changes in their environment and perform tasks. In the field of anomaly detection in computer vision, probabilistic and deep learning methods have been used to calculate the image distance. These methods calculate distances by focusing on image pixels. In contrast, this study aims to detect semantic changes in the daily life environment using the current development of large-scale vision-language models. Using its Visual Question Answering (VQA) model, we propose a method to detect semantic changes by applying multiple questions to a reference image and a current image and obtaining answers in the form of sentences. Unlike deep learning-based methods in anomaly detection, this method does not require any training or fine-tuning, is not affected by noise, and is sensitive to semantic state changes in the real world. In our experiments, we demonstrated the effectiveness of this method by applying it to a patrol task in a real-life environment using a mobile robot, Fetch Mobile Manipulator. In the future, it may be possible to add explanatory power to changes in the daily life environment through spoken language.
Design, Characterization and Control of a Whole-body Grasping and Perching (WHOPPEr) Drone

Flying robots can exploit perching abilities to position themselves on strategically-chosen locations and monitor the areas of interest from a critical vantage point. Moreover, they can significantly extend their battery life by turning off the propulsion systems when carrying out a surveillance mission. However, unknown disturbances arise from the physical interactions between the robot and the object, making it challenging to stabilize the robot during perching. In this paper, we present a Whole-body Grasping and Perching (WHOPPEr) Drone, which is capable of fast and robust perching by utilizing its entire body as the grasper in lieu of an add-on grasper. We first present the design concept, parameter selection and characterization of the novel whole-body grasping drone. Next, we analyze the grasping ability of the morphing chassis and present an aerodynamic analysis for the effect of motor thrust on the compliant arm. We finally demonstrate, via real-time experiments, the performance of WHOPPEr in autonomous perching and payload delivery tasks.
Development of Logical Methods for Extracting Emotional Assessments from Natural Language Texts

The article is devoted to the problem of extracting emotional assessments from natural language texts. The article analyzes existing methods of sentiment analysis and explores modern methods of natural language processing to determine the sentiment of Uzbek texts. Existing approaches to the study and description of emotions are explored. Particular attention is paid to emotional assessments of situations presented in natural language texts. Logical-semantic methods and technologies based on deep machine learning methods are considered. Formalization of emotional assessments contained in natural language texts is carried out using the theory of partial models. The task of classification is to assign a class to objects based on their characteristics. In the case of the emotion model, the emotion texts are object-class pairs. Since the number of emotions is not binary, the classification is not binary. To solve the classification problem, a training data set consisting of labeled text with emotions is required.
VARIQuery: VAE Segment-Based Active Learning for Query Selection in Preference-Based Reinforcement Learning

Human-in-the-loop reinforcement learning (RL) methods actively integrate human knowledge to create reward functions for various robotic tasks. Learning from preferences shows promise as alleviates the requirement of demonstrations by querying humans on state-action sequences. However, the limited granularity of sequence-based approaches complicates temporal credit assignment. The amount of human querying is contingent on query quality, as redundant queries result in excessive human involvement. This paper addresses the often-overlooked aspect of query selection, which is closely related to active learning (AL). We propose a novel query selection approach that leverages variational autoencoder (VAE) representations of state sequences. In this manner, we formulate queries that are diverse in nature while simultaneously taking into account reward model estimations. We compare our approach to the current state-of-the-art query selection methods in preference-based RL, and find ours to be either on-par or more sample efficient through extensive benchmarking on simulated environments relevant to robotics. Lastly, we conduct an online study to verify the effectiveness of our query selection approach with real human feedback and examine several metrics related to human effort.
Reactive and Safe Co-Navigation with Haptic Guidance

We propose a co-navigation algorithm that enables a human and a robot to work together to navigate to a common goal. In this system, the human is responsible for making high-level steering decisions, and the robot, in turn, provides haptic feedback for collision avoidance and path suggestions while reacting to changes in the environment. Our algorithm uses optimized Rapidly-exploring Random Trees (RRT*) to generate paths to lead the user to the goal, via an attractive force feedback computed using a Control Lyapunov Function (CLF). We simultaneously ensure collision avoidance where necessary using a Control Barrier Function (CBF). We demonstrate our approach using simulations with a virtual pilot, and hardware experiments with a human pilot. Our results show that combining RRT* and CBFs is a promising tool for enabling collaborative human-robot navigation.
Dynamic Object Tracking for Quadruped Manipulator with Spherical Image-Based Approach

Exactly estimating and tracking the motion of surrounding dynamic objects is one of important tasks for the autonomy of a quadruped manipulator. However, with only an onboard RGB camera, it is still a challenging work for a quadruped manipulator to track the motion of a dynamic object moving with unknown and changing velocities. To address this problem, this manuscript proposes a novel image-based visual servoing (IBVS) approach consisting of three elements: a spherical projection model, a robust super-twisting observer, and a model predictive controller (MPC). The spherical projection model decouples the visual error of the dynamic target into linear and angular ones. Then, with the presence of the visual error, the robustness of the observer is exploited to estimate the unknown and changing velocities of the dynamic target without depth estimation. Finally, the estimated velocity is fed into the model predictive controller (MPC) to generate joint torques for the quadruped manipulator to track the motion of the dynamical target. The proposed approach is validated through hardware experiments and the experimental results illustrate the approach's effectiveness in improving the autonomy of the quadruped manipulator.
POTENTIALS OF DISTANCE COURSES FOR THE DEVELOPMENT OF PEDAGOGICAL MASTERY IN TEACHERS OF GENERAL EDUCATION DISCIPLINES

Relevance. The development of professional pedagogical skills in teachers of general education disciplines throughout their lifetime is a key condition for preparing competitive specialists in all fields of activity. For a competent educator, continual self-improvement, self-development, and the development of their abilities through self-education and distance professional development courses are vital. This article highlights the potentials of distance courses for the development of pedagogical mastery in teachers of general education disciplines. Purpose. The article aims to illuminate the potentials of distance courses for enhancing the pedagogical skills of teachers of general education disciplines. Methods. In the scientific search, methods of analysis and synthesis were used to determine the potentials of distance courses for the development of pedagogical mastery; methods of generalization and systematization were applied for developing a distance course to enhance pedagogical skills. Results. Professional development is a process of paramount importance, through which the growth of an educator as a professional is achieved. In today's fast-paced life, with constant time constraints, the development of pedagogical skills in teachers of general education disciplines at vocational education institutions is effectively accomplished through distance education. The peculiarities of learning using distance educational technologies are highlighted, and their advantages and disadvantages are analyzed. A specialized distance course for enhancing the professional mastery of educators, "Development of Pedagogical Skills in Teachers of General Education Disciplines at Vocational Education Institutions," has been characterized. This course includes five educational modules designed to improve all aspects of the pedagogical skills of teachers in these institutions. Conclusions. The organization of the distance course "Development of Pedagogical Skills in Teachers of General Education Disciplines at Vocational Education Institutions" is aimed at providing informational and methodological support to educators in their continuous professional education and is highly relevant today.