Reviewerly/exHarmony huggingface dataset:
TWO-STAGE METHOD FOR PARAMETER ESTIMATING IN UNKNOWN NOISE ENVIRONMENT

This paper addresses the parameter estimation problem in the case of an unknown noise environment. A two-stage method consisting of the tuning and estimating stages is pro-posed for solving this problem. At the tuning stage, the estimator is tuned to the noise envi-ronment by minimizing the estimation root-mean-square error for a known data fragment in the space of the three free parameters; these free parameters allow modifying the behavior of the minimization problem objective function. At the estimating stage, estimation is car-ried out by solving the corresponding minimization problem using already tuned free pa-rameters. The features of the proposed method, including algorithms for the tuning and es-timating stages, are demonstrated for estimating a Gaussian pulse that slowly moves in the unknown noise environment and locates on a known constant background. The numerical simulations confirm the high performance of the proposed method.
OpenCitations and EC funding: OpenAIRE Nexus and RISIS2

&lt;strong&gt;The incentives for new OpenCitations innovative solutions&lt;/strong&gt; Two years ago, in their canonical 2020 QSS paper on OpenCitations, Silvio Peroni and David Shotton anticipated the creation of the new database, OpenCitations Meta, able to “offer a faster and richer service” by storing bibliographic metadata “in house”. Meta would “&lt;em&gt;avoid duplication of data by efficiently permitting us to keep […] a single copy of the metadata for each of the bibliographic entities involved as citing or cited entities in the different OpenCitations’ citation indexes&lt;/em&gt;”, would remove the req
Generality imaging for optimized face classification using deep learning techniques

Relied on discernible or corporeal attributes, human beings are recognized by employing biometric scheme. In computer perception and design ratification domain, progressive studies are carried out in face recognition. Given the constant development in the discipline of imaging sensor, a legion of rest of the novel problems has occurred. The chief issue remains how to discover focus region more precisely for multi-focus face detection. Several studies have been proliferated in face discernment, spotting, and protection acknowledgment; the key problem remains in this is considering those images into contemplation that had “disparate dimensions” and “disparate aspect ratio” in a singular frame avoiding the progression to attain or surpass human-level accuracy in human facial aspect like noise in face pictures, defying lighting conditions and posture ratio.
Hayo Reinders: Making sense of big (and not so big) data with language learning analytics

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Modeling the Decomposition Signal and Correcting Bulk Organic Data from a Peat Deposit: a Case Study at Low Latitudes (Cameroon)

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Sergei Rachmaninoff: Cross Rhythms of the Soul by Valeria Z.Nollan. Lanham: Lexington Books, 2022. 398 pp. $125.00. ISBN 978‐1‐66691‐759‐8

The Russian ReviewVolume 82, Issue 4 p. 724-725 BOOK REVIEW Sergei Rachmaninoff: Cross Rhythms of the Soul by Valeria Z. Nollan. Lanham: Lexington Books, 2022. 398 pp. $125.00. ISBN 978-1-66691-759-8 Klara Moricz, Corresponding Author Klara Moricz [email protected] orcid.org/0009-0009-1809-1741 Amherst College Email: [email protected]Search for more papers by this author Klara Moricz, Corresponding Author Klara Moricz [email protected] orcid.org/0009-0009-1809-1741 Amherst College Email: [email protected]Search for more papers by this author First published: 13 August 2023 https://doi.org/10.1111/russ.12552Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume82, Issue4October 2023Pages 724-725 RelatedInformation
A Survey on Light-weight Convolutional Neural Networks: Trends, Issues and Future Scope

Today with the substantial increase in the computing power of small devices and systems new challenges are emerging. For example, how to control a small handheld device which has the computing capabilities of a desktop Personal computer (PC) used five years ago. Devolving decision-making power to the device in order to make it more intelligent e.g. in the case of autonomous driving, is an interesting area. Deep learning has paved the way for this task due to its reliable decision-making capabilities which are quite popular. However for small devices there are constraints like availability of limited computation hardware, less power due to small batteries, need for real-time as well as accurate decision-making abilities, etc. In this regard, light-weight Convolutional Neural Networks (CNNs) are a valuable tool. Lightweight CNNs like MobileNets, ShuffleNets, CondenseNets, etc. are deep networks which have a much lesser number of layers and a much smaller number of parameters as compared to their larger CNN counterparts like GoogLeNet, Inception, ResNets, etc. Due to their unique advantages for small stand-alone systems, light-weight CNNs are used in these systems. In this literature survey the notable light-weight CNNs along with their architecture, design features, performance metrics, advantages, etc are discussed. The trends, issues and future scope in the area are also discussed. It is hoped that by studying this survey, the reader will engage in research in this interesting area.
Compensation of Optical Nonlinear Waveform Distortion Using DSP-Based Reservoir Computing with Tapped Delay Lines

We propose a novel reservoir-computing-based nonlinear equalizer, designed to compensate for fiber-optic nonlinearity. Signals are fed to the equalizer through tapped delay lines. We clarified that the equalizer outperforms conventional alternatives.
Detection of SQL Injection and Cross-Site Scripting Based on Multi-Model CNN Combined with Bidirectional GRU and Multi-Head Self-Attention

Both SQL injection and cross-site scripting (XSS) are critical threats in the field of web application and API security. According to the literatures and our experiments, use individual deep learning architecture such as DNN (Dense Neural Network), LSTM (Long short-term memory), RNN (Recurrent Neural Network) or transformer to detect SQL injection and XSS that requires much time and large amounts of data for training. For overcoming the above issues to balance the training performance and detection accuracy, we integrate CNN and GRU with the attention mechanism inspired by modern language models to construct an innovative and efficient detection system that can achieve higher accuracy, smaller training dataset and shorter training time. Moreover, the experimental results were even better than other baseline techniques and close to what we had anticipated.
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://github.com/sanket0707/GNN-Mixer.git
Trial frame for evaluating eye movements

Eye movement research is an established methodology and widely used in fundamental research fields such as psychology or neuroscience. Most research is performed in controlled environments with screen-based eye trackers, but demand for a wearable requirement is on the rise. Yet, a state of the art wearable eye tracking system thus far has not been feasible for subjects with refractive errors; therefore, a trial frame is proposed for researching the eye movement of people with or without refractive errors. Two iris cameras and several infrared radiation LEDs were fixed behind the frame for recording eye images. The images were sent to a host computer using 2.4G wireless, which was also fixed on the frame. The entire electronic system in the trial frame was driven using a battery. Eye position in the images was recognized and segmented by a model that was trained through machine learning. Then the pupil and blinks were detected from the segmented eye area using ellipse fitting and the eye aspect ratio. Experiments showed that the correct rate of pupil and blink detections were 97.24% and 96.14%, respectively.
Multi-sensor Ensemble-guided Attention Network for Aerial Vehicle Perception Beyond Visible Spectrum

Researchers from different market domains have made significant developments in Artificial Intelligence (AI) enabling more advanced automated sensing systems and, thus, eliminating the need for the time-consuming manual analysis of data, which is prone to human errors. However, successful deployment of such systems in real world applications requires careful design and analysis of the proposed models. This work focuses on perception done on Unmanned Aerial Vehicles (UAV) using multi-task learning. There are multiple challenges when considering such platforms. First of all, they often operate in difficult and dynamic conditions affected by various factors, such as background noises, ego-noise of the motors and occluded views. At the same time, they require high performance local compute, co-designed with optimized software solutions that meet small size, weight, and power (SWaP) requirements. Therefore, the AI models designed for such systems should not introduce computational and memory overheads to allow for real time processing at the embedded edge. Taking this into account, this work proposes a novel neural network-based system that utilizes ensemble-guided modulations of audio path fused with the infrared (IR) visual embedding using the attention mechanism. The ensemble mechanism doesn’t require spawning new ensemble members, but instead operates on FiLM (Feature-wise Linear Modulation) activation, making it suitable for resource-constraints embedded edge platforms. The performed experiments show that the proposed network outperforms a single FiLM network by 15% and is more robust to noise.
Technical Decision-Making

As a young aspiring app developer during my university days in 2012, I had an idea to build an app that would allow students to listen to a lecturer over a frequency using their phones. I was thrilled at the prospect of creating something that could revolutionize the way students learn in lecture halls. I imagined how students would be able to listen to the lecturer using just their phones, without having to strain their ears to hear what was being said.
Wearable Smart Rings for Multifinger Gesture Recognition Using Supervised Learning

This paper presents a wearable smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers’ motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine pre-defined finger movements were introduced to verify the feasibility of the proposed method. Data pre-processing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and k-nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine learning algorithms were used for gesture classification purposes, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time, and prevents overfitted model but also provides approximately similar machine learning prediction accuracy compared to when all elements of feature vectors were used. Using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively.
IndicSTR12: A Dataset for Indic Scene Text Recognition

The importance of Scene Text Recognition (STR) in today’s increasingly digital world cannot be overstated. Given the significance of STR, data-intensive deep learning approaches that auto-learn feature mappings have primarily driven the development of STR solutions. Several benchmark datasets and substantial work on deep learning models are available for Latin languages to meet this need. On more complex, syntactically and semantically, Indian languages spoken and read by 1.3 billion people, there is less work and datasets available. This paper aims to address the Indian space’s lack of a comprehensive dataset by proposing the largest and most comprehensive real dataset - IndicSTR12 - and benchmarking STR performance on 12 major Indian languages (Assamese, Bengali, Odia, Marathi, Hindi, Kannada, Urdu, Telugu, Malayalam, Tamil, Gujarati, and Punjabi). A few works have addressed the same issue, but to the best of our knowledge, they focused on a small number of Indian languages. The size and complexity of the proposed dataset are comparable to those of existing Latin contemporaries, while its multilingualism will catalyse the development of robust text detection and recognition models. It was created specifically for a group of related languages with different scripts. The dataset contains over 27000 word-images gathered from various natural scenes, with over 1000 word-images for each language. Unlike previous datasets, the images cover a broader range of realistic conditions, including blur, illumination changes, occlusion, non-iconic texts, low resolution, perspective text etc. Along with the new dataset, we provide a high-performing baseline on three models: PARSeq (Latin SOTA), CRNN, and STARNet.
Norm conflicts and epistemic modals

Statements containing epistemic modals (e.g., “by spring 2023 most European countries may have the Covid-19 pandemic under control”) are common expressions of epistemic uncertainty. In this paper, previous published findings (Knobe & Yalcin, 2014; Khoo & Phillips, 2018) on the opposition between Contextualism and Relativism for epistemic modals are re-examined. It is found that these findings contain a substantial degree of individual variation. To investigate whether participants differ in their interpretations of epistemic modals, an experiment with multiple phases and sessions is conducted to classify participants according to the three semantic theories of Relativism, Contextualism, and Objectivism. Through this study, some of the first empirical evidence for the kind of truth-value shifts postulated by semantic Relativism is presented. It is furthermore found that participants’ disagreement judgments match their truth evaluations and that participants are capable of distinguishing between truth and justification. In a second experimental session, it is investigated whether participants thus classified follow the norm of retraction which Relativism uses to account for argumentation with epistemic modals. Here the results are less favorable for Relativism. In a second experiment, these results are replicated and the normative beliefs of participants concerning the norm of retraction are investigated following work on measuring norms by Bicchieri (2017). Again, it is found that on average participants show no strong preferences concerning the norm of retraction for epistemic modals. Yet, it was found that participants who had committed to Objectivism and had training in logic applied the norm of retraction to might-statements. These results present a substantial challenge to the account of argumentation with epistemic modals presented in MacFarlane (2014), as discussed.
IMUSHIELD: defending against inaudible attacks via IMUs on smartphones

Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like “Siri, open the door”. It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90%.
Digital image correlation strain measurement study based on smoothing displacement gradient field

Digital image correlation (DIC) technology has been widely used in engineering measurement. In the process of DIC strain measurement, the commonly used gradient method and the local least squares method for displacement field fitting often produce large errors. To reduce the strain measurement error and improve the measurement results, a method of local weighted regression smoothing and moving means smoothing for displacement gradient is proposed to measure strain. The simulated Gaussian speckle is generated by a computer, and the reference image is deformed to verify the effect of these two methods, then compared with the gradient and local least squares methods. The results show that the strain field obtained by smoothing the displacement gradient has a good denoising effect, a more accurate solution, a more stable result, and less error.
Promising Role of Visual IoT: Challenges and Future Research Directions

The Visual Internet of Things (VIoT) has gained significant attention in computer science due to the emergence of miniature video sensors and advancements in computer vision technology. Despite being resource-constrained devices, VIoT is widely used in a range of applications such as industrial inspection, health monitoring, smart cities, sports analytics and many more. Along with their advantages, VIoTs present unique challenges and opportunities. As the popularity of VIoT applications continues to grow, novel use cases with distinct characteristics are being identified. Therefore, we comprehensively examine existing frameworks for computation, communication and storage in VIoT applications. Then we briefly discuss emerging technologies that could be applicable to designing the VIoT network. In order to demonstrate the applicability of VIoT, we also covered a number of practical scenarios. We propose a reference model for analyzing VIoT data in a distributed setting. Finally, we outline several open research areas for future exploration.
Domain-Centroid-Guided Progressive Teacher-based Knowledge Distillation for Source-Free Domain Adaptation of Histopathological Images

Deep neural networks are commonly used for histopathology image analysis. However, such data-driven models are sensitive to style variances across scanners and suffer a significant performance degradation as a result. Although the network performance can be improved by using domain adaptation methods, the source dataset required to perform the adaptation process is generally unavailable. This study shows that the performance degradation of deep neural networks when applied to histopathology images is the result partly of the wide distribution of the features generated when inferring the features of the target model using the feature centers of the source model. To address this problem, a teacher-student framework, designated as Domain-Centroid-Guided Progressive Teacher-based Knowledge Distillation (DCGP-KD), is proposed which aims to learn compact target features in order to provide more accurate pseudo labels for the target model without the need for the original source dataset. In the proposed framework, the class-wise feature centers of the source data are progressively adapted to the distribution of the target data, and compact target features are then generated by gathering the features based on their class-wise centers. A strategy is additionally proposed to prevent catastrophic forgetting during the progressive adaption process. Finally, a prediction consistency loss function is introduced to improve the robustness of the target dataset. The feasibility of the proposed framework is demonstrated experimentally for the illustrative case of the tumor classification of histopathological images with staining variations. The results show that DCGP-KD provides a promising assistive tool for pathologists in various histopathological analysis tasks.
Quantum Bayesian computation

Abstract Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed‐up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.