Reviewerly/exHarmony huggingface dataset:
The Impact of Randomized Algorithm over Recommender System

In today’s world, the amount of information is increasing drastically. There are 2.5 quintillion bytes of data generated each day at a current time. With this rapid increase of data, it is important to filter the huge amount of data from the existing unnecessary available storage. To handle this issue, the researchers have incorporated the concept of recommender system. Recommender system tries to filter the users’ data to come up with the exact amount of information required according to the needs and preferences of the users. Hence there is an increase in the demand of recommender system to tackle the data overload. However, the existing recommender systems do perform well but face some issues in the corporate world. Cold start, user bias, precision and speed are some of the major problems faced by recommender system which are yet to be addressed. To minimize such issues, we incorporate the concept of randomization such that random factor can be integrated in design of recommender systems. In this paper, we have also outlined the advantages as well as the procedure of using a randomized algorithm for recommender system. The proposed approach would provide a platform for researchers to enhance the recommender system using the randomized algorithms in the future.
Joint Disaster Classification and Victim Detection using Multi-Task Learning

Recent advances in deep learning and computer vision have transformed surveillance into an important application for smart disaster monitoring systems. Based on the detected number of victims and activity of disasters, emergency response unit can dispatch manpower more efficiently, which could save more lives. However, most of existing disaster detection methods fall into the class of single-task learning, which can either detect victim or classify disaster. In contrast, this paper proposes a YOLO-based multi-task model which performs the aforementioned tasks simultaneously. This is accomplished by attaching a disaster classification head model to the backbone of a victim detection model. The head model is inherited from the MobileNetv2 architecture, and we precisely select the backbone feature map layer to which the head model is attached. For the victim detection, results reveal that the solution achieves up to 0.6938 and 20.31 in terms of average precision and frame per second, respectively. Whereas for the disaster classification, the algorithm is comparable with most deep learning models that are specifically trained for single task. This shows that our solution is flexible and robust enough to handle both victim detection and disaster classification.
Plug-and-Play Deblurring for Robust Object Detection

Object detection is a classic computer vision task, which learns the mapping between an image and object bounding boxes + class labels. Many applications of object detection involve images which are prone to degradation at capture time, notably motion blur from a moving camera like UAVs or object itself. One approach to handling this blur involves using common deblurring methods to recover the clean pixel images and then the apply vision task. This task is typically ill-posed. On top of this, application of these methods also add onto the inference time of the vision network, which can hinder performance of video inputs. To address the issues, we propose a novel plug-and-play (PnP) solution that insert deblurring features into the target vision task network without the need to retrain the task network. The deblur features are learned from a classification loss network on blur strength and directions, and the PnP scheme works well with the object detection network with minimum inference time complexity, compared with the state of the art deblur and then detection solution.
A parameter-free approach to lossless summarization of fully dynamic graphs

In large dynamic graphs, it is often impractical to store and process the entire graph. To contend with such graphs, lossless graph summarization is a compression technique that can provide a succinct graph representation without losing information. However, given a fully dynamic graph stream, the problem of lossless summarization is computationally and technically challenging. Although the state-of-the-art method performs well with respect to efficiency, its compression quality is usually unstable. This outcome occurs because it is a randomized algorithm and depends heavily on the pretuned parameters. In this paper, we propose a parameter-free approach to lossless summarization of a fully dynamic graph stream. In response to edge insertions and deletions, we first develop an incremental algorithm to maintain the summarization result by carefully exploring the influenced subgraph. Furthermore, we present a similarity-based algorithm to control the movement of vertices on the subgraph and thus guarantee the optimal result at each summarization update. To enhance the performance of our approach, we also propose two optimization techniques regarding candidate supernode refinement and destination supernode selection. The experimental results demonstrate that the proposed methods outperform the state-of-the-art methods by a large margin in terms of compression quality with comparable running time on the majority of datasets.
eGHWT: The Extended Generalized Haar–Walsh Transform

Abstract Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose the extended generalized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the efficiency of graph-domain partitions but also that of “sequency-domain” partitions simultaneously . Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals significantly improve the performance of the previous GHWT with the similar computational cost, $$O(N \log N)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>N</mml:mi> <mml:mo>log</mml:mo> <mml:mi>N</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where N is the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than $$(1.5)^N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1.5</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>N</mml:mi> </mml:msup> </mml:math> possible orthonormal bases in $$\mathbb {R}^N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>N</mml:mi> </mml:msup> </mml:math> , the eGHWT best-basis algorithm can find a better one by searching through more than $$0.618\cdot (1.84)^N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.618</mml:mn> <mml:mo>·</mml:mo> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1.84</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>N</mml:mi> </mml:msup> </mml:mrow> </mml:math> possible orthonormal bases in $$\mathbb {R}^N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>N</mml:mi> </mml:msup> </mml:math> . This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its effectiveness on applications such as image approximation.
Generative Adversarial Networks for synthetic wellbore data: Expert perception vs mathematical metrics

We study the applicability of Generative Adversarial Networks (GANs) for generating the synthetic data related to well construction and geological characterisation of the near-wellbore area. We focus on 1D mud logs (time-series) and 2D core images. GANs are known to have difficulties with their quality assessment in general. Moreover, generic GAN's performance assessment methods cannot be suitable for the petroleum domain. A petroleum engineer expects the GANs to generate data with specific physical and geological properties, not just a colourful picture. We have trained over 40 GAN models and generated synthetic data with them. Then, we have involved several experts to analyse the generated data in order to address the question of whether it is possible to substitute human analysis with a mathematical metric. We found that some quantitative mathematical metrics can represent our experts' perceptions. In particular, we show that for 2D core images, Mode Score metric with standard inception v3 model is the best proxy for all considered qualitative metrics of expert's perception according to the Kendall correlation (for two qualitative metrics the correlation is strong, the absolute value is above 0.7, and for other two it is moderate, the absolute value is between 0.5 and 0.7); for mud logs time-series, Mode Score and Frechet Inception Distance with the InceptionTime model provide the strong (above 0.7) correlation with objects reconstruction quality, whereas Inception Score has almost strong correlation (with Kendalls'-tau coefficient 0.69) with experts' perception of objects generation quality. With these results, experts manual annotation of generated objects during GAN model selection process can be reduced to calculating the corresponding quantitative metrics.
: Using emoji to practice impromptu speaking

Impromptu speaking is a typical exercise in communication-focused courses. This format is often used to practice reducing or assess speaking anxiety, getting to know one another, and delivering project updates and ceremonial remarks. For students to practice their skills in quick thinking and speech preparation, instructors commonly have students draw random written topics and speak in front of their classmates. This procedure, while efficient, can be uninspiring and induce anxiety. The activity outlined increases creative engagement while reducing speech anxiety in impromptu speaking scenarios. It uses randomized collections of emoji as speaking prompts and embraces students' use of texting and visual communication to create livelier classroom experiences while reinforcing impromptu speaking skills by mitigating weaknesses in traditional approaches.Courses This single-class activity would be useful in public-speaking courses or during presentation and public-speaking units of any course. For example, this activity has also been successfully deployed in an Introduction to Professional Development course's unit on impromptu speaking.Objectives The learning objectives of this activity are: (1) to practice impromptu speaking and (2) to encourage creative engagement in class.
Artificial Intelligence Education System Based on Differential Evolution Algorithm to Optimize SVM

The artificial intelligence education system promotes the rooting of artificial intelligence in the education field and accelerates its entry into the era of intelligent education. This article focuses on the development of the artificial intelligence education system and proposes an artificial intelligence education system based on differential evolution algorithm optimization support vector machine. First, the processing of educational demand information data is automated, then a differential evolution algorithm is built to optimize the support vector machine model, and the model is used to implement various educational tasks to achieve automated education. The test results show that the model classification accuracy, classification recall rate, classification accuracy rate, and F1-score value are 4 items. Performances have been improved to improve the efficiency of education work and provide a reference for exploring the application and practice of artificial intelligence in education.
Covid Vaccine Sentiment Analysis by Geographic Region

In this paper, we provide a sentiment analysis of conversations surrounding Covid-19 vaccine adoption on Twitter. We focus on key regions of the US, particularly urban areas with high African American populations. We utilize machine learning models such as logistic regression, Support Vector Machines, and Naive Bayes to provide baseline models. Furthermore, we develop fined-tuned Transformer-based language models that provide a classification of sentiments with high accuracy. The results from our analysis show that fine-tuning our dataset on a Transformer-based model, Covid-BERT v2, performs better than our baseline models however the accuracy is still relatively low. This might be as a result of the very limited training dataset. Future work will explore the use of a higher quality dataset and also evaluate other transformer-based models.
Does One Size Fit All? A Case Study to Discuss Findings of an Augmented Hands-Free Robot Teleoperation Concept for People with and without Motor Disabilities

Hands-free robot teleoperation and augmented reality have the potential to create an inclusive environment for people with motor disabilities. It may allow them to teleoperate robotic arms to manipulate objects. However, the experiences evoked by the same teleoperation concept and augmented reality can vary significantly for people with motor disabilities compared to those without disabilities. In this paper, we report the experiences of Miss L., a person with multiple sclerosis, when teleoperating a robotic arm in a hands-free multimodal manner using a virtual menu and visual hints presented through the Microsoft HoloLens 2. We discuss our findings and compare her experiences to those of people without disabilities using the same teleoperation concept. Additionally, we present three learning points from comparing these experiences: a re-evaluation of the metrics used to measure performance, being aware of the bias, and considering variability in abilities, which evokes different experiences. We consider these learning points can be extrapolated to carrying human–robot interaction evaluations with mixed groups of participants with and without disabilities.
A depth camera-based system to enable touch-less interaction using hand gestures

Touch-less interaction is an active area of research due to its numerous applications ranging from medical systems to gaming. In this paper we propose a novel interactive system based on RealSense camera to track the pose of hand and to recognize gestures. Our approach is based on MediaPipe model introduced by Google to detect the landmarks of the hand. The system utilizes the camera calibration information and user actions to get the geometry of the deployed environment, which shows high accuracy in measurement of hand position. The touch-less interaction is demonstrated using a graphical user interface for painting.
Research on the Spread Path and Evolution Causes of Oral Language in the Digital Era

Visual orientation seems to indicate the decline of oral communication, but oral communication has its own living space under the new media ecology. Research has found that in the digital media era, voice communication is manifested as a single-level feature that simulates current interaction and information communication. Although voice communication is a lie constructed by individuals, the interaction between the subject’s discourse and the actual field of interaction separate the emotional distance, but the situation is harmonious and inclusive. The following voice communication and new media technologies are still trustworthy. Aiming at multifactor evolutionary algorithm (MFEA), the most classical multifactor evolutionary algorithm in multitask computation, we theoretically analyze the inherent defects of MFEA in dealing with multitask optimization problems with different subfunction dimensions and propose an improved version of the multifactor evolutionary algorithm, called HD-MFEA. In HD-MFEA, we proposed heterodimensional selection crossover and adaptive elite replacement strategies, enabling HD-MFEA to better carry out gene migration in the heterodimensional multitask environment. At the same time, we propose a benchmark test problem of multitask optimization with different dimensions, and HD-MFEA is superior to MFEA and other improved algorithms in the test problem. Secondly, we extend the application scope of multitask evolutionary computation, and for the first time, the training problem of neural networks with different structures is equivalent to the multitask optimization problem with different dimensions. At the same time, according to the hierarchical characteristics of neural networks, a heterodimensional multifactor neural evolution algorithm HD-MFEA neuro-evolution is proposed to train multiple neural networks simultaneously. Through experiments on chaotic time series data sets, we find that HD-MFEA neuro-evolution algorithm is far superior to other evolutionary algorithms, and its convergence speed and accuracy are better than the gradient algorithm commonly used in neural network training.
An Electromagnetic Environment Situation Assessment and Abnormal Detection Technology

Electromagnetic environment situation is an important part of battlefield situation, which plays an important reference role in battlefield situation and battlefield threat assessment, and can provide a strong basis for combat command. To better grasp the electromagnetic environment situation, from the time domain, spatial domain, frequency domain, energy and so on, we put forward 10 from the trend of the state and characterization of indicators, to systematically describes the electromagnetic environment situation, based on the adaptive neural fuzzy system (ANFIS) electromagnetic environment situation assessment model was constructed, according to the evaluation results to detect electromagnetic environment changes. The evaluation ability of the model is verified by experiments with the short-wave spectrum monitoring data, and the function of abnormal situation detection in electromagnetic environment is realized.
The Effect of Different Occupational Background Noises on Voice Recognition Accuracy

Abstract Voice recognition has become an integral part of our lives, commonly used in call centers and as part of virtual assistants. However, voice recognition is increasingly applied to more industrial uses. Each of these use cases has unique characteristics that may impact the effectiveness of voice recognition, which could impact industrial productivity, performance, or even safety. One of the most prominent among them is the unique background noises that are dominant in each industry. The existence of different machinery and different work layouts are primary contributors to this. Another important characteristic is the type of communication that is present in these settings. Daily communication often involves longer sentences uttered under relatively silent conditions, whereas communication in industrial settings is often short and conducted in loud conditions. In this study, we demonstrated the importance of taking these two elements into account by comparing the performances of two voice recognition algorithms under several background noise conditions: a regular Convolutional Neural Network (CNN)-based voice recognition algorithm to an Auto Speech Recognition (ASR)-based model with a denoising module. Our results indicate that there is a significant performance drop between the typical background noise use (white noise) and the rest of the background noises. Also, our custom ASR model with the denoising module outperformed the CNN-based model with an overall performance increase between 14–35% across all background noises. Both results give proof that specialized voice recognition algorithms need to be developed for these environments to reliably deploy them as control mechanisms.
Tellus regional stream water geochemistry: environmental and mineral exploration applications

Regional stream water geochemistry acquired as part of the Tellus programme in Ireland has been analysed to assess its potential for application to environmental assessment and mineral exploration. Interpolated geochemical maps and multivariate statistical analysis, including principal component analysis and random forest classification, demonstrate broad geogenic control of stream water chemistry, with both bedrock and subsoil contributing to the patterns observed. Surface water regulations set Environmental Quality Standard values for individual Priority Substances and Specific Pollutants that may depend on background concentrations and/or water hardness. The high resolution of Tellus stream water data and their location on low-order streams have allowed estimation of background concentrations and water hardness in the survey area, with significant implications for water monitoring programmes. Anthropogenic inputs to stream water in the survey area come mainly from agricultural sources and Tellus data suggest few catchments are unaffected. Comparison of Tellus stream water geochemistry with stream sediment and topsoil geochemistry suggest that stream water geochemistry has strong potential for use in mineral exploration, with the same base metal and gold pathfinder anomalies apparent in all three data sets. Cluster analysis indicates that base metals in stream water are associated with organic matter but statistical analysis may be employed to distinguish mineralization-related signatures. Supplementary material: Comparison of cation/anion associations using Piper plots and principal component analysis is available at https://doi.org/10.6084/m9.figshare.c.5683094 Thematic collection: This article is part of the Hydrochemistry related to exploration and environmental issues collection available at: https://www.lyellcollection.org/cc/hydrochemistry-related-to-exploration-and-environmental-issues
An implementation framework for vision-based bat-like inverted perching with bi-directionalthrust quadrotor

This paper presents an implementation framework to perform a vision-guided, bat-like inverted perching maneuver with a bi-directional thrust quadrotor platform. The framework consists of several distinct modules (guidance, motion planning, control, state estimation) that can be easily be individually customized in the future to meet specific research requirements. The main contribution of this paper lies in the whole framework pipeline with a modular structure developed for implementing a generalized framework for an agile quadrotor to achieve inverted perching. A computationally-light guidance module has been developed as an example to demonstrate the capability while being independent of accurate pre-known target information, and does not require the state estimation of the quadrotor to be provided by an external motion capture system as in our previous work. A motion planning module based on an optimization method has been introduced to generate a two-stage inverted perching trajectory aiming at minimizing altitude loss during the half-flip maneuver. A control module has been developed to enable a bi-directional quadrotor to fly in both upright and inverted states and closely follow the intended trajectory. The compensation strategy used in the control module is key to minimizing the transition time between the upright and inverted states. Finally, an experimental flight platform has been developed to demonstrate the capabilities of the framework. During testing, the proposed framework has achieved an 80 % success rate. To the best of our knowledge, this paper presents the first time a quadrotor has achieved the inverted perching maneuver using onboard vision guidance.