Reviewerly/exHarmony huggingface dataset:
Symmetries of Touch: Reconsidering Tactility in the Age of Ubiquitous Computing

Engaging with the specific ways current media technologies interact with, or directly access the human body, we suggest developing a ‘symmetrical’ theory of touch. Critically referring to Bruno Latour’s invocation of ‘symmetrical anthropology’, we reconsider tactile agency as ‘technological agency’, arguing that the concept of touch – traditionally viewed as an exclusively human ability – should be extended to non-human actors and analysed in view of the cultural logic of capitalism. Its systematic focus, then, is on the productive intersections and contact zones between biology and technology: from phenomena of non-human touch in industrial production (lathing, printing, etc.) to the material touching taking place in the instrumental grasp on the living in the 19th-century physiological laboratories and the invisible operations of tracing, tracking and sensing taking place in the technological milieus of today’s (media) environments. In highlighting the tactile dimension of digital modernity and its economic genealogies, this article aims to advance a combined concept of human and non-human touch which provides a crucial angle for reconsidering bodies and technologies in the age of ubiquitous computing.
An Automated Approach for the Prediction of the Severity Level of Bug Reports Using GPT-2

Manual investigation is warranted in traditional approaches for estimating the bug severity level, which adds to the effort and time required. For bug severity report prediction, numerous automated strategies have been proposed in addition to manual ones. However, the current bug report predictors by facing several issues, such as overfitting and weight computation, and therefore, their efficiency for specific levels of data noise needs to improve. As a result, a bug report predictor is required to solve these concerns (e.g., overfitting and avoiding weight calculation, which increases computing complexity) and perform better in the situation of data noise. We use GPT-2’s features (limiting overfitting and supplying sequential predictors rather than weight computation) to develop a new approach for predicting the severity level of bug reports in this study. The proposed approach is divided into four stages. First, the bug reports are subjected to text preprocessing. Second, we assess each bug report’s emotional score. Third, each report is presented in vector format. Finally, an emotion score is assigned to each bug report, and a vector of each bug report is produced and sent to GPT-2. We employ statistical indicators like recall, precision, and F1-score to evaluate the suggested method’s effectiveness and efficacy. A comparison was also made using state-of-the-art bug report predictors such as Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Support Vector Machine (SVM), XGBoost, and Naive Bayes Multinomial (NBM). The proposed method’s promising result indicates its efficacy in bug information retrieval.
Layer-Wise Analysis of Neuron Activation Values for Performance Verification of Artificial Neural Network Classifiers

Object classification in dynamic, uncontrolled environments is one of the functional elements of safety-critical Autonomous Systems. It is crucial to develop methods for the specification and verification of these elements, and the associated algorithms, in order to gain confidence in the overall safety of Autonomous Systems and their functional and behavioural correctness and adequacy. Artificial Neural Network (ANN) object classifiers must therefore be assured and need to be verified with respect to requirements. A classifier might be required to generalize to a satisfactory extent, in the sense that its classification performance must be maintained at an acceptable level when the input data differs from the training data. This requirement would apply when data received during operation is drawn from a different distribution to the training data. The specification and verification of classifier generalization capability can be based on measures of the dissimilarity between operational and training data. A requirement could state the permitted forms of the relationship between classification performance and a data dissimilarity measure. We have previously proposed such a dissimilarity measure, which we have termed the Neuron Region Distance (NRD). The NRD is a function of network activation values. In this paper, we analyze neuron activation values layer-by-layer across a neural network. This is in order to advance our progress towards the conception of a novel, generalized form of the NRD. This new measure is called the Per Neuron Ranking (PNR) measure. The activation value analysis provides insight into the required formulation of the PNR measure.
Adversarially Robust Edge-Based Object Detection for Assuredly Autonomous Systems

Edge-based and autonomous, deep learning computer vision applications, such as those used in surveillance or traffic management, must be assuredly correct and performant. However, realizing these applications in practice incurs a number of challenges. First, the constraints on edge resources precludes the use of large-sized, deep learning computer vision models. Second, the heterogeneity in edge resource types causes different execution speeds and energy consumption during model inference. Third, deep learning models are known to be vulnerable to adversarial perturbations, which can make them ineffective or lead to incorrect inferences. Although some research that addresses the first two challenges exists, defending against adversarial attacks at the edge remains mostly an unresolved problem. To that end, this paper presents techniques to realize robust and edge-based deep learning computer vision applications thereby providing a level of assured autonomy. We utilize state-of-the-art (SOTA) object detection attacks from the TOG (adversarial objectness gradient attacks) suite to design a generalized adversarial robustness evaluation procedure. It enables fast robustness evaluations on popular object detection architectures of YOLOv3, YOLOv3-tiny, and Faster R-CNN with different image classification backbones to test the robustness of these object detection models. We explore two variations of adversarial training. The first variant augments the training data with multiple types of attacks. The second variant exchanges a clean image in the training set for a randomly chosen adversarial image. Our solutions are then evaluated using the PASCAL VOC dataset. Using the first variant, we are able to improve the robustness of YOLOv3-tiny models by 1–2% mean average precision (mAP) and YOLOv3 realized an improvement of up to 17% mAP on attacked data. The second variant saw even better results in some cases with improvements in robustness of over 25% for YOLOv3. The Faster RCNN models also saw improvement, however, less substantially at around 10–15%. Yet, their mAP was improved on clean data as well.
Quantum Weighted Fractional Fourier Transform

Quantum Fourier transform (QFT) is an important part of many quantum algorithms. However, there are few reports on quantum fractional Fourier transform (QFRFT). The main reason is that the definitions of fractional Fourier transform (FRFT) are diverse, while some definitions do not include unitarity, which leads to some studies pointing out that there is no QFRFT. In this paper, we first present a reformulation of the weighted fractional Fourier transform (WFRFT) and prove its unitarity, thereby proposing a quantum weighted fractional Fourier transform (QWFRFT). The proposal of QWFRFT provides the possibility for many quantum implementations of signal processing.
English Multimedia Online Teaching Resource Processing System Based on Intelligent Cloud Platform

In order to improve the quality of online English teaching, starting from English online teaching resources, this paper applies cloud computing technology to the teaching resource integration process to design a comprehensive cloud storage control method based on user habits. Moreover, this paper uses the user habit extraction algorithm to extract the user’s habit information and then realizes the control of cloud storage with the help of these habit information. After that, the system can realize intelligent management of cloud storage in space by analyzing the information and converting it into corresponding cloud storage control instructions. In addition, this paper constructs an integrated system of English multimedia network teaching resources under the support of cloud storage technology. Finally, this paper uses simulation experiments to verify that the integrated system of English multimedia network teaching resources based on cloud storage has a good effect.
Renewing learning: Reimagining the newness of families' sociotechnical practices towards ecologically sustainable media engagement

This paper reports on an ethnographic study of families using new media and mobile technologies around their homes. While I was originally studying how families' everyday practices were being reconfigured by these new tools, what I found unsettles assumptions about learning with “new” media and about processes of technological change. This paper examines how older and newer technologies coexist in practice, and offers renewability as a key phenomenon for children learning in the context of family life. Drawing on observations and interviews with two sisters, nine-year-old Lara and twelve-year-old Latasha, I develop the concept of renewability through their sociotechnical practices of recycling and interleaving media forms as they went about routine activities like studying and reading after school. I present the cases narratively and adopt a media archaeological approach to studying material culture, where moment-to-moment interactions with technology provide proleptic evidence of future media strata and substrata. I then discuss families' changing practices in terms of processes of technology development, focusing on paper technologies and the contradictions of contravening on complex social-ecological conditions. I conclude that designing for renewability of learning is a way of envisioning responsible forms of technologically re-mediated action in an era of climate change.
Enhancement of Hand Gesture Recognition Using Convolutional Neural Networks Integrating a Combination of an Autoencoder Network and PCA

Hand gestures offer people a convenient way to interact with computers, in addition to give them the ability to communicate without physical contact and at a distance, which is essential in today’s health conditions, especially during an epidemic infectious viruses such as the COVID-19 coronavirus. However, factors, such as the complexity of hand gesture patterns, differences in hand size and position, and other aspects, can affect the performance of hand gesture recognition and classification algorithms. Some deep learning approaches such as convolutional neural networks (CNN), capsule networks (CapsNets) and autoencoders have been proposed by researchers to improve the performance of image recognition systems in this particular field: While CNNs are arguably the most widely used networks for object detection and image classification, CapsNets and Autoencoder seem to resolve some of the limitations identified in the first approach. For this reason, in this work, a specific combination of these networks is proposed to effectively solve the ASL problem. The results obtained in this work show that the proposed group with a simple data augmentation process improves precision performance by 99.43%.
Cryptocurrency: The Future of Plastic Surgery Payments?

Cryptocurrencies are digital currencies that allow for peer-to-peer transactions, maintained by a decentralized, secure, and trust-based system. Similar to the patient-centered care approach, cryptocurrency empowers the individual to transact, without a third-party, in a safe, largely transparent, and fast way. Awareness of the current economic state and the ongoing growth of cryptocurrency can place plastic surgeons in a unique and opportune position to broaden payment acceptance methods and create a more effective business model. By first creating a digital wallet for storage, and then employing a payment processing tool, transacting with cryptocurrency becomes practical and convenient. This paper explores the advantages and limitations of cryptocurrency and provides a high-level breakdown of the basics of accepting and transacting with cryptocurrency within a successful plastic surgery practice.
Coevolution and learning symbolic concepts: statistical validation

The paper reports an empirical study done to statistically validate the preliminary findings obtained in previous research by the author on the small disjunct problem. Thus additional support to the working hypothesis that cooperative evolution (co-evolution) can be successfully applied in learning symbolic concepts and that co-evolution when carefully exploited can produce more robust classification rule (symbolic concepts) with higher statistical validity. In the paper we will compare the effect of applying a specific co-evolutive learning strategy with the results obtained by running a learning system without any coevolution. Thus we can measure the add-on effect produced by the coevolutive strategy. As learning systems we will use the system REGAL that combines distributed learning and genetic algorithms to find symbolic classifiers. As a future extension of this research, we note that the described co-evolutive strategy can be applied to other learning methods.
Calibration Error Prediction: Ensuring High-Quality Mobile Eye-Tracking

Gaze calibration is common in traditional infrared oculographic eye tracking. However, it is not well studied in visible-light mobile/remote eye tracking. We developed a lightweight real-time gaze error estimator and analyzed calibration errors from two perspectives: facial feature-based and Monte Carlo-based. Both methods correlated with gaze estimation errors, but the Monte Carlo method associated more strongly. Facial feature associations with gaze error were interpretable, relating movements of the face to the visibility of the eye. We highlight the degradation of gaze estimation quality in a sample of children with autism spectrum disorder (as compared to typical adults), and note that calibration methods may improve Euclidean error by 10%.
Vehicle motion segmentation via combining neural networks and geometric methods

Motion segmentation is important for autonomous robots’ activities in dynamic scenes, which has been a challenging problem due to the dual motion caused by the camera and moving objects. The existing methods based on the optical or scene flow are sensitive to the environment, whereas deep learning-based methods commonly do not consider geometric constraints. Aiming at a vehicle, as the most common object in outdoor urban dynamic scenes because other objects, such as people, can be almost directly determined as a moving object, this paper proposes an object-level motion segmentation method for multiple moving objects combining geometric methods and deep learning. First, we choose three kinds of learned information, constructing object-level three-dimensional (3D) scene structure. And then, to infer motion segmentation results from learned information, we propose an instance cross-matching method to obtain instance correspondence and a motion segmentation extraction method based on instance reprojection residual to determine whether the objects are moving or not. The proposed approach is simple and direct, and it can obtain vehicle motion segmentation results from the original RGB images without using the optical or scene flow commonly used to extract motion information. Experiments on public motion segmentation datasets demonstrate that the proposed method can effectively improve the performance of vehicle motion segmentation and is superior to the most advanced methods in terms of accuracy.
Learning the Architectural Features That Predict Functional Similarity of Neural Networks

The mapping of the wiring diagrams of neural circuits promises to allow us to link the structure and function of neural networks. Current approaches to analyzing such connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph-theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features—the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs—predicts the functional similarity of networks of up to 1000 neurons, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks. These architectural features conform with experimental evidence of homeostatic synaptic mechanisms.Received 25 September 2020Revised 14 July 2021Accepted 22 September 2021DOI:https://doi.org/10.1103/PhysRevX.12.021051Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNetwork structureNeuronal network activitySpiking neuronsPhysical SystemsNeuronal networksTechniquesNetwork ModelsNetworks Analysis ToolsNetworksBiological PhysicsInterdisciplinary Physics
An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Cloud computing distributes task-parallel among the various resources.Applications with self-service supported and on-demand service have rapid growth.For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements.Proper resource allocation is vital for fulfilling user requirements.In contrast, improper resource allocations result to load imbalance, which leads to severe service issues.The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations.The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex.This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users.The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them.The proposed system introduced three -threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding).Along with it, task merging enables minimum energy consumption and response time.The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM).With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models.The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models.The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration.
The Impact of COVID-19 on Latin American STEM Higher Education: A Systematic Review

Since 2020, the COVID-19 pandemic has impacted the education system, causing a shift towards online distance education. This systematic review aims to systematize research studies focused on science, technology, engineering, and mathematics (STEM) higher education in Latin America. The search was conducted on the Web of Science, IEEE Xplore, ACM Library, and Scielo databases. In total, 18 studies were selected from the fields of biology (01), chemistry (02), computer science (04), information science (02), engineering (06), systems engineering (02), and interdisciplinary (01). The results show that online learning partially followed a constructivist approach, but sometimes caused stress and depression in students. While online teaching employed gamification, and virtual laboratories, it increased professors’ workload. Universities implemented online courses, and some promoted interdisciplinary research. This review discusses the challenges and opportunities of online distance education by demonstrating the efforts made to introduce this modality of education in Latin American higher education institutions.
Diversity-inclusion nexus: assessing the role of ethnic and religious diversity in financial inclusion; a global perspective

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Valuable unintended learning outcomes when practicum for student teachers in kindergartens is carried out online

This article presents a study of an educational experiment conducted at the early childhood education programme at UiT, the Arctic University of Norway. As COVID-19 made social distancing an issue, the traditional practicum in kindergartens was moved to online platforms. Constructive alignment was used as an analytical framework to investigate the possibilities and limitations of student teachers' learning outcomes in a collaborative online learning activity. Overall, 8 out of 9 practicum teachers found the online practicum form a better alternative than supplying the student teacher with a written assignment, and 72.7% of the student teachers agreed or somewhat agreed with facilitating learning outcomes from the online practicum regardless of social distancing. The need for professional digital competence is central, as digital technology is not simply part of pedagogical practices but is becoming an integrated part of communicating and collaborating with colleagues and parents. This study broadens the understanding of how collaborative online learning can facilitate unintended valuable learning outcomes and critically debates the limitations related to emphasising a too-strong focus on intended learning outcomes as a premise for constructive alignment in education.