Reviewerly/exHarmony huggingface dataset:
Computing with Kubernetes

This Chapter introduces Kubernetes, a container orchestration system created and open sourced by Google. Kubernetes Engine is Google Cloud Platform's (GCP's) managed Kubernetes service. With this service, GCP customers can create and maintain their own Kubernetes clusters without having to manage the Kubernetes platform. Kubernetes runs containers on a cluster of virtual machines (VMs). It determines where to run containers, monitors the health of containers, and manages the full lifecycle of VM instances. This collection of tasks is known as container orchestration. A Kubernetes cluster consists of a cluster master and one or more nodes, which are the workers of the cluster. The cluster master controls the cluster and can be replicated and distributed for high-availability and fault tolerance. The cluster master manages services provided by Kubernetes, such as the Kubernetes API, controllers, and schedulers. All interactions with the cluster are done through the master using the Kubernetes API. The cluster master issues the command that performs an action on a node.
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction

Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce. In this work, we propose a new unsupervised approach to detect the landmarks in images, validating it on the popular task of human face key-points extraction. The method is based on the idea of auto-encoding the wanted landmarks in the latent space while discarding the non-essential information (and effectively preserving the interpretability). The interpretable latent space representation (the bottleneck containing nothing but the wanted key-points) is achieved by a new two-step regularization approach. The first regularization step evaluates transport distance from a given set of landmarks to some average value (the barycenter by Wasserstein distance). The second regularization step controls deviations from the barycenter by applying random geometric deformations synchronously to the initial image and to the encoded landmarks. We demonstrate the effectiveness of the approach both in unsupervised and semi-supervised training scenarios using 300-W, CelebA, and MAFL datasets. The proposed regularization paradigm is shown to prevent overfitting, and the detection quality is shown to improve beyond the state-of-the-art face models.
Drone Classification with a Convolutional Neural Network Applied to Raw IQ Data

With the increasing popularity of civilian drones, the need for technical detection and classification systems rises. In this paper a machine learning based approach for detection and classification of radio frequency signals from drones is proposed. As data source the DroneDetect_V2 data set is used. The raw IQ data is processed by a convolutional neural network, without the need for much pre-processeing or any feature engineering. With this approach an accuracy of 99 % for detection and between 72 % and 94 % for classifi-cation is reached.
On Non-Convexity of the Nonclassicality Measure via Operator Ordering Sensitivity

In quantum optics, nonclassicality in the sense of Glauber-Sudarshan is a valuable resource related to the quantum aspect of photons. A desirable and intuitive requirement for a consistent measure of nonclassicality is convexity: Classical mixing should not increase nonclassicality. We show that the recently introduced nonclassicality measure [Phys. Rev. Lett. 122 , 080402 (2019)] is not convex. This nonclassicality measure is defined via operator ordering sensitivity, which is an interesting and significant probe (witness) of nonclassicality without convexity but can be intrinsically connected to the convex Wigner-Yanase skew information [Proc. Nat. Acad. Sci. United States 49 , 910 (1963)] via the square root operation on quantum states. Motivated by the Wigner-Yanase skew information, we also propose a faithful measure of nonclassicality, although it cannot be readily computed, it is convex.
Emotion Recognition of Online Education Learners by Convolutional Neural Networks

At present, the facial expression recognition model in video communication has problems such as weak network generalization ability and complex model structure, which leads to a large amount of computation. Firstly, the Inception architecture is adopted as a design philosophy. The Visual Geometry Group Network (VGGNet) model is improved. Multiscale kernel convolutional layers are constructed to obtain more expressive features. Secondly, the attention mechanism is integrated into a multiscale feature fusion network to form a multiattention mechanism Convolutional Neural Network (CNN) model. Novel spatial and multichannel attention models are designed. The effects of redundant information and noise are reduced. Finally, experiments are carried out on the Fer2013 dataset and the Extended Cohn-Kanade Dataset (CK+) to verify the detection accuracy of the model. The results show that the Delivered Duty Unpaid (DDU) loss can be used for facial expression recognition in complex environments. After the attention module is added, the overall recognition accuracy of the network on Fer2013 and CK+ has been improved to varying degrees. The addition of the channel attention module has a more obvious effect on the recognition accuracy compared with the spatial attention module. The addition of the attention module enables the network to increase the attention to error-prone samples. The improved network model can better extract the key features of facial expressions, enhance the feature discrimination ability, and improve the recognition accuracy of error-prone expressions. The accuracy rate of facial expression recognition with larger movements is over 98%. Facial expressions are an important way of communication between people, and online video has greatly limited this communication method. The proposed CNN model based on multiscale feature fusion will effectively solve these network limitations and have an important and positive impact on future network information exchange.
Geometric Algebra Computing for Computer Graphics using GAALOP

course Share on Geometric Algebra Computing for Computer Graphics using GAALOP Authors: Dietmar Hildenbrand View Profile , Alyn Rockwood View Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 CoursesAugust 2022 Article No.: 9Pages 1–173https://doi.org/10.1145/3532720.3535655Published:02 August 2022Publication History 0citation103DownloadsMetricsTotal Citations0Total Downloads103Last 12 Months103Last 6 weeks13 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Modeling from Time Series of Complex Brain Signals

Signals obtained from most of real-world systems, especially from living organisms, are irregular, often chaotic, non-stationary, and noise-corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). In the paper given a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time series. Brain signals can be recorded by brainwave controlled applications, such as EMotiv Epoc +14. The paper investigates the models of the observed brain signals using time series, analyzes their applicability and develops new statistical models for their study.
C2PA: the world’s first industry standard for content provenance (Conference Presentation)

Given the deluge of digital content and rapidly advancing technology, it is challenging for consumers to trust what they see online. Deceptive content, such as deepfakes generated by artificial intelligence or more traditionally manipulated media, can be indistinguishable from the real thing, so establishing the provenance of media is critical to ensure transparency, understanding, and trust. The C2PA specification provides platforms with a open standards-based method to define what information is associated with each type of asset (e.g., images, videos, audio, or documents), how that information is presented and stored, and how evidence of tampering can be identified.
Object Detection using Different Point Feature Techniques: A Comparative Analysis

The image Recognition system is a vital problem in the field of computer vision because it must be precise, successful in the desired goal, strong, healthy, and self-loading. The following are the most critical essential phases in image alignment/registration: feature matching, feature detection, derivation of transformation function based on related features in pictures, and reconstruction of images based on generated transformation function. In many applications, the goal of computer vision is to create an ideal and accurate image, which is dependent on optimal feature matching and detection. This paper's inquiry summarizes the similarity among five alternative approaches for robust features/interest points (or landmarks) detector and picture identification. This research also focuses on the extraction of unique features from photos that may be utilized to conduct effective matching of diverse perspectives of the images/objects/scenes.
High Throughput of Image Processing with Keccak Algorithm using Microprocessor on FPGA

Transmitting images between people has been a crucial aspect of daily communication in our digital world. Therefore, methods are needed to check the integrity and accuracy of the transmitted data. The most famous and secure way today is hashing. This paper focuses on the Keccak algorithm for hashing image sizes $256\times 256$ pixels utilizing our specialized FPGA implementations. Our experiments were performed on the FPGA Intel DE2-115 (EP4CE115F29C7) and the Nios II microprocessor. Additionally, research on computational metrics like throughput, entropy, NPCR, and UACI demonstrates how the Keccak algorithm is specialized, secure, and has a wide range of potential applications for hashing images. Finally, we propped our method using the Block Floating Point Hardware 2 (FP2). We contrasted the outcomes of our analysis with other recent optimization techniques used by other researchers, and the results demonstrate our advantages.
Framework to Improve the Traceability of the Coffee Production Chain in Perú by Applying a Blockchain Architecture

The value of coffee is decreasing in the international market caused by the poor traceability that coffee cooperatives offer to the rest of the public in the production chain. There are paper records or storage in centralized databases of the details of the product that is delivered and received at each stage, generating high cost and inefficiency in paper-based processes, fraud, corruption and errors in both processes. Other investigations pose the problem proposing solutions with opportunities to improve. A framework is proposed to improve the traceability of the coffee production chain using blockchain. The proposed framework is composed of 3 phases: (1) Blockchain architecture design, (2) Smart contracts design and (3) Web application design. Blockchain technology is used to guarantee immutable traceability of the production chain. The proposed framework was validated in a coffee cooperative located in Cajamarca (Peru) applying 2 experiments and a survey based on expert opinion. The results show that with the proposed framework, time of process and the number of errors can be reduced in the defined scenarios, where in turn a reduction of 99.87% of the time in the generation of coffee traceability is evidenced. In addition, the results of the survey show that the “performance”, “traceability parameters” and “usability” of the system have an average value of 4.6 (Value close to 5).
Learning English with the mobile language learning application 'Duolingo': the experiences of three working adults at different proficiency levels

Duolingo, one of the most popular mobile applications for English learning, has been studied extensively to assess the effectiveness of Duolingo-Assisted Language Learning (DALL). However, most studies have focused on learning outcomes, few have discussed the learning process, and even fewer have investigated working adults. Accordingly, the present study investigated the DALL experiences of three Chinese working adults who graduated from colleges and continued to learn English for various purposes. The three who were at the basic, intermediate and advanced levels had been learning English with Duolingo for a month. Throughout this month, they reported their learning experiences through think-aloud protocols and reflective learning logs, after which they were interviewed. The results indicated that the basic-level learner used more strategies than the intermediate- and advanced-level learners, which enriched the literature. All three participants showed generally positive perceptions of Duolingo. They moved up to higher course levels after one-month Duolingo learning, indicating that language learners may consider using Duolingo for self-access language learning.
Adaptive resource management for spot workers in cloud computing environment

Due to flexible scheduling requirements of various service applications, a cloud platform usually has some temporarily unleased machines. To make cost-effective of the platform, such a considerable number of idle workers can be collected to perform malleable tasks. However, these workers are considered unstable since they can be interrupted unexpectedly by the resource broker. This paper proposes a resource management approach that employs replication to increase resource availability. We will show that increasing the replication factor can improve the worker reliability, but on the contrary, it also worsens the overhead of computational redundancy. An algorithm that can effectively control the replication factor so as to adapt to the changing workload and maintain the system performance is also developed.
BERT-Based Emotion Classification Approach with Analysis of COVID-19 Pandemic Tweets

The continuous spread of coronavirus disease, asks for active research on interactions, activities, and emotional flows of people as it is significant to policymakers. While social media are broadly used to express opinions and emotions, emotion detection and classification from text is a challenging problem due to the lack of facial or voice expressions. This paper proposes a machine-learning model that, unlike manual feature extraction techniques, leverages the neural attention approach to detect emotions through meaningful feature extraction. Firstly, this research contributes to take a deep learning approach for the classification of six types of emotions from the text using the benchmark ISEAR dataset. Secondly, The model predictions have been explained using the model agnostic approach. Thirdly, emotions in the tweets related to COVID-19 have been classified and quantified along with an illustration of the emotion dynamics during the Corona Pandemic before and after the start of the vaccination. Finally, N-gram analysis has been used for the summarization of the contributing factors. In terms of emotion classification from ISEAR data, our approach comes out with a better F1 score of .72 compared to some other state-of-the-art works.
Coarse-to-fine Disentangling Demoiréing Framework for Recaptured Screen Images

Removing the undesired moiré patterns from images capturing the contents displayed on screens is of increasing research interest, as the need for recording and sharing the instant information conveyed by the screens is growing. Previous demoiréing methods provide limited investigations into the formation process of moiré patterns to exploit moiré-specific priors for guiding the learning of demoiréing models. In this paper, we investigate the moiré pattern formation process from the perspective of signal aliasing, and correspondingly propose a coarse-to-fine disentangling demoiréing framework. In this framework, we first disentangle the moiré pattern layer and the clean image with alleviated ill-posedness based on the derivation of our moiré image formation model. Then we refine the demoiréing results exploiting both the frequency domain features and edge attention, considering moiré patterns’ property on spectrum distribution and edge intensity revealed in our aliasing based analysis. Experiments on several datasets show that the proposed method performs favorably against state-of-the-art methods. Besides, the proposed method is validated to adapt well to different data sources and scales, especially on the high-resolution moiré images.
Modified RNN for Solving Comprehensive Sylvester Equation With TDOA Application

The augmented Sylvester equation, as a comprehensive equation, is of great significance and its special cases (e.g., Lyapunov equation, Sylvester equation, Stein equation) are frequently encountered in various fields. It is worth pointing out that the current research on simultaneously eliminating the lagging error and handling noises in the nonstationary complex-valued field is rather rare. Therefore, this article focuses on solving a nonstationary complex-valued augmented Sylvester equation (NCASE) in real time and proposes two modified recurrent neural network (RNN) models. The first proposed modified RNN model possesses gradient search and velocity compensation, termed as RNN-GV model. The superiority of the proposed RNN-GV model to traditional algorithms including the complex-valued gradient-based RNN (GRNN) model lies in completely eliminating the lagging error when employed in the nonstationary problem. The second model named complex-valued integration enhanced RNN-GV with the nonlinear acceleration (IERNN-GVN) model is proposed to adapt to a noisy environment and accelerate the convergence process. Besides, the convergence and robustness of these two proposed models are proved via theoretical analysis. Simulative results on an illustrative example and an application to the moving source localization coincide with the theoretical analysis and illustrate the excellent performance of the proposed models.
Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code

Bosonic codes provide an alternative option for quantum error correction. An important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP) code has aroused much interest recently. Theoretically, the error correction ability of the GKP code is limited since it can only correct small shift errors in position and momentum quadratures. A natural approach to promote the GKP error correction for large-scale, fault-tolerant quantum computation is concatenating encoded GKP states with a stabilizer code. The performance of the XZZX surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX surface code is investigated in this paper under two different noise models. Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code with parameter $\ensuremath{\lambda}$ is introduced. Using the minimum weight perfect matching decoder combined with the continuous-variable GKP information, the optimal threshold of the XZZX-surface GKP code reaches $\ensuremath{\sigma}\ensuremath{\approx}0.67$ when $\ensuremath{\lambda}=2.1$, compared with the threshold $\ensuremath{\sigma}\ensuremath{\approx}0.60$ of the standard surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in the actual implementation and build the full circuit-level noise model. By setting the appropriate bias parameters, the logical error rate is reduced by several times in some cases. These results indicate the XZZX surface-GKP codes are more suitable for asymmetric concatenation under the general noise models. We also estimate the overhead of the XZZX-surface GKP code which uses about 291 GKP states with the noise parameter 18.5 dB ($\ensuremath{\kappa}/g\ensuremath{\approx}0.71%$) to encode a logical qubit with the error rate $2.53\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}$, compared with the qubit-based surface code using 3041 qubits to achieve almost the same logical error rate.
Asynchronous measurement-device-independent quantum key distribution with hybrid source

The linear constraint of secret key rate capacity is overcome by the twin-field quantum key distribution (QKD). However, the complex phase-locking and phase-tracking technique requirements throttle the real-life applications of the twin-field protocol. The asynchronous measurement-device-independent (AMDI) QKD, also called the mode-pairing QKD, protocol can relax the technical requirements and keep the similar performance of the twin-field protocol. Here, we propose an AMDI-QKD protocol with a nonclassical light source by changing the phase-randomized weak coherent state to a phase-randomized coherent-state superposition in the signal state time window. Simulation results show that our proposed hybrid source protocol significantly enhances the key rate of the AMDI-QKD protocol, while exhibiting robustness to imperfect modulation of nonclassical light sources.
Co-constructing Stories Based on Users Lived Experiences to Investigate Visualization Design for Collective Stress Management

Collective stress is the stress within a group or an organization. It affects individuals’ well-being and group productivity. HCI research has started exploring collective stress visualization to facilitate group awareness and collective coping via testing prototypes in controlled settings. However, an in-depth understanding of users’ needs and envisaged scenarios based on their authentic experiences are still lacking. In this study, we utilized a participatory approach called co-constructing stories to investigate how a collective stress visualization would be used in office workers’ authentic workday routines. We constructed use case stories with a group of office workers separately based on their personal lived experiences, using a design probe called AffectiveGarden. Our results categorized six clusters of benefits for collective coping through visualization and their implications for future design practice.
Power consumption reduction for IoT devices thanks to Edge-AI: Application to human activity recognition

Edge-AI uses Artificial Intelligence algorithms directly embedded on a device, contrary to a remote AI that uses an AI on a cloud or remote server for prediction. Recent improvements in microcontroller computing capabilities and enhanced deep learning algorithms and conversion frameworks made it easier to run small AI models directly on microcontroller units. Is the current interest in on-device AI justified in terms of its energy consumption on resource-constrained devices when compared to AI on the cloud? This study presents how an embedded deep convolutional neural network (DCNN) is used for real-time human activity recognition with more than 98% classification accuracy and its impact on battery life. Experiments conducted on a triaxial accelerometer with data collected and processed by an ARM Cortex-M4-based development board showed that energy consumption could be reduced up to 21% when inferences are run on an edge device versus using a remote server/cloud without compromising the overall classification precision and accuracy. We can reduce energy consumption by limiting data transmission by considering pseudo-real-time or non-real-time application scenarios.