Reviewerly/exHarmony huggingface dataset:
Advancing our understanding and assessment of cognitive effort in the cognitive fit theory and data visualization context: Eye tracking-based approach

In Cognitive Fit Theory (CFT) based research, there is a consensus about cognitive effort as the underlying mechanism impacting performance. Although critical to the theory, cognitive effort and its direct empirical assessment remain a challenge. In this repeated measures experimental study, we introduce a research model and develop hypotheses based on the fundamental relationships underlying CFT while integrating eye tracking as an approach for assessing cognitive effort. Our study finds that eye tracking technology, specifically fixation-based metrics, can be used in the understanding of cognitive processes initiated by our data representation choices. Specifically, we find that in all tasks except the complex-symbolic task, users experience meaningful change in the physiological assessment of cognitive effort based on the condition of cognitive fit. We contrast our findings to existing research and find that physiological indicators of cognitive effort can provide critical insights often missed in traditional CFT research.
Beyond word embeddings: A survey

The goal of this paper is to provide an overview of the methods that allow text representations with a focus on embeddings for text of different lengths, specifically on works that go beyond word embeddings. Analyzing pieces of text can be more challenging in comparison to the analysis of single words, because several additional factors come into play. For this reason, representations of longer pieces of text can be obtained with different strategies, leveraging additional information with respect to what is done for single words. A text is defined by its components and how these are combined together, and this should be taken into account when integrating information to obtain a single document embedding. In addition, multimodal approaches are described to show how it is possible to fuse information of different nature (aural, visual and knowledge) in order to obtain enriched representations. The aim of this survey is to help navigate through the existing methods proposed in the literature and understand which strategies are most suitable to specific needs.
An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT)

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events.
Interacting with Chatbots: Message type and consumers' control

With advances in technology, personalized services provided by offline salespeople are replaced by new sales assistant methods, such as personalized chatbots in online and mobile environments. However, providing conversation-based recommendations may be insufficient to support consumers in online or mobile stores because they cannot experience the product in real-time. A salesperson often provides one-to-one customer support in an offline store, including verbal and visual recommendations. In this context, chatbots, the sales assistants, may better support consumers in the online and mobile environments by providing additional real-time visual information. This study aims to determine the condition of chatbots as online and mobile sales assistants, and the mechanisms by which consumers accept chatbots. The results indicate that a higher level of personalized chatbot messages enhances purchase intention through a sense of ease and understanding of the product. Moreover, additional real-time visual information (i.e., AR) supports chatbots in acting as successful sales assistants.
QoS-Aware Co-Scheduling for Distributed Long-Running Applications on Shared Clusters

To achieve a high degree of resource utilization, production clusters need to co-schedule diverse workloads – including both batch analytic jobs with short-lived tasks and long-running applications (LRAs) that execute for a long time frame from hours to months – onto the shared resources. Microservice architecture advances the manifestation of distributed LRAs (DLRAs), comprising multiple interconnected microservices that are executed in long-lived distributed containers and serve massive user requests. Detecting and mitigating QoS violation become even more intractable due to the network uncertainties and latency propagation across dependent microservices. However, current resource managers are only responsible for resource allocation among applications/jobs but agnostic to runtime QoS such as latency at application level. The state-of-the-art QoS-aware scheduling approaches are dedicated for monolithic applications, without considering the temporal-spatio performance variability across distributed microservices. In this paper, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Toposch</small> , a new scheduling and execution framework to prioritize the QoS of DLRAs whilst balancing the performance of batch jobs and maintaining high cluster utilization through harvesting idle resources. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Toposch</small> tracks footprints of every single request across microservices and uses critical path analysis, based on the end-to-end latency graph, to identify microservices that have high risk of QoS violation. Based on microservice and node level risk assessment, we intervene the batch scheduling by adaptively reducing the visible resources to batch tasks and thus delaying their execution to give way to DLRAs. We propose a prediction-based vertical resource auto-scaling mechanism, with the aid of resource-performance modeling and fine-grained resource inference and access control, for prompt recovery of QoS violation. A cost-effective task preemption is leveraged to ensure a low-cost task preemption and resource reclamation during the auto-scaling. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Toposch</small> is integrated with Apache YARN and experiments show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Toposch</small> outperforms other baselines in terms of performance guarantee of DLRAs, at an acceptable cost of batch job slowdown. The tail latency of DLRAs is merely 1.12x of the case of executing alone on average in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Toposch</small> with a 26% JCT increase of Spark analytic jobs.
Digital image correlation based on convolutional neural networks

As an indispensable non-destructive testing technique, digital image correlation (DIC) has been increasingly applied to various engineering areas concerning deformation characterization. Inspired by artificial intelligence-related technologies, we here develop a new convolutional neural network-based theoretical framework for DIC analyses, hereafter called DIC-Net. A pyramidal structure is designed to ensure robustness and reliability of measurement results. Simultaneously, the second-order shape function is adopted to create training dataset, making the DIC-Net more suitable for solving complex deformation fields. Different from conventional DIC algorithms, the developed DIC-Net does not require specific correlation criterion, nor is it necessary to perform numerical iterative computations, which greatly enhances the efficiency of correlation calculations. The proposed DIC-Net not only provides an alternative approach to achieve accurate, precise and reliable deformation measurements, but also paves the way for developing high-efficiency DIC with real-time processing capabilities.
On the Grounds of Solutionism: Ontologies of Blackness and HCI

Why is the solution the end point to a problem? While many in HCI and design have examined the impulse to solve problems–the solutionist or techno-solutionist mindset–we examine the logic that binds the solution and the problem together as a pair. Focusing on the timely and consequential problem of systemic racial injustice, we think through the paradoxical possibility that the pairing of the problem and solution (so often treated as the default in design and HCI) perpetuates the very conditions we seek to improve. With Calvin Warren’s profound Afro-pessimism, we recognize how the tools used to solve structural inequities around Black life are constructed with inequities themselves. The problem-solution, therefore, is a dead end. We use this paradox as an invitation to rethink ongoing efforts to seek equity and justice more broadly, setting out a fragile but hopeful path for HCI and design.
Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning

SUMMARY To provide timely and accurate seismic alerts for potential users during the earthquake early warning (EEW) process, several algorithms have been proposed and implemented. Some of the most common rely on the characterization of the earthquake magnitude and location, and then use a ground motion model to forecast shaking intensity at a user's location. It has been noted that with this approach the scatter in the forecasted intensities can be significant and may affect the reliability and usefulness of the warnings. To ameliorate this, we propose a single station machine learning (ML) algorithm. We build a four-layer convolutional neural network (CNN), named it CONIP (Convolutional neural network ONsite Intensity Prediction), and test it using two data sets to study the feasibility of seismic intensity forecasting from only the first few seconds of a waveform. With only limited waveforms, mainly P waves, our CONIP model will forecast the on-site seismic intensity. We find that compared with existing methods, the forecasted seismic intensities are much more accurate. To understand the nature of this improvement we carry out a residual decomposition and quantify to what degree the ML model learns site, regional path, and source information during the training. We find that source and site effects are easily learned by the algorithm. Path effects, on the other hand, can be learned but will depend largely on the number, location, and coverage of stations. Overall, the ML model performance is a substantial improvement over traditional approaches. Our results are currently only applicable for small and moderate intensities but, we argue, could in future work be supplemented by simulations to supplement the training data sets at higher intensities. We believe that ML algorithms will play a dominant role in the next generation of EEW systems.
Parameterized Hamiltonian Learning With Quantum Circuit

Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution. Then, a PHL algorithm is developed to prepare a specific Hamiltonian system by iteratively updating the gradient of the loss function about circuit parameters. Finally, the experiments are conducted on Origin Pilot, and it demonstrates that the PHL algorithm can deal with the image segmentation problem and provide a segmentation solution accurately. Compared with the classical Grabcut algorithm, the PHL algorithm eliminates the requirement of early manual intervention. It provides a new possibility for solving practical application problems with quantum devices, which also assists in solving increasingly complicated problems and supports a much wider range of application possibilities in the future.
Distributed Real-Time Object Detection Based on Edge-Cloud Collaboration for Smart Video Surveillance Applications

Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of “smart city.” Video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable; thus, the centralized framework is not sufficient for real-time processing of media data needed for smart video surveillance. To tackle this problem, edge computing - a technique for accelerating the development of AIoT (AI across IoT) in smart cities - can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and low-throughput media data transmission.
Reschedule Gradients: Temporal Non-IID Resilient Federated Learning

Federated learning is a popular framework designed to perform the distributed machine learning while protecting client privacy. However, the heterogeneous data distribution in real-world environments makes it difficult to converge when performing model training. In this article, we propose the federated gradient scheduling (FedGS), an improved historical gradient sampling utilization method for optimizers that utilize historical gradients in the federated learning to alleviate the instability problem of historical gradient information due to non-IID. FedGS consists of two main steps to improve federated learning performance. First, clustering uses clients’ label distributions, which relabel clients and their submitting gradients. Second, sampling gradient clusters to generate an IID gradient set, which feeds to optimizers to derive valid momentum information. Besides, we introduce differential privacy to collaborate with FedGS to enhance clients’ privacy protection strength. Compared to previous non-IID federated learning solutions, our method can achieve greater resistance to temporal non-IID. Moreover, experiments show that FedGS can achieve faster convergence and performance gain of up to 10% over existing state-to-art methods in some scenarios. FedGS can combine with existing methods easily to achieve better performance. We further verify that our method has robust performance gains in different non-IID scenarios, demonstrating the adaptability of FedGS for different scenarios.
Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art.
A modified Lévy flight distribution for solving high-dimensional numerical optimization problems

Lévy flight distribution is a recent meta-heuristic inspired by lévy flight random walk for exploring unknown large search spaces. Similar to other original metaheuristic algorithms, Lévy flight distribution can suffer from drawbacks, such as being trapped in minimum local areas and imbalance between the exploitation and exploration. To overcome these weaknesses and enhance the ability of Lévy flight distribution in solving high-dimensional numerical optimization problems, a modified Lévy flight distribution, called MLFD, is presented. Firstly, Lévy flight distribution has good exploration ability; secondly, the symbiosis organisms search has a strong exploitation capability in the mutualism phase. By introducing the mutualism phase, the exploitation ability of the algorithm is improved effectively and help avoid premature convergence. Moreover, a new differential variation strategy is proposed to enhance the diversity of the population and make the algorithm jump out of the local optimum in time. Seventeen well-known high-dimensional unconstrained problems are utilized to compare the proposed algorithm with other nine classical algorithms. The experimental results and statistical analysis demonstrate that MLFD algorithm has promising effectiveness and performance compared with other nine classical algorithms.
Effect of Imbalance Data Handling Techniques to Improve the Accuracy of Heart Disease Prediction using Machine Learning and Deep Learning

Heart disease is the prominent life-threating cause of death. Early stage of prediction can reduce the death percentage. This paper investigates the effect of different imbalance data handling techniques on the accuracy to predict the heart disease using machine learning, deep learning methods and an ensemble method. Most of the algorithms show better accuracy in balanced data instead of imbalance data. Support Vector Machine, Multilayer Perceptron, ensemble of Logistic Regression and Multilayer Perceptron shows 96% accuracy on balanced data using SMOTETomek hybrid balancing techniques. Accuracy, Precision, Recall, F-1 score, Specificity, Cohen Kappa, AUC score and ROC curve is used to measure the performance of each algorithm.
Individual disturbance and neighborhood mutation search enhanced whale optimization: performance design for engineering problems

Abstract The whale optimizer is a popular metaheuristic algorithm, which has the problems of weak global exploration, easy falling into local optimum, and low optimization accuracy when searching for the optimal solution. To solve these problems, this paper proposes an enhanced whale optimization algorithm (WOA) based on the worst individual disturbance (WD) and neighborhood mutation search (NM), named WDNMWOA, which employed WD to enhance the ability to jump out of local optimum and global exploration, adopted NM to enhance the possibility of individuals approaching the optimal solution. The superiority of WDNMWOA is demonstrated by representative IEEE CEC2014, CEC2017, CEC2019, and CEC2020 benchmark functions and four engineering examples. The experimental results show that thes WDNMWOA has better convergence accuracy and strong optimization ability than the original WOA.
A topic modelling analysis of white papers in security token offerings: Which topic matters for funding?

Security token offerings (STOs), based on blockchain technology, are attracting increasing attention as an innovative alternative means of venture financing. Information about specific STOs is generally provided in white papers. This study analyses the content of white papers using a unique sample of 188 STOs from 2017 to 2021 to identify which topic is related to campaign success. We leverage latent Dirichlet allocation (LDA) topic modelling to identify the topics and themes in white papers. Nine topics are identified through LDA: company description, distributed ledger technology components, energy and green issues, financial and legal issues, artificial intelligence, and tech applications in different industries—specifically, healthcare, manufacturing and construction, education, and financial services. We find that energy and green issues represent one of the most prominent topics among all types of projects and that their disclosure is positively related to the probability of campaign success and the amount of funding raised. Another prominent topic that affects campaign outcomes is technology in the healthcare industry, reflecting wider investment trends in this sector. Our results may help entrepreneurs to improve their campaign disclosures and present new issues for policymakers regarding investments in digital tokens. • An integrated framework of STOs success is proposed by combining topic modelling with other campaigns' indicators. • Company's energy consumption and green impact is one of the most dealt topics among all types of projects. • The technological components in the healthcare field is a topic related to the campaign success • Energy consumption and green issues must be adequately disclosed to affect campaign success.