Reviewerly/exHarmony huggingface dataset:
A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data

The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of one stimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are an indicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collect eye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitive interference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, we trained different Machine Learning models to recognize tasks performed in the different conditions (i.e., with interference, without interference). The models achieved good classification performances when distinguishing between similar tasks performed with or without cognitive interference. This suggests the presence of characterizing patterns common among subjects, which can be captured by machine learning algorithms despite the individual variability of visual behavior.
A catalogue with semantic annotations makes multilabel datasets FAIR

Abstract Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robust, and trustworthy benchmarking is of utmost importance for the further development of the field. We believe that this can be achieved by adhering to the recently emerged data management standards, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, and Technology) principles. We introduce an ontology-based online catalogue of MLC datasets originating from various application domains following these principles. The catalogue extensively describes many MLC datasets with comprehensible meta-features, MLC-specific semantic descriptions, and different data provenance information. The MLC data catalogue is available at: http://semantichub.ijs.si/MLCdatasets .
Research on Bus Passenger Flow Statistics Based on Video Images

In order to meet the travel needs of inhabitant, buses as a travel mode of “low-carbon, environmental protection and green travel” have been widely used, so the statistics of public transport passenger flow has gradually become an important reference data source for rational traffic planning and optimization of public vehicle route scheduling. Aiming at the impact of incorrect counting caused by complex conditions such as movement, crowding and occlusion in the actual bus surveillance video scene, this paper adopts the passenger flow statistics system with the passenger's head area as the target detection. The algorithm proposed in this paper mainly uses the head area of the passengers getting on and off as the target feature to be extracted, and on this basis, the image processing and recognition operations are performed. Then input the above results into the target tracking algorithm to be used to estimate and preprocess it, and then use the data information obtained by the algorithm to achieve the association matching between multiple targets. Finally, the counting is completed by analyzing the target motion trajectory, so as to realize the statistics of boarding and alighting passenger flow. The experimental results show that the passenger flow statistics have strong robustness, track and count the target passengers accurately, and meet the needs of actual passenger flow statistics.
Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning

Hyperspectral imaging (HSI), a non-invasive imaging modality, has been successfully used in many different biological and medical applications. One such application is in the field of oncology, where hyperspectral imaging is being used on histologic samples. This study compares the performances of different image classifiers using different imaging modalities as training data. From a database of 33 fixed tissues from head and neck patients with follicular thyroid carcinoma, we produced three different datasets: an RGB image dataset that was acquired from a whole slide image scanner, a hyperspectral (HS) dataset that was acquired with a compact hyperspectral camera, and an HS-synthesized RGB image dataset. Three separate deep learning classifiers were trained using the three datasets. We show that the deep learning classifier trained on HSI data has an area under the receiver operator characteristic curve (AUC-ROC) of 0.966, higher than that of the classifiers trained on RGB and HSI-synthesized RGB data. This study demonstrates that hyperspectral images improve the performance of cancer classification on whole histologic slides. Hyperspectral imaging and deep learning provide an automatic tool for thyroid cancer detection on whole histologic slides.
High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.
Functional characterization of correct and incorrect feature integration

Abstract Our sensory system constantly receives information from the environment and our own body. Despite our impression to the contrary, we remain largely unaware of this information and often cannot report it correctly. Although perceptual processing does not require conscious effort on the part of the observer, it is often complex, giving rise to errors such as incorrect integration of features (illusory conjunctions). In the present study, we use functional magnetic resonance imaging to study the neural bases of feature integration in a dual task that produced ~30% illusions. A distributed set of regions demonstrated increased activity for correct compared to incorrect (illusory) feature integration, with increased functional coupling between occipital and parietal regions. In contrast, incorrect feature integration (illusions) was associated with increased occipital (V1–V2) responses at early stages, reduced functional connectivity between right occipital regions and the frontal eye field at later stages, and an overall decrease in coactivation between occipital and parietal regions. These results underscore the role of parietal regions in feature integration and highlight the relevance of functional occipito-frontal interactions in perceptual processing.
Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention

Recently, Siamese-based trackers have achieved excellent performance in object tracking.However, the high speed and deformation of objects in the movement process make tracking difficult.Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers.The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations.Thus, the features of different layers are enhanced by the coordinate attention block.We then send these features separately into the cascaded RPN for classification and regression.According to the two classification and regression results, the final position of the target is obtained.To verify the effectiveness of the proposed method, we conducted comprehensive experiments on the OTB100, VOT2016, UAV123, and GOT-10k datasets.Compared with other state-of-the-art trackers, the proposed tracker achieved good performance and can run at real-time speed.
On the Application of Explainable Artificial Intelligence to Lightpath QoT Estimation

We demonstrate the potentialities of explainable AI when applied to distill knowledge from a trained supervised machine learning model for lightpath quality of transmission estimation in optical networks, with synthetic datasets.
The Development of a UAV Target Tracking System Based on YOLOv3-Tiny Object Detection Algorithm

Unmanned Aerial Vehicle (UAV) machine vision target tracking has a wide range of applications. The most important thing in machine vision target tracking is the real-time performance of the object detection algorithm. At present, the amount of computation of the mainstream target detection algorithms used is too large. The processing speed of the target detection algorithm to the image collected by the computer is too slow, which makes the UAV lack real-time tracking. Therefore, it is very necessary to use a fast object detection algorithm in machine vision target tracking in UAVs. YOLOv3-Tiny is an object detection method based on deep learning. YOLOv3-Tiny optimizes the network structure based on YOLOv3 and reduces the output of one scale. In this work, we use human images to train YOLOv3-Tiny to make it can recognize humans as tracking targets. Then, we use YOLOv3-Tiny to analyze the images collected by the UAV for target tracking. Finally, the control signal of the tracking system is output to the flight controller of the UAV. For the tracking effect of the system designed in the research, we tested the tracking system in a complex indoor environment. It is found that the UAV indoor target tracking system based on YOLOv3-Tiny can effectively achieve target tracking.
Global and Regional Evaluation of the CERES Edition-4A Surface Solar Radiation and Its Uncertainty Quantification

This article presents a comprehensive evaluation of the 2000-2018 Clouds and Earth's Radiant Energy System Synoptic 1°Ed4A (CERES SYN1deg Edition 4A) surface solar radiation (SSR) product.In particular, the global assessment is conducted over different temporal scales (i.e., hourly, daily, and monthlyaverage) with special attention given to the impact of clouds, and a regional evaluation is further implemented over the Mainland of China (MC) using SSR measurements from a denser observational network provided by the China Meteorological Administration.Evaluation across all valid station-grid pairs yields mixed performance with |MBE|≤2.8(6.2) W m -2 , RMSE≤89.5 (31.6)W m -2 , and R≥0.95 (0.93) over the globe (MC) for different temporal scales, and the monthly CERES SSR, with RMSE≤20 W m -2 , is found to hold promise for global numerical weather prediction and climate monitoring.In addition, CERES is found to generally underestimate and overestimate SSR over land and ocean, respectively.Comparison between year-round and cloudy-season suggests that the presence of clouds may potentially impact the SSR retrievals, especially at the hourly temporal scales, with an increase in RMSE values larger than 10 W m -2 for most stations.Further investigation of subgrid heterogeneity suggests that most in situ SSR measurements can reasonably represent the 1°grid average except for some stations with specific geographic deployments, which may raise significant spatial representativeness issues and, therefore, need to be used with great caution.
Image Classification and Generation Based on GAN Model

The topic of image processing is becoming more and more popular in the field of artificial intelligence, and it can be applied to fields of biology, medicine, video games, art, and etc. In order to have a deeper understanding of how to optimize the image processing, this paper mainly proposed the Generative Adversarial Network (GAN), which is an emerging deep learning model with the ability to continuously improve modeling under the game, and there are already many applications related to image processing, such as video prediction, 3-dimensional object generation, image super-resolution and etc. In this paper, we mainly implement image generation and image classification based on GAN model. In order to indicate the performance of GAN model in image generation in detail, GAN models with linear layers and with convolution layers are trained and compared based on MNIST datasets. Furthermore, we train GAN model with linear layers, and GAN model with convolution layers, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Residual Network (ResNet) models in image classification based on the Mixed National Institute of Standards and Technology database (MNIST), and receive the training loss and testing accuracy of these models for different epochs in image classification. The experimental results demonstrated that GAN model with convolution layers performs best in both image generation and image classification.
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction

We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way.Especially, we address the two-way interaction between COREF and RE that has not been the focus by previous work, and propose to introduce explicit interaction namely Graph Compatibility (GC) that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference.Our experiments are conducted on DocRED and DWIE; in addition to GC, we implement and compare different multi-task settings commonly adopted in previous work, including pipeline, shared encoders, graph propagation, to examine the effectiveness of different interactions.The result shows that GC achieves the best performance by up to 2.3/5.1 F1 improvement over the baseline.
Children expect others to prefer handmade items.

Although children frequently engage in creative activities (in which they make foods and objects by hand), the development and scope of children's thinking about handmade items is largely unexplored. In the present studies, we examined whether 4- to 12-year-old children at a local children's museum (54% girls, 46% boys; 51% White, 11% Asian/Asian American, 10% more than 1 group, 4% Latinx, 3% Black/African American, 18% did not report race/ethnicity) would expect other people to prefer handmade over factory-made items, including foods and nonfoods. In Experiments 1 (n = 124) and 2 (n = 122), participants expected a child character to prefer items the character made themselves and items made by the character's parent or a local person. However, this expectation did not persist at all costs: When considering imperfect handmade items in Experiment 3 (n = 122), children demonstrated a handmade preference when considering nonfoods made by a parent but demonstrated a factory-made preference when considering foods made by a parent. Children's explanations were associated with their choices: When children's explanations referred to emotions or relationships, they were more likely to select handmade items. When children referred to item features, they were more likely to select factory-made items. Across studies, we observed persistent age and gender effects: Children's handmade preference increased with child age and girls demonstrated a more robust handmade preference than boys. These findings highlight children's developing and nuanced reasoning about object value. At an early age, children consider who made an object as a contributor to its value. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Digital pathology: the effect of experience on visual search behavior

Purpose: The introduction of whole slide imaging and digital pathology has enabled greater scrutiny of visual search behaviors among pathologists. We aim to investigate zooming and panning behaviors, external markers of visual processing capabilities, and the changes with experience. Approaches: Twenty digitized breast core needle biopsy histopathology slides were obtained from the circulating slides from the main digital pathology trial (IRAS number: 258799). These were presented to five pathologists with varying experience (1.5 to 40 years) whose examinations were recorded. Data of visual fixations were collected using eye-tracking cameras, and the magnification data and zooming behaviors were extracted in an objective fashion by an automated algorithm. The relationship between experience and metrics was analyzed using mixed-effects regression analyses. Results: There was a significant association between experience and both reading times (p < 0.001) and a number of fixations (p < 0.001), with these relationships being inversely proportional. The greater experience was also associated with greater diagnostic accuracy (p = 0.033). We found that experience was significantly associated with greater use of magnification changes (p < 0.001). Conversely, less experience showed a near significant association with the increased proportion of time spent panning (p = 0.070). Conclusions: Fewer fixations needed to reach a diagnosis and quicker reading times are indicative of greater cognitive and visual processing capabilities with greater experience. These cognitive capabilities may be a prerequisite for the more frequent zooming changes that are more prevalent with increasing experience.
Unmasking Hate on Twitter

Notions of free speech and expectations of speaker anonymity are instrumental aspects of online information practice in the United States, which manifest in greater protections for speakers of hate, while making targets of trolling and hate speech more vulnerable. In this chapter, we argue that corporate digital media platforms moderate and manage “free speech” in ways that disproportionately harm vulnerable populations. After being targets of racist and misogynist trolling ourselves, we investigated whether new modes of analysis could identify and strengthen the ties between the online personas of anonymous speakers of hate and their identities in real life, which may present opportunities for intervention to arrest online hate speech, or at least make speakers known to those who are targets or recipients of their speech.
An automated cloud security framework based on FCM in user-cloud environment

In this paper an efficient secure cloud computing framework has been developed. This framework consists of data grouping based on fuzzy c-means (FCM). It has been used for the individual and associative rankings of uploaded text data on the cloud. For the decision selection ranking of the data simple additive weighting (SAW) method have been used. For data security RC6, RSA and AES algorithms have been used collectively and individually based on the condition. RC6, AES and RSA algorithms have been used as a combination for the complex key security. Based on the decision performance ranking top higher rank which supports are &gt;=50% adopted all the three security algorithms, only one key is applied for the remaining data. The maximum number of keys applied is 5 but there are total three key variants mainly applicable. So we have considered three keys. It has been clear from our results that the keys spreading are automatically increased on the basis of number of files. So in case of high risk the keys are increased automatically and applied.
Decoupled delay and bandwidth centralized queue-based QoS scheme in OpenFlow networks

There are an increasing of scenarios that require the independent bandwidth and delay demands. For instance, in a data center, the interactive message would not occupy much bandwidth, but it requires the rigorous demands for the delay. However, the existing QoS approaches are mainly bandwidth based, which are inappropriate for these scenarios. Hence, we propose the decoupled scheme in the OpenFlow networks to provide the centralized differential bandwidth and delay control. We leverage the mature HTB to manage the bandwidth. And we design the Queue Delay Management Scheme (QDMS) for queuing delay arrangement, as well as the Comprehensive Parameters based Dijkstra Route algorithm (CPDR) for the propagation delay control. The evaluation results verify the decoupling effectiveness. And the decoupled scheme can reduce the delay for high priority flows.
Rethinking capabilities in information security risk management: a systematic literature review

Information security risk management capabilities have predominantly focused on instrumental onsets, while largely ignoring the underlying intentions and knowledge these management practices entail. This article aims to study what capabilities are embedded in information security risk management. A theoretical framework is proposed, namely rethinking capability as the alignment between intent and knowing. The framework is situated around four general risk management practices. A systematic literature review utilising the framework was conducted, resulting in the identification of eight identified capabilities. These capabilities were grouped into respective practices: integrating various perspectives and values to reach a risk perception aligned with the intended outcome (identify); adapting to varying perspectives of risks and prioritising them in accordance with the intended outcome (prioritise); security controls to enable resources, and integrate/reconfigure beliefs held by various stakeholders (implement); and sustaining the integrated resources and competences held by stakeholders to continue the alignment with the intended outcome (monitor).
Application of mobile education in assisted autonomous learning platforms in intelligent campus

In order to solve the problems of low teaching effect and poor student performance in traditional methods of building intelligent campus, an application method of mobile education in intelligent campus assisted autonomous learning platform is proposed. This method analyses the connotation and characteristics of mobile learning, and studies the influence of mobile education on assisted autonomous learning in intelligent campus. By means of questionnaires, the teachers who have carried out mobile education and those who have not carried out mobile education are investigated. And compare the examination results of mobile education classes with those of non-mobile education classes. The results of the survey show that the application of mobile education in intelligent autonomous learning platform can diversify teaching methods, improve teachers' teaching effect, and improve students' learning performance on the premise of improving students' enthusiasm in class, thereby improving the teaching level of intelligent campus.