Reviewerly/exHarmony huggingface dataset:
FedAAA-SDN: Federated Authentication, Authorization and Accounting in SDN controllers

Modern technology advancements such as Software Defined Networking (SDN) have been employed in distinct scenarios such as cloud computing and wireless networks, and have made network administration simpler since it gives greater flexibility in centralizing the configurations of a network and its data. Several SDN controllers have started to offer Authorization, Authentication, and Accounting (AAA) services to improve user management and security, including support for OAuth2.0. However, basic authentication schemes using the tuple username and password are not considered secure nowadays (prone to brute force attacks), or the use of basic tokens in OAuth 2.0 is propitious to security vulnerabilities not meeting the security requirements in current and future use cases dealing with authentication. Therefore, the main objective of this work is the design and evaluation of the FedAAA-SDN framework enabling authentication, authorization, and accounting mechanisms in SDN controllers controlling different types of networks. In particular, the FedAAA-SDN framework enables federated authentication and authorization processes for network functions by enabling the conveyance of identity and authentication information across heterogeneous domains/networks, allowing also network operators to enforce authorization policies for network functions in wired and wireless networks. A FedAAA-SDN proof of concept is implemented with OpenDaylight as the SDN controller, OpenID Connect as the authentication and authorization mechanism, and Keycloak as the OpenID Provider. FedAAA-SDN also includes the concept of trust levels and trust policies that rely on the context information of a user’s device and associated connected access networks. The implementation of the FedAAA-SDN framework proof of concept involves modifications in the OpenDaylight AAA filter component to secure all the interactions with the several network applications/subcomponents of the OpenDaylight SDN controller. STRIDE security analysis demonstrates that the FedAAA-SDN framework is able to reduce the number of threats.
A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

The objective of this study is to create a Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity with an aim to develop an artificial intelligence (AI)-based chatbot used to analyze Aedes-borne diseases through natural language processing. The analysis occurred when the symptoms provided by users on the chatbot were assessed to select relevant words as text attributes using the term frequency-inverse document frequency (TF-IDF) before the Jaccard similarity was used to measure the similarity of the information on the mosquito-borne disease database. The Line Messaging API was applied to facilitate communication between users and the chatbot through the Line application. The chatbot applied PHP 7.2.34 and MySQL 5.7.32 for database management, with Apache 2.2.29 serving as the bot server. The performance evaluation of the chatbot revealed that the chatbot accurately understood user intentions with an intent accuracy of 85.00%. Likewise, the usability of the chatbot was assessed using the system usability scale (SUS), and it received a score of 89.75, indicating a high level of user-friendliness. Furthermore, it has been found that appropriate tokenization enables accurate feature selection. This leads to improved accuracy in measuring Jaccard similarity. Consequently, the chatbot is capable of providing precise responses that align with the user's intent.
Learning feature contexts by transformer and CNN hybrid deep network for weakly supervised person search

Person search is a computer vision task that aims to locate and re-identify specific pedestrians in images captured by non-overlapping cameras. However, the identity annotation in person search is labor-intensive, especially as the amount of data increases. Therefore, more and more studies consider training person search models using weakly supervised learning with only location annotations. The context information is useful to improve feature representations in the absence of pedestrian identity as supervision. Existing weakly supervised person search methods focus on logic-driven contexts while ignoring feature contexts. In this paper, we propose a hybrid deep network for weakly supervised person search. The hybrid architecture consists of a Transformer-based feature extraction network and a fully convolution-based region recognition head network. The purpose is to enable the model to learn feature contexts at different levels. In our network, hierarchical vision Transformers are used to extract features in order to obtain discriminative representations of scene images. The context-enhanced head network is designed to integrate different features for candidate pedestrians. In addition, a pedestrian proposal network is proposed to improve the quality of predicted proposals. Experiments are conducted on the CUHK-SYSU and the PRW benchmarks to evaluate the effectiveness of the proposed method.
Systematic review on weapon detection in surveillance footage through deep learning

In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action. Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts. A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.
Determination of earthquake focal mechanism via multi-task learning

A multi-task learning-based focal mechanism network (MTFMN) is proposed for calculating parameters of the focal mechanism of earthquakes by regression with incorporating expert prior knowledge. The model automatically learns feature representations of seismic waveforms and transforms the inversion task of the focal mechanism into multi-task learning. Experimental results suggest that MTFMN outperforms traditional methods in the task of earthquake focal mechanism and improves the accuracy of parameter estimation of focal mechanism. In addition, comparative experiments demonstrate MTFMN's enhanced robustness and generalization capabilities compared to other methods. Our proposed methodology presents a more precise regression approach for focal mechanism inversion, with the potential to provide a better understanding of seismic events.
Multi-perspective thought navigation for source-free entity linking

Neural entity-linking models excel at bridging the lexical gap of multiple facets of facts, such as entity-related claims or evidence documents. Despite advancements in self-supervised learning and pretrained language models, challenges persist in entity linking, particularly in interpretability and transferability. Moreover, these models need many aligned documents to adapt to emerging entities, which may not be available due to data scarcity. In this work, we propose a novel Demonstrative Self-TrAining fRamework (D-STAR) that leverages multi-perspective thought navigation. D-STAR iteratively optimizes a question generator and an entity retriever by navigating thoughts on a dynamic graph reasoning across multiple perspectives for question generation. The generated question–answer pairs, along with hard negatives shared in the graph, enable adaptation with minimal computational overhead. Additionally, we introduce a new task, source-free entity linking, focusing on unsupervised transfer learning without direct access to original domain data. To demonstrate the feasibility of this task, we provide a generated question–answering dataset, FandomWiki, for novel entities. Our experiments show that D-STAR significantly improves baselines on SciFact, Zeshel, and FandomWiki.
EIGAN: An explicitly and implicitly feature-aligned GAN for degraded image classification

The implementation of classification networks encounters a substantial decline in performance when subjected to degraded images due to factors such as blur, noise, and low resolution. Existing methods focus on addressing a specific kind of degraded images and thus cannot simultaneously adapt to multiple degradation scenarios. Besides, insufficient attention has been given to the causes of the decline. In this paper, we discuss about the reasons for the decrease and propose an Explicitly and Implicitly feature-aligned Generative Adversarial Network to guide the model to learn features that are more consistent with high-quality(HQ) images, named EIGAN. Initially, we introduce a feature matching loss to enable the model to focus on target regions as it does on high-quality images. Subsequently, we propose an adversarial loss intended to steer the model toward aligning with the feature distribution observed in high-quality images. As a result, our method demonstrates an enhancement in the classification accuracy of degraded images without introducing additional parameters. Extensive experiments across four types of degraded datasets indicate that as degradation intensifies, the advantages of our proposed method compared to other methods become notably more pronounced.
EffCause: Discover Dynamic Causal Relationships Efficiently from Time-Series

Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However, this study argues that it is easy to break in real-world scenarios. Fortunately, our paper presents an essential observation: if we consider a sufficiently short window when discovering the rapidly changing causalities, they will keep approximately static and thus can be detected using the static way correctly. In light of this, we develop EffCause, bringing dynamics into classic Granger causality. Specifically, to efficiently examine the causalities on different sliding window lengths, we design two optimization schemes in EffCause and demonstrate the advantage of EffCause through extensive experiments on both simulated and real-world datasets. The results validate that EffCause achieves state-of-the-art accuracy in continuous causal discovery tasks while achieving faster computation. Case studies from cloud system failure analysis and traffic flow monitoring show that EffCause effectively helps us understand real-world time-series data and solve practical problems.
An Augmented Reality Hand-Eye Sensorimotor Impairment Assessment for Spaceflight Operations

INTRODUCTION: Following a transition from microgravity to a gravity-rich environment (e.g., Earth, Moon, or Mars), astronauts experience sensorimotor impairment, primarily from a reinterpretation of vestibular cues, which can impact their ability to perform mission-critical tasks. To enable future exploration-class missions, the development of lightweight, space-conscious assessments for astronauts transitioning between gravity environments without expert assistance is needed. METHODS: We examined differences in performance during a two-dimensional (2D) hand–eye multidirectional tapping task, implemented in augmented reality in subjects ( N = 20) with and without the presence of a vestibular-dominated sensorimotor impairment paradigm: the binaural bipolar application of a pseudorandom galvanic vestibular stimulation (GVS) signal. Metrics associated with both the impairment paradigm and task performance were assessed. RESULTS: Medial-lateral sway during balance on an anterior-posterior sway-referenced platform with eyes closed was most affected by GVS (effect size: 1.2), in addition to anterior-posterior sway (effect size: 0.63) and the vestibular index (effect size: 0.65). During the augmented reality task, an increase in time to completion (effect size: 0.63), number of misses (effect size: 0.52), and head linear accelerations (effect size: 0.30) were found in the presence of the selected GVS waveform. DISCUSSION: Findings indicate that this multidirectional tapping task may detect emergent vestibular-dominated impairment (near landing day performance) in astronauts. Decrements in speed and accuracy indicate this impairment may hinder crews’ ability to acquire known target locations while in a static standing posture. The ability to track these decrements can support mission operations decisions. Allred AR, Weiss H, Clark TK, Stirling L. An augmented reality hand–eye sensorimotor impairment assessment for spaceflight operations . Aerosp Med Hum Perform. 2024; 95(2):69–78.
Mining significant local spatial association rules for multi-category point data

Spatial association rule mining can reveal the inherent laws of spatial object interdependence and is an important part of spatial data mining. Most of the existing algorithms for mining local spatial association rules are oriented towards the spatial association between two categories of points and cannot fully reflect the spatial heterogeneity of complex spatial relations among multiple categories of points. In addition, the interactions between points in different categories are often asymmetrical. However, the existing algorithms ignore this asymmetry. To address the above problems, an algorithm for mining local spatial association rules for point data of multiple categories based on position quotients is proposed. First, the proximity relationship between points is determined by an adaptive filter, and the spatial weight value is given according to Gaussian kernel function. Then, the multivariate local colocation quotient of each point is calculated to measure the strength of the local regional spatial association rule. Finally, the Monte Carlo simulation function is used to generate a random sample distribution to test the significance of the results. The algorithm is verified on artificial simulation data and real Point of Interest (POI) data. The experimental results show that the algorithm can identify significant association regions of different spatial association rules for point sets.
Optimization-based trajectory planning for transport collaboration of heterogeneous systems

Abstract This paper describes an optimization-based trajectory planning scheme for handing over an object between a quadrotor and a wheeled robot in a transportation scenario. Concretely, a quadrotor should pick up an object from a moving ground mobile robot and deliver it to its destination. An optimization framework based on discrete mechanics and complementarity constraints is utilized here to jointly ensure dynamic feasibility and determine the position, timing, and coordination of the handover autonomously. Cooperative trajectories of the heterogeneous robot system can be generated simultaneously to satisfy different requirements by adjusting the objective function and constraints. The proposed planning scheme provides a novel paradigm combining trajectory planning and handover decision-making within an optimal control problem.
Physics bachelors’ outcomes

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Susan C. White; Physics bachelors’ outcomes. Phys. Teach. 1 February 2024; 62 (2): 149. https://doi.org/10.1119/5.0195446 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
BRsyn-Caps: Chinese Text Classification Using Capsule Network Based on Bert and Dependency Syntax

Text classification is a fundamental task in natural language processing, which finds extensive applications in various domains, such as spam detection and sentiment analysis. Syntactic information can be effectively utilized to improve the performance of neural network models in understanding the semantics of text. The Chinese text exhibits a high degree of syntactic complexity, with individual words often possessing multiple parts of speech. In this paper, we propose BRsyn-caps, a capsule network-based Chinese text classification model that leverages both Bert and dependency syntax. Our proposed approach integrates semantic information through Bert pre-training model for obtaining word representations, extracts contextual information through Long Short-term memory neural network (LSTM), encodes syntactic dependency trees through graph attention neural network, and utilizes capsule network to effectively integrate features for text classification. Additionally, we propose a character-level syntactic dependency tree adjacency matrix construction algorithm, which can introduce syntactic information into character-level representation. Experiments on five datasets demonstrate that BRsyn-caps can effectively integrate semantic, sequential, and syntactic information in text, proving the effectiveness of our proposed method for Chinese text classification.
How to improve the ability of informatization teaching for secondary teachers

Information teaching is a hot topic in the current education field, and it is also a very real problem. With the rapid development of information technology today, the field of education is also undergoing continuous changes, such as education informatization and teaching reform. As one of the main bodies of vocational education in China, the teachers of secondary vocational schools have also achieved certain results in the deep integration of information technology and teaching. However, it is undeniable that there are still many problems as far as secondary teachers in China are concerned. By analyzing the current status and existing problems of secondary vocational teachers' informatization teaching ability, this paper puts forward corresponding solution strategies, in order to promote the improvement of secondary vocational teachers' informatization teaching ability, and lay a good foundation for promoting the reform and development of secondary vocational education in China and realizing educational modernization.
Adopting Technology to Revolutionise and Accelerate the Flow of Seismic Data from Sensor to Customer

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Careers: Manan Suri: His brain-inspired computers run AI on sensors and drones

In work and in life, it's easy to get stuck in your ways. That's why Manan Suri has always looked to expand his horizons both professionally and personally.
Comment on ‘The Generation of Eocene Mafic Dike Swarms During the Exhumation of a Core Complex, Biarjmand Area, NE Iran’ by Azizi et al. (2023), <i>Journal of Petrology</i>, 64, 1–18

Abstract Azizi, H., Daneshvar, N., Asahara, Y., Minami, M. &amp; Anma, R. (2023). The generation of Eocene mafic dike swarms during the exhumation of a Core complex, Biarjmand area, NE Iran. Journal of Petrology64, 1–18 have attributed the E–W-oriented mafic dike swarm in the Biarjmand metamorphic core complex to an Eocene extensional event which is much younger than a previously suggested Late Jurassic–Early Cretaceous age. They proposed that the emplacement of these dikes occurred in a rapid extensional regime coeval with the exhumation of the core complex after gravitational instability in the Central Iran/Eurasia collision zone. I appreciate the opportunity this article provides to shed light on specific aspects of the Late Mesozoic–Early Cenozoic continental extension within the Eurasian sector of the Neotethys subduction system. However, I here bring to attention certain discrepancies within Azizi et al.'s publication. Specifically, the assignment of an Eocene age to the emplacement of the E–W-oriented dike swarm, even though purportedly supported by U–Pb zircon dating, appears to be at odds with field observations and previously published geochronological data. Furthermore, the article contains internal contradictions in its presentation of the core complex model for the study area. It is important to note that Malekpour-Alamdari, A., Axen, G. J., Heizler, M. T. &amp; Hassanzadeh, J. (2017). Large-magnitude continental extension in the northeastern Iranian plateau: insight from K-feldspar 40Ar/39Ar thermochronology from the Shotor Kuh-Biarjmand metamorphic core complex. Geosphere13, 1207–1233 previously documented the geochronological-based metamorphic core complex model of the area. Regrettably, despite its direct relevance, these earlier works have not been acknowledged in Azizi et al.'s paper. In this comment, I outline the problems with the structural and regional geology, the zircon U–Pb age of the dike samples, the age of the dike swarm, and the geodynamic interpretations in Azizi et al.'s work.
The Mediating Role of Organizational Citizenship Behavior (OCB) on Employee Performance: Locus of Control and Self-Efficacy

This research aimed to determine and analyze the influence of locus of control, self-efficacy, and Organizational Citizenship Behavior (OCB) on employee performance, on OCB, and through OCB and to find out and analyze the influence of self-efficacy, on employee performance through OCB at a Hospital Company in Medan. The population in this study was 180 employees of the Hospital Company in Medan, with a research sample of 64 employees who were measured using the Slovin formula. The data collection techniques used in this research were questionnaires and interviews. Meanwhile, the analysis technique used SmartPLS (Partial Least Square), starting from model measurement (outer model), model structure (inner model), and hypothesis testing. The research results showed that locus of control, self-efficacy, and OCB influence employee performance, locus of control and self-efficacy influence OCB, locus of control influences employee performance through OCB and self-efficacy influences employee performance through OCB at a Hospital Company in Medan.