A novel dual-stage progressive enhancement network for single image deraining

Abstract: The dense rain accumulation in heavy rain can significantly wash out images and thus destroy the background details of images. Although existing deep rain removal models lead to improved performance for heavy rain removal, we find that most of them ignore the detail reconstruction accuracy of rain-free images. In this paper, we propose a dual-stage progressive enhancement network (DPENet-v2) to achieve effective deraining with structure-accurate rain-free images. Three main modules are included in our framework, namely a rain streaks removal network (R2Net), a details reconstruction network (DRNet) and a cross-stage feature interaction module (CFIM). The former aims to achieve accurate rain removal, and the latter is designed to recover the details of rain-free images. We introduce two main strategies within our networks to achieve trade-off between the effectiveness of deraining and the detail restoration of rain-free images. Firstly, a dilated dense residual block (DDRB) within the rain streaks removal network is presented to aggregate high/low level features of heavy rain. Secondly, an enhanced residual pixel-wise attention block (ERPAB) within the details reconstruction network is designed for context information aggregation. Meanwhile, CFIM learns the long-range dependencies and achieves cross-stage information communication. We also propose a comprehensive loss function to highlight the marginal and regional accuracy of rain-free images. Extensive experiments on benchmark public datasets show both efficiency and effectiveness of the proposed method in achieving structure-preserving rain-free images for heavy rain removal.


Neural-Based Compression Scheme for Solar Image Data

Abstract: Studying the solar system and especially the Sun relies on the data gathered daily from space missions.These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make.Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data.On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression.In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions.We chose NASA's Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm.In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs.The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective.We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image.Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data.As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.


Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory

Abstract: This paper presents a novel deep learning-based approach for anomaly detection in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested approach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and convolutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is employed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XDViolence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with ConvLSTM can increase precision and reduce false positives, achieving a high accuracy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cuttingedge techniques mentioned in the comparison.


A start-stop points CenterNet for wideband signals detection and time-frequency localization in spectrum sensing

Abstract: Recently, deep learning (DL) based object detection methods have attracted significant attention for wideband multisignal detection, which has been viewed as an essential part in the field of cognitive radio spectrum sensing. However, the existing DL methods are difficult or very likely fail to detect discontinuous burst signals, not to mention the signals with wide, instantaneous, dynamic bandwidth, and multiple channels. To solve this problem, the present study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, namely the Fast Spectrum-Size Self-Training network (FSSNet). Considering the horizontal rectangle form of a wideband signal in the time-frequency domain, we innovatively utilize the start-stop points of the two-dimensional (2D) Box to build the signal model. Specifically, We propose a fast Start-stop HeatMap where the proposed LPS-YXE simultaneously labels and divides the start-stop points positions in the X-Y axis of a single HeatMap. We attribute the method’s success in discontinuous signal detection to the multidimensional space transformation of HeatMap, which is used to locate the start-stop points and extract features separated from the signal regions of start-stop points. Furthermore, FSSNet can realize the 2D Box estimation of the wideband signal by regressing only a single variable, and thus with satisfactory detection speed. Simulation results verify the effectiveness and superiority of the proposed start-stop based wideband signal detection scheme with practical received signals. All our models and code are available athttps://github.com/jn-z/SSNet2.


Sensing complicated meanings from unstructured data: a novel hybrid approach

Abstract: The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.


Single-board device individual authentication based on hardware performance and autoencoder transformer models

Abstract: The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74±0.13 and an average maximum False Positive Rate (FPR) of 0.06±0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.


FedAAA-SDN: Federated Authentication, Authorization and Accounting in SDN controllers

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Abstract: Preview this article: Adopting Technology to Revolutionise and Accelerate the Flow of Seismic Data from Sensor to Customer, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/fb/42/2/fb2024016-1.gif


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

Abstract: 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: Abstract Azizi, H., Daneshvar, N., Asahara, Y., Minami, M. & 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. & 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

Abstract: 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.


Digital fluency – a dynamic capability continuum

Abstract: The impact of digital technologies on the ways people work, learn and live has been debated and researched for half a century. Digital literacy approaches have recurrently been the focus of educational and industry learning; however, current framings of digital literacy are not sufficient to support students’ digital capability development, nor do static digital literacies reflect the dynamic and contextual nature of digital capabilities. A digital capability continuum that fluidly moves between digital foundation skills, digital literacies and digital fluency is a more robust model for education. By unpacking the digital capability continuum and responding to both learning and curriculum paradigms, this paper expands on an earlier framework (Coldwell-Neilson, 2020), the decoding digital literacy framework, as well as building on our research and academic experiences, to inform higher education. A key agenda is that the higher education sector frames digital fluency as a mindset and an attitude. The model and framework underscore that capabilities need to be flexible and transferable across technologies, disciplines and the world of work.


Strategic Implementation of AWS Security Services: A Focus on Best Practices, SSM and Secrets Manager

Abstract:
Purpose: As organizations increasingly migrate to the cloud, ensuring robust data security becomes paramount. This technical paper explores the purpose, methodology, findings, and unique contributions in cloud security, focusing on AWS Systems Manager (SSM) and Secrets Manager. The objective is to provide a comprehensive guide for engineers and architects seeking to enhance data security in their cloud environments.
 Methodology: The comparative study conducted involves analyzing the features, capabilities, and integration possibilities of AWS SSM and Secrets Manager. Real-world use cases, best practices, and security measures associated with these services are explored through a methodology that includes hands-on exploration, case studies, and a survey of existing literature to distill critical insights.
 Findings: The paper uncovers a rich landscape of security measures facilitated by AWS SSM and Secrets Manager. Findings highlight the strengths and limitations of each service, emphasizing the importance of their integration for a holistic security approach. Automated rotation of credentials, encryption options, IAM policies, and monitoring strategies emerge as critical findings, contributing to the overall understanding of secure cloud data management.
 Unique contributor to theory, policy, and practice: This work uniquely provides a detailed comparative analysis that empowers engineers and architects to make well-informed decisions. The paper offers insights into the nuances of AWS SSM and Secrets Manager, enabling professionals to tailor their security strategies based on specific use cases and requirements. The focus on real-world scenarios and identifying best practices make this contribution practical and applicable, serving as a valuable resource for those navigating the complex landscape of cloud security engineering and architecture.


REVIEWS OF ACOUSTICAL PATENTS

Abstract: The purpose of these acoustical patent reviews is to provide enough information for a Journal reader to decide whether to seek more information from the patent itself. Any opinions expressed here are those of the reviewers as individuals and are not legal opinions. Patents are available via the internet at the USPTO website.


Efficient Selection Methods in Evolutionary Algorithms

Abstract: Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the possibility of producing offspring play a huge role in the process of natural evolution. This process is called a natural selection. This mechanism is responsible for eliminating poor population members and gives the possibility of development for good ones. The evolutionary algorithm - an instance of evolution in the computer environment also requires a selection method, a computer version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness and some kind of ``fight'' among individuals. But computer environment is quite different from nature. Computer populations of individuals are usually small, they easily suffer from a premature convergence to local extremes. To avoid this drawback, computer selection methods must have different features than natural selection. In the computer selection methods randomness, fight and competition should be controlled or influenced to operate to the desired extent. Several new methods of individual selection are proposed in this work: several kinds of mixed selection, an interval selection and a taboo selection. Also advantages of passing them into the evolutionary algorithm are shown, using examples based on searching for the maximum α-clique problem and traditional TSP in comparison with traditionally considered as very efficient tournament selection, considered ineffective proportional (roulette) selection and similar classical methods.


An algorithm for optimal control of a closed labor market system at a given time interval

Abstract: The ratio of supply and demand in the labor market is an important criteria for the effectiveness of the functioning of the economic system at the macro level. Improving the effectiveness of employers' personnel policy requires the development of new control tools and forecasting of dynamic labor market processes at the macro and micro levels. The problem of optimal control of the previously proposed closed labor market system is considered. The problem of optimal control of a system described by a system of nonlinear differential equations is formulated in general form. Aim. Finding piecewise constant control values at which the price-quality ratio in the labor market will be maximum over the next three years. Materials and methods. According to the concept of the model, the subjects of the labor market are divided into three categories depending on the amount of demand for their labor: subjects of high, low and medium qualifications. A hypothesis has been adopted according to which vacant places in the labor market are considered to be occupied by low-skilled subjects. Due to the previously proven uniqueness of solving the problem of optimal control of a closed labor market, a greedy algorithm for finding the maximum value of the price-quality ratio in the labor market is justified. Using Wolfram Mathematica, according to the method found earlier, the formulated optimal control problem was solved at each time half-interval. Results. Control values have been obtained at which the price-quality ratio in the labor market of the village of Sylva in the Perm Territory will be maximum over the next three years. The results substantiate the recommendation to introduce stricter requirements for applicants for vacant positions at the city-forming enterprise of the village of Sylva in the Perm Territory.


Deepfake Creation and Detection of Multimedia Data

Abstract: As the increasing number of deepfake content poses a growing threat to multimedia integrity, this paper proposes a robust deepfake detection approach based on a hybrid architecture. The proposed framework combines the power of Residual Networks (ResNet) for spatial feature extraction and Long Short-Term Memory (LSTM) with Convolutional Neural Networks (CNN) for temporal dependency modeling. The ResNet component captures complicated patterns in facial and contextual information, whereas the LSTM-CNN module identifies dynamic facial expressions and movements across multiple frames. Transfer learning strategies are used to improve model generalization by combining pre-training on a large dataset with fine-tuning on deepfake data. Experimental evaluations on a variety of deepfake datasets show superior accuracy, precision, and recall, demonstrating the hybrid architecture's efficiency in dealing with the evolving challenges posed by more advanced deepfake generation techniques. In conclusion, our novel deepfake detection framework employs an effective combination of ResNet and LSTM-CNN, demonstrating a promising solution that not only advances the state-of-the-art in multimedia forensics but is also resistant to adversarial attacks. This hybrid model, which effectively combines spatial and temporal information, has the potential to significantly improve the accuracy and reliability of deepfake detection systems in the face of emerging digital threats.


An Emotional Artificial Intelligence Based Attendance System for Class Room

Abstract: Attendance through a camera using Artificial Intelligence (AI) and Deep Learning (DL) is a mod-ern approach to monitoring and tracking attendance in various settings such as schools, offices, and man-ufacturing facilities. It uses computer vision techniques and deep learning algorithms to automatically detect and identify individuals in an image or video captured by the camera. The process typically in-volves training the system on a dataset of labelled images of the individuals who will be taking attend-ance. This training data can include images of the individual's face, iris, or fingerprints, depending on the specific approach used. Once the system is trained, it can then use this knowledge to recognize these in-dividuals in new images captured by the camera. When an individual is recognized, the system can log their attendance in a database or other record-keeping system. This can be done in real-time, allowing for immediate tracking of attendance, or it can be done at a later time for batch processing. However, AI has been expanding its horizon and face recognition with deep learning techniques can be augmented with emotion recognition as well. Sometimes, students feel really sad and overwhelmed by their school work and other responsibilities. They might feel like they can't keep up or that they are not good enough. When these feelings last for a long time, it's called depression. Depression can make it hard for students to do their school work, be with friends and family, or even get out of bed in the morning. When students are feeling really sad and hopeless, they might think about hurting themselves or ending their lives. This is called suicide. It's important to know that suicide is preventable and there are people who can help. It's important to take care of our mental health, just like we take care of our physical health. If you ever feel sad or overwhelmed, it's important to talk to someone you trust and get help. One way to prevent suicide is to detect if someone is feeling sad and hopeless, which can be a sign of depression. One way to do this is by using a CCTV camera and AI and DL technology to analyze the person's facial expression, body language, and speech patterns. Emotional AI and DL can be a powerful tool in detecting depression but it's not a substitute for a professional diagnosis. It's always advisable to consult with a mental health pro-fessional if you suspect that you or someone you know may be struggling with depression.


Impact of Stress on Eating Behavior among College Students

Abstract: The study explores the impact of stress on eating behavior among college students, noting that stress can lead to unhealthy dietary choices. The transition from school to college life is highlighted, emphasizing the influence of factors like parental expectations, peer pressure, and adjusting to a new environment on students' eating habits. The research, conducted in Pune, Maharashtra, involved a cross-sectional questionnaire study with 247 participants.The results indicate a variety of dietary behaviors, with differences based on living arrangements. Notably, 39.3% of participants reported undereating during stress, and coping strategies varied, including phone use and emotional eating. Lack of social activities and poor academic performance were identified as common stressors. The study suggests a need for college support and interventions, advocating for mandatory psychological counseling and stress management seminars to promote healthier eating habits among students, especially those in hostels or living alone.In conclusion, the study explores the correlation between stress and dietary habits among students, revealing diverse patterns influenced by living conditions. Recognizing the impact of living environment on long-term health, the study emphasizes the need for college support and interventions to address unhealthy eating habits, particularly among hostel-dwelling and solitary students. Advocating for mandatory psychological counseling in colleges and conducting seminars on stress management and healthy eating habits, the study emphasizes the importance of initiating stress management before college to safeguard students' ability to adopt and maintain healthy dietary practices for long-term well-being.


The Issue of Malaysian Art Culture Inspired for Fashion Design Course

Abstract: Malaysia is a multi-ethnic and multi-religious society country with a rich artistic and cultural background. This diversity contributes to significant inspiration in creating cultural heritage such as clothing, dancing, art sculptures, weapons architecture and diets for the civilization. Therefore, the study on the influence of Malaysian art culture towards Fashion Design students’ inspiration was conducted on Malaysian Polytechnic second to fifth-semester diploma students majoring in Fashion Design and Clothing. The objective is to facilitate the student by measuring the tendency to be inspired by suitable Malaysian art culture characteristics elements and principles while maintaining its authenticity during producing and developing the research for their fashion design portfolio. The study is quantitative and implemented a set of questionnaires as its research instrument that was distributed to 92 respondents as its data collection techniques. It was found that 96% of them agreed with the design framework proposed during the design development process to achieve the purpose of the syllabus. The overall result will be used to upgrade the guidelines and improvements in the syllabus to achieve the conditions of sustainability in Quality Education Practices (SDG4) and Decent Work and Economic Growth (SDG8) as recommended in the SDG17 plan.


Curriculum and Pedagogy in Indian Knowledge System: A Relevance Educational Practice for Today's Educational Scenario

Abstract: India has had a rich tradition of education and learning since ancient times, especially during the Renaissance period, which was the golden age of Indian culture. The principle of karma holds great importance in the Indian education system and has evolved during the transition. The four Vedas, namely, Rigveda, Yajurveda, Samaveda and Atharvaveda were configured through ideals, practices and conduct. In the Vedic period, two methods of teaching were prevalent, first, the oral/verbal method, and second, the method based on thinking (contemplation). Bloom's taxonomy defines three domains: learning, cognitive, affective, and psychomotor. Ancient education systems are also based on three domains, developing higher- order learning by building lower-level cognitive skills. Ancient to modern education. The current Higher Education 2020 also suggests a multidisciplinary approach on similar lines. The Vedic period saw teaching being student-centred. Listening, understanding and discussion method of oral work, reasoning used gave rise to generalization and application of knowledge. Santiniketan which follows the philosophy of Naturalism in its curricular dealings. Many other experiments of Gurukul's system are being explored in different parts of the country. It becomes imperative that we learn from our past and adopt ways of designing our curriculum and pedagogy to meaningfully address societal needs. What are the academic subjects from the past that can be included in the present day curriculum? How can the curriculum be based on Indian teachings? What instructional strategies can be integrated from the past? Through the answers to these questions, we can prepare better citizens for an advanced society by introducing an educational practice relevant to today's educational scenario. Because in ancient times, educational practices included such subjects which have relevance even today.


Are The Sudanese Banks Financially Sound?

Abstract: This study attempts to investigate whether the banks operating in Sudan are solvent and financially sound as well as to examine whether there are roots for a banking panic. The research is also intended to determine whether there are candidate bankrupt banks. The study employs quantitative and qualitative research methods and utilizes both secondary and primary data and covers the eight-year period 2013-2020. The annual audited financial reports of banks published for the period under study represent the source of the secondary data and the primary data is collected through questionnaires distributed to depositors. The sample comprises 30 banks out of a total population of 37 banks. Also, responses from 416 participants in the questionnaire are considered for constructing the depositors’ confidence index. To test the hypotheses a number of quantitative models, namely, univariate financial ratios models, Ahmed (2003) Z-score model, Altman (2002) emergent markets Z-score model, and depositor’s confidence index (DCI) model are utilized. The statistical results of three out of the four models, namely, the univariate financial ratios model, Ahmed (2003) Z-score model, and depositor’s confidence index, document that banks operating in Sudan are financially unsound and financially distressed and none of those banks is thoroughly healthy. However, the results of EM Z-score model show that banks operating in Sudan can be categorized as healthy and financially sound and that there are no roots for a banking panic in the country.


Application of the AIGC Large Language Model in College English Writing Teaching

Abstract: In the present era of the internet, the use of artificial intelligence is gradually permeating into higher education, especially in foreign language teaching. The co-teaching model of artificial intelligence is becoming an irreversible trend. This paper deeply studies the AIGC large language model and uses the GPT-4 version to design a university English writing class and use it for auxiliary teaching after much experimentation. The author designs the class around the theme of “travel” according to the unit teaching objectives and lesson teaching objectives. Using the dialogue mode and text analysis function in the language model, the Prompt that meets the classroom needs is designed and implemented in each link of the classroom writing teaching. The process gradually builds scaffolding to train students’ abilities to absorb, select, analyze, and integrate information. After writing, AI is used for evaluation, and the language model’s usage in each link realizes the human-computer collaboration in classroom teaching. Through practical teaching, the author concludes that the AIGC large language model has a helpful role in improving undergraduates’ English writing ability. It can effectively enhance students’ ability to organize information, and the students can quickly summarize titles and write an article outline. The language model significantly improves students’ word spelling, grammar structure, paragraph development, article layout, and logical structure.


"Heterogeneity and Criticality in Neural Networks: A Computational Model of the Griffiths Phase"

Abstract: This article presents a computational exploration into the dynamics of neural networks, focusing on the manifestation of heterogeneity and critical behavior akin to the Griffiths phase observed in condensed matter physics. Drawing inspiration from the rich tapestry of neural activities within the human neocortex, we develop a simplified model to investigate how networks with diverse propensities for critical behavior can exhibit complex, adaptive functionalities. Utilizing an Erdős–Rényi random graph as the foundation, we introduce a proportion of nodes designated as "critical," possessing a higher likelihood of activation, to simulate the nuanced interplay between different regions of a theoretical brain network. Through iterative simulations, we examine the activation patterns over time, paying close attention to the influence of critical nodes on the overall network dynamics. The model integrates fundamental principles from network theory and neuroscience, offering insights into the potential mechanisms underlying the brain's remarkable ability to process information efficiently, adapt to new stimuli, and maintain robustness in the face of perturbations. Our findings highlight the importance of heterogeneity and criticality in enhancing the computational capabilities of neural networks, providing a conceptual bridge between theoretical physics and cognitive neuroscience. This work contributes to a deeper understanding of the brain's operational principles and opens avenues for further research into the applications of criticality and Griffiths phases in artificial intelligence and neural network design.


Lesson Learned from the Initial Phase of the Development Process of CEFR-Based Uttaradit Rajabhat University Test of English Proficiency

Abstract: Background and Aims: The development of the CEFR-Based Test of English Proficiency enriches the broader field of language assessment. By sharing valuable insights into the complexities of aligning assessments with international frameworks. Thus, this academic article aims to present the lesson learned during the initial phase of the development process of the CEFR-Based Uttaradit Rajabhat University Test of English Proficiency (URU-TEP). Linking the URU-TEP to the Common European Framework of Reference for Languages (CEFR), widely used as a reference in English as a Foreign Language (EFL) education, is crucial if test results are to be interpreted under the CEFR levels. Methodology: This study used the method of studying documents and related research, using thematic analysis and presenting descriptive information. Results: the finding is divided into four sections: the current state of the English proficiency test at URU, the process of developing the five-skill URU-TEP based on the CEFR, the process of obtaining test validity and reliability, and opportunities and challenges. Conclusion: The essence of the discovery is to enhance understanding in various areas. of the English proficiency testing process at Uttaradit Rajabhat University It also emphasizes consideration of the validity and reliability of the test to support quality and improve the examination process in the future.


STRATEGI DAN TANTANGAN MANAJEMEN TEKNOMEDIA DI LEMBAGA PENDIDIKAN ANAK USIA DINI

Abstract: Digital learning media management is a crucial skill required by the heads of educational institutions to navigate the digital era. This research aims to depict: (1) the challenges faced by the leaders of Early Childhood Education (PAUD) institutions in Merauke City in managing technomedia in the learning process, and (2) the strategies employed by these leaders to overcome challenges in technomedia management during the learning process. The informants for this study were purposefully sampled from school principals in Merauke City. The primary challenges confronted by school principals in managing digital learning media in Early Childhood Education institutions (PAUD) include the availability of adequate technological resources, the development of teachers' skills in using technology, and the management of student data security and privacy. School principals also need to take into account parental involvement in supporting the use of digital media at home and ensure that technology use remains balanced with the physical and social activities of children. To tackle these challenges, strategies employed by school principals include organizing training and developing teachers' skills in technology use, selecting quality digital learning content, and establishing clear policies and guidelines related to technology use. Additionally, careful monitoring of students' technology use and maintaining a balance between technology use and traditional learning experiences are also emphasized. By implementing these strategies, PAUD school principals can effectively manage digital learning media, enhance the quality of early childhood education, and ensure that technology is used judiciously in the learning process.


Chatbot Informasi Penerimaan Mahasiswa Baru Menggunakan Metode FastText dan LSTM

Abstract: New Student Admission (PMB) is an important stage in the continuity of education in an educational institution. The Faculty of Science and Informatics (FSI) at Jenderal Achmad Yani University (UNJANI) provides information services about PMB to prospective students and parents/guardians of prospective students but is still inefficient, so it is necessary to improve PMB information services by using Chatbots as a solution that is able to serve questions effectively and consistent. This study aims to develop a PMB information Chatbot system for FSI using the FastText and Long Short-Term Memory (LSTM) methods. Several methods have been used in Chatbot development research, such as Term Frequency–Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Convolutional Neural Networks (CNN). However, these studies still have certain limitations, such as the inability to grasp the meaning of words and difficulties in handling certain inputs. In this study, the text classification model uses the FastText method as the stage for representing words in vector form, then combined with several pre-processing methods (Tokenization & Casefolding) and LSTM for the classification stage. Then put it into the Chatbot component according to the architecture that was made. In testing, the Black Box Testing method is used to ensure the functionality of the Chatbot system. The test results show that the Chatbot system is able to understand the topic of questions asked by users properly. The interaction between users and Chatbots also runs smoothly, resulting in appropriate and informative responses. The results of this study are expected to be an effective and consistent solution for providing information about PMB to prospective students and parents/guardians of prospective students at FSI.


GAMBARAN UMUM TOKSOPLASMOSIS OKULER DI RUMAH SAKIT DR KARIADI SEMARANG

Abstract: Introduction: Ocular toxoplasmosis is the most common infectious etiology of posterior uveitis. The aim of this study is to describe the clinical characteristics of ocular toxoplasmosis in Dr. Kariadi Hospital, Semarang. Methods: A retrospective review of out-patient medical records of ocular toxoplasmosis at Dr. Kariadi Hospital Semarang from January 2018 to December 2022 was done. All toxoplasmic uveitis were included, whereas incomplete medical records and loss of follow-up were excluded. The clinical characteristics of patients were recorded and analyzed. Results: During 5 years a total of 160 patients were recruited of which 123 fulfilled the criteria, consisting of 47 males (38.2%) and 76 females (61.8%), mostly were acquired infection (97.6%) with immunocompetent status (75.6%), and affected unilateral eye (65%). The predominant age was 26-45 years old (41.5%). Primary infection (70.7%) was greater than recurrent infection (8.9%) in which recurrence affects 46-65 years old (72.7%). Visual acuity at the initial presentation was <6/18-6/60 (26.2%), and the final follow-up increased to ?6/18 (33.1%). Posterior uveitis (35.1%) with posterior pole lesion (66%) was a common clinical sign found. Medications given were trimethoprim-sulfamethoxazole (51,3%), and corticosteroid (27.5%) as an adjuvant. The complication detected was cataract (13%). Conclusion: Ocular toxoplasmosis was mostly found in immunocompetent male patients, acquired, unilateral, and in the posterior pole. A good response was found by trimethoprim-sulfamethoxazole and corticosteroid medications.


Microwave Photon-Number Amplification

Abstract: A new photon-number amplification scheme, which combines the advantages of a single-photon detector and a power meter, could lead to new photon-detection possibilities in quantum-sensing and quantum-computing applications.


Quantifying the unextendibility of entanglement

Abstract: Abstract Entanglement is a striking feature of quantum mechanics, and it has a key property called unextendibility. In this paper, we present a framework for quantifying and investigating the unextendibility of general bipartite quantum states. First, we define the unextendible entanglement, a family of entanglement measures based on the concept of a state-dependent set of free states. The intuition behind these measures is that the more entangled a bipartite state is, the less entangled each of its individual systems is with a third party. Second, we demonstrate that the unextendible entanglement is an entanglement monotone under two-extendible quantum operations, including local operations and one-way classical communication as a special case. Normalization and faithfulness are two other desirable properties of unextendible entanglement, which we establish here. We further show that the unextendible entanglement provides efficiently computable benchmarks for the rate of exact entanglement or secret key distillation, as well as the overhead of probabilistic entanglement or secret key distillation.


Text summarization using modified generative adversarial network1

Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99% . The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively.


Enhancing Semi Supervised Semantic Segmentation Through Cycle-Consistent Label Propagation in Video

Abstract: Abstract To perform semantic image segmentation using deep learning models, a significant quantity of data and meticulous manual annotation is necessary (Mani in: Research anthology on improving medical imaging techniques for analysis and intervention. IGI Global, pp. 107–125, 2023), and the process consumes a lot of resources, including time and money. To resolve such issues, we introduce a unique label propagation method (Qin et al. in IEEE/CAA J Autom Sinica 10(5):1192–1208, 2023) that utilizes cycle consistency across time to propagate labels over longer time horizons with higher accuracy. Additionally, we acknowledge that dense pixel annotation is a noisy process (Das et al. in: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 5978–5987, 2023), whether performed manually or automatically. To address this, we present a principled approach that accounts for label uncertainty when training with labels from multiple noisy labeling processes. We introduce two new approaches; Warp-Refine Propagation and Uncertainty-Aware Training, for improving label propagation and handling noisy labels, respectively, and support the process with quantitative and qualitative evaluations and theoretical justification. Our contributions are validated on the Cityscapes and ApolloScape datasets, where we achieve encouraging results. In later endeavors, the aim should be to expand such approaches to include other noisy augmentation processes like image-based rendering methods (Laraqui et al. in Int J Comput Aid Eng Technol 18(5):141–151, 2023), thanks to the noisy label learning approach.


Generative adversarial network for car following trajectory generation and anomaly detection

Abstract: Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes 'TrajGAN', an unsupervised learning approach based on the Generative Adversarial Network (GAN) to exploit vehicle car following trajectory data for generation and anomaly detection. The proposed TrajGAN consists of two modules, an encoder-decoder Long Short-Term Memory (LSTM)-based generator and an LSTM-multilayer perceptron (MLP) based discriminator, whose former component is used to generate vehicular car following trajectories and the latter one is for trajectory anomaly detection. By letting these two modules game with each other in training, we can simultaneously achieve robust trajectory generators and anomaly detectors. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Simulation results indicate that the proposed approach is efficient in reproducing artificial trajectories and identifying anomalous driving behaviors.


Exploring and reflecting on the influences that shape teacher professional digital competence frameworks

Abstract: The importance of Digital Competence in teacher education has increased in recent years resulting in a range of digital competency frameworks aimed at guiding national and regional governments in their integration of digital competence in teacher education. The discourses and assumptions underpinning digital competence frameworks are discussed in this paper using two influential supra-national competency frameworks, the UNESCO ICT Competency Framework for Teachers and the DigCompEdu, as examples. The theoretical reflection argues that several influences have shaped the current discourse. These influences include the rise of teacher performance and competency frameworks, the tradition of ranking teachers' levels of technology use and the techno-centric education discourses that align technology use with constructivist practices. Through this theoretical reflection, the paper raises questions about their use in, and implications for, teacher education arguing that, while such frameworks provide helpful guidance for policy makers and teacher educators, they may stifle teachers' professional autonomy and straight-jacket technology use in a linear and deterministic manner.


Comparison of Modern Deep Learning Models for Speaker Verification

Abstract: This research presents an extensive comparative analysis of a selection of popular deep speaker embedding models, namely WavLM, TitaNet, ECAPA, and PyAnnote, applied in speaker verification tasks. The study employs a specially curated dataset, specifically designed to mirror the real-world operating conditions of voice models as accurately as possible. This dataset includes short, non-English statements gathered from interviews on a popular online video platform. The dataset features a wide range of speakers, with 33 males and 17 females, making a total of 50 unique voices. These speakers vary in age from 20 to 70 years old. This variety helps in thoroughly testing speaker verification models. This dataset is especially useful for research on speaker verification with short recordings. It consists of 10 clips for each person, each clip being no longer than 10 s, adding up to 500 recordings in total. The total length of all recordings is about 1 h and 30 min, which averages to roughly 100 s for each speaker. This dataset is a valuable tool for research in speaker verification, particularly for studies involving short audio clips. The performance of these models is evaluated using common biometric metrics such as false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER) and detection cost function (DCF). The results reveal that the TitaNet and ECAPA models stand out by presenting the lowest EER (1.91% and 1.71%, respectively) and thus exhibiting higher discriminative features, ensuring, on the one hand, a reduction in intra-class distance (the same speaker), and, on the other hand, maximizing the distance between different speaker embeddings. This analysis also highlights the ECAPA model’s advantageous balance of performance and efficiency, achieving an inference time of 69.43 milliseconds, slightly longer than the PyAnnote models. This study not only compares the performance of models but also provides a comparative analysis of respective model embeddings, offering insights into their strengths and weaknesses. The presented findings serve as a foundation for guiding future research in speaker verification, especially in the context of short audio samples or limited data. This may be particularly relevant for applications requiring quick and accurate speaker identification from short voice clips.


Synthetic Car Dataset for Vehicle Detection: Integrating Aerial and Satellite Imagery

Abstract: Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048 × 2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.


Dynamic Transitive Closure-Based Static Analysis through the Lens of Quantum Search

Abstract: Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this paper studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover’s algorithm). We first introduce our oracle implementation in Grover’s algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic ( \(O(N^2\sqrt {N}{polylog}(N)) \) ), yielding better results than the upper bound ( O ( N 3 /log N )) of existing classical algorithms. Finally, we conducted a classical simulation of Grover’s search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.


<i>RE-Trace</i> : Re-Identification of Modified GPS Trajectories

Abstract: GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace – a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all data sets and identifies modified GPS trajectories effectively and efficiently.


Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

Abstract: As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this paper, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools Marabou and Planet as the underlying verification engines, and evaluate on widely-used benchmarks for three datasets ACAS Xu , MNIST and CIFAR-10 . The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster.


A novel CNN-based method for localization of temperature anomalies in RDTS system

Abstract: In this study, the effect of convolutional neural network combined with cosine similarity approach on the localization of temperature anomalous events was investigated by designing Raman-based Distributed Temperature Sensing (RDTS) experiments. We first constructed convolutional neural network (CNN) models to extract temperature anomaly features. RDTS experiments were designed to obtain the dataset for training the CNN models. Subsequently, the cosine similarity between the anomalous features and the original signal was calculated to overcome the disadvantage of feature offset. Finally, the feature with the highest cosine similarity was used as the final temperature anomaly event feature. Moreover, another experiment was designed to verify the performance of the proposed method. Based on optical fiber data with a resolution of 1 m, we cut the original signal into data segments with a length of 100 sampling points and used them as input to the CNN model. The main results show that the proposed method has an accuracy of 98.37 %, a precision of 93.53 %, a recall of 75.54 %, and an overall evaluation index F1 score of 0.84 (0.81 for the Copula-based outlier detection method, and 0.75 for the quartile method). The proposed method shows excellent generalization performance on datasets with different signal-to-noise ratios, providing a novel and effective solution for locating temperature abnormal events in RDTS systems.


Mixed Graph Contrastive Network for Semi-Supervised Node Classification

Abstract: Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed M ixed G raph C ontrastive N etwork (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.


Emergence of sequential dynamical invariants in central pattern generators from auto-organized constraints in their sequence time intervals

Abstract: Motor coordination by the nervous system involves generating and coordinating muscle sequential activity. Such activity must be robust to produce effective motion that relies on patterned motor events ordered in a specific sequence and, at the same time, flexible in timing and duration to adapt to intrinsic and environmental circumstances. Motor neural circuits produce the signaling that muscles use for their sequential action. Even in rhythmic repetitive motion such as walking, breathing and chewing, variability in the timing of the events that build the sequence can be observed within the overall ordered rhythm of the motion. In this context, central pattern generators (CPGs) are key neural circuits to study the cycle-by-cycle balance between the robustness of the sequential order of neural events and the irregularity of the intracycle intervals that shape a rhythmic sequence. Recently, the presence of sequential dynamical invariants in the form of robust cycle-by-cycle relationships between specific sequence time intervals was unveiled in living CPGs. Variability of other sequence intervals remains unrelated and coexists with the presence of the invariants. Sequential dynamical invariants have been proposed to underlie the cycle-by-cycle CPG coordination. In this work, we used a minimal CPG circuit building block of two interacting inhibitory model neurons to assess the emergence of coordinated variability in sequential neural activity. We studied the conditions that produce sequential dynamical invariants in the circuit using intrinsically irregular neuron models and symmetric and asymmetric network topologies. We quantified the circuit irregularity and related it to the presence of invariants under different configurations. We found the conditions to propagate and sustain coordinated variability between time intervals, and thus our study illustrates the auto-organized constraint-based mechanisms for motor sequence coordination that shape sequential dynamical invariants. This is the first model study in which sequential dynamical invariants are observed considering only the intrinsic variability of CPG neurons without external stimuli. Finally, we discuss possible applications of this research in the context of autonomous robotics and artificial intelligence.


A novel technique for image steganography based on maximum energy seam

Abstract: Abstract Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.


Clustering-based attack detection for adversarial reinforcement learning

Abstract: Abstract Detecting malicious attacks presents a major challenge in the field of reinforcement learning (RL), as such attacks can force the victim to perform abnormal actions, with potentially severe consequences. To mitigate these risks, current research focuses on the enhancement of RL algorithms with efficient detection mechanisms, especially for real-world applications. Adversarial attacks have the potential to alter the environmental dynamics of a Markov Decision Process (MDP) perceived by an RL agent. Leveraging these changes in dynamics, we propose a novel approach to detect attacks. Our contribution can be summarized in two main aspects. Firstly, we propose a novel formalization of the attack detection problem that entails analyzing modifications made by attacks to the transition and reward dynamics within the environment. This problem can be framed as a context change detection problem, where the goal is to identify the transition from a “free-of-attack” situation to an “under-attack” scenario. To solve this problem, we propose a groundbreaking “model-free” clustering-based countermeasure. This approach consists of two essential steps: first, partitioning the transition space into clusters, and then using this partitioning to identify changes in environmental dynamics caused by adversarial attacks. To assess the efficiency of our detection method, we performed experiments on four established RL domains (grid-world, mountain car, carpole, and acrobot) and subjected them to four advanced attack types. Uniform, Strategically-timed, Q-value, and Multi-objective. Our study proves that our technique has a high potential for perturbation detection, even in scenarios where attackers employ more sophisticated strategies.


Who spies on whom? Unravelling the puzzle of state-sponsored cyber economic espionage

Abstract: Traditional conceptions of state-sponsored cyber economic espionage suggest that countries with different product profiles should experience high levels of espionage between them. However, this is not what we observe empirically. Incidence of economic espionage tends to be prevalent between countries with similar product and manufacturing profiles. This suggests that we may be missing critical parts of what drives state-sponsored cyber economic espionage. To help unravel this puzzle, I develop a novel theoretical framework that proposes that because attackers seek to maximize the expected utility of stolen information, they target countries that possess similar productive capabilities as themselves. Consequently, countries with dissimilar product profiles should avoid targeting each other for espionage. I test this argument using data on states’ product complexities and cyber economic espionage for a global sample of countries in a dyadic analytical framework. The results robustly show that for any pair of countries, as the complexities of their products diverge, they become significantly less likely to aim espionage attempts at each other. This study thus contributes new insights to explain why cyber economic espionage appears restricted to only a small number of advanced economies. It also illustrates the utility of large-N dyadic approaches in studying state-sponsored cyber espionage.


Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification

Abstract: Background The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty. Purpose In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass. Methods We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy controls, discriminating the former and latter classes based on morphometric images exclusively. Results We demonstrate how combining estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select the cases to be recommended for, e.g., expert human evaluation due to excessive uncertainty. Importantly, our framework circumvents additional workload during the training phase by using our network “turned into Bayesian” to implicitly investigate the loss landscape in the neighborhood of each test sample in order to determine the reliability of the predictions. Conclusion We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.


An unrestricted Arnold’s cat map transformation

Abstract: Abstract The Arnold’s Cat Map (ACM) is one of the chaotic transformations, which is utilized by numerous scrambling and encryption algorithms in Information Security. Traditionally, the ACM is used in image scrambling whereby repeated application of the ACM matrix, any image can be scrambled. The transformation obtained by the ACM matrix is periodic; therefore, the original image can be reconstructed using the scrambled image whenever the elements of the matrix, hence the key, is known. The transformation matrices in all the chaotic maps employing ACM has limitations on the choice of the free parameters which generally require the area-preserving property of the matrix used in transformation, that is, the determinant of the transformation matrix to be $$\pm 1.$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> </mml:mrow> </mml:math> This reduces the number of possible set of keys which leads to discovering the ACM matrix in encryption algorithms using the brute-force method. Additionally, the period obtained is small which also causes the faster discovery of the original image by repeated application of the matrix. These two parameters are important in a brute-force attack to find out the original image from a scrambled one. The objective of the present study is to increase the key space of the ACM matrix, hence increase the security of the scrambling process and make a brute-force attack more difficult. It is proved mathematically that area-preserving property of the traditional matrix is not required for the matrix to be used in scrambling process. Removing the restriction enlarges the maximum possible key space and, in many cases, increases the period as well. Additionally, it is supplied experimentally that, in scrambling images, the new ACM matrix is equivalent or better compared to the traditional one with longer periods. Consequently, the encryption techniques with ACM become more robust compared to the traditional ones. The new ACM matrix is compatible with all algorithms that utilized the original matrix. In this novel contribution, we proved that the traditional enforcement of the determinant of the ACM matrix to be one is redundant and can be removed.