Another view of sequential sampling in the birth process with immigration

Abstract: Abstract We explore properties of the family sizes arising in a linear birth process with immigration (BI). In particular, we study the correlation of the number of families observed during consecutive disjoint intervals of time. Letting S ( a , b ) be the number of families observed in ( a , b ), we study the expected sample variance and its asymptotics for p consecutive sequential samples $$S_p =(S(t_0,t_1),\dots , S(t_{p-1},t_p))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>p</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>S</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mo>⋯</mml:mo> <mml:mo>,</mml:mo> <mml:mi>S</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mi>p</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , for $$0=t_0<t_1<\dots <t_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo><</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo><</mml:mo> <mml:mo>⋯</mml:mo> <mml:mo><</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:mrow> </mml:math> . By conditioning on the sizes of the samples, we provide a connection between $$S_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:math> and p sequential samples of sizes $$n_1,n_2,\dots ,n_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>n</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>⋯</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:mrow> </mml:math> , drawn from a single run of a Chinese Restaurant Process. Properties of the latter were studied in da Silva et al. (Bernoulli 29:1166–1194, 2023. https://doi.org/10.3150/22-BEJ1494 ). We show how the continuous-time framework helps to make asymptotic calculations easier than its discrete-time counterpart. As an application, for a specific choice of $$t_1,t_2,\dots , t_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>⋯</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>t</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:mrow> </mml:math> , where the lengths of intervals are logarithmically equal, we revisit Fisher’s 1943 multi-sampling problem and give another explanation of what Fisher’s model could have meant in the world of sequential samples drawn from a BI process.


Distance learning technologies and their legal regulation in the Republic of Kazakhstan

Abstract: This study addresses the pressing relevance of implementing distance learning technology in the Republic of Kazakhstan across various educational levels, guided by the framework of legal regulation. The study aims to investigate the benefits of using diverse distance learning technologies in modern education, improving access, motivation, and flexibility while fostering environmental awareness and societal prosperity. The chosen methodology is based on the diagnostic testing method to assess and analyze the educational needs and preferences of students. The study involved 100 participants, ranging in age from 19 to 26, from the Abai Kazakh National Pedagogical University. The study shows important signs and criteria for incorporating distance learning into legal rules in a way that works. These include factors related to the environment, motivation, cognition, analysis, and culture. This transformation of education aligns with real-life situations, adapting to changing circumstances and allowing students to select distance learning when necessary. The implementation of modern technology in distance education facilitates profound knowledge acquisition while accommodating evolving personal circumstances, ultimately fostering a conducive environment for continued high-quality education at cognitive and social levels.


Class imbalance-sensitive approach based on PLMs for the detection of cyberbullying in English and Arabic datasets

Abstract: Social Networking increases allowed the spreading of cyberbullying worldwide. The latter invaded cyberspace, kids and adolescents are no more safe in their virtual playgrounds. Indeed, online bullying is attracting considerable concern due to the societal and health issues it causes, ranging from depression, anxiety, and low self-esteem to sui cide attempts. Automatic cyberbullying detection is becoming a vital factor in protecting individuals' lives. It has received much attention in the last decade. Researchers use machine learning and deep learning models to detect online bullying content. An automatic cyberbullying detection model would flag any bullying text as efficiently as possible. Yet, several challenges lie ahead for the development of such a robust model. Our study discerned class imbalance and bullying text representation as being the major issues concerning cyberbullying classification. In this context, we tried to handle the class imbalance problem through data augmentation, cost-sensitive learning, and lever- aging a Computer Vision loss function for the task. Moreover, we consider a prominent solution for bullying content representation, which consists of fine-tuning Pre-trained Language Models for cyberbullying detection and using these latter as feature extractors for Multichannel ConvNets and Bidirectional LSTMs. The results show the effectiveness of the proposed models, which outperform several past works and provide high Recall values (78%–96%) on English and Arabic datasets.


Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

Abstract:
INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine.
 OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing.
 METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise.
 RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images.
 CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier. The entire system developed using python programming.


Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

Abstract: (1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling.


Vision-Language Model for Generating Textual Descriptions From Clinical Images: Model Development and Validation Study

Abstract: Background The automatic generation of radiology reports, which seeks to create a free-text description from a clinical radiograph, is emerging as a pivotal intersection between clinical medicine and artificial intelligence. Leveraging natural language processing technologies can accelerate report creation, enhancing health care quality and standardization. However, most existing studies have not yet fully tapped into the combined potential of advanced language and vision models. Objective The purpose of this study was to explore the integration of pretrained vision-language models into radiology report generation. This would enable the vision-language model to automatically convert clinical images into high-quality textual reports. Methods In our research, we introduced a radiology report generation model named ClinicalBLIP, building upon the foundational InstructBLIP model and refining it using clinical image-to-text data sets. A multistage fine-tuning approach via low-rank adaptation was proposed to deepen the semantic comprehension of the visual encoder and the large language model for clinical imagery. Furthermore, prior knowledge was integrated through prompt learning to enhance the precision of the reports generated. Experiments were conducted on both the IU X-RAY and MIMIC-CXR data sets, with ClinicalBLIP compared to several leading methods. Results Experimental results revealed that ClinicalBLIP obtained superior scores of 0.570/0.365 and 0.534/0.313 on the IU X-RAY/MIMIC-CXR test sets for the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluations, respectively. This performance notably surpasses that of existing state-of-the-art methods. Further evaluations confirmed the effectiveness of the multistage fine-tuning and the integration of prior information, leading to substantial improvements. Conclusions The proposed ClinicalBLIP model demonstrated robustness and effectiveness in enhancing clinical radiology report generation, suggesting significant promise for real-world clinical applications.


SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection

Abstract: Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet).


Optimizing Performance of Quantum Operations with Non-Markovian Decoherence: The Tortoise or the Hare?

Abstract: The interaction between a quantum system and its environment limits our ability to control it and perform quantum operations on it. We present an efficient method to find optimal controls for quantum systems coupled to non-Markovian environments, by using the process tensor to compute the gradient of an objective function. We consider state transfer for a driven two-level system coupled to a bosonic environment, and characterize performance in terms of speed and fidelity. This allows us to determine the best achievable fidelity as a function of process duration. We show there can be a trade-off between speed and fidelity, and that slower processes can have higher fidelity by exploiting non-Markovian effects.


Efficient high-fidelity flying qubit shaping

Abstract: Matter qubit to traveling photonic qubit conversion is the cornerstone of numerous quantum technologies such as distributed quantum computing, as well as several quantum internet and networking protocols. We formulate a theory for stimulated Raman emission which is applicable to a wide range of physical systems, including quantum dots, solid-state defects, and trapped ions, as well as various parameter regimes. We find the upper bound for the photonic pulse emission efficiency of arbitrary matter qubit states for imperfect emitters and show a path forward to optimizing the fidelity. Based on these results, we propose a paradigm shift from optimizing the drive to directly optimizing the temporal mode of the flying qubit using a closed-form expression. Protocols for the production of time-bin encoding and spin-photon entanglement are proposed. Furthermore, the mathematical idea to use input-output theory for pulses to absorb the dominant emission process into the coherent dynamics, followed by a non-Hermitian Schr\"odinger equation approach, has great potential for studying other physical systems.


Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

Abstract: The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.


Local Disclosure of Quantum Memory in Non-Markovian Dynamics

Abstract: Non-Markovian processes may arise in physics due to memory effects of environmental degrees of freedom. For quantum non-Markovianity, it is an ongoing debate to clarify whether such memory effects have a verifiable quantum origin, or whether they might equally be modeled by a classical memory. In this contribution, we propose a criterion to test locally for a truly quantum memory. The approach is agnostic with respect to the environment, as it solely depends on the local dynamics of the system of interest. Experimental realizations are particularly easy, as only single-time measurements on the system itself have to be performed. We study memory in a variety of physically motivated examples, both for a time-discrete case, and for time-continuous dynamics. For the latter, we are able to provide an interesting class of non-Markovian master equations with classical memory that allows for a physically measurable quantum trajectory representation.


Beyond Legal Constraints: Understanding Barriers to Humanizing Responses in the Aftermath of Hate Speech at Private Universities

Abstract: Supreme Court rulings prevent public institutions from censoring hate speech for content—even if that content has damaging consequences for marginalized students and conflicts with other institutional objectives. This case study examines administrator responses to hate speech and impacts for racial inclusion at a private university unconstrained by First Amendment protections. The findings illustrate that, even in contexts where administrators have both the constitutional leeway to enact stricter speech policies and a deep investment in building an anti-racist community, normative understandings about freedom of expression hamper efforts for racial inclusion and humanizing responses. We propose an “inclusive freedom” approach that leverages norms of academic freedom as a path forward for postsecondary institutions to address harm in the aftermath of hate speech.


An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian

Abstract: The presence of unknown heavy-tailed noise can lead to inaccuracies in measurements and processes, resulting in instability in nonlinear systems. Various estimation methods for heavy-tailed noise exist. However, these methods often trade estimation accuracy for algorithm complexity and parameter sensitivity. To tackle this challenge, we introduced an improved variational Bayesian (VB)-based adaptive iterative extended Kalman filter. In this VB framework, the inverse Wishart distributionis used as the prior for the state prediction covariance matrix. The system state and noise parameter posterior distributions are then iteratively updated for adaptive estimation. Furthermore, we make adaptive adjustments to the IEKF filter parameters to enhance sensitivity and filtering accuracy, thus ensuring robust prediction estimation. A two-dimensional target tracking and nonlinear numerical UNGM simulation validated our algorithm. Compared to existing algorithms RKF-ML and GA-VB, our method showed significant improvements in RMSEpos and RMSEvel, with increases of 21.81% and 22.11% respectively, and a 49.04% faster convergence speed. These results highlight the method’s reliability and adaptability.


Measuring Lexical Diversity in Texts: The Twofold Length Problem

Abstract: Abstract The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. Analysis of three data sets of texts produced by English language learners revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length‐dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.


Constructing Envelopes: How Institutional Custodians Can Tame Disruptive Algorithms

Abstract: The infusion of algorithms into organizational fields—accelerated by advances in artificial intelligence—can have disruptive effects that trigger defensive responses. One important response involves establishing a boundary around an algorithm to delimit its interactions with its environment—in engineering terms, constructing an "envelope." Yet, we know little about the process through which such envelopes are constructed. We address this issue by exploring how institutional custodians construct envelopes around disruptive algorithms. We empirically examine custodians' responses to the high-frequency trading algorithms that disrupted the field of U.S. securities trading, focusing on the years 2009–2016. Our inductive analysis shows that custodians created an envelope with interconnected normative, governance, and practice "layers" that jointly constrained high-frequency trading. Each layer emerged as custodians "coupled" one element of the field (e.g., its values) to one aspect of the disruptive algorithms (e.g., their impacts). Our study contributes to research on the social dynamics of algorithms by generating novel theory of how envelopes around algorithms are constructed, and to research on institutional custodianship by highlighting the constructing of envelopes as a custodial response to a wide range of threats—including, but not restricted to, disruptive algorithms.


Leveraging Coverless Image Steganography to Hide Secret Information by Generating Anime Characters Using GAN

Abstract: There has been a growing need for secure communication in recent years, particularly in the digital realm. Researchers have developed various text-hiding methods to address this need, but these methods often suffer from limitations such as low embedding capacity or high embedding distortion. However, current methods for information hiding require a large image database or have a low capacity for hiding information, which makes them impractical. To address these issues, we propose a coverless information-hiding method that generates anime characters through generative adversarial networks (GANs). This method converts secret information into an attribute label set for anime characters and uses the label set to generate anime characters directly. The quality of the generated anime characters was improved by the super-resolution GAN (SRGAN) model. The resulting images were used to communicate secret information on the digital channel. Numerous experiments were conducted to evaluate the performance of the proposed framework, including hiding capacity, image clarity, robustness and security. Results showed that the proposed framework outperforms existing text-hiding methods in terms of hiding capacity and robustness while maintaining image clarity and computation time. In conclusion, our proposed framework provides a secure and efficient solution for text encryption and decryption in feasible computation time using anime characters generated by a GAN. The framework has a high embedding capacity and low embedding distortion, making it a promising solution for secure communication in the digital world.


MPFC-Net: A multi-perspective feature compensation network for medical image segmentation

Abstract: Image segmentation is one of the vital steps in medical image analysis. The segmentation precision and delicacy of targets affect the diagnosis accuracy to a great extent. Deep learning-based technology is currently a hot research direction in the field of medical image analysis. Compared with manual labeling, deep convolutional neural networks (DCNN) can extract valuable deep features that are difficult for humans to identify. However, some existing DCNN methods tend to only focus on extracting deep features and ignore the semantics loss in the process of continuous pooling and convolution. In addition, medical image datasets are characterized by small-scale, mass noise, and imperceptible differences between foreground and background. How to construct sufficient contextual dependency and feature compensation from multiple perspectives to improve the segmentation performance is still faced with great challenges. In this paper, we propose a deep attention network with multi-perspective feature compensation, called Multi-Perspective Feature Compensation Network (MPFC-Net), to supplement semantics loss during continuous pooling and convolution as much as possible and establish strong contextual dependency. Merits of MPFC-Net are as follows: (1) Serializing Multi-scale Aggregation Blocks (SMAB) are constructed to extract features with different scale receptive fields, capture semantics loss in convolutions, and enhance the nonlinear fitting capability of the network. (2) Space and Channel Compound Multidimensional Interaction Attention (SCMI Attention) is designed to consider the weights of feature maps of space and channel and build long-term information interactions between different dimensions. (3) Multi-Perspective Feature Compensation (MPFC) is proposed to establish dense context interaction and compensate for semantics loss in continuous pooling and convolution. (4) Deep Feature Reuse (DFR) module is adopted to strengthen the propagation and advanced features reuse. We conduct extensive experiments on the 2018 Data Science Bowl (2018DSB), the ISIC-2018 Challenge (ISIC2018), the JSUAH-Cerebellum, and the DRIVE dataset, and the results show that MPFC-Net achieves competitive performance with simultaneous increases in various metrics.


Abstract: Editorial| February 08, 2024 Early Publication Is the Era of Broadband Seismometry Coming to an End? Horst Rademacher Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Publisher: Seismological Society of America First Online: 08 Feb 2024 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © Seismological Society of America Seismological Research Letters (2024) https://doi.org/10.1785/0220240007 Article history First Online: 08 Feb 2024 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Horst Rademacher; Is the Era of Broadband Seismometry Coming to an End?. Seismological Research Letters 2024; doi: https://doi.org/10.1785/0220240007 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search It was more than fifty years ago, that seismologists in what was then Czechoslovakia deployed the first experimental broadband feedback seismometers in seismic stations in Poland and in Czechoslovakia (Plešinger and Horalek, 1976). Based on their experience Erhard Wielandt and his student Gunar Streckeisen at the Eidgenössische Technische Hochschule in Zürich, Switzerland, developed the first commercial broadband instrument, dubbed STS‐1 (Wielandt and Streckeisen, 1982). For a detailed overview of the history of the development of broadband seismometers see Rademacher (2023). Several of these new STS‐1 seismometers were first deployed at the Seismologisches Zentralobservatorium Gräfenberg in... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.


Node-personalized multi-graph convolutional networks for recommendation

Abstract: Graph neural networks have revealed powerful potential in ranking recommendation. Existing methods based on bipartite graphs for ranking recommendation mainly focus on homogeneous graphs and usually treat user and item nodes as the same kind of nodes, however, the user-item bipartite graph is always heterogeneous. Additionally, various types of nodes have varying effects on recommendations, and a good node representation can be learned by successfully differentiating the same type of nodes. In this paper, we develop a node-personalized multi-graph convolutional network (NP-MGCN) for ranking recommendation. It consists of a node importance awareness block, a graph construction module, and a node information propagation and aggregation framework. Specifically, a node importance awareness block is proposed to encode nodes using node degree information to highlight the differences between nodes. Subsequently, the Jaccard similarity and co-occurrence matrix fusion graph construction module is devised to acquire user-user and item-item graphs, enriching correlation information between users and between items. Finally, a composite hop node information propagation and aggregation framework, including single-hop and double-hop branches, is designed. The high-order connectivity is used to aggregate heterogeneous information for the single-hop branch, while the multi-hop dependency is utilized to aggregate homogeneous information for the double-hop branch. It makes user and item node embedding more discriminative and integrates the different nodes’ heterogeneity into the model. Experiments on several datasets manifest that NP-MGCN achieves outstanding recommendation performance than existing methods.


ImGame Project

Abstract: The study presents the authors' research for the purpose of designing ImGame, a virtual environment inviting users to playfully learn about the concept of immersion and its historical antecedents. The authors describe ImGame's current pre-production stage and examine the basic characteristics of the feeling of immersion. They intend to deepen the current understanding of the immersive experience in art, taking into account its broad cultural connotations. The article suggests that the aesthetics of immersion can be classified into two psychological modes that have not been explicitly defined in the discourse of immersivity: a calm reflection and one of awe. From the technical standpoint, the project offers a simple handling of triggering animations and events of the game as well as storing of their state in order to create gamification elements, interactions for quizzes and other activities for ImGame or any other game using the a-frame framework to create WebXR experiences.


A Literature Survey on Transaction and Phishing URL Fraud Detection System

Abstract: The literature survey provides a comprehensive overview of the complexities surrounding cryptocurrencies, focusing on fraud detection and regulation within the global financial system. It traces the historical evolution of monetary systems, the emergence of cryptocurrencies, and regulatory approaches. Significant findings include the application of machine learning algorithms like LGBM and random forest in Ethereum fraud detection, and the effectiveness of unsupervised learning for anomaly mining in Bitcoin transactions. Novel frameworks for fraud detection through ensemble stacking models are also highlighted. The survey underscores the need for effective approaches to combat fraudulent activities within blockchain platforms, such as Ponzi schemes and phishing scams. Proposed solutions utilize methodologies like graph neural networks and ensemble learning, exhibiting high accuracy. Regulatory measures, classification technique refinement, and future research directions are emphasized to enhance fraud detection models. Overall, the survey illuminates the potential impact of cryptocurrencies on the global financial system, the challenges of unregulated circulation, and promising advancements in fraud detection and regulatory efforts within the digital economy


A two-stream deep model for automated ICD-9 code prediction in an intensive care unit

Abstract:
Abstract
 Assigning medical codes for patients is essential for healthcare organizations, not only for billing purposes but also for maintaining accurate records of patients' medical histories and analyzing the outputs of certain procedures. Due to the abundance of disease codes, it can be laborious and time-consuming for medical specialists to manually assign these codes to each procedure. To address this problem, we discuss the automatic prediction of ICD-9 codes, the most popular and widely accepted system of medical coding. We introduce a two-stream deep learning framework specifically designed to analyze multi-modal data. This framework is applied to the extensive and publicly available MIMIC-III dataset, enabling us to leverage both numerical and text-based data for improved ICD-9 code prediction. Our system uses text representation models to understand the text-based medical records; the Gated Recurrent Unit (GRU) to model the numerical health records; and fuses these two streams to automatically predict the ICD-9 codes used in the intensive care unit. We discuss the preprocessing and classification methods and demonstrate that our proposed two-stream model outperforms other state-of-the-art studies in the literature.


A novel IoT trust model leveraging fully distributed behavioral fingerprinting and secure delegation

Abstract: The pervasiveness and high number of Internet of Things (IoT) applications in people’s daily lives make this context a very critical attack surface for cyber threats. The high heterogeneity of involved entities, both in terms of hardware and software characteristics, does not allow the definition of uniform, global, and efficient security solutions. Therefore, researchers have started to investigate novel mechanisms, in which a super node (a gateway, a hub, or a router) analyzes the interactions of the target node with other peers in the network, to detect possible anomalies. The most recent of these strategies base such an analysis on the modeling of the fingerprint of a node behavior in an IoT; nevertheless, existing solutions do not cope with the fully distributed nature of the referring scenario. In this paper, we try to provide a contribution in this setting, by designing a novel and fully distributed trust model exploiting point-to-point devices’ behavioral fingerprints, a distributed consensus mechanism, and Blockchain technology. In our solution we tackle the non-trivial issue of equipping smart things with a secure mechanism to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it. Beyond the detailed description of our framework, we also illustrate the security model associated with it and the tests carried out to evaluate its correctness and performance.


Customized Video Summarization with Thumbnail Containers and 2D CNN

Abstract: This project focuses on acquiring customized video summaries using thumbnail container-based summarization framework and 2D CNN model to select and extract specific features from thumbnails. This framework creates a custom keyshot summary for two or more concurrent users by leveraging the computing power of the user’s device. The most advanced methods that collect and analyze complete video data to create video summaries consume a lot of computing power. In this context, we use the thumbnail containers framework which implements light thumbnails to manage the complex detection of events. This minimizes computational complexity and increases communication and storage performance by overcoming computational and privacy issues in resource constrained end-user environments. We aim on developing a user interactive customized video summarization tool which will be trained utilizing diverse datasets leading to the generation of personalized video summaries for feature-length videos.


Case-based selection of explanation methods for neural network image classifiers

Abstract: Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users’ trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users’ perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.


Constructing MRD codes by switching

Abstract: Abstract Maximum rank‐distance (MRD) codes are (not necessarily linear) maximum codes in the rank‐distance metric space on ‐by‐ matrices over a finite field . They are diameter perfect and have the cardinality if . We define switching in MRD codes as the replacement of special MRD subcodes by other subcodes with the same parameters. We consider constructions of MRD codes admitting switching, such as punctured twisted Gabidulin codes and direct‐product codes. Using switching, we construct a huge class of MRD codes whose cardinality grows doubly exponentially in if the other parameters (, the code distance) are fixed. Moreover, we construct MRD codes with different affine ranks and aperiodic MRD codes.


An enhanced multilevel secure data dissemination approximate solution for future networks

Abstract: Sensitive data, such as financial, personal, or classified governmental information, must be protected throughout its cycle. This paper studies the problem of safeguarding transmitted data based on data categorization techniques. This research aims to use a novel routine as a new meta-heuristic to enhance a novel data categorization based-traffic classification technique where private data is classified into multiple confidential levels. As a result, two packets belonging to the same confidentiality level cannot be transmitted through two routers simultaneously, ensuring a high data protection level. Such a problem is determined by a non-deterministic polynomial-time hardness (NP-hard) problem; therefore, a scheduling algorithm is applied to minimize the total transmission time over the two considered routers. To measure the proposed scheme’s performance, two types of distribution, uniform and binomial distributions used to generate packets transmission time datasets. The experimental result shows that the most efficient algorithm is the Best-Random Algorithm ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> ), recording 0.028 s with an average gap of less than 0.001 in 95.1% of cases compared to all proposed algorithms. In addition, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> is compared to the best-proposed algorithm in the literature which is the Modified decreasing Estimated-Transmission Time algorithm ( MDETA ). The results show that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M3"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> is the best one in 100% of cases where MDETA reaches the best results in only 48%.


Genetic link between Mississippi Valley-type (MVT) Pb–Zn mineralization and hydrocarbon accumulation in the Niujiaotang ore field, SW China

Abstract: The Niujiaotang MVT Pb-Zn ore field is located at the southwestern end of the West Hunan-East Guizhou (WHEG) metallogenic belt, which overlaps spatially with the Majiang paleo-oil reservoir. The Pb-Zn mineralization is closely associated with oil-gas accumulation processes. However, the genetic link between the paleo-oil reservoir and the Pb-Zn deposits remains unclear. In this paper, we present new findings of bitumen (paleo-oil reservoir) and its features in the deposits, and apply various isotopic geochemical methods and 2D seismic data to elucidate the source rock and coupling relationship of the paleo-oil reservoir and the Pb-Zn deposits. This study reveals that the ore has a simple mineral composition, mainly comprising sphalerite, pyrite, and dolomite. Solid bitumen occurs widely in the ore and the host dolostone, and coexists with minerals from the mineralization stage. Laser Raman spectroscopy shows that the bitumen in the wall rocks of the Chengong deposit and the Danzhai paleo-oil reservoir are both overmature, and their carbon isotope compositions (δ13CPDB = –29.17 ‰ – –31.20 ‰, average –30.10 ‰, n = 6) and initial 187Os/188Os ratio (1.08±0.19) suggest that they have the Lower Cambrian source rock similar to the Majiang paleo-oil reservoir. The δ34S value of pyrite in the bitumen from the wall rocks in the Chengong deposit ranges from +28.15 ‰ to +44.12 ‰ (average +33.28 ‰, n = 18), which is consistent with the sulfur isotope composition of the ore sulfide reported previously, indicating that they both derived from sulfate in the Middle-Upper Cambrian strata. Bitumen Re-Os and sphalerite Rb-Sr isotopic dating indicate that oil reservoir accumulation in the ore-hosting strata preceded Pb-Zn mineralization, occurring at 424±160 Ma (MSWD = 1.3) and 398±4.7 Ma (MSWD = 0.66), respectively. The initial 87Sr/86Sr ratio of sphalerite is 0.709983±0.000027, which, after comparison and in combination with previous studies, suggests that the metallogenic metals mainly originated from the underlying strata and basement. 2D seismic data show that the Zaolou fault (F2) that controls the mineralization is a deep fault that extends to the basin basement, and acts as a migration pathway for hydrocarbon fluids and ore-forming materials. We propose that liquid hydrocarbons played a significant role in Pb-Zn mineralization, acting as reductants to reduce sulfate through the thermochemical sulfate reduction (TSR) reaction and provide abundant reduced sulfur (H2S/HS−) for Pb-Zn mineralization. Finally, we present a comprehensive model of hydrocarbon accumulation and mineralization in the Niujiaotang ore field.


The role of digital skills in the acceptance of artificial intelligence

Abstract: Purpose The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model. Design/methodology/approach Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model. Findings The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills. Originality/value The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.


Decentralization in the Digital Age

Abstract: This chapter explores how digital technologies are reshaping decentralization in our fast-evolving technological era. It defines decentralization and examines its diverse aspects in politics, economics, and technology. The chapter emphasizes blockchain technology, highlighting its immutability, decentralized consensus, and smart contracts as pivotal elements driving decentralized applications and disrupting traditional paradigms. To provide context, it conducts a thorough literature review, tracing decentralization's historical evolution, exploring its impact on various sectors like decentralized finance, governance, financial inclusion, and system resilience. The chapter also addresses challenges such as regulatory uncertainties, scalability issues, and security concerns, drawing from authoritative sources and academic research. In conclusion, it sets the stage for a deeper exploration of decentralization's applications and impact across sectors, acknowledging both its potential and complexity as a transformative paradigm shift.


Disruptive Business Models and the Shift Towards Decentralized Marketplaces

Abstract: In today's rapidly evolving business landscape, the emergence of disruptive technologies and innovative business models have revolutionized traditional market structures. One such transformation is the shift towards decentralized marketplaces, driven by blockchain technology, smart contracts, and decentralized finance (DeFi) solutions. This chapter aims to explore the concept of disruptive business models and their profound impact on market dynamics, focusing on the rise of decentralized marketplace. This chapter addresses a pressing need for comprehensive, up-to-date, and insightful information on a topic that is rapidly changing the business landscape. It provides a valuable resource for a diverse audience, ranging from industry professionals to academics and policymakers. It can be highly beneficial for several reasons, given the ongoing evolution and significance of decentralized marketplaces.


Decision Support Framework for Parkinson's Disease Using Novel Handwriting Markers

Abstract: Parkinson's disease (PD) is a neurological disorder that affects millions of people worldwide. Among its motor symptoms are bradykinesia, tremors, rigidity, and postural instability. Handwriting variations known as “Parkinsonian handwriting” or “micrographia” are frequently linked to Parkinson's disease. The condition is characterized by tiny, cramped handwriting that gets smaller with time. The illness may make writing challenging and frequently results in less readable text. Innovative handwriting markers are becoming more and more popular as methods for diagnosing and monitoring PD. A new handwriting marker-based Parkinson's disease decision support system is covered in this chapter, with an emphasis on how useful it may be for early detection and tracking.


DeFi's Transformative Influence on the Global Financial Landscape

Abstract: The rise of decentralized finance (DeFi) has fundamentally reshaped the financial industry, challenging traditional banking systems and opening up a world of possibilities in global finance. This chapter explores the multifaceted impact of DeFi on the global economic landscape, addressing critical themes through a series of subtitles. DeFi is disrupting traditional banking models by offering alternative financial services directly on blockchain networks, such as lending, borrowing, and trading. One of the remarkable achievements of DeFi is its ability to provide financial services to previously underserved and unbanked populations. Tokenization is a crucial aspect of DeFi, enabling the representation of real-world assets as digital tokens on the blockchain. DeFi offers numerous advantages but poses security challenges, including smart contract vulnerabilities and hacks. This chapter provides an overview of the major themes and implications of DeFi's influence on finance, highlighting its opportunities and challenges.


The Digital Disruption of Distribution

Abstract: Digital transformation across human activity has emerged as a critical evolutionary trend. It has not only led to the emergence of neo-business models but has disrupted traditional industries like automobile manufacturers and stakeholders. A ripple effect is visible in the distribution phase of the automobile value chain due to the rapidly evolving needs of automobile customers, awareness and access to digital platforms, and the need to enhance customer lifetime value by manufacturers. In this chapter, the digitalization of automobile distribution has been discussed in three aspects – customer acquisition, retail experience, and customer life-cycle management. Digitalization has not only helped automobile dealers reduce their customer acquisition costs but also driven higher customer engagement and satisfaction. As more customers become tech-savvy and digitally aware, automobile dealers and manufacturers will need to quickly adapt to the new normal of digitalization in order to remain relevant to customers, innovate sustainable business models, and unlock customer lifetime value.


Unpacking the Role of Service Quality of AI Tools in Catalyzing Digital Transformation

Abstract: The aim of this chapter is to do a comprehensive examination of the existing scholarly literature pertaining to artificial intelligence (AI) along with the service quality of AI tools within the framework of digital transformation, managing the effects of deglobalization, and fostering the use of technology. An in-depth study was carried out on a dataset consisting of 156 articles that were taken from the Scopus database. For the purpose of conducting this inquiry, bibliometric methods were utilized, and more specifically, VOSviewer software was utilized, in order to assess the performance analysis and science mapping of the literature that was under investigation. The findings of the study imply that there is a taxonomical representation of current scientific research on the incorporation of artificial intelligence with technological adoption across a variety of fields, with a particular emphasis on online shopping and the level of service quality provided by AI tools.


Exploring the Educational Value of an Immersive Virtual Reality Method Within a Continuing Education Module in Nursing: A Mixed Methods Study

Abstract: Background: Few studies have explored the potential educational value of immersive 360° video in continuing education. This study explored the potential value of immersive 360° video as an acceptable educational method in a continuing education module in nursing. Method: A convergent parallel mixed methods design was adopted. The setting was a nursing and midwifery school at a university. The 11 participants were RNs. Data were collected at three time points with surveys and focus groups. Results: Participants found educational value in the triggering of a deep reflective process, supported by a subsequent classroom discussion. Further, there were nuances and complexities to be considered, with a need to tailor material toward high-acuity, low-frequency, or challenging clinical events when considering content. Conclusion: Immersive 360° videos are a potentially useful method for providing continuing education; however, the content must be tailored to students' learning needs. A reflective model may provide a valuable structure for discussions after the use of immersive 360° video. [ J Contin Educ Nurs . 202x;5x(x):xx–xx.]


A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

Abstract: Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.


Oxidative fluids facilitate Cd enrichment in a magmatic-hydrothermal system: An example from the Kangjiawan Pb-Zn polymetallic deposit, South China

Abstract: The Kangjiawan, as one of the representative magmatic-hydrothermal Pb + Zn polymetallic deposits in the central Qin-Hang Metallogenic Belt (QHMB), South China, contains 16.59 million tons of ore reserves with 0.5 Mt Pb metal reserves and 0.56 Mt Zn metal reserves. Episodic magmatic and associated hydrothermal ore fluids produced a variety of sphalerite generations with distinct mineral assemblages and compositions, making the Kangjiawan an excellent target for studying the controls on the enrichment of critical metals such as Cd, In, and Sn in Pb-Zn deposits. In this contribution, we systematically collected the samples from the different horizontal and vertical levels. Then, a series of microscopic identification, electron probe micro-analyzer (EPMA), and laser ablation inductively coupled plasm mass spectrometer (LA-ICP-MS) analyses were carried out. We identified four generations of sphalerite, that is, Sp1, Sp2, Sp3, and Sp4, with largely different mineral assemblages. A group of minor and trace elements such as Mn, Fe, Cd, Cu, Ag, and Pb with great variations in sphalerite are detected. It is notable that Cd in sphalerite displays an obvious elevation from Sp1 (mean = 2583 ppm), Sp2 (2489 ppm), and Sp3 (3249 ppm) to Sp4 (6324 ppm, a maximum up to 9356 ppm). Further temperature estimation via empirical GGIMFis geothermometer shows there is a clear drop from higher-temperature Sp1, Sp2, and Sp3 (mean = 320 ℃) to low-temperature Sp4 (160 ℃). Moreover, the concentrations of Mn sensitive to redox state also have a significant drop from early Sp1 (mean 3461 ppm), Sp2 (6373 ppm), and Sp3 (6763 ppm) to late Sp4 (5.28 ppm). Combined with a massive of pyrrhotite inclusions in Sp1, Sp2, and Sp3, against Sp4, we deduce that Sp1, Sp2, and Sp3 are formed in a relatively reductive condition, but Sp4 is strongly oxidative. Collectively, we conclude that the redox state, besides temperature, acts as an important factor for Cd enrichment, and the oxidized, low-temperature hydrothermal fluids facilitate Cd enrichment in the Kangjiawan magmatic-hydrothermal system.


Temperature as a major control on Cd enrichment in a skarn system: A case study of the Yiliu Pb-Zn-As deposit, South China

Abstract: The Yiliu Pb-Zn-As deposit is located in the Nanling Metallogenic Belt (NMB), South China, representing an extremely As-enriched skarn system, but the endowment of critical metals, such as Cd and In, and their recording metallogenic physicochemical condition have never been investigated. To address these questions, we conducted a series of field survey, microscopic observation, electron probe micro-analyzer (EPMA) and laser ablation inductively coupled plasm mass spectrometer (LA-ICP-MS) studies. The lenticular orebodies are closely associated with the altered Devonian limestone and Yanshanian Baoshan and Benggangling granitic intrusions, and the massive and disseminated ores comprise arsenopyrite, pyrite, sphalerite, chalcopyrite, galena and tetrahedrite, in a decreasing order. We identified three generations of sphalerite, that is, Sp1, Sp2 and Sp3, with different colors and mineral assemblages. EPMA and LA-ICP-MS data show that two groups of minor and trace elements are hosted in the Yiliu sphalerites: (1) Fe, Cd, Mn and In mainly occurring as lattice-bound (solid-solution) or homogeneously distributed nanoparticles, and (2) Cu, Sn, Pb, Sb and Ag mainly occurring as micro-mineral inclusions. It is notable that a relatively high Cd (4814–10149 ppm, mean = 6133 ppm) against low In (10–301 ppm, mean = 67 ppm) concentration is identified in all the three sphalerite generations. Calculation of sphalerite crystallization temperatures by GGIMFis geothermometer show that a clearly decreasing trend from the earlier Sp1 (273–319 ℃; median = 296 ℃) to the later Sp2 (267–301 ℃; median = 286 ℃) and Sp3 (278–290 ℃; median = 282 ℃). And there is a decrease for Cd concentrations from Sp1 to Sp2 and Sp3, i.e., 4903–10149 ppm (median 6964 ppm) for Sp1, and 4814–7078 ppm (median 5435 ppm) for Sp2 and 5756–6894 ppm (median 6420 ppm) for Sp3, respectively. Additionally, the enrichment of Cd is also affected by the content of Fe whose enrichment is controlled by the relatively high temperature. Collectively, we deduce that the temperature acts as the major control for Cd enrichment in the sphalerite of the Yiliu Pb-Zn-As deposit, and the higher temperature facilitate Cd enrichment in a skarn system.


Reinforcement learning tutor better supported lower performers in a math task

Abstract: Abstract Resource limitations make it challenging to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a pivotal tool to decrease the development costs and enhance the effectiveness of intelligent tutoring software, that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.


IoT Based Railway Track Faults Detection and Localization using Acoustic Analysis and Signalling

Abstract: Ensuring the safety and efficiency of rail transportation is paramount, necessitating timely inspection of railway tracks to prevent potential hazards. This research introduces an innovative approach by implementing an Internet of Things (IoT)-based autonomous railway track fault detection system. Various track components, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels, can lead to hazardous situations if not properly monitored. This master's thesis proposes an efficient method for classifying track components using image processing techniques and deep learning algorithms. Specifically, it focuses on detecting missing fasteners by analyzing images captured by a line camera. Experimental results demonstrate the method's effectiveness and robustness in complex environments. Future work aims to develop a comprehensive model capable of classifying all track components simultaneously. Image processing is utilized to extract individual components such as fasteners, rail, ballast, and sleepers, with the model trained to classify fastener states. This research contributes to advancing railway inspection methods, mitigating operational costs, preventing accidents, and enhancing passenger experience


Sensor-Based Authentication in Smartphone; a Systematic Review

Abstract: With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone’s accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven’t been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized; ScienceDirect, IEEE Xplore, Web of Science, and Scopus. The total number of studies found was n=256 articles, only n=46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area.


Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Abstract: With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.


Federated Freeze BERT for text classification

Abstract: Abstract Pre-trained BERT models have demonstrated exceptional performance in the context of text classification tasks. Certain problem domains necessitate data distribution without data sharing. Federated Learning (FL) allows multiple clients to collectively train a global model by sharing learned models rather than raw data. However, the adoption of BERT, a large model, within a Federated Learning framework incurs substantial communication costs. To address this challenge, we propose a novel framework, FedFreezeBERT, for BERT-based text classification. FedFreezeBERT works by adding an aggregation architecture on top of BERT to obtain better sentence embedding for classification while freezing BERT parameters. Keeping the model parameters frozen, FedFreezeBERT reduces the communication costs by a large factor compared to other state-of-the-art methods. FedFreezeBERT is implemented in a distributed version where the aggregation architecture only is being transferred and aggregated by FL algorithms such as FedAvg or FedProx. FedFreezeBERT is also implemented in a centralized version where the data embeddings extracted by BERT are sent to the central server to train the aggregation architecture. The experiments show that FedFreezeBERT achieves new state-of-the-art performance on Arabic sentiment analysis on the ArSarcasm-v2 dataset with a 12.9% and 1.2% improvement over FedAvg/FedProx and the previous SOTA respectively. FedFreezeBERT also reduces the communication cost by 5 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> compared to the previous SOTA.


Social construction of XAI: Do we need one definition to rule them all?

Abstract:
Summary
 In this opinion, Upol Ehsan and Mark Riedl argue why a singular monolithic definition of explainable AI (XAI) is neither feasible nor desirable at this stage of XAI's development.


Stability of a quantum skyrmion: Projective measurements and the quantum Zeno effect

Abstract: Magnetic skyrmions are vortexlike quasiparticles characterized by long lifetime and remarkable topological properties. That makes them a promising candidate for the role of information carriers in magnetic information storage and processing devices. Although considerable progress has been made in studying skyrmions in classical systems, little is known about the quantum case: quantum skyrmions cannot be directly observed by probing the local magnetization of the system, and the notion of topological protection is elusive in the quantum realm. Here we explore the potential robustness of quantum skyrmions in comparison to their classical counterparts. We theoretically analyze the dynamics of a quantum skyrmion subject to local projective measurements and demonstrate that the properties of the skyrmionic quantum state change very little upon external perturbations. We further show that by performing repetitive measurements on a quantum skyrmion, it can be completely stabilized through an analog of the quantum Zeno effect.


Quantum state tomography of undetected photons

Abstract: The measurement of quantum states is one of the most important problems in quantum mechanics. We introduce a quantum state tomography technique in which the state of a qubit is reconstructed, while the qubit remains undetected. The key ingredients are (i) employing an additional qubit, (ii) aligning the undetected qubit with a known reference state by using path identity, and (iii) measuring the additional qubit to reconstruct the undetected qubit state. We establish theoretically and demonstrate experimentally the method with photonic polarization states. The principle underlying our method could also be applied to quantum entities other than photons.


Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Abstract: Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.


Text-independent speaker identification system using discrete wavelet transform with linear prediction coding

Abstract: Abstract One of the key problems of the modern day is the presentation of an identity verification system that can perform sufficient accuracy in identity verification, is resilient to assaults and noises, and can be recorded in the simplest possible method. In this study, a new speaker feature extraction which based on discrete wavelet transform (DWT) and linear prediction coding (LPC) algorithm (WLPCA) are investigated. This paper's primary objective is to evidence the performance of the new method for speaker identification by a Gaussian mixture model (GMM). The proposed method improves the recognition rate over the Mel-frequency cepstral coefficient (MFCC). Experimental evaluation of the process performance is performed on two speech databases; our recorded database and the publicly available TIMIT database. We show that the speech features derived by the newly proposed method are more suitable for GMM (91.53%), in terms of the time-consumed, by requiring less Gaussian mixtures than MFCC (85.77%). For testing the presented method in a noisy environment, Additive white Gaussian noise (AWGN) was added to the TIMIT database, where a slight improvement in the performance of the presented method (60.02%) over the MFCC (59.89%) was observed.


Fuzzy system of automatic tuning of electromagnetic excitation system of vibration-frequency liquid density meter

Abstract: Abstract A fuzzy system for setting the location of electromagnetic exciter of vibration-frequency density meter is presented, taking into account the possible nonlinearities of the object. This paper proposes a system and algorithm for tuning the location of electromagnetic exciter of vibration-frequency density meter using fuzzy set theory. The use of fuzzy control methods allowed us to obtain a system for tuning the location of the electromagnetic exciter of the vibration-frequency density meter without the use of cumbersome computational procedures characteristic of the classical control method.


A multiparameter mathematical model for the problem of nonlinear filtration of fluids in two-layer media

Abstract: Abstract In the realm of fluid dynamics and porous media, the nonlinear filtration of fluids in two-layer media presents a complex and intriguing challenge that demands a comprehensive mathematical framework for accurate representation and analysis. This paper introduces a multiparameter mathematical model designed to elucidate the intricacies of fluid filtration in heterogeneous environments, specifically focusing on two-layer media. The authors research the construction of a multiparameter mathematical model and numerical solution of nonlinear problems of the theory of filtration of unstructured and structured fluids in hydrodynamically connected multilayer formations with different characteristics. The developed models and solution algorithms were tested on hypothetical data.


Should Decisions in QCDCL Follow Prefix Order?

Abstract: Abstract Quantified conflict-driven clause learning (QCDCL) is one of the main solving approaches for quantified Boolean formulas (QBF). One of the differences between QCDCL and propositional CDCL is that QCDCL typically follows the prefix order of the QBF for making decisions. We investigate an alternative model for QCDCL solving where decisions can be made in arbitrary order. The resulting system $$\textsf{QCDCL}^\textsf {{A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is still sound and terminating, but does not necessarily allow to always learn asserting clauses or cubes. To address this potential drawback, we additionally introduce two subsystems that guarantee to always learn asserting clauses ( $$\textsf{QCDCL}^\textsf {{U\tiny {\MakeUppercase {ni}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>U</mml:mi> <mml:mi>NI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> ) and asserting cubes ( $$\textsf{QCDCL}^\textsf {{E\tiny {\MakeUppercase {xi}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>E</mml:mi> <mml:mi>XI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> ), respectively. We model all four approaches by formal proof systems and show that $$\textsf{QCDCL}^\textsf {{U\tiny {\MakeUppercase {ni}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>U</mml:mi> <mml:mi>NI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is exponentially better than $$\mathsf{{QCDCL}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>QCDCL</mml:mi> </mml:math> on false formulas, whereas $$\textsf{QCDCL}^\textsf {{E\tiny {\MakeUppercase {xi}}-A\tiny {\MakeUppercase {ny}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>QCDCL</mml:mi> <mml:mrow> <mml:mi>E</mml:mi> <mml:mi>XI</mml:mi> <mml:mo>-</mml:mo> <mml:mi>A</mml:mi> <mml:mi>NY</mml:mi> </mml:mrow> </mml:msup> </mml:math> is exponentially better than $$\mathsf{{QCDCL}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>QCDCL</mml:mi> </mml:math> on true QBFs. Technically, this involves constructing specific QBF families and showing lower and upper bounds in the respective proof systems. We complement our theoretical study with some initial experiments that confirm our theoretical findings.


Relative Norm Alignment for Tackling Domain Shift in Deep Multi-modal Classification

Abstract: Abstract Multi-modal learning has gained significant attention due to its ability to enhance machine learning algorithms. However, it brings challenges related to modality heterogeneity and domain shift. In this work, we address these challenges by proposing a new approach called Relative Norm Alignment (RNA) loss. RNA loss exploits the observation that variations in marginal distributions between modalities manifest as discrepancies in their mean feature norms, and rebalances feature norms across domains, modalities, and classes. This rebalancing improves the accuracy of models on test data from unseen (“target”) distributions. In the context of Unsupervised Domain Adaptation (UDA), we use unlabeled target data to enhance feature transferability. We achieve this by combining RNA loss with an adversarial domain loss and an Information Maximization term that regularizes predictions on target data. We present a comprehensive analysis and ablation of our method for both Domain Generalization and UDA settings, testing our approach on different modalities for tasks such as first and third person action recognition, object recognition, and fatigue detection. Experimental results show that our approach achieves competitive or state-of-the-art performance on the proposed benchmarks, showing the versatility and effectiveness of our method in a wide range of applications.


Revisiting Bag of Words Document Representations for Efficient Ranking with Transformers

Abstract: Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes prohibitively expensive for long input as required in document retrieval. Instead of focusing on the model itself to improve efficiency, this paper explores different bag of words document representations that encode full documents by only a fraction of their characteristic terms, allowing us to control and reduce the input length. We experiment with various models for document retrieval on MS MARCO data, as well as zero-shot document retrieval on Robust04, and show large gains in efficiency while retaining reasonable effectiveness. Inference time efficiency gains are both lowering the time and memory complexity in a controllable way, allowing for further trading off memory footprint and query latency. More generally, this line of research connects traditional IR models with neural “NLP” models and offers novel ways to explore the space between (efficient, but less effective) traditional rankers and (effective, but less efficient) neural rankers elegantly.


Grid-like entorhinal representation of an abstract value space during prospective decision making

Abstract: Abstract How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.


TransMI: a transfer-learning method for generalized map information evaluation

Abstract: Information Theory of Cartography plays an important role in guiding map design, generalization, and evaluation, and measurement of map information is the most basic topic of this theory. However, there are many problems in current measurement methods, and there is a long way to go to form a theoretically rigorous algorithm that can effectively depict spatial information and comprehensively consider the feeling of map readers. Luckily, we can now propose an evaluation metric that exhibits a certain correlation with map information based on deep learning to benefit actual cartography, and we term it as generalized map information evaluation to demonstrate differentiation. Specifically, this paper first constructs a subjective data set to support the supervised learning paradigm. Also, considering the difficulty of large-scale subjective data set collection, this paper proposes a Transfer-learning method for generalized Map Information evaluation (TransMI). Technically, a Siamese Network is pre-trained to explicitly acquire prior knowledge about the reasons for changes to mapped information. On this basis, one branch of the network is extracted and fine-tuned on the subjective data set to achieve the goal of predicting the quality of generalized map information. The results and the analysis of ablation studies prove the feasibility of our method.


Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

Abstract: <ns3:p>Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA i or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects’ ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.</ns3:p>


Fast Model Selection and Hyperparameter Tuning for Generative Models

Abstract: Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The performance of generative models depends critically on a good set of hyperparameters. Yet, finding the right hyperparameter configuration can be an extremely time-consuming task. In this paper, we focus on speeding up the hyperparameter search through adaptive resource allocation, early stopping underperforming candidates quickly and allocating more computational resources to promising ones by comparing their intermediate performance. The hyperparameter search is formulated as a non-stochastic best-arm identification problem where resources like iterations or training time constrained by some predetermined budget are allocated to different hyperparameter configurations. A procedure which uses hypothesis testing coupled with Successive Halving is proposed to make the resource allocation and early stopping decisions and compares the intermediate performance of generative models by their exponentially weighted Maximum Means Discrepancy (MMD). The experimental results show that the proposed method selects hyperparameter configurations that lead to a significant improvement in the model performance compared to Successive Halving for a wide range of budgets across several real-world applications.


A risk-oriented ontological model for supporting management decision-making in a cargo port based on multi-level goal setting

Abstract: In today's unstable market environment, transport logistics enterprises face the task of predicting possible risk situations in order to avoid them, as well as developing tools that allow them to form management decisions, evaluate the effectiveness of each of them and choose the most appropriate one, taking into account specific tasks and various influencing factors. Situations of failure to achieve the goals of a cargo port, as well as cases of failure by indicators to assess these goals of normative values, are considered as risks. The goals and indicators are outlined and described in the previously built Balanced Scorecard (MTSP). A set of links between goals and indicators of their assessment with risks at various levels of management is presented in the form of a developed multi-level hybrid logical-probabilistic (LV) risk model of a cargo port, as well as in detailed scenario models of this LV model. The integrated use of SSP and LV modeling technologies allowed us to build a general concept of multi-level goal setting, the main advantage of which lies in the detailed elaboration of the company's goals subordinate to the main strategic one, which allows, if necessary, to influence operational events and obtain a positive result in the longer term, in tactical and strategic plans. The ontological model contains all the information about the interaction of elements that affect the occurrence of risky events, and allows you to select options for getting out of a risky situation in accordance with various specified conditions. The results of queries to the risk-oriented ontological model are variants of management decisions aimed at reducing risks in the cargo port.


Blockchain Enabled Smart Contracts for Digital Assets

Abstract: The diverse applications and benefits of blockchain technology and smart contracts across various sectors are explored in this overview. Decentralized Finance (DeFi) projects, supply chain management, tokenization of assets, NFT platforms, and identity verification stand out as prominent examples. Smart contracts are lauded for their cost efficiency and security, underpinned by blockchain's cryptographic features that fortify system integrity. Blockchain's global accessibility facilitates cross-border transactions, while decentralized exchanges (DEX) mitigate censorship and counterparty risks, empowering users with asset control. Moreover, blockchain expands investment opportunities through asset tokenization, encompassing diverse classes such as real estate and intellectual property, bolstering security and transparency through immutable ownership records. Oracles play a vital role in integrating real-time data into DeFi ecosystems, informing decision-making by linking off-chain data with blockchain networks.Diverse token standards like ERC-20 and ERC-721 are pivotal, catering to fungible and non-fungible token ecosystems respectively. Lastly, the advantages of proof of stake (PoS) over proof of work (PoW) are highlighted, emphasizing PoS's energy efficiency, transaction throughput, and scalability


Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

Abstract: The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.


Autonomous Quadcopter for Surveillance

Abstract: The use of drone technology is becoming increasingly popular. Finding applications, across various industries. In agriculture drones are employed for tasks such as applying pesticides and fertilizers monitoring crops and creating maps. The construction sector utilizes drones to inspect job sites and track progress. Additionally, the film industry benefits from photography using drones while search and rescue operations rely on them to reach areas. Overall drone technology has the potential to revolutionize industries by offering solutions to previously challenging problems. We propose the development of an AI based autonomous drone system that can be deployed in areas for applications including surveillance and warehouse management. This system incorporates a PI CAM V2 camera mounted on a quadcopter equipped with GPS technology. The drone operates autonomously without the need for a pilot thanks to software, like Robot Operating System (ROS) other cutting-edge technologies


Applicability of Neural Combinatorial Optimization: A Critical View

Abstract: Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a variety of combinatorial problems. However, the incorporation of such models in the conventional optimization portfolio raises many questions related to their performance compared to other existing methods, such as exact algorithms, heuristics or metaheuristics. This paper aims to present a critical view of these new proposals, discussing their benefits and drawbacks with respect to the tools and algorithms already present in the optimization field. For this purpose, a comprehensive study is carried out to analyse the fundamental aspects of such methods, including performance, computational cost, transferability and reusability of the trained model. Moreover, this discussion is accompanied by the design and validation of a new neural combinatorial optimization algorithm on two well-known combinatorial problems, the Linear Ordering Problem and the Permutation Flowshop Scheduling Problem. Finally, new directions for future work in the area of Neural Combinatorial Optimization algorithms are suggested.


A Security Information Transmission Method Based on DHR for Seafloor Observation Network

Abstract: A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity.


The optimizing method of centralized multi-agent systems organizational structure in automatic mode

Abstract: The organizational structure of a multi-agent system (MAS) is a set of roles and relationships of agents, com-ponents that control their behavior, as well as rules governing the interaction of system elements. The effectiveness of the MAS largely depends on the characteristics of the organizational structure used. Existing solutions in this area have a significant disadvantage, namely low adaptability to changes in environmental parameters or adjustment of the conditions of the task, which consists in the need to restart the procedure for synthesizing the organizational structure of the MAS. The problem of automatic optimization of the organizational structure of centralized MAS in the conditions of changed environmental parameters or the task is of particular relevance for centralized MAS with a strict hierarchical structure, whose agents can be divided into two classes – agents-managers, agents-subordinates. The subject of the study is the methods of synthesis of the organizational structure of the MAS. The aim of the work is to develop a method for optimizing the organizational structure of centralized MAS in automatic mode. To achieve this goal, the concepts of primary and secondary agent resources were introduced, based on a biogeographic algorithm, a method was developed that regulates the movement of subordinate agents between groups depending on the degree of attractiveness of the agent manager (depends on the sum of estimates of the effectiveness of subordinate agents managed by him, as well as on the distance from this subordinate agent). The developed method can find its practical application in the implementation of the following tasks: inspection or patrolling (protection) of infrastructure facilities by mobile robots, the implementation of artificial intelligence in computer games.


Empowering Product Teams to Advance Inclusive Language and Mitigate Hateful Speech on Social Media Products

Abstract: As social media use proliferates, so does hateful language on such platforms among users. How might product teams be empowered to tackle hateful speech and promote inclusive language proactively among users? We developed an intervention that targets the organizational culture of tech companies and their product teams as a driver for change. More specifically, the intervention aimed to advance inclusive language practices within product teams, while introducing product development frameworks for advancing inclusive language and mitigating hateful language among social media product users. For this study, we partnered with a large U.S.-headquartered tech firm that owns several social media products to develop and pilot the intervention. The intervention was implemented with 238 participants—representing employees of the firm across several global locations, with particular representation from the United States—in seven workshops. Forty participants were tracked semilongitudinally across three surveys—prior to the intervention, immediately after, and 4 months after. Two participants participated in in-depth interviews 6 months after the intervention. Survey data were submitted to ordinal regression models that examined difference in workshop attendees’ confidence levels across the three time periods. Findings reveal positive impacts over time among product teams in regard to enhanced agency in advancing inclusive language and mitigating hateful speech among users. More specifically, confidence levels to guide one’s team in preventing or mitigating harmful language among product users significantly increased by 13.6% ( p < .0001) from before the intervention to both postworkshop surveys. Confidence levels to guide one’s team in enhancing inclusive language among users significantly increased by 18.1% ( p < .0001) pre- to postworkshop. Positive correlations are present between the measures. Lastly, qualitative responses in the surveys and interviews express appreciation of learning gained in the workshops, but also the need to continue such interventions as an effort to maintain critical understandings of inclusive language practices.


Quantum Measurement Classification using Statistical Learning

Abstract: Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with qubit states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This paper puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which show a substantial decrease (88%) in single qubit readout error compared to state of the art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.


The Influence of Affect in Help-Seeking Behaviors and Performance in a Math Intelligent Tutoring System

Abstract: This study investigates the association between help-seeking behaviors (hints, hints per step, hints with steps requests, and hint to error), affect (boredom, confusion, frustration, happiness, and engagement), and performance in seventh and eighth-grade students using the Cognitive Tutor Bridge to Algebra as a self-regulated learning environment. Analyses focused on correlations between students’ help-seeking behaviors and their affect in units 4 and 14. Affect was also used to predict help-seeking behaviors in the next units (e.g., 5 and 15). Moreover, we examined how associations between help-seeking behaviors and performance differed as a function of affect. The results showed that a pattern emerged in which students showed more executive help-seeking behaviors rather than instrumental ones. Students feeling bored, confused, and frustrated tended to use more hints, and they were less likely to switch to external help-seeking sources. Also, those feeling happy or engaged were less likely to use hints.