Oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution

Abstract: Abstract An on-demand source of bright entangled photon pairs is desirable for quantum key distribution (QKD) and quantum repeaters. The leading candidate to generate such pairs is based on spontaneous parametric down-conversion (SPDC) in non-linear crystals. However, its pair extraction efficiency is limited to 0.1% when operating at near-unity fidelity due to multiphoton emission at high brightness. Quantum dots in photonic nanostructures can in principle overcome this limit, but the devices with high entanglement fidelity (99%) have low pair extraction efficiency (0.01%). Here, we show a measured peak entanglement fidelity of 97.5% ± 0.8% and pair extraction efficiency of 0.65% from an InAsP quantum dot in an InP photonic nanowire waveguide. We show that the generated oscillating two-photon Bell state can establish a secure key for peer-to-peer QKD. Using our time-resolved QKD scheme alleviates the need to remove the quantum dot energy splitting of the intermediate exciton states in the biexciton-exciton cascade.


Blockchain-Based Electronic Voting System: Significance and Requirements

Abstract: In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology.


Spatio-temporal neural network with handcrafted features for skeleton-based action recognition

Abstract: Abstract The task of human action recognition (HAR) can be found in many computer vision practical applications. Various data modalities have been considered for solving this task, including joint-based skeletal representations which are suitable for real-time applications on platforms with limited computational resources. We propose a spatio-temporal neural network that uses handcrafted geometric features to classify human actions from video data. The proposed deep neural network architecture combines graph convolutional and temporal convolutional layers. The experiments performed on public HAR datasets show that our model obtains results similar to other state-of-the-art methods but has a lower inference time while offering the possibility to obtain an explanation for the classified action.


Convolution smoothing and non-convex regularization for support vector machine in high dimensions

Abstract: The support vector machine (SVM) is a well-known statistical learning tool for binary classification. One serious drawback of SVM is that it can be adversely affected by redundant variables, and research has shown that variable selection is crucial and necessary for achieving good classification accuracy. Hence some SVM variable selection studies have been devoted, and they have an unified “empirical hinge loss plus sparse penalty” formulation. However, a noteworthy issue is the computational complexity of existing methods is high especially for large-scale problems, due to the non-smoothness of the hinge loss. To solve this issue, we first propose a convolution smoothing approach, which turns the non-smooth hinge loss into a smooth surrogate one, and they are asymptotically equivalent. Moreover, we construct computationally more efficient SVM variable selection procedure by implementing non-convex penalized convolution smooth hinge loss. In theory, we prove that the resulting variable selection possesses the oracle property when the number of predictors is diverging. Numerical experiments also confirm the good performance of the new method.


Dual-perspective semantic-aware representation blending for multi-label image recognition with partial labels

Abstract: Recently, multi-label image recognition with partial labels (MLR-PL) has attracted increasing attention, in which only some labels are known while others are unknown for each image. However, current algorithms rely on pre-trained image similarity models or iteratively updating the image classification models to generate pseudo labels for unknown labels. Thus, they depend on a certain amount of annotations and inevitably suffer from obvious performance drops. To address this dilemma, we propose a dual-perspective semantic-aware representation blending (DSRB) framework that blends multi-granularity category-specific semantic representation across different images, from an instance and prototype perspective, respectively, to transfer information of known labels to complement unknown labels. Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels. Meanwhile, a prototype-perspective representation blending (PPRB) module is introduced to learn more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels in a location-sensitive manner to complement these unknown labels. Extensive experiments on various datasets show that the proposed DSRB consistently outperforms current state-of-the-art algorithms on all known label proportion settings.


AdaMO: Adaptive Meta-Optimization for cold-start recommendation

Abstract: Meta-learning has been proven effective in tackling the cold-start problem of recommendation systems. Most work in this line adopts the meta-optimization idea that learns global knowledge to initialize the base recommender and adapts it for unseen recommendation tasks. Typically, the process is non-adaptive in the sense that both the initialization and model fitting are not task-specific. Such non-adaptive approaches do not work well for the strict cold-start problem, where historical interaction data of the new users is scarce or not available at all. In this paper, we propose a novel adaptive meta-optimization approach, called Adaptive Meta-Optimization (AdaMO), to address the problem. AdaMO is fully adaptive, namely, it simultaneously customizes the initialization and model fitting according to task-specific information. During the cold-start phase, it aggregates task-specific information with transferred knowledge from all historical tasks to tune the initialization of the base model. In the subsequent warm-up phase, AdaMO mines the adaptive transition information from the behavior patterns and parameter status to effectively fit the model using transitional learning. By making a sophisticated balance between shared knowledge and task-specific information, AdaMO outperforms the state-of-the-art methods in various cold-start scenarios.


A contrastive learning based unsupervised multi-view stereo with multi-stage self-training strategy

Abstract: Recent years, unsupervised multi-view stereo (MVS) methods have achieved excellent success that can produce comparable results to earlier supervised work. However, as unsupervised MVS uses image reconstruction as pretext task, it faces two vital drawbacks: RGB value, which is the measurement of image, is not robust enough across views due to complicated environment like lighting conditions and reconstruction itself cannot reflect quality of depth estimation linearly. These problems cause the actual optimization goal to diverge from the expected optimization goal, thus could impair the training process. To enhance robustness of pretext task, we propose a contrastive learning based constraint. The constraint adds featuremetric consistency across views by forcing the features between matching points similar and the features between unmatched points opposite. To add linear properties to overall training procedure, we propose a multi-stage training strategy that uses pseudo label as supervision after unsupervised training at the beginning. On the other hand, we adopt an iterative optimizer that proven to be quite effective in supervised MVS to accelerate training. Finally, we conduct a series of experiments on the DTU dataset and Tanks and Temples dataset that demonstrate the efficiency and robustness of our method compared with the state-of-the-art methods in terms of accuracy, completeness and speed.


Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment

Abstract: The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.


Online course as a means of developing master's professional competencies: results of a pedagogical experiment

Abstract: The article proposes a solution to a scientific problem, which is to substantiate the feasibility of using online courses in the professional training of masters in the pedagogical university, to develop an online course and experimentally test its effectiveness as a means of developing the professional competencies of masters in the speciality 014~Secondary Education (Mathematics). The author used the following methods of scientific research: pedagogical experiment, observation (for systematic perception and analysis of learning activities, motivation and personal growth of students in the context of blended learning), testing (to determine the level of logical thinking and development of students’ professional competences), questionnaire (to monitor the ergonomics and efficiency of the online course and the Mathematics teacher’s e-portfolio), expert evaluation of the developed online course, and methods of mathematical statistics (for proper processing of experimental data and verification of the online course’ effectiveness). The results of the control stage of the study indicate a positive trend in the development of mathematical, information and educational, methodological, psychological and pedagogical, and professional and technological competencies of students in the experimental group. The final test of the online course was chosen as a tool for determining the level of mathematical competencies of the masters in the experimental group. The reliability of the assessment of the masters’ professional competencies was confirmed by testing the hypothesis of the normal distribution of test results according to the Shapiro-Wilk test. In addition, a close correlation between students' academic achievement level in an online course and the level of logical thinking development was substantiated using the Student’s t-test. Prospects for further research include the development of online courses on Mathematics teaching methods with an emphasis on methodical, professional, and technological competencies; a detailed study of the impact of the latest information technologies (artificial intelligence, virtual and augmented reality, web programming, etc.) on the development of students’ information and educational professional competences in distance and blended learning.


Programmable photonic system for quantum simulation in arbitrary topologies

Abstract: Synthetic dimensions have generated great interest for studying many types of topological, quantum, and many-body physics, and they offer a flexible platform for simulation of interesting physical systems, especially in high dimensions. In this paper, we describe a programmable photonic device capable of emulating the dynamics of a broad class of Hamiltonians in lattices with arbitrary topologies and dimensions. We derive a correspondence between the physics of the device and the Hamiltonians of interest, and we simulate the physics of the device to observe a wide variety of physical phenomena, including chiral states in a Hall ladder, effective gauge potentials, and oscillations in high-dimensional lattices. Our proposed device opens new possibilities for studying topological and many-body physics in near-term experimental platforms.


Conv-Attention: A Low Computation Attention Calculation Method for Swin Transformer

Abstract: Abstract Transformer networks have excellent performance in various different vision tasks, especially object detection. However, in practical applications, Transformer is difficult to use on-board due to its large computational complexity. In this paper, we propose a new approach for reducing the computation of self-attention, which is called conv-attention. Different from the work of self-attention, conv-attention is inspired by NetVLAD and adopts the probability obtained by soft classification to replace the similarity calculation between query and key. Moreover, we combine the three convolution operations for computing the attention matrix in order to reduce the computational effort. Using the Swin Transformer as a comparison, experiments show that the parameters and FLOPs are reduced by 15% and 16%. Meanwhile, MAP is improved in vehicle object detection, including both FILR and Nuscenses datasets.


A Hybrid Scene Text Script Identification Network for regional Indian Languages

Abstract: In this work, we introduce WAFFNet, an attention-centric feature fusion architecture tailored for word-level multi-lingual scene text script identification. Motivated by the limitations of traditional approaches that rely exclusively on feature-based methods or deep learning strategies, our approach amalgamates statistical and deep features to bridge the gap. At the core of WAFFNet, we utilized the merits of Local Binary Pattern —a prominent descriptor capturing low-level texture features with high-dimensional, semantically-rich convolutional features. This fusion is judiciously augmented by a spatial attention mechanism, ensuring targeted emphasis on semantically critical regions of the input image. To address the class imbalance problem in multi-class classification scenarios, we employed a weighted objective function. This not only regularizes the learning process but also addresses the class imbalance problem. The architectural integrity of WAFFNet is preserved through an end-to-end training paradigm, leveraging transfer learning to expedite convergence and optimize performance metrics. Considering the under-representation of regional Indian languages in current datasets, we meticulously curated IIITG-STLI2023, a comprehensive dataset encapsulating English alongside six under-represented Indian languages: Hindi, Kannada, Malayalam, Telugu, Bengali, and Manipuri. Rigorous evaluation of the IIITG-STLI2023, as well as the established MLe2e and SIW-13 datasets, underscores WAFFNet’s supremacy over both traditional feature-engineering approaches as well as state-of-the-art deep learning frameworks. Thus, the proposed WAFFNet framework offers a robust and effective solution for language identification in scene text images.


Toward Genre Access: A Micro-level Analytical Approach

Abstract: Many genre scholars have focused on how individuals might build genre knowledge, generally understood as the enculturation processes, gradual stages, or ingredients that lead to one’s facility with a genre in context. While genre knowledge describes whether people can engage genres, it does not describe the various factors that shape how people may engage genres. By consolidating scholarship across Rhetorical Genre Studies (RGS), this article characterizes genre access as the power, opportunity, permission, and/or right to engage genre. Furthermore, this article integrates Network Gatekeeping Theory to develop a micro-level analytical approach for explicitly describing genre access. The author demonstrates and develops genre access as a concept and analytical approach with an illustrative example from a larger ethnographic project. Specifically, this illustrative example explores genre access for the Staff Report, a common genre in local government that proposes recommendations from individual departments to their elected City Commissioners for voted approval. Overall, the purpose of this article is (1) to consolidate and extend RGS’s exploration of the power, opportunity, permission, and/or right to engage genres; (2) to identify and name genre access as a fundamental aspect of how genres work; and (3) to provide a micro-level analytical language for researchers to tease out the various factors the shape genre access.


Video stabilization based on low‐rank constraint and trajectory optimization

Abstract: Abstract Video stabilization plays a pivotal role in enhancing video quality by eliminating unwanted jitter in shaky videos. This paper introduces a novel video stabilization algorithm that leverages low‐rank constraint and trajectory optimization to effectively eliminate undesirable motion and generate stabilized videos. In the proposed algorithm, a low‐rank constraint regularization term is incorporated to enhance the smoothness of motion trajectories. Additionally, a predictive path smoothness term is integrated to ensure the consistency of motion across neighbouring frames. To address the problem of excessive cropping resulting from aggressive smoothing, a flexible local window strategy that emphasizes local motion relationships within the trajectories is introduced. The experimental results show that, compared to other excellent video stabilization algorithms, the proposed algorithm improves the stability metric by approximately 2.3%. Furthermore, in the stabilized videos generated by the algorithm, an approximate improvement of 2.18 dB in average image temporal fidelity and a 5.7% increase in average structural similarity between adjacent frames are achieved. The code that implements the proposed method is publicly accessible at https://github.com/CZS0319/VS_Low‐Rank_Constraint_Trajectory_Optimization .


An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems

Abstract: Abstract Software managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers.


Adversities in Abstract Interpretation

Abstract: Robustness is a key and desirable property of any classifying system, in particular, to avoid the ever-rising threat of adversarial attacks. Informally, a classification system is robust when the result is not affected by the perturbation of the input. This notion has been extensively studied, but little attention has been dedicated to how the perturbation affects the classification. The interference between perturbation and classification can manifest in many different ways, and its understanding is the main contribution of the present paper. Starting from a rigorous definition of a standard notion of robustness, we build a formal method for accommodating the required degree of robustness — depending on the amount of error the analyst may accept on the classification result. Our idea is to precisely model this error as an abstraction . This leads us to define weakened forms of robustness also in the context of programming languages, particularly in language-based security — e.g., information-flow policies — and in program verification. The latter is possible by moving from a quantitative (standard) model of perturbation to a novel qualitative model, given by means of the notion of abstraction. As in language-based security, we show that it is possible to confine adversities, which means to characterize the degree of perturbation (and/or the degree of class generalization) for which the classifier may be deemed adequately robust. We conclude with an experimental evaluation of our ideas, showing how weakened forms of robustness apply to state-of-the-art image classifiers.


An optimal linear-combination-of-unitaries-based quantum linear system solver

Abstract: Solving systems of linear equations is one of the most important primitives in many different areas, including in optimization, simulation, and machine learning. Quantum algorithms for solving linear systems have the potential to provide a quantum advantage for these problems. In this work, we recall the Chebyshev iterative method and the corresponding optimal polynomial approximation of the inverse. We show that the Chebyshev iteration polynomial can be efficiently evaluated both using quantum singular value transformation (QSVT) as well as linear combination of unitaries (LCU). We achieve this by bounding the 1-norm of the coefficients of the polynomial expressed in the Chebyshev basis. This leads to a considerable constant-factor improvement in the runtime of quantum linear system solvers that are based on LCU or QSVT (or, conversely, a several orders of magnitude smaller error with the same runtime/circuit depth).


Enhancing corporate brands through service robots: The impact of anthropomorphic design metaphors on corporate brand perceptions

Abstract: Abstract The increasing introduction of intelligent, interactive robots in the service industry raises concerns about the potential dehumanization of service provision and its influences on corporate brand perceptions. To avoid adverse effects, new service development (NSD) managers seemingly favor service robots that feature anthropomorphic design metaphors, so they appear more human‐like. The current research investigates explicitly how customers' perception of a robot's anthropomorphic design metaphors might spill over to affect corporate brand perceptions. Study 1, a picture‐based scenario study with 109 participants, reveals the impact of anthropomorphic design metaphors on untested corporate brand outcomes, such as brand trust and brand experience. Then Study 2, a video‐based scenario study with 530 participants, addresses whether these effects depend on the service context. In Study 3, a field study of 393 participants, the authors examine how anthropomorphic design metaphors influence other firm‐related outcomes (e.g., shopping enjoyment, sales). The combined results confirm that anthropomorphic design metaphors strongly affect brand trust and brand experience, as well as other critical firm‐related outcomes; they also reveal notable context effects, such that customers of people‐processing (e.g., care services) and mental‐stimulus‐processing (e.g., shopping assistance) services appear more likely to use anthropomorphic design metaphors as corporate brand cues. Our research encourages NSD managers and scholars to consider the effects of introducing anthropomorphic service robots on corporate brands.


Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

Abstract: Abstract In order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.


Multi-bit per cycle true random number generator based on XOR-XNOR ring oscillator unit

Abstract: To achieve a fast and resource-efficient entropy source, we present a novel oscillator unit, called XOR-XNOR ring oscillator (XXRO) which consists of an XOR gate and an XNOR gate. The XXRO unit has an internal feedback loop that allows the accumulation of jitter within the unit. Connecting multiple XXRO units forms a new entropy source. We utilize the differences between the XXRO units in the entropy sources to output two bits pre cycle, increasing the throughput of the TRNG while optimizing the use of hardware resources. In this paper, we use four proposed entropy source to form a TRNG, by sampling the two outputs of each entropy source individually and performing an XOR computation based on the corresponding positions to obtain random numbers. Experimental results indicate that when working at a throughput of 250 Mbps under a 125 MHz sampling clock, both outputs of the proposed TRNG pass the randomness tests and other performance tests.


An Improved <i>k</i>‐Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules

Abstract: To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced.An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure.A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results.A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.


Military camouflaged object detection with deep learning using dataset development and combination

Abstract: Camouflaged object detection (COD) is one of the emerging artificial intelligence technologies. COD identifies objects that require attention and time to detect with human eyes due to the similarity in texture or color to the surrounding environment. Despite the importance of camouflage and its detection in military, there is a lack of military camouflaged object detection research. Previous studies point out that the general COD has not been well studied due to the lack of camouflaged datasets, and the situation is worse in the military domain. This study aims at tackling the challenge in two directions. First, we carefully assemble the military camouflaged object (MCAM) dataset, including camouflaged soldiers and people as well as camouflaged military supplies for military COD. The experiment shows that MCAM can generate better performance results than the other benchmark datasets (CAMO, COD10K). Second, military (MCAM) and nonmilitary camouflage datasets (benchmark datasets) are combined and tested to overcome data scarcity. The experiment shows that the nonmilitary camouflage datasets are effective for military COD at a certain level, and a proper combination of military and nonmilitary camouflage datasets can improve the detection performance.


A Dirichlet Process Mixture regression model for the analysis of competing risk events

Abstract: We develop a regression model for the analysis of competing risk events. The joint distribution of the time to these events is flexibly characterized by a random effect which follows a discrete probability distribution drawn from a Dirichlet Process, explaining their variability. This entails an additional layer of flexibility of this joint model, whose inference is robust with respect to the misspecification of the distribution of the random effects. The model is analysed in a fully Bayesian setting, yielding a flexible Dirichlet Process Mixture model for the joint distribution of the time to events. An efficient MCMC sampler is developed for inference. The modelling approach is applied to the empirical analysis of the surrending risk in a US life insurance portfolio previously analysed by Milhaud and Dutang (2018). The approach yields an improved predictive performance of the surrending rates.


Financial Access and Income Inequality in Sub-Saharan Africa: Does Ethnic Fragmentation Give New Evidence?

Abstract: There is a large body of work documenting the non-consensual effects of financial access on income inequality. Despite this extensive literature and the predominance of ethnic fragmentation in Sub Saharan Africa countries (SSA), little is known about its mediating effect on the above relation. This paper focuses on assessing the effect of ethnic fragmentation on financial access income inequality nexus. Based on Kripfganz and Schwarz's (2019) dynamic panel estimator of time-invariant variables in a sample of thirty-seven (37) SSA countries over the period of 1990-2019, it is observed that ethnic fragmentation hinders financial access to reduce inequality in SSA. The results remain stable following several sensitivity tests related to corruption, urbanization, financial literacy and socio-geographic factors. They are also robust to the use of alternative measures of financial access and to change estimation technique.


Multimodal learning with only image data: A deep unsupervised model for street view image retrieval by fusing visual and scene text features of images

Abstract: Abstract As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval .


Harnessing generative artificial intelligence to support nature‐based solutions

Abstract: Abstract The ongoing biodiversity and climate change crises require society to adopt nature‐based solutions that integrate and enhance ecosystems. To achieve successful implementation of nature‐based solutions, it is vital to communicate scientific information about their benefits and suitability. This article explores the potential of generative artificial intelligence (GenAI) as a tool for automating and scaling up science communication, outreach, and extension for nature‐based solutions. To illustrate the potential of GenAI, we present three case study examples; (1) reporting scientific information on ecosystem services, future land use options, and nature‐based solutions for farms (2) interactively providing guidance in response to homeowner questions about biodiversity‐friendly garden design and (3) visualising potential future scenarios of landscape change that incorporate diverse nature based and technological solutions. These examples demonstrate potential applications which may be relevant to other systems and types of nature‐based solutions. While GenAI for nature‐based solutions offers significant opportunities, this new technology brings risks of bias, false information, data privacy, mistrust, and high energy usage. Alongside technological development, we require integrated social research into ethics, public acceptability, and user experience, to maximise the benefits of GenAI while limiting these risks. GenAI offers an opportunity to accelerate the dissemination of nature‐based design strategies and reach a broader audience, by synthesising information and producing tailored content for specific users and locations. By harnessing the power of GenAI alongside human expertise, we can support nature‐based solutions to tackle the complex challenges of future sustainability. Read the free Plain Language Summary for this article on the Journal blog.


Multimodal computation or interpretation? Automatic vs. critical understanding of text-image relations in racist memes in English

Abstract: This paper discusses the epistemological differences between the label 'multimodal' in computational and sociosemiotic terms by addressing the challenges of automatic detection of hate speech in racist memes, considered as germane families of multimodal artifacts. Assuming that text-image interplays, such is the case of memes, may be extremely complex to disentangle by AI-driven models, the paper adopts a sociosemiotic multimodal critical approach to discuss the challenges of automatic detection of hateful memes on the Internet. As a case study, we select two different English-language datasets, 1) the Hateful Memes Challenge (HMC) Dataset, which was built by the Facebook AI Research group in 2020, and 2) the Text-Image Cluster (TIC) Dataset, including manually collected user-generated (UG) hateful memes. By discussing different combinations of non-hateful/hateful texts and non-hateful/hateful images, we will show how humour, intertextuality, and anomalous juxtapositions of texts and images, as well as contextual cultural knowledge, may make AI-based automatic interpretation incorrect, biased or misleading. In our conclusions, we will argue the case for the development of computational models that incorporate insights from sociosemiotics and multimodal critical discourse analysis.


Developing a cognitive model of solid geometry based on Interpretive Structural Modeling method

Abstract: With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry. This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.


Cryptanalysis and improvement of “group public key encryption scheme supporting equality test without bilinear pairings”

Abstract: Public key encryption with equality test (PKEET) is a novel primitive which supports equality comparisons on two encrypted messages. Currently, most of the existing PKEET schemes are based on bilinear pairing and require heavy computational overheads. To address this issue, Shen et al. recently proposed an efficient group public key encryption supporting equality test without bilinear pairings scheme. Compared with other schemes, their scheme reduces the usage of expensive bilinear pairing operations and enjoys higher computation efficiency. They claimed that their scheme achieved one-wayness security in the random oracle model and resisted offline message recovery attack. In this letter, we analyze Shen et al.'s scheme through two concrete attacks and demonstrate that their scheme can not support the above two security requirements. An improved scheme is provided to overcome the security vulnerabilities in their scheme. Performance analysis shows that our improved scheme has certain advantages in both computation overhead and storage overhead.


ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network

Abstract: Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating active learning (AL) and semi-supervised learning (SSL) into heterogeneous graph network. Our framework consists of three components: heterogeneous information network (HIN), HGAT and active semi-supervised learning strategy (ASSL). The adjustable HIN is first constructed by fusing multi-modal features. Then the HGAT is used to encode HIN and the double-layer attention mechanism can mitigate noises during information fusion. The ASSL is further designed to enrich the high-quality labeled training data for model train. Samples selected by uncertainty-aware AL and samples labeled by pseudo-label selection based SSL are finally mixed to iteratively train the ASSL-HGAT framework. Experimental results prove that ASSL-HGAT surpasses the compared state-of-the-art methods on five representative semi-supervised benchmark datasets under the same experimental settings.


Abstract: Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing phase. In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method.


JAHAN: A framework for procedural generation of game maps from design specifications

Abstract: Procedural content generation is a field that focuses on creating game assets employing algorithmic procedures. A critical asset in many game genres is the map. Due to the sophistication of the game map development, procedural map generation has been the subject of many studies. Considering the fundamental complexity of the game maps, many of these studies try to engage human designers in the generation process to improve the output of algorithmic procedures. However, these studies have not paid adequate attention to important non-functional requirements such as generality, comprehensiveness, controllability, and adaptability to iterative development. This paper presents a procedural map generation framework that places map designers at the center of a pipeline architecture. This framework allows designers to configure different pipeline modules and specify the characteristics of their desired map. Moreover, this framework has assertion checking features that facilitate map evaluation. We demonstrate the capabilities of this framework through one running example, along with three case studies that generate maps for first-person shooter, real-time strategy, and dungeon crawler games. Furthermore, we present a user study to show the suitability of this framework in iterative development processes and demonstrate how it reduces the labor of human designers.


Engineering Qubit Coupling to Reservoir Mode: Optimizing Circuitry to Extend Coherence Time

Abstract: The research investigates how reservoir modes influence the decay and dephasing rates of a qubit, utilizing a circuit model where the qubit interacts with N distinct oscillators. The Lagrangian and Hamiltonian of the system are deduced theoretically, and the analysis delves into how circuit components like the qubit and coupling capacitors affect the decay rate. Notably, the study reveals that the coupling capacitor exerts a more pronounced influence on the relaxation time compared to the qubit capacitor. Moreover, it quantifies the repercussions of bosonic modes affected by coupling to reservoir modes on the qubit coherence time. As a result, the primary objective of the investigation is to examine the impact of circuit elements on qubit behavior, offering valuable insights into the determinants of qubit decay and dephasing rates.


Quantum-enhanced chaotic image encryption: Strengthening digital data security with 1-D sine-based chaotic maps and quantum coding

Abstract: The proposed work delves into the utilization of chaotic maps along with the quantum mechanisms to strengthen the security of the digital images along with addressing the limitations posed by conventional 1-D chaotic systems characterized by their pseudorandom and periodic attributes. Leveraging the inherent uncertainty in quantum theory, the proposed research introduces a new image encryption scheme. This method is built upon the concepts of two-dimensional quantum coding and the 1-D sine-based chaotic map (1-D SBCM). In the proposed encryption scheme, initially, a random sequence is created using 1-D SBCM by varying the seed parameters. This sequence is subsequently utilized for the purpose of scrambling. After that, a pseudorandom number generator (PRNG) is meticulously devised, drawing inspiration from the concepts of quantum coherence. This PRNG generates a confidential code stream that defies predictability and remains incongruent with the plaintext image, thereby contributing to heightened security levels. Subsequently, the research employs the novel enhanced quantum representation (NEQR) model, capitalizing on its advanced capabilities. Within this framework, the study introduces a quantum right cyclic shift operator, as well as a quantum XOR operator. These operators play a vital role in the formation of highly robust encrypted images. The application of these quantum operators not only enhances the security measures but also increases the complexity of the encryption procedure Statistical evidence derived from comprehensive experimentation corroborates the efficacy of the proposed image encryption scheme. Through rigorous analysis, it becomes evident that the system exhibits a robust performance in terms of strong security. The integration of quantum principles and quantum coding demonstrates that the proposed encryption scheme is resilient against potential threats.


An approach for handwritten Chinese text recognition unifying character segmentation and recognition

Abstract: Text line recognition methods can be categorized into explicit segmentation based and implicit segmentation based ones. Explicit segmentation based methods require character-level annotation during training, while implicit segmentation based methods, trained on line-level annotated data, face alignment drift challenges. Though some methods have been proposed to address these challenges using weakly supervised object detection, they often rely on cumbersome pseudo-box generation processes and complex decoding. In this paper, we propose a unified framework to overcome these challenges, achieving high accuracy in text recognition and character segmentation. To eliminate the need of character-level annotated real text line data in training, we introduce a novel training paradigm that utilizes character-level annotated synthetic data and line-level annotated real data jointly. For synthetic data, candidate characters are explicitly aligned with labeled characters to generate hard labels for supervising model training. For real data, implicit alignment is produced by Connectionist Temporal Classification (CTC) mapping to provide soft labels for weakly-supervised model training. And for inference, we propose two decoding strategies leveraging the advantages of Non-Maximum Suppression (NMS) and CTC decoding. Extensive experiments on benchmark datasets demonstrate the superior performance of our method in text recognition and character localization, even with minimal amounts of character-level annotated line data.


Open hardware solutions in quantum technology

Abstract: Quantum technologies, such as communication, computing, and sensing, offer vast opportunities for advanced research and development. While an open-source ethos currently exists within some quantum technologies, especially in quantum computer programming, we argue that there are additional advantages in developing open quantum hardware (OQH). Open quantum hardware encompasses open-source software for the control of quantum devices in labs, blueprints, and open-source toolkits for chip design and other hardware components, as well as openly accessible testbeds and facilities that allow cloud-access to a wider scientific community. We provide an overview of current projects in the OQH ecosystem, identify gaps, and make recommendations on how to close them at present. More open quantum hardware would accelerate technology transfer to and growth of the quantum industry and increase accessibility in science.


An Empirical Investigation on Awareness Regarding Cryptocurrencies Among Investors Under Different Market Regimes

Abstract: Cryptocurrency is one of the most notable financial innovations in recent years. The present study provides a novel methodology to understand the level of awareness regarding cryptocurrencies among investors under different market regimes. The study used a survey questionnaire for a sample of 352 investors in sample Asian countries (India, Japan, South Korea, Singapore, Saudi Arabia, Bahrain, Oman, and Dubai). The study has developed eight hypotheses that have been tested considering independent (Statement of Responses About cryptocurrency-SORAC) and dependent variables (Market Factors-MF, Social Sentiment Factors-SF, Technological Factors-TF, Stocks-ST, Bank Deposits-BD) by incorporating t-test, regression, and correlation. The results revealed that cryptocurrency has a significant and positive influence on dependent variables among investors under different market regimes. The results from the analytical findings pave the way for the policymakers of these countries to help design robust mechanisms to regulate their cryptocurrencies under different market regimes.


Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections

Abstract: Abstract Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms.


AYURVEDIC INTERVENTION IN THE MANAGEMENT OF PANDU ROGA (DIMORPHIC ANAEMIA)

Abstract: Pandu Roga is an Ayurvedic ailment clinically comparable to many types of Anaemia. This study was done to establish the effect of Ayurvedic management on the patient of Pandu Roga (Dimorphic Anaemia). In the IPD, Arogyashala Rugnalaya, Nashik, from 08/01/2022 to 17/01/2022, A 69-year Male patient, suffering from Pandu Roga (Dimorphic Anaemia) was treated with Ayurvedic management. Shamana Chikitsa (ayurvedic medicinal treatment) And Virechana Karma (Therapeutic Purgation) were administered. The patient felt a significant increase in Hb g% and other hematological investigations. Symptoms were assessed as per the criteria of assessment, and improvement was seen in symptoms. Panduta (Pallor) 75%, Daurbalya (General weakness) 75%, Ayasaja Shwasa (Dyspnoea on exertion) 66.66%, Pindikodveshtana (Leg cramps) 50%, Nidraluta (Sleepiness) 50%, Shiroruk (Headache)100%, Bhrama (Giddiness)0%, Sadana (Fatigue) 66.66%, Gatra Shoola (Body aches) 66.66%, Agnimandya (Impaired appetite) 66.66%, Aruchi (Anorexia) 66.66%. This case study demonstrates that Ayurvedic management was significant in Pandu Roga (Dimorphic Anaemia).


A Distributed System Fault Diagnosis System based on Machine Learning

Abstract: Now days distributed system becomes the mainstream system of information storage and processing. Compared with traditional systems, distributed systems are larger and more complex.However, the average probability of failure is higher and the difficulty, complexity of operation and maintenance are greatly increased. Therefore, it is necessary to use efficient methods to diagnose the system. Our aim is to use the trained model to diagnose the fault data of the distributed system, so we can obtain as high diagnostic accuracy as possible, and create a web side for users to use. The technique we proposed uses the integrated learning approach of Stacking to model the superposition of the raw data. To realize this, we trained with a dataset of 10,000 pieces of data and assessed accuracy every once in a while. Our best training results are about 80.69% accurate and can be used on the web side. By training data sets and analyzing distributed system faults with Stacking technology, a model with a test accuracy of 80.69% was obtained. Through this model and the web platform we built, the fault of distributed system can be diagnosed, and the diagnosis results are better than other models.


Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

Abstract: This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.


Artificial Intelligence in “Ivory Tower”

Abstract: This article is devoted to the problem of adapting the higher education system to the new realities of introducing artificial intelligence into everyday life. The author proposes new constructs in communication between teachers and students, based on the use of modern research and development tools (neural networks, big data, artificial intelligence, etc.), which make it possible to improve the mastery of the material with minimal losses due to obsolescence and a decrease in the relevance of the proposed studying courses. Thus it maximizes the competitive advantages of university graduates who acquire competencies that are in high demand in the modern world.


Text Summarization for Online and Blended Learning

Abstract: Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age.


Patriotic Agenda in the Information Flows of Russian Universities: Key Areas of Efficiency Improvement

Abstract: The article presents the results of an empirical study of the patriotic agenda in the information flows of the largest Russian universities. The relevance of the study is determined by the set of global socio-political challenges facing the state at the current stage of development. Based on modern methods and tools of social media analytics, the authors determine the basic characteristics of thematic information flows in social media and make a comprehensive assessment of their effectiveness in interacting with target groups of students. Based on the results of the study the authors differentiate contexts and discourses regarding the level of engagement and personal involvement of young people in patriotic activities, as well as achievement practical results in such work. The main directions for improving the information work of higher educational institutions as subjects of formation and promotion of patriotic information flows in the new media should be focused on systemic activities that simultaneously consider the current external contexts and the needs of student youth.


Efficient ground state preparation in variational quantum eigensolver with symmetry-breaking layers

Abstract: A variational quantum eigensolver (VQE) solves the ground state problem of a given Hamiltonian by finding the parameters of a quantum circuit Ansatz that minimizes the Hamiltonian expectation value. Among possible quantum circuit Ansätze, the Hamiltonian variational Ansatz (HVA) is widely studied for quantum many-body problems as the Ansatz with sufficiently large depth is theoretically guaranteed to express the ground state. However, since the HVA shares the same symmetry with the Hamiltonian, it is not necessarily good at finding symmetry-broken ground states that prevail in nature. In this paper, we systematically explore the limitations of the HVA for solving symmetry-broken systems and propose an alternative quantum circuit Ansatz with symmetry-breaking layers. With extensive numerical simulations, we show that the proposed Ansatz finds the ground state in depth significantly shorter than the bare HVA when the target Hamiltonian has symmetry-broken ground states.


Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm

Abstract: With the rapid growth of network traffic, in order to monitor network traffic, the author proposes a baseline based traffic inspection method. The main objective is to develop a global system for identifying malicious traffic, rather than a precise method for detecting the types of worms produced by malicious traffic. Although traffic is caused by the causes, network administrators can use this international search technique to detect malicious traffic data. The system based approach mainly includes designing time based on the traditional traffic model, detecting various equipments and network traffic process, and configuring the traffic flow according to each time frame. This method uses Cisco's NetFlow Collector, a NetFlow Collector (NFC), to collect raw NetFlow data transmitted by the device through UDP every 5 minutes. the Then, three-dimensional data such as communication port, communication time, and traffic flow (bytes or packets) is used to filter, remove the different values, calculate the base values, and compare the real-time results with the base values to check the traffic defects in the current network. If there are differences between the monitoring data and the system configuration at the same time, the system will issue an abnormal warning, and as time accumulates, the alarm level will gradually escalate.


New Model of Inclusive Higher Education Practices in Megacities’ Universities

Abstract: This study analyzes the problem of higher professional education for people with disabilities in multinational megacities from different countries. The aim of this paper is to investigate the emerging changes and perspectives of educational inclusion in the universities of Moscow, Jakarta, Barcelona, and Cairo and to develop a new model of inclusive educational practice for universities based on an inclusive ideology of understanding the position of people with disabilities in the education system and society overall. To perform this study and verify the new model proposed by the authors, participants were selected from ten universities that indicated parameters such as accessibility, participation, quality, students’ education results, and government funding. The survey aimed to collect participants’ perspectives and experiences from diverse elements of the higher education community in the aforementioned locations. 751 participants were selected for the survey with a balanced gender distribution: 48.6% men and 51.4% women. Age distribution was rather diverse: the age group of 18–24 years was 25,7%, the age group of 25–34 years was 27,3%, the age group of 35–44 years was 25,6%, and the age group of 45 years 21,4%. Another distribution was people without disabilities (N=250) and with disabilities (N=551). The reliability of the research was achieved using several methods, including ANOVA. The results of this research show similarities and differences in implementing inclusive education practices across selected locations, providing a detailed picture of the current state and future perspectives of higher inclusive education. The scientific novelty of this research lies in the theoretical-methodological rationale of the new model of inclusive higher education practices in universities of major megacities and in forming the propositions on this model’s implementation in higher education institutions. Doi: 10.28991/ESJ-2024-SIED1-03 Full Text: PDF


Maximizing Penetration Testing Success with Effective Reconnaissance Techniques Using ChatGPT

Abstract:
Background/Objective: The study investigates the integration of ChatGPT, a generative pretrained transformer language model into the reconnaissance phase of penetration testing. The research aims to enhance the efficiency and depth of information gathering during critical security assessments offering potential improvements to traditional approaches.
 Research Problem: The research study addresses the challenge of optimizing the reconnaissance phase in penetration testing. It seeks to provide a solution by exploring the capabilities of ChatGPT in extracting valuable data, such as various aspects of the digital footprint or infrastructure of a system or an organization. The scope of the research relies in demonstrating how ChatGPT can contribute to the planning phase of penetration testing, guiding the selection of tactics, tools, and techniques for identifying and mitigating potential risks that could be used to assist with securing Internet accessible assets of a system or an organization.
 Methodology: The research adopts a case study methodology to assess the effectiveness of ChatGPT in reconnaissance. Tailored questions are formulated to extract specific information relevant to penetration testing. The study highlights the importance of prompt engineering emphasizing the need for carefully constructed questions to ensure usable results.
 Results: The research showcases the ability of ChatGPT to provide diverse and insightful reconnaissance information. The extracted data includes IP address ranges, domain names, vendor technologies, SSL/TLS ciphers, and network protocols. The information gathering improves efficiency of the reconnaissance phase aiding penetration testers in planning subsequent phases of the assessment.
 Discussion: The research study extends to the broader field of cybersecurity where artificial intelligence language models can play a valuable role in enhancing the success of reconnaissance in penetration testing. The research suggests that integrating ChatGPT into penetration testing can bring about positive changes in the efficiency and depth of information obtained during reconnaissance.
 Conclusion: The results of the study determine that incorporating ChatGPT in the reconnaissance phase significantly benefits penetration testers by offering valuable insights and streamlining subsequent assessment planning. The results affirm ChatGPT as a pivotal tool in maximizing success in penetration testing, contributing to ongoing advancements in cybersecurity practices.


Investigating delay of the media access control protocols for IoT‐RoF using quantum entanglement

Abstract: Abstract One of the significant challenges of the Radio over fibre (RoF) Medium Access Control (MAC) protocol is the propagation delay. This delay can lead to serious issues, such as higher propagation delay resulting in collisions and unnecessary retransmissions. Quantum entanglement is an excellent candidate to overcome the propagation delay of the RoF MAC protocol. A new quantum MAC protocol is proposed, named the Quantum Entanglement‐based MAC protocol (QE‐MAC), in which Quantum Teleportation is utilised to address the propagation delay. Four entanglement states are employed to represent the control packets of the classical MAC protocol, and data is transmitted over the classical channel. Instead of using control packets such as acknowledgement, request to send, and Clear to send, state transitions are employed. This approach avoids the delay and collision issues associated with control packets, resulting in a significant improvement in network performance. The delay, duty cycle (DC), and power consumption of the proposed QE‐MAC protocol are formulated and derived. The protocol is evaluated in terms of delay, DC, and power consumption, demonstrating superior performance compared to the classical RoF MAC protocol. In comparison to published works, our proposed approach has successfully reduced both delay and power consumption by 35%.


Determining mammalian cells state by fractal micromotion

Abstract: We applied four fractal dimension estimation algorithms on the temporal electrical impedance signal of normal MDCK type II cell cultures monitored by ECIS technique and showed that the fractal dimension due to micromotion allows discriminating processes not sensed by the spectral impedance of the culture. In this work we subjected cell cultures to electric current damage and drug exposure to analyze the changes in the fractal structure of the temporal signal. Among the changes presented and detected are the differentiation between a healthy monolayer and one exposed to a drug, as well as the distinction between a seeding process and a wound-healing process performed by electric current. The four algorithms used were validated by applying them on topological functions of known fractal dimension, a study that determined the necessary conditions for a correct estimation.


MAPPING RADIOACTIVITY IN CONTINENTAL TERMINAL 3 (CT3) FORMATIONS: THE CASE OF THE THREE SISTERS, NIAMEY REGION (EASTERN EDGE OF THE WEST AFRICAN CRATON, ILLUMMEDEN BASIN)

Abstract: The Continental Terminal 3 (Ct3) formations, which rest in major unconformity on the Birimian basement of the West African Craton, correspond to the last filling deposits of the Iullemmeden basin in the Niamey region (West, Niger). The general objective of this study is to produce a surface radiometric map of the Niamey Three Sisters Hills area. The methodology used included field and laboratory work, with radiometric data processed using Surfer software. Radioactivity values in the Three Sisters area ranged from 5 to 25 shocks per second (C/s). High radioactivity values (15 to 25 c/s) are recorded in the upper terms, corresponding to the breastplates. Low values of radioactivity (5 to 15 c/s) are measured in the basal layers, corresponding to clays and alluvium. This radioactivity is thought to be due to trace amounts of uranium in these outcrops.


Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging

Abstract: The controlled-swap and controlled-controlled-not gates are at the heart of the original proposal of reversible classical computation by Fredkin and Toffoli. Their widespread use in quantum computation, both in the implementation of classical logic subroutines of quantum algorithms and in quantum schemes with no direct classical counterparts, has made it imperative early on to pursue their efficient decomposition in terms of the lower-level gate sets native to different physical platforms. Here, we add to this body of literature by providing several logically equivalent circuits for the Toffoli and Fredkin gates under all-to-all and linear qubit connectivity, the latter with two different routings for control and target qubits. Besides achieving the lowest cnot counts in the literature for all these configurations, we also demonstrate the remarkable effectiveness of the obtained decompositions at mitigating coherent errors on near-term quantum computers via equivalent circuit averaging. We first quantify the performance of the method in silico with a coherent-noise model before validating it experimentally on a superconducting quantum processor. In addition, we consider the case where the three qubits on which the Toffoli or Fredkin gates act nontrivially are not adjacent, proposing a novel scheme to reorder them that saves one cnot for every swap. This scheme also finds use in the shallow implementation of long-range cnots. Our results highlight the importance of considering different entangling gate structures and connectivity constraints when designing efficient quantum circuits.


Quo vadis higher education? Post-pandemic success digital competencies of the higher educators – a Hungarian university case and actions

Abstract: Abstract The purpose of this paper is to show the necessary competence sets for Higher Education (HE) lecturers in the framework of the COVID and post-COVID. A COVID-situated competence survey was carried out among university lecturers. Exploratory and confirmatory factor analyses (EFA, CFA) were applied to show the significant digital education competence factors. The representative online survey of 322 respondents shows that three big competence sets can be identified: Awareness, Professional, and Digital. Those having higher scores in Professional competencies foresee more digital programs and communications in the future, and not only the digital competencies but also the professional ones should be developed to meet the requirements of the digital education transformation process. The findings emphasize that the forced and drastic changes in the application of digital education to the intensification of COVID-19 should become sustainable and find its proper place and role in the future HE. The structured and closely managed use of the results was followed by a set of digital and professional competence development initiatives carried out within the framework of the Digital Education and Learning Support Centre, founded in 2020 at the University of Pécs.


UNVEILING THE LIVED EXPERIENCES OF 5-6S MONEY LENDERS: A PHENOMENOLOGICAL INQUIRY

Abstract: The purpose of this study was to uncover and understand the experiences of 5-6 money lenders in Kapalong, Davao del Norte. In-depth interviews were done with 10 purposively chosen money lenders. The gathered information was analyzed thematically. The experiences of the participants encompass the struggle of collecting debt amortization and lack of working capital. Hence, they stressed the need for financial agreements, additional investment, efficiency & amiability, and aid through training, capital, and tax incentives. The results imply that government agencies may craft a program to improve the lending experiences of the money lenders and enhance the lending industrys overall quality. KEYWORDS: Business management, micro-money lenders, plight, qualitative-phenomenological inquiry, thematic analysis, Philippines


Orman yangınları çalışmalarında uygulanan makine öğrenmesi (ML) tekniklerine genel bir bakış

Abstract: With the increasing frequency of forest fires globally, causing substantial environmental and economic damages, there is an imperative need for early fire prediction and detection. This study aims to examine the utility of machine learning techniques in predicting and identifying forest fires. A comprehensive review was conducted on various technologies and techniques proposed for forest fire prediction. Particular emphasis was placed on understanding the pros and cons of each machine learning algorithm, with an aim to identify the most effective approaches. It was observed that while numerous machine learning methods exist for forecasting forest fires, each possesses unique strengths and limitations. Some techniques, when tailored to specific forest characteristics, displayed enhanced predictive capabilities. Machine learning (ML) plays a pivotal role in advancing the field of forest fire studies. Identifying and utilizing the most suited ML technique, based on forest characteristics and the nature of data, can significantly augment prediction accuracy.


Iterative Ensemble Learning over High Dimensional Data for Sentiment Analysis

Abstract: For sentiment analysis in particular, the problem of processing and analyzing high-dimensional data becomes more prominent in recent past. This is where the IEL-HDDSA model, which aims to increase accuracy and performance in complex, high-dimensional data streams sentiment analysis comes into play. Iterative approach in ensemble learning; a contribution to the field. It integrates preprocessing techniques such as tokenization, stop word removal, lemmatization and the collection of sentiment-related features. Then the training corpus is divided by label, and features with high mutual information are selected. Highly replicated points of data for model training can also be identified at this point. First a Naive Bayes model is trained, then later it's placed in an ensemble as part of bagging. Its major advantage over earlier methods is that IEL-HDDSA can iteratively train on selected subsets of data until the performance in sentiment analysis for high-dimensional objects reaches an optimum level. A 10-fold cross validation method was used to rigorously evaluate the performance of this model, which showed consistently high levels of operation with almost no variation across different measures. IEL-HDDSA's precision ranged from 0.9359 to 0.9492, and its specificity was between 0. Its accuracy differed from 0.93 to around 0.95, and its F1-measure fluctuated between the values of about 0.94 and above; so here too balance was well maintained in a manner that satisfied both precision and recall requirements equally. The false alarming rate fell from 0.056 to 0.1, a fairly low ratio of incorrect positive classifications; Moreover, MCC quantities ranged from 0.8668 to 0. These results testify to the IEL-HDDSA model's stable effectiveness and high reproducibility in sentiment analysis applications, especially for massive data flows.


Optimal Usage of Resources through Quality Aware Scheduling in Containers based Cloud Computing Environment

Abstract: For cloud computing, the Quality Aware Scheduling of Containers (QASC) model has been proposed for delay-sensitive tasks. Plan your cloud tasks. Typically, time constraints are met while resources are used effectively. It's a really difficult undertaking. In order to distribute containers more effectively, QASC takes into account a number of performance factors. Containers and their make-span logs, as well as input quality metrics like I/O-intensive workload, startup time, hot standby failure rate, and inter-container dependencies, are collected by the QASC model. A metric coefficient that indicates each container's overall rating is calculated by normalizing and averaging these values to determine it. In order to determine how well scheduling performed, the model also includes a quality coefficient that calculates this metric-coefficient threshold. It's also critical for QASC to be able to determine the remaining energy in each container, which represents its request capacity. In order to optimize cloud resources, energy use is also taken into account by the model. From the cloud-sim simulation, an experimental dataset including 50 containers and 1,200 internet protocol-capable users was employed. For the make-span ratio, round-trip time, and energy consumption analysis, this produced 20,000 data points. The RLSched, DSTS, and ADATSA models were contrasted with the QASC model. The outcomes showed that QASC performed better than these models in a number of crucial areas. Tasks may be managed better with the higher average make-span ratio and lower volatility. Its superior job scheduling and resource use were further demonstrated by its shorter round-trip durations and lower energy usage across loads. The QASC model is an extremely complex scheduling method for container-based systems and a significant advancement in cloud computing research. Its approaches and methods enable for more intelligent energy use as well as high-quality services while also improving system performance, particularly for tasks that are delay-sensitive.


Common transversals and complements in abelian groups

Abstract: Abstract Given a finite abelian group G and cyclic subgroups A , B , C of G of the same order, we find necessary and sufficient conditions for A , B , C to admit a common transversal for the cosets they afford. For an arbitrary number of cyclic subgroups, we give a sufficient criterion when there exists a common complement. Moreover, in several cases where a common transversal exists, we provide concrete constructions.


Software Defined Netconomics

Abstract: This paper is based on the fair analysis on the economic benefits of software defined networking. The paper will explain vividly the concept of software defined networking and its applications together with the economic advantage(s) with respect to both the client perspective and server perspective. In general, SDN provides lower hardware and operational cost in an excessive traffic load of nowadays users. It has tighter security and faster response time with improved management and planning of server controller. SDN when deployed provides better service(s) with respect to lowering cost and of course with better efficiency. Software Defined Network (SDN) is a great revolutionary idea to change IT Industry of which includes cloud computing and many others. The beauty of its architecture is the decoupling of the essential control and data planes from the application layer, thus the underlying infrastructure is abstracted but the network intelligence is logically centralized with state. This architecture gives access to the enterprise to control, program, automate, scale and secure the network according to the business needs, together with securing a safe benefit in competiveness that has risen due to fast advancement in technology. This architecture is even more important to fulfilling the demands of various clients in the current model of networking. The concept of SDN has the potential to change the current networking model. With SDN, for network services, the underlying network infrastructure can be abstracted by the administrators for many applications. In theory, it can be shown that SDN has many benefits over current IT systems but in practical systems, IT companies are reluctant to work with, tend to oppose nor are they ready to deploy SDN. In this paper, the economic benefits are explored and discussed without bias, and it will be shown that new protocol of SDN provides many economic benefits and guidance to IT companies to establish more secure and efficient network.


An ontology‐based semantic description model of ubiquitous map images

Abstract: Abstract Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.


Correlational Analysis: Knowledge, Training, and Attitude Towards First Aid Injuries Among Students of Astate Senior High School in Banjarmasin

Abstract: ABSTRACT Childhood injury is a growing public health concern that requires special attention. Students frequently participate in activities without considering the influence on their own body, resulting in injuries that can have a negative impact on their health and daily activities. The goal of this study was to assess the association between students' knowledge, training, and attitude toward first aid for injuries in a state senior high school in Banjarmasin. A correlational study design and a cross-sectional approach were utilized in this investigation. Data was obtained from a total of 1280 respondents, and a sample of 114 respondents was analyzed using a basic random sampling technique with probability sampling, with Spearman's rho as the statistical test utilized. With a p value of 0.05, the results suggest that there is a relationship between the level of knowledge, training, and attitude and the level of accuracy in first aid. It can be concluded that understudies' knowledge, training, and state of mind influence the precision of giving first aid for injuries, so it is critical for understudies to have positive knowledge, preparation, and demeanor in giving first aid to avoid mistakes that can lead to crises. Keywords: Attitude, First Aid for Injuries, Level of Knowledge, Training


Integration Of Electronic Learning Resources in The Educational Process: Stages and Significance

Abstract: This article describes the stages of implementation of the integration of electronic educational resources to increase the efficiency of independent education of students of higher educational institutions and the results of research on determining the practical importance of integrating electronic educational resources


Librarians’ Indices And Utilization Of Electronic Information Resources In Bowen University Iwo

Abstract: This study determined the Librarians’ Indices on Utilization of Electronic Information Resources in Bowen University Iwo. Three research questions and three null hypotheses were formulated to guide the study. The study covered librarians’ (academic librarians) of Bowen University Iwo. Ex-post facto research design was used for the study. The population of the study was 7 academic librarians’. Census sampling technique was used for the study; and a sample size of 7 academic librarians was used. The instrument titled “Librarians’ Indices and Utilization of Electronic Information Resources Questionnaire (LIUEIRQ)” was developed by the researcher and validated by three librarians. Cronbach Alpha reliability method was used to compute the reliability of the research instrument which yielded a co-efficient of .72. The data collected were analyzed using mean and standard deviation for research question 1- 3, and t-test was used to test null hypotheses 1- 3. All the null hypotheses were tested at .05 level of significance. The findings indicated that librarians’ indices influenced the level of utilization of electronic information resources in Bowen Universities Iwo. The study concluded that Librarians’ Indices are significant determinants for the influence in the utilization of electronic information resources in Bowen University Iwo. Librarians’ indices and utilization of electronic information resources in Bowen University Iwo is very crucial for acquiring current, timely, relevant and up-to-date electronic information resources for academic activities. Based on the findings, the study recommended among others, that the librarians’ indices should not be barrier on their utilization of electronic information resources. Every librarian should be able and interested in making use of electronic information resources no matter of their status


Analysis of School Library Utilization in Developing a Literacy Culture for Elementary School Students at SDN Bendelan 1

Abstract:
Background: The cultural climate of literacy for elementary school students is intricately linked to the crucial role played by school libraries. Recognizing the significance of this connection, the research delves into understanding how school libraries contribute to the development of a robust literacy culture among elementary school students.
 Purpose: The primary aim of this research is to analyze the role of school libraries in fostering a literacy culture among elementary school students. Employing a descriptive qualitative research method, the study seeks to investigate the extent of library utilization, focusing on aspects such as book variety, literacy programs, and strategies designed to captivate students' interest in reading. Additionally, the research aims to explore collaborative efforts between librarians and teachers in enhancing students' literacy culture.
 Design and methods: The research employs a descriptive qualitative research method, utilizing interviews, observation, and documentation to collect data from school principals, teachers, and students. The data analysis focuses on evaluating the extent of library usage, the adequacy of book collections, and the effectiveness of literacy programs and strategies. The design also incorporates an examination of collaborative initiatives between librarians and teachers to attract students' interest in developing their literacy culture.
 Results: The findings of the research highlight the current state of the library collection at SDN Bendelan 1, indicating its adequacy but emphasizing the need for improvement in alignment with the Merdeka Curriculum. The results underscore the importance of ongoing development to ensure the relevance of library materials to the curriculum, ultimately meeting the evolving learning needs of elementary school students. This research provides valuable insights for educators, librarians, and policymakers to strategically enhance literacy initiatives in elementary schools through focused library collection development.


Library Promotion Strategy in the Digital Era

Abstract:
Background: The library is an institution that has an essential role in society. The efforts made by the library to carry out this role ensure that the public knows about the library's existence. The public can only use the library if they know its role. Thus, so that libraries can continue to carry out their role, libraries need to develop library promotion strategies. Promotion as a marketing activity carried out by libraries is essential so that the public knows about the products that the library can use. Society also uses digital technology to meet information needs in the digital era. However, society's tendency to use digital technology does not reduce the role of libraries as information institutions to meet society's information needs. The development of digital technology can be utilized to increase the existence of libraries through library promotion strategies.
 Purpose: This article reviews various promotional strategies libraries can implement using digital technology.
 Design and methods: The research method used is qualitative, with data collection techniques through document study and library observation via websites.
 Results: Promotional strategies that libraries can carry out are publications, advertising, sales promotions, personal sales, and digital marketing. Obstacles encountered in carrying out promotional activities are a consideration for libraries in determining promotional strategies that are appropriate to the conditions of the library.


SICURO+: A web platform to raise awareness on seismic risk in Italy

Abstract: Italy is a country with high seismic risk. In the past many catastrophic earthquakes have occurred causing severe damage to buildings and infrastructures, serious economic impacts, and the loss of thousands of human lives. To mitigate the impact of seismic risk, as well as of other risks, many efforts have been made, implementing preventive structural measures and acting to raise awareness in the community. In Italy, this need was strengthened with the emanation of the new Civil Protection Code (D.Lgs January 2, 2018 n.1), with the purpose of encouraging the participation of citizens and spreading risk knowledge and the culture of civil protection, thus contributing to “increase the resilience of communities” (art. 31). As part of the agreement between the Italian Civil Protection Department (ICPD) and EUCENTRE (European Centre for training and research in Earthquake Engineering), an interactive web-tool was developed to allow citizens to display the most updated seismic risk maps. These maps were produced in 2018 by the ICPD, with the relevant support of the scientific community operating in the field of seismic risk, in particular of ReLUIS (The Laboratories University Network of seismic engineering) and EUCENTRE. The web-tool, named SICURO+ (Information System for Risk Communication, www.sicuropiu.it), was created to raise awareness in citizens on seismic risk in the place where they live, work, or go on holiday. Communication strategies and behavioural techniques were designed and tested experimentally with the support of behavioural scientists, as part of an agreement between the National School of Administration and the Prime Minister's Office.


KONSEP POLIGAMI MENURUT OEMAR BAKRY

Abstract: This research is motivated by the widespread public response to polygamists, both for and against, with various views. Therefore, this research focuses on studying Oemar Bakry in interpreting verses that relate to polygamy in Tafsir Rahmat. Modern thinking and scientific approaches make Oemar Bakry have a different view in studying polygamy verses. This research uses a qualitative method approach library research. This research resulted in the conclusion that Oemar Bakry considers that the text which implicitly allows polygamy cannot be removed or abolished but requires a re-examination of the verse. So, according to Oemar Bakry, polygamy is used as an emergency exit which can be used in case of emergency. Second, the practice of polygamy must be based on justice both physically and spiritually, which is the main requirement, although it is adjusted to the abilities of the polygamist (husband). Third, must be connected mu'asyarah bil ma'ruf in marriage. Fourth, every problem needs to find a solution and the last resort is divorce if the problem cannot be fixed. Therefore, the ideal marriage according to Oemar Bakry is a monogamous marriage.


Music Player System for User Facial Recognition using CNN Algorithm

Abstract: A strong language for expressing your emotion is music . Many pepole utilise music therapy to get through difficult time in their life. Emotion and moods can be easily reflected in music We often listen to energetic music when playing sports, and the same goes for fatigued or nervous people—a lovely, calming tune can help them relax."Melancholic Music" is a music player which play song based on your mood ,it uses neural network to categorise the many emotions on a person’s face, such as anger ,disgust ,fear etc. Neural network is a method in artificial intelligence .It is a type of machine learning process called deep learning .These neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in diverse domains .The project also aims to create a playlist according to different emotion.Thus deep learning algorithms helps one automate a task that can take a long time to perform.


An Affect of Leadership and Compensation towards Employee Performance

Abstract: This study aims to analyze the magnitude of the affect of leadership and compensation on the performance of PT Mutu Prima Utama employees both partially and together. This research is an explanatory research with a quantitative approach. The sample in the study was 43 respondents with a saturated sample method. Data collection techniques use observation techniques, questionnaires, and literature studies. The data analysis with multiple linear regression. The results showed that leadership and compensation were proven to positively and significantly affect the performance of PT Mutu Prima Utama employees partially or together, as for the impact of the contribution is strong at 54.1 percent.