Complexity of a class of first-order objective-function-free optimization algorithms

Abstract: A parametric class of trust-region algorithms for unconstrained non-convex optimization is considered where the value of the objective function is never computed. The class contains a deterministic version of the first-order Adagrad method typically used for minimization of noisy function, but also allows the use of (possibly approximate) second-order information when available. The rate of convergence of methods in the class is analysed and is shown to be identical to that known for first-order optimization methods using both function and gradients values, recovering existing results for purely-first order variants and improving the explicit dependence on problem dimension. This rate is shown to be essentially sharp. A new class of methods is also presented, for which a slightly worse and essentially sharp complexity result holds. Limited numerical experiments show that the new methods' performance may be comparable to that of standard steepest descent, despite using significantly less information, and that this performance is relatively insensitive to noise.


Native Approach to Controlled- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:math> Gates in Inductively Coupled Fluxonium Qubits

Abstract: The fluxonium qubits have emerged as a promising platform for gate-based quantum information processing. However, their extraordinary protection against charge fluctuations comes at a cost: when coupled capacitively, the qubit-qubit interactions are restricted to XX interactions. Consequently, effective ZZ or XZ interactions are only constructed either by temporarily populating higher-energy states, or by exploiting perturbative effects under microwave driving. Instead, we propose and demonstrate an inductive coupling scheme, which offers a wide selection of native qubit-qubit interactions for fluxonium. In particular, we leverage a built-in, flux-controlled ZZ interaction to perform qubit entanglement. To combat the increased flux-noise-induced dephasing away from the flux-insensitive position, we use a continuous version of the dynamical decoupling scheme to perform noise filtering. Combining these, we demonstrate a 20 ns controlled-z gate with a mean fidelity of 99.53%. More than confirming the efficacy of our gate scheme, this high-fidelity result also reveals a promising but rarely explored parameter space uniquely suitable for gate operations between fluxonium qubits.Received 12 September 2023Accepted 8 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.060602© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum gatesQuantum information with solid state qubitsPhysical SystemsSuperconducting qubitsQuantum Information, Science & TechnologyCondensed Matter, Materials & Applied Physics


Locked entropy in partially coherent optical fields

Abstract: The authors study partially coherent optical fields, for which polarization and the spatial degrees of freedom (DoF) are relevant, by exploring the rank of the associated coherence matrix. They show that rank-2 fields can always be rendered separable and can concentrate entropy entirely into one DoF, whereas all rank-3 fields are nonseparable and always have some locked entropy in one DoF.


The role of artificial intelligence in corporate digital strategies: evidence from China

Abstract: Purpose In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure. Design/methodology/approach To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings. Findings The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs. Originality/value This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.


Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Abstract: Background The accumulation of vast electronic medical records (EMRs) through medical informatization creates significant research value, particularly in obstetrics. Diagnostic standardization across different health care institutions and regions is vital for medical data analysis. Large language models (LLMs) have been extensively used for various medical tasks. Prompt engineering is key to use LLMs effectively. Objective This study aims to evaluate and compare the performance of LLMs with various prompt engineering techniques on the task of standardizing obstetric diagnostic terminology using real-world obstetric data. Methods The paper describes a 4-step approach used for mapping diagnoses in electronic medical records to the International Classification of Diseases, 10th revision, observation domain. First, similarity measures were used for mapping the diagnoses. Second, candidate mapping terms were collected based on similarity scores above a threshold, to be used as the training data set. For generating optimal mapping terms, we used two LLMs (ChatGLM2 and Qwen-14B-Chat [QWEN]) for zero-shot learning in step 3. Finally, a performance comparison was conducted by using 3 pretrained bidirectional encoder representations from transformers (BERTs), including BERT, whole word masking BERT, and momentum contrastive learning with BERT (MC-BERT), for unsupervised optimal mapping term generation in the fourth step. Results LLMs and BERT demonstrated comparable performance at their respective optimal levels. LLMs showed clear advantages in terms of performance and efficiency in unsupervised settings. Interestingly, the performance of the LLMs varied significantly across different prompt engineering setups. For instance, when applying the self-consistency approach in QWEN, the F1-score improved by 5%, with precision increasing by 7.9%, outperforming the zero-shot method. Likewise, ChatGLM2 delivered similar rates of accurately generated responses. During the analysis, the BERT series served as a comparative model with comparable results. Among the 3 models, MC-BERT demonstrated the highest level of performance. However, the differences among the versions of BERT in this study were relatively insignificant. Conclusions After applying LLMs to standardize diagnoses and designing 4 different prompts, we compared the results to those generated by the BERT model. Our findings indicate that QWEN prompts largely outperformed the other prompts, with precision comparable to that of the BERT model. These results demonstrate the potential of unsupervised approaches in improving the efficiency of aligning diagnostic terms in daily research and uncovering hidden information values in patient data.


Digital Identity and Data Sovereignty

Abstract: This chapter explores the intersection of digital identity and global data sovereignty, focusing on blockchain's impact. The authors analyze its transformative potential, security, and challenges concerning data regulations. This chapter advocates for a user-centric, ethical, and secure approach to digital identity, addressing societal implications and emphasizing user sovereignty. The chapter calls for interdisciplinary collaboration and outlines future directions for a secure digital identity framework.


Navigating the Digital Era

Abstract: In the midst of the 21st-century digital revolution, we find ourselves navigating a complex landscape where our analog instincts clash with our digital dependencies. Our smartphones, compact marvels of technology, house the entirety of our daily existence, from groceries to fashion and medicine. Yet, amidst this convenience, fundamental questions about data safety, security controls, and data ownership linger ominously in our minds. This chapter delves into the vital trifecta of privacy, security, and data ownership in our increasingly digitized world. In an era of ever-evolving technology, the ordinary user constantly frets about the sanctity of their data. While technology has undoubtedly bestowed countless benefits upon humanity, it also bears a dark side, epitomized by the surge in digital frauds and scams. Such concerns propel individuals to ponder deeply: Is my data truly private? and Who ultimately lays claim to, or safeguards, my data? This exploration ventures into the realms of artificial intelligence, virtual reality, the metaverse, online payments, and virtual classrooms.


Rethinking Attention Mechanisms in Vision Transformers with Graph Structures

Abstract: In this paper, we propose a new type of vision transformer (ViT) based on graph head attention (GHA). Because the multi-head attention (MHA) of a pure ViT requires multiple parameters and tends to lose the locality of an image, we replaced MHA with GHA by applying a graph to the attention head of the transformer. Consequently, the proposed GHA maintains both the locality and globality of the input patches and guarantees the diversity of the attention. The proposed GHA-ViT commonly outperforms pure ViT-based models using small-sized CIFAR-10/100, MNIST, and MNIST-F datasets and a medium-sized ImageNet-1K dataset in scratch training. A Top-1 accuracy of 81.7% was achieved for ImageNet-1K using GHA-B, which is a base model with approximately 29 M parameters. In addition, with CIFAR-10/100, the existing ViT and parameters are reduced 17-fold and the performance increased by 0.4/4.3%, respectively. The proposed GHA-ViT shows promising results in terms of the number of parameters and operations and the level of accuracy in comparison with other state-of-the-art ViT-lightweight models.


Efficient Error and Variance Estimation for Randomized Matrix Computations

Abstract: .Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nyström approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices.Keywordsjackknife resampling low-rank approximation error estimation randomized algorithmsMSC codes62F4065F5568W20


Graph neural networks: a survey on the links between privacy and security

Abstract: Abstract Graph neural networks (GNNs) are models that capture the dependencies between graph data by passing messages between graph nodes and they have been widely used to process graph data that contains relational information. Example application areas include social networks, recommendation systems, and life sciences. However, like all neural networks, there are underpinning security and privacy concerns associated with GNN deployments in practice. For example, attackers can perturb a graph’s data to undermine a model’s effectiveness, or they can steal the model’s data and/or parameters, thus threatening the privacy of the model. In this survey, we provide a comprehensive review of recent research efforts on security and/or privacy in GNNs. We also systematically describe the distinctions and relationships between security and privacy, as well as providing an outlook on future directions of research in this area.


Minimal orthonormal bases for pure quantum state estimation

Abstract: We present an analytical method to estimate pure quantum states using a minimum of three measurement bases in any finite-dimensional Hilbert space. This is optimal as two bases are insufficient to construct an informationally complete positive operator-valued measurement (IC-POVM) for pure states. We demonstrate our method using a binary tree structure, providing an algorithmic path for implementation. The performance of the method is evaluated through numerical simulations, showcasing its effectiveness for quantum state estimation.


Paraconsistent logic and query answering in inconsistent databases

Abstract: This paper concerns the paraconsistent logic LPQ⊃,F and an application of it in the area of relational database theory. The notions of a relational database, a query applicable to a relational database, and a consistent answer to a query with respect to a possibly inconsistent relational database are considered from the perspective of this logic. This perspective enables among other things the definition of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. In an earlier paper, LPQ⊃ ,F is presented with a sequent-style natural deduction proof system. In this paper, a sequent calculus proof system is presented instead because such proof systems are generally considered more suitable as the basis of proof search procedures than natural deduction proof systems and proof search procedures can serve as the core of algorithms for computing consistent answers to queries.


Generative approaches for solving tangram puzzles

Abstract: Abstract The Tangram is a dissection puzzle composed of seven polygonal pieces that can form different patterns. Solving the Tangram is an irregular shape packing problem known to be NP-hard. This paper investigates the application of four deep-learning architectures—Convolutional Autoencoder, Variational Autoencoder, U-Net, and Generative Adversarial Network—specifically designed for solving Tangram puzzles. We explore the potential of these architectures in learning the complex spatial relationships inherent in Tangram configurations. Our experiments show that the Generative Adversarial Network competes well with other architectures and converges considerably faster. We further prove that traditional evaluation metrics based on pixel accuracy often fail in assessing the visual quality of the generated Tangram solutions. We introduce a loss function based on a Weighted Mean Absolute Error that prioritizes pixels representing inter-piece sections over those covered by individual pieces. Extending this loss function, we propose a novel evaluation metric as a more fitting measure for assessing Tangram solutions compared to traditional metrics. This investigation advances our understanding of the capabilities of artificial intelligence in complex geometrical problem domains.


A Hardware Realization Framework for Fuzzy Inference System Optimization

Abstract: Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.


Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation

Abstract: In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments.


A Design Language for Prototyping and Storyboarding Data-Driven Stories

Abstract: Data-driven stories (DDS) are digital forms of storytelling that arrange data and visualizations to communicate a narrative of information to an audience. They have been growing rapidly over the past decades. As a result, a great degree of versatility appears in the forms of published DDS. The recent structures of DDS are more complex, respecting their arrangement, composition, features, and inner parts. In the current academic research, neither storytelling techniques nor any taxonomies suggest visual mechanisms to distinguish between different layouts, compositions, and arrangements. The lack of an expressive visual solution that integrates different parts of DDS under one structure prevents the authors from trying more alternative design paths in the story design process. In this proposed work, we unify all the constructing parts of DDS to define the narrative structure as a visually structured representation of the DDS narrative, which is formed and designed by their constructing elements. This solution proposes a design language consisting of a set of design rules that integrate the visual elements to represent the DDS narrative structure. Our evaluation of the audit process out of 100 DDS examples confirms that the design language is comprehensive, expressive, and versatile. Additionally, we developed DataStoryDesign, a system that incorporates this visual solution to facilitate prototyping and storyboarding DDS for a team of DDS authors. The preliminary result of the exploratory evaluation indicates that such a solution is effective in prototyping and storyboarding DDS. In addition, our findings confirmed that the existence of our design language improves the visual communication between different personas in the DDS production workflow.


Automated Identification of Sensitive Financial Data Based on the Topic Analysis

Abstract: Data governance is an extremely important protection and management measure throughout the entire life cycle of data. However, there are still data governance issues, such as data security risks, data privacy breaches, and difficulties in data management and access control. These problems lead to a risk of data breaches and abuse. Therefore, the security classification and grading of data has become an important task to accurately identify sensitive data and adopt appropriate maintenance and management measures with different sensitivity levels. This work started from the problems existing in the current data security classification and grading work, such as inconsistent classification and grading standards, difficult data acquisition and sorting, and weak semantic information of data fields, to find the limitations of the current methods and the direction for improvement. The automatic identification method of sensitive financial data proposed in this paper is based on topic analysis and was constructed by incorporating Jieba word segmentation, word frequency statistics, the skip-gram model, K-means clustering, and other technologies. Expert assistance was sought to select appropriate keywords for enhanced accuracy. This work used the descriptive text library and real business data of a Chinese financial institution for training and testing to further demonstrate its effectiveness and usefulness. The evaluation indicators illustrated the effectiveness of this method in the classification of data security. The proposed method addressed the challenge of sensitivity level division in texts with limited semantic information, which overcame the limitations on model expansion across different domains and provided an optimized application model. All of the above pointed out the direction for the real-time updating of the method.


Use of Augmented Reality as a Tool to Support Cargo Handling Operations at the CARGO Air Terminal

Abstract: (1) Background: A current trend observed in the logistics sector is the use of Industry 4.0 tools to improve and enhance the efficiency of cargo handling processes. One of the popular solutions is an augmented reality system that supports operators in everyday tasks. The article aims to present design assumptions for implementing an augmented reality system to support air cargo handling at the warehouse. (2) Methods: Research was carried out based on a five-stage analytical procedure, aiming to analyze the current state and identify the potential for implementing the AR system. The following methods were used to collect data: co-participant observations, process analysis, direct interviews, analysis of internal documentation, and applicable legal regulations. (3) Results: The conducted research allowed for identifying information flows accompanying cargo flows and developing a project to automate selected information flows. The obtained results made it possible to identify operations for which the AR system’s implementation will increase their effectiveness and efficiency. (4) Conclusions: The obtained results identified the need to develop a hybrid algorithm for arranging cargo in the warehouse and to build a system supporting self-verification of markings on air cargo.


Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection

Abstract: Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.


Semi-Supervised Implicit Augmentation for Data-Scarce VQA

Abstract: Vision-language models (VLMs) have demonstrated increasing potency in solving complex vision-language tasks in the recent past. Visual question answering (VQA) is one of the primary downstream tasks for assessing the capability of VLMs, as it helps in gauging the multimodal understanding of a VLM in answering open-ended questions. The vast contextual information learned during the pretraining stage in VLMs can be utilised effectively to finetune the VQA model for specific datasets. In particular, special types of VQA datasets, such as OK-VQA, A-OKVQA (outside knowledge-based), and ArtVQA (domain-specific), have a relatively smaller number of images and corresponding question-answer annotations in the training set. Such datasets can be categorised as data-scarce. This hinders the effective learning of VLMs due to the low information availability. We introduce SemIAug (Semi-Supervised Implicit Augmentation), a model and dataset agnostic strategy specially designed to address the challenges faced by limited data availability in the domain-specific VQA datasets. SemIAug uses the annotated image-question data present within the chosen dataset and augments it with meaningful new image-question associations. We show that SemIAug improves the VQA performance on data-scarce datasets without the need for additional data or labels.


Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies

Abstract: In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a "black box" nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the "black box" of AI’s decision-making process.


Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT

Abstract: Abstract Developing a clinical AI model necessitates a significant amount of highly curated and carefully annotated dataset by multiple medical experts, which results in increased development time and costs. Self-supervised learning (SSL) is a method that enables AI models to leverage unlabelled data to acquire domain-specific background knowledge that can enhance their performance on various downstream tasks. In this work, we introduce CypherViT , a cluster-based histo-pathology phenotype representation learning by self-supervised multi-class-token hierarchical Vision Transformer (ViT). CypherViT is a novel backbone that can be integrated into a SSL pipeline, accommodating both coarse and fine-grained feature learning for histopathological images via a hierarchical feature agglomerative attention module with multiple classification (cls) tokens in ViT. Our qualitative analysis showcases that our approach successfully learns semantically meaningful regions of interest that align with morphological phenotypes. To validate the model, we utilize the DINO self-supervised learning (SSL) framework to train CypherViT on a substantial dataset of unlabeled breast cancer histopathological images. This trained model proves to be a generalizable and robust feature extractor for colorectal cancer images. Notably, our model demonstrates promising performance in patch-level tissue phenotyping tasks across four public datasets. The results from our quantitative experiments highlight significant advantages over existing state-of-the-art SSL models and traditional transfer learning methods, such as those relying on ImageNet pre-training.


A dynamic approach for visualizing and exploring concept hierarchies from textbooks

Abstract: In this study, we propose a visualization technique to explore and visualize concept hierarchies generated from a textbook in the legal domain. Through a human-centered design process, we developed a tool that allows users to effectively navigate through and explore complex hierarchical concepts in three kinds of traversal techniques: top-down, middle-out, and bottom-up. Our concept hierarchies offer an overview over a given domain, with increasing level of detail toward the bottom of the hierarchy which is consisting of entities. In the legal use case we considered, the concepts were adapted from section headings in a legal textbook, whereas references to law or legal cases inside the textbook became entities. The design of this tool is refined following various steps such as gathering user needs, pain points of an existing visualization, prototyping, testing, and refining. The resulting interface offers users several key features such as dynamic search and filter, explorable concept nodes, and a preview of leaf nodes at every stage. A high-fidelity prototype was created to test our theory and design. To test our concept, we used the System Usability Scale as a way to measure the prototype's usability, a task-based survey to asses the tool's ability in assisting users in gathering information and interacting with the prototype, and finally mouse tracking to understand user interaction patterns. Along with this, we gathered audio and video footage of users when participating in the study. This footage also helped us in getting feedback when the survey responses required further information. The data collected provided valuable insights to set the directions for extending this study. As a result, we have accounted for varying hierarchy depths, longer text spans than only one to two words in the elements of the hierarchy, searchability, and exploration of the hierarchies. At the same time, we aimed for minimizing visual clutter and cognitive overload. We show that existing approaches are not suitable to visualize the type of data which we support with our visualization.


Exploring the performance of automatic speaker recognition using twin speech and deep learning-based artificial neural networks

Abstract: This study assessed the influence of speaker similarity and sample length on the performance of an automatic speaker recognition (ASR) system utilizing the SpeechBrain toolkit. The dataset comprised recordings from 20 male identical twin speakers engaged in spontaneous dialogues and interviews. Performance evaluations involved comparing identical twins, all speakers in the dataset (including twin pairs), and all speakers excluding twin pairs. Speech samples, ranging from 5 to 30 s, underwent assessment based on equal error rates (EER) and Log cost-likelihood ratios (Cllr). Results highlight the substantial challenge posed by identical twins to the ASR system, leading to a decrease in overall speaker recognition accuracy. Furthermore, analyses based on longer speech samples outperformed those using shorter samples. As sample size increased, standard deviation values for both intra and inter-speaker similarity scores decreased, indicating reduced variability in estimating speaker similarity/dissimilarity levels in longer speech stretches compared to shorter ones. The study also uncovered varying degrees of likeness among identical twins, with certain pairs presenting a greater challenge for ASR systems. These outcomes align with prior research and are discussed within the context of relevant literature.


Behind every good research there are data. What are they and their importance to forensic science

Abstract: Data underpinning science have become one of the most precious assets in research, and while the principles of FAIR (Findable, Accessible, Interoperable and Reusable) have been put forward as a guide to how to approach data handling, data sharing and long-term storage still remain a challenge for many research areas including forensic science. The reporting and the sharing of data can be made easier by giving them structure, the use of suitable labels and the inclusion of descriptors collated into metadata prior to their deposition in repositories with persistent identifiers. Such a systematic approach would strengthen the quality and the integrity of research while providing greater transparency to published materials.


An approach based on maximal cliques and multi-density clustering for regional co-location pattern mining

Abstract: Spatial co-location pattern (SCP) mining aims to mine the implicit relationships between different spatial features. These features often have certain connections and co-occur in close geographical proximity. Regional co-location pattern (RCP) mining is a branch of SCP mining, which is usually used to discover some sets of spatial features that do not often co-occur in large spatial scales but co-occur in local regions. Discovering RCPs is still very challenging, because different RCPs will be obtained under different region partitions. However, existing region division methods still suffer from ignoring the influence of the density of individual feature instances, low recognition rate of regions with low density distribution but containing RCPs, and lack of semantic information in the delineated regions. To this end, first, we propose a novel multi-density clustering method based on maximal cliques (MCs) during the partitioning phase of RCP mining. Second, we design a two-stage mining algorithm based on MCs in the mining phase, which fully exploits the advantages of the MC to improve the mining efficiency, and the algorithm can quickly obtain new mining results when changing the prevalence threshold. Third, regional similarity is defined based on RCPs over regions to merge similar sub-regions. Finally, the proposed method is compared with a state-of-the-art method on both synthetic and real datasets. The experimental results show that the proposed method can not only effectively solve the issues of existing methods to make the divided sub-regions more closely matched with the real spatial distribution, but also quickly obtain new mining results when changing the prevalence threshold.


Re-scoring using image-language similarity for few-shot object detection

Abstract: Few-shot object detection, which focuses on detecting novel objects with few labels, is an emerging challenge in the community. Recent studies show that adapting a pre-trained model or modified loss function can improve performance. In this paper, we explore leveraging the power of Contrastive Language-Image Pre-training (CLIP) and hard negative classification loss in low data setting. Specifically, we propose Re-scoring using Image-language Similarity for Few-shot object detection (RISF) which extends Faster R-CNN by introducing Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss (BNRL). The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset. Extensive experiments on MS-COCO and PASCAL VOC show that the proposed RISF substantially outperforms the state-of-the-art approaches. The code will be available.


Practitioners' expectations on automated release note generation techniques

Abstract: Abstract The software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.


Orientation-aware leg movement learning for action-driven human motion prediction

Abstract: The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as “Walk” or “Run”. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive experimental evaluations on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness.


Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements

Abstract: Geochemical mapping of risk element concentrations in soils is performed in many countries around the world. It results in numerous large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive exploratory data analysis of big data while respecting the natural variability of soil composition. To distinguish anthropogenic contamination from natural variations, the analysis of the entire data distribution for smaller subareas is key. In this article, we propose a new data mining methodology for geochemical mapping data based on functional data analysis of probability densities in the framework of Bayes spaces after post-stratification of a big dataset to smaller districts. The tools we propose allow us to analyse the entire distribution, going well beyond a superficial detection of extreme concentration anomalies. We illustrate the proposed methodology on a dataset gathered according to the Czech national legislation (1990–2009), whose information content has not yet been fully exploited. Taking into account specific properties of probability density functions and recent results for orthogonal decomposition of multivariate densities enabled us to reveal real contamination patterns that were so far only suspected in Czech agricultural soils. We process the above Czech soil composition dataset for Cu, Pb, and Zn by first compartmentalizing it into spatial units, the so-called districts, and by subsequently clustering these districts according to diagnostic features of their uni- and multivariate distributions at high concentration levels. These clusters were seen to correspond to compartments that show known features of contamination, such as historical metallurgy of non-ferrous metals and iron and steel production. Comparison between compartments, notably neighbouring districts with similar natural factors controlling soil variability, is key to the reliable distinction of diffuse contamination. In this work, we used soil contamination by Cu-bearing pesticides as an example for empirical testing of the proposed data mining approach. In general, there are no natural and justifiable thresholds of risk element concentrations that would be valid for geographical areas with too much natural heterogeneity. Therefore, national (or larger) soil geochemistry datasets cannot be processed as a whole. As we demonstrate in this paper, empirical knowledge and careful tailoring of statistical tools for the characteristic types of soil contamination are essential for unequivocal identification of the anthropogenic component in real datasets.


GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

Abstract: Abstract Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.


Expanding information behaviour boundaries: A study with religious leaders

Abstract: Information behaviour is a very productive research area in information science, although some study contexts are still little explored, such as religious and spiritual practices, particularly related to minority religions. Thus, this study investigated the information behaviour of religious leaders of Santo Daime, a religion originating in the Brazilian Amazon. Exploratory research with a qualitative approach and semi-structured interviews with four leaders of the doctrine were carried out. The most used sources of information were documents about the Daime religion, personal experience, contact with other leaders and the institution. Information was sought to guide the participants in their rituals, solve administrative problems, improve their knowledge and strengthen their faith. Used in a critical, reflective and creative way, it seeks coherence with the Daimist principles. The findings may contribute to the understanding of the information behaviour of this little-studied social group and the appreciation of cultural and religious diversity in society.


From NeurODEs to AutoencODEs: A mean-field control framework for width-varying neural networks

Abstract: Abstract The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks, which has provided interesting results of both theoretical and practical significance. However, by construction, NeurODEs have been limited to describing constant-width layers, making them unsuitable for modelling deep learning architectures with layers of variable width. In this paper, we propose a continuous-time Autoencoder, which we call AutoencODE, based on a modification of the controlled field that drives the dynamics. This adaptation enables the extension of the mean-field control framework originally devised for conventional NeurODEs. In this setting, we tackle the case of low Tikhonov regularisation, resulting in potentially non-convex cost landscapes. While the global results obtained for high Tikhonov regularisation may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.


Modification of the wave algorithm for Euclidean distances calculation in transport applications

Abstract: Many engineering problems could be either reduced to or directly involve the mathematical problem of finding the shortest path in some confined space with obstacles. To solve this problem, graph theory and dynamic programming offers quite a lot of exact and heuristic algorithms that work with directed and undirected, weighted and unweighted, connected and disconnected graphs. All these methods are based on their own techniques of calculating the distance between the vertices, the choice of which is dictated by the convenience of implementation and methodological considerations. As a result, the resulted paths found by the algorithms usually are characterized by a certain abstract length, which is often difficult to convert into Euclidean one. Nevertheless, spatial problems in engineering practice often require an answer in terms of real physical distances. Specifically, such a task fully arises in engineering applications related to the design of seaports, namely, in the laying of routes of intra-terminal transport, power supply networks, in the design of approach channels. A modification of the most common method for finding the shortest paths, the wave orthogonal-diagonal algorithm, is described in the paper. It allows you to include the geometric characteristics of its individual sections in the calculation of the length of the found route, which makes it possible to accurately estimate the length in the sense of the Euclidean distance.


The ecology of online hate speech: Mapping expert perspectives on the drivers for online hate perpetration with the Delphi method

Abstract: Abstract Online hate speech (OHS) is a prevalent issue on social media with harmful consequences for individuals and society. Counteracting this issue requires a better understanding of the factors that drive behaviors such as posting hate speech or making hateful comments. The present study contributes to this need by investigating the personal, social, and digital determinants for OHS perpetration. By conducting a two‐round Delphi study, data was collected from an interdisciplinary sample of experts. Practitioners were included in this sample to bridge gaps between research and fieldwork on OHS. The first survey collected information through open‐ended questions, which was then organized in different themes and lists of determinants in a thematic analysis. These determinants were rated for relevance in a follow‐up survey. Data analysis of the second survey used nonparametric statistics. From a total list of 129 determinants, the participants reached a consensus on 27 determinants as most relevant (median > 4) for facilitating the production and dissemination of OHS. An ecological perspective was applied to interpret these findings. As a result, we propose an ecological model of personal, social, and digital determinants for online hate perpetration to illustrate their influence on individual behavior as well as their possible connections. The model provides a comprehensive overview of factors facilitating OHS perpetration, which can help to identify points for intervention.


One-step Bayesian example-dependent cost classification: The OsC-MLP method

Abstract: Example-dependent cost classification problems are those where the decision costs depend not only on the true and the attributed classes but also on the sample features. Discriminative algorithms that carry out such classification tasks must take this dependence into account. In some applications, the decision costs are known for the training set but not in production, which complicates the problem. In this paper, we introduce a new one-step Bayesian formulation to train Neural Networks and solve the above limitation for binary cases with one-step Learning Machines, avoiding the drawbacks that unknown analytical forms of the example-dependent costs create. The formulation is based on defining an artificial likelihood ratio by using the available training classification costs in its definition, and proposes a test that does not require the values of the costs for unseen samples. Furthermore, it also includes Bayesian rebalancing mechanisms to combat the negative effects of class imbalance. Experimental results support the consistency and effectiveness of the corresponding algorithms.


Life cycle, education, and statistical cognitive ability

Abstract: High statistical cognitive ability is an essential factor to achieve high-quality development in the era of artificial intelligence and big data. In this research, we use the machine learning local weighted regression algorithm to analyze the change curve of Chinese statistical cognitive ability throughout the life cycle, as well as the impact of individual education and parental education on statistical cognitive ability of 26,000 individuals from different groups of gender, age, educational background, and family background. All the data analyzed is from the China Family Panel Studies (CFPS). We find that the statistical cognitive ability curve is inverted U-shaped throughout the life cycle, and the years of education, parental education and individual are proportional to statistical cognitive ability. Keywords: statistical cognitive ability, machine learning, robust locally weighted and smoothing scatterplots, education, life cycle.


Research on denoising and enhancing methods of medical images based on convolutional neural networks

Abstract: Summary In the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.


Fast simulation for multi-photon, atomic-ensemble quantum model of linear optical systems addressing the curse of dimensionality

Abstract: Abstract Photons are elementary particles of light in quantum mechanics, whose dynamics can be difficult to gain detailed insights, especially in complex systems. Simulation is a promising tool to resolve this issue, but it must address the curse of dimensionality, namely, that the number of bases increases exponentially in the number of photons. Here we mitigate this dimensionality scaling by focusing on optical systems composed of linear optical objects, modeled as an ensemble of two-level atoms. We decompose the time evolutionary operator on multiple photons into a group of time evolution operators acting on a single photon. Since the dimension of a single-photon time evolution operator is exponentially smaller than that of a multi-photon one in the number of photons, the decomposition enables the multi-photon simulations to be performed at a much lower computational cost. We apply this method to basic single- and multi-photon phenomena, such as Hong–Ou–Mandel interference and violation of the Bell-CHSH inequality, and confirm that the calculated properties are quantitatively comparable to the experimental results. Furthermore, our method visualizes the spatial propagation of photons hence provides insights that aid experiment designs for quantum-enabled technologies.


Online and Mobile-Assisted Language Learning

Abstract: Online language classes and mobile-assisted language learning applications (Apps) are non-traditional methods of education that differ from traditional classes, which involve face-to-face instruction and teacher-led lessons. Web or mobile-assisted language learning applications cater to the changing world, making them convenient and affordable for those with limited time. These applications can be used at the learners’ convenience, making them a qualitative leap in the field of language learning. There is a world full of language applications some of which are dedicated to the education of the English Language as the Global language and others have many languages to learn. In the explorations of language applications, the Google Bundle is one of the best for online classes as it is the most comprehensive bundle and Duolingo is the best language application, if we considered the increasing number of its downloads and its continuously increasing revenue. Most of the applications have free and Pro or Premium features, and some only offer a free trial of one lesson or a seven-day trial. Each application has different teaching methods; some use gamification, interactive exercises, communicative approach, spaced repetition, contextual learning, audio-visual learning, and personalization. Moreover, most of the applications employ Artificial intelligence (AI) for immediate feedback.


Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement

Abstract: This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations.


Self-organizing maps: a novel approach to identify and map business clusters

Abstract: Business cluster identification is an essential topic for helping understand regional and global supply chains and establishing economic policies and logistics. This work aims to leverage the benefits of self-organizing maps (SOM), combined with traditional clustering algorithms and image processing techniques, to identify business clusters that are described by high-dimensionality feature vectors. It is advantageous over previous work because the algorithm is unsupervised and makes no assumptions about the number of clusters for a given feature set. The proposed algorithm was evaluated using recent datasets for US metropolitan cities from the Indiana Business Research Center (Innovation 2.0) and the Occupational Employment Statistics Survey. Data involving innovation metrics, education levels, economic well-being, connectivity, local GDP, and STEM are aggregated to demonstrate the effectiveness of the proposed neural network. The clustering results are compared to traditional approaches, including K-means clustering, both quantitatively and qualitatively. The unsupervised nature of the proposed SOM approach, and the acceptable computational complexity of the overall algorithm, suggests that self-organizing maps offer several advantages over traditional methods. In this work, we present a novel architecture coupling a SOM model with processing techniques for automatically identifying business clusters derived from high-dimensionality feature vectors, the first use case of SOMs in business cases affecting supply chains and other economic decisions. Preliminary results confirm the viability of architecture as an unsupervised approach for identifying business clusters.


Accessing the artwork in covid-19: loss, recovery and reimagination

Abstract: Being ‘there’ and present within a physical space has been a long-accepted part of any art museum visitor experience. Once the sole access route, the emergence of digitisation and the Internet has since offered a new flexibility for visitors. Such online provision is often considered supplementary, ancillary, yet the year 2020 was witness to a seismic shift, when museums were compelled to unilaterally close their physical doors in response to Covid-19. This paper explores the sense of loss engendered by the abrupt removal of a direct interaction with physical artworks and examines the online initiatives that took its place. The theme of loss is explored through phenomenological experience of artworks/objects, an experience which cannot be recaptured virtually, in relation to the interchange between the physical and the virtual at both an individual artwork level and within a curated museum space. Advances in digital access, exciting new realities and future possibilities are examined, considering how this sense of loss might be accommodated in lieu, mitigated online. It is argued that a multiplicity of approaches that seek to redefine rather than replicate will engage new audiences experientially and offer a refreshed look at access between the physical and virtual art museum space.


Extended Reality as an Educational Resource in the Primary School Classroom: An Interview of Drawbacks and Opportunities

Abstract: The use of Extended Reality in Primary Education classrooms has emerged as a transformative element that enhances the teaching and learning process of students. In this context, examining the various effects that this tool can generate is essential to identify both the opportunities and limitations that teachers face when incorporating this technology into their practices. The aim of this research is to analyse the impact of the use of Extended Reality as an educational resource in Primary Education, focusing on teachers’ perceptions. The information was collected through semi-structured interviews with 36 active teachers in Primary Education. The analysis of the data obtained identifies the benefits and functionalities offered by the implementation of Extended Reality in Primary Education classrooms, as well as the uncertainties and concerns that teachers have with the implementation of Extended Reality. The results highlight the significant opportunities that Extended Reality offers in the teaching–learning process, provided that teachers are adequately trained. Furthermore, this study offers valuable recommendations to guide future teachers and researchers in the successful integration of this technology into the educational process.


Rainfall's Symphony: Understanding its Influence on Communication Systems in Nigeria

Abstract: This study investigates the significant issue of rain-induced signal attenuation in satellite communication, specifically focusing on television receive-only (TVRO) stations operating in the Ku band across diverse Nigerian locations. Utilizing a comprehensive dataset of point rain rate distribution and the ITU rain attenuation model, the study comprehensively assesses how rainfall impacts signal quality. The findings highlight that southern regions consistently display high decibel (dB) values, indicating increased susceptibility to signal disruptions during heavy rainfall, while a comparative analysis between two key satellites, EUTELSAT 36B and INTELSAT 20, consistently favors the former in terms of signal resilience during adverse weather conditions. In contrast, northern regions generally exhibit lower dB values, suggesting a higher degree of signal resilience during rainfall events. These insights underscore the importance of considering location-specific and satellite-specific factors when designing satellite communication systems, emphasizing the need for robust infrastructure and strategic satellite selection to mitigate the effects of rain-induced attenuation. This study provides valuable guidance to engineers and service providers, enabling them to make informed decisions to minimize signal disruptions and enhance overall network reliability, particularly in regions susceptible to rain-induced attenuation.


Paintings in naked-eye virtual reality: a parallax view between the surface and volumetric depth

Abstract: Abstract This article explores a subgenre of naked-eye virtual reality (VR) video that features two-dimensional paintings in a three-dimensional space and is circulated on the Chinese video streaming website Bilibili. In contrast to traditional VR, which requires the spectator to wear a head-mounted display, naked-eye VR offers a stereoscopic view on the screen, eliminating the need for VR glasses. The apparent incongruity between the screen’s physical flatness and the volumetric depth of the painting becomes even more pronounced when the naked-eye VR image emphasises the painting’s inherent two-dimensionality. Employing an interdisciplinary humanities’ method that connects media archaeology, film studies, art history, and the field of science, technology, and society, this paper delves into this distinctive juxtaposition between the painting’s flatness and the volumetric depth characteristic of naked-eye VR, a subgenre that remains mostly uncharted in the field of film and media studies. Taking a media archaeological perspective, this paper introduces a parallax media history, suggesting that the aesthetics of VR can be traced not only to the stereoscope but also to scroll paintings, which are paradoxically defined by their flat compositions. In addition, naked-eye VR references pictorial traditions by generating a spatial illusion that leads spectators to feel as if they are delving beneath the surface of a painting, transitioning from spatial extensivity on the x-y axis to perceived depth along the z-axis. This oscillation between surface and depth is engendered by the horizontal parallax rooted in binocular disparity, thereby positioning naked-eye VR as a “parallax media.” In analysing the illusion created by naked-eye VR, this paper proposes a paradigmatic shift in the framework of media study from conceiving of an image as a mirror to experiencing it as a door, representing the oscillation between volumetric depth and two-dimensional surface.


The visual content of neural networks as a return to the third-order simulacrum

Abstract: This article is part of a larger study of design as a cultural phenomenon. In this part of the study, the author examines the process that is currently taking place in the semiotic structure of design, associated with the active introduction of neural networks into the creation of visual content. Artificial intelligence products stylistically take the design away from the fourth-order simulacrum (from the flat design style) and return the design to using the third-order simulacrum as the main iconic form.The object of the research is the transformation of the semiotic design system. The subject of the study is a return to the third–order simulacrum in modern design. The purpose of the study is to show and try to explain how in modern design there is a return to the use of a third-order simulacrum. The research method is a semiotic analysis of modern design based on the methodology of R. Barth's semiotic analysis. The study is also based on J. Baudrillard's theory of semiosis in hyperreality and three orders of simulacra. The author sees the philosophical justification of the art of neural networks in the concept of flat ontologies. The study of the semiotic structure of design allows us to see that the logic of design development carries this phenomenon through successive stages of semiosis associated with a decrease in meaning and a diminution of being. In its development, the design consistently uses first index signs, a second-order simulacrum, then a third-order simulacrum as the main sign form. Nowadays, the main iconic form in design has become a fourth-order simulacrum. Next, design had to either end as a profession and phenomenon, or move into a new cultural paradigm that was not related to simulation. However, unexpected transformations have begun to occur in design, due to the active involvement of non–human agents - neural networks – in the creation of visual content. Neural network products are a typical example of a third-order simulacrum. Thanks to the use of neural networks, modern design finally acquires the vector of transhumanism and closes in simulation.


An Attention-Averaging-Based Compression Algorithm for Real-Time Transmission of Ship Data via Beidou Navigation System

Abstract: The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems.


IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Abstract: Abstract The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.


The Development and Evaluation of an Animated Video for Pre- and Postoperative Instructions for Patients with Osteoarthritis—A Design Science Research Approach

Abstract: Osteoarthritis (OA) is a condition in the hip or knee joints that develops during a long period of time and sometimes needs hip or knee joint replacement surgery when pain gets too intense for the patient. This paper describes how an animated video for pre- and postoperative instructions for patients with osteoarthritis was designed. The design science research (DSR) approach was followed by creating a web-based animated video. The web-based animated video is used to support surgical departments with education for patients suffering from OA. In the web-based animated video, information about OA surgical treatment and its pre- and post-arrangements was included. The relevance, the rigor, and the design cycles were focused on, with some iterations of and improvements in the animations. Even after implementation, there was a feedback-loop with comments from the surgeons and their patients. Moreover, as more departments will use the web-based animated video, they want to make their special mark on it, so that further changes will be made. This paper presents the design and successful implementation of an animated video for pre- and postoperative instructions for patients with osteoarthritis, tightly linked to the patient journey and the workflow of healthcare professionals. The animated video serves not only as a tool to improve care but also as a basis for further scientific research studies.


Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction

Abstract: Abstract Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–relations, implicit entity–relations, and entity normalization. Recent advances have proposed an effective solution based on generative language models, which cast entity–relation extraction as a sequence-to-sequence text generation task. Inspired by the observation that humans learn by getting to the bottom of things, we propose a novel framework, namely GenRE, Generative multi-turn question answering with contrastive learning for entity–relation extraction. Specifically, a template-based question prompt generation first is designed to answer in different turns. We then formulate entity–relation extraction as a generative question answering task based on the general language model instead of span-based machine reading comprehension. Meanwhile, the contrastive learning strategy in fine-tuning is introduced to add negative samples to mitigate the exposure bias inherent in generative models. Our extensive experiments demonstrate that GenRE performs competitively on two public datasets and a custom dataset, highlighting its superiority in entity normalization and implicit entity–relation extraction. (The code is available at https://github.com/lovelyllwang/GenRE ).


Analysis of Coordination Mechanisms during Collaborative Problem-Solving on an Interactive Tabletop Display

Abstract: Abstract Coordination is an important aspect of group work. Previous studies have shown how collocated multi-user interfaces, such as interactive tabletops, support coordination by providing a shared space that enhances workspace awareness. However, only little is known about the coordination processes that occur during problem-solving on such shared devices and how the design of features and interaction techniques can impact coordination behaviour. In this paper we analyse users’ coordination mechanisms during joint manipulation tasks in Orbitia, an interactive tabletop-based problem-solving activity. The proposed design integrates a series of “breaches” seeking to challenge participants’ collaboration by confronting them to different limitations. We report on a case study with five groups of three users ( N = 15), jointly solving tasks while facing different challenges related to the activity interface and the environment. By analysing 135 min of video material along with their transcripts, we identified nine different coordination mechanisms that relate to either coordination of information or coordination of actions. By exploring the occurrences of these mechanisms, we found that sharing unsolicited task-relevant information is a common coordination behaviour that can be observed at interactive tabletops, and that with breaches, more explicit coordination in the form of direct requests, orders, or shadowing is used.


A novel Cosine-Cosine chaotic map-based video encryption scheme

Abstract: Abstract The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping.


The Serbian Sign Language Alphabet: A Unique Authentic Dataset of Letter Sign Gestures

Abstract: Language barriers and the communication difficulties of individuals with developmental disabilities are two major causes of communication problems that societies worldwide encounter. A particularly challenging group is hearing-impaired people who have difficulties with communication, reading, writing, learning, and social interactions, which have a substantial impact on their quality of life. This article focuses on detailing a Serbian Sign Language alphabet database and the method for creating it in order to provide a foundation for answering the various societal challenges of persons who use the Serbian language. In front of a computer camera, 41 people performed Serbian Sign Language sign movements that replicated the Serbian alphabet for this study’s aims. Hand and body key points were identified using the recorded video clips, and the numerical values of the identified key points were then stored in a database for further processing. In total, 8.346 video clips of people making recognized hand gestures were gathered, processed, classed, and archived. This paper provides a thorough technique that may be applied to comparable tasks and details the process of constructing a dataset based on Serbian Sign Language alphabet signs. This dataset was created using custom-made Python 3.11 software. Data regarding dynamic video clips that capture entire subject movement were incorporated into this dataset to fill in the gaps in other similar efforts based on static photographs. Thus, the purpose of this investigation is to employ innovative technology to support the community of hearing-impaired people in areas such as general inclusion, education, communication, and empowerment.


Modern Trends in Multi-Agent Systems

Abstract: The term “multi-agent system” is generally understood as an interconnected set of independent entities that can effectively solve complex and time-consuming problems exceeding the individual abilities of common problem solvers [...]


Removing Coding and Inter Pixel Redundancy in Image Compression

Abstract: The digital image plays an important role in today’s digital world. Storing and transmitting digital images efficiently is a challenging job. There are lots of techniques for reducing the size of digital pictures. This paper adapts the following method. The digital technique is separated into high and low resolutions. The low intensity and high intensity pixels single-handedly is dense and decompressed using three diverse algorithms to hit upon out the occurrence of low down intensity pixels in the picture. Totally six algorithms are experienced by means of benchmark images and the most excellent scheme is selected for concluding compression. A Comparison is made between the results obtained using these techniques and those obtained using JPEG 2000.


Air Writing Detection and Recognition

Abstract: Air Writing, a groundbreaking concept, revolutionizes the act of writing by allowing users to create characters or words in free space through hand or finger movements and coloured light. Unlike traditional pen-and-paper methods, this approach replaces pen-up and pen-down motions with colour shifts or light toggling to indicate the beginning and end of characters or words. The Air Handwriting Recognition project combines computer vision object tracking with handwriting recognition using machine learning techniques. Using a computer's webcam, the system tracks the characters users write in the air, following a user-selected colour with the aid of a mask. These tracked movements are then transcribed onto a virtual canvas, mimicking a plain whiteboard. The resulting canvas image serves as input for the recognition model, employing machine learning to interpret air-written words and characters. The integration of colour-based tracking and advanced recognition algorithms ensures the avoidance of plagiarism, making Air Handwriting Recognition a cutting-edge solution for hands-free writing in the digital realm.. A brief history of CNN and other approaches to characters detection and recognition are discussed in this paper


Pronoun interpretation in Italian

Abstract: Abstract We explore potential effects of prosody on pronoun interpretation in Italian, building on previous research which has shown that second language learners/users (L2ers) assign non-target interpretations to overt pronouns. We investigate effects of contrastive stress and pause, proposing that these will result in changes to default antecedent preferences for overt and null pronouns, for L2ers and for native speakers. An experiment was conducted, involving English-speaking L2ers of Italian and Italian native speakers. Participants were presented with auditory stimuli like Lorenzo ha scritto a Roberto quando Ø/lui si è trasferito a Torino ‘Lorenzo wrote to Roberto when (he) moved to Turin’ and indicated their preferred antecedent for the pronoun. Overt versus null pronouns, presence versus absence of stress on overt pronouns, and presence versus absence of pause between clauses were manipulated. The results yielded significant differences for antecedent choices between null and overt pronouns, consistent with earlier literature. In addition, stress was significant for both groups. Implications of a prosodic approach to ambiguity resolution are discussed.


A Literature Survey on Online Voting System using Face Recognition

Abstract: This paper presents a creative technique for overseeing modernizing the larger part rule cycle through the mix of face certification improvement into an internet projecting a surveying structure framework. By utilizing facial certification calculations, occupants can safely certify their characters from a decent ways, guaranteeing the uprightness and security of the discretionary association. The proposed structure means to overhaul straightforwardness, accuracy, and straightforwardness in fair strategies, tending to stresses related with character check and fake exercises. Through thorough testing and underwriting, this plan offers a promising development for driving the capacity and reliability of web based projecting a surveying structure systems in contemporary social orders


Exploiting PlanetScope Imagery for Volcanic Deposits Mapping

Abstract: During explosive eruptions, tephra fallout represents one of the main volcanic hazards and can be extremely dangerous for air traffic, infrastructures, and human health. Here, we present a new technique aimed at identifying the area covered by tephra after an explosive event, based on processing PlanetScope imagery. We estimate the mean reflectance values of the visible (RGB) and near infrared (NIR) bands, analyzing pre- and post-eruptive data in specific areas and introducing a new index, which we call the ‘Tephra Fallout Index (TFI)’. We use the Google Earth Engine computing platform and define a threshold for the TFI of different eruptive events to distinguish the areas affected by the tephra fallout and quantify the surface coverage density. We apply our technique to the eruptive events occurring in 2021 at Mt. Etna (Italy), which mainly involved the eastern flank of the volcano, sometimes two or three times within a day, making field surveys difficult. Whenever possible, we compare our results with field data and find an optimal match. This work could have important implications for the identification and quantification of short-term volcanic hazard assessments in near real-time during a volcanic eruption, but also for the mapping of other hazardous events worldwide.


Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

Abstract: This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm.


MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model

Abstract: Transit riders’ feedback provided in ridership surveys, customer relationship management (CRM) channels, and, in more recent times, through social media, is key for transit agencies to better gauge the efficacy of their services and initiatives. Getting a holistic understanding of riders’ experience through the feedback shared in those instruments is often challenging, mostly because of the open-ended, unstructured nature of text feedback. In this paper, we propose leveraging traditional transit CRM feedback to develop and deploy a transit-topic-aware large language model (LLM) capable of classifying open-ended text feedback to relevant transit-specific topics. First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority. We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM—MetRoBERTa—with classical machine learning approaches utilizing keyword-based and lexicon representations. Our model outperforms those methods across all evaluation metrics, providing an average topic classification accuracy of 90%. Finally, we provide a value proposition of this work demonstrating how the language model, alongside additional text processing tools, can be applied to add structure to open-ended text sources of feedback such as Twitter. The framework and results we present provide a pathway for an automated, generalizable approach for ingesting, visualizing, and reporting transit riders’ feedback at scale, enabling agencies to better understand and improve customer experience.


Quantum Poisson solver without arithmetic

Abstract: Abstract Solving differential equations is one of the most promising applications of quantum computing. The Poisson equation has applications in various domains of physics and engineering, including the simulation of ocean current dynamics. Here, we propose an efficient quantum algorithm for solving the one-dimensional Poisson equation based on the controlled R y rotations. Our quantum Poisson solver (QPS) removes the need for expensive routines such as phase estimation, quantum arithmetic or Hamiltonian simulation. The computational cost of our QPS is 3 n in qubits and 5/3 n 3 in one- and two-qubit gates, where n is the logarithmic of the number of discrete points. An overwhelming reduction of the constant factors of the big-O complexity is achieved, which is critical to evaluate the practicality of implementing the algorithm on a quantum computer. In terms of the error ε , the complexity is log(1/ ε ) in qubits and poly(log(1/ ε )) in operations. The algorithms are demonstrated using a quantum virtual computing system, and the circuits are executed successfully on the IBM real quantum computers. The present QPS could exhibit a potential real-world application for solving differential equations on noisy intermediate-scale quantum (NISQ) devices.


Commonsense Knowledge in Foundation and Large Language Models

Abstract: The development and continuous expansion of the transformer deep-learning architecture have produced enormous effects across various domains, including but not limited to natural language processing. The power of deep learning models has sparked a fresh interest in commonsense knowledge, which has been aided by transformer-based language models. Most of the recent research has concentrated on delving into the commonsense already built into these models' pre-trained parameters and finding ways to fill in any gaps in commonsense utilizing knowledge graphs and fine-tuning. In order to broaden a limited commonsense knowledge network that was originally generated solely from visual data, we are building on the demonstrated linguistic understanding of extremely large transformer-based language models. Compared to language models that are fine-tuned on a huge starting corpus, few-shotprompted pre-trained models are able to acquire the context of an initial knowledge graph with less bias. It has also been demonstrated that these models can contribute novel ideas to the visual knowledge networkIt is a new development in the field of commonsense knowledge generation that, as far as we can tell, can lead to a fivefold decrease in cost when compared to the current state of the art. Fuzzy language names assigned to the produced triples are another addition. Applying knowledge graphs as a framework, the procedure is comprehensive. It implies that the triples are expressed in natural language, analyzed, and then added to the commonsense knowledge network as triples again.


Cloud-integrated cyber–physical systems: Reliability, performance and power consumption with shared-servers and parallelized services

Abstract: Abstract Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of visualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.