Quantum random number generator based on LED

Abstract: Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a light-emitting diode. To achieve a robust and reliable QRNG, we compare some usual post-processing methods and select the finite impulse response method for a real-time device. This device could pass the National Institute of Standards and Technology tests, the generation rate is 1 Mbit/s, and the randomness of the output data is invariant in time.


Synthetic Data for Sentinel-2 Semantic Segmentation

Abstract: Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. ‘Samples’ extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.


Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Abstract: The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.


Predictive Factors for Difficulty in Simulation Methodology in Teacher Education

Abstract: Background Simulation methodology in teacher education offers a wide range of practice opportunities in a controlled environment. However, even though not much has been written about the benefits of simulation in teacher training, even less has been said about the difficulties perceived by the participants in doing simulation. Method In this study we conducted an exploratory longitudinal study which includes 205 postgraduate participants scattered around the globe who took part in a computer mediated simulation in the years 2019 to 2022 as an assessment tool in a master’s degree for teacher education. Aside from the positive impact on learning, which has already been disseminated (autor1), some recurring difficulties have been identified and presented in this study. A mixed analysis is performed based on the triangulation between qualitative and quantitative approaches. The qualitative data were analysed using a qualitative content analysis method through an open question about participants’ perceptions post-treatment, video recordings and individual final reports. The quantitative data was gathered through a Likert-type questionnaire and analysed using statistical methods. Findings Findings indicate that (1) simulation entails several concerns about task overload and time consumption along with lack of understanding of what the simulation phases involve (learning focus vs performance focus); and (2) simulation can cause uneasiness and anxiety related to the use of technological devices and teamwork. Contributions Thus, these results have several implications for research, theory and practice when it comes to applying simulation as a pedagogical strategy.


Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Abstract: Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.


THE IMPROVEMENT OF THE ASSESSMENT OF DIGITAL TRANSFORMATIONS IN SOCIOECONOMIC SYSTEMS USING AI

Abstract: This article aims to improve the methods of assessing digital transformations in socioeconomic systems. The relevance of the research topic is driven by the rapid development of digital technologies and their impact on the economy and society, which contributes to sustainable development. Research methods include analysing statistical data, expert methods, and modelling the dynamics of digital transformations. The results show that throughout the study period of 2017-2021, six out of eight dynamic indicators reflect that digital transformations in socioeconomic systems meet installed criteria besides the involvement of government institutions in providing access to electronic democracy tools and the percentage of the adult population subscribed to mobile phones. Six out of eight dynamic indicators show a trend, suggesting an increase in these specific indicators over time. Additionally, the consolidated dynamic indicator of digital transformations in socioeconomic systems meets the criteria, pointing towards an overall improvement over time for indicators of digital transformations in socioeconomic systems. It is important to note that this does not necessarily imply a simultaneous improvement in all individual indicators. Consequently, enhancing the indicators experiencing deterioration over time is crucial to improve the overall situation. This emphasises the need to make managerial decisions regarding the correction of digital transformations in socioeconomic systems in those areas, the indicators of which decreased during 2017-2021. Identifying key aspects of these problems allows for defining directions to improve the digital transformations. The practical value of the study lies in the potential use of refined scientific and methodological approaches to obtaining information necessary for making managerial decisions to promote digital transformations.


Ranking‐based architecture generation for surrogate‐assisted neural architecture search

Abstract: Abstract Architectures generation optimization has been received a lot of attention in neural architecture search (NAS) since its efficiency in generating architecture. By learning the architecture representation through unsupervised learning and constructing a latent space, the prediction process of predictors is simplified, leading to improved efficiency in architecture search. However, searching for architectures with top performance in complex and large NAS search spaces remains challenging. In this paper, an approach that combined a ranker and generative model is proposed to address this challenge through regularizing the latent space and identifying architectures with top rankings. We introduce the ranking error to gradually regulate the training of the generative model, making it easier to identify architecture representations in the latent space. Additionally, a surrogate‐assisted evolutionary search method that utilized neural network Bayesian optimization is proposed to efficiently explore promising architectures in the latent space. We demonstrate the benefits of our approach in optimizing architectures with top rankings, and our method outperforms state‐of‐the‐art techniques on various NAS benchmarks. The code is available at https://github.com/outofstyle/RAGS‐NAS .


Dynamic ensemble of regression neural networks based on predictive uncertainty

Abstract: In a data-decentralized environment where data are distributed across individual local nodes, inter-node data sharing is often restricted due to security concerns and communication costs. Under these restrictions, using entire data to train a global predictive model is challenging. A viable strategy is to leverage local predictive models trained independently on local nodes. However, this adversely affect generalization performance, particularly when the data distribution varies between local nodes. To address this issue in regression problems, we propose a dynamic ensemble method that combines the predictions of locally trained neural networks without the need to access data from local nodes. For a query instance, the prediction and its uncertainty from each neural network are computed by applying Monte Carlo dropout. Subsequently, smaller/larger weights are assigned to neural networks with higher/lower predictive uncertainty. The final prediction result is derived using the weighted average of the individual predictions. The proposed method is applicable to a data-decentralized environment where data are inaccessible and thus cannot be shared across local nodes. Transmitting query instances to local nodes and receiving predictions and weights from the local nodes are the only requirements, thereby ensuring low communication costs. Experimental results on simulated data-decentralized environments using benchmark regression datasets validated the superior generalization performance of the proposed method compared with baseline methods. The proposed method was found to be more effective for regression neural networks that demonstrate higher performance at their respective local nodes.


The spheroidal trackball: generalising the fixed trackball for virtual camera navigation

Abstract: Abstract Virtual trackball techniques have become a standard in 3D applications, particularly for interfaces with limited degrees of freedom such as touchscreens or mice. The fact that we are used to them does not mean that they cannot be improved upon. Recent research has highlighted the significance of considering users’ mental models of a preferred rotation axis, as it can improve performance, perceived usability and perceived workload. Building upon these findings, this paper introduces the spheroidal trackball framework—a novel method for orbiting the virtual camera around elongated objects. The paper presents the mathematical formulation and the evaluation of the technique. The formulation offers enough information to implement the approach. The evaluation shows the advantages of this approach over the fixed spherical trackball for this class of objects, in terms of task performance, usability and perceived workload. This research constitutes an advancement in the refinement of 3D user interaction techniques, opening new avenues of innovation in this still evolving field.


An efficient secure predictive demand forecasting system using Ethereum virtual machine

Abstract: Abstract Predictive demand forecasting plays a pivotal role in optimizing supply chain management, enabling businesses to effectively allocate resources and minimize operational inefficiencies. This paper introduces a novel approach to enhancing demand forecasting processes by leveraging the Ethereum virtual machine within a blockchain framework. The proposed system capitalizes on the inherent security, transparency, and decentralized nature of blockchain technology to create a secure and efficient platform for predictive demand forecasting. The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. The current research focused on Ethereum virtual machine characteristics, features, components, and implementation details. A secured framework for the prediction of demand forecasting systems is proposed. Finally, the authors tried to validate and optimize the gas cost by using distinguished statistics and analysis.


Identifying exploitable memory objects for out‐of‐bound write vulnerabilities

Abstract: Abstract Exploiting an out‐of‐bounds write vulnerability in general‐purpose applications has become a current research focus. Given the large scale of code in programs, selecting appropriate memory objects for exploitation is challenging. This letter proposes a corrupted data propagation‐guided fuzzing method. By tracking the propagation process of corrupted data among memory objects, a multi‐level fuzzing schedule is proposed to search the execution paths. Experimental results show that this proposed method, EMOFuzz, can effectively identify exploitable objects under various overflow lengths, significantly enhancing the efficiency of exploitability analysis.


Recent advances in blockchain technology: prospects, applications and constraints in the minerals industry

Abstract: One of the most important sectors of the world economy is the mining and metals sector. Although, many operational and commercial procedures continue to be ineffective and out-of-date, which results in crucial data omissions, security flaws, and sometimes corruption. Given that the mining industry wants to increase the emphasis on ethical and open mining practices, the industry has been looking for ways to incorporate these practices. It is envisioned that such practices will contribute to the modernisation of supply chains in addition to helping to reduce risks related to sustainability and reputation. The application of blockchain technology in the minerals industry capable of tracking natural resources has been discussed in this study, giving a much-needed layer of transparency of this technology. However, there are a lot of difficulties and problems that come up when thinking about blockchain technology; if it is to advance as an industry standard, stakeholders will need to evaluate the usefulness and scalability of the technology. While blockchain technology can be applied to a wide range of areas in the minerals industry, this study shines more light on the application of this technology to conflict minerals tracking, mineral resources reporting cheating scandals, rock mechanics designs and monitoring strategy, blasting designs and operation, mine ventilation designs and applications, mining machinery maintenance and management, and mining surveying. The study reveals that blockchain technology is starting to be seriously evaluated and used by a variety of stakeholders, even though broad acceptance has not yet been attained.


Automated curation of large‐scale cancer histopathology image datasets using deep learning

Abstract: Background Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole‐slide images (WSIs) is a very time‐consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep‐learning (DL)‐based method for automatic curation of large pathology datasets with several slides per patient. Methods We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry‐stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro‐averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. Results In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994–0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979–0.985] and 0.875 [95% CI: 0.864–0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. Conclusion Our findings show that using the low‐resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.


Removal of salt and pepper noise using adaptive switching modified decision-based unsymmetric trimmed median filter optimized with Hyb-BCO-FBIA

Abstract: An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods.


Mass robotics: How do people communicate with, use, and feel about Alexa? A cross-cultural, user perspective

Abstract: For the first time, voice-based assistants (VBAs) allow studying the mass consumption of a robotic product in a ‘natural’ environment. The present paper investigates users' perspectives concerning Alexa, informed by the scholarly literature on the diffusion and appropriation of digital media, VBAs, and social robots, in general, in the domestic sphere. Besides CASA, a paradigm widely applied in robotics, we draw on theories on users and theories on the use of users. We explored individuals' use of Alexa in two countries—the US and Italy— through an online survey. Results indicated that: (1) Alexa's use follows the same modalities of the previous digital media; (2) Alexa's use is in an early phase of domestication, and thus, the role of users in reshaping this technological artifact is still limited; and (3) updates to the CASA paradigm are needed.


An efficient feature pyramid attention network for person re-identification

Abstract: For person re-identification, occlusion, appearance similarity and background clutter have always been challenges. In order to effectively address the challenges, we propose an efficient feature pyramid attention network (FPA-Net), which combines visual features from different levels to focus on both detail features and information. Specifically, we embed a pair of attention mechanisms that complement each other in the backbone network to focus on the discriminant features of person areas. In addition, we designed a novel feature pyramid structure, which propagates the feature information from the cross-level through the top feature to the bottom feature and from the bottom feature to the top feature to supplement the detail information of the feature. Finally, we integrate features form different scales through a lightweight transition block to generate more discriminant features. Our method performed experimental analysis on four datasets: Market-1501, DukeMTMC-ReID, CUHK03(Detected) and MSMT17. A large number of experimental results prove that the performance of the method is significantly better than the existing state-of-the-art methods.


Adopting the metaverse in the luxury hotel business: a cost–benefit perspective

Abstract: Purpose This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations. Design/methodology/approach An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed. Findings The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel). Practical implications Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse. Originality/value This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.


Crossing the Andes: Challenges and opportunities for digital pathology in Latin America

Abstract: The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.


Software bug localization based on optimized and ensembled deep learning models

Abstract: Abstract An automated task for finding the essential buggy files among software projects with the help of a given bug report is termed bug localization. The conventional approaches suffer from the challenges of performing lexical matching. Particularly, the terms utilized for describing the bugs in the bug reports are observed to be irrelevant to the terms used in the source code files. To resolve these problems, we propose an optimized and ensemble deep learning model for software bug localization. These features are reduced by the principle component analysis (PCA). Then, they are selected by the weighted convolutional neural network (CNN) model with the support of the Modified Scatter Probability‐based Coyote Optimization Algorithm (MSP‐COA). Finally, the optimal features are subjected to the ensemble deep neural network and long short‐term memory (DNN‐LSTM), with parameter tuning by the MSP‐COA. Experimental results show that the proposed approach can achieve higher bug localization accuracy than individual models.


DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

Abstract: During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at smaller number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm is proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose a pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.


Predicting pragmatic functions of Chinese echo questions using prosody: evidence from acoustic analysis and data modeling

Abstract: Echo questions serve two pragmatic functions (recapitulatory and explicatory) and are subdivided into two types (yes-no echo question and wh-echo question) in verbal communication. Yet to date, most relevant studies have been conducted in European languages like English and Spanish. It remains unknown whether the different functions of echo questions can be conveyed via prosody in spoken Chinese. Additionally, no comparison was made on the diversified algorithmic models in predicting functions by the prosodity of Chinese echo questions, a novel linguistic cognition in nature. This motivated us to use different acoustic cues to predict different pragmatic functions of Chinese echo questions by virtue of acoustic experiment and data modeling. The results showed that for yes-no echo question, explicatory function exhibited higher pitch and intensity patterns than recapitulatory function whereas for wh-echo question, recapitulatory function demonstrated higher pitch and intensity patterns than explicatory function. With regard to data modeling, the algorithm Support Vector Machine (SVM) relative to Random Forest (RF) and Logistic Regression (LR) performed better when predicting different functions using prosodic cues in both yes-no and wh-echo questions. This study from a digitized perspective adds evidence to the cognition of echo questions’ functions on a prosodic basis.


<scp>CerviFormer</scp>: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Abstract: Abstract Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer‐a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross‐attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large‐scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3‐state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2‐state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process.


Comprehensive evaluation of intrinsic safety of railway facilities and equipment based on improved cloud model-fuzzy Petri net

Abstract: In order to achieve the goal of transforming China into a modernized railway powerhouse, a comprehensive evaluation model of intrinsic safety is constructed in this paper by focussing on the intrinsic safety of railway facilities and equipment. Firstly, the index system for evaluating the intrinsic safety of railway facilities and equipment is divided. Secondly, the fuzzy Petri net (FPN) model for intrinsic safety evaluation is constructed according to the fuzzy generative rules, and the similarity calculation method of the cloud model based on shape and distance is applied to derive the initial state value of the FPN model and carry out the calculation of fuzzy reasoning of the intrinsic safety. Finally, the comprehensive evaluation of intrinsic safety is carried out using the example of railroad facilities and equipment at one China Railway Group bureau’s station section. The results indicate that the intrinsic safety level of the station section falls into Class IV intrinsic safety, exhibiting a few vulnerabilities. However, the risk level is low, aligning with the findings of the actual research. Moreover, the evaluation results based on the FPN, the cloud model’s similarity calculation method and the suggested method are compared and analysed with the findings of the actual research. The comparative results demonstrate the accuracy and scientificity of the proposed method, providing a reference for enhancing the intrinsic safety of railway facilities and equipment.


SR-TTS: a rhyme-based end-to-end speech synthesis system

Abstract: Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like human-computer interaction. However, conventional TTS models still face challenges, as the synthesized speeches often lack naturalness and expressiveness. Additionally, the slow inference speed, reflecting low efficiency, contributes to the reduced voice quality. This paper introduces SynthRhythm-TTS (SR-TTS), an optimized Transformer-based structure designed to enhance synthesized speech. SR-TTS not only improves phonological quality and naturalness but also accelerates the speech generation process, thereby increasing inference efficiency. SR-TTS contains an encoder, a rhythm coordinator, and a decoder. In particular, a pre-duration predictor within the cadence coordinator and a self-attention-based feature predictor work together to enhance the naturalness and articulatory accuracy of speech. In addition, the introduction of causal convolution enhances the consistency of the time series. The cross-linguistic capability of SR-TTS is validated by training it on both English and Chinese corpora. Human evaluation shows that SR-TTS outperforms existing techniques in terms of speech quality and naturalness of expression. This technology is particularly suitable for applications that require high-quality natural speech, such as intelligent assistants, speech synthesized podcasts, and human-computer interaction.


An anomaly affected discrete LTI systems: a moving horizon approach for estimating position and temperature measurements

Abstract: A real-time system may be subjected to various anomalies that can affect the quality of the observations. The main motivation of the article arises from the need in addressing challenges posed by the presence of anomalies in discrete linear time-invariant (LTI) systems with a focus on the estimation processes, in the context of position and temperature measurements. The proposed approach leverages the properties of discrete LTI systems and takes advantage of the predictive capabilities of the moving horizon strategy (MHS). It operates recursively updating estimates of new measurement while fairly considering its past estimates that occur within the window of the moving horizon. The estimation framework will be designed to handle disturbances and provide robust estimates, to ensure the effectiveness of the system. In order to validate the proposed approach simulation studies were conducted on different and only scenarios in different order LTI system. Comparative studies with different estimation techniques demonstrate the capability of the proposed approach in terms of performance and efficiency. The proposed approach can be applied to systems with changing system dynamics. Future research may be conducted to utilize this strategy in other domains to mitigate anomalies while enhancing performance.


Algorithm design of a combinatorial mathematical model for computer random signals

Abstract: To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to its fuzzy function (FF) and the parameters of conventional FHSs are estimated according to the time-frequency distribution corresponding to the KF. Then, simulation experiments are carried out in different types of interference noise environments. The proposed combinatorial mathematical model for computer random signals shows a practical impact, and can effectively improve the effect of random signal combination.


Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm

Abstract: Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments.


Toward a Universal Dependencies Treebank of Old English: Representing the Morphological Relatedness of Un-Derivatives

Abstract: This article deals with one of the aspects involved in the compilation of a treebank of Old English within the framework of Universal Dependencies. More specifically, this study addresses the question of how to account for the remarkable degree of Old English morphological relatedness in a type of treebank designed to stress syntactic similarities across languages. The solution proposed and assessed in this study is the addition of an extra field of annotation for morphological relatedness. The data of this analysis comprise 1106 derivatives attaching the prefix un-. Out of these, there are around 80 morphologically complex nouns, adjectives, verbs, and adverbs whose derivation cannot be described gradually, 33 of which are unique formations or hapax legomena according to the attestations provided by the Dictionary of Old English Corpus. The main conclusion is that the specification of short-distance and long-distance morphological relatedness provides the Old English treebank with a paradigmatic dimension that can be particularly relevant for languages with relatively generalised and transparent derivational morphology.


Analyzing Vulnerabilities of Disaster Risk Management Systems of Local Governments in South Korea: Evidence from National Audit Office Reports

Abstract: The capacity of local governments to act as frontline disaster management agencies is crucial to urban sustainability and disaster risk management systems. However, vulnerabilities in the management systems can hinder the effectiveness of disaster risk management, affecting the resilience and sustainable development of urban areas. This study examines vulnerable areas of disaster risk management from a practical perspective, based on audit findings conducted by the Board of Audit and Inspection (BAI). The stages of disaster risk management are classified as prevention, preparedness, response, and recovery. The disaster risk management activities that local governments should undertake at each stage have been identified and summarized. The vulnerabilities and associated cases related to disaster risk management were comprehensively analyzed by compiling the results of local government disaster risk management audits conducted after 2015. This study revealed vulnerabilities in areas such as disaster management funds, prevention facilities, safety inspections, forecasting and warning systems, and resident evacuation, all of which are integral to maintaining urban sustainability. To avoid the recurrence of these issues, this study suggests that local governments should develop and implement improvement measures for each vulnerable area. The findings of this study can serve as valuable guidelines for local governments on ways to enhance their disaster risk management systems.


Evaluating how extended reality delivery device and preservice teacher major impact presence in immersive learning environments

Abstract: Teacher education has begun to embrace the use of 360 videos to improve preservice teachers' (PSTs) engagement and immersion. While recent research on such use is promising overall, there are specific questions that have been left unanswered about the construct of presence in 360 videos. More specifically, research has yet to fully explore how video delivery devices and PST characteristics may impact presence. The purpose of this study was to respond to this gap in the literature by examining PST major, delivery device (ie, head mounted display vs. flat screen), and the interaction between the two in informing presence. A total of 93 PSTs watched 360 videos and then completed a questionnaire. Findings indicated that presence in head‐mounted displays was related to emotions and agency. Conversely, presence with flat screens meant familiarity and control. Moreover, PST major was a predictor of presence, suggesting that 360 videos should be personalized according to PST career and professional goals. Finally, the interaction effect of preparing to teach K‐12 mathematics and wearing a head‐mounted display had a negative and statistically significant effect on participants' perceived presence, thus indicating a non‐cumulative effect. Practitioner notes What is already known about this topic 360 videos are increasingly used to support preservice teacher training. 360 videos for teacher education foster immersion, presence, and noticing skills. What this paper adds Empirical evidence that 360 videos experienced with head‐mounted display facilitate agency and emotional involvement. Empirical evidence that 360 videos experienced with flat screens facilitate feelings of control. Data showed that preservice teachers' content area (math) was a positive predictor of presence in 360 videos. Implications for practice and/or policy Head‐mounted displays show promise as a delivery mode for 360 videos in teacher education. 360 videos should be personalized according to PST career and professional goals. The eXtended Reality Presence Scale is confirmed as a robust instrument to measure presence in immersive environments. More research is needed to evaluate the scalability of this approach in other contexts.


Cascaded Searching Reinforcement Learning Agent for Proposal-Free Weakly-Supervised Phrase Comprehension

Abstract: Phrase comprehension (PC) aims to locate a specific object in an image according to a given linguistic query. The existing PC methods work in either a fully supervised or proposal-based weakly supervised manner, which rely explicitly or implicitly on expensive region annotations. In order to completely remove the dependence on the supervised region information, this paper proposes to address PC in a proposal-free weakly supervised training paradigm. To this end, we developed a novel cascaded searching reinforcement learning agent (CSRLA). Concretely, we first leveraged a visual language pre-trained model to generate a visual–textual cross-modal attention heatmap. Accordingly, a coarse salient initial region of the referential target was located. Then, we formulated the visual object grounding as a Markov decision process (MDP) in a reinforcement learning framework, where an agent was trained to iteratively search for the target’s complete region from the salient local region. Additionally, we developed a novel confidence discrimination reward function (ConDis_R) to constrain the model to search for a complete and exclusive object region. The experimental results on three benchmark datasets of Refcoco, Refcoco+, and Refcocog demonstrated the effectiveness of our proposed method.


Development of an Automated Downtime System for Small and Medium sized Enterprises

Abstract: The implementation of Industry 4.0 can be a challenge for SMEs as often the existing equipment cannot be simply replaced or data cannot be easily extracted from existing equipment. A system that automatically records downtime with minimal operator interaction can be beneficial by giving a clear picture of how a manufacturing process is running, along with providing information on duration, frequency and reasons for stoppages. When this information is available, further analysis can be performed to identify the main reasons for downtime and identify which sections of the production line are more prone to stopping; therefore, corrective actions can be put in place to reduce this. Off-the-shelf IoT equipment was used to develop this system, which reads the status from the signal towers of the line. These data were recorded in an SQL database and an interactive user interface was developed so that downtime codes could be assigned to stoppages and downtime data could be presented in different ways. This case study has shown that a small investment in IoT can make a huge improvement on the ability to monitor and analyse the downtime of a machining line for an SME.


Teachable Q&A Agent: The Effect of Chatbot Training by Students on Reading Interest and Engagement

Abstract: Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young readers. Currently, AI techniques are primarily used in chatbots as tutors, with limited focus on tutee chatbots that employ the learning-by-teaching pedagogy. Therefore, this study adopted a teachable Q&A agent and probed into the effect of chatbot training, employing AI techniques and utilizing student-generated questions and answers, with the aim of enhancing students’ reading interest and engagement. Ninety-five fifth graders participated in a 9-week reading program. A quasi-experimental design was conducted. The results proved that incorporating a learning-by-teaching approach into the chatbot training activity significantly enhanced their reading interest and engagement. However, the quantity of certain question types is negatively correlated with interest and engagement. This implies that asking diverse questions poses a certain level of challenge to young readers, which requires deliberate training and incubation. Additionally, the identification of four distinct student clusters exhibited the affordances and limitations of tutee chatbots for reading.


Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery

Abstract: Abstract Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operations and passenger real-time information.


Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference

Abstract: In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates attribute and user behavior information into distinct channels and leverages hypergraphs to capture high-order correlations among vertices, offering a more natural approach to modeling. Furthermore, we introduce behavior-based attribute confidence (BAC) for assessing the reliability of inferred attributes concerning the corresponding behaviors and update the most credible portions to enhance recommendation quality. Extensive experiments conducted on three public benchmarks demonstrate the superiority of our model. It consistently outperforms other state-of-the-art approaches, with ablation experiments further confirming the effectiveness of our proposed method.


Staff Affection, the Key Factor in the Loyalty of University Students using Virtual Library Services that are Accessible by a Physical Access Space, in Developing Countries

Abstract: Virtual libraries are essential in strengthening research and education, especially in universities in the Dominican Republic. Despite facing various challenges, they offer services in physical spaces. This study examines the relevance of system quality, service quality, information quality, and staff interaction on student satisfaction and loyalty toward these physical virtual libraries. A survey of 531 students was analyzed using SPSS and PLS-SEM. Findings show that staff interaction positively affects user loyalty, though not satisfaction. Additionally, system quality, information quality, and service quality continue to positively influence user satisfaction, which, in turn, affects user loyalty. This research deepens our understanding of staff's influence on user loyalty in developing countries' virtual libraries.


UHD Database Focus on Smart Cities and Smart Transport

Abstract: “Smart city” refers to a modern solution to organizing a city’s services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity.


Paraconsistent Labeling Semantics for Abstract Argumentation

Abstract: Dung’s abstract argumentation framework is a popular formalism in formal argumentation. The present work develops paraconsistent labeling semantics for abstract argumentation such that the incomplete and inconsistent information can be expressed, and it introduces a Hilbert-style axiomatic system which is proven to be sound and complete. Additionally, we make a comparison between the logic developed in the present work and some relevant theories of abstract argumentation.


CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

Abstract: The availability of large data sets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small data sets, mainly due to practical and cost-effective deployment issues, as well as the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering Structures (CACTUS) is presented as a means of improving secure analytics by effectively employing explainable artificial intelligence. CACTUS achieves this by providing additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It exposes to the user the frequency of the attributes in each class and ranks them by their discriminative power. Performance is assessed by applying it to various domains, including Wisconsin Diagnostic Breast Cancer, Thyroid0387, Mushroom, Cleveland Heart Disease, and Adult Income data sets.


An Optimal Edge-weighted Graph Semantic Correlation Framework for Multi-view Feature Representation Learning

Abstract: In this paper, we present an optimal edge-weighted graph semantic correlation (EWGSC) framework for multi-view feature representation learning. Different from most existing multi-view representation methods, local structural information and global correlation in multi-view feature spaces are exploited jointly in the EWGSC framework, leading to a new and high quality multi-view feature representation. Specifically, a novel edge-weighted graph model is first conceptualized and developed to preserve local structural information in each of the multi-view feature spaces. Then, the explored structural information is integrated with a semantic correlation algorithm, labeled multiple canonical correlation analysis (LMCCA), to form a powerful platform for effectively exploiting local and global relations across multi-view feature spaces jointly. We then theoretically verified the relation between the upper limit on the number of projected dimensions and the optimal solution to the multi-view feature representation problem. To validate the effectiveness and generality of the proposed framework, we conducted experiments on five data sets of different scales, including visual-based (University of California Irvine (UCI) iris database, Olivetti Research Lab (ORL) face database and Caltech 256 database), text-image-based (Wiki database) and video-based (Ryerson Multimedia Lab (RML) audio-visual emotion database) examples. The experimental results show the superiority of the proposed framework on multi-view feature representation over state-of-the-art algorithms.


Tandem language exchange application: A telecollaborative experience of linguistic and cultural exchange

Abstract: Limited opportunities for language practice, especially productive skills, have constantly been a concern voiced by language learners. Recent technological advancements have addressed this need through mobile applications. One such application is <i>Tandem: Language Exchange</i>, bringing together language learners with similar goals worldwide to practice their language of interest. To this end, this review evaluated the app in focus: An introduction, utilizing the technology for language teaching and challenges in using the technology for language teaching, and a conclusion. Following this framework, the evaluation revealed that the app provided customized learning preferences and fostered productive skills practice, multimodal feedback, and intercultural awareness. However, the misuse of the app, the participants’ linguistic expertise, the potential emergence of cultural issues, and data security raised concerns over its use. Ultimately, the review recommended the app as an educational opportunity and an entertaining digital learning environment.


MEANINGFUL ORIENTATIONS IN THE SYSTEM OF PROFESSIONAL BURNOUT FACTORS OF HIGH SCHOOL TEACHERS

Abstract:
The article presents the results of a theoretical and empirical study of meaningful life orientations indicators f in the system of teacher’s of higher education institutions professional burnout factors.
 The results of the teacher’s of higher education institutions professional burnout phenomenon theoretical analysis, the system of factors of it’s occurrence are presented. Research methods and sample characteristics are described.
 The indicators of the general level and components of professional burnout of teachers of higher education institutions with different levels of meaningfulness of life are analyzed. The relationship between the level of meaningfulness of life and the characteristics of professional burnout of teachers is outlined.


Quantum limits of parameter estimation in long-baseline imaging

Abstract: Telescope systems with distributed apertures are a well-established approach for boosting resolution in astronomical imaging. However, theoretical limits on quantitative imaging precision, and the fundamentally best possible mode-sorting, beam-combining, and detection schemes to use with such arrays, remain largely unexplored. Using mathematical tools of the quantum and classical Cr\'amer-Rao bounds, we perform analyses showing the fundamental origins of the enhancement provided by distributed imaging systems, over and above a single monolithic telescope, and consider the precision with which one can estimate any desired parameter embedded in a scene's incoherent radiation with a multi-aperture imaging system. We show how quantum-optimal measurements can be realized via beam-combination strategies of two classes: (1) multi-axial, where light from different apertures is directed to a common focal plane, e.g., of a segmented-aperture telescope; and (2) co-axial, where light collected at each aperture, e.g., telescope sites of a long-baseline array, is routed to an optical interferometer. As an example, we show an explicit calculation of the quantum Fisher information (QFI) for estimating the angular separation between two-point emitters using two identical apertures separated by a baseline distance. We show that this QFI splits instructively into additive contributions from the single apertures and from the baseline. We quantify the relative benefits of intratelescope (e.g., spatial-mode) optical processing and intertelescope beam combination. We show how both receiver designs can be used to capture both sources of information and discuss how similar methods could be extended to more general imaging tasks. We discuss translating QFI-attaining measurements to explicit receiver designs, and the use of preshared entanglement to achieve the QFI when it is impractical to colocate and combine light collected by the apertures.


Use of Chatbots at the Intersection of Technology and Education

Abstract: This chapter presents a comprehensive review of all the chatbots available and working in the field of education. Different methodologies are used according to the different requirement for chatbots (i.e., rule based, domain based, etc.), so this chapter presents evaluation-based analysis of methodologies used for designing these chatbots, along with exploring it with future directions considering the change in designing for the requirement of a new way of learning during and after the pandemic from a micro-education view. In this chapter, a critical study has been performed by measuring the performance of chatbots in different scenarios and on several homogeneous and heterogeneous parameters. In the last, a complete review and comparison between many chatbots have been performed, and by analyzing their grey areas, recommendations have been given so that in the future better communication between chatbots and humans can be achieved from a learning point of view.


Chatbots in Practical Science, Technology, Engineering, and Mathematics (STEM) Education

Abstract: This chapter explores and analyzes the use of chatbots in practical STEM education. It presents a literature review that examines the various ways in which chatbots have been utilized to facilitate learning and engagement among students in science, technology, engineering, and mathematics (STEM) courses. Chatbots have emerged as versatile educational tools that can interact with students in real-time, simulating human-like conversations to answer questions, deliver content, and provide feedback. The integration of chatbots in STEM education presents numerous advantages, including scalability, accessibility, and the ability to adapt to individual learning needs. The chapter highlights the benefits, challenges, and future prospects of integrating chatbots in practical STEM education.


Multipartite spin coherent states and spinor states

Abstract: Multipartite generalizations of spin coherent states are introduced and analyzed. These are the spin analogs of multimode optical coherent states as used in continuous variable quantum information, but generalized to possess full spin symmetry. Two possible generalizations are given, one of which is a simple tensor product of a given multipartite quantum state. The second generalization uses the bosonic formulation in the Jordan-Schwinger map, which we call spinor states. In the unipartite case, spinor states are equivalent to spin coherent states; however, in the multipartite case, they are no longer equivalent. Some fundamental properties of these states are discussed, such as their observables and covariances with respect to symmetric operators, form-preserving transformations, and entanglement. We discuss the utility of such multipartite spin coherent and spinor states as a way of storing quantum information.


The Study on Software Architecture Smell Refactoring

Abstract: Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes. The goal of refactoring is to fix smells in the system. Fixing architectural smells requires more effort than other smells because it is tangled in multiple components in the system. Architecture smells refer to commonly used architectural decisions that negatively impact system quality. They cause high software coupling, create complications when developing new requirements, and are hard to test and reuse. This paper presented a tool to analyze the causes of architectural smells such as cyclic dependency and unstable dependency and included a priority metric that could be used to optimize the smell with the most refactoring efforts and simulate the most cost-effective refactoring path sequence for a developer to follow. Using a real case scenario, a refactoring path was evaluated with real refactoring execution, and the validity of the path was verified.


Advancing document-level relation extraction with a syntax-enhanced multi-hop reasoning network

Abstract: Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model’s robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model’s focus, reducing its efficiency. To concentrate the model’s attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods.


Measurement-Free Fault-Tolerant Quantum Error Correction in Near-Term Devices

Abstract: Logical qubits can be protected from decoherence by performing quantum error-correction (QEC) cycles repeatedly. Algorithms for fault-tolerant QEC must be compiled to the specific hardware platform under consideration in order to practically realize a quantum memory that operates for in principle arbitrary long times. All circuit components must be assumed as noisy unless specific assumptions about the form of the noise are made. Modern QEC schemes are challenging to implement experimentally in physical architectures where in-sequence measurements and feed forward of classical information cannot be reliably executed fast enough or even at all. Here we provide a novel scheme to perform QEC cycles without the need of measuring qubits that is fully fault-tolerant with respect to all components used in the circuit. Our scheme can be used for any low-distance CSS code since its only requirement towards the underlying code is a transversal cnot gate. Similarly to Steane-type EC, we coherently copy errors to a logical auxiliary qubit but then apply a coherent feedback operation from the auxiliary system to the logical data qubit. The logical auxiliary qubit is prepared fault tolerantly without measurements, too. We benchmark logical failure rates of the scheme in comparison to a flag-qubit-based EC cycle. We map out a parameter region where our scheme is feasible and estimate physical error rates necessary to achieve the break-even point of beneficial QEC with our scheme. We outline how our scheme could be implemented in ion traps and with neutral atoms in a tweezer array. For recently demonstrated capabilities of atom shuttling and native multiatom Rydberg gates, we achieve moderate circuit depths and beneficial performance of our scheme while not breaking fault tolerance. These results thereby enable practical fault-tolerant QEC in hardware architectures that do not support midcircuit measurements.13 MoreReceived 30 August 2023Revised 20 December 2023Accepted 22 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum circuitsQuantum error correctionQuantum protocolsQuantum Information, Science & Technology


Pairwise CNN-Transformer Features for Human–Object Interaction Detection

Abstract: Human–object interaction (HOI) detection aims to localize and recognize the relationship between humans and objects, which helps computers understand high-level semantics. In HOI detection, two-stage and one-stage methods have distinct advantages and disadvantages. The two-stage methods can obtain high-quality human–object pair features based on object detection but lack contextual information. The one-stage transformer-based methods can model good global features but cannot benefit from object detection. The ideal model should have the advantages of both methods. Therefore, we propose the Pairwise Convolutional neural network (CNN)-Transformer (PCT), a simple and effective two-stage method. The model both fully utilizes the object detector and has rich contextual information. Specifically, we obtain pairwise CNN features from the CNN backbone. These features are fused with pairwise transformer features to enhance the pairwise representations. The enhanced representations are superior to using CNN and transformer features individually. In addition, the global features of the transformer provide valuable contextual cues. We fairly compare the performance of pairwise CNN and pairwise transformer features in HOI detection. The experimental results show that the previously neglected CNN features still have a significant edge. Compared to state-of-the-art methods, our model achieves competitive results on the HICO-DET and V-COCO datasets.


Soft Contrastive Cross-Modal Retrieval

Abstract: Cross-modal retrieval plays a key role in the Natural Language Processing area, which aims to retrieve one modality to another efficiently. Despite the notable achievements of existing cross-modal retrieval methodologies, the complexity of the embedding space increases with more complex models, leading to less interpretable and potentially overfitting representations. Most existing methods realize outstanding results based on datasets without any error or noise, but that is extremely ideal and leads to trained models lacking robustness. To solve these problems, in this paper, we propose a novel approach, Soft Contrastive Cross-Modal Retrieval (SCCMR), which integrates the deep cross-modal model with soft contrastive learning and smooth label cross-entropy learning to boost common subspace embedding and improve the generalizability and robustness of the model. To confirm the performance and effectiveness of SCCMR, we conduct extensive experiments comparing 12 state-of-the-art methods on three multi-modal datasets by using image–text retrieval as a showcase. The experimental results show that our proposed method outperforms the baselines.


Development of teachers’ attitudes and self-efficacy expectations for inclusive mathematics instruction: effects of online and blended learning programs

Abstract: Abstract This paper presents the results of a quantitative study investigating the development of teachers' attitudes and self-efficacy expectations for inclusive mathematics instruction in the context of an in-service training that was designed in a blended learning format and compared to an unsupported online offer. In the blended learning format, 101 teachers participated in an in-service training, designed as a combination of six workshops with concrete activities based on materials for inclusive mathematics teaching and intermittent practical phases with collaborative learning environments for children aged 6–12 years. The teachers' attitudes toward inclusive mathematics instruction and self-efficacy expectations are collected by using published scales (KIESEL) and scales under development. The effects of the blended learning program are analyzed by the t test for dependent samples or the nonparametric Wilcoxon signed-rank test and compared to the effects of an online offer without further guidance and support. On the one hand, the results indicate the importance of the blended learning program in comparison with the unsupported online offer. On the other hand, they show insights into the relevance of the participants' interaction with the materials during the concrete activities as well as of their try outs in and their reflection on teaching practice during the intermittent practical phases.


Systematic Review of Morphological and Semantic Analysis in a Low Resource Language

Abstract: Natural language processing discusses the applications of computational technique analysis and synthesis of natural languages. Semantic and morphological analysis are the two basic percepts in the natural language processing domain. Semantic analysis is the process of analyzing the lexical, grammatical, and syntactical parts of the words. The study of words known as morphology focuses on the meaning and structure of words. In this chapter, the authors focus on various morphological analyzers developed for Tamil language. Developing a highly accurate and adaptable morphological analyser is a challenging task. Morphological analyser basically identifies the morphemes and parts of speech for tagging. The atomic version of a word that retains the original meaning is called a morpheme. Morphological analyzer type includes phrase level and word level analyzers. Universal networking language (UNL) is a declarative kind used to express the natural language text using a semantic network. The major applications of UNL are information retrieval system, machine translation system, and UNL-based search engine.


Twitter Sentiment Analyser Using NLP Techniques

Abstract: Twitter is a popular platform where users express their opinions on various topics, including social, political, and economic issues. By monitoring the sentiment of tweets related to a particular topic, companies or governments can identify potential problems before they escalate into full-blown crises, allowing them to take appropriate action in a timely manner. The chapter typically involves collecting a large dataset of tweets, cleaning and pre-processing the data, and then using natural language processing (NLP) and machine learning techniques to classify the sentiment of each tweet.


Cross-Lingual Transfer Learning for Bambara Leveraging Resources From Other Languages

Abstract: Bambara, a language spoken primarily in West Africa, faces resource limitations that hinder the development of natural language processing (NLP) applications. This chapter presents a comprehensive cross-lingual transfer learning (CTL) approach to harness knowledge from other languages and substantially improve the performance of Bambara NLP tasks. The authors meticulously outline the methodology, including the creation of a Bambara corpus, training a CTL classifier, evaluating its performance across different languages, conducting a rigorous comparative analysis against baseline methods, and providing insights into future research directions. The results indicate that CTL is a promising and feasible approach to elevate the effectiveness of NLP tasks in Bambara.


A Case Study on Tools and Techniques of Machine Translation of Indian Low Resource Languages

Abstract: India, celebrated for its vast linguistic diversity with 1,599 languages (including 122 prominent ones), exemplifies the richness of language variation. Indo-Aryan languages, spoken by 70% of the population, and Dravidian languages, used by 19%, create this diverse linguistic tapestry. Effective communication in this interconnected society is crucial, and machine translation plays a pivotal role in bridging language gaps. Speech is a cost-effective mode of interaction, particularly important in a globalized world. Machine translation, a part of natural language processing (NLP), aids in overcoming language barriers. This chapter presents enlightening case studies and valuable best practices, inspiring meaningful translation efforts for low-resource language communities. Together, we can break linguistic barriers, fostering a world of boundless communication and enduring cultural treasures. Challenges like limited linguistic resources, technological deficiencies, and community engagement issues persist.


Datraw, Advancing Collaborative Data Collection, and Recognition Models for Low Resource Languages in the Research Community

Abstract: There is a significant need for a platform for all that promotes collaborative data collecting and supports community-driven advances in task recognition in the current era of increasing low resource data-driven research. “Datraw,” a groundbreaking platform meant to connect researchers with the larger research community, serves as a centralized hub, empowering researchers to upload sample data with labels, while inviting linguists, common individuals, and fellow researchers to contribute by drawing data, whether in the form of characters or complete works and build rich and expansive dataset for low resource language and offers the opportunity to leverage the amassed datasets for training basic classification models. Through comprehensive validation and review methods, the platform assures data integrity and quality, ensuring that the obtained data stays correct and relevant for recognition tasks.


Transformer-Based Memes Generation Using Text and Image

Abstract: Memes are gaining popularity day by day to convey humor, news, social commentary, etc., but they can spread hatred and offence in the society. In this chapter, the authors create an automatic meme generator that generates relevant memes. In this chapter, Memebotics tool takes input as image or text and uses multiple pre-trained models of GPT to make memes more creative and quicker. Memebotics uses sentiment analysis to verify that created memes are relevant and do not hurt the sentiment of any society. The authors use VADAR, SVM techniques to identify the best model for sentiment analysis. They tested the model with different versions of EleutherAI GPT-Neo to find the model that can work on low configuration and saves time and money. As a result, they have created a model that generates memes that are relevant to all culture, language, and religion.


Named Entity Recognition (NER) in Low Resource Languages of Ho

Abstract: The Ho tribe is an indigenous community that primarily inhabits the Indian states of Odisha, Jharkhand, West Bengal, Assam, and Chhattisgarh. The Ho language, which belongs to the Austroasiatic language of Munda family, is their primary means of communication. Warang Chiti is the script for writing Ho language. Creating user-friendly tools, applications, and resources that support Ho language users in various aspects, such as typing, spell-checking, dictionary lookup, text conversion between UNICODE and 8-bit encodings, speech-to-text, and text-to-speech translation, this chapter discusses data augmentation techniques, transfer learning methods, domain adaptation strategies, and the importance of resource creation. It also emphasizes the need for collaborative efforts and community-driven initiatives to advance NER research in low resource language settings.


Machine Learning Approach for Kashmiri Word Sense Disambiguation

Abstract: Studying the senses of words in a given data is crucial for analysing and understanding natural languages. The meaning of an ambiguous word varies based on the context of usage and identifying its correct meaning in the given situation is a famous problem known as word sense disambiguation (WSD) in natural language processing (NLP). In this chapter, the authors discuss the important WSD research works carried out in the context of different languages using different techniques. They also explore a supervised approach based on the hidden Markov model (HMM) to address the WSD problem in the Kashmiri language, which lacks research in the NLP domain. The performance of the proposed approach is also examined in detail along with future improvement directions. The average results produced by the proposed system are accuracy=72.29%, precision=0.70, recall= 0.70, and F1-measure=0.70.


Fast and robust clustering of general-shaped structures with tk-merge

Abstract: In real-world applications, the group of provenance of data can be inherently uncertain, the data values can be imprecise and some of them can be wrong. We handle uncertain, imprecise and noisy data in clustering problems with general-shaped structures. We do it under very weak parametric assumptions with a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical agglomeration. The algorithm has low computational complexity and effectively identifies the clusters also in presence of data contamination. We also present natural generalizations of the approach as well as an adaptive procedure to estimate the amount of contamination in a data-driven fashion. Our proposal outperforms state-of-the-art robust, model-based methods in our numerical simulations and real-world applications related to color quantization for image analysis, human mobility patterns based on GPS data, biomedical images of diabetic retinopathy, and weather data.


Anchor-based scalable multi-view subspace clustering

Abstract: Multi-view clustering is a major topic in pattern recognition and machine learning. Common multi-view clustering algorithms construct similarity graphs from original samples and use them to perform spectral clustering. The time complexity of the singular value decomposition process in graph construction and spectral clustering is high, leading to high computational and memory costs. In addition, subsequent K-means clustering is sensitive to the initial points, yielding unstable clustering results. To address these issues, this study proposes a novel approach to reduce time overhead and memory space from two perspectives. First, a new anchor selection method is proposed to reduce the dimension of the original data by lowering the cost. Second, the self-representation matrix of multi-views is fused into a consistent graph matrix using the post-fusion technique, and the fused graph is directly processed in postprocessing. Furthermore, the proposed method directly obtains clustering results based on the connectivity of the fusion graph, eliminating the need for K-means postprocessing, which avoids the issue of unstable clustering results. Experimental results on artificial and real multi-view datasets indicate that the proposed algorithm is superior to existing algorithms.


Remittance inflows and financial development IN Sub-Saharan African countries: Does governance matter?

Abstract:
Abstract

Background
 The surge in remittances in most sub-Saharan African countries motivated this study to establish whether remittance inflows enhance financial development and whether governance plays any significant mediating role in the nexus between financial development and remittance inflows.
Purpose
 The study examines the impact of remittance inflows on bank-based financial development in 26 sub-Saharan African (SSA) countries using three proxies of bank-based financial development. The study also examines whether government regulatory quality and effectiveness modulate the impact of remittance on bank-based financial development.
Method
 The generalised method of moments (GMM) estimation technique is used to examine this linkage.
Results
 The results show that when financial development is proxied by liquid liabilities and bank deposits, remittance inflow is found to have an unconditional positive impact on bank-based financial development, while governance is found to reinforce the positive relationship between remittance and financial development. However, when financial development is proxied by deposit money bank assets, remittance is found to have no profound effect on bank-based financial development, but it was found to interact with government effectiveness to yield a positive influence on financial development.
Conclusion
 Overall, remittance inflows were found to have an overwhelming positive impact on the banking sector development. It was also found that good governance generally tends to reinforce the positive impact of remittances on banking sector development.
Novelty
 This study adds value to the extant literature by providing answers to the role that governance plays in enhancing the impact of remittances on financial development in sub-Saharan African countries.


Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Abstract: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.


Tswinpose: Enhanced monocular 3D human pose estimation with JointFlow

Abstract: Monocular estimation of 3D human poses is challenging due to ambiguity in depths and partial occlusion. Most recent works define this as a 2D-to-3D lifting task, taking 2D key point sequences and using spatial and temporal relationships. However, prior works focus on capturing spatio-temporal correlations but ignore the motion of joints that is needed for continuous estimation. To extend the potential of 2D-to-3D pose estimation, we propose TSwinPose, which learns multi-scale spatio-temporal representations from 2D key point locations and patterns of motion. The input 2D key point sequences are enhanced by JointFlow, which encodes the motion of each human joint. Based on Swin-Transformer, we designed a temporal domain Swin-Unet structure to model multi-scale spatio-temporal relationships of human joints across different temporal windows. The final 3D pose generated by multi-stage representations is consistent temporally and has a higher accuracy. Experiments conducted on three benchmark datasets, Human3.6M, MPI-INF-3DHP, and HumanEva-I, demonstrate that TSwinPose achieves performance that is on par with state-of-the-art methods. Moreover, the introduction of JointFlow as a plug-in extension enhances performance significantly, particularly benefiting long-term 2D-to-3D lifting human pose estimation methods.


Secure decentralized firmware update delivery service for Internet of Things

Abstract: With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.


Applying large language models and chain-of-thought for automatic scoring

Abstract: This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of artificial intelligence-based automatic scoring tools among researchers and educators. With a testing dataset comprising six assessment tasks (three binomial and three trinomial) with 1650 student responses, we employed six prompt engineering strategies to automatically score student responses. The six strategies combined zero-shot or few-shot learning with CoT, either alone or alongside item stem and scoring rubrics, developed based on a novel approach, WRVRT (prompt writing, reviewing, validating, revising, and testing). Results indicated that few-shot (acc = 0.67) outperformed zero-shot learning (acc = 0.60), with 12.6% increase. CoT, when used without item stem and scoring rubrics, did not significantly affect scoring accuracy (acc = 0.60). However, CoT prompting paired with contextual item stems and rubrics proved to be a significant contributor to scoring accuracy (13.44% increase for zero-shot; 3.7% increase for few-shot). We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks. We also found that GPT-4 demonstrated superior performance over GPT -3.5 in various scoring tasks when combined with the single-call greedy sampling or ensemble voting nucleus sampling strategy, showing 8.64% difference. Particularly, the single-call greedy sampling strategy with GPT-4 outperformed other approaches. This study also demonstrates the potential of LLMs in facilitating explainable and interpretable automatic scoring, emphasizing that CoT enhances accuracy and transparency, particularly when used with item stem and scoring rubrics.


Search Space Division Method for Wrapper Feature Selection on High-Dimensional Data Classification

Abstract: Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper techniques become computationally very expensive. To solve this problem of scalability, this paper proposes the concept of search space division (SSD) which leads to smaller search spaces and hence reduced computational cost. The proposed SSD approach is generic in nature and can be integrated with any wrapper-based FS technique. The SSD approach divides search space into multiple parts and the wrapper-based FS is independently applied to each part. To facilitate the interaction of features over the complete search space, all feature subsets obtained from each part are combined and the wrapper-based FS is again applied to get the final feature subset. Moreover, a new wrapper FS technique named Binary Rao (BRAO) Algorithm has been proposed. BRAO is based on the metaphor-less, parameter-less meta-heuristic optimization algorithm Rao-1. The proposed combination of SSD and BRAO algorithm named SSDR has produced better classification accuracies with a smaller number of features in a much shorter time as compared to other well-regarded wrapper FS techniques as well as classical FS techniques over thirteen benchmark high-dimensional datasets.