The effects of NMT as a de facto dictionary on vocabulary learning: a comparison of three look-up conditions

Abstract: Many language learners have reported using Neural machine translation (NMT) such as Google Translate as a reference tool for writing production or as a dictionary substitute to facilitate reading comprehension. Although increasing evidence has suggested that editing with NMT can help learners make improvement on the vocabulary level, no study has explored how consulting NMT for unknown vocabulary may impact the retention of word meaning compared to more conventional look-up conditions. Using a 3 × 3 quasi-experimental design, this study investigated the extent to which the use of NMT to learn unknown target words during reading comprehension activities yielded vocabulary retention compared to that of an online dictionary and how the findings varied by learners' proficiency levels. The research questions focused on the comparison of the outcomes of three different look-up conditions: (1) NMT word search, (2) NMT sentence search, and (3) online dictionary search. ANOVA results showed that learners who performed online dictionary search and NMT sentence search demonstrated significantly higher levels of word retention than those who carried out NMT single-word search, and the same pattern was observed regardless of their proficiency levels.


Flexible learning spaces as an arena for developing professional digital competence through small group collaboration

Abstract: Flexible technology-rich learning spaces such as Future Classroom Labs (FCL) have a large potential for developing teacher education (TE) students’ professional digital competence through engaging in authentic collaborative learning experiences with digital technology. However, there are limited reports on how students perceive and enact the affordances existing in such learning spaces. This paper reports on how one such FCL can facilitate collaborative learning among TE students. The paper poses two research questions: 1) How does the FCL as a learning space accommodate for collaborative learning? and 2) How do the artefacts promote or limit collaboration among students at the FCL? The data were collected through observations of four sessions of group work at the FCL, each having a different group of 15–16 first-year TE students. The results suggest that the FCL promoted collaborative work in terms of finding and building groups, monitoring, and resource-sharing across the small groups. However, students depended on external guidance from a teacher or student assistant to understand the technical sides of the artefacts, and their educational potential. Therefore, the implication is that in their first visits to FCLs, students need guidance to get started with the distinct tasks, and to master the various devices.


Dunhuang murals image restoration method based on generative adversarial network

Abstract: Abstract Murals are an important part of China’s cultural heritage. After more than a 1000 years of exposure to the sun and wind, most of these ancient murals have become mottled, with damage such as cracking, mold, and even large-scale detachment. It is an urgent work to restore these damaged murals. The technique of digital restoration of mural images refers to the reconstruction of structures and textures to virtually fill in the damaged areas of the image. Existing digital restoration methods have the problems of incomplete restoration and distortion of local details. In this paper, we propose a generative adversarial network model combining a parallel dual convolutional feature extraction depth generator and a ternary heterogeneous joint discriminator. The generator network is designed with the mechanism of parallel extraction of image features by vanilla convolution and dilated convolution, capturing multi-scale features simultaneously, and reasonable parameter settings reduce the loss of image information. A pixel-level discriminator is proposed to identify the pixel-level defects of the captured image, and its joint global discriminator and local discriminator discriminate the generated image at different levels and granularities. In this paper, we create the Dunhuang murals dataset and validate our method on this dataset, and the experimental results show that the method of this paper has an overall improvement in the evaluation metrics of PSNR and SSIM compared with the comparative methods. The restored resultant image is more in line with the subjective vision of human beings, which achieves the effective restoration of mural images.


A multimodal virtual vision platform as a next-generation vision system for a surgical robot

Abstract: Abstract Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot. Graphical Abstract


Towards an obstacle detection system for robot obstacle negotiation

Abstract: Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.


LCPCWSC: a Web service classification approach based on label confusion and priori correction

Abstract: Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.


Relationships among class climate, students' internal help‐seeking inclinations, participation in peer help and achievement in an online class

Abstract: Abstract Help seeking has traditionally been studied in face‐to‐face settings using self‐report instruments measuring learners' internal inclinations for help seeking (eg, perceived benefits and threats). Much less is known about help seeking in online learning. Furthermore, external environmental factors such as a positive climate were found to encourage help seeking. Utilizing peer‐help analytics as well as self‐report measures, this study simultaneously modelled the structural relationships among students' perceived class climate, internal help‐seeking inclinations, help‐seeking engagement, participation in peer help and performance in an online class. Results from structural equation modelling indicated that students' perceived goal structure of an online class had a significant influence on their internal help‐seeking inclinations, which subsequently affected their help‐seeking engagement, participation in peer help and learning outcomes. Instructors of online classes should pay particular attention to fostering a mastery‐oriented climate in order to encourage help seeking and improve academic performance. Practitioner notes What is already known about this topic Help seeking is important for academic success. Due to a variety of individual and contextual factors, students often do not seek help despite the need. Existing research on help seeking focuses on the face‐to‐face setting and relies on self‐report measures. Students' help‐seeking inclinations and patterns may differ between in‐person classes and the online environment. What this paper adds This study examined help seeking in an online class through self‐report measures and objective analytics data. This study modelled the structural relationships among the external climate of an online class, students' internal help‐seeking inclinations, engagement in help seeking, participation in peer help and course performance. Structural equation modelling found that the goal structure of an online class influenced students' internal help‐seeking inclinations. Online students' internal help‐seeking inclinations affected their grades through their participation in help seeking. Implications for practice and/or policy A mastery‐oriented class climate elicits online learners' positive help‐seeking inclinations while discouraging negative ones. The design and teaching of online classes should aim to foster a mastery‐oriented climate. Online classes should offer peer‐help opportunities and encourage student participation, which is conducive to academic performance.


Color watermarking algorithm combining the quantum discrete cosine transform with the sinusoidal–tent map

Abstract: To overcome the drawbacks of the existing sinusoidal map and tent map, this paper proposes the design of a sinusoidal–tent (ST) map. The test results indicate that the new chaotic system exhibits more significant advantages in chaos control. Compared with the sinusoidal map and tent map, the proposed sinusoidal–tent map performs better in terms of bifurcation diagram and Lyapunov exponents. The trajectories of the sinusoidal–tent map can occupy all the phase planes over (0,4), while those of the two classic maps only occupy a small phase space, and the Lyapunov exponents of the ST map are all positive within the range of control parameters, higher than those of seed maps. Simultaneously, a novel quantum scrambling operation is devised based on the sinusoidal–tent map to avoid the periodicity of the quantum Arnold scrambling method. Initially, two chaotic sequences are generated to scramble the pixel positions of the watermark image, further enhancing the security of the watermarking algorithm. Subsequently, the host image is processed by the quantum discrete cosine transform, and finally, the scrambled watermark image is inserted into the medium-frequency band of the transformed host image, ensuring the invisibility of the watermarking. According to the simulation results, the quantum watermarking algorithm has excellent invisibility and robustness.


Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials

Abstract: Abstract Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized implemented distributed identity management by the distributed verifiable credentials scheme that possesses the desirable properties, i.e., selective disclosure, multi-show unlinkability, threshold traceability, and public verifiability. Finally, we presented a fair trading mechanism without trusted third parties based on smart contracts, and we employed blockchain and multi-signature to ensure data integrity during data storage and trading. The security and performance analysis shows that our proposal is feasible for sensitive data trading for multiple data owners and provides a useful exploration for future industrial data trading and management.


Super‐resolution reconstruction based on generative adversarial networks with dual branch half instance normalization

Abstract: Abstract This paper proposes a super‐resolution reconstruction model, SRPGANto improve the visual quality of images based on generative adversarial networks (GANs) by improving the network structures of the generator and the discriminator. In the generator, a dual branch residual block is designed instead of the residual block, including a branch with an attention mechanism and a branch without an attention mechanism, to extract more differentiated features. Normalization methods are explored to avoid unstable training and bath normalization artifacts and use a half instance normalization layer that is more suitable for underlying visual problems compared with traditional batch normalization. In the discriminator, PatchGAN is applied instead of typical GAN to improve the generation of local texture by discriminating each patch rather than the global image. The experimental results on the public datasets demonstrate that the proposed SRPGAN can achieve excellent quantitative evaluation while improving the visual quality of reconstructed images.


Optimization on Manifolds via Graph Gaussian Processes

Abstract: .This paper integrates manifold learning techniques within a Gaussian process upper confidence bound algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective. Query points are sequentially chosen using the posterior distribution of the surrogate model given all previous queries. We establish regret bounds in terms of the number of queries and the size of the point cloud. Several numerical examples complement the theory and illustrate the performance of our method.KeywordsBayesian optimizationmanifold learninggraph Gaussian processesMSC codes60G1562F1505C8590C26


Research on path planning technology of mobile robot in the restaurant scene based on A* and D* algorithm

Abstract: Intelligent robots have found widespread applications in various fields including medical treatment, industrial production, and social services. These robots are specifically designed to fulfill diverse tasks in different environments, the design and control of these robots play a crucial role in ensuring their efficiency and security. In response to the high demand for fast and convenient service, the catering field is a crucial and promising application field for intelligent robots. This article is primarily focused on the robot in the restaurant scene where the robot could automatically deliver food to the customers, thus reducing the burden on human service providers. In this special environment, the delivery robot must possess the capability to identify its surroundings within the restaurant and autonomously calculate an obstacle-free path from the starting point to the desired destination without colliding with either obstacles or individuals. In this article, the restaurant scene is built in the Gazebo, and map scanning and conversion methods are utilized to enable the robots to recognize the environment effectively. Furthermore, A* and D* algorithms are employed in Python to achieve global and local path planning, respectively, thus validating the feasibility of the methods. The combination of these two algorithms demonstrates a successful path-planning application within the context of a restaurant scene.


Mixed Reality: Present and future

Abstract: Mixed Reality (MR) is an emerging technology that can create a visual environment connecting the real world and the virtual environment. With this technology, the way information is presented has changed in a great number of domains. However, for common people, this technology still looks far away from life and seems unintelligible. Therefore, this essay reviews the development and recent research of MR, trying to help people without professional backgrounds understand this emerging technology and offer researchers another perspective on MR. By using the methods of text mining, this essay has reviewed more than twenty papers related to MR on different topics, including the definition, the principles, and the applications. The objective of this essay is to provide a comprehensive understanding of MR, which may promote wider applications and research.


Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system

Abstract: The Multi-Armed Bandit (MAB) algorithm holds significant prominence as a recommendation system technique, effectively presenting user-centric content preferences based on the analysis of collected data. However, the application of the basic MAB algorithm in real-world recommendation systems is not without challenges, including issues related to data volume and data processing accuracy. Therefore, the optimization algorithm based on the MAB algorithm is more widely used in the recommendation system. This paper briefly introduces the multi-armed bandit algorithm, that is, the use of MAB in the recommendation system and the problems of the basic MAB algorithm. Aiming at the problems of the basic MAB algorithm, it introduces the MAB-based optimization algorithm used in the recommendation system. At the same time, this paper also analyzes and summarizes such algorithms. This paper introduces two different MAB-based optimization algorithms, namely The Details of Dynamic clustering based contextual combinatorial multi-armed bandit (DC3MAB) and Binary Upper Confidence Bound (BiUCB). In addition, this paper also introduces the application of algorithm in recommended system. Finally, this paper summarizes the introduced algorithms and proposes the future prospects for MAB optimization algorithms.


The Interaction Between Morphosyntactic Features and the Performance of Machine Translation Tools: The Case of Google Translate, Systran, and Microsoft Bing in English-Arabic Translation

Abstract: The present study compares the performance of three machine translation tools in English-Arabic translation to answer the questions of (a) whether the three machine translation tools, Google Translate, Systran and Microsoft Bing can be ordered in a hierarchy of performance, and (b) whether they can handle lexically and structurally ambiguous sentences and garden path sentences. Using a number of constructed and selected English sentences, the morphosyntactic features of number, gender, case, definiteness, and humanness, agreement between cardinal numerals and their head nouns, lexically and structurally ambiguous sentences and garden path sentences are used to test the three machine translation tools for performance. The results show that (a) as far as handling the morphosyntactic features of subject-verb agreement in Standard Arabic, all three machine translation tools perform equally well, and no machine translation tool seems to perform significantly better than the other two (b) some marked features (e.g. dual number and humanness) of SA seem to pose some problems for machine translation tools, and (c) lexically and structurally ambiguous sentences and garden path sentences seem to be the most challenging sentences for the three machine translation tools.


Investigating the Effectiveness of Synchronous E-Learning Tools on Developing EFL Learners' Language Communication Skills Among the Hashemite University Students

Abstract: After COVID-19, today’s language classes have become blended with e-learning tools that have become indispensable to the process of learning in many situations. Synchronous e-learning classrooms have been studied for their impact on EFL communication skills, but few studies have been conducted in the Jordanian institutions. The purpose of this study is to investigate the EFL students’ viewpoints on the efficiency of online classrooms in improving their verbal communication abilities. This study consisted of 284 participants who were EFL learners from the Hashemite University. It was conducted during the academic year 2022-2023, and it followed a mixed qualitative approach. Two means of data collection were used: a questionnaire and observations. The results indicated that students have a positive viewpoint toward online learning effectiveness in developing their communication. Nevertheless, students showed that low self-assurance, nervousness over making errors, and poor lexical knowledge are their main concerns when communicating in EFL classrooms.


Unravelling Yu Guangzhong’s Identity Through His Poetic Reflections: A Corpus-Based Investigation

Abstract: This study examines Yu Guangzhong’s identity presented in his self-translated poetry collection, The Night Watchman. It employs corpus linguistic methodologies and utilizes the corpus linguistic tool AntConc for analysis. This study highlights Yu Guangzhong’s identity, which is presented in different categories and topics of poems. It is found that Yu Guangzhong’s identity is ever-changing, varying at different times, and occasionally conflicting and opposing. This study may have practical implications for Chinese literature and culture looking to enhance their international influence, and it may also provide academic insights for investigating other literary giants.


Research and application analysis of typical recommendation methods

Abstract: Recommendation systems are vital tools in the modern digital landscape for handling vast amounts of online data and are now employed across a variety of sectors. This manuscript underlines the importance of video recommendation algorithms and delves into the three main types: content-based, collaborative filtering, and hybrid algorithms. Content-based algorithms offer suggestions by assessing and matching the descriptive attributes of videos. These attributes could range from the genre, the actors involved, the director, or any other related metadata. On the other hand, collaborative filtering algorithms provide recommendations by comparing the user's historical data with that of others. They function on the principle that individuals who have agreed in the past are likely to agree again in the future. Hybrid recommendation algorithms, as the name suggests, are a combination of both content-based and collaborative filtering algorithms. They aim to harness the strengths of the two while mitigating their individual weaknesses, providing more balanced and accurate recommendations. This detailed exploration of these algorithms not only underscores their significance but also paves the way for the future, heralding the potential advancements and adaptations these systems could undergo. Continued innovation in this realm has the potential to revolutionize how information is processed and shared, ultimately enriching the user experience across the digital platform landscape.


Single‐Transistor Optoelectronic Spiking Neuron with Optogenetics‐Inspired Spatiotemporal Dynamics

Abstract: Abstract The visual system is crucial for human perception and learning, and the retina is responsible for pre‐processing visual information. Mimicking the neurobiological functions of the retina provides the basis for neuromorphic vision hardware. Here, an optogenetics‐inspired prototype capable of modifying neuron‐spiking behavior in a retina‐like manner is demonstrated. In addition to basic information processing, neuronal signal transmission in the retina is bio‐faithfully emulated using optical modulation, that is, neuronal firing is inhibited in the dark and activated upon illumination. The proposed single threshold‐switch field‐effect transistor optoelectronic neuron features an ultra‐compact design by integrating a gate‐modulated 2D MoS 2 channel with a volatile threshold switch, and optoelectronic performance is achieved through a multi‐physics resistance‐matching mechanism. This study provides a promising pathway toward highly scalable and hierarchically designed spiking electronics, expanding the spectrum of neuromorphic hardware applications in the emerging field of biomimetic vision.


DECISION SUPPORT SYSTEM IN IMPROVING THE QUALITY OF BANJARMASIN TOURISM, GET TOUR APPLICATION USING THE SAW METHOD

Abstract: Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.


Social Media English in Nigeria and the New Crime Wave

Abstract: This study explores the social media English in Nigeria and the new crime wave. Utilizing the theoretical frameworks of systemic functional linguistics and forensic linguistics, the study adopts a qualitative data analysis approach to examine the linguistic devices used in the portrayal of criminal activities. The research delves into the language of crime, reviewing related literature and analyzing selected posts for their use of specific linguistic devices. These devices include slang and code words, metaphors and euphemisms, sensational headlines, and biased language. The study reveals that these linguistic devices play a crucial role in the construction and perception of criminal activities on social media platforms. Slang and code words, for instance, often serve as a form of coded communication among criminals, while metaphors and euphemisms are used to downplay the severity of criminal acts or to disguise illegal activities. Sensational headlines and biased language, on the other hand, can influence public opinion and perpetuate stereotypes about crime and criminals. This study contributes to the understanding of how language is used in the context of crime on social media platforms, offering valuable insights for law enforcement agencies, policy makers, and researchers in the fields of linguistics and criminology. It underscores the need for further research in this area, particularly in the era of digital communication where language plays a pivotal role in shaping societal narratives about crime.


Graph neural network based method for robot path planning problem

Abstract: Sampling-based path planning is widely used in robotics, particularly in high-dimensional state spaces. In the path planning process, collision detection is the most time-consuming operation. Therefore, we propose a learning-based path planning method that reduces the number of collision checks. We develop an efficient neural network model based on graph neural networks. The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments. The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.


Deep Learning for Daily 2‐m Temperature Downscaling

Abstract: Abstract This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future.


Controlling chaotic maps using next-generation reservoir computing

Abstract: In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.


Assembling an illustrated family‐level tree of life for exploration in mobile devices

Abstract: Abstract Since the concept of the tree of life was introduced about 150 years ago, a considerable fraction of the scientific community has focused its efforts on its reconstruction, with remarkable progress during the last two decades with the advent of DNA sequences. However, the assemblage of a comprehensive and explorable tree of life has been a difficult task to achieve due to two main obstacles: (i) information is scattered into several individual sources and (ii) practical visualization tools for exploring large trees are needed. To overcome both challenges, we aimed to synthesize a family‐level tree of life by compiling over 1400 published phylogenetic studies, choosing the source trees that represent the best phylogenetic hypotheses to date based on a set of objective criteria. Moreover, we dated the tree by employing over 550 secondary calibrations using publicly available sequences for more than 5000 taxa and by incorporating age ranges from the fossil record for over 2800 taxa. Additionally, we developed a mobile app for smartphones to facilitate the visualization and exploration of the resulting tree. Interactive features include exploration by the zooming and panning gestures of touch screens, collapsing branches, visualizing specific clades as subtrees, a search engine, and a timescale to determine extinction and divergence dates, among others. Small illustrations of organisms are displayed at the terminals to better visualize the morphological diversity of life. Our assembled tree currently includes over 7000 families, and its content will be expanded through regular updates to cover all life on earth at the family level.


Effects of decades of physical driving experience on pre-exposure postural precursors of motion sickness among virtual passengers

Abstract: Previous research has shown that motion sickness associated with virtual vehicles is more common among passengers than among drivers. Separately, other studies have shown that postural precursors of motion sickness during virtual driving differ as a function of prior experience driving physical vehicles. We investigated the intersection of those prior effects: We asked whether decades of physical driving experience 1) would influence motion sickness among passengers in a virtual vehicle, and 2) would influence postural precursors of motion sickness among passengers in a virtual vehicle. In our study, middle-aged adults were exposed to a virtual vehicle as passengers. Some participants (Physical Drivers) had decades of experience driving physical automobiles, while others (Physical Non-Drivers) had rarely or never driven a physical vehicle. First, we measured head and torso movement as standing participants performed simple visual tasks. Then, each participant watched a recording of the motion of a virtual vehicle, which induced motion sickness in some participants. Afterward, neither the incidence nor the severity of motion sickness differed between Physical Drivers and Physical Non-Drivers. Our analysis of pre-exposure standing body sway revealed postural precursors of motion sickness in measures of the spatial magnitude and temporal dynamics of movement. In statistically significant interactions, these precursors (Well vs. Sick) differed as a function of physical driving experience (Physical Drivers vs. Physical Non-Drivers). Overall, our results indicate that, among virtual passengers, long-term real-world driving experience influenced the postural precursors of motion sickness, but not the incidence or severity of motion sickness. We discuss these results in terms of relationships between perception and motor control in theories of motion sickness etiology.


Data reusability for migration research: a use case from SoDaNet data repository

Abstract: This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers.


Associating UAS images through a graph‐based guiding strategy for boosting structure from motion

Abstract: Abstract Structure from motion (SfM) using optical images has been an important prerequisite for reconstructing three‐dimensional (3D) landforms. Although various algorithms have been developed in the past, they suffer from many image pairs for feature matching and recursive searching for the most suitable image to add to SfM reconstruction. Thus, carrying out SfM is computationally costly. This research proposes a boosting SfM (B‐SfM) pipeline containing two phases, indexing graph network (IGN) and graph tracking, to accelerate SfM reconstruction. The IGN intends to form image pairs presenting desirable spatial correlation to reduce the time costs spent for feature matching. Building on the IGN, graph tracking integrates ant colony optimisation and greedy sorting algorithms to encode an optimum image sequence to boost SfM reconstruction. Compared to the results derived from other available means, the experimental results show that the proposed approach can accelerate the two phases, feature matching and 3D reconstruction, by up to 14 times faster. The quality of the camera poses recovered is retained or even slightly improved. As a result, the developed B‐SfM can efficiently achieve SfM reconstruction by suppressing the time cost in the fashion of image pair selection for feature matching and image order determination for more efficient SfM reconstruction.


Modelling Data Poisoning Attacks Against Convolutional Neural Networks

Abstract: Cybersecurity has become a great concern in many real-world applications involving adversaries with Machine Learning (ML) algorithms being more widely used. This concern is more challenging in Internet of Things (IoT) platforms. As IoT-enabled applications are growing at a rapid pace in every sector there are growing security related incidents as well. ML algorithms are widely deployed to perform data analysis, reasoning and decision-making over the data emanating from IoT devices. Security of this data while collection, communication and computing is a major challenge. Various attackers are trying to find weaknesses in the ML algorithms and trying to deceive these ML algorithms to learn the wrong information from the data. Countermeasures need to be developed to evaluate the security of the ML models. To develop such countermeasures, one needs to understand all possible attacks on the ML models. Data poisoning attacks are a class of adversarial attacks on ML where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier to satisfy certain objectives. In order to develop an attack-resistant ML model, one needs to know all possible attacks on these models. The recent data poisoning technique such as the Fast Gradient Sign Method (FGSM) is static and provides very micro control to the attacker on creating adversarial data. In this research, we develop a more robust data poisoning technique for deep neural networks using Generative Adversarial Networks (GANs) to create a data poisoning attack. We then evaluate the performance of the proposed algorithm and also compare the results obtained by FGSM.


Research on Network Teaching Collaboration Platform Using Flipped Classroom Teaching Mode

Abstract: The quick development of intelligent information technology in modern society has accelerated education informatisation. Accordingly, the traditional modes of instruction and methods of instruction are no longer able to satisfy the educational needs of modern society, so the flipped classroom teaching method emerges at the historic moment. While injecting fresh vitality into education, this model can also reflect good teaching effects, which is of enormous practical relevance to the development of education. Additionally, flipped classroom teaching is made possible by the online teaching resources offered by colleges and institutions. In view of the unreasonable distribution of curriculum resources in the current university network teaching platform, this study proposes a university network platform combining flipped classroom teaching mode and improved Apriori algorithm. First of all, to achieve the acceptable allocation and suggestion of learning materials in accordance with students’ learning demands, the Apriori algorithm is introduced to address the university network teaching platform’s lack of adequate planning and organisation. After that, the flipped classroom teaching mode is introduced into the online teaching platform for empirical research. According to the trial findings, more than 90.22 % of the students had improved their study habits. The network teaching platform proposed by the research institute can stimulate college students’ learning interest and improve their learning ability to a certain extent.


Concentration‐number (C‐N) fractal models reveal the distribution pattern of the elements in ancient nephrite measured by portable X‐ray fluorescence: Based on nephrite objects excavated from different sites in Nanyang, Henan Province

Abstract: Abstract Changes in the composition of various chemical elements in ancient nephrite artifacts due to prolonged burial are critical factors that should not be underestimated. However, the increasingly stringent heritage management has made many techniques impractical. Consequently, portable X‐ray fluorescence (pXRF) has become an indispensable nondestructive field analysis method. This paper aims to make use of the pXRF dataset to distinguish which elements tested are endogenous or exogenous, as well as to gauge which elements have been affected by stronger burial effects. More specifically, we carry out this work on the example of 103 pieces of nephrite excavated from different cemeteries in the Nanyang area. In addition to traditional statistical techniques, a new tool, the concentration‐number (C‐N) fractal method can shed new light on the analysis of the distribution patterns of different elements in excavated nephrite. The anomalous boundaries generated by the method have clear geochemical significance and can be delineated between background zone and disturbed regions. The degree of resistance of different elements to fluctuations in external factors was assessed, which has a direct relationship with the content of the buried soil. Considering the richness of the model, it has the potential to be used in archaeometrics.


Unsupervised novelty detection for time series using a deep learning approach

Abstract: In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets. Introducing the novel DeepMaly method, this approach provides a practical tool for SHS developers. Functioning seamlessly in an unsupervised manner, DeepMaly distinguishes between seasonal and actual anomalies through a unique process of training on unlabeled pristine features extracted from time series data. Leveraging a combination of Long Short-Term Memory (LSTM) and Deep Convolutional Neural Network (DCNN), the model is primed to detect anomalies in real-time. The research culminates in a comprehensive data prediction and classification process into normal and abnormal data based on specified anomaly thresholds and fraction percentages. Notably, this function operates seamlessly unsupervised, eliminating the need for labeled datasets. The study concludes with a complete data forecasting and sorting method that divides data into normal and abnormal categories based on defined anomaly thresholds and fraction percentages. Working in an unsupervised mode reduces the requirement for labeled datasets. The results highlight the model's prowess in new detection, which has been successfully applied to benchmark datasets. However, there is a restriction since deep learning algorithms can recognize noise as abnormalities. Finally, the investigation enhances SHS anomaly detection, providing a crucial tool for real-time anomaly identification in the ever-changing IoT and Smart Homes scene.


Few‐shot object detection based on global context and implicit knowledge decoupled head

Abstract: Abstract The acquisition cycle of remote sensing images is slow, and the labelling process encounters challenges, which have become prominent with the rapid development of remote sensing image object detection research. Therefore, this article provides a way to make the model better capture the diversity and contextual relationships in the data, and solve this problem by more than just data augmentation. Specifically, this method is a few‐shot object detection method for remote sensing based on global context combined with implicit knowledge decoupled head (GC‐IKDH). This method first uses a segmentation strategy to convert high‐resolution images into low‐resolution images and expands the sample size through a generative model. Secondly, GC attention is introduced to generate a GC vector by weighting and averaging the information of each position in the input sequence, which helps the model better understand the semantics of the input sequence. Finally, an IKDH is added to improve the model head, which is used to learn specific features in the data so that the model can better handle the diversity in the data. Experimental results show that GC attention and IKDH boosting provide a good performance boost to the baseline model. Compared with other few‐shot samples, this method achieves state‐of‐the‐art performance under different shot settings and highly competitive results on two benchmark datasets (NWPU VHR‐10 and DIOR).


Experimental and simulation data of hole bearing type connections

Abstract: This paper presents a comprehensive dataset comprising experimental test data and numerical simulations of hole bearing tests involving 32 single-bolt, 20 two-bolt, and 20 four-bolt specimens. The dataset encompasses load-deformation curves obtained from experimental tests and displacement data acquired via the Digital Image Correlation (DIC) system, which covers specific regions of the specimens. Additionally, the dataset incorporates force-deformation curves derived from corresponding numerical simulations. The numerical simulation procedure is outlined, involving a simplified model employing solid elements for the specimen and rigid shell elements for the bolts. A "hard-contact" is employed to define the normal behavior of surface-to-surface contact between the specimen and the bolts. Material behavior modeling utilizes true stress-strain curves obtained from experimental tensile tests, encompassing both material properties extracted from these tests and the ensuing input parameters for numerical simulations. Furthermore, the DIC-system measurements provide data on displacements and strain distributions across various regions of the specimens. These strain measurements are meticulously evaluated and presented. The validation of the numerical simulations against experimental results substantiates the robustness of the numerical methodology, instilling confidence in its application for simulating bearing-type connections. Moreover, this dataset serves as a valuable resource for comparative analysis, enhancing the comprehension of these connections, and providing reference points for further numerical simulations.


Leaving no one behind: Information (re)packaging for rural dwellers in South Africa

Abstract: In most rural communities in South Africa, there is inadequate access to information due to a lack of public facilities and infrastructure, such as libraries and internet connections. This has a negative impact on the day-to-day activities of rural dwellers as it deprives them of the opportunity to access information, which is a fundamental right. This qualitative study used the Delphi technique, interviewing five experts in the Library and Information Services (LIS) fraternity in three rounds. The format in which information is accessed, as well as available information sources and resources, need (re)packaging in order to ensure free and equal access to information on a daily basis. This can be done through the provision of free Wi-Fi by rural municipalities in churches, gathering places, and royal houses to bridge the digital chasm. Provision of free Wi-Fi would give rural dwellers an opportunity to have access to online information without any hurdles as most rural dwellers are unemployed and lack money to buy data bundles for information access. With technological advancements, lot of information is shared online be it job advertisements, tertiary education applications, news, among others. The study provides a framework for addressing issues of information inaccessibility in remote rural areas. There is a need for increasing high-speed internet access for those in rural communities.


MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification

Abstract: Background Immunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis. Method In this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients’ survival outcomes. Result Experiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients. Conclusion To the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.


The importance of cybersecurity disclosures in customer relationships

Abstract: Abstract The escalating use of digital technologies has spotlighted the crucial role of cybersecurity in safeguarding sensitive information within companies. This study explores the relationship between a firm's major customers and its cybersecurity awareness. Drawing on SEC‐mandated disclosures, we employ four proxies to measure changes in customer‐supplier relationships. Our findings reveal that customers increase their purchases from suppliers whose cybersecurity awareness scores improve. Additionally, we examine the interplay between customers and suppliers more susceptible to nonpublic adverse news, particularly during cyber events. The study emphasizes the importance of cybersecurity disclosure for regulators, supply chain partners, and corporate management. It also contributes to the literature on factors influencing the duration of customer‐supplier relationships and underscores the significance of supplier characteristics. “Understanding and disclosing cybersecurity risks are” paramount in an increasingly digital business landscape.


Convex Body Collision Detection using the Signed Distance Function

Abstract: We present a new algorithm to compute the minimum distance and penetration depth between two convex bodies represented by their Signed Distance Function (SDF). First, we formulate the problem as an optimization problem suitable for arbitrary non-convex bodies, and then we propose the ellipsoid algorithm to solve the problem when the two bodies are convex. Finally, we benchmark the algorithm and compare the results in collision detection against the popular Gilbert–Johnson–Keerthi (GJK) and Minkowski Portal Refinement (MPR) algorithms, which represent bodies using the support function. Results show that our algorithm has similar performance to both, providing penetration depth like MPR and, with better robustness, minimum distance like GJK. Our algorithm provides accurate and fast collision detection between implicitly modeled convex rigid bodies and is able to substitute existing algorithms in previous applications whenever the support function is replaced with the SDF.


A survey on blockchain deployment for biometric systems

Abstract: Abstract Blockchain technology has become an emerging area in recent years due to its capacity to improve the security, dependability, and resilience of distributed systems. Research based on this technique has impacted several firms, including banking, healthcare, data processing, remote sensing, and many others. The key characteristics of blockchain technology that make it appealing are data immutability, transparency, privacy, decentralization, and distributed ledgers. However, there is a chance of a privacy breach with sensitive biometric data. The purpose of this investigation is to examine blockchain‐based biometric applications research. It begins by determining the myriad ways that biometrics and blockchain may work together, including the storage and protection of biometric templates, identity management, and biometric authentication systems. Different biometric applications with respect to blockchain technology are also identified, along with the types of biometric data taken into account, features and capabilities of blockchain technology exploited, and blockchain technology frameworks employed. Finally, the authors seek to investigate blockchain concepts in the biometric domain by evaluating their pros and cons and summarizing the methods developed on blockchain for diverse biometric applications. Additionally, the applications of blockchain‐based biometric systems are highlighted before moving on to open research questions and potential future research areas.


Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure

Abstract: Random-scan Gibbs samplers possess a natural hierarchical structure. The structure connects Gibbs samplers targeting higher-dimensional distributions to those targeting lower-dimensional ones. This leads to a quasi-telescoping property of their spectral gaps. Based on this property, we derive three new bounds on the spectral gaps and convergence rates of Gibbs samplers on general domains. The three bounds relate a chain's spectral gap to, respectively, the correlation structure of the target distribution, a class of random walk chains, and a collection of influence matrices. Notably, one of our results generalizes the technique of spectral independence, which has received considerable attention for its success on finite domains, to general state spaces. We illustrate our methods through a sampler targeting the uniform distribution on a corner of an n-cube.


Analysis of the ensemble Kalman–Bucy filter for correlated observation noise

Abstract: Ensemble Kalman–Bucy filters (EnKBFs) are an important tool in data assimilation that aim to approximate the posterior distribution for continuous time filtering problems using an ensemble of interacting particles. In this work we extend a previously derived unifying framework for consistent representations of the posterior distribution to correlated observation noise and use these representations to derive an EnKBF suitable for this setting as a constant gain approximation of these optimal filters. Existence and uniqueness results for both the EnKBF and its mean field limit are provided. The existence and uniqueness of solutions to its limiting McKean-Vlasov equation does not seem to be covered by the existing literature. In the correlated noise case the evolution of the ensemble depends also on the pseudoinverse of its empirical covariance matrix, which has to be controlled for global well-posedness. These bounds may also be of independent interest. Finally the convergence to the mean field limit is proven. The results can also be extended to other versions of EnKBFs.


SPCTRE: sparsity-constrained fully-digital reservoir computing architecture on FPGA

Abstract: This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge.


ARGo: augmented reality-based mobile Go stone collision game

Abstract: Abstract In this study, we present a mobile Go stone collision game based on augmented reality, which we call ARGo, inspired by the traditional Korean board game, Alkkagi. ARGo aims to resolve two main issues: (1) the portability and space constraints of the original Alkkagi and (2) the limited sense of reality due to the touchscreen-based interface of the existing mobile Alkkagi games. To improve a sense of the reality of the game, ARGo provides a gameplay interface similar to the original Alkkagi by recognizing the user‘s hand motion based on AR. Additionally, it provides a customization mechanism for each user to improve the recognition of the hand motion and the strength of the attack considering each user‘s characteristics. Finally, we make the following three main contributions. First, we employ the automata theory to design the game and collision scenarios between stones. Consequently, we can clearly define the complicated states incurred by AR-based motion recognition and collisions between virtual objects. Second, we propose a collision equation based on Continuous Collision Detection tailored to ARGo, i.e., Go stones and their collisions. Through experimental studies, we demonstrate that the collision equation enables the simulation of the exact collision effects. Third, through user experience studies, we verify the effectiveness of ARGo by showing the effects of the functions implemented in ARGo and its superiority over the existing mobile game Alkkagi Mania.


Single-photon scattering and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system

Abstract: Abstract We theoretically investigate coherent scattering of single photons and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system. Using the real-space Hamiltonian, analytical expressions are derived for the transport spectra scattered by these two giant atoms with four azimuthal angles. Fano-like resonance can be exhibited in the scattering spectra by adjusting the azimuthal angle difference. High concurrence of the entangled state for two atoms can be implemented in a wide angle-difference range, and the entanglement of the atomic states can be switched on/off by modulating the additional azimuthal angle differences from the giant atoms. This suggests a novel handle to effectively control the single-photon scattering and quantum entanglement.


Evolution of automated weakness detection in Ethereum bytecode: a comprehensive study

Abstract: Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.


Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS)

Abstract: Various mineral prospectivity modelling (MPM) approaches are available for targeting mineral deposits, each method capable of predicting areas of high prospectivity. Given the diversity of MPM approaches, the modelled areas of high prospectivity can differ across different MPMs. However, rather than a negative, different MPM outputs can benefit mineral exploration targeting because each method has its advantages. Rather, the problem lies in the lack of consensus over how to best select and delimit mineral exploration targets from different MPM results. Here we aim to address the challenges outlined above whilst quantifying and mitigating the effects of inherent uncertainties. We first generate eleven different prospectivity models utilising deep learning, machine learning, fuzzy logic, and geometric average integration methods. Then, we adopt a majority voting ensemble technique to incorporate and combine the predictions of each prospectivity model. Next, we propose a confidence index designed to mitigate uncertainty associated with our multi-technique approach to MPM. The confidence index quantifies variation in prospectivity values for each cell of the MPM target area. The conjunction of a confidence index and majority voting model facilitates consistent and robust algorithm-driven extraction of exploration targets based on an ensemble of prospectivity models.


On subsets of asymptotic bases

Abstract: Let h≥2 be an integer. In this paper, we prove that if A is an asymptotic basis of order h and B is a nonempty subset of A, then either there exists a finite subset F of A such that F∪B is an asymptotic basis of order h, or for any ε>0, there exists a finite subset F ε of A such that d L (h(F ε ∪B))≥hd L (B)-ε, where d L (X) denotes the lower asymptotic density of X and hX denotes the set of all x 1 +⋯+x h with x i ∈X (1≤i≤h). This generalizes a result of Nathanson and Sárközy.


A multi‐label social short text classification method based on contrastive learning and improved <scp>ml‐KNN</scp>

Abstract: Abstract Short texts on social platforms often have the problems of diverse categories and semantic sparsity, making it challenging to identify the diverse intentions of users. To address this issue, this article proposes a multi‐label social short text classification method (IML‐CL) based on contrastive learning and improved ml‐KNN. First, a contrastive learning approach is employed to train a multi‐label text classification model. This approach improves semantic sparsity by leveraging the knowledge from the existing samples to enrich the feature representation of short texts. Simultaneously, an improved ml‐KNN algorithm is developed to enhance the accuracy of label prediction. This algorithm utilizes a two‐layer nearest neighbor rule and introduces a penalty function and weight optimization. Next, the model generates the feature representation for the test sample and predicts its label. Additionally, the improved ml‐KNN algorithm retrieves neighbors of the test sample and uses their label information for prediction. Finally, the two predictions are combined to obtain the final prediction, which accurately identifies the user's intention. The experimental results demonstrate that, on the dataset constructed in this article, the IML‐CL method effectively boosts the performance of the baseline model.


One-way semi-quantum private comparison protocol without pre-shared keys based on unitary operations

Abstract: Abstract Semi-quantum private comparison is a method for private comparison with fewer quantum resources, enabling classical participants to collaborate with a semi-honest third party possessing complete quantum capabilities. A one-way quantum private comparison protocol is devised only by unitary operations. The protocol facilitates one-way transmission between third party (TP) and classical participants in quantum communication, where the classical participants only need to perform unitary operations and measurement operations on the transmitted qubits. In addition, classical participants do not require pre-shared keys. It is shown that the qubit efficiency of this protocol is 12.5%. Finally, security analysis and the simulation results on the IBM Quantum Experience demonstrate the security and the feasibility of this protocol.


Hand gesture recognition based on micro‐Doppler radar using graph neural network

Abstract: Abstract Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.


Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

Abstract: The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.


A Study of a blockchain-based judicial evidence preservation scheme

Abstract: To address the challenges of low credibility, difficult data sharing, and regulatory supervision issues involving electronic evidence storage in the judicial preservation process, this paper proposes a blockchain-based judicial evidence preservation scheme. The scheme utilizes the characteristics of blockchain's immutability to achieve credible forensics of electronic evidence on the chain and employs the decentralized storage of the interplanetary file system for secure and efficient off-chain storage. Simultaneously, it resolves the problem of declining throughput due to limited block capacity. Additionally, it leverages smart contract technology to encompass major aspects of the judicial process, including user case registration, authority management, judicial evidence uploading and downloading, case data sharing, partial disclosure of case information, and regulatory review. Simulation experiments demonstrate that the scheme significantly improves throughput and stability. Performance tests indicate that the transfer speed of the interplanetary file system can meet the data-sharing needs among judicial organizations.


Protecting Sensitive Data with Secure Data Enclaves

Abstract: A Secure Data Enclave is a system that allows data owners, such as governments and private firms, to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party's access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave, provide a reference architecture for its implementation on Amazon Web Services using managed cloud services, and describe four use cases of this architecture in partnerships with state governments to control access to sensitive administrative data.


“Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System

Abstract: With the advancement of AI, even people without professional experience can create artworks using AI-based image generation systems like DALL-E 2. However, little is known about how users interact with these new AI algorithms, much less how AI-infused systems can be designed. We explore the user experience of these new technologies and their potential to foster creativity. A user study was carried out where 13 participants executed tasks of creating artworks using DALL-E 2 alongside in-depth interviews related to their experience. The results showed that users had ambivalent opinions regarding the algorithm's performance. When users were informed of the system's capabilities, they subsequently utilized more specific prompts to generate the intended output. Users also optimized their prompts (the queries they entered to create artworks) based on how algorithms worked to achieve their desired outcome. The users wanted a two-way interaction where AI explained the outcome and accepted feedback rather than simply accepting unilateral instructions. We discuss the implications for designing interfaces that maximize creativity while providing comfort for the users.


A shape-aware enhancement Vision Transformer for building extraction from remote sensing imagery

Abstract: Convolutional neural networks (CNN) have been developed for several years in the field of extracting buildings from remote sensing images. Vision Transformer (ViT) has recently demonstrated superior performance over CNN, thanks to its ability to model long-range dependencies through self-attention mechanisms. However, most existing ViT models lack shape information enhancement for the building objects, resulting in insufficient fine-grained segmentation. To address this limitation, we construct an efficient dual-path ViT framework for building segmentation, termed shape-aware enhancement Vision Transformer (SAEViT). Our approach incorporates shape-aware enhancement module (SAEM) that perceives and enhances the shape features of buildings using multi-shapes of convolutional kernels. We also introduce multi-pooling channel attention (MPCA) to exploit channel-wise information without squeezing the channel dimension. Furthermore, we propose a progressive aggregation upsampling model (PAUM) in the decoder to aggregate multilevel features using a progressive upsampling methodology, coupled with the utilization of the soft-pool algorithm operating on the channel axis. We evaluate our model on three public building datasets. The experimental results show that SAEViT obtains a significant improvement on various datasets, confirming its efficacy. Compared with several state-of-the-art models, SAEViT achieves a comprehensive transcendence in overall performance.


StegoBackoff: Creating a Covert Channel in Smart Grids Using the Backoff Procedure of IEEE 802.11 Networks

Abstract: A smart grid constitutes an electrical infrastructure that integrates communication technologies to optimize electricity production, distribution, and consumption. Within the smart grid, IEEE 802.11 networks play a crucial role in facilitating communication between smart meters and data collectors, operating within a shared transmission medium. However, a notable challenge arises due to the lack of certainty regarding the genuine identity of data recipients. In response, we present a solution—a novel covert channel leveraging the IEEE 802.11 backoff procedure—to transmit data that requires special protection. Implemented using the ns-3 simulator, our covert channel achieved a throughput of 140,000 bps when single covert station realized transmission in the wireless channel, and 880 bps in a populated environment characterized by high traffic volumes. This performance metric shows that our mechanism is better than other covert channels, where the performance in saturated conditions usually does not exceed several hundred bps. This covert channel represents a new approach to fortifying data integrity and privacy within smart grid communication.


Fear of Missing Out: Constrained Trial of Blockchain in Supply Chain

Abstract: Blockchain’s potential to revolutionize supply chain and logistics with transparency and equitable stakeholder engagement is significant. However, challenges like scalability, privacy, and interoperability persist. This study explores the scarcity of real-world blockchain implementations in supply chain and logistics since we have not witnessed many real-world deployments of blockchain-based solutions in the field. Puzzled by this, we integrate technology, user experience, and operational efficiency to illuminate the complex landscape of blockchain integration. We present blockchain-based solutions in three use cases, comparing them with alternative designs and analyzing them in terms of technical, economic, and operational aspects. Insights from a tailored questionnaire of 50 questions addressed to practitioners and experts offer crucial perspectives on blockchain adoption. One of the key findings from our work shows that half of the companies interviewed agree that they will miss the potential for competitive advantage if they do not invest in blockchain technology, and 61% of the companies surveyed claimed that their customers ask for more transparency in supply chain-related transactions. However, only one-third of the companies were aware of the main features of blockchain technology, which shows a lack of knowledge among the companies that may lead to a weaker blockchain adaption in supply chain use cases. Our readers should note that our study is specifically contextualized in a Netherlands-funded national project. We hope that researchers as well as stakeholders in supply chain and logistics can benefit from the insights of our work.


Quantum Tomography: From Markovianity to Non-Markovianity

Abstract: The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE)-based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography, utilizing some symmetry properties of the target. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.


Forensic Audio and Voice Analysis: TV Series Reinforce False Popular Beliefs

Abstract: People’s perception of forensic evidence is greatly influenced by crime TV series. The analysis of the human voice is no exception. However, unlike fingerprints—with which fiction and popular beliefs draw an incorrect parallel—the human voice varies according to many factors, can be altered deliberately, and its potential uniqueness has yet to be proven. Starting with a cursory examination of landmarks in forensic voice analysis that exemplify how the voiceprint fallacy came about and why people think they can recognize people’s voices, we then provide a thorough inspection of over 100 excerpts from TV series. Through this analysis, we seek to characterize the narrative and aesthetic processes that fashion our perception of scientific evidence when it comes to identifying somebody based on voice analysis. These processes converge to exaggerate the reliability of forensic voice analysis. We complement our examination with plausibility ratings of a subset of excerpts. We claim that these biased representations have led to a situation where, even today, one of the main challenges faced by forensic voice specialists is to convince trial jurors, judges, lawyers, and police officers that forensic voice comparison can by no means give the sort of straightforward answers that fingerprints or DNA permit.


More than malware: unmasking the hidden risk of cybersecurity regulations

Abstract: Abstract Cybersecurity investments are made within a complex and ever-evolving environment, where regulatory changes represent a significant risk factor. While cybersecurity regulations aim to minimize cyber risks and enhance protection, the uncertainty arising from frequent changes or new regulations can significantly impact organizational response strategies. This paper explores the determinants and implications of regulatory risks associated with cybersecurity, aiming to provide a deeper understanding of how these risks influence strategic decision-making. The study delves into the suggestion of preventive and mitigative controls that enable businesses to adapt to and mitigate potential disruptions caused by regulatory changes, thereby preserving their established cybersecurity practices. Another key contribution of this study is the introduction of a stochastic econometric model that illustrates how regulatory risks and uncertainties can affect investment behaviors, often prompting a “wait-and-see” stance. This model synthesizes the complex relationship among investment choices, regulatory changes, and cybersecurity risks, providing insights into the dynamic nature of cybersecurity investment strategies. The research findings offer valuable guidance for risk management and strategic planning in cybersecurity investments. By comprehensively understanding the drivers and impacts of regulatory risks, businesses and policymakers can develop more effective risk evaluation and management approaches. This is essential for sustaining a strong cybersecurity posture while navigating the changing regulatory environment.


Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix

Abstract: In this paper, two randomized block Kaczmarz methods to compute inner inverses of any rectangular matrix A are presented. These are iterative methods without matrix multiplications and their convergence is proved. The numerical results show that the proposed methods are more efficient than iterative methods involving matrix multiplications for the high-dimensional matrix.


Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

Abstract: This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.


Granite Exposure Mapping Through Sentinel‐2 Visible and Short Wave Infrared Bands

Abstract: Abstract Nonmetallic minerals like granite and limestone have calcite and biotitic ingredients as their major part which exhibit wonderful absorption features in the visible and short wave range of the electromagnetic spectrum. This research puts emphasis on delineating granite and limestone deposits of the Mardan district through the latest multispectral Landsat‐9 and Sentinel‐2 sensors of which the latter provided 94% mapping accuracy in delineating granites (biotitic bearing minerals) and limestone (calcite‐bearing minerals). The Image processing techniques of minimum noise fraction, which is double cascaded principal components analysis and pixel purity index algorithms proved helpful to bring significant improvements in classification results and in the reduction of noise and data size. The outcomes of the research study show that supervised machine learning algorithms are impactful to map such minerals provided that the data must be well organized and limited in size. The results achieved were verified through validation steps like, (a) Independent reference data of high‐resolution Google Earth maps and (b) Ground survey reports. Arc GIS 10.2 and Envi 5.3 software suite were used to create (a) ground truth points at random for accuracy assessment (b) portraying study area maps (c) Image Processing and Preprocessing tools and (d) implementation of machine learning algorithms. Access to the data and software suite is being provided for open research work.


Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Abstract: Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.


Model of IoT design decision-making processes in Flow Based Programming systems

Abstract: The aim of the article is to present a model of decision-making processes in the design of flow-based Internet of Things systems. This is an attempt to fill the research gap in the construction of decision-making systems for designing both infrastructure and the application development environment. Therefore, the aim of the article is to show a decision-making model for selecting infrastructure and design environment for the development of IoT systems. In order for these processes to be implemented, it is necessary to use containerization processes and Flow Based Programming systems, both at the stage of system prototyping and its subsequent implementation.


A Comparison of One- and Two-Handed Gesture User Interfaces in Virtual Reality—A Task-Based Approach

Abstract: This paper presents two gesture-based user interfaces which were designed for a 3D design review in virtual reality (VR) with inspiration drawn from the shipbuilding industry’s need to streamline and make their processes more sustainable. The user interfaces, one focusing on single-hand (unimanual) gestures and the other focusing on dual-handed (bimanual) usage, are tested as a case study using 13 tasks. The unimanual approach attempts to provide a higher degree of flexibility, while the bimanual approach seeks to provide more control over the interaction. The interfaces were developed for the Meta Quest 2 VR headset using the Unity game engine. Hand-tracking (HT) is utilized due to potential usability benefits in comparison to standard controller-based user interfaces, which lack intuitiveness regarding the controls and can cause more strain. The user interfaces were tested with 25 test users, and the results indicate a preference toward the one-handed user interface with little variation in test user categories. Additionally, the testing order, which was counterbalanced, had a statistically significant impact on the preference and performance, indicating that learning novel interaction mechanisms requires an adjustment period for reliable results. VR sickness was also strongly experienced by a few users, and there were no signs that gesture controls would significantly alleviate it.


Number detection of cylindrical objects based on improved Yolov5s algorithm

Abstract: As deep learning continues to advance, object detection technology holds potential and promising prospects in the recognition of cylindrical objects’ quantity, such as in industries like timber processing, construction, and pipeline engineering. The traditional manual counting methods have lower efficiency, a higher error rate, and demand a greater amount of manpower. The introduction of object detection technology can effectively address these issues, enhance work efficiency, and reduce labor costs. Therefore, this research paper introduces a novel variant of the YOLOv5s algorithm, called YOLOv5-COC, specifically designed to tackle the task of counting cylindrical objects. This paper makes the following significant contributions: Firstly, introducing the utilization of data augmentation techniques to augment the dataset, thereby enhancing the generalization ability of the model. Secondly, the K-means++ algorithm is employed as an alternative to the conventional K-means algorithm in order to enhance the initialization of anchor boxes. Thirdly, introduce distinct methodologies, including the incorporation of a coordinated attention mechanism, the amalgamation of the Bidirectional Feature Pyramid Network (BiFPN), and the substitution of the loss function, in order to further refine the model and enhance its recognition precision. Finally, employ ablation experiments to assess the optimization outcomes of the aforementioned methodologies. The experimental results reveal that the YOLOv5-COC model proposed in this study attains an mAP of 98.7%, operates at a frame rate of 60 FPS, attains a Precision of 98.3%, and boasts a Recall of 99.1%. The mAP@0.5:0.95 stands at 72.4%. In comparison to the original YOLOv5s model, the mAP value exhibits an improvement of 1.3%, the FPS experiences a remarkable surge of 27.7%, detection accuracy elevates by 1%, the recall rate advances by 1%, and the mAP@0.5:0.95 escalates by 3.5%. In summary, the YOLOv5-COC model demonstrates a sufficiently high level of accuracy in object detection tasks, mitigating instances of both false negatives and false positives. It efficiently accomplishes the task of object detection.


A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining

Abstract: In an e-commerce platform recommendation system, accurately mining users' preferences and hidden information has always been an important initiative. In this study, based on the potential preferences and hidden characteristics of users, we propose a recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining. First, considering the advantages of the VGAE (variational Graph Auto-Encoder) in reconstructing data samples, we propose an EI-VGAE (Explicit Information-Variational Graph Auto-Encoder) to compensate for the lack of explicit feedback. Second, to address the problem that hidden information in e-commerce platforms is difficult to mine, an xDeepFM (eXtremely Deep Factorization Machine) is used to jointly learn higher-order interaction features explicitly and implicitly. A time decay function is also introduced to reduce the impact of higher-order interaction features on the representation of hidden information. Finally, considering the advantages of the attention mechanism for obtaining implicit relational attention and the quantification of the user's degree of preference in local representation, a GAT (Graph Attention Network)-based recommendation model for the e-commerce platform EI-GAT (Explicit and Implicit Graph Attention Network) is proposed. Our experiments show that the proposed method effectively alleviates the problem of low recommendation accuracy owing to insufficient explicit feedback from consumers and further improves recommendation accuracy by quantifying user preferences.


Integrating LSTM and NLP techniques for essay generation

Abstract: This research explores the potential of Long Short-Term Memory (LSTM) and Natural Language Processing (NLP) for automated essay generation. The goal of the study is to create a model that can produce high-quality essays that are not only grammatically correct but also semantically meaningful and contextually relevant. The rise of NLP and Deep Learning has made it possible to generate text that is coherent and semantically sound. In this research, there is an advantage by leveraging the ability of LSTMs to capture long-term dependencies and context within the text, and combining it with NLP techniques, such as word embeddings, to process and encode textual data. The results of experiments show that the proposed model can effectively generate essays that are coherent, contextually relevant, and semantically meaningful. This is a significant advancement in the field of text generation and has potential applications in areas such as education, content creation, and language translation. In education, for example, the model could be used to generate essays for language proficiency tests or as a writing aid for students. In content creation, it could be used to generate articles, blog posts, and other written content. In language translation, the model could be used to generate essays in the target language that are semantically and contextually equivalent to the source language essay. The findings of this study contribute to the advancement of NLP and deep learning techniques in the area of text generation and open up new avenues for future research. Short after the proposed model was deployed, it was discovered that it outscored Multi-Topic Aware LSTM (MTA-LSTM), Topic-Attention LSTM (TAT-LSTM), and Topic-Averaged LSTM (TAV-LSTM) in human evaluation by 6.84 percent, 25.40 percent, and 34.94 percent, respectively. Furthermore, it enhanced automatic BLEU score evaluation scores by 11.68 percent, 26.23 percent, and 54.11 percent in MTA-LSTM, TAT-LSTM, and TAV- LSTM, respectively.