Measuring gaze angle changes to maintain fixation upon a small target during motion: 3D motion tracking versus wearable eye-tracker

Abstract: Recently we demonstrated how changes in gaze angle can be determined without an eye-tracker. The approach uses 3D motion-capture, to track the viewed target in the head’s reference frame and assumes head or target movement causes a gaze-angle change. This study determined the validity of this “assumed-gaze” method. Participants read information presented on a smartphone whilst walking. Changes in gaze angles were simultaneously assessed with an eye-tracker and our assumed-gaze method. The spatial and temporal agreement of the assumed-gaze approach with the eye-tracker were ∼1deg and ∼0.02s, respectively, and spatial congruence indicated the direction of changes in the assumed-gaze angle were in accordance with those determined with the eye tracker for ∼81% of the time. Findings indicate that when the head is moving and gaze is continually directed to a small target, our assumed-gaze approach can determine changes in gaze angle with comparable precision to a wearable eye-tracker.


FDNet: Feature decoupling for single-stage pose estimation in complex scenes

Abstract: Severe error accumulation is a key reason that makes existing multi-person pose estimation challenging in complex scenes. Single-stage pose estimation methods detect human and keypoints in parallel, avoiding serial error accumulation compared to two-stage methods. However, two distinct tasks share identical features in the existing method, which causes error accumulation. Therefore, we propose a Feature Decoupling Network (FDNet) to further extend the pipeline of single-stage methods and reconstruct task-specific features, including Feature Decoupling Module (FDM), Human Aware Loss (HAL) and Keypoint Aware Loss (KAL). FDM can adaptively perceive spatial and channel features and allocate separate feature domains for various tasks. To learn distinctive feature representation for compact human bodies, HAL and KAL measure and suppress feature similarities and distances for different human and keypoints, thus alienating feature interference. Experiments on crowded datasets show that our method is superior, outperforming the state-of-the-art method CID by 1.5%, 0.8% on OCHuman and CrowdPose.


LL-WSOD: Weakly supervised object detection in low-light

Abstract: Weakly supervised detection performs significantly lower than the fully supervised methods due to the lack of detailed and precise annotations. Especially, its performance deteriorates more severely in low-light conditions with the lack of low-light datasets. To overcome these issues, we propose a new Low-Light Weakly Supervised Object Detection (LL-WSOD) framework. First, we propose a Progressive Low-light NoiseModule (PLNM) to train the model progressively with low light using the common datasets with normal light, greatly reducing the training difficulty. Next, a Residual Self-refinement Low-light Rebuild Module (ResSLRM) is proposed to allow convolutional neural networks to learn sharper features by rebuilding low-light features into normal-light images. Finally, a Pseudo Boundingbox Assisted Learning Module (PBALM) is designed to perform better low-light training using salient priors. The results show that the proposed LL-WSOD algorithm effectively detects objects under low-light conditions and achieves great results on the real low-light dataset ExDark.


Codes and pseudo-geometric designs from the ternary m-sequences with Welch-type decimation d = 2 ⋅ 3(−1)/2 + 1

Abstract: Pseudo-geometric designs are combinatorial designs which share the same parameters as a finite geometry design, but are not isomorphic to that design. As far as we know, many pseudo-geometric designs have been constructed by the methods of finite geometries and combinatorics. However, none of pseudo-geometric designs with the parameters S(2,q+1,(qn−1)/(q−1)) is constructed by the approach of coding theory. In this paper, we use cyclic codes to construct pseudo-geometric designs. We firstly present a family of ternary cyclic codes from the m-sequences with Welch-type decimation d=2⋅3(n−1)/2+1, and obtain some infinite family of 2-designs and a family of Steiner systems S(2,4,(3n−1)/2) using these cyclic codes and their duals. Moreover, the parameters of these cyclic codes and their shortened codes are also determined. Some of those ternary codes are optimal or almost optimal. Finally, we show that one of these obtained Steiner systems is inequivalent to the point-line design of the projective space PG(n−1,3) and thus is a pseudo-geometric design.


Secret image sharing in the encrypted domain

Abstract: Nowadays, digital images have a greatly widespread and profound influence on human life, and its security protection problem is increasingly prominent. Image encryption is one of the most effective solutions for image protection but may result in secret concentration or single point of failure. Secret image sharing (SIS) with loss tolerance provides a feasible method to protect secret images in a distributed way. However, existing SIS schemes are most utilized in natural images, and the application of SIS to encrypted images which are widely used in practical scenarios is rarely discussed. Thus, this paper proposes a novel concept of SIS in the encrypted domain (SIS-ED) for the first time on the basis of synthesizing the merits of SIS and image encryption. SIS-ED integrating the encryption and SIS means applying general or user-defined encryption algorithms to encrypt the secret image in the plaintext domain, and in the encrypted domain using appropriate SIS schemes on the encrypted image to share secrets. SIS-ED can prevent secret images from being reconstructed illegally and enhance security against malicious attacks such as differential attacks, which can be widely applied in practical scenarios with high-security requirements such as authentication, blockchain, and cloud systems. In addition, we present a concrete SIS-ED scheme to instantiate the SIS-ED model. The proposed SIS-ED scheme with high security can realize lossless recovery without pixel expansion, and reduced-size shares. Theoretical analysis, experiments, and comparisons are conducted to show the effectiveness and advantages of SIS-ED.


Multisensor Multitarget Tracking Arithmetic Average Fusion Method Based on Probabilistic Time Window

Abstract: The deployment of sensors in multisensor network systems needs to measure the actual state of the target. There may be significant errors in transmitting information to each sensor node through the sensor network system, which cannot accurately reflect the actual target state, resulting in the sensor network generating wrong measurements and instructions. The accuracy of target state estimation is affected by two factors: random noise in the process of sensor measurement and random delay of sensor data information in the network transmission process. Because of the above problems, this article designs a probabilistic time-window multisensor arithmetic average (AA) fusion multitarget tracking algorithm that can resist these interference factors, compensate for the random delay, and calculate the correct probability cumulative value of different sensor nodes in the time window, and then adjusts the probability hypothesis density filter update value as the multisensor fusion weight to reduce the negative impact of random interference on the sensor measurement. The simulation results show that under the condition of accurate probability information of random noise and random delay, compared with the traditional AA fusion and generalized covariance intersection (GCI) fusion algorithms, the proposed probabilistic time-window multisensor network AA fusion algorithm has the most minor tracking error and is more suitable for complex tracking scenarios with clutter and detection probability changes.


Determinants of business management in the digital age

Abstract: Abstract Purpose The 21st century is an era of rapidly expanding digitization in which cyber security is a key global challenge. Digitization creates cyber security risks. One of the problems of cyber security are issues related to accessibility of an organization's systems. The subject of this article is the management of an enterprise in the digital age. Digital amenities simultaneously generate new development opportunities and dynamic progress for enterprises, which are also susceptible to the threats occurring in cyberspace. The analytical goal was to identify the determinants of enterprise management in the context of their operation in cyberspace, which is characterized by a lack of borders, dynamism, and anonymity, and represents one of the most expanding areas of threats in the modern world. Design/methodology/approach In achieving the goal, the starting point was to present the cyber security of the business entity as an area that has a decisive impact on the management of the company in the digital age. Then, in order to analyse the actual market situation, part of the survey research on cyber security in the business unit was presented. In May 2022, survey research using a questionnaire method by the author of this article with the use of a telephone interview was conducted on behalf of the IPC Research Institute Ltd. based in Wroclaw. The survey was conducted on a sample of 100 enterprises from various industries operating on the territory of the Republic of Poland. The following research methods and techniques were used in the article: survey methods, induction methods as a form of going from the particular to the general, analytical methods, deduction method as a form of generalization and inference, literature analysis. Findings Research has confirmed that the cyber security of the business unit is a key determinant in the digital age. The publication indicates the need to conduct and develop research on the determination, analysis, and presentation of mechanisms for managing the business unit in the area of cyber security as a security protection in the digital age. Originality/value The value of the article is the analysis of the author's survey conducted on a sample of 100 companies from various industries operating in the Republic of Poland, and the identification of cyber security as one of the main challenges in the digital age. The article is addressed to executives in order to make them aware of the need to conduct an ongoing analysis of the company's performance in cyberspace in order to locate weak areas of operations and adjust the strategy accordingly to the company's capabilities and goals.


A novel robust multivariate Laplace distribution-based distributed consensus information fusion

Abstract: The multivariate Laplace distribution (MLD) possesses a lesser set of prior parameters compared to the Student's t-distribution, thus simplifying the modeling of measurement data subject to heavy-tailed noise corruption. This short communication introduces a novel distributed hybrid consensus fusion algorithm utilizing the MLD modeling and variational Bayesian approach. Firstly, we formulate the measurement noise associated with individual sensor nodes and introduce the hierarchical Gaussian form of MLD to enhance the feasibility of variational inference. Secondly, we opt for the inverse-Wishart (IW) distribution to serve as the conjugate prior to the positive definite matrix, enabling adaptive parameter learning within the MLD context. As a result, a novel hierarchical Gaussian state–space model (SSM) is established for multi-sensors network. Subsequently, we derive a novel local information filter to acquire higher precision information and innovation vector–matrix pairs, outperforming the Student's t distribution-based filter. These derivations are grounded in the new hierarchical SSM, and the vector–matrix pairs aid in information fusion across the network. Lastly, we present a distributed robust consensus filter for fusing local information using a hybrid consensus approach that devoid of a central fusion node. Simulation results demonstrate that the proposed consensus filter can obtain better estimation accuracy under the interference of heavy-tailed measurement outliers.


Detect malicious websites by building a neural network to capture global and local features of websites

Abstract: With the development of the digital age, the Internet has become an integral part of our daily lives. However, it has also brought about a series of security challenges, among which malicious websites are particularly prominent. These websites often lure ignorant users by disguising themselves as legitimate services or through various fraudulent means to commit identity theft, distribute malware, or launch other forms of cyberattacks. Therefore the detection of malicious websites is very necessary. Traditionally, many malicious website detection methods rely on machine learning techniques, some of which require manual extraction of features, which may result in a time-consuming prediction process. Despite the existence of machine learning models that can automatically extract features, including unsupervised ones, capturing the subtleties of malicious website features is still a challenge. In recent years, deep learning has been gaining attention as a method for automated feature learning. It is capable of capturing and understanding the content of a website in greater depth, thus making classification and detection more accurate and efficient. Although deep learning shows its potential in capturing advanced features, its performance depends on the input data and the chosen model architecture. Both efficiently constructing feature representations of input data and building efficient model architectures to capture features are currently major challenges. For this reason, we propose a new approach for malicious website detection. This method uses wordpiece-level features to represent the information of malicious websites. Combination of multi-filter text convolutional neural network and multi-head self-attention mechanism is used for model construction. This enables the model to capture both global and local features of the input data. Compared to common deep learning methods, our approach captures the features of malicious websites better.


Augmented reality training for improved learnability

Abstract: In the current era of Industry 4.0, many new technologies offer manufacturing industries to achieve high productivity. Augmented Reality (AR) is one of the emerging technologies that has been adopted in industries to aid users in acquiring complex skills and carrying out many complicated tasks such product assembly and maintenance. Nevertheless, most AR applications have been developed without clear understanding of how such technology can facilitate improved learnability in terms of knowledge reusability. This paper proposed an enhanced AR-based training system that provides multimodal information with a contextualized information to improve task comprehension and knowledge reusability compared with traditional AR that presents unimodal and decontextualized information. An empirical test was carried out to assess the task performance and the task learnability aspects of this enhanced AR compared to the traditional AR and the paper-based document. The experiment consisted of a training phase where participants carried out an electrical connection task of a sensor followed by a knowledge reuse phase where participants had to wire a second sensor using their previous training. A pre-test quiz was given before the experiment followed by the post-tests phase after the training. Post-tests consist of one post-test given directly after the experiment (short-term retention test) and a second post-test quiz given one week later (long-term retention test) to measure information retention. The results indicated that AR-based approaches could enhance knowledge acquisition by around 18 % for traditional AR and almost 25 % for enhanced AR as compared to paper-based approach. While all training systems achieved relatively equivalent well for short-term retention test, trainees who used the enhanced AR training systems statistically outperformed those in the paper-based group for long term retention test. Furthermore, there was a positive correlation between the score of short-term retention test and the score in the knowledge reusability which was also shown by the higher scores in knowledge reusability for the enhanced AR training system compared to the other two approaches. These findings are discussed in relation to the Industry 5.0′s human centric core value.


AdvEWM: Generating image adversarial examples by embedding digital watermarks

Abstract: With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific ways (also referred to as adversarial examples) aiming to mislead the DNN models to output incorrect classification results. Similarly, many efforts are proposed to detect and mitigate adversarial examples, usually for certain dedicated attacks. In this paper, we propose a novel framework called AdvEWM based on embedding digital watermark to generate image adversarial examples for fooling DNN models. Specifically, partial main features of the watermark image are embedded into the host image almost invisibly, aiming to tamper with and damage the recognition capabilities of the DNN models. We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples. The experimental results illustrate that the attack success rate on common DNN models can reach an average of 95.47% on the CIFAR-10 dataset and the highest at 98.71%. Besides, our scheme is able to generate a large number of adversarial examples efficiently, concretely, an average of 1.17 s for completing the attacks on each image on the CIFAR-10 dataset. In addition, we design a baseline experiment using the watermark images generated by Gaussian noise as the watermark image dataset that also displays the effectiveness of our scheme. Similarly, we also propose the modified discrete cosine transform (DCT) based Patchwork watermarking algorithm. To ensure repeatability and reproducibility, the source code is available on GitHub.


Decidim.viz: A data dashboard prototype for a digital democracy platform

Abstract: In this work, we present Decidim.viz, a tool to display metrics and statistical information about Decidim instances. Decidim is a digital platform for political participation. We have created a Proof of Concept that analyses a participatory process launched by the European Commission in 2022. Decidim.viz was co-designed with the community of users and researchers of Decidim to guarantee the usefulness of the tool. Decidim.viz is designed as a client-server application that has a web application as a user interface. In addition, the tool provides a RESTful API to make its data reproducible and available to the community.


MNEARO: A meta swarm intelligence optimization algorithm for engineering applications

Abstract: Artificial rabbits (AR) optimization is a newly proposed swarm intelligence algorithm. The idea of AR optimization (ARO) is inspired by the nature behaviors of the AR including the detour foraging, the random hiding and the energy shrink. Although ARO has better performance than many classical natural heuristic algorithms, it still has some disadvantages such as lacking the diversity of the population and easy to trap in the local optimal. To mitigate the influence of the above defects, this paper respectively introduces the mutation strategy, the prey identification strategy and the elite opposition-based learning strategy into the original algorithm, and proposes a new meta swarm intelligence optimization algorithm termed MNEARO. Firstly, the mutation strategy is introduced into the two natural behaviors of ARO to expand its exploration range in the solution space and increase the probability of finding the global optimal. Secondly, replacing the captured individuals with the predators that take better action trajectories to achieve the population evolution. Another candidate population is generated by the prey identification strategy, thus improving the population diversity and dynamics of ARO. Finally, the elite opposition-based learning strategy improves the exploitation ability of ARO and the overall quality of the population by selecting some individuals with the better fitness values as the elites and using the greedy selection before the end of each iteration. A series of performance tests are conducted on the CEC2020 test set in four dimensions and the CEC2022 test set in the highest dimension respectively to verify that the proposed MNEARO has better convergence and robustness. In addition, the practicality of MNEARO is further highlighted through five mechanical optimization designs, two truss topology optimization designs and the vehicle cruise control system. The experimental results show that the proposed MNEARO has a strong competitiveness in solving complex optimization problems with different dimensions.


Modeling Awareness Requirements in Groupware: From Cards to Diagrams

Abstract: Up to now, groupware has enjoyed a certain stability in terms of the users' technical requirements, being the awareness dimension one of its key services to provide usability and improve collaboration.Nonetheless, currently, groupware technologies are being stressed: on the one hand, the pandemic of COVID-19 has greatly driven the massive use of groupware tools to overcome physical distancing; on the other hand, the new digital worlds (with disruptive devices, changing paradigms, and growing productive needs) are introducing new collaboration settings.This, and the fact that software engineering methods are not paying enough attention to the awareness, makes us concentrate on facilitating its design.Thus, we have created a visual modeling technique, based on a conceptual framework, to be used by the developers of groupware systems to describe awareness requirements.This visual language, called the awareness description diagrams, has been validated in some experimental activities.The results obtained show that this is a valid technique in order to model the awareness support, that it is useful and understandable for groupware engineers, and that the visual representation is preferred to a more textual one in terms of expressiveness.


<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si240.svg" display="inline" id="d1e223"><mml:mi mathvariant="normal">SimplMM</mml:mi></mml:math>: A simplified and abstract multicore hardware model for large scale system software formal verification

Abstract: This paper introduces SimplMM, a novel subsystem within the Certified Concurrent Abstraction Layers (CCAL) modular software verification framework, designed specifically for fine-grained concurrent software. SimplMM aims to provide a generic, practical, and realistic multicore machine model for verifying software within the CCAL framework. While formal multicore hardware semantics have seen extensive development, their integration with large-scale software verification has received limited attention. To address this gap, we propose a novel approach: a toolkit comprising a generic sequentially consistent multicore semantics, contextual refinement templates, and libraries. These components establish crucial connections between the machine model and verified program modules (layers) using CCAL. We demonstrate the practicality of our framework by successfully integrating it with existing large-scale proofs, specifically for CertiKOS running on top of the x86 hardware architecture. This research significantly advances the field of accurate and efficient concurrent software verification and development tools for multicore systems. Our provision of a practical and formal multicore machine model, seamlessly integrated within the CCAL framework, equips developers with a powerful toolkit for large-scale concurrent software verification. The effectiveness of our approach, validated through successful integration with existing large-scale proofs such as CertiKOS, establishes a robust foundation for the design and verification of concurrent software in multicore systems.


WinTA: An Efficient Reconfigurable CNN Training Accelerator With Decomposition Winograd

Abstract: Convolutional neural networks (CNNs) are expected to bridge the domain shift between the training data and real-world tasks. Moreover, the efficient training of CNNs on resource-constrained platforms has become more important because of communication latency and privacy concerns. However, deploying CNN training on edge devices is challenging due to the intensive computation and diverse computational patterns. In this work, we firstly propose a hybrid decomposition Winograd (HDW) method that significantly reduces the number of multiplications and flexibly handles various convolution operations during training. Secondly, we design a reconfigurable CNN training accelerator, named WinTA, utilizing a set of unified transformation units to support various Winograd operations. Thirdly, we implement an efficient and flexible data access scheme using a hierarchical barrel shifter network (HBSN). Experimental results on the Xilinx Alveo U50 FPGA Card demonstrate that WinTA effectively accelerates CNN training. Compared to CPU and GPU implementations, WinTA achieves speedups of 7.1 texttimes and 1.65 texttimes, respectively, while improving energy efficiency by 26.6 texttimes and 10.4 texttimes, respectively. Additionally, our design provides 1.24 texttimes and 2.04 texttimes improvements in terms of throughput and resource efficiency compared to prior-art FPGA-based training accelerator.


CBD2023: A Hypercomplex Bangla Handwriting Character Recognition Data for Hierarchical Class Expansion

Abstract: Object recognition technology has made significant strides, but recognizing handwritten Bangla characters (including symbols, compound forms, etc.) remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script and individual's unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. Compared to other major languages, there are limited options for recognizing handwritten Bangla characters. This research has described a new dataset to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language spoken by over 200 million people worldwide. This dataset aims to investigate and recognize Bangla handwritten characters, focusing on enlarging the recognized character classes. To achieve this, a new challenging dataset for handwriting recognition is introduced, collected from numerous students' handwriting from two institutions.


Role of boiling for the formation of vertical metal zoning and bonanza zone in low-sulfidation epithermal gold‑silver deposits: A geochemical modeling approach

Abstract: Boiling and mixing are thought to be the most important processes for mineralization in the low-sulfidation epithermal gold‑silver deposits. Some low-sulfidation epithermal gold‑silver deposits show vertical zoning of metals including gold, silver, copper, lead, and zinc, but effect of boiling and mixing for the vertical zoning of metals through mineralization has been scarcely debated. Therefore, we applied geochemical modeling for an open-isenthalpic boiling system, with a hydrothermal fluid with different initial temperatures, carbon dioxide contents, salinities, and total sulfur contents to numerically evaluate the effect of boiling phenomenon for the vertical zoning in low-sulfidation epithermal gold‑silver deposits. Geochemical modeling of boiling phenomenon suggests that boiling is effective for depositing gold, silver, copper, lead, and zinc and that it could form a bonanza zone enriched in gold and silver in the shallow zone above the lead and zinc-rich zone. Temperature, carbon dioxide content, salinity, and hydrogen sulfide content affect the deposited amount of metals, vertical extent, depth of mineralization, and gold/silver ratio of the ore zone, although other factors such as permeability, gangue mineralogy, duration of mineralization, kinetics of mineral precipitation, change in porosity, thermal conductivity, and fractures of host rocks could affect mineralization as well. Our modeling of boiling phenomenon demonstrates similar characteristics with natural low-sulfidation epithermal gold‑silver deposits, suggesting that boiling is an important mechanism to cause vertical metal zoning in the low-sulfidation epithermal gold‑silver deposits, even though mixing could also be important to precipitate metals in some deposits.


Quantum sensitivity of squeezed Schrodinger cat states

Abstract: Theoretically it is shown that high quantum sensitivity of the squeezed state of the Schrodinger cat relative to the non-squeezed state can be achieved. For realization of squeezing the Schrodinger cat state, a parametric process in a nonlinear optical crystal is used. To visualize the squeezed state of the Schrodinger cat with and without a displacement, the values of the Wigner functions are calculated.


Joint-ID: Transformer-Based Joint Image Enhancement and Depth Estimation for Underwater Environments

Abstract: Underwater imaging is a challenging task due to factors such as scattering, absorption, and turbulence, which degrade image quality and limit visibility. In this article, we propose a novel approach for enhancing underwater images that leverages the benefits of joint learning for simultaneous image enhancement and depth estimation. We introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint-ID</monospace> , a transformer-based neural network that can obtain high-perceptual image quality and depth information from raw underwater images. Our approach formulates a multimodal objective function that addresses invalid depth, lack of sharpness, and image degradation based on color and local texture. We design an end-to-end training pipeline that enables joint restoration and depth estimation in a shared hierarchical feature space. In addition, we propose a synthetic dataset with various distortions and scene depths for multitask learning. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint-ID</monospace> on synthetic and standard datasets, as well as real underwater images with diverse spectra and harsh turbidity, demonstrating its effectiveness for underwater image enhancement (UIE) and depth estimation. Furthermore, we demonstrate the ability of Joint-ID to perform feature matching and saliency detection for visually guided underwater robots. Our proposed method has the potential to improve the visual perception of underwater environments and benefit applications such as oceanography and underwater robotics. Supplements are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/joint-id/home</uri> .


Transformation of Bell states using linear optics

Abstract: Bell states form a complete set of four maximally polarization entangled two-qubit quantum state. Being a key ingredient of many quantum applications such as entanglement based quantum key distribution protocols, superdense coding, quantum teleportation, entanglement swapping etc, Bell states have to be prepared and measured. Spontaneous parametric down-conversion (SPDC) is the easiest way of preparing Bell states and a desired Bell state can be prepared from any entangled photon pair through single-qubit logic gates. In this paper, we present the generation of complete set of Bell states, only by applying unitary transformations of half-wave plate (HWP) on the initial Bell state. The initial Bell state is prepared using a combination of a nonlinear crystal and a beam-splitter (BS) and the rest of the Bell states are created by applying single-qubit logic gates on the entangled photon pairs using HWPs. Our results can be useful in many quantum applications, especially in superdense coding where control over basis of maximally entangled state is required.


An unsupervised end-to-end approach to fault detection in delta 3D printers using deep support vector data description

Abstract: Fault detection in 3D printers is crucial for safety and quality assurance, emphasizing proactive prediction over reactive rectification based on manufacturing factors. Presently, most detection techniques rely on shallow models with limited representational capabilities, necessitating manual feature extraction from the captured signals. This manual process is not only cumbersome and potentially costly but often requires intricate domain-specific knowledge. Additionally, these handcrafted features might not optimally distinguish between normal and faulty samples, potentially reducing prediction accuracy. In this study, we introduce an end-to-end approach using the Deep Support Vector Data Description model for fault detection in 3D printers. This design inherently facilitates automatic feature learning, where the features are synergistically optimized for fault detection. Our experiments leverage magnetic field signals for fault detection in 3D printers, using 1D convolutional layers to discern temporal signal patterns and wide kernels in the initial layer to mitigate high-frequency noise. Furthermore, our model can be easily adapted to integrate multi-channel signals for enhanced accuracy. Evaluations on real-world data from a delta 3D printer underscore the superiority of our method compared to existing alternatives.


Real-world applications of BIM and immersive VR in construction

Abstract: The integration of immersive Virtual Reality (VR) and Building Information Modeling (BIM) has many applications within the Architecture, Engineering, and Construction (AEC) industries. In this context VR is often highlighted for its ability to convey scale and details, especially when compared to non-immersive visualizations. However, despite being an active area of research, there is currently a lack of real-world studies exploring immersive VR in a construction-oriented context. In addition, there are still technical challenges and barriers for an efficient integration, such as rendering performance and interoperability issues. This paper addresses these issues by investigating the use of immersive, single- and multi-user VR within the openBIM ecosystem. The contribution is two-fold: (a) an in-depth presentation of algorithms and technical details of a multi-user VR application for immersive visualization of large and complex BIMs and (b) an evaluation of this VR system on several real-world construction projects. In all cases the VR visualization has been directly realized from the design teams IFC-models and the multi-user sessions has been performed both co-located as well as fully remote. The results show that multi-user VR improves communication, understanding, and collaboration, and by letting staff with knowledge and experience from construction production review the project in VR, design errors and constructability issues can be identified and resolved before reaching the actual production stage. Moreover, the use of VR is helpful regarding sequencing and planning, and to identify alternative design solutions.


Harnessing federated generative learning for green and sustainable Internet of Things

Abstract: The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.


Discriminative object tracking by domain contrast

Abstract: Multi-domain tracking method as a novel learning paradigm improves object tracking by sharing domain information with a common backbone whilst learning private information with domain-specific layers. In that context, each individual video sequence as a specific domain serves for a domain-specific layer. In this paper, we observe an intriguing finding that target features from different domains are highly confused with each other, thus having weak discriminative ability, for lack of domain interaction. To this end, we propose a simple yet effective domain interaction training paradigm called domain contrast to boost discriminative object features by effectively using amounts of instances from all the domains in two novel aspects: (1) a light-weight memory-saving training algorithm is proposed to solve the “out-of-the-memory” problem, which paves the way to couple with previous multi-domain trackers, and (2) a composite class-balanced loss is explored to tackle a more practical imbalanced problem, which not only involves the usual class imbalance problem but also accounts for the case of the totally mere negative instances. Experiments on multiple popular tracking benchmark datasets show that our mechanism consistently achieves the tracking performance gain of both base multi-domain tracker and its real-time variant thereof, without any other changes made on the original network.


R2: Optimal vector-based and any-angle 2D path planning with non-convex obstacles

Abstract: A novel vector-based path planner, R2 (R in two dimensions), is introduced in this paper. R2 is optimal and online, returning any-angle paths by applying heuristic costs to vector-based searches. R2 delays line-of-sight checks to expand the most promising path that has the least detours from the start and goal points. As delayed checks can cause severe path cost underestimates, R2 infers the smallest known convex hull, the best hull, of obstacles while moving around them. To construct the best hull, phantom points are introduced, which are imaginary turning points lying on non-convex corners to guide future searches. Tracing rules are introduced to ensure that the estimated path cost from the best hull increases admissibly and monotonically between queues to the open-list. The distance between the start and goal points have little impact on R2’s performance when compared to the number of line-of-sight checks that collide with obstacles. While having an exponential search time in the worst case with respect to the number of collided line-of-sight checks, R2 is much faster than state-of-the-art when the optimal path is expected to turn around few obstacles, especially on large maps with few disjoint and non-convex obstacles.


Constrained continuous-action reinforcement learning for supply chain inventory management

Abstract: Reinforcement learning (RL) is a promising solution for difficult decision-making problems, such as inventory management in chemical supply chains. However, enabling RL to explicitly consider known environment constraints is crucial for safe deployment in practical applications. This work incorporates recent tools for optimization over trained neural networks to introduce two algorithms for safe training and deployment of RL, with a focus on supply chains. Specifically, we use optimization over trained neural-network state–action value functions (i.e., a critic function) to directly incorporate constraints when computing actions in a continuous action space. Furthermore, we introduce a second algorithm that guarantees constraint satisfaction during deployment by directly implementing actions from constrained optimization of a trained value function. The algorithms are compared against state-of-the-art algorithms TRPO, CPO, and RCPO using a computational supply chain case study.


Improved zeroing neural models based on two novel activation functions with exponential behavior

Abstract: A family of zeroing neural networks based on new nonlinear activation functions is proposed for solving various time-varying linear matrix equations (TVLME). The proposed neural network dynamical systems, symbolized as Li-VPZNN1 and Li-VPZNN2, include an exponential parameter in nonlinear activation function (AF) that leads to faster convergence to the theoretical result compared to previous categories of nonlinearly activated neural networks. Theoretical analysis as well as numerical tests in MATLAB's environment confirm the efficiency and accelerated convergence property of the novel dynamics.


Improving rare relation inferring for scene graph generation using bipartite graph network

Abstract: Scene graph generation (SGG) task is plagued by insufficient and long-tailed training samples. Thus external text knowledge has been introduced into SGG task, which augments the training dataset. However, rare relations or low-frequency events, such as <dog, standing on, surfboard>, are still hard to be discovered since they also rarely appear in external texts. Addressed these issues, this paper proposes a model-agnostic Bipartite Graph Network with Dual-Group Message Passing (DG-BGN). It extends the relation space of each object pair in training dataset based on the similarity of different object pairs, which cooperates external text knowledge and internal visual information. Specifically, the main framework of DG-BGN consists of three parts, that is, bipartite graph construction (BGC), dual-group message propagation (DG-MP), and pseudo label generation (PLG). For BGC, alignment between external texts and visual dataset is performed to mine information from multimodal resources. Taking object pair as the index of the grouping, dual bipartite graph containing intra/inter-group graph is established to represent predicate label probability distribution of object pair instances. Among them, inter-group bipartite graph is built based on the similarity between object pair instance. For DG-MP, efficient message passing is running on both kinds of bipartite graph through Graph Convolution Neural Networks to refine the graphs, in order to let each object pair instance learn from its similar instances. Finally in PLG, pseudo labels, which are used to train SGG models in fully supervised way, are obtained by aggregating these refined graphs. Systematic experiments on Visual Genome dataset and Conceptual Captions show that our method performs better on discovering rare relations.


Quantum sub-phase sensitivity of a Mach–Zehnder interferometer with the superposition of Schrödinger’s cat-like state with vacuum state as an input under product detection scheme

Abstract: We study the phase sensitivity of a Mach–Zehnder interferometer (MZI) with product detection (PD) scheme by using the superposition of Schrödinger’s cat-like state with the vacuum state (SCVS) and the coherent state as the inputs. We compare the results derived from PD with the parity detection scheme’s results. We found that PD performs better than parity detection and approaches the single parameter quantum Cramér-Rao bound in some cases. Therefore, we expect that SCVS may play as an alternative quantum resource with PD for the improvement in the phase sensitivity of an MZI having potential application in quantum metrology and quantum sensing.


Predicting new hackers’ criminal careers: A group-based trajectory approach

Abstract: The current study employs group-based trajectory modeling to assess the longitudinal attack patterns of new hackers involved in website defacement. Specifically, we track the activity of 241 emergent hackers for one year following their first verified website defacement. In doing so, we find four distinct criminal trajectories: low threat (29.0%), naturally desisting (26.5%), increasingly prolific (22.3%), and persistent threat (22.1%). Hackers classified as low threats engage in few defacements, whereas persistent threats engage in high-frequency attacks. Those labeled as naturally desisting begin their careers with velocity but become less prolific with time. Conversely, those classified as increasingly prolific engage in more attacks as they advance in their criminal careers. Using a series of regression models, we find that digital artifacts and open-source intelligence are predictive of group involvement. The findings presented in this study contribute to our theoretical understanding of the developmental trajectories of hackers, while providing valuable insights in fostering targeted intervention strategies aimed at effectively mitigating and preventing cyber-attacks.


Quantum simulation for partial differential equations with physical boundary or interface conditions

Abstract: This paper explores the feasibility of quantum simulation for partial differential equations (PDEs) with physical boundary or interface conditions. Semi-discretization of such problems does not necessarily yield Hamiltonian dynamics and even alters the Hamiltonian structure of the dynamics when boundary and interface conditions are included. This seemingly intractable issue can be resolved by using a recently introduced Schrödingerisation method [1], [2] – it converts any linear PDEs and ODEs with non-Hermitian dynamics to a system of Schrödinger equations, via the so-called warped phase transformation that maps the equation into one higher dimension. We implement this method for several typical problems, including the linear convection equation with inflow boundary conditions and the heat equation with Dirichlet and Neumann boundary conditions. For interface problems we study the (parabolic) Stefan problem, linear convection, and linear Liouville equations with discontinuous and even measure-valued coefficients. We perform numerical experiments to demonstrate the validity of this approach, which helps to bridge the gap between available quantum algorithms and computational models for classical and quantum dynamics with boundary and interface conditions.


An executable domain-specific modeling language for simulating organizational auction-based coordination strategies for crisis response

Abstract: Emergency response environments in which the crisis occurs include many homogenous and heterogeneous entities, e.g., organizations and persons. These entities need to collaborate to address a crisis. However, most of the time, the organizations that are involved in a crisis suffer from communication and coordination problems. This may cause unfair resource distribution. To mitigate the problem, there is a pressing need to construct models of emergency response environments and simulate the coordination strategies proactively, before the onset of any crisis. This paper highlights the complexity of the system formed by interacting organizations during a crisis and the difficulty in programming for the simulation of such complex systems. To cope with this issue, this paper presents an executable domain-specific modeling language called CoorERE. The language facilitates modeling emergency response environments for the simulation of one of the most common coordination strategies, i.e., auction-based coordination strategies in crisis response. CoorERE is used for the graphical modeling of emergency response environments and simulation of both inter and intra-organizational auction-based coordination strategies. The paper presents two case studies with different complexities to show the applicability of CoorERE. An empirical study is conducted to compare CoorERE with MDD4ABMS, which is one of the most recent tools for modeling and simulation of multi-agent systems. The evaluation includes both objective and subjective assessments. The objective assessment considers the development effort required for modeling a complete case study, while the subjective assessment compares the tools in terms of satisfying modeling method requirements and quality metrics such as usability, productivity, and expressiveness. The results of the evaluation indicate that CoorERE reduces the development effort by about 47%. In terms of quality metrics, CoorERE obtained higher scores than MDD4ABMS for all the measurements considered, indicating its superior usability, productivity, and expressiveness.


A comprehensive knowledge map for AI improving security management of cyber-physical system enabled smart manufacturing

Abstract: The enhancement of security management for smart manufacturing has garnered considerable attention, prompting extensive research efforts that explore AI-based secure Cyber-Physical System (CPS) methods. While previous studies have made valuable contributions to this field, the current state of literature reviews still requires a comprehensive examination to construct the knowledge map for future research. This study employed scientometric analyses, including collaboration network analysis, co-occurrence network analysis, and co-citation network analysis, to examine 1133 previous articles conducted between 2015 and 2023 concerning the research topic of AI application in secure CPS-enabled smart manufacturing. The findings reveal that the academic communities in this field comprise influential authors affiliated with leading academic institutions in China, India, and the USA. The key research topics of AI application in secure CPS-enabled smart manufacturing show evolutionary trends during different research periods. Existing research on AI application in secure CPS-enabled smart manufacturing is supported by classical references, which can be divided into four significant co-citation clusters, including the theory of security and privacy in CPS, DL-based IDS, DDoS attacks on CPS by botnet, and FL method for privacy-preserved data sharing. Finally, this study predicted four potential future research directions for the research field of AI application in secure CPS-enabled smart manufacturing: the well-structured and representative dataset for model training, methods for resisting PUF attacks against ML techniques, DL-based methods for detecting and resisting botnets, and lightweight FL-based methods for privacy-preserved data sharing. The findings of this study can guide researchers in this field for future collaboration and work.


SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

Abstract: In the cloud environment, the virtual computing node has become the dominant form of cloud services used by users. Hence, it is increasingly critical to guarantee the trusted operation of the virtual computing node's operating system (VCNOS). However, previous schemes suffer a lot, such as insufficient consideration of the comprehensive of the measured objects, ignoring the dynamic of trusted, and the trusted measurement mechanisms rarely consider their security. Thus, a three-dimensional dynamic trusted measurement model SABDTM, which integrates the integrity measurement of kernel static data, trusted evaluation of operating system behavior (OSB), and the feedback trust of interacting nodes, is proposed. First, SABDTM divided OSB into multiple atomic behaviors and introduced the Bayesian decision theory to predict trusted expectations of OSBs. Second, the feedback trust of interacting nodes is considered to improve the comprehensiveness of the trusted measurement and evaluate its value based on the Euclidean distance function to reduce the impact of inaccurate feedback from malicious nodes. Subsequently, we set the appropriate weight for trusted measurement values of different moments based on Induced Ordered Weighted Averaging to accurately portray the actual state of VCNOS. Moreover, we designed a lightweight and independent subsystem to perform the trusted measurement, which guarantees the security of the measurement service. The security of our model is proved rigorously based on the non-interference theory. Finally, the experiments and comparative analysis demonstrated our model has better functionality and superiority.


Quantum computing enhanced distance-minimizing data-driven computational mechanics

Abstract: The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.


Hydra: Hybrid-model federated learning for human activity recognition on heterogeneous devices

Abstract: Federated Learning (FL) has recently received extensive attention in enabling privacy-preserving edge AI services for Human Activity Recognition (HAR). However, users’ mobile and wearable devices in the HAR scenario usually possess dramatically different computing capability and diverse data distributions, making it very challenging for such heterogeneous HAR devices to conduct effective collaborative training (co-training) with the traditional FL schemes. To address this issue, we present Hydra, a Hybrid-model federated learning mechanism that facilitates the co-training among heterogeneous devices by allowing them to train models that well fit their own computing capability. Specifically, Hydra leverages BranchyNet to design a large-small global hybrid-model and enables heterogeneous devices to train the proper parts of the model tailored to their computing capability. Hydra drives co-training among the devices and clusters them based on model similarity to mitigate the impact of HAR data heterogeneity on model accuracy. In order to deal with the issue that large model may lack sufficient training data due to the limited number of high-performance devices in FL, we introduce a pairing scheme between high and low performance devices for effective co-training, and further propose sample selection approach to select more valuable samples to participate in co-training. We then formulate a constrained co-training problem within a cluster that is proved to be NP-hard and devise a fast greedy-based heuristic algorithm to solve it. In addition, to address the low accuracy of small models, we also propose a Large-to-Small knowledge distillation algorithm for resource-constrained devices to optimize the efficiency of transferring knowledge from large models to small models. We conduct extensive experiments on three HAR datasets and the experimental results demonstrate the superior performance of Hydra for achieving outstanding model accuracy improvement compared with other state-of-the-art schemes.


Robust GEPSVM classifier: An efficient iterative optimization framework

Abstract: The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-known pattern classification method. GEPSVM, however, is prone to outliers due to its use of the squared L2-norm distance criterion. A robust GEPSVM version is proposed to tackle this problem using L1-norm distance optimization technique (GEPSVML1). As optimizing a GEPSVML1 with L1-norm terms can be challenging, we have developed an iterative algorithm to address the L1-norm ratio problem associated with GEPSVML1. Furthermore, an efficient iterative optimization framework has been developed to conveniently address related optimization problems. The research contribution of this paper lies in providing a theoretical analysis of the algorithm’s convergence. Besides, we find that the L1-norm distance-based methods in real-world applications, especially for handling samples with outliers, sometimes provides an unsatisfactory recognition result. Thus, a generalized version of GEPSVML1 is proposed. The L1-norm distance is replaced with a Lp-norm distance in GEPSVML1 (GEPSVMLp). It is the robust counterpart of GEPSVML1 and GEPSVM. It is worth noting that we fine-tune GEPSVMLp’s parameters to strike a balance between training time and classification accuracy, an especially crucial step for larger datasets. Our experiments indicate that the proposed GEPSVMLp is more efficient and robust than the competitors in numerous experimental settings. Overall, our work demonstrates the importance of developing robust pattern classification methods in the presence of outliers and provides a practical solution for handling such cases in real-world applications.


Advancing financial fraud detection: Self-attention generative adversarial networks for precise and effective identification

Abstract: The burgeoning prominence of electronic payment methods has significantly intensified the importance of financial fraud detection, especially concerning credit card fraud. The challenges encompass data imbalance, overfitting, and a limited capacity to realistically emulate the complexity and dynamics of fraudulent activities. This paper introduces self-attention generative adversarial networks (SAGANs) for the detection of credit card fraud. Leveraging self-attention mechanisms, SAGANs can distinguish salient features and patterns within extensive transaction datasets, thereby fostering a more profound understanding and refined identification of credit card fraud. By incorporating generative adversarial networks (GANs), our model is capable of generating data that mirrors actual fraudulent behavior, which consequently enhances and optimizes fraud detection algorithms. Empirical findings from numerous credit card transaction datasets underscore the considerable superiority of our approach in terms of accuracy and recall. Our proposed SAGANs more competently emulate the complexity and diversity inherent in fraudulent behavior, thereby augmenting both the precision and efficacy of fraud detection.


DPAFD-net: A dual-path adaptive fusion dehazing network

Abstract: Image dehazing is an ill-posed problem that has been extensively studied in recent years. Unfortunately, most existing deep dehazing models have high computational complexity and lack the dynamic adjustment of details, which hinders their application to high-resolution images in computational vision tasks. In this paper, we propose an efficient dual-path adaptive fusion dehazing network (DPAFD-Net) to directly restore a clear image from a hazy input. Moreover, we propose a pure subnetwork with encoder and decoder structures to further extract the structural information and progressively restore the haze-free image. To evaluate the effectiveness of the proposed method, we validate our approach on synthetic and real hazy images, where our method performs favourably against the state-of-the-art dehazing approaches.


Ensemble strategy using particle swarm optimisation variant and enhanced local search capability

Abstract: Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing suitable mechanisms at appropriate instances in the evolutionary process. This study entailed the development of an ensemble strategy that uses a variant of the modified particle swarm optimisation algorithm with a covariance matrix adapted to the retreat phase and sequential quadratic programming. The modified particle swarm optimisation algorithm employs nonlinear population size reduction and uses the candidate elite best solution as the stochastic learning strategy and the fitness-distance balance as the terminal updating mechanism. The proposed algorithm was compared with the most recently proposed particle swarm optimisation-based variants through testing on CEC2017 benchmark functions. In the experimental results, the proposed method achieved the best ranking and exhibited excellent performance. Further effectiveness tests demonstrated that the proposed combination of algorithms and mechanisms exhibits tacit cooperation and significantly improves performance.


A control theoretic framework for adaptive gradient optimizers

Abstract: Gradient-based optimization and control frameworks have been utilized in several applications. The learning rate parameter is typically chosen following a schedule or using methods such as line search to enhance the convergence rate. Recently, the machine learning community has developed methodologies for automated tuning of the learning rate, known as adaptive gradient methods. This paper develops a control theory-inspired framework for modeling adaptive gradient methods that solve non-convex optimization problems. We first model the adaptive gradient methods in a state–space framework, which allows us to present simpler convergence proofs of prominent adaptive optimizers, such as AdaGrad, Adam, and AdaBelief. The proposed framework is constructive because it allows synthesizing new adaptive optimizers. To illustrate this fact, we then utilize the transfer function paradigm from classical control to propose a new variant of Adam, coined AdamSSM, and prove its convergence. We add an appropriate pole-zero pair in the transfer function from squared gradients to the second moment estimate. Applications on benchmark machine learning tasks of image classification using CNN architectures and language modeling using LSTM architecture demonstrate that the AdamSSM algorithm improves the gap between generalization accuracy and faster convergence than the recent adaptive gradient methods.


Addressing posterior collapse by splitting decoders in variational recurrent autoencoders

Abstract: Variational recurrent autoencoder model (VRAE) is an appealing technique for capturing the variabilities underlying complex sequential data, which is realized by introducing high-level latent random variables as hidden states. Existing models suffer from the well-known ‘posterior collapse’ problem, meaning that a powerful autoregressive decoder equipped in the model itself could capture all the variabilities and hence leave the latent variables learning nothing from the data. From the perspective of model training, the posterior collapse problem can result in a very low Kullback–Leibler divergence (KL-divergence) value, which means the posteriors of the latent variables tend to be just the priors. In this paper, we address this problem by proposing a Bayesian variational recurrent neural network (BVRNN) model, in which two additional decoders are added into the original VRAE. These extra decoders can force the latent variables to learn meaningful knowledge during the training process. We conduct experiments on MNIST and Fashion-MNIST dataset. The experimental results show that the proposed model outperforms several baseline models. We further adapt the proposed model to a very challenging task in natural language processing, namely Named-Entity Recognition (NER). Experimental results show that our model is competitive to the state-of-the-art models on NER.


Approximate DBSCAN on obfuscated data

Abstract: With the emergence of remote storage, computation facilities, and the availability of high-speed data connectivity — cloud computation has become the call for the day. In this scenario, security and computability of data have emerged as two crucial aspects which often conflict with each other. An efficient solution is to build a trade-off between the two. In this work, we propose a novel Hilbert-Curve-based data encoding scheme (HC encoding) which obfuscates the data points. We use the obfuscated data in DBSCAN clustering and obtain an approximated output with respect to the original clustering (without invoking the plaintext data at any stages of the computation). HC encoding has a provision to modulate the degree of obfuscation. The correctness of the clustering performance decreases with the increase in obfuscation and vice versa. The empirical study is carried out on 8 regular datasets and a large, color image dataset, CIFAR10. Two key applications are explored– (i) clustering of homogeneous images and (ii) clustering of heterogeneous images originating from two different sources. The empirical results show the effectiveness of the HC encoding to provide approximate clustering performances on the obfuscated multimedia data as well as regular datasets. The outcomes also manifest HC encoding’s superiority against the comparing methods which operate under a similar fully private constraint.


Filtered data augmentation approach based on model competence evaluation

Abstract: The scale of parallel corpus plays an important role in training high-quality neural machine translation models. In order to expand the scale of parallel corpus in low-resource scenarios, researchers have proposed a series of data augmentation approaches, in which the most representative work is the back-translation. The back-translation approach uses a basic translation model to translate the target monolingual corpus into the source language, and then combines it into a pseudo parallel corpus to expand the training data. Due to the simple and efficient data expansion strategies, the back-translation approach becomes the mainstream data augmentation approach of neural machine translation. However, in low-resource scenarios, only low-precision basic translation models can be used. The pseudo-parallel corpus translated by this model contains low-quality sentence pairs, which inevitably introduces noises and leads to negative impacts to translation procedure. To improve the performance of low-resource neural machine translation, we propose a filtered data augmentation method based on the model competence evaluation, which can effectively improve the quality of training data by using both the filtered back-translation and dynamic evaluation of translation model capabilities. Comparative experiments show that the proposed approach can improve the quality of training data and the performance of low-resource neural machine translation.


Trustworthy adaptive adversarial perturbations in social networks

Abstract: Deep neural networks have achieved excellent performance in various research and applications, but they have proven to be susceptible to adversarial examples. Generating adversarial examples can help identify the vulnerability of the deep neural networks and further enhance the robustness and reliability of these models. However, the existing adversarial attacks can hardly achieve the balance between robustness and imperceptibility, which is not trustworthy in social networks. To solve these problems, we propose adaptive adversarial perturbation (AAP) to improve the universal robustness of the adversarial examples while ensuring imperceptibility. To optimize the imperceptibility of the perturbation, we design a noise visibility function (NVF) to reflect the features of the original images based on the human visual system (HVS). By further calculating a coefficient matrix based on the NVF, the perturbation intensity of different pixels can be adjusted dynamically to improve the robustness. The experimental results prove that the proposed method alleviates the trade-off between robustness and imperceptibility, and outperforms existing attack methods in both one-step and iterative ways. Our method makes the adversarial attack more reliable and applicable in social networks.


An interaction model applied to optimize photovoltaic farm location: A case study of China

Abstract: Given the escalating environmental challenges and the looming carbon peaking and carbon neutrality goals, developing renewable energies becomes a global imperative. The pivotal initial stage involves site selection; however, this process has been plagued by the ambiguity of subjective decision-making and the inconsistency of multi-factor decisions. In this regard, this study proposes a new evaluation system which combines Geodetector and Interaction Linear Model to minimize subjective interventions and reveal the mechanisms by which interaction terms influence siting. China is selected to unveil the model's capability through solar PV site selection. The results indicate that compared with the Basic Linear Model, the Interaction Linear Model increased the estimation accuracy by 8%, with some interactive terms showing higher determinant power than single factors. Based on this interaction model, there is a total of 13% of China's land is developable for solar PV plants, consisting of 46% highly suitable areas (581,576 km2), 39% moderately suitable areas (492,615 km2), and 15% marginally suitable areas (198,088 km2). Finally, Xinjiang was identified as the most suitable place in China. Above all, the new siting system proposed here provides a new perspective on renewables' site selection, promoting the high-quality energy transition globally.


Timeframe of eastern Paleo-Tethys closure: Constraint on the Songpan-Ganzi Complex by big data-based multiproxy provenance analysis

Abstract: The Songpan-Ganzi Complex (SGC) is one of the largest turbidite complexes on Earth. The source-to-sink processes of the SGC are closely linked to the closure of the Paleo-Jinsha Ocean in the eastern Paleo-Tethys realm, so the spatiotemporal transitions in SGC provenance provide the potential to constrain the closure time. A comprehensive multiproxy provenance investigation, integrating detrital zircon UPb geochronology, whole-rock SmNd isotope data, and paleocurrent analysis, shed light on the origin of the Triassic SGC turbidites. In this study, it was determined that a significant proportion (exceeding 70%) of the detrital material comprising the SGC turbidites was sourced from the northwestern margin of the Yangtze Craton and the Qinling Orogen. A comprehensive and quantitative provenance study revealed that the Songpan-Ganzi turbidites primarily originated from collisional orogenic belts at convergent continental plate margins and secondarily from magmatic arcs and other tectonic units surrounding the basin. In this study, it was demonstrated that the SGC contains important information on the convergence of Eurasia and the tectonic evolution of surrounding terranes. (1) The South Qinling Orogen experienced significant rapid exhumation during the Ladinian to early Carnian. (2) The closure of the Paleo-Jinsha Ocean most likely occurred during the transition period between the upper and lower members of the Zhuwo Formation (ca. 231 Ma, middle Carnian). (3) During the Late Triassic, the crust of the Yidun Terrane underwent significant vertical accretion, resulting in a topography exceeding that of the Jomda-Weixi arc by the late Norian.


SCTracker: Multi-Object Tracking With Shape and Confidence Constraints

Abstract: Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this article proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an intersection of union (IoU) distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman filter to improve the track performance with the inaccurate detection result. Experimental results on the MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking when compared with the state-of-the-art methods.


Development of an OTDR-Based Hand Glove Optical Sensor for Sign Language Prediction

Abstract: This article demonstrates a wearable optical fiber-based sensing glove for predicting hand gestures for sign language. The sensors were created using macrobends on single mode fiber (SMF) for each finger to simultaneously monitor the change in bending radius due to the change in position of each finger by creating different hand gestures. The glove sensing capability is based on the change in macrobending loss due to a change in bending radius. The system includes an optical time-domain reflectometer (OTDR), optical fiber, and hand glove. The functionality and performance of the glove were thoroughly evaluated on different participants for repeatability. We retrieved hand gesture information using data from each finger macrobend loss through the sensor’s response on OTDR—the developed glove acquired real-time raw data on alphabet and numerical hand postures. A set of unique features were extracted from the acquired raw data. The selected features were used to classify hand gestures using the ensemble classifier. The performance of this classifier was optimized using the training dataset (70%), and the performance of this trained algorithm was evaluated to recognize hand gestures using a testing dataset (30%). Compared to some existing methods, the proposed method enhances sign language recognition accuracy and improves tracking of hand gesture movement. This article presents statistical results with 93.57% classification accuracy on test data representing good dynamic gesture recognition scenarios.


MAD-MEL: Combining Entity and Metric Learning for Anomaly Detection in Multivariate Time Series

Abstract: Detection of anomalies in multivariate time series (MVTS) data has been crucial for various application areas. Though recent approaches have addressed such issues, still the problem is challenging not only due to identifying anomalous behavior but also to reducing false alarms. The problem becomes more exciting and challenging when the model takes care of errors due to measurement, environmental effects, and temporal dependency among the observations. Toward this, we propose a framework MAD-MEL, an MVTS Anomaly Detection Method using metric and entity learning (EL) approaches. The metric learning-based model identifies the behavior of individual series as well as the relationship between different attributes, whereas the entity-based model learns the behavior of MVTS as a single entity. An empirical mode decomposition-based feature learning (FL) component is incorporated to learn more generalized behavior of data and to handle the problem of minor disturbances. Also, the proposed method combines a linear component to control the spatial scaling effect in the train and test set. The idea is to learn the normal behavior of MVTS by combining all the components as a prediction-based model and to use prediction error for anomaly detection. We adopt transfer learning to reduce the time complexity while learning the individual series in case of a large number of attributes or multiple instances of MVTS. Our proposed multivariate time series anomaly detection using metric and entity learning (MAD-MEL) achieves the F1-score of 94% on public dataset, which is an improvement of 3% from SOTA.


Intra-Inter Region Adaptive Graph Convolutional Networks for skeleton-based action recognition

Abstract: To effectively capture the spatio-temporal dependencies of the skeletal data, graph convolution has been widely applied. However, it tends to emphasize the dependence relationship between adjacent joints and does not consider long-distance dependence relationships among joints. Another problem is single-structure temporal convolution, which is difficult to extract global temporal features. To address the above issues, we propose Intra-Inter Region Adaptive Graph Convolutional Networks (IIR-AGCN), which models the long-distance relationships of skeleton sequences at temporal and spatial scales. Our contributions are three-fold: First, to enhance global topological learning capabilities of graph convolution, we propose a regional-coupled attention module, which divides joint features into multiple sub-regions, and then constructs coupled relationships between intra-inter regions by self-attention mechanism, which realizes global joint information interaction. Second, to address the issue of difficulty in extracting global temporal features, we propose a pyramid multi-scale temporal module that extracts deep multi-scale temporal features and implements adaptive cross-scale feature fusion. Third, IIR-AGCN adopts a two-stream architecture, evaluating performances on two large-scale human skeleton datasets, NTU-RGB+D 60 and NTU-RGB+D 120, respectively.


Stabilized activation scale estimation for precise Post-Training Quantization

Abstract: In recent years, there have been more and more studies on Post-Training Quantization (PTQ). Many outstanding works have emerged, which has greatly promoted the availability of PTQ methods. However, in terms of low-bit quantization, there is still a wide gap between PTQ and the current state-of-the-art Quantization-Aware Training (QAT) methods. In this work, we find that the current way of obtaining the activation scale is not completely reasonable in PTQ, which leads to the fact that the weight cannot be well adapted to the biased quantized activation during inference. Based on experiments and analysis, we propose a method called StablePTQ. It obtains a stable activation scale and mixes rich input into the block reconstruction to achieve an improving quantization accuracy. StablePTQ achieves remarkable improvements in several different bit quantization especially W2A4, and can be applied to other quantization algorithms as a plug-and-play approach. The source code of StablePTQ is avaiable at .


Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

Abstract: In location-based social networks, calibrating a point-of-interest (POI) recommendation system is as important as its accuracy for improving user satisfaction. POI recommendation calibration is primarily classified as categorical or geographical calibration. Categorical calibration ensures that the recommended items are distributed proportionally among the past interest categories of the target user. When a target user checks 80 Chinese, 10 Japanese, and 10 French restaurants, a recommendation list with a ratio of 8:1:1 for Chinese, Japanese, and French restaurants can be reasonably expected. In addition to categorical calibration, geographical calibration has been proposed to increase user interest in the recommended results. Users have a high probability of revisiting locations in their subareas. Therefore, the POIs recommended in multiple subareas of interest are more suitable than those from one small and frequently visited subarea. However, improving the calibration and accuracy are conflicting tasks. To achieve high calibration while maintaining accuracy, previous studies proposed reranking-based techniques to rerank the candidate list and return POIs with high calibration. However, optimizing the calibration by reranking is independent of the basic-candidate-item generation model, resulting in a suboptimal system. To tackle the problem, we propose a novel sampling-based differentiation technique to merge the task of improving calibration into the GCN model training process and directly generate the final recommendation list. The model is flexible and can be applied to different domains, where a domain can be a subarea or category. In a three-layer GCN, the layer one represents the historical check-ins of the user, whereas layer three includes the candidate POIs from which the target user aggregates information. We trained the model to make the distribution of the POI domains at layer three approximated the distribution at layer one. Experimental results on Philadelphia and Tucson datasets confirmed that the proposed method outperforms all state-of-the-art GCN+ geo-reranking and GCN+ MCF baselines, improving Recall@ 5 from 0.0394 to 0.0412 (4.57%) and Jensen–Shannon measure (JS)@ 5 from 0.5931 to 0.6734 (13.54%) on the Philadelphia dataset and improving Recall@ 5 from 0.0495 to 0.0517 (4.40%) and JS@ 5 from 0.5869 to 0.6598 (12.42%) on the Tucson dataset for categorical calibration. The model was also tested in the geographical domain and a similar trend was observed.


Deep style transfer to deal with the domain shift problem on spheroid segmentation

Abstract: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumor behaviour, and for testing different preclinical and clinical treatments. Image analysis of spheroids is crucial to understand the formation and behaviour of such tumors, but when performed manually is a time-consuming task. Deep convolutional models can help in the analysis of spheroid images since they provide accurate results for the segmentation of tumors, a task that is necessary to further study features of spheroids. However, the images from tumor spheroids greatly vary depending on the experimental conditions, and also on the equipment (microscopes) and conditions (focus and magnification) employed to capture the images. This issue, known as domain shift, hinders the applicability of deep learning models for spheroid segmentation. In this work, we address this problem by studying style transfer methods based on both convolutional neural networks and generative adversarial networks to transfer the style from the images used for training segmentation models to the images from a new domain. This approach successfully handled the domain shift problem in the context of spheroid segmentation for 6 different deep learning models. Finally, we developed a high-level API that facilitates the of our approach to other contexts where the domain-shift problem might occur.


Consistency–exclusivity guided unsupervised multi-view feature selection

Abstract: Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for multi-view data. It aims to obtain the important feature subset from multi-view data, which can significantly minimize the impacts of noises, outliers and redundancy. Although previous UMFS methods achieve remarkable achievements, they fail to fully take into account the consistency or the exclusivity hidden in multi-view data. To address this, this article presents a consistency–exclusivity guided unsupervised multi-view feature selection (CE-UMFS) method. Specifically, we design a multi-view matrix factorization model to simultaneously explore the consistency and exclusivity in multi-view data. Meanwhile, we employ the Hilbert–Schmidt independence criterion (HSIC) to preserve the exclusivity of different views. Furthermore, we impose a nuclear norm on the consistent representation matrix to explore the consistency across views. At last, promising experimental results demonstrate the superiority of the proposed method compared with some state-of-the-art methods.


Theoretical analysis of co-existing periodic orbits in sparse binary neural networks

Abstract: A sparse binary neural network is a discrete-time recurrent neural network characterized by local binary connections and signum-type neuron models. The dynamics is described by an autonomous difference equation of binary state variables. Depending on the parameters, the network generates various periodic orbits of binary vectors. Multiple periodic orbits can co-exist and the network exhibits one of them depending on initial condition. Real/potential applications of the periodic orbits include control signals of switching circuits and basic signals for time-series approximation. The network is well suited for theoretical analysis and simple hardware implementation. This paper considers shift-type periodic orbits in the networks. As the main theoretical results, we clarify the number, period, and stability of the periodic orbits for key parameters. As a first step to the applications in the future, we present an FPGA-based hardware prototype and confirm typical periodic orbits experimentally.


GlocalFuse-Depth: Fusing transformers and CNNs for all-day self-supervised monocular depth estimation

Abstract: In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieves remarkable results on standard benchmarks. However, most of existing methods only focus on either daytime or nighttime images, their performance degrades on the other domain because of the large gap between daytime and nighttime images. To address this problem, we propose a two-branch network named GlocalFuse-Depth for self-supervised depth estimation of all-day images in this paper. The daytime and nighttime images in input image pair are fed into the two branches: CNN branch and Transformer branch, respectively, where both local details and global dependency can be effectively captured. Besides, a novel fusion module is proposed to fuse multi-dimensional features from the two branches. Extensive experiments demonstrate that GlocalFuse-Depth achieves state-of-the-art results for all-day images of the Oxford RobotCar dataset, which proves the superiority of our method.


An efficient multi-order cascade distillation model for the detection of small targets and occluded objects in transmission line inspection

Abstract: The cascade distillation detection model is proposed to address the problems of low accuracy of multiple small target defect detection and lack of robustness under complex environments in transmission line UAV intelligent inspection. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. Secondly, a high-order aggregated feature extraction backbone network is proposed to enhance the feature extraction of small and medium-sized targets and obscured targets on transmission lines. Then, a spatially deformable attention mechanism is designed to optimize the selection of sampling points for transmission line targets and defects and the calculation of their attention weights by aggregating contextual information in the vicinity of the targets, enhancing the module's ability to refine and adjust the bounding boxes. Finally, a large dataset of transmission line defect targets is constructed and generated, and the validity of the model is verified by experiments.


An educational simulation of a country’s electric power system

Abstract: We have created an educational simulation of the electric power system of a country. The simulation can be used by high school or university students to get a hands-on experience with some of the complexities of meeting the demand for electrical power, while optimizing for cost and CO2 emission. The simulation parameters are primarily based on real historical data. The simulation is bundled with an instruction manual including background information and the tasks to be completed. The program was first tried out by the contestants of the Leó Szilárd Physics competition in Hungary. The contestants’ result and our experience with the program are also discussed.


Language relatedness evaluation for multilingual neural machine translation

Abstract: Multilingual neural machine translation (MNMT) has attracted more and more attention in recent days because it can use a single neural machine translation (NMT) model to translate between multiple languages. As several languages are involved in MNMT, recent studies have shown that using part of these languages rather than all of them to train the model leads to comparable results. However, previous work on this topic mainly focuses on language clustering and features defined by linguists. The semantic relationship and language distance are not fully considered. How to select the most related language pairs to current low-resource pair to optimize the performance of MNMT is still an open question. In this paper, we propose to take language relatedness computation as a ranking problem, where features such as language distance, linguistic typological information and semantic relatedness features are incorporated into a random decision forest to improve the language relatedness evaluation (LRE) for MNMT. Since the model only focuses on monolingual LRE in general cross-lingual natural language processing tasks, we also propose two features related to machine translation (data size and bilingual relatedness) to predict the final language pairs. Experimental results on IWSLT and WMT datasets show that our proposed LRE method can achieve significant improvements compared with other models. We also conducted several groups of experiments on IWSLT and WMT datasets to further evaluate the effectiveness of the proposed method on MNMT. The results show that the MNMT model trained on language pairs predicted by the LRE method outperforms other language selection methods.


Economic feasibility of Gabal Um Takha leucogranitic intrusion, South Sinai, Egypt: Integrated remote sensing, geochemical, aeromagnetic, and geotechnical approach

Abstract: Variable granitic rocks are widely used as decorative stones. The present work conducted accurate mapping and the utilization of a large leucogranitic intrusion (Um Takha area) as a decorative stones through physico-mechanical tests, mineralogical and geochemical compositions. These leucogranites are medium-grained, classified as syenogranites, peraluminous, calc-alkaline, and highly evolved granites developed during the post-collisional setting. This study utilized various remote sensing techniques to identify the Um Takha leucogranitic using ASTER and Landsat-9 data. A novel FCC band ratio of the ASTER image (RGB: b1+b3/b2, b2/b1, b7+b9/b8) was created, along with the Landsat-9 FCC band ratio of (4/7, 3/4, and 2/1) in the RGB, to effectively map the distribution of in the study area. These results, combined with field observation, led to the creation of a new modified geological map of the Um Takha area. The analysis of magnetic data reveals that the depth of magnetic sources under the study area ranges between 170 m and 2215 m, as calculated by source parameter image and Euler deconvolution techniques. The application of horizontal gradient, tilt derivatives and improved logistic techniques reveals that the major structural trend affecting the study area is in the N–S, NNE-SSW and NNW-SSE direction. The physico-mechanical properties of leucogranitic rocks (M13) fulfilled the requirements of dimension stone in terms of bulk density, compressive strength, and to some extent water absorption. Therefore, they could be acceptable for interior use and the light-duty purposes of exterior use, such as facades and cladding. This suggest that a large resources of leucogranitic can be used as a decorative stones.


A quantum-enhanced solution method for multi classification problems

Abstract: With the increasing data size of multi classification problems, the running efficiency of classical algorithms is seriously affected. In the paper, in order to improve the implementation efficiency of the algorithm, we propose a quantum-enhanced solution method for multi classification problems. The method mainly introduces the quantum-enhanced technology into the classical algorithm. Aiming at the two steps of solving Euclid distance and kernel function in the classical algorithm, the paper relates the classical inner product principle with the amplitude evolution of quantum states. On the basis of quantizing the sample data, a general quantum circuit that can calculate the inner product is designed and constructed. The circuit can make full use of the advantages of quantum parallel computing to achieve exponential acceleration of computing efficiency. Aiming at solving linear equations in the classical algorithm, a quantum circuit based on the quantum singular value estimation is designed and constructed. The circuit makes use of the acceleration advantage of quantum computing in matrix computing to achieve polynomial acceleration of matrix computing. The experimental results show that the method can not only find the optimal solution for multi classification problems, but also greatly improve the operation efficiency of the algorithm. Compared with the classical methods, the method has at least polynomial improvement in time complexity and spatial complexity.


A visible-infrared clothes-changing dataset for person re-identification in natural scene

Abstract: Person re-identification (Re-ID) has been widely used in intelligent surveillance systems, aiming at retrieving specific pedestrian images across different cameras. Although existing person Re-ID methods have achieved inspiring success, there are still limitations in practical monitoring system applications. To narrow the gap with the practical application, we introduce the cross-modality person Re-ID problem in the clothes-changing scene. Meanwhile, we construct the first Visible-Infrared Clothes-Changing (NEU-VICC) dataset, which contained 16632 RGB images and 8374 infrared images of 107 pedestrians. The critical challenge of the cross-modality person Re-ID problem in the clothes-changing scene lies in the vast modality discrepancy and the intra-class discrepancy caused by changing clothes. So, we propose a novel Semantic-Constraint Clothes-Changing Augmentation Network (SC3ANet) based on current cross-modality person Re-ID methods to solve this problem. Specifically, we design a semantic-constraint clothes-changing module that guides the model to learn clothes-irrelevant features by randomly changing pedestrians' clothes. In addition, we devise a dual-granularity constraint loss module to mitigate inter-modality and intra-class differences. Experiments on our NEU-VICC dataset show that the SC3ANet achieves the best results. The dataset and code are available at: https://github.com/VDT-2048/NEU-VICC.


Fully Pipelined FPGA Acceleration of Binary Convolutional Neural Networks with Neural Architecture Search

Abstract: In this paper, we present a fully pipelined and semi-parallel channel convolutional neural network hardware accelerator structure. This structure can trade off the compute time and the hardware utilization, allowing the accelerator to be layer pipelined without the need for fully parallelizing the input and output channels. A parallel strategy is applied to reduce the time gap in transferring the output results between different layers. The parallelism can be decided based on the hardware resources on the target FPGA. We use this structure to implement a binary ResNet18 based on the neural architecture search strategy, which can increase the accuracy of manually designed binary convolutional neural networks. Our optimized binary ResNet18 can achieve a Top-1 accuracy of 60.5% on the ImageNet dataset. We deploy this ResNet18 hardware implementation on an Alphadata 9H7 FPGA, connected with an OpenCAPI interface, to demonstrate the hardware capabilities. Depending on the amount of parallelism used, the latency can range from 1.12 to 6.33[Formula: see text]ms, with a corresponding throughput of 4.56 to 0.71 TOPS for different hardware utilization, with a 200[Formula: see text]MHz clock frequency. Our best latency is [Formula: see text] lower and our best throughput is [Formula: see text] higher compared to the best previous works. The code for our implementation is open-source and publicly available on GitHub at https://github.com/MFJI/NASBRESNET .


Abstract: The performance of deep neural networks is typically associated with their depth and width. As the performance of a model improves, it demands greater computational resources and memory. However, self-distillation offers a pathway to augment this performance by internally transferring knowledge. Traditional self-distillation methods lack guidance from teacher networks, thus lacking the ability to obtain correct and key information. In this paper, we propose a new self-distillation framework—Global key knowledge distillation framework (GK-KD). We implement error correction mechanisms to network logit outputs and assess the importance of feature maps through rank to obtain correct and important knowledge. Experiments demonstrate the effectiveness of this framework: on the CIFAR100 dataset, ResNet18 achieved a 4.56% accuracy improvement over the baseline, ResNet152 also achieved a 5.26% accuracy improvement, and for the VGG series, the average accuracy increased by 5.82%, outperforming previous research methods without adding additional computational costs. Further experiments show that this framework can effectively enhance the robustness and calibration of the network.