Reviewerly/exHarmony huggingface dataset:
Atmospheric dependence of the direct, diffuse, and global clear-sky conversion ratios between solar photosynthetic active irradiance and photon flux

The ratio of the global solar photosynthetic active radiation (PAR) photon flux (in μmol/m2s) to global solar PAR irradiance (in W/m2) is of interest to convert one into another. This ratio is usually considered as a constant value close to its extraterrestrial value, 4.55μmol/J. However, this ratio depends on the spectral composition of solar radiation at ground level and it is different for the diffuse and beam components of solar irradiance. Under clear-sky conditions, the three PAR ratios (global, beam and diffuse) are determined by the local atmospheric composition and the relative air mass. In this work, the SMARTS spectral irradiance model with MERRA-2 atmospheric inputs is used to evaluate the dependence of these ratios under clear-sky conditions with air mass, aerosol optical depth (AOD), precipitable water vapor and ozone column. The accuracy of the SMARTS beam spectral irradiance is previously assessed using local spectroradiometer measurements. The clear-sky ratios for the diffuse and direct components increase with increasing air mass, while the global ratio shows only a weak air mass dependence. The clear-sky ratios can be modeled with simple bi-variate linear models in air mass and AOD. These results can be used in similar climatic regions to convert PAR flux to PAR irradiance and vice versa with increased accuracy for the global, direct, and diffuse radiation under cloud-free conditions.
Modeling and Assessing Hip-Knee Joint Coordination Based on Cyclograms Using a Portable Motion Capture System

Cyclograms are useful for describing interjoint coordination in clinical gait analyses. In this paper, we establish a model for generating hip-knee cyclograms at different walking speeds by using B-Spline curves, which could provide reference standards for evaluating hip-knee joint coordination. We use the shape similarity between individual and reference cyclograms to develop a hip-knee joint coordination assessment metric: the dynamic time warping-shape context (DTW-SC). The DTW algorithm is used to match corresponding points between the hip-knee cyclograms, and the matched results are visualized to show abnormal changes in gait patterns. The distance between corresponding points is determined through shape context descriptors and applied to quantify the hip-knee joint coordination of the subjects. To verify the effectiveness of the proposed approach, a stereo vision-based portable lower limb motion capture system is developed to collect hip and knee joint angle data from five healthy and five hemiplegic subjects walking at comfortable speeds. Experimental results show that the range of the modeling error is [0.84°, 2.76°]. DTW-SC metric can visualize and quantify the abnormal patterns of the subject’s hip-knee joint coordination. The work is meaningful in evaluating the hip-knee joint coordination of hemiplegic patients.
Sampling-Based Reactive Synthesis for Nondeterministic Hybrid Systems

This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy – a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.
Measuring gaze angle changes to maintain fixation upon a small target during motion: 3D motion tracking versus wearable eye-tracker

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

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

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

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

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

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 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

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

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

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

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

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.