Reviewerly/exHarmony huggingface dataset:
Image Edge Detection Algorithm of Machined Parts Based on Mathematical Morphology

At present, the edge detection method filters image edge points according to extreme image points, resulting in an unclear detected edge image and a low average energy value. An edge detection algorithm for machining part images based on mathematical morphology is proposed. The binary morphology of mathematical morphology is used to remove image noise. Using a two-dimensional wavelet transform, the edge information matrix in the horizontal and vertical directions of the image is calculated to determine the edge scale of the image. The mask operator in two directions of the image is calculated as a multiscale and multidirectional detection operator. According to the multiscale and multidirectional detection operator, the target edge detection process is designed to smooth the denoised machined part image, screen the image target edge points according to the maximum value points of the image edge gradient, segment the image target and background, and realize target edge detection. The images of machined parts collected in the normal machining room are divided into two categories according to the noise gradient, and the two groups of methods are compared. Experimental results: Compared with the method selected in this experiment, the average energy value of the detected target edge image is higher.
Adversarial Sampling-Based Motion Planning

There are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT* may have low path cost, but their optimality makes them predictable. Similarly, a deceptive path that features numerous zig-zags may take too long to reach the goal. We address this trade-off by drawing inspiration from adversarial machine learning. We propose a new planning algorithm, which we title Adversarial RRT*. Adversarial RRT* attempts to deceive machine learning classifiers by incorporating a predicted measure of deception into the planner cost function. Adversarial RRT* considers both path cost and a measure of predicted deceptiveness in order to produce a trajectory with low path cost that still has deceptive properties. We demonstrate the performance of Adversarial RRT*, with two measures of deception, using a simulated Dubins vehicle. We show how Adversarial RRT* can decrease cumulative RNN accuracy across paths to 10&#x0025;, compared to 46&#x0025; cumulative accuracy on near-optimal RRT* paths, while keeping path length within 16&#x0025; of optimal. We also present an example demonstration where the Adversarial RRT* planner attempts to safely deliver a high value package while an adversary observes the path and tries to intercept the package.
Application of multi-camera digital image correlation in the stability study of the long timber column with the circular cross-section under axial compression

Displacement meter synthesis (DMS), as a traditional method, is widely used to capture the local deformation of a specimen. However, for axis-symmetrical circular columns, the axial compression instability cannot be accurately evaluated using the DMS method due to the uncertain bending direction. To address this problem, a 360° full-surface digital image correlation (DIC) system composed of eight cameras is proposed to capture the full-field deformation information of the column surfaces under axial compression. The objective of this study was to experimentally validate the efficiency of the proposed novel measurement in tracking the full-filled compressive deformation of a long wooden column with the circular cross-section. The test results showed that the columns experience instable failure. The multi-camera DIC system can completely reconstruct the three-dimensional shape of the circular column and obtain the whole process deformation state at any position on the surface of the timber column. The DIC method also can capture the uncertain lateral deformation direction and obtain the data of lateral deflection at the mid-span of timber columns. The multi-camera DIC provides an intuitive and comprehensive new test method for the test and analysis of the stability of axisymmetric long timber columns.
Design of Intelligent Garbage Classification Bin Based on LD3320

In this paper, based on LD3320 non-specific person speech recognition chip, Arduino UNO R3 MCU, LX-225 serial bus intelligent steering gear, an intelligent trash can is designed, which realizes the functions of voice input of garbage name, intelligent retrieval of garbage type, intelligent opening and closing of garbage can cover, etc.
POGT: A Peking Opera Gesture Training System Using Infrared Sensors

Peking opera is one of the national cultural heritages in China. However, it is difficult for people to learn the gestures in Peking opera performance, which limits the spread of this traditional culture. To address this issue, we propose a Peking opera gesture training system using infrared sensors. Specifically, we build a character avatar for demonstrating the gestures in Peking opera in the proposed system. Based on the data collected by infrared sensors, a method for calculating gesture similarity is proposed and is applied for the training of Peking opera gestures, which allows natural interactions and provides interactive feedback for user gestures. We conducted multiple experiments to verify the feasibility and effectiveness of the training system. The experimental results showed that the proposed system can overcome the difficulties in the traditional learning process of Peking opera gestures, which helps users to achieve the goal of learning standard Peking opera gestures. The proposed training system greatly eases the learning of Peking opera gestures, adding vitality into the culture of traditional Peking opera.
Parallel Particle Swarm Optimization Using Apache Beam

The majority of complex research problems can be formulated as optimization problems. Particle Swarm Optimization (PSO) algorithm is very effective in solving optimization problems because of its robustness, simplicity, and global search capabilities. Since the computational cost of these problems is usually high, it has been necessary to develop optimization algorithms with parallelization. With the advent of big-data technology, such problems can be solved by distributed parallel computing. In previous related work, MapReduce (a programming model that implements a distributed parallel approach to processing and producing large datasets on a cluster) has been used to parallelize the PSO algorithm, but frequent file reads and writes make the execution time of MRPSO very long. We propose Apache Beam particle swarm optimization (BPSO), which uses Apache Beam parallel programming model. In the experiment, we compared BPSO and PSO based on MapReduce (MRPSO) on four benchmark functions by changing the number of particles and optimizing the dimensions of the problem. The experimental results show that, as the number of particles increases, MRPSO remains largely constant when the number of particles is small (&lt;1000), while the time required for algorithm execution increases rapidly when the number of particles exceeds a certain amount (&gt;1000), while BPSO grows slowly and tends to yield better results than MRPSO. As the dimensionality of the optimization problem increases, BPSO can take half the time of MRPSO and obtain better results than it does. MRPSO requires more execution time than BPSO, as the problem complexity varies, but both MRPSO and BPSO are not very sensitive to problem complexity. All program code and input data are uploaded to GitHub.
E-Module Design Using Kvisoft Flipbook Application Based on Mathematics Creative Thinking Ability for Junior High Schools

Mathematics learning innovations in teaching materials used in schools are still in the form of printed books from the government. In 4.0 era, the use of technology can be used to make teaching materials in the form of e-modules or electronic modules using the Kvisoft Flipbook application. The purpose of this study was to determine the validity, practicality, and student responses to the design of the number pattern e-module based on mathematical creative thinking skills using the kvisoft flipbook application so that it can be used as a learning resource for students both in the classroom learning process and independent learning. The method used in this study is a research and development (R&amp;D) method using the ADDIE model. The data collection technique used is through a validation questionnaire, user practicality questionnaire and student response questionnaire to e-modules. The results of the study show that this e-module is very feasible to use with the results of material expert validation, media expert validation, user practicality, and student responses to e-modules. Based on the results of this study, the design of the e-module using the kvisoft flipbook maker application based on mathematical creative thinking skills is appropriate for use in the learning process for class VIII number pattern material
Image Cryptosystem for IoT Devices Using 2-D Zaslavsky Chaotic Map

Recently, the proliferation of the Internet of Things (IoT) services and applications has prompted several governments to deploy many cameras in critical sites, such as embassies, military institutions, and ministries.The images must be encrypted to protect data shared between IoT sensors/devices and embedded subsystems.Often, IoT devices are smaller and less powerful.A lot of traditional encryption methods are computationally inefficient.They require many rounds of coding, which takes up much space on a device.Despite this, this paper has developed a lightweight encryption algorithm based on chaos theory suitable for IoT devices with limited processing power.The confusion-diffusion structure is often divided into two independent components in chaotic image cryptosystems.However, it reduces the security of the cryptosystem since the independent design may be subjected to cryptanalysis individually.This paper presents an efficient chaotic image encryption algorithm based on a plaintext-associated approach.It incorporates confusion-diffusion operation to increase encryption reliability.The Zaslavsky map initial values are determined by the values and locations of an input image and random values to resist chosen-plaintext cryptanalysis.Furthermore, the proposed method uses a simultaneous confusion-diffusion process to resist any separate attack.The simulation and experimental analysis demonstrate that the proposed encryption algorithm has 0.3927 seconds as a fast encryption time.Moreover, it provides a high performance compared to several other chaotic-based image cryptosystems.as measured by the histogram, entropy (7.99723), pixel change rate (99.7194), unified average changing intensity (33.4635) within the critical interval, pixel correlation, global and local entropy, CDR (ciphertext difference rate), and gray difference degree (GDD).It also has higher security than other chaoticbased image cryptosystems.
Large DOF microscopic fringe projection profilometry with a coaxial light-field structure

Fringe projection profilometry (FPP) has been widely researched for three-dimensional (3D) microscopic measurement during recent decades. Nevertheless, some disadvantages arising from the limited depth of field and occlusion still exist and need to be further addressed. In this paper, light field imaging is introduced for microscopic fringe projection profilometry (MFPP) to obtain a larger depth of field. Meanwhile, this system is built with a coaxial structure to reduce occlusion, where the principle of triangulation is no longer applicable. In this situation, the depth information is estimated based on the epipolar plane image (EPI) of light field. In order to make a quantitative measurement, a metric calibration method which establishes the mapping between the slope of the line feature in EPI and the depth information is proposed for this system. Finally, a group of experiments demonstrate that the proposed LF-MFPP system can work well for depth estimation with a large DOF and reduced occlusion.
Color and Shape efficiency for outlier detection from automated to user evaluation

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representation itself. To make coherent design choices about visual attributes, the visual search field proposes guidelines based on the human brain’s perception of features. However, information visualization representations frequently need to depict more data than the amount these guidelines have been validated on. Since, the information visualization community has extended these guidelines to a wider parameter space. This paper contributes to this theme by extending visual search theories to an information visualization context. We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors. Stimuli are defined by color and shape features for the purpose of visually encoding categorical data. The experimental protocol is made of a parameters space reduction step (i.e., sub-sampling) based on a machine learning model, and a user evaluation to validate hypotheses and measure capacity limits. The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier. When redundantly encoded, the display heterogeneity has no effect on the task. When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded with two attributes simultaneously, performances drop drastically even with minor heterogeneity.
The determinants of an information security policy compliance culture in organisations: the combined effects of organisational and behavioural factors

Purpose This paper aims to examine the individual and combined effects of organisational and behavioural factors on employees’ attitudes and intentions to establish an information security policy compliance culture (ISPCC) in organisations. Design/methodology/approach Based on factors derived from the organisational culture theory, social bond theory and accountability theory, a testable research model was developed and evaluated in an online survey that involves the use of a questionnaire to collect quantitative data from 313 employees, from ten different organisations in Ghana. The data collected were analysed using the partial least squares-structural equation modelling approach, involving the measurement and structural model tests. Findings The study reveals that the individual measures of accountability – identifiability (2.4%), expectations of evaluation (38.8%), awareness of monitoring (55.7%) and social presence (−41.2%) – had weak to moderate effects on employees’ attitudes towards information security policy compliance. However, the combined effect showed a significant influence. In addition, organisational factors – supportive organisational culture (15%), security compliance leadership (2%) and user involvement (63%) – showed positive effects on employees’ attitudes. Further, employees’ attitudes had a substantial influence (65%), while behavioural intentions demonstrated a weak effect (24%) on the establishment of an ISPCC in the organisation. The combined effect also had a substantial statistical influence on the establishment of an ISPCC in the organisation. Practical implications Given the findings of the study, information security practitioners should implement organisational and behavioural factors that will have an impact on compliance, in tandem, with the organisational effort to build a culture of compliance for information security policies. Originality/value The study provides new insights on how to address the problem of non-compliance with regard to the information security policy in organisations through the combined application of organisational and behavioural factors to establish an information security policy compliance culture, which has not been considered in any past research.
IDART: An Improved Discrete Tomography Algorithm for Reconstructing Images With Multiple Gray Levels

The discrete algebraic reconstruction technique has many advantages in computed tomography and electron tomography. However, the number of gray levels and the absolute gray values that should be known in advance are typically not available in experiments especially when there are many gray levels in the image. In this paper, we report an automatic discrete tomography reconstruction algorithm to improve its feasibility in practice, without needing to know these two parameters. In our algorithm, the number of gray levels is estimated by labeling the connected components in the tomogram and the absolute values of them are determined by the modal value of each domain. The proposed algorithm was extensively validated on both simulated and experimental datasets. The results show that our algorithm can accurately recover not only the morphology but also the gray levels of the interested objects, even in the images with multiple gray levels. It is demonstrated that the presented algorithm is robust for eliminating missing wedge artifacts and tolerable for noisy data.
Salient object detection network with center pooling and distance-weighted affinity loss function

Due to the rapid development of deep learning, salient target detection models have achieved significant progress. However, these models ignore the center prior in the collected images, that is, there is a greater probability of being in the center area for the salient object. The hidden information in the image is not fully utilized, which hinders further improvement of the detection result. We propose a center pooling algorithm that realizes the combination of image center prior and deep learning. The algorithm dynamically modifies the size of the receptive field according to the probability of the existence of salient objects and solves the problem of ignoring the importance of different regions when using the same pooling receptive field in the entire image by the standard pooling method. At the same time, the proportion of the foreground in the image is increased, and the problem of category imbalance is alleviated. In addition, a distance-weighted affinity loss function is also proposed to supplement the keyspace information by learning the grouping or separating force between pixel pairs at different distances and increase the confidence of edge regions. To further improve the detection results, a background detection subnetwork is introduced. Through the interactive learning of the foreground and background subnetworks, the opposite relationship between the predicted maps is explored. Finally, experiments on six different related datasets are conducted, and comparisons with other models prove that our model achieves the best performance.
A multimodal deep learning infused with artificial algae algorithm – An architecture of advanced E-health system for cancer prognosis prediction

Cancer becomes life-threatening when it affects vital organs and their potential to function; therefore, early discovery is crucial for prognosis management. Medical research benefits from the rapid growth of Artificial Intelligence (AI) technology, notably machine learning (ML), deep learning (DL), and reinforcement learning (RL) which has resulted in the development of several approaches and techniques for improving cancer prediction and survival analysis. To improve cancer prognosis prediction and survival analysis, a novel framework that fuses multimodal features via early and late fusion techniques using DL and RL is presented. The approach utilizes an artificial algae algorithm (AAA) to extract the most resilient features from the dataset and build a robust model for cancer prognosis and prediction. The uniqueness of this technique is illustrated by using AAA to extract valuable features from a variety of data modalities infused with Double DEEP Q-NETWORK (DDQN), Convolution eXtreme Gradient Boosting (CNN-XGBOOST), and Convolution Support Vector Machine (CNN-SVM) models for cancer prognostic prediction. The effectiveness of the proposed framework was evaluated using publicly-available datasets: Lower Grade Glioma in the Brain(BRAIN-TCGA), Prostate Cancer, Bladder Cancer, Metastatic Colorectal Cancer (MSKCC), and the METABRIC dataset. The optimum accuracy achieved was 0.99 for the BRAIN-TCGA dataset, surpassing a variety of state-of-the-art techniques. The performance assessment results reveal that the proposed model outperforms currently available cancer prognostic prediction models in terms of predictive performance.
Weight coefficient calculation through equivalent ray tracing method for light field particle image velocimetry

Light field particle image velocimetry (LF-PIV) can measure three-dimensional (3D) flow velocity from a single snapshot of a light field camera based on the 3D reconstruction of tracer particles of light field images. However, it requires light field intensity calibrations to calculate accurate weight coefficients. Conventionally, the weight coefficients are calculated through in-situ calibration approaches whereas the translation of the calibration board within the entire measurement area is required. Therefore, these approaches are inapplicable for internal industrial flows and space-constrained applications. This study presents an equivalent ray tracing method for the weight coefficients calculation. With a light field snapshot of a smart calibration board, a mapping relationship is established to relate the target points sampled in the flow field with their equivalent points in the air. The weight coefficients are then calculated through the ray tracing method by changing the starting points of ray tracing from the target points to their equivalent points. The calculated weight coefficients are employed to reconstruct the feature marks of the calibration board. Results show that the spatial locations of the marks can be reconstructed accurately with a mean lateral and depth error of 0.63% and 5.6%, respectively. Experiments were also carried out on a low-speed laminar flow. The result indicates that the equivalent ray tracing method provides a similar measurement accuracy with the in-situ calibration method. The overall error of 6.77% is achieved for the velocity measurement. It is demonstrated that the proposed method is capable of measuring the 3D flow velocity for internal industrial and space-constrained applications without translating the calibration board within the entire measurement area.