Reviewerly/exHarmony huggingface dataset:
Quantitative Measurement of Perceptual Attributes and Artifacts for Tone-Mapped HDR Display

Measuring electronic display quality, as perceived by human observers, has attracted high attention in current consumer displays. With limited dynamic range of consumer-level standard dynamic range (SDR) displays, high dynamic range (HDR) scenes are often rendered by different tone mapping operators (TMOs). Due to the lack of quantitative measurement and analysis of tone mapping in the existing image quality measurement (IQM) methods, it is of considerable significance to establish new IQM protocols that can differentiate the display quality of SDR electronic devices. We first propose an IQM model to exhibit the essential perceptual attributes and artifacts that are peculiar to tone-mapping. Furthermore, we characterize the overall image quality (OiQ) resulting from linear regression and various machine learning techniques. Finally, the execution of without HDR reference ablation experiments demonstrate the relative contribution of these attribute measurement to the OiQ. The use of IQM protocols helps with well-founded quality measurement between TMOs during tone-mapping processing. Our effort is not only useful to get into the tone-mapping field or when implementing a TMO, but it also sets the stage for quantitative measurement of TMOs. By monitoring these attributes and artifacts after different tone-mapping process, user-driven or optimal display are made possible.
fASLR: Function-Based ASLR via TrustZone-M and MPU for Resource-Constrained IoT Systems

The address space layout randomization (ASLR) has been widely deployed on modern operating systems against code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP). However, porting ASLR to resource-constrained IoT devices is a great challenge due to the limited memory space for randomization. We propose a function-based ASLR scheme (fASLR) for IoT runtime security utilizing the ARM TrustZone-M technology and the memory protection unit (MPU) supported by ARM Cortex-M processors. fASLR loads a function from the flash and randomizes its base address in a randomization region in RAM when the function is being called. We design novel mechanisms on cleaning up finished functions from the RAM and memory addressing to tackle the complexity of function relocation and randomization. Optimizations are applied to effectively reduce overhead introduced by runtime memory management. We also formally prove that user applications will run correctly with fASLR enabled. Compared with the related work, a prominent advantage of fASLR is that fASLR can run an application even if the application code cannot be completely loaded into RAM for execution. We test fASLR with 21 applications. The experimental results show that fASLR achieves a high randomization entropy and incurs a runtime overhead of less than 10%.
An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption of medical images is very important to secure patient information.Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem.In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network.On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach: The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution.In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination of Mean Square Error (MSE), RMSprop, three layers for the auto-encoder, and ReLU, which had the best classification accuracy of 65%, the auto-encoder gives MAE = 0.31 after 50 epochs.The third case is the worst, which is the combination of the hinge, RMSprop, three layers for the auto-encoder, and ReLU, providing accuracy of 20% and MAE = 0.485.
Dynamic Knowledge Distillation with Noise Elimination for Rgb-D Salient Object Detection

RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data. Inevitably, an independent stream is introduced to extract features from depth images, leading to extra computation and parameters. This methodology sacrifices the model size to improve the detection accuracy which may impede the practical application of SOD problems. To tackle this dilemma, we propose a dynamic distillation method along with a lightweight structure, which significantly reduces the computational burden while maintaining validity. This method considers the factors of both teacher and student performance within the training stage and dynamically assigns the distillation weight instead of applying a fixed weight on the student model. We also investigate the issue of RGB-D early fusion strategy in distillation and propose a simple noise elimination method to mitigate the impact of distorted training data caused by low quality depth maps. Extensive experiments are conducted on five public datasets to demonstrate that our method can achieve competitive performance with a fast inference speed (136FPS) compared to 10 prior methods.
Image Inpainting Based on Interactive Separation Network and Progressive Reconstruction Algorithm

Recently, learning-based image inpainting has gained much attention. It widely utilizes an auto-encoder structure and can obtain compact feature representation in the encoder to achieve high-quality image inpainting. Although this approach has achieved encouraging inpainting results, it inevitably reduces the high-resolution representation due to interval downsampling. In order to solve this problem and achieve an excellent image inpainting effect, this paper proposes a brand-new generative network, Interactive Separation Network, which retains the high-resolution information and extracts the semantic features from corrupted images. Furthermore, this paper also discusses network designs with different complexity in different application scenarios. Finally, to improve the effectiveness and robustness of our proposal to large corrupted regions in the inpainting image, we further propose a flexible and highly reusable reconstruction scheme to complete the inpainting in the prediction process gradually. Experiments show that our proposed generation network and reconstruction scheme can significantly improve the quality of repaired images. The proposed method significantly outperforms the state-of-the-art image inpainting approaches in image quality.
Genetic Algorithm for Bayesian Knowledge Tracing: A Practical Application

Online intelligent tutoring systems have developed rapidly in recent years. Analyzing educational data to help students personalize learning has become a research hotspot. Knowledge Tracing (KT) aims to assess students' changing cognitive states of skills by analyzing their performance on answers. As a representative KT model, Bayesian Knowledge Tracing (BKT) has good interpretability due to the use of the Hidden Markov Model. However, BKT needs to model students' performance on different skills separately. If BKT simultaneously traces the cognitive states of students' multiple skills, its time complexity increases exponentially with the number of skills. Therefore, we introduce a genetic algorithm to solve this problem and propose a Multi-skills BKT. This approach allows the BKT model to handle multiple skills simultaneously. Experiments on real datasets show that the model has a significant improvement in prediction performance over the BKT.
Detail Perception Network for Semantic Segmentation in Water Scenes

Semantic segmentation in the water scene is significant for water environment monitoring. Recent water scene segmentation methods usually regard all floating objects as only one foreground category that limits the understanding of the water scene. Considering various floating objects, we propose a Detail Perception Network (DPNet) to address two challenges for water scene segmentation with multi-categories floating objects. One is the sample imbalance among objects of different scales, which leads to low accuracy on small objects covering a few pixel samples. Another is the weak discriminability of features among the categories that are close to the blurred edge, which leads to the miss-segmentation in the blurred edge region. For sample imbalance, we design Distance Field Loss (DF Loss) to strengthen the learning of small objects by a pixel-wise weight calculated from a distance field during training. To address the weak discriminability of features among categories that are close to the blurred edge, we propose a Category Edge Perception Pyramid (CEPP) module that learns the edge feature of each category as prior knowledge to enhance edge features. For training and evaluating relative models, we also establish a dataset named ColorWater, which contains 1279 images with 9 semantic labels over various water scenes. Extensive experiments demonstrate that our model performs favorably against the state-of-the-art models on our ColorWater dataset and the public Aeroscapes dataset.
Chaotic Pseudo Random Number Generator (cPRNG) Using One-Dimensional Logistic Map

In this paper, a method of generating non-periodic pseudo random numbers has been proposed based on a chaotic PRNG using one-dimensional logistic map. PRNGs generate deterministic sequences of numbers that appear random; such a sequence is reproducible given the state of the generator is known. The proposed cPRNG is based on the one-dimensional logistic map; by adjusting the value of the control parameter lambda, a sequence of random numbers is generated. The sequence so generated is then put through a test of randomness. In this paper, the Wald–Wolfowitz runs test has been used as a test of randomness.
Easy Hand Gesture Control of a ROS-Car Using Google MediaPipe for Surveillance Use

Hand gestures are a relatively new way for humans to communicate with computers. The goal of gesture recognition is to bridge the physical and digital worlds. Hand gestures make it much easier to communicate our intentions and ideas to the computer. There are numerous methods for a computer to recognize a hand gesture, one of which is image recognition. The use of a Convolutional Neural Network (CNN) allows for the detection of human gestures. However, training a CNN necessitates a massive dataset of human gesture images. In this paper, we employ Google MediaPipe, a Machine Learning (ML) pipeline that combines Palm Detection and Hand Landmark Models, to develop a simple hand tracking method to control a Robot Operating System (ROS) based surveillance car with socket programming. The study demonstrates control of a ROS car's steering direction and speed. Hand-gesture-controlled surveillance vehicles could aid in the improvement of security systems.
Rapid and High-Purity Seed Grading Based on Pruned Deep Convolutional Neural Network

The crop seed grading method based on deep learning has achieved ideal recognition results. However, an effective deep neural network model for seed grading usually needs a relatively high computational complexity, memory space, or inference time, which critically hampers the utilization of complex CNNs on devices with limited computational resources. For this reason, a method of combining layer pruning and filter pruning is proposed to realize fast and high-purity seed grading. First, we propose an effective approach based on feature representation to eliminate redundant convolutional layers, which greatly reduces the model’s consumption of device storage resources. Then, the filter-level pruning based on the Taylor expansion criterion is introduced to further eliminate the redundant information existing in the convolutional layer. Finally, an effective and practical knowledge distillation technology (MEAL V2) is adopted to transfer knowledge of well-performing models, to compensate for the information loss caused by the pruning of the network. Experiments on red kidney bean datasets demonstrate that the method is effective and feasible. We proposed the Vgg_Beannet, which can achieve 4 $$\times $$ inference acceleration while the accuracy is only reduced by 0.13% when the filter is pruned by 90%. Moreover, we also compared some handcrafted lightweight architectures such as MobileNetv2, MixNet, etc. The results show that the pruned network outperforms the above network in inference time (2.07 ms vs. 7.83 ms, 22.23 ms) and accuracy (96.33% vs. 95.94%, 94.89%).
An RGB Image Encryption Algorithm Based on Clifford Attractors with a Bilinear Transformation

This work represents a new contribution in the field of encryption of RGB images, using Clifford attractors, which are the basic constituents of chaos theory, considering the dynamic behavior and random appearance, we generated three substitution boxes $$K^{(1)}, K^{(2)},$$ $$K^{(3)}$$ are enerated, each of them helps us to successively encrypt the channels of the original RGB image (red, green, blue). This random permutation is done by digram (sequence of two digits), which means that each pixel of the image to be encrypted with the red color will be replaced by the cell of position (i, j). In fact, the value of i is extracted from the channel $$K^{(1)}$$ with an increasing step and j is obtained from the channel $$K^{(2)}$$ of decreasing step. While the modification of the green matrix is done by the choice of the channels $$K^{(2)}$$ and $$K^{(3)}$$ , on the other side, the blue color uses the channels $$K^{(3)}$$ and $$K^{(1)}$$ while following the same principle. On the purpose of making the present approach more efficient in the unreadable and fuzzy image while increasing the decryption time, we resort to the application of an XOR mask between each substituted matrix and each examined matrix with a bilinear transformation of different steps. Based on several criteria such as histogram, differential attacks, entropy, and correlation analysis, this approach has yielded experimental results that justify its performance and reliability in protecting data against any malicious attack. In addition, it marks its competence in front of other existing methods in the literature.
Multistructure Graph Classification Method With Attention-Based Pooling

Graph neural networks (GNNs) have achieved effective performance in many graph-related tasks involving recommendation systems, social networks, and bioinformatics. Recent studies have proposed several graph pooling operators to obtain graph-level representations from node representations. Nevertheless, they usually adopt a single strategy to evaluate the importance of nodes, which may generate node rankings with weak robustness. Also, they cannot capture the different substructures of a graph since they shrink the graph layer by layer. To solve the above problems, this article proposes a Multistructure graph classification method with Attention mechanism and Convolutional neural network (CNN), called MAC. In particular, we propose a novel pooling operator, which adopts multiple strategies to evaluate the importance of nodes and updates node representations through an attention mechanism. Also, we design a hierarchical architecture for MAC to capture multiple different substructures of a graph. To further reduce the loss of graph information, we utilize 2-D CNN to generate a graph-level representation. Comparative experiments are performed on public benchmark datasets deriving from social systems, and the experimental results indicate that our method outperforms a range of state-of-the-art graph classification methods.
Towards Intelligent Vision Surveillance for Police Information Systems

Traditional surveillance systems detect suspicious activities with the assistance of human operators watching screens showing video streams of activities captured from different cameras, which often leads to fatigue and failure to identify the suspicious activities. This is an area of considerable interest for security agents like police carrying out surveillance operations. Besides the current literature effort, this research investigates how suspicious loitering in a location can be detected before the crime occurs. Since human behavior is dynamic, this research develops an intelligent vision framework based on an integrated convolutional neural network (CNN) adaptive to specific locations at a time rather than building a generalized model prone to errors. We demonstrate the efficiency of the proposed system with preliminary results obtained on real-life image frames captured from multiple cameras. This model outperforms the conventional approaches in terms of the detection of suspicious locations with an average F1-score of 0.9666, a false positive rate of 0.0922, and an accuracy of 94.49%. The deployment of this new model can help to augment the work of police information systems.
Heatmap Assisted Accuracy Score Evaluation Method for Machine-Centric Explainable Deep Neural Networks

There have existed many studies about the explainable artificial intelligence (XAI) that explains the logic behind the complex deep neural network called a black box.At the same time, researchers have tried to evaluate the explainability performance of various XAIs.However, most previous evaluation methods are human-centric, that is, subjective, where they rely on how much the results of explanation are similar to what people's decision is based on rather than what features actually affect the decision in the model.Their XAI selections are also dependent of datasets.Furthermore, they are focusing only on the output variation of a target class.On the other hand, this paper proposes a robust heatmap assisted accuracy score (HAAS) scheme over datasets that helps selecting machine-centric explanation algorithms to show what actually leads to the decision of a given classification network.The proposed method modifies the input image with the heatmap scores obtained by a given explanation algorithm and then puts the resultant heatmap assisted (HA) images into the network to estimate the accuracy change.The resultant metric (HAAS) is computed as a ratio of accuracies of the given network over HA and original images.The proposed evaluation scheme is verified in the image classification models of LeNet-5 for MNIST and VGG-16 for CIFAR-10, STL-10, and ILSVRC2012 over totally 11 XAI algorithms of saliency map, deconvolution, and 9 layer-wise relevance propagation (LRP) configurations.Consequently, for LRP1 and LRP3, MINST showed largest HAAS values of 1.0088 and 1.0079, CIFAR-10 achieved 1.1160 and 1.1254, STL-10 had 1.0906 and 1.0918, and ILSVRC2012 got 1.3207 and 1.3469.While LRP1 consists of ϵ-rules for input, convolutional, and fully-connected layers, LRP3 adopts a bounded-rule for an input layer and the same ϵ-rules for other layers as LRP1.The consistency of evaluation results of HAAS and AOPC has been compared by means of Kullback-Leibler divergence, ensuring that HAAS is the more robust evaluation method than AOPC independently of datasets since HAAS has much lower average divergence of 0.0251 than AOPC of 0.3048.In addition, the validity of the proposed HAAS scheme is further investigated through the inverted HA test that employs inverted HA images made up with inverted heatmap scores and estimates the accuracy degradation caused by applying them to the network.The XAI algorithms with largest HAAS results experience biggest accuracy degradation in the inverted HA test.
Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems. Based on a qualitative assessment and classification of freely available mobile applications, ten relevant fun selfie filters are selected to create a database. To this end, the selected filters are automatically applied to face images of public face image databases. Different state-of-the-art methods are used to evaluate the influence of fun selfie filters on the performance of face detection using dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The obtained results indicate that selfie filters negatively affect face recognition modules, especially if fun selfie filters cover a large region of the face, where the mouth, nose, and eyes are covered. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed which consists of a segmentation module, a perceptual network, and a generation module. In a cross-database experiment the application of the presented selfie filter removal technique has shown to significantly improve the biometric performance of the underlying face recognition systems.
Implementing Web and Mobile Applications From Linked Open Data

The Linked Data initiative has successfully attracted many data providers who agree to adhere to the linked data principles and W3C standards. This movement aims to adopt a unified format, understandable by machines and easily discoverable and exploitable. As a result of this success, there has been a continuous expansion of linked open data available on the cloud. However, a limited number of applications utilize this wealth of data. Therefore, several governmental initiatives were launched to encourage the exploitation and use of public utility data to create applications that improve citizens' lives. This work investigates how linked open data, including government data, can provide public utility applications. Furthermore, this paper proposes a generic approach for creating mobile and web apps based on linked open data.
Aircraft Pipe Gap Inspection on Raw Point Cloud From a Single View

The pipe is a common part of aviation equipment. The pipe gap is one of the important factors that affect the safety of an aviation equipment system. It is crucial to quickly and accurately measure the gaps among the pipes within the aviation equipment. Due to the complexity of the aviation pipeline system, inspecting the spacing efficiently among different pipes is challenging. In this paper, we propose an effective method to inspect the gaps among aircraft pipes over the raw point data from a single view. First, we design a probe function to extract the information of pipe structures based on the cylinder feature consistency among proximity patches. The probe function is intended to avoid the interference of other structures and also provides an appropriate starting location when extracting the pipe structure information. We then design a growth guidance strategy based on the continuity of pipe axial directions, which can ensure the continuity and accuracy of pipe centerline extraction. Since the curved smooth pipe can be subdivided into many straight cylinders, we devise an adaptive growing method which can ensure the centerline extraction efficiency of multi-curvature aviation pipes. Extensive experiments demonstrate that our algorithm outperforms the state-of-the-art methods in terms of inspection accuracy and efficiency.