Reviewerly/exHarmony huggingface dataset:
Attention-Based Deep Driving Model for Autonomous Vehicles with Surround-View Cameras

Experienced human drivers always make safe driving decisions by selectively observing the front, rear and side- view mirrors. Several end - to-end methods have been pro-posed to learn driving models with multi-view visual infor-mation. However, these benchmark methods lack semantic understanding of multi-view image contents, where human drivers usually reason these information for decision making with different visual region of interests. In this paper, we propose an attention-based deep learning method to learn a driving model with input of surround-view visual information and the route planner, in which a multi-view attention module is designed for obtaining region of interests from human drivers. We evaluate our model on the Drive360 dataset with comparison of benchmarking deep driving models. Results demonstrate that our model achieves a competitive accuracy in both steering angle and speed prediction than benchmarking methods. Code is available at https://githuh.com/jet-uestc/MVA-Net.
Pilot Hand Detection Based on Semi-Supervised Learning

In this paper, we propose a pilot hand detection method that combines the semi-supervised DenSe Learning (DSL) with a coordinate attention (CA) module, which we call CA-DSL. In our suggested approach, the CA module is added to Resnet, the backbone network of DSL. The CA module enhances the DSL’s capacity for learning and boosts the effectiveness of pilot hand detection. In addition, we establish a dataset for pilot hand detection that we call Pilot Hand (PH). On the PH dataset, we evaluate the suggested hand detection algorithm. and compared it with the state-of-the-art models. Experimental results show that the detection accuracy of CA-DSL outperforms the state-of-the-art models by 2%-7%.
Applied Alternative Tools and Methods in the Replacement of the Game Design Document

This chapter proposes and experiments alternatives to replace or optimize the use of the game design document (GDD). The creation and development of a game is accomplished by relying on the GDD, which contains all the information, such as the script, mechanics, and relevant details, so the team can use as a guide. There is no exact formula for creating a GDD, and several formats are proposed and used nowadays. Information misinterpreted or misunderstood at different levels of responsibility can create irreparable problems after the start of production. This chapter proposes the use of analog prototyping associated with benchmarking techniques and agile development as efficient alternatives to GDD, which are tested in the development of the game Forsaken Dungeons, created by one of the authors.
Mapping Blockchain Technology Prospects and Solutions in the Healthcare Industry for Pandemic Crises

Throughout history, pandemics have extensively impacted humans’ lives in many different ways. Despite the World Health Organisation (WHO), governments and supporting health agencies and non-profit organisations’ widespread efforts to identify, control, and manage pandemics, such crises are increasing in frequency. Using measures to prevent, address, or overcome them has become challenging and overwhelming. Overall, the current pandemic influenced life, has left scientists, practitioners and politicians no choice other than to find solutions to support societies to live the new normal in a more thriving way. Their focus has been on helping individuals and communities prepare for the ‘ad-hoc future’ health-related crises. One of the most recent promising discoveries and creative developments that seems to be playing a vital role in everyone’s daily life is ‘Blockchain Technology’. In recent years, blockchain has received increased interest and has moved beyond finance, and its emergence is revolutionising many industries in various ways. For instance, blockchain has been actively researched and used for multiple purposes such as monitoring supply chains, managing retail loyalty rewards programs, certifying digital credentials, etc. While blockchain has positively disrupted many of the past and current healthcare-related practices, still it’s essential to gain an in-depth understanding of blockchain technology’s intriguing benefits and open challenges. Such comprehensive knowledge can enhance the development and utilisation of further promising healthcare products/services and processes within societies. This chapter will support the readers in understanding the blockchain technology prospects and solutions for the healthcare industry in general and healthcare-related crises in particular.
Vision-Based Gesture Recognition for Smart Light Switching

Detecting human gestures is important in many areas for humans. The creation of new technologies and methods to simplify the creation of such sensors is important for their rapid and cheap implementation in new applications. We used a Convolutional Neural Network for the visual recognition of a hand or a wand signal and tested it by switching on/off a normal lamp. By taking the difference between two contiguous images from a single video file and processing them to get one filtered image as the CNN input, we simplified the collection of samples to train the neural network. In this way, the images can be small enough to considerably speed up the system throughput. We added a redundancy layer to increase the robustness of the system to confusing signals and obtained 100% accuracy. We also achieved good system performance under different lighting conditions without specifically training the neural network for this.
Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual Learning

Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.
RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment.
Empirical Study on Frequency Adversarial Perturbation for Medical Image Privacy Protection

The purpose of this research is to explore the possibility of leveraging the adversarial perturbation to protect medical images against illegal and unauthorized access. We especially look into the adversarial perturbation in the frequency domain in terms of reliability against noise mitigation methods. A new frequency adversarial perturbation method is proposed based on the DAG method. Both the spatial adversarial perturbation and the frequency adversarial perturbation are evaluated in terms of segmentation accuracy and reliability. In mIoU segmentation accuracy comparison, the spatial adversarial perturbation has 0.798970 and 0.890291 in DAG and random noise respectively, while the frequency adversarial perturbation has 0.798992 and 0.897046. In the reliability test, the DAG frequency adversarial perturbation has 0.855191, while the DAG spatial adversarial perturbation has 0.8955231. The accuracy comparison shows the frequency adversarial perturbation has a comparable impact on segmentation accuracy to spatial adversarial perturbation. The reliability comparison shows frequency adversarial perturbations are more tolerant to low pass filters than spatial adversarial perturbations. Adversarial perturbation is a promising solution for medical image privacy protection. Especially, the proposed adversarial perturbation generation method has some capability to countermeasure the noise mitigation methods.
3-Disjoint Path Multistage Interconnection Network with Low Cost and High Reliability

The 3-disjoint path partially chained multistage interconnection network (3-DP PCMIN) is a new multistage interconnection network that we present in this paper. It is a modified version of the GIN that offers three disjoint paths between any source-destination (S-D) node pair in order to accommodate two switch/link failures. It has lower hardware cost than GIN, 3DGIN and enhanced IADM. Here the compact expression for 2-terminal reliability of proposed design is also calculated using hybrid MVI algorithm. To show the advantage features of proposed design layout (3-DP PCMIN) we also make a comparison between 3-DCGIN, enhanced IADM, 3 DGIN and proposed design layout of 3-disjoint path partly chained MIN.
Breast Tumor Localization by Prone to Supine Landmark Driven Registration for Surgical Planning

Breast cancer is the most common cancer in women worldwide. Screening programs and imaging improvements have increased the detection of clinically occult non-palpable lesions requiring preoperative localization. Wire guided localization (WGL) is the current standard of care for the excision of non-palpable carcinomas during breast conserving surgery. Due to the current limitations of intraoperative tumor localization approaches, the integration of multimodal imaging information may be especially relevant in surgical planning. This research proposes a novel method for performing preoperative image-to-surgical surface data alignment to determine the position of the tumor at the time of surgery and aid preoperative planning. First, the volume of the breast in the surgical position is reconstructed, and a set of surface correspondences is defined. Then, the preoperative (prone) and intraoperative (supine) volumes are co-registered using landmark driven non-rigid registration methods. We compared the performances of diffeomorphic and Bspline based registration methods. Finally, our method was validated using clinical data from 67 patients considering as target registration error (TRE) the distance between the estimated tumor position and the reference surgical position. The proposed method achieved a TRE of 16.21 ± 8.18 mm and it could potentially assist the surgery planning and guidance of breast cancer treatment in the clinical practice.
PHILOSOPHICAL ANALYSIS OF CONCEPTUAL FUNDAMENTALS OF EDUCATION FOR SUSTAINABLE DEVELOPMENT

Summary. In the work on the basis of interdisciplinary and metaparadigmatic approaches dominated by systems analysis and culturological approach, the study of education for sustainable development as a scientific object that combines the subject of philosophy of education and pedagogy. A generalized analysis of existing approaches to understanding education for sustainable development is given. Modern education for sustainable development is a harmonious complement to humanistic education in the part related to the interaction of man and the environment. Today, as always, there is a need to maximize humanization processes in various spheres of human life. However, along with this, new priorities are coming to education, which reflect the processes of globalization and the need to harmonize human-nature relations. Accordingly, the focus of education for sustainable development is the problems of optimal organization of the environment of mankind in general and each person in particular. It is concluded that the studied phenomenon acts as an integral construct that can be used for systematic analysis of changes in the education of the post-classical model. It is shown that the main directions of education development are determined by the principles of sustainable development society and the reflection of these principles in the educational sphere. The interrelation of the formation of a safe and favorable environment for human development and changes in modern education, as well as areas of education for sustainable development in specific pedagogical representations. in modern conditions, education for sustainable development goes far beyond traditional environmental issues. It serves as a basis for the integration of almost all innovation processes in the field of education, ensuring the complementarity of changes in education and society. The basic dimensions of modern education (mediality, subject-centeredness, openness, etc.) are also characteristics of education for sustainable development. The main direction of further research should be considered the development of methodology and methods of conceptual synthesis of educational innovations based on the principles of sustainable development.
Homology-Class Guided Rapidly-Exploring Random Tree For Belief Space Planning

In this work, an efficient homology guided belief space planning method for obstacle-cluttered environments is presented. The proposed planner follows a two-step approach. First, a h-signature guided rapidly-exploring random tree (HRRT) algorithm is proposed to provide nominal trajecto-ries in different homology classes by constructing homology aware sub-trees in a parallel manner. The HRRT planner is extended to a h-signature guided RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, where an inter-homology-class rewire procedure is proposed, increasing the probability of discovering homology classes in narrow space/passages. The iLQG-based belief space planning algorithm is then employed to find locally optimal trajectories minimizing uncertainties in each homology class.
Can keypoint descriptors be used in outdoor image classification?

In this paper, we approach a Content Based Image Retrieval problem using keypoints and texture. We employ an algorithm that allows computing a fixed number of keypoints for all the images in the dataset, by adapting a certain parameter of the keypoints computation method. The SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features) algorithms yield the required keypoints. We also used texture features to improve the retrieval results. The texture features are computed using Local Binary Patterns (LBP), Gray Level Co-occurrence Matrices (GLCM), Dual Tree Complex Wavelet Transform (DTCWT), and Gabor filters. The methods were tested on our own dataset, ODIDS which contains outdoor images, mainly acquired in the city of Iaşi, Romania. We performed location identification with good results.
A circular shifting binary descriptor for efficient rotation invariant image matching

Image matching is a key component in computer vision and has many applications. In this work, we propose a circular shifting binary descriptor to increase the speed of rotation invariant image matching algorithms. We can compute the descriptors of the rotated image patches without rotating either the sample points or the image patch by circularly shifting the binary descriptor. Thus, operations such as multiplications and divisions from the orientation estimation step are eliminated from the image matching process which significantly reduces the number of operations for computing the descriptor. In addition, our experiments illustrate that the circular shifting binary descriptor shows limited rotation error in comparison with other descriptors such as ORB while attaining comparable mean average precision.
The Minimum Value State Problem in Actor-Critic Networks

Deep reinforcement learning (RL) methods are vulnerable to adversarially perturbed states. Though the nature of such states is difficult to characterize, popular actor-critic architectures provide a natural way to detect such pathological states by determining whether their value head is below some value. In this paper, we leverage this capacity of actor-critic architectures to generate low-value states that can be used to define a training regimen for RL algorithms, which we call the Pathological Ensemble of Actions for RL (PEARL). We pose the problem of synthesizing a low-value state for a given architecture, prove that it is NP-hard, and present a solution based on integer linear programming. The generated states can then be used to augment the training data with these pathological states. We demonstrate the gains obtained by PEARL when compared to standard Proximal Policy Optimization and Monte-Carlo Tree Search baselines on board games such as Go and Checkers.
Classification of soil types using convolution neural network

Soil classification is primarily concerned with systematic identification of soils dependent on differentiating properties and criteria that guide usage decisions. We can advise farmers on crop types, tillage methods, irrigation, and soil fertility by assessing soil characteristics, among many other factors that can affect crop production. One of many archaeological research topics is identifying components and nanostructures in bottom sediments of thin layers, as these attributes can disclose the details about its deposit from which they have been retrieved, along with its evolution and nature, and also hints about their relation to individual and social contexts or deformation processes over time. We designed a Deep Learning based system using Convolutional Neural Networks (CNN) for the classification of variety of old soil formations. The observed findings are positive, indicating that model offered may be applied to this classification algorithm successfully. The developed training model is used for calculation of number of distinct individual nanostructures in testing images, with an accuracy of 84 percent and a median error of 52 percent.
Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence

Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in the field of knowledge graph reasoning and completion on account of that its have strong interpretability. However, reasoning methods based on path features still have several problems in the following aspects: Path search is inefficient, insufficient paths for sparse tasks and some paths are not helpful for reasoning tasks. In order to solve the above problems, this paper proposes a method called DC-Path that combines dynamic relation confidence and other indicators to evaluate path features, and then guide path search, finally conduct relation reasoning. Experimental result show that compared with the existing relation reasoning algorithm, this method can select the most representative features in the current reasoning task from the knowledge graph and achieve better performance on the current relation reasoning task.
Realtime Texture Upsampling on Graphics Hardware Using Fractal Coding

The texture upsampling technique reduces the memory and manual labor required to create a high-definition texture by procedurally increasing the image resolution on the fly. We propose a real-time texture upsampling technique that utilizes an underlying fractal structure of surface textures found in natural phenomena, such as stone surfaces, tree bark, and animal skin. Our method encodes the source texture by a fractal compression technique in the authoring process. The cross-scale correspondences between two mipmap layers are encoded as compact fractal codes and packed in three types of GPU-friendly formats. The runtime computation increases the resolution of encoded textures by recursively applying fractal decoding. Owing to the concurrent data structures, texture upsampling by any power of 2 is efficiently executed on GPUs. Furthermore, we propose an iterative algorithm to refine the fractal code, thus improving upsampling quality. We demonstrate the effectiveness of our proposed algorithm by synthesizing a high-resolution texture with semi-random patterns.
Rethinking the Adaptiveness of Conditional Flow A Novel Distribution Adaptation Strategy for Image Super-Resolution

The conditional normalized flow model can alleviate the ill-posed nature of super-resolution problems by learning the conditional distribution of the output for a given low-resolution input, which allows multiple predictions for a given low-resolution image. However, the model may generate confusing artifacts in the high-frequency region of the image. While giving a smaller reduction in temperature would solve this problem, it would also smooth out the model's output, which would lead to a decrease in perceptual quality. In this paper, we propose a novel conditional normalizing flow-based distribution adaptation strategy for image Super-Resolution. More specifically, we demonstrate that the part of the latent variable that differs significantly between the latent variables of HR and the Bicubic of LR, contains mainly high-frequency information of the image. We adopt a Bicubic image of LR and a continuous threshold function to evaluate different temperatures in different latent variables. In this way, We can further alleviate generation of confusing artifacts without reducing perceptual quality. Extensive experiments show that our model can outperform state-of-the-art methods and generate more visually favorable results.
Backdoor Attacks against Deep Neural Networks by Personalized Audio Steganography

In the world of cyber security, backdoor attacks are widely used. These attacks work by injecting a hidden backdoor into training samples to mislead models into making incorrect judgments for achieving the effect of the attack. However, since the triggers in backdoor attacks are relatively single, defenders can easily detect backdoor triggers of different corrupted samples based on the same behavior. In addition, most current work considers image classification as the object of backdoor attacks, and there is almost no related research on speaker verification. This paper proposes a novel audio steganography-based personalized trigger backdoor attack that embeds hidden trigger techniques into deep neural networks. Specifically, the backdoor speaker verification uses a pre-trained audio steganography network that employs specific triggers for different samples to implicitly write personalized information to all corrupted samples. This personalized method can significantly improve the concealment of the attack and the success rate of the attack. In addition, only the frequency and pitch were modified and the structure of the attacked model was left unaltered, making the attack behavior stealthy. The proposed method provides a new attack direction for speaker verification. Through extensive experiments, we verified the effectiveness of the proposed method.