Reviewerly/exHarmony huggingface dataset:
Towards Pragmatic Semantic Image Synthesis for Urban Scenes

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of the input mask and the appearance of real images. This new task reframes the well-known unsupervised SIS task in a more practical setting, where we leverage cheaply available synthetic data from a driving simulator to learn how to generate photorealistic images of urban scenes. This stands in contrast to previous works, which assume that labels and images come from the same domain but are unpaired during training. We find that previous unsupervised works underperform on this task, as they do not handle distribution shifts between two different domains. To bypass these problems, we propose a novel framework with two main contributions. First, we leverage the synthetic image as a guide to the content of the generated image by penalizing the difference between their high-level features on a patch level. Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches. Extensive comparisons on the benchmarks benchmarks GTA-V → Cityscapes and GTA-V → Mapillary show the superior performance of the proposed model against state-of-the-art on this task.
Toward Map Updates with Crosswalk Change Detection Using a Monocular Bus Camera

Detecting when road maps change is useful for autonomous vehicles to drive safely and legally, for city planners to make more educated decisions, and web maps to better serve consumers. Many public vehicles drive around the city on a regular basis and collect road data for security and safety purposes through dash cams, yet few cities and companies have considered this as a data source for city monitoring. We present an automatic method and system for crosswalk change detection at city intersections using a monocular camera on a city bus and analyze longitudinal results over the course of a year. Using images recorded by a bus two years ago as reference, multiple city intersections are reconstructed, fitted for ground planes, and labelled for crosswalks. Subsequent images from the bus are imported and processed to detect if changes have occurred since intersections were first seen by first localizing current images with respect to the reference images, detecting for crosswalks, and computing detection overlaps in the bird’s-eye-view. Our method makes improvements upon baseline methods by checking for crosswalk visibility and localization errors, is able to generate results typically seen by using more expensive LiDAR sensors, and has been successfully deployed live for one month.
Factors affecting parental intention to use AI-based social robots for children’s ESL learning

Abstract The use of AI-based social robots has been shown to be beneficial for learning English as a Second Language (ESL). Not much is known, however, about the drivers of parental intention to use those robots in support of their children’s ESL learning. This study aims to explore the factors that drive parental intention to adopt AI-based social robots for children’s ESL learning. The research model is proposed based on the theories and literature regarding motivations, product smartness, personality traits and physical risk perception. Data collected from 315 participants are analyzed using the partial least squares structural equation modeling (PLS-SEM) method. The results show that utilitarian and hedonic motivations positively affect parental intention to adopt AI-based social robots. In addition, utilitarian motivation is influenced by robots’ autonomy and parental personal innovativeness, while hedonic motivation is influenced by robots’ autonomy and humanlike interaction, and parental personal innovativeness. From the findings, important implications for promoting parental intention to adopt AI-based social robots in children’s ELS learning are offered.
Adaptive Multi-Robot Communication System and Collision Avoidance Algorithm for Precision Agriculture

In precision farming technology, the interest of the researchers has been focused on the applications of autonomous mobile robots for agricultural operations such as planting, inspection, spraying, and harvesting. However, each autonomous robot generally performs a single agricultural task. In this context, complete autonomy in precision farming can be achieved by using coordinated multi-robot systems that can easily and safely cooperate to accomplish agricultural tasks. The efficiency of the multi-robot system depends on the number of robots, the size of the robots, the distance between each robot, the instant location and heading angle of the robots, and the size of the farmland. This paper describes the development of wireless Robot to Robot (R2R) communication system architecture and the collision avoidance algorithm for multi-robot precision farming applications. The developed system uses the fusion of a digital compass and GPS receiver for wirelessly broadcasting the spatial and temporal data of the mobile robots through WiFi. In this study, WiFi broadcasting was chosen for reasons such as the advantages of long wireless signal range and strength, not being easily affected by weather and dust, low cost, and so on. The proposed system realizes the real-time wireless broadcasting of the mobile robot information for eliminating the collision of mobile robots and improving the level of safety management. The results show that the system has flexible, reliable, and adaptable solution, and thus can increase the efficiency of the multi-robot system in precision farming applications.
An Online Website Fingerprinting Defense Based on the Non-Targeted Adversarial Patch

Website Fingerprinting (WF) attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim's destination website, even if traffic is sophisticatedly anonymized by Tor. Many offline defenses have been proposed and claimed to have achieved good effectiveness. However, such work is more of a theoretical optimization study than a technology that can be applied to real-time traffic in the practical scenario. Because defenders generate optimized defense schemes only if the complete traffic traces are obtained. The practicality and effectiveness are doubtful. In this paper, we provide an in-depth analysis of the difficulties faced in porting existing offline defenses to the online scenarios. And then the online WF defense based on the non-targeted adversarial patch is proposed. To reduce the overhead, we use the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm to identify critical segments that have high contribution to the classification. In addition, we optimize the adversarial patch generation process by splitting patches and limiting the values, so that the pre-trained patches can be injected and discarded in real-time traffic. Extensive experiments are carried out to evaluate the effectiveness of our defense. When bandwidth overhead is set to 20%, the accuracies of the two state-of-the-art attacks, DF and Var-CNN, drop to 10.83% and 15.49%, respectively. Furthermore, we implement the real-time patch traffic injection based on WFPadTools framework in the online scenario, and achieve a defense accuracy of 95.50% with 12.57% time overhead.
Modern Artificial Intelligence-An Overview

Modern Artificial Intelligence (AI) is a rapidly evolving field that encompasses a range of techniques and approaches, including machine learning, deep learning, natural language processing, computer vision, robotics, and more. The development of AI technologies has enabled unprecedented levels of accuracy in tasks such as image and speech recognition, natural language understanding, and game playing. This has been made possible by the rise of deep learning, which involves training artificial neural networks on vast amounts of data to recognize patterns and make predictions with high accuracy. Other recent advances in modern AI include the development of generative models and reinforcement learning. Despite the significant progress made in modern AI, there are still many challenges that need to be addressed, including issues related to data privacy, fairness, and bias, and the need for more explainable AI systems that can provide clear and transparent reasoning for their decisions. This study provides an overview of modern AI and its applications, as well as the challenges and opportunities that lie ahead in this rapidly evolving field.
Critical success factors for Security Education, Training and Awareness (SETA) programme effectiveness: an empirical comparison of practitioner perspectives

Purpose Cyber security has never been more important than it is today in an ever more connected and pervasive digital world. However, frequently reported shortages of suitably skilled and trained information system (IS)/cyber security professionals elevate the importance of delivering effective Security Education,Training and Awareness (SETA) programmes within organisations. Therefore, the purpose of this study is the questionable effectiveness of SETA programmes at changing employee behaviour and an absence of empirical studies on the critical success factors (CSFs) for SETA programme effectiveness. Design/methodology/approach This exploratory study follows a three-stage research design to give voice to practitioners with SETA programme expertise. Data is gathered in Stage 1 using semi-structured interviews with 20 key informants (the emergence of the CSFs), in Stage 2 from 65 respondents to a short online survey (the ranking of the CSFs) and in Stage 3 using semi-structured interviews with nine IS/cyber security practitioners (the emergence of the guiding principles). Using a multi-stage research design allows the authors to propose and evaluate the 11 CSFs for SETA programme effectiveness. Findings This study conducted a mean score analysis to evaluate the level of importance of each CSF within two independent groups of IS/cyber security professionals. This multi-stage analysis produces a ranked list of 11 CSFs for SETA programme effectiveness, while the difference in the rankings leads to the emergence of five CSF-specific guiding principles (to increase the likelihood of delivering an effective SETA programme within an organisational context). This analysis also reveals that most of the contradictions/differences in CSF rankings between IS/cyber security practitioners are linked to the design phase of the SETA programme life cycle. While two CSFs, “maintain quarterly evaluation of employee performance” (CSF-DS6) and “build security awareness campaigns” (CSF-EV1), represent the most significant contradiction in this study. Originality/value The 11 CSFs for SETA programme effectiveness, along with the five CSF-specific guiding principles, provide a greater depth of knowledge contributing to both theory and practice and lays the foundation for future studies. Therefore, the outputs of this study provide valuable insights on the areas that practice needs to get right to deliver effective SETA programmes.
A multi-scale 3-stacked-layer coned U-net framework for tumor segmentation in whole slide images

The contribution of deep learning in medical image diagnosis has gained extensive interest due to its excellent performance. Furthermore, the interest has also grown in digital pathology since it is considered the golden standard for tumor detection and diagnosis in digital Whole Slide Images (WSIs). This paper proposes an end-to-end cone-shaped encoder-decoder framework called a Multi-scale 3-stacked-Layer coned U-Net (Ms3LcU-Net) framework. It boosts performance by using many enhancements and integrating techniques such as blended mutual attention, dilated fusion, edge enhancement, and atrous pooling. Furthermore, the morphological post-processing and test time augmentation techniques are used in Ms3LcU-Net to refine and smooth the generated segmentations. The experimental results from a quantitative perspective using multiple evaluation metrics and from a qualitative viewpoint by visualizing the generated segmentation predictions conducted on the public PAIP 2019 and DigestPath datasets demonstrated the effectiveness and competitiveness of the proposed model for tumor segmentation in WSIs. The proposed framework yielded an average clipped Jaccard Index value of 0.7211 on the validation set of the PAIP 2019 dataset. In contrast, the DigestPath dataset achieved an average dice coefficient and F1-score of 0.833 and 0.897, respectively. The code will be available publicly upon acceptance of the paper at https://github.com/Heba-AbdeNabi/Ms3LcU-Net-.
Probabilistic multi-modal depth estimation based on camera–LiDAR sensor fusion

Abstract Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution, or LiDAR sensors, due to the precise geometric data they provide. However, each of these suffers from some inherent drawbacks, such as high sensitivity to changes in illumination conditions in the case of cameras and limited resolution for the LiDARs. Sensor fusion can be used to combine the merits and compensate for the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They process the sensor data streams independently and combine the high-level estimates obtained for each sensor. In this paper, we tackle the problem at a low level, fusing the raw sensor streams, thus obtaining depth estimates which are both dense and precise, and can be used as a unified multi-modal data source for higher-level estimation problems. This work proposes a conditional random field model with multiple geometry and appearance potentials. It seamlessly represents the problem of estimating dense depth maps from camera and LiDAR data. The model can be optimized efficiently using the conjugate gradient squared algorithm. The proposed method was evaluated and compared with the state of the art using the commonly used KITTI benchmark dataset.
DLIRIR : Deep learning based improved Reverse Image Retrieval

Deep feature search has transpired as a sub-problem of large-scale Reverse Image Retrieval (RIR) algorithms in computer vision. In light of recent progress, transforming deep features into binary codes and employing various search strategies to delineate similar images has gained popularity. However, these binary codes cannot accurately define the similarity between two images, which is essential for image retrieval. Moreover, when retrieving k closest images, the existing RIR approaches show a drop in precision value as k increases. In this paper, we put forward a method, Deep Learning based Improved Reverse Image Retrieval (DLIRIR), which solves the RIR problem by utilizing a deep neural network, clustering methodology, and dimensionality reduction technique. Motivated by the need to prevent information loss in converting deep features to binary codes and reduce search time, we cluster image features and select some features from each cluster called Representative Points (RPs). These RPs are then compared to features of an image (query) to retrieve images similar to it from the database. The Euclidean distances of the query feature and the RPs indicate similarity between the query and the cluster of images represented by that RP. We reduce the number of comparisons by selecting as few RPs as possible. We evaluate the performance of DLIRIR by comparing it with other state-of-the-art algorithms in the RIR domain. Our method achieves a precision greater than 99% in benchmark datasets Caltech 256, Corel 10k, and Adaptiope, while a precision between 80%–85% on large size datasets Cifar 100, Tiny ImageNet, and VGGFace2.
A Segmentation Method of 3D Liver Image Based on Multi-scale Feature Fusion and Coordinate Attention Mechanism

Due to the high similarity of organs in 3D liver image and the use of simple connection by U-Net to fuse different semantic features, the segmentation accuracy of network needs to be improved. To solve these problems, this paper proposes a 3D liver semantic segmentation method based on multi-scale feature fusion and coordinate attention mechanism. Firstly, in the encoder section of U-Net, the multi-scale feature fusion module was used to capture multi-scale features; Then, coordinate attention mechanism was used to fuse low-level features and high-level features to locate regions of interest; Finally, the segmentation effect of edge details was improved through a deep supervision mechanism. The experimental results show that: on the LiTS dataset, the dice similarity coefficient (DSC) of this method reaches 96.5%. Compared with the U3-Net + DC method, the DSC increases by 0.1%, and the relative volume difference (RVD) decreases by 1.09%; On the CHAOS dataset, the DSC of this method reaches 96.8%, and compared with CANet, the DSC increases by 0.2%; On the MRI dataset of a hospital, the DSC of this method reaches 97.2%.
Cross-Scale Dynamic Alignment Network for Reference-Based Super-Resolution

Image super-resolution aims to recover high-resolution (HR) images from corresponding low-resolution (LR) images, but it is prone to lose significant details in reconstruction progress. Reference-based image super-resolution can produce realistic textures using an external reference (Ref) image, thus reconstructing pleasant images. Despite the remarkable advancement, there are two critical challenges in reference-based image super-resolution. One is that it is difficult to match the correspondence between LR and Ref images when they are significantly different. The other is how the details of the Ref image are accurately transferred to the LR image. In order to solve these issues, we propose improved feature extraction and matching method to find the matching relationship corresponding to the LR and Ref images more accurately, propose cross-scale dynamic correction module to use multiple scale related textures to compensate for more information. Extensive experimental results over multiple datasets demonstrate that our method is better than the baseline model on both quantitative and qualitative evaluations.
Joint Spatiotemporal Collaborative Relationship Network for Skeleton-Based Action Recognition

Skeleton-based human action recognition has achieved a great interest in recent years, as skeleton data has been demonstrated to be robust to illumination changes, body scales, dynamic camera views, and complex background. Nevertheless, efficient modeling of relationships between non-adjacent nodes in 3D skeleton spacetime is still an open problem. In this work, we propose a Joint Spatiotemporal Collaborative Relationship (JSCR) network which model utilizes the query and key vectors of each node to construct the relationship feature matrix between nodes, and realizes the feature capture and reorganization of nodes in the space-time dimension. In our JSCR model, Spatial Collaborative Relation Module (SCRM) is used to model the relationship between non-adjacent nodes within a frame, and Temporal Collaborative Relation Module (TCRM) is used to understand the interaction between non-adjacent nodes across multiple frames. The two are combined in a two-stream network which outperforms state-of-the-art models using the same input data on both NTU-RGB + D and Kinetics-400.
A Novel Multi-task Architecture for Vanishing Point Assisted Road Segmentation and Guidance in Off-Road Environments

Despite the widespread application of convolutional neural network (CNN) based and transformer based models for road segmentation task to provide driving vehicles with valuable information, there is currently no reliable and safe solution specifically designed for harsh off-road environments. In order to address this challenge, we proposed a multi-task network (VPrs-Net) capable of simultaneously learning two tasks: vanishing point (VP) detection and road segmentation. By utilizing road clue provided by the VP, VPrs-Net achieves more accurate performance in identifying drivable areas of harsh off-road environments. Moreover, the model guided by the VP can further enhance the safety performance of driving vehicles. We further proposed a multi-attention architecture for learning of task-specific features from the global features to solve the problem of attentional imbalance in multi-task learning. The public ORFD off-road dataset was used to evaluate performance of our proposed VPrs-Net. Experimental results show that compared to several state-of-the-art algorithms, our model achieved not only 96.91% accuracy in the segmentation task, but also a mean error of NormDist of 0.03288 in road VP detection task. Therefore, the proposed model has demonstrated its potential performance in challenging off-road environments.
Do Tissue Source Sites Leave Identifiable Signatures in Whole Slide Images Beyond Staining?

Why can deep learning predictors trained on Whole Slide Images fail to generalize? It is a common theme in Computational Pathology to see a high performing model developed in a research setting experience a large drop in performance when it is deployed to a new clinical environment. One of the major reasons for this is the batch effect that is introduced during the creation of Whole Slide Images resulting in a domain shift. Computational Pathology pipelines try to reduce this effect via stain normalization techniques. However, in this paper, we provide empirical evidence that stain normalization methods do not result in any significant reduction of the batch effect. This is done via clustering analysis of the dataset as well as training weakly-supervised models to predict source sites. This study aims to open up avenues for further research for effective handling of batch effects for improving trustworthiness and generalization of predictive modelling in the Computational Pathology domain.
PFGE: Parsimonious Fast Geometric Ensembling of DNNs

Ensemble methods are commonly used to enhance the generalization performance of machine learning models. However, they present a challenge in deep learning systems due to the high computational overhead required to train an ensemble of deep neural networks (DNNs). Recent advancements such as fast geometric ensembling (FGE) and snapshot ensembles have addressed this issue by training model ensembles in the same time as a single model. Nonetheless, these techniques still require additional memory for test-time inference compared to single-model-based methods. In this paper, we propose a new method called parsimonious FGE (PFGE), which employs a lightweight ensemble of higher-performing DNNs generated through successive stochastic weight averaging procedures. Our experimental results on CIFAR-{10,100} and ImageNet datasets across various modern DNN architectures demonstrate that PFGE achieves 5x memory efficiency compared to previous methods, without compromising on generalization performance. For those interested, our code is available at https://github.com/ZJLAB-AMMI/PFGE .