Reviewerly/exHarmony huggingface dataset:
Opportunities and Challenges of Blockchain Technology for Tourism Industry in Future Smart Society

The tourism is a trending industry in the current era of the economic growth of the country. Tourism can be explained as travelling to a place which is different from your home city or country for numerous reasons. But the tourism industry has been facing problems like Identity theft, Intermediaries, Payment failures, fake reviews, transparency, settlement, overbooking, policy and most importantly safety concerns due to COVID-19 pandemic. To overcome the above drawbacks after referring to some of the research work come up with a solution called Blockchain. Blockchain is a chain of Blocks where each block contains a set of transactions and the blocks are connected using cryptography. Mainly Blockchain and tourism have the potential to show into a really advantageous combination as this technology will offer additional security, Safety, eradication of middleman, data integrity, decentralized business models, immutability, transparency and accurate contact tracing app for Tourism Industry in Future Smart Society. This research gives the clear idea of how blockchain technology helps to overcome difficulties/Challenges in tourism industries. Our future proposed architecture BLOBIM (Blockchain Based Integrated Model) removes the third party intervention, security, safety, trust, transparency etc.
Privacy Preservation for Federated Learning With Robust Aggregation in Edge Computing

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this article, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks.
Cluster Embedding Joint-Probability-Discrepancy Transfer for Cross-Subject Seizure Detection

Transfer learning (TL) has been applied in seizure detection to deal with differences between different subjects or tasks. In this paper, we consider cross-subject seizure detection that does not rely on patient history records, that is, acquiring knowledge from other subjects through TL to improve seizure detection performance. We propose a novel domain adaptation method, named the Cluster Embedding Joint-Probability-Discrepancy Transfer (CEJT), for data distribution structure learning. Specifically, 1) The joint probability distribution discrepancy is minimized to reduce the distribution shift in the source and target domains, and strengthen the discriminative knowledge of classes. 2) A clustering is performed on the target domain, and the class centroids of sources is used as the clustering prototype of the target domain to enhance data structure. It is worth noting that the manifold regularization is used to improve the quality of clustering prototypes. In addition, a correlation-alignment-based source selection metric (SSC) is designed for most favorable subject selection, reducing the computational cost as well as avoiding some negative transfer. Experiments on 15 patients with focal epilepsy from the Children's Hospital, Zhejiang University School of Medicine (CHZU) database shown that CEJT outperforms several state-of-the-art approaches, and can promote the application of seizure detection.
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning

The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions.In this setting, models operate on a huge output space, containing all possible state-object compositions.While previous works tackle the problem by learning embeddings for the compositions jointly, here we revisit a simple CZSL baseline and predict the primitives, i.e. states and objects, independently.To ensure that the model develops primitive-specific features, we equip the state and object classifiers with separate, non-linear feature extractors.Moreover, we estimate the feasibility of each composition through external knowledge, using this prior to remove unfeasible compositions from the output space.Finally, we propose a new setting, i.e.CZSL under partial supervision (pCZSL), where either only objects or state labels are available during training, and we can use our prior to estimate the missing labels.Our model, Knowledge-Guided Simple Primitives (KG-SP), achieves state of the art in both OW-CZSL and pCZSL, surpassing most recent competitors even when coupled with semi-supervised learning techniques.
Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe

The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.
Do Explanations Explain? Model Knows Best

It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the problem. One peculiar observation is that these explanations (attributions) point to different features as being important. The phenomenon raises the question, which explanation to trust? We propose a framework for evaluating the explanations using the neural network model itself. The framework leverages the network to generate input features that impose a particular behavior on the output. Using the generated features, we devise controlled experimental setups to evaluate whether an explanation method conforms to an axiom. Thus we propose an empirical framework for axiomatic evaluation of explanation methods. We evaluate well-known and promising explanation solutions using the proposed framework. The framework provides a toolset to reveal properties and drawbacks within existing and future explanation solutions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/CAMP-eXplain-AI/Do-Explanations-Explain
Quantifying Explainability of Saliency Methods in Deep Neural Networks With a Synthetic Dataset

Post-hoc analysis is a popular category in eXplainable artificial intelligence (XAI) study. In particular, methods that generate heatmaps have been used to explain the deep neural network (DNN), a black-box model. Heatmaps can be appealing due to the intuitive and visual ways to understand them but assessing their qualities might not be straightforward. Different ways to assess heatmaps' quality have their own merits and shortcomings. This paper introduces a synthetic dataset that can be generated adhoc along with the ground-truth heatmaps for more objective quantitative assessment. Each sample data is an image of a cell with easily recognized features that are distinguished from localization ground-truth mask, hence facilitating a more transparent assessment of different XAI methods. Comparison and recommendations are made, shortcomings are clarified along with suggestions for future research directions to handle the finer details of select post-hoc analysis methods. Furthermore, mabCAM is introduced as the heatmap generation method compatible with our ground-truth heatmaps. The framework is easily generalizable and uses only standard deep learning components.
Dynamic Dual-Output Diffusion Models

Iterative denoising-based generation, also known as denoising diffusion models, has recently been shown to be comparable in quality to other classes of generative models, and even surpass them. Including, in particular, Generative Adversarial Networks, which are currently the state of the art in many subtasks of image generation. However, a major drawback of this method is that it requires hundreds of iterations to produce a competitive result. Recent works have proposed solutions that allow for faster generation with fewer iterations, but the image quality gradually deteriorates with increasingly fewer iterations being applied during generation. In this paper, we reveal some of the causes that affect the generation quality of diffusion models, especially when sampling with few iterations, and come up with a simple, yet effective, solution to mitigate them. We consider two opposite equations for the iterative denoising, the first predicts the applied noise, and the second predicts the image directly. Our solution takes the two options and learns to dynamically alternate between them through the denoising process. Our proposed solution is general and can be applied to any existing diffusion model. As we show, when applied to various SOTA architectures, our solution immediately improves their generation quality, with negligible added complexity and parameters. We experiment on multiple datasets and configurations and run an extensive ablation study to support these findings.
Real-Time Medical Data Security Solution for Smart Healthcare

Cyberattacks pose a serious threat to the wireless transfer of sensitive healthcare data, hampering the level of privacy offered. Numerous cyber-security modules have been developed. However, many of these methods are unsuitable for real-time medical data processing. In this article, we present a cybersecurity framework developed for medical images in a smart healthcare system. We propose two novel two-dimensional chaotic maps, called the logistic regulated quadratic map (LRQ) and quadratic regulated quadratic map (QRQ), which have compound chaotic properties and a large chaotic range. We present an encryption technique based on the LRQ and QRQ map and demonstrate that the exceedingly chaotic pseudorandom number sequence generated by the maps results in a highly robust cipher image. Our fast cryptosystem can encrypt an image of size 128 × 128 in approximately 0.03 s. The proposed cybersecurity solution protects data against cyberattacks and ensures a seamless treatment experience.
Optical flow-based branch segmentation for complex orchard environments

Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.
Few-Shot Object Counting and Detection

We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: Counting-DETR. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter leverages the pseudo ground-truth provided by the former but takes the necessary steps to account for the imperfection of pseudo ground-truth. To validate the performance of our method on the new task, we introduce two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scenes, multiple object classes per image, and a huge variation in object shapes, sizes, and appearance. Our proposed approach outperforms very strong baselines adapted from few-shot object counting and few-shot object detection with a large margin in both counting and detection metrics. The code and models are available at https://github.com/VinAIResearch/Counting-DETR .
Attention-Based Interrelation Modeling for Explainable Automated Driving

Automated driving desires better performance on tasks like motion planning and interacting with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high performance in these tasks with remarkable visual scene understanding and generalization abilities. However, when common scene-parsing methods are used to train end-to-end models, limitations of explainability in such algorithms inhibit their implementations in fully automated driving. The main challenges include algorithm performance deficiencies and inconsistencies, insufficient AI transparency, degraded user trust, and undermining human-AI interactions. This research aids the decision-making performance and transparency of automated driving systems by providing multi-modal explanations, especially when interacting with pedestrians. The proposed algorithm combines global visual features and interrelation features by parsing scene images as self-constructed graphs and using an attention-based module to capture the interrelationship among the ego-vehicle and other traffic-related objects. The output modules make decisions while simultaneously generating semantic text explanations. The results show that the fusion of the features from global frames and interrelational graphs improves decision-making and explanation predictions compared to two state-of-the-art benchmark algorithms. The interrelation module also enhances algorithm transparency by disclosing the visual attention used for decision-making. The importance of interrelation features on the two prediction tasks is further revealed along with the underlying mechanism of multitask learning on the datasets with hierarchical labels. The proposed model improves driving decision-making during pedestrian interactions with intelligible reasoning cues for building an appropriate mental model of automated driving performance for human users.
Towards Multimodal Depth Estimation from Light Fields

Light field applications, especially light field rendering and depth estimation, developed rapidly in recent years. While state-of-the-art light field rendering methods handle semi-transparent and reflective objects well, depth estimation methods either ignore these cases altogether or only deliver a weak performance. We argue that this is due current methods only considering a single “true” depth, even when multiple objects at different depths contributed to the color of a single pixel. Based on the simple idea of outputting a posterior depth distribution instead of only a single estimate, we develop and explore several different deep-learning-based approaches to the problem. Additionally, we contribute the first “multimodal light field depth dataset” that contains the depths of all objects which contribute to the color of a pixel. This allows us to supervise the multimodal depth prediction and also validate all methods by measuring the KL divergence of the predicted posteriors. With our thorough analysis and novel dataset, we aim to start a new line of depth estimation research that overcomes some of the longstanding limitations of this field.
Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation

Co-speech gestures are a principal component in conveying messages and enhancing interaction experiences between humans and critical ingredients in human-agent interaction, including virtual agents and robots. Existing machine learning approaches have yielded only marginal success in learning speech-to-motion at the frame level. Current methods generate repetitive gesture sequences that lack appropriateness with respect to the speech context. To tackle this challenge, we take inspiration from successes in natural language processing on context and long-term dependencies, and propose a new framework that views text-to-gesture as machine translation, where gestures are words in another (non-verbal) language. We propose a vector-quantized variational autoencoder structure as well as training techniques to learn a rigorous representation of gesture sequences. We then translate input text into a discrete sequence of associated gesture chunks in the learned gesture space. Ultimately, we use translated gesture tokens from the input text as an input to the autoencoder's decoder to produce gesture sequences. Subjective and objective evaluations confirm the success of our approach in terms of appropriateness, human-likeness, and diversity. We also introduce new objective metrics using the quantized gesture representation.
DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio

In wireless communication, signal demodulation under non-ideal conditions is one of the important research topic. In this paper, a novel non-coherent binary phase shift keying demodulator based on deep neural network, namely DeepDeMod, is proposed. The proposed scheme makes use of neural network to decode the symbols from the received sampled signal. The proposed scheme is developed to demodulate signal under fading channel with additive white Gaussian noise along with hardware imperfections, such as phase and frequency offset. The time varying nature of hardware imperfections and channel poses a additional challenge in signal demodulation. In order to address this issue, additionally we propose transfer learning based DeepDeMod scheme. Pilot symbols along with data is transmitted in a packet which is used to learn the time varying parameters from the pilot reception followed by data demodulation. Results show that compared with the conventional demodulators and other machine learning based demodulators, our proposed DeepDeMod provides significantly better performance in term of bit error rate. We also implement the proposed DeepDeMod on software defined radio and present the experimental results.
Zero-Shot Category-Level Object Pose Estimation

Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally. In this paper we tackle the problem of estimating the pose of novel object categories in a zero-shot manner. This extends much of the existing literature by removing the need for pose-labelled datasets or category-specific CAD models for training or inference. Specifically, we make the following contributions. First, we formalise the zero-shot, category-level pose estimation problem and frame it in a way that is most applicable to real-world embodied agents. Secondly, we propose a novel method based on semantic correspondences from a self-supervised vision transformer to solve the pose estimation problem. We further re-purpose the recent CO3D dataset to present a controlled and realistic test setting. Finally, we demonstrate that all baselines for our proposed task perform poorly, and show that our method provides a six-fold improvement in average rotation accuracy at 30 $$^\circ $$ C. Our code is available at https://github.com/applied-ai-lab/zero-shot-pose .
AD-RCNN: Adaptive Dynamic Neural Network for Small Object Detection

With the large-scale commercialization of 5G networks, Internet of Things (IoT) applications keep on emerging in recent years. Real-time environmental awareness is an essential part of various IoT applications, e.g., self-driving vehicles. Object detection plays a fundamental role in real-time environmental awareness, which is responsible for acquiring valuable object information from the environment automatically. Despite of the fast progress for object detection in general, small object detection still faces challenges. Because of the restricted scales, small objects are only capable of generating relatively week features after multiple convolutional layers, thus causing low detection accuracy. Existing schemes mostly focus on extracting rich multiscale features, e.g., generating high-resolution features through generative adversarial networks (GANs), or generating multiscale features through feature combination. Nevertheless, these schemes require complex network implementation, and usually suffer from high processing delay because of high-resolution images. To resolve the problems mentioned above, we propose an adaptive dynamic neural network (AD-RCNN) that consists of three fundamental improvements. We first propose a dynamic region proposal network to improve the quality of region proposals. We then introduce a visual attention scheme to generate features of regions. Finally, we put forward an adaptive dynamic training module to optimize final detection results. Experimental results demonstrate that AD-RCNN outperforms the state-of-the-art from the perspectives of mAP and frames per second (FPS). Specifically, at the resolution of 1024 of TT100K data set, AD-RCNN achieves 68.8% mAP, which outperforms the baseline Faster RCNN by 8.52%.