Reviewerly/exHarmony huggingface dataset:
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis

Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero Shot Learning (GZSL) problem. Most models achieve competitive performance but still suffer from two problems: (1) Feature confounding, the overall representations confound task-correlated and task-independent features, and existing models disentangle them in a generative way, but they are unreasonable to synthesize reliable pseudo samples with limited samples; (2) Distribution uncertainty, that massive data is needed when existing models synthesize samples from the uncertain distribution, which causes poor performance in limited samples of seen classes. In this paper, we propose a non-generative model to address these problems correspondingly in two modules: (1) Task-correlated feature disentanglement, to exclude the task-correlated features from task-independent ones by adversarial learning of domain adaption towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to synthesize edge-pseudo and center-pseudo samples with certain characteristics towards more diversity generated and intuitive transfer. In addation, to describe the new scene that is the limit seen class samples in the training process, we further formulate a new ZSL task named the ‘Few-shot Seen class and Zero-shot Unseen class learning’ (FSZU). Extensive experiments on four benchmarks verify that the proposed method is competitive in the GZSL and the FSZU tasks.
A Blockchain-Empowered Federated Learning in Healthcare-Based Cyber Physical Systems

With the rapid development of healthcare-based cyber physical systems (CPSs), more and more healthcare data is collected from clinical institutions or hospitals. Due to the private and fragmented nature, healthcare data is quite suitable to be processed by federated learning (FL) paradigm, where a shared global model is aggregated by a central server while keeping the sensitive healthcare data in local hospitals. However, there are two practical issues: (1) the centralized FL server may not honestly aggregate the final model, and (2) the FL participants lack incentive to contribute their efforts. In this study, we propose a blockchain-empowered FL framework for healthcare-based CPSs. A distributed ledger is maintained by a task agreement committee which is composed by the representators of the hospitals who execute FL tasks. A secure FL task model training-based consensus process is proposed to generate consistent blocks. Furthermore, a contribution point-based incentive mechanism is designed to fairly reward FL participators for contributing their local data. We evaluate the proposed system base on real healthcare data and the numerical results demonstrate its effectiveness in achieving FL model aggregation truthfulness and efficiency in providing incentives for FL participants.
Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots (MRs) in both research and education.In this paper, an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives, first, the MR obstacle-free environment is modeled as a diagraph including nodes, edges and weights.Second, Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement, the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim, the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found, the MR updates its diagraph by excluding the corresponding node.Then, Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach, a simulation was carried out on a hand-made MR and an environment including 9 nodes, 19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.
Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features

Liver tumor segmentation from CT images plays an important role in disease diagnosis and treatment planning. In this paper, we propose an automatic segmentation framework dedicated to accurate liver tumor extraction from CT images. To reduce the segmentation complexity, 3D U-Net is first employed to extract liver region. Then, the liver region is divided into homogeneous superpixels by applying a LI-SLIC based hierarchical iterative segmentation strategy, in which the superpixels are decomposed recursively according to their intensity standard deviation in order to adhere to tumor boundaries precisely. Meanwhile, each pixel in the liver region is roughly classified into tumor or non-tumor by SVM using its local intensity and texture features. Finally, a voting model is developed to identify tumor regions from superpixels based on the pixel-wise classification results. Extensive experiments on two public clinical datasets and comparisons with many state-of-the-art methods demonstrate the superiority of our method on liver tumor segmentation especially for the images with noises, ambiguous boundaries, and low contrast.
M-YOLO: Traffic Sign Detection Algorithm Applicable to Complex Scenarios

Traffic signs can be seen everywhere in daily life. Traffic signs are symmetrical, and traffic sign detection is easily affected by distortion, distance, light intensity and other factors, which also increases the potential safety hazards of assisted driving in practical application. In order to solve this problem, a symmetrical traffic sign detection algorithm M-YOLO for complex scenes is proposed. The algorithm optimizes the delay problem by reducing the computational overhead of the network, and speeds up the speed of feature extraction. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in complex environments, such as scale and illumination changes. Experimental results on CCTSDB dataset containing traffic signs in complex scenes and HRRSD small target dataset show that M-YOLO algorithm has good detection performance. Compared with other algorithms, it has higher detection accuracy and detection speed. The test results in real complex scenes show that the detection effect of this algorithm is better than that of YOLOv5l algorithm, and M-YOLO algorithm can accurately detect the traffic signs that cannot be detected by YOLOv5l algorithm. Therefore, the algorithm proposed in this article can effectively improve the detection accuracy of traffic signs, is suitable for complex scenes, and has a good detection effect on small targets.
Intra-hour cloud index forecasting with data assimilation

We introduce a computational framework to forecast cloud index (CI) fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.
Augmented Reality Apps for Tourism Destination Promotion

Current chapter focuses on the effectiveness of Augmented Reality (AR) supported applications for mobile and wearable devices during the visitation of tourist destinations. AR as an innovative technology tool becomes inseparable part of destinations' marketing in different countries across the world. However, the use of AR for marketing purpose in tourism destinations is fully dependent on access and resource availabilities. Hence the effective of AR application in tourism destination marketing remains a subject to detailed and accurate information availability of a particular destination or a region. This chapter draws particular reference to the AR supported ‘Mobile guides' that replaced conventional human tourist guides. The chapter is indebted to AR literature written in the Turkish. Results demonstrate that AR as an innovative technology can be applied for tourism destination promotion.
Artificial Intelligence

Artificial Intelligence is going to support every human activity, from communication to healthcare, business, entertaining, and so forth. It is a very active field of research with an uncountable spectrum of applications. A picture of the field is provided, with a brief overview of history, recent achievements, and directions to detailed information on the various facets of AI.
Nearest neighbor classifiers over incomplete information

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications remains elusive. In this paper, we present a formal study of this impact by extending the notion of Certain Answers for Codd tables , which has been explored by the database research community for decades, into the field of machine learning. Specifically, we focus on classification problems and propose the notion of "Certain Predictions" (CP) --- a test data example can be certainly predicted (CP'ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of data would yield the same prediction. We study two fundamental CP queries: (Q1) checking query that determines whether a data example can be CP'ed; and (Q2) counting query that computes the number of classifiers that support a particular prediction (i.e., label). Given that general solutions to CP queries are, not surprisingly, hard without assumption over the type of classifier, we further present a case study in the context of nearest neighbor (NN) classifiers, where efficient solutions to CP queries can be developed --- we show that it is possible to answer both queries in linear or polynomial time over exponentially many possible worlds. We demonstrate one example use case of CP in the important application of "data cleaning for machine learning (DC for ML)." We show that our proposed CPClean approach built based on CP can often significantly outperform existing techniques, particularly on datasets with systematic missing values. For example, on 5 datasets with systematic missingness, CPClean (with early termination) closes 100% gap on average by cleaning 36% of dirty data on average, while the best automatic cleaning approach BoostClean can only close 14% gap on average.
Data mining tools

The development and application of data mining algorithms requires the use of powerful software tools. With challenges such as big data encountered in economy or gene sequencing for life science, data mining is important for daily problems as well as specialized fields. However, the large variety of requirements and user groups lead to a huge number and diversity of software tools. We give an overview by discussing the historical development and presenting a range of existing state‐of‐the‐art data mining and related tools. This paper is an update of our previous article from 2011 following the encyclopedic aspect of Wiley Interdisciplinary Reviews to include new findings or references and changing outdated information. However, since the paper should be able to stand alone, it includes many still valid elements of the previous article. Following the original paper, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different categories. The typical characteristics of these types are explained and a selection of the most important tools is categorized. This article is categorized under: Application Areas &gt; Data Mining Software Tools
Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks

Most monocular object tracking algorithms work in the 2D domain of the image. However, object trajectories, which are very simple in a fixed 3D world space, result in complex motions on the image plane, especially when the camera is moving. Therefore, in absence of any 3D representation, aforementioned approaches are only able to perform the measurement-to-track association based on rough similarity of 2D bounding box parameters. Recent advances in monocular 3D object detection allow to extract additional parameters like the pose and spatial extent of a 3D bounding box. In this paper, we present a multi-object tracking approach composed of an Extended Kalman filter estimating the 3D state by using these detections for track initialization. In subsequent time steps 2D bounding boxes are used to avoid filtering temporally correlated 3D measurements. This ensures properly estimated state uncertainties. We show that this 3D representation is very valuable as we achieve state-of-the-art results on the KITTI dataset with an association solely based on 2D bounding box comparison. We use state uncertainties transformed into the measurement space while completely ignoring appearance features.
Holographic Construction

We present a method for generating holographic construction information from parametric models. Holographic models replace 2D drawings and templates with unambiguous, contextual, shared and interactive design information. We show that our method enabled a team of expert bricklayers to complete a section of wall in a fraction of expected construction time and within typical tolerances, measured through comparative analysis of digital models to 3D point cloud scans of as built conditions.
Turning the (virtual) world around: Patterns in saccade direction vary with picture orientation and shape in virtual reality

Research investigating gaze in natural scenes has identified a number of spatial biases in where people look, but it is unclear whether these are partly due to constrained testing environments (e.g., a participant with their head restrained and looking at a landscape image framed within a computer monitor). We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influence eye and head movements in virtual reality (VR). Both the eyes and head were tracked while observers looked at natural scenes in a virtual environment. In line with previous work, we found a bias for saccade directions parallel to the image horizon, regardless of image shape or content. We found that, when allowed to do so, observers move both their eyes and head to explore images. Head rotation, however, was idiosyncratic; some observers rotated a lot, whereas others did not. Interestingly, the head rotated in line with the rotation of landscape but not fractal images. That head rotation and gaze direction respond differently to image content suggests that they may be under different control systems. We discuss our findings in relation to current theories on head and eye movement control and how insights from VR might inform more traditional eye-tracking studies.
Big Data Analysis and Mining

Big data analysis and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big databases that contain high volumes of valuable veracious data collected or generated at a high velocity from a wide variety of data sources. Among different big data mining tasks, this chapter focuses on big data analysis and mining for frequent patterns. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover frequent patterns from (1) big databases of precise data in a breadth-first manner or in a depth-first manner and/or from (2) big databases of uncertain data. Such a big data analysis and mining process can be sped up. The resulting (constrained or unconstrained) frequent patterns mined from big databases provide users with new insights and a sound understanding of users' patterns. Such knowledge is useful is many real-life information science and technology applications.
Transformer-based Long-Term Viewport Prediction in 360° Video: Scanpath is All You Need

Virtual Reality (VR) multimedia technology has dramatically advanced in recent years. Its immersive and interactive natures enable users to view any direction in 360° content freely. Users do not see the entire 360° content at a glance, but only a portion in the viewport. Viewport-based adaptive streaming, which streams only the user’s viewport of interest with high quality, has emerged as the primary technique to save bandwidth over the best-effort Internet. Thus, users’ viewport prediction in the forthcoming seconds becomes an essential task for informing the streaming decisions in the VR system. Various viewport prediction methods based on deep neural networks have been proposed. However, typically they are composed of complex Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) that require heavy computation. To achieve high prediction accuracy in limited computation time in a streaming system, we propose a new transformer-based architecture, named 360° Viewport Prediction Transformer (VPT360), that only leverages the past viewport scanpath to predict a user’s future viewport scanpath. We evaluate VPT360 over three widely-used datasets and compare the computation complexity with the state-of-the-art methods. The experiments show that our VPT360 provides the highest accuracy for short-term and long-term prediction and achieves the lowest computation complexity. The code is publicly available at https://github.com/FannyChao/VPT360 to further contribute to the community.
Privacy and Transparency in CBDCs: A Regulation-by-Design AML/CFT Scheme

Central banks and governments all over the world are increasingly exploring digital versions of fiat money, known as retail Central Bank Digital Currencies (CBDCs). Most initiatives rely on Distributed Ledger Technologies and are presented as alternatives to physical cash. Consequently, anonymity-related regulatory questions have naturally started to arise in terms of Anti-Money Laundering and Counter-Terrorist Financing compliance. Against this backdrop, this paper provides a techno-legal taxonomy of approaches to balance privacy and transparency in CBDCs without thwarting accountability, but it also underlines cross-sectoral impacts. The contribution heeds regulation-by-design as its core methodological foundation, with Privacy-Enhancing Technologies as the relevant use case. Thus, it highlights that not only technology aids legal purposes, but also that some regulatory requirements ought to be designed into technology for one to reach agreed-upon results and/or standards.
LifeSeeker 2.0: Interactive Lifelog Search Engine at LSC 2020

In this paper we present our interactive lifelog retrieval engine in the LSC'20 comparative benchmarking challenge. The LifeSeeker2.0 interactive lifelog retrieval engine is developed by both Dublin City University and Ho Chi Minh University of Science, which represents an enhanced version of the two corresponding inter-active lifelog retrieval engines in LSC'19. The implementation of LifeSeeker 2.0 has been designed to focus on the searching by text query using a Bag-of-Words model with visual concept augmentation and additional improvements in query processing time, enhanced result display and browsing support, and interacting with visual graphs for both query and filter purposes.
Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring

The increasingly widespread use of smartphones as real cameras on drones has allowed an ever-greater development of several algorithms to improve the image’s refinement. Although the latest generations of drone cameras let the user achieve high resolution images, the large number of pixels to be processed and the acquisitions from multiple lengths for stereo-view often fail to guarantee satisfactory results. In particular, high flight altitudes strongly impact the accuracy, and result in images which are undefined or blurry. This is not acceptable in the field of road pavement monitoring. In that case, the conventional algorithms used for the image resolution conversion, such as the bilinear interpolation algorithm, do not allow high frequency information to be retrieved from an undefined capture. This aspect is felt more strongly when using the recorded images to build a 3D scenario, since its geometric accuracy is greater when the resolution of the photos is higher. Super-Resolution algorithms (SRa) are utilized when registering multiple low-resolution images to interpolate sub-pixel information The aim of this work is to assess, at high flight altitudes, the geometric precision of a 3D model by using the the Morpho Super-Resolution™ algorithm for a road pavement distress monitoring case study.
Blockchain Applications in Agriculture: A Scoping Review

Blockchain is a distributed, immutable ledger technology initially developed to secure cryptocurrency transactions. Following its revolutionary use in cryptocurrencies, blockchain solutions are now being proposed to address various problems in different domains, and it is currently one of the most “disruptive” technologies. This paper presents a scoping review of the scientific literature for exploring the current research area of blockchain applications in the agricultural sector. The aim is to identify the service areas of agriculture where blockchain is used, the blockchain technology used, the data stored in it, its combination with external databases, the reason it is used, and the variety of agricultural products, as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR methodology. The purpose of conducting these scoping reviews is to identify the evidence in this field and clarify the key concepts. The literature search was conducted in April 2021 using Scopus and Google Scholar, and a systematic selection process identified 104 research articles for detailed study. Our findings show that in the field, although still in the early stages, with the majority of the studies in the design phase, several experiments have been conducted, so a significant percentage of the work is in the implementation or piloting phase. Finally, our research shows that the use of blockchain in this domain mainly concerns the integrity of agricultural production records, the monitoring of production steps, and the monitoring of products. However, other varied and remarkable blockchain applications include incentive mechanisms, a circular economy, data privacy, product certification, and reputation systems. This study is the first scoping review in this area, following a formal systematic literature review methodology and answering research questions that have not yet been addressed.