Reviewerly/exHarmony huggingface dataset:
Multi-view Outlier Detection in Deep Intact Space

Recently, multi-view outlier detection has emerged as a challenging research topic in outlier detection because of complex distributions of data across different views. There are mainly three types of outliers, i.e., attribute outliers, class outliers and class-attribute outliers. Most existing multi-view outlier detection approaches only detect part of the three types of outliers in a pairwise manner across different views, which is not able to accomplish the task of multi-view outlier detection comprehensively and uniformly. Outlier detection in a pairwise manner across different views also leads to time-consuming computation. We propose a new algorithm termed Multi-view Outlier Detection in Deep Intact Space (MODDIS) to find all the three types of outliers simultaneously and avoid comparing different views in a pairwise manner. Rather than leveraging subspace clustering, the performance of which is seriously affected by the dependence of subspaces on most real datasets, neural networks are employed in MODDIS in that neural networks have a stronger representation learning ability. Meanwhile, based on the view insufficiency assumption, a multi-view intact outlierness space assumption is proposed. Based on this assumption, a multi-view latent intact space is constructed to encode outlierness information of all views, where outlierness in any view is a snapshot from some perspective. Finally, an outlier detection measurement is defined in the latent intact space. Experiments are conducted on several UCI datasets and the empirical results demonstrate the effectiveness of our proposed method.
U2Eyes: A Binocular Dataset for Eye Tracking and Gaze Estimation

Theory shows that huge amount of labelled data are needed in order to achieve reliable classification/regression methods when using deep/machine learning techniques.However, in the eye tracking field, manual annotation is not a feasible option due to the wide variability to be covered.Hence, techniques devoted to synthesizing images show up as an opportunity to provide vast amounts of annotated data.Considering that the well-known UnityEyes tool provides a framework to generate single eye images and taking into account that both eyes information can contribute to improve gaze estimation accuracy we present U2Eyes dataset, that is publicly available.It comprehends about 6 million of synthetic images containing binocular data.Furthermore, the physiology of the eye model employed is improved, simplified dynamics of binocular vision are incorporated and more detailed 2D and 3D labelled data are provided.Additionally, an example of application of the dataset is shown as work in progress.Employing U2Eyes as training framework Supervised Descent Method (SDM) is used for eyelids segmentation.The model obtained as result of the training process is then applied on real images from GI4E dataset showing promising results.
Distribution of Node Embeddings as Multiresolution Features for Graphs

Graph classification is an important problem in many fields, from bioinformatics and neuroscience to computer vision and social network analysis. That said, the task of comparing graphs for the purpose of graph classification faces several major challenges. In particular, an effective graph comparison method must (1) expressively and inductively compare graphs; (2) efficiently compare large graphs; and (3) enable the use of fast machine learning models for graph classification. To address such challenges, we propose Randomized Grid Mapping (RGM), a fast-to-compute feature map that represents a graph via the distribution of its node embeddings in feature space. We justify RGM with close connections to kernel methods: RGM provably approximates the Laplacian kernel mean map and has the multiresolution properties of the pyramid match kernel. We also show that RGM can be extended to incorporate node labels using the Weisfeiler-Lehman framework. Extensive experiments show that graph classification accuracy with RGM feature maps is better than or competitive with many powerful graph kernels, unsupervised graph feature mappings, and deep neural networks. Moreover, comparing graphs based on their node embeddings with RGM is up to an order of magnitude faster than competitive baselines, while maintaining high classification accuracy.
Player Tracking in Sports Videos

This paper considers the problem of tracking the players in handball videos using a single video source. Tracking of handball players in the video is a difficult task as they can frequently leave and re-enter the camera field of view, often change directions quickly and occlude each other. Players wear similar team uniforms, play indoor under artificial illumination, with the background than can vary significantly as the handball court is often painted in various colors. The continually improving accuracy of CNN-based object detectors makes tracking-by-detection methods an attractive choice for tracking players in sports videos as they can perform online and with low computational requirements on top of object detection. Here we consider the use of three tracking-by-detection methods in conjunction with the YOLO object detector, namely the standard Hungarian assignment algorithm, the Simple Online, and Real-time Tracking (SORT) algorithm that adds a motion model, and its extension Deep SORT. The methods are tested on a custom dataset of handball video scenes.
Direct Target Tracking by Distributed Gaussian Particle Filtering for Heterogeneous Networks

In this article, we consider the distributed direct target tracking using the received radio signal by exploiting time delay and Doppler for heterogeneous wireless sensor networks. We develop herein a distributed Gaussian particle filtering (D-GPF) algorithm for diffusion networks, along with an adaptive combiners (AC) scheme and a particle number adaptation (PNA) method. We transform the online AC optimization problem into the minimum variance unbiased estimation problem with the nonnegative constraint of the combiners imposed, and solve this constrained problem by establishing the Karush-Kuhn-Tucker (KKT) conditions. Further, we develop a variable bin-size scheme for the PNA method to improve the efficiency of particle filters in conformity with the underlying state uncertainty at each sensor of the heterogeneous networks. Simulations involving heterogeneous networks illustrate i) that the proposed AC method could improve robustness of the D-GPF against the spatial variation of Signal-to-Noise-Ratios over the network, and ii) that the proposed PNA method could achieve a tradeoff between the tracking performance and computational complexity of the D-GPF.
Blockchain and Tourism

Blockchain technology has the potential to substantially transform the tourism industry. Its salient features such as immutability, transparency, programmability, and decentralization allow for innovative ways to design customer relationships, enable novel organizational structures and processes, and facilitate new forms of interorganizational collaboration. In this chapter, I first elaborate on the basic functioning of the blockchain and highlight those characteristics which are crucial for understanding the rest of the chapter. I not only equip scholars and practitioners with the knowledge needed to better comprehend blockchain technology and how to apply it in the context of the tourism industry but also highlight shortcomings and important research topics. Furthermore, I investigate the disruptive potential of the blockchain on an economic level by discussing various ways in which it can alter existing market structures und potentially lead to the disintermediation of incumbents in the tourism industry and the emergence of new players. Economic theory is referenced to better understand how blockchain characteristics might shape the future of the tourism industry and who the main beneficiaries will be. I end the chapter with several suggestions for future research and expected future developments.
Research on investment decision-making model from the perspective of “Internet of Things + Big data”

With the continuous improvement of the global securities market, the market competition is unprecedented fierce. In the aspect of investment decision support system, there is an urgent need to continuously absorb new information processing technologies and improve the scientific and standardization of decision-making, so as to achieve the goal of improving investment decision-making efficiency and stabilizing investment returns. Firstly, this paper introduces the defect that the database, model base and knowledge base are designed and implemented independently in the traditional decision support system. Then, constructing a unified and efficient data processing platform for Internet of Things based on DMFS technology, which realizes the integration of database and model, and innovatively establishes a data mining model facing market big data, dynamically analyzes and proposes investment decisions. Finally, the operating income data of a listed company in recent ten years are selected for simulation, which verifies the efficiency of the model system in processing dynamic data and the stability of investment income.
The Offense-Defense Balance of Scientific Knowledge

There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework elucidates the factors governing whether the published research will be more useful for attackers or defenders, such as the possibility for adequate defensive measures, or the independent discovery of the knowledge outside of the scientific community. The balance will vary across scientific fields. However, we show that the existing conversation within AI has imported concepts and conclusions from prior debates within computer security over the disclosure of software vulnerabilities. While disclosure of software vulnerabilities often favours defence, this cannot be assumed for AI research. The AI research community should consider concepts and policies from a broad set of adjacent fields, and ultimately needs to craft policy well-suited to its particular challenges.
Multimodal image registration using histogram of oriented gradient distance and data-driven grey wolf optimizer

Multimodal image registration is becoming increasingly important in remote sensing. However, due to the significant nonlinear intensity differences between multimodal images, conventional registration methods tend to get trapped into local optima. To address this issue, we present a new approach for multimodal image registration using histogram of oriented gradient distance (HOGD) and data-driven grey wolf optimizer (DDGWO). First, we propose a novel similarity measure for area-based registration methods that is HOGD. We investigate the performance of HOGD by analyzing its similarity curve. HOGD has a large range of values, which is helpful to find the global optimum. Second, we use GWO to optimize the transformation parameters. Since it is time-consuming to calculate HOGD, we propose DDGWO to minimize HOGD. In DDGWO, the iterations are divided into two parts: the training and prediction iterations. A support vector machine (SVM) regression model is trained by the historical HOGD computed in the training iterations. The trained SVM model predicts HOGD instead of calculating in the prediction iterations, which can reduce the computational time. Finally, we test the proposed approach that uses HOGD as the similarity measure and DDGWO as the search algorithm on 12 real and four simulated image pairs. Extensive experiments demonstrate that our approach saves up to 83.35-84.15% of computational time and outperforms the state-of-the-art algorithms in terms of registration accuracy.
Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.
Sentiment analysis on customer satisfaction of digital payment in Indonesia: A comparative study using KNN and Naïve Bayes

Abstract Indonesia payment behaviour has turned from traditional to digital as the impact of the technology growth. Digital payment usage in Indonesia have increased rapidly in recent years. Many companies offer this service with different terms and tariff options. Social media is one of the places where people can express their feeling and opinion, including Twitter. In this research, sentiment analysis and opinion mining is conducted to see public satisfaction towards the digital payment service in Indonesia (OVO, GO-PAY and LinkAja). This research is using Twitter data and has several stages, which are data crawling from Twitter, data cleaning, feature selection and classification using two machine learning approach (Naïve bayes classifier and K-Nearest Neighbour or KNN). The raw data is processed to get the clean data, and to get the appropriate feature for classification algorithm and then perform classification and validation to the model. As for the classification algorithm, this research finds out that KNN has better accuracy than Naive Bayes. The result of this research also shows that LinkAja and G0-PAY has more neutral sentiment or customers nearly satisfied of the services provided, and OVO has more negative sentiment than neutral sentiment
SMT-based variability analyses in FeatureIDE

Handling configurable systems with thousands of configuration options is a challenging problem in research and industry. One of the most common approaches to manage the configuration options of large systems is variability modelling. The verification and configuration process of large variability models is manually infeasible. Hence, they are usually assisted by automated analyses based on solving satisfiability problems (SAT). Recent advances in satisfiability modulo theories (SMT) could prove SMT solvers as a viable alternative to SAT solvers. However, SMT solvers are typically not utilized for variability analyses. A comparison for SAT and SMT could help to estimate SMT solvers potential for the automated analysis. We integrated two SMT solvers into FeatureIDE and compared them against a SAT solver on analyses for feature models, configurations, and realization artifacts. We give an overview of all variability analyses in FeatureIDE and present the results of our empirical evaluation for over 122 systems. We observed that SMT solvers are generally faster in generating explanations of unsatisfiable requests. However, the evaluated SAT solver outperformed SMT solvers for other analyses.
An Introduction to the Use of zk-SNARKs in Blockchains

The advent of blockchain brings a wide horizon of opportunities to the world. The first such example, Bitcoin, strongly advocates a principle of total openness, which is reflected in the fact that all transactions are public and the history of each account can easily be reconstructed. Although an account cannot immediately be linked to a real-world identity, this does not grant strong guarantees of anonymity, and such a feature of Bitcoin and similar blockchains prevents it from reaching wide acceptance for financial use-cases, where users often desire strong confidentiality of their balances and financial history. As a consequence, there has recently been a growing interest in privacy-enhancing technologies that ensure public permissionless blockchains can keep the details of transactions private according to particular use cases. One of the most promising technologies in this area is Zero-Knowledge Proofs, and in particular zk-SNARKs, due to their very short proofs and verification times. This makes them well suited to be used as transaction data, hiding all the private details at the same time they guarantee the integrity and accuracy of the transaction, and to be verified on-chain by a smart contract. This paper is an introductory presentation of this topic, what advantages zk-SNARKs bring to the blockchain ecosystem and how they can be tailored to specific applications.
Of robots and humans: Creating user representations in practice

In this study, we explore the constitution of user representations of robots in design practice. Using the results of ethnographic research in two robot laboratories, we show how user representations emerge in and are entangled with design activities. Our study speaks to the growing popularity of and investment in robotics, robots and other forms of artificial intelligence. Scholars in Science and Technology Studies (STS) have shown that it is often difficult for designers and engineers to develop accurate ideas about potential users of such technologies. However, the social context of robots and design settings themselves have received significantly less attention. Based on our laboratory ethnographies, we argue that the practices in which engineers are engaged are important as they can shape the kind of user images designers create. To capture these dynamics, we propose two new concepts: ‘image-evoking activities’ as well as ‘user image landscape’. Our findings provide pertinent input for researchers, designers and policy-makers, as they raise questions with regards to contemporary fears of robots replacing humans, for the effectiveness of user involvement and participatory design, and for user studies in STS. If design activities co-constitute the user images that engineers develop, a greater awareness is needed specifically of the locales in which the design of robots and other types of technologies takes place.
Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.
Distributed fusion estimation for multisensor systems with non-Gaussian but heavy-tailed noises

Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multivariate t distribution and variational Bayesian estimation, the information filter, the centralized batch fusion, the distributed fusion, and the suboptimal distributed fusion algorithms are derived, respectively. The centralized fusion is given in two forms, namely, from t distribution based filter and the proposed t distribution based information filter, respectively. The distributed fusion is deduced by the use of the newly derived information filter, and it has been demonstrated to be equivalent to the centralized batch fusion. The suboptimal distributed fusion is obtained by a parameter approximation from the derived distributed fusion to decrease the computation complexity. The presented algorithms are shown to be the generalization of the classical Kalman filter based traditional algorithms. Theoretical analysis and exhaustive experimental analysis by a target tracking example show that the proposed algorithms are feasible and effective.
Robust Human Pose Estimation for Rotation via Self-Supervised Learning

The detection of abnormal postures, such as that of a reclining person, is a crucial part of visual surveillance. Further, even regular poses can appear rotated because of incongruity between the image and the angle of a pre-installed camera. However, most existing human pose estimation methods focus on small rotational changes, i.e., those less than 50 degrees, and they seldom consider robust human pose estimation for more drastic rotational changes. To the best of our knowledge, there have been no reports on the robustness of human pose estimation for rotational changes through large angles. In this study, we propose a robust human pose estimation method by creating a path for learning new rotational changes based on a self-supervised method and by combining the results with those obtained from a path based on a supervised method. Furthermore, a combination module composed of a convolutional layer is trained complementarily by both paths of the network to produce robust results for various rotational changes. We demonstrate the robustness of the proposed method with extensive experiments on images generated by rotating the elements of standard benchmark datasets. We fully analyze the rotational characteristics of the state-of-the-art human pose estimators and the proposed method. On the COCO Keypoint Detection dataset, the proposed method attains more than 15% improvement in the mean of average precision compared to the state-of-the-art method, and the standard deviation of the performance is improved by more than 4.7 times.