Reviewerly/exHarmony huggingface dataset:
Preconditions for a General-Purpose Central Bank Digital Currency

<ns3:p>Over the last few years, interest in the potential issuance of a general-purpose central bank digital currency (CBDC) has increased. Introducing and operating a CBDC would require actions by many stakeholders and not just the central bank. In view of the far-reaching implications of introducing a new form of money to the public, the decision cannot be taken lightly. This paper outlines foundational preconditions and proposes areas of work that may help achieve them prior to the possible implementation of a potential future general-purpose CBDC in the United States. These foundational preconditions include clear policy objectives, broad stakeholder support, a strong legal framework, robust technology, and readiness for market acceptance and adoption.</ns3:p>
On Refined Neutrosophic Matrices and Their Application in Refined Neutrosophic Algebraic Equations

The objective of this paper is to introduce the concept of refined neutrosophic matrices as matrices such as multiplication, addition, and ring property. Also, it determines the necessary and sufficient condition for the invertibility of these matrices with respect to multiplication. On the contrary, nilpotency and idempotency properties will be discussed.
A hybrid global structure from motion method for synchronously estimating global rotations and global translations

Over the last few decades, the methods of global image orientation, which is also called global SfM, have attracted a lot of attention from researchers, mainly thanks to its advantage of time efficiency. Based on the input of relative orientation results, most conventional global SfM methods employ a two-step strategy consisting of global rotation estimation and global translation estimation. This paper, on the contrary, introduces a hybrid global approach that intends to solve global rotations and translations synchronously, but hierarchically. To improve the robustness and time efficiency, we first propose a novel efficient method that is much faster than the previous approaches for extracting an optimal minimum cover of a connected image triplet set (OMCTS). The OMCTS makes all the available images contained in a minimum number of connected image triplets, as well as all of those selected triplets, satisfy the constraint that the three corresponding relative orientations are as compatible as possible to each other. In order to solve non-collinear triplets in the OMCTS, some fundamental characterizations of essential matrices in the multiple-image setting are used, and image pose parameters are then estimated via averaging the constrained essential matrices. For the collinear triplets, the above approach is invalid and the image pose parameters are then alternatively determined from the relative orientations using the depth of tie points from each individual local spatial intersection. Finally, all image orientations are moved to a common coordinate system by traversing the solved connected triplets using similarity transformations. Compared to the state-of-the-art global SfM methods, the performance and capability of the proposed hybrid approach are thoroughly demonstrated on various public datasets (mainly including ordered and unordered internet images, oblique aerial images, hard and complex datasets, etc.).
VA2Mass: Towards the Fluid Filling Mass Estimation via Integration of Vision and Audio Learning

Robotic perception of filling mass estimation via multiple sensors and deep learning approaches is still an open problem due to the diverse pouring durations, small pixel ratio for target objects and complex pouring scenarios. In this paper, we propose a practical solution to tackle this challenging task via estimating filling level, filling type and container capacity simultaneously. The proposed method is inspired by how humans observe and understand the pouring process via the cooperation among multiple modalities, i.e., vision and audio. In a nutshell, our proposed method is divided into three folds to help the agent shape a rich understanding of the pouring procedure. First, the agent obtains the prior of container categories (i.e., cup, glass or box) through the object detection framework. Second, we integrate the audio features with the prior to make the agent learn a multi-modal feature space. Finally, the agent infers the distribution of both the container capacity and fluid properties. The experimental results show the effectiveness of the proposed method, which ranked as \(2^{nd}\) runner-up in the CORSMAL Challenge of Multi-modal Fusion and Learning For Robotics in ICPR 2020.
Phishing Site Detection Using Similarity of Website Structure

The number of phishing sites is increasing and becoming a problem. General phishing sites often have very short lives. Phishers are thought to construct phishing sites using tools such as phishing kits. Phishing sites constructed using the same tools have similar website structures. We propose a new method based on the similarity of website structure information defined by the types and sizes of web resources that make up these websites. Our method can detect phishing sites that is not registered with blocklists or do not have similar URL strings with targeting legitimate sites. In addition, our method can identify phishing sites that differed in appearance but have similar website structures. Our method is particularly effective for detecting phishing sites constructed by the same phishers or using the same tools, as our method identifies structural similarity between websites. We conducted an evaluation to confirm the correctness of our assumption using phishing sites constructed using phishing kits and the PhishTank dataset. We found a large number of phishing sites that were structurally similar to phishing sites constructed using phishing kits. We applied our method to web access logs provided by ordinary Japanese citizens, and detected some unknown phishing sites. We have also examined the possibility of improving our method based on the importance of web resources, determined using the number of occurrences in web access logs.
Breast Cancer Detection from Histopathological Biopsy Images Using Transfer Learning

Breast cancer is one of the most common deadly diseases among women around the world. Early detection of the disease improves the cure rate for patients. In this paper, a dataset named VBCan is introduced, which is composed of images of hematoxylin and eosin (H&E) stained lymph node sections, collected from two specialized hospitals in Vietnam. The dataset has 3529 images at resolution of 512x512. Then, a two-stage method is introduced to evaluate the accuracy of the breast cancer detection, i.e. a combination of feature extraction by one of state-of-the-art CNNs namely VGG-16, GoogLeNet or ResNet-50 and various conventional machine learning classifiers. In addition, two transfer learning techniques are applied as firstly we fine-tune the networks by updating parameters of all layers on a benchmark dataset namely Patch Camelyon 2017 by initializing their weights from the pretrained models on ImageNet. Then, the features are extracted from the CNNs to train different machine learning classifiers on VBCan dataset to improve the deep learning models. The optimization results show that the topmost accuracy on VBCan is 96.98% by using ResNet-50 model to extract features and Softmax as a classifier. It is also recognized that the recall of VGG-16 model using its original classifier is the highest value of 97.76% among other models while GoogLeNet model achieves the highest precision of 98.58%.
Knowledge Graphs for Semantic-Aware Anomaly Detection in Video

Video understanding, surveillance and analytics fields have been dynamically expanding over the recent years due to the enormous amount of CCTV, dashcams and phone cameras which generate video data stored on cloud servers, in social networks, in public and private repositories. The video data has a great potential to be used for improving situation awareness, prediction and prevention of unwanted events and disasters in various settings. Still, there is a significant need for methods and ways to understand the large amount of video recordings and to extract hidden patterns and knowledge. Deep learning networks have been successfully applied for video object and anomaly detection tasks. However, while neural networks focus on utilizing features within an object to be detected, the vast amount of background knowledge remains unnoticed. We propose a semantics centered method for video anomaly detection which allows to identify entities that are inconsistent with the scene and thus can be marked as a potential anomaly. Our method is inspired with the way humans comprehend the surroundings with incorporating external knowledge and previous experience. As a source of external knowledge for deep learning networks we maintain a knowledge graph which allows to compute semantic similarity between the detected objects. Similarity of the entities in the frame depends on the distance between the graph vertices which represent the recognized entities. The object which is semantically distinct from other entities in the video is an anomalous one. We conduct experiments on real-life data to empirically prove the efficiency of our approach and provide an enhanced framework that leads to anomaly detection in video with higher accuracy and better interpretability.
QoE-aware Video Adaptive Streaming over HTTP

In recent years, the demand for video streaming over Internet has been increasing so as it exceeds the responsiveness of the network infrastructure. Beside improving the network infrastructure, the improvement of adaptive streaming algorithms to users’ conditions is also very important to provide the highest possible Quality of Experience (QoE) to users. In this paper, we propose a new adaptive streaming algorithm that employs a QoE model to make adaptation decisions and flexibly change the number of next adaptation segments based on the current state of the network and of the client towards obtaining the highest QoE. Based on experimental results, it is shown that the proposed algorithm offers significantly higher QoE values up to 2.67 MOS in comparison with four existing algorithms.
A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.
Hybrid Neural Network Text Classification Combining TCN and GRU

Aiming at the problems of insufficient feature extraction and low classification accuracy in the classification of Chinese news texts by the neural network, this paper proposes a hybrid neural network text classification model which integrates time series convolutional network TGNet. The new model utilizes Temporal Convolutional Network to capture the relationship between hidden features on different time scale, and uses Gated Tanh-ReLU Units (GTRU) as the activation layer to improve the expression ability of neural network to the model. Meanwhile, the Gated Recurrent Unit networks (GRU) is used to learn the semantic features of context. Finally, the extracted features are fused and input into Softmax for classification. Experimental results show that the text classification model proposed in this paper has achieved better classification results in the published Chinese news data sets SougoCS and FuDan.
Research of the influence of reliability on qualitative parameters of the transport subnet of the infocommunication system

The article proposes a model of research the influence of reliability parameters of the transport subnet of an infocommunication system on qualitative network parameters, based on queuing theory, shows the degree of influence of the availability coefficient on the values of the probability of uptime and the probability of loss of message packets of external traffic.
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation

Reinforcement Learning (RL), among other learning-based methods, represents powerful tools to solve complex robotic tasks (e.g., actuation, manipulation, navigation, etc.), with the need for real-world data to train these systems as one of its most important limitations. The use of simulators is one way to address this issue, yet knowledge acquired in simulations does not work directly in the real-world, which is known as the sim-to-real transfer problem. While previous works focus on the nature of the images used as observations (e.g., textures and lighting), which has proven useful for a sim-to-sim transfer, they neglect other concerns regarding said observations, such as precise geometrical meanings, failing at robot-to-robot, and thus in sim-to-real transfers. We propose a method that learns on an observation space constructed by point clouds and environment randomization, generalizing among robots and simulators to achieve sim-to-real, while also addressing partial observability. We demonstrate the benefits of our methodology on the point goal navigation task, in which our method proves to be highly unaffected to unseen scenarios produced by robot-to-robot transfer, outperforms image-based baselines in robot-randomized experiments, and presents high performances in sim-to-sim conditions. Finally, we perform several experiments to validate the sim-to-real transfer to a physical domestic robot platform, confirming the out-of-the-box performance of our system.
Multi-View Clustering With Learned Bipartite Graph

In this paper, we propose a late fusion multi-view clustering via a learned bipartite graph (MVC-LBG). Firstly, we obtain the base partition from each single view. Then, a consensus bipartite graph is constructed by these base partitions for final clustering results. Inspired by anchors used in spectral clustering on a bipartite graph, we introduce representative points that are updated during the iterating of MVC-LBG. Compared with early fusion multi-view clustering algorithms, MVC-LBG not only dramatically improves the clustering accuracy but also has lower computational complexity. By adopting the base partitions in a new way, the proposed algorithm achieves better performance than existing late fusion multi-view clustering algorithms. Extensive experiments on six data sets demonstrate the effects and efficiency of our algorithm. Finally, we extend MVC-LBG to large-scale datasets.
Comparison of single- and multiple entry point PBFT for IoT blockchain systems

This work deals with problem of deployment of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm in Internet of things (IoT) applications requiring strict order among records linked in blockchains at multiple geographical points. Due to the needs of reliability and coverage of larger geographical areas it becomes necessary to extend current PBFT systems with single entry node towards multiple entry nodes. In this work we model PBFT systems with single and multiple entry points and compare their performance. We have implemented multiple entry system using CSMA/CA algorithm over fully connected P2P networks. We have compared two systems with four orderers against increasing system load and against increasing geographical coverage. Our results indicate that system with four entry points has double capacity over the system with single entry point.
The Timer Inremental Compression of Data and Information

The ability to find short representations, i.e. to compress data, is crucial for many intelligentsystems. This paper is devoted to data compression and a transform-based quantitative data compressiontechnique involving quick enumeration in a unary-binary time-based numeral system (NS). The symbolscomprising the alphabets of human-computer interaction languages (HCIL), which are used in an informationalmessage (IM), are collected in primary code tables, such as the ASCII table. The statistical-oriented datacompression method using unconventional timer encryption and encoding information are proposed by us. Itwas constructed probability - discrete model of the set of character sequences and characterized someprobabilistic algorithms associated with the recovery of text by its public key and its cipher. We find thepossibility of parallel implementation of this method by building a block of timer tags. The necessaryestimations of complexity are obtained. The method can be used to compress SMS messages. Probabilisticstatistical analysis and evaluation of their effectiveness are obtained.
Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion

Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach.
Unsupervised Depth and Confidence Prediction from Monocular Images using Bayesian Inference

In this paper, we propose an unsupervised deep learning framework with Bayesian inference for improving the accuracy of per-pixel depth prediction from monocular RGB images. The proposed framework predicts confidence map along with depth and pose information for a given input image. The depth hypotheses from previous frames are propagated forward and fused with the depth hypothesis of the current frame by using Bayesian inference mechanism. The ground truth information required for training the confidence map prediction is constructed using image reconstruction loss thereby obviating the need for explicit ground truth depth information used in supervised methods. The resulting unsupervised framework is shown to outperform the existing state-of-the-art methods for depth prediction on the publicly available KITTI outdoor dataset. The usefulness of the proposed framework is further established by demonstrating a real-world robotic pick-and-place application where the pose of the robot end-effector is computed using the depth predicted from an eye-in-hand monocular camera. The design choices made for the proposed framework is justified through extensive ablation studies.
A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph

Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.
Video Monitoring System using Facial Recognition: A Facenet-based Approach

Reductions in installation and storage costs have increased the demand for security systems, including video surveillance and digital authentication. The video surveillance systems, when monitored by humans, are subject to errors and are challenging to scale. Authentication systems can validate someone using a password or a card from another user. Facial recognition algorithms can solve this fault by the traffic monitoring of known individuals or intruders as well as for individual biometric authentication. Hence, this paper evaluates the FaceNet approach using the Labeled Faces in the Wild benchmark, as well as evaluates a machine learning technique known as support vector machine (SVM) for the classification of embedding generated using FaceNet. The suggested approach also models a real-time facial recognition system combining FaceNet and SVM, reaching 90% of accuracy using a medium webcam.
Detection of Information Hiding at Anti-Copying 2D Barcodes

This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentally dependent. Thus, an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes for existing anti-copying 2D barcodes. The first scheme directly uses the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred to as the Pixel Distance Based Detection (PDBD) scheme. The second scheme first calculates the variance of raw signal and the covariance between the recovered and raw signals, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred to as the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced Illegitimately-Copying (IC) attacks to evaluate the security of two existing anti-copying 2D barcodes. We conduct extensive performance comparisons among the proposed schemes and prior schemes under different capturing devices, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , different scanners or camera phones. Our experimental results show that the PVBD scheme can correctly detect the existence of hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the successful attacking probability of the proposed IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.