Reviewerly/exHarmony huggingface dataset:
Single-Image Smoker Detection by Human-Object Interaction with Post-refinement

This paper addresses the problem of smoker detection in a single image. Previous smoker detection works usually focus on cigarette detection, yet often neglect the rich information of smoking behavior (especially the interaction information between smoker and cigarette). Though some attempts have been made to detect the smoking behavior, they typically rely on the temporal information in videos and are not suitable for single images. To tackle these issues, this paper proposes a novel smoker detection framework based on human-object interaction (HOI) and post-refinement. In particular, based on deep neural networks, we develop a one-stage HOI module to identify the interaction between smoker and cigarette, and exploit an additional fine-grained detector to further improve the HOI accuracy in the post-refinement module. Remarkably, we present a new benchmark dataset named SCAU smoker detection (SCAU-SD), which, to the best of our knowledge, is the first benchmark dataset for the specific task of smoker detection in single images with HOI annotations. Extensive experimental results demonstrate the superior single-image smoker detection performance of the proposed framework.
Research on fast construction of question answering system based on target graph

Aiming at the problems of chaotic structure and lack of visual query methods in small sample domain data, this paper proposes a fast construction framework of question answering (QA) system based on target graph. Firstly, we construct a knowledge graph by extracting entities, properties and relationships from open source datasets. Secondly, the method based on dictionary technology and Aho-Corasick (AC) automaton is used to realize fast entity recognition and question word extraction. Then a problem base is established for problem template matching. Finally, we use query statements to retrieve answers in the graph database. In the military field, experiments show that through the framework proposed in this paper, a more comprehensive target graph can be quickly constructed, on this basis, accurate QA can be realized.
Unsupervised multi-spectral image feature point matching under different lighting scenes

In the case of low illumination condition or strong illumination variation, traditional visual SLAM often fails in feature point extraction or tracking. In order to improve the illumination robustness of SLAM front-end and facilitate localization in the same scene of day and night, we propose the UnsuperPoint-CLR network which integrates multi-spectral information, namely the RGB and thermal imaging, with an improved descriptor loss for image feature point matching. In this paper, two kinds of single mode information are evaluated in terms of repeatability score, localization error, homography estimation and matching score. An RGB-T fusion architecture based on RGB and thermal image features is established and trained by a self-supervised learning method, which learns the cross-modal feature matching. The proposed approach is trained and validated on our proposed RGB-T aligned dataset. The results reveal that the learned descriptor is with an improved illumination robustness compared with traditional methods.
Multi-Antenna Pre-processing for Improved RFML in Congested Spectral Environments

In this work, a novel signal detection approach for dense co-channel environments is developed that leverages the intelligent combination of traditional signal preprocessing and deep radio frequency machine learning. Specifically, a novel multi-antenna preprocessing stage is developed to ease the signal processing burden of the deep learning algorithm. Easing this burden enables deep learning to be focused on specifically solving the sensing problem which helps minimize its footprint, improves its convergence during training, and reduces the required size of training datasets. Performance results of the proposed approach demonstrate that this intelligent combination of traditional and deep learning approaches leads to a detector that minimizes the impact of interference sources and nuisance signals and compensates for challenging propagation environments.
Privacy Risk of Document Data and a Countermeasure Framework

A huge number of documents such as news articles, public reports, and personal essays have been released on websites and social media. Once documents containing privacy-sensitive information are published, the risk of privacy breaches increases, thus requiring very careful review of documents prior to publication. In many cases, human experts redact or sanitize documents before publishing them; however, this approach can be inefficient with regard to cost and accuracy. Furthermore, such measures do not guarantee that critical privacy risks are eliminated from the documents. In this paper, we present a generalized adversary model and apply it to document data. This work devises an attack algorithm for documents using a web search engine, and then proposes a privacy-preserving framework against the attacks. We evaluate the privacy risks for actual accident reports from schools and court documents. In experiments using these reports, we show that human-sanitized documents still contain privacy risks and that our proposed approach can contribute to risk reduction.
Flipping Data Augmentation of Convolutional Neural Networks Using Discrete Cosine Transforms

Convolutional neural networks (CNNs) are widely used in many areas. The problem now is to collect large numbers of labeled images in order to improve network performance. Data augmentation is to increase the number of images for training, where images are artificially generated by transformation, such as rotation, translation, scaling, and flipping. In this paper, we focus on flipping data augmentation and present a novel algorithm of convolution that involves flipping data augmentation in CNNs. Without generating flipped images beforehand, we can obtain information of flipped images in computation on convolutional layers using discrete cosine transforms. The proposed algorithm on a simple CNN is demonstrated and the efficacy of the proposed algorithm is testified.
Genetic Algorithms based on Bhattacharyya Distance for Quantum Measurement Error Mitigation

Quantum computing is a fascinating research area which promises a revolution in computing performance. Since the launch of the IBM Quantum Experience project in 2016, the research activities in this area are strongly increased. This project provides the public access to quantum processors composed of superconducting physical computing elements known as qubits. Unfortunately, qubits are sensitive to noise and, for this reason, quantum computation can be affected by errors. As a consequence, there is a strong emergence for so- called quantum error mitigation methods aimed to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error- prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms whose fitness function uses Bhattacharyya distance is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method outperforms the traditional approach.
Semantic Models of Pottery Making

We explore semantic models for workflows in digital humanities. Specifically, we consider modeling the production of Celadon pottery and focus on the description of processes and transitions for state changes in that context. Semantic models are based on ontologies and other structured information resources. The Getty Art and Architecture Thesaurus (AAT) is a valuable resource for humanities, but is purely hierarchical and does not include rich semantic relationships across its facets. We coordinate AAT with the Suggested Upper Merged Ontology (SUMO) and use the combination to model an executable workflow for pottery making. In addition, we present a prototype interactive interface for exploring those workflows.
Hand Gesture Identification System Using Convolutional Neural Networks

Recognition of hand movements is a key to conquering several difficulties and building warmth for human life. In an enormous number of applications, human actions and their significance are used in an array of applications to grasp the flexibility of machines. Sign language interpretation is one particular area of interest. Following paper describes a practical and interactive procedure for hand gesture detection by making use of a Convolutional Neural Network. The techniques are suitably graded into various stages during the process, such as the data acquisition, pre-processing, segmentation, extraction of features, and classification. The different algorithms that have done their task at each location are elaborated, along with their merits. Challenges and limitations faced during the process are discussed. Overall, it is hoped that the analysis might provide a detailed introduction into the sector of machine-driven gesture and signing acknowledgment and further facilitation of future research efforts in this sector. The proposed methodology has been tested over the 8700 images, and it classifies the images with an approximate accuracy of above 95%.
nPTAS: A Novel Platform for Text Annotation and Service

Natural Language Processing (NLP) is a critical research area in artificial intelligence, which has spawned a variety of useful applications. However, creating an online NLP service from scratch is still challenging for non-expert users, which has to go through complex steps such as corpus annotation, model training and deployment. Existing tools mainly focus on corpus annotation, which rarely support collaborative annotation or online model training and deployment. To meet the requirement of rapid deployment of NLP services, we develop a novel full process platform nPTAS, which supports collaborative annotation, online model training and model deploying with RESTful interfaces. To validate the effectiveness of the platform, we build a clinical entities recognition service from Chinese Electronic Medical Records on it. The experimental results show that nPTAS improves the efficiency and quality of corpus annotation and greatly reduces the effort to build online NLP services. The platform is available at http://nptas.c2cloud.cn.
Analytical Investigation of Anomaly Detection Methods based on Time-Domain Features and Autoencoders in Satellite Power Subsystem

This study investigates the potential of autoencoders in detecting anomalies in satellite power subsystem telemetry data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and time-domain features. Considering the unique learning mechanism of autoencoders, two specific methods have been designed to evaluate the autoencoder performance. The research results show that the combination of kurtosis feature and convolutional autoencoder has stronger detection ability for satellite power subsystem.
Parameterization of Ambiguity in Monocular Depth Prediction

Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps, the result often has poor metric accuracy. Moreover, the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we relax the monocular depth estimation task by proposing a network that allows us to complement image features with a set of auxiliary variables. These allow disambiguation when image features are not enough to accurately pinpoint the exact depth map and can be thought of as a low dimensional parameterization of the surfaces that are reasonable monocular predictions. By searching the parameterization we can combine monocular estimation with traditional photoconsistency or geometry based methods to achieve both visually appealing and metrically accurate surface estimations. Since we relax the problem we are able to work with smaller networks than current architectures. In addition we design a self-supervised training scheme, eliminating the need for ground truth image depth-map pairs. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches.
Tractable Planning for Coordinated Story Capture: Sequential Stochastic Decoupling

We consider the problem of deploying robots to observe the evolution of a stochastic process in order to output a sequence of observations that fit some given specification. This problem often arises in contexts such as event reporting, situation depiction, and automated narrative generation. The paper extends our prior work by formulating and examining the multi-robot case: a team of robots move about, each recording what they observe, and, if they manage to capture some event, communicating that fact with the group. In the end, all events from all the robots are collated to provide a cumulative output. A plan is used to decide what each robot will attempt to capture next, based on the state of the world and the events that have been captured (collectively) so far. This paper focuses on the question of how to compute effective multi-robot plans. A monolithic treatment, involving the optimal selection of joint choices, i.e., choosing the next elements to attempt to capture by all robots, is formulated where costs are minimized in an expected sense. Since such plans are prohibitive to compute, variants based on an approximation scheme based on solving a sequence of individual planning problems are then introduced. This scheme sacrifices some solution quality but requires far less computational expense; we show this permits one to scale to greater numbers of robots.
K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Char-Farsakh, Iran

Due to the efficiency of data mining science for analyzing and reviewing extensive data, especially geochemical data, essential methods and techniques such as the hierarchical method, K-Means method, densitybased methods, Cohennon method, and so forth, have been developed and utilized by numerous researchers for clustering.One of the most notable and widely used algorithms in the field of clustering is the K-Means algorithm.This algorithm divides the data into K clusters by emphasizing the distance criterion.This study focuses on applying this method according to lithogeochemical data taken from the 1:100,000 scale map of Chahar-Farsakh in South Khorasan province for the elements of copper, cobalt and nickel to the sampling coordinates.The optimal value of K was classified according to the desirability of the selection and the data, and thus the relationships between these elements in the range were determined.This was analyzed by changing the value of K from 3 to 15 criteria mentioned in each class to reveal the optimal K.According to the observations, the existence of a quadratic relationship with negative concavity between copper and cobalt elements, as well as a special exponential relationship between copper and nickel and a positive linear relationship between nickel and cobalt, were reported.Finally, considering the coordinates of the samples and the concentration of cobalt and nickel, the quantity of copper was predicted using a General Regression Neural Network (GRNN).The accuracy of this method was estimated to be 0.99 on training data and 0.76 on test data.Therefore, using the proposed method (K-means Clustering and GRNN) in this paper, it is possible to examine the extent of changes in other elements in the analysis.Also, it is possible to make deeper and broader explorations via determining the relationship between the elements.
A Precise Prognosis Model for Breast Cancer Data by Comparing Supervised Algorithms

According to global statistics, newer cancer cases and cancer-related deaths have become a significant public health problem. Breast cancer is a common familiar disease among women worldwide. The early diagnosis of breast cancer promotes the patients can significantly improve the chance of survival and receive timely clinical treatment. The high accurate classification of tumors can obviate the patients undergoing superfluous treatment. The cause of peculiar avails in critical features detection from complex breast cancer datasets is machine learning, widely recognized as the methodology in breast cancer classification and prediction. The breast cancer dataset classification and the prediction of cancer are the pretensions of this research work. Here bestow seven supervised algorithms and compared their metrics to finalize an accurate model. Spawned an exact prophetic model to fetch individual details of cancer patients from the extensive breast cancer data in a short time, especially the model proclaims whether the tumor is malignant or benign.
A Security Enhanced Blockchain System Supporting Standard Elliptic Curve Digital Signature Algorithm Signature (ECDSA) and SM2 Signature Simultaneously

Blockchain provides a promising way that could ensure the traceability of transactions as well as provide strong trust establishment of users. Considering that digital signatures are a typical way to achieve mutual authentication, Elliptic Curve Digital Signature Algorithm (ECDSA) is implemented in most blockchain platforms such as Bitcoin, Etherum and Hyperleger Fabric since its superiority in security and efficiency. In 2010, the Chinese State Cryptography Administration published SM2 algorithm as a standard for commercial applications. The security, efficiency and practicality have been proven and recognized. Therefore, to improve the security and computing efficiency of the blockchain system, this paper proposed a blockchain system that supports the ECDSA algorithm and SM2 algorithm simultaneously. Each blockchain node or payer/payee is able to generate and verify both SM2 signature and ECDSA signature. we also analyze the performance and security of ECDSA and ECDSA-SM2.
Formalising lie algebras

Lie algebras are an important class of algebras which arise throughout mathematics and physics. We report on the formalisation of Lie algebras in Lean's Mathlib library. Although basic knowledge of Lie theory will benefit the reader, none is assumed; the intention is that the overall themes will be accessible even to readers unfamiliar with Lie theory.
Bitcoin Usage: Study on Bitcoin Usage Around the World 2020

Bitcoin usage has grown from zero to hundreds of billions of dollars in just over a decade. However, the research and studies of bitcoin usage fall short. This is mainly due to the nature of bitcoin, but also the myths of the early days. In this study, we go through the user data from LocalBitcoins, the largest peer-to-peer bitcoin marketplace in the world to study the bitcoin usage and needs. By analyzing and classifying major use cases for bitcoin through surveys and statistical analysis, this study will bring a better understanding of the different use cases for bitcoin and the role they play in bitcoin adoption. This research shows that the scope of bitcoin usage is noticeably more diverse and not merely limited to illegal usage or speculation.
Computer Vision Enabled Drowning Detection System

Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.
GPU enabled Improved Reference Ideal Method (I-RIM) for Web Service Selection

Web services are globally utilized by clients to accomplish the required functionality over the web. As a result of its popularity and flexibility in usage, thousands of functionally similar web services are available over the network. Hence, it becomes necessary to select the optimal web service to satisfy the clients’ need. Various methodologies like machine learning, genetic algorithm, bio-inspired techniques, multi-criteria decision making (MCDM) methods and many others aid in the process of selecting the best web service from thousands of alternatives. This paper aims in proposing a relatively new MCDM approach to solve the selection issue and thereby proposes a novel framework incorporating the proposed MCDM method to aid in the process of service selection. Reference ideal method (RIM) is a state-of-the-art MCDM technique to select the optimal web service based on user inputs. In spite of its popularity, this method is found to have multiple pitfalls which make the selection process less effective. This paper proposes a novel MCDM methodology named improved RIM (I-RIM) to overcome the existing pitfalls in RIM. The paper also proposes a novel framework which combines the power of graphics processing unit (GPU) and I-RIM to enhance the efficiency of the selection process. The proposed I-RIM when parallelized using GPU is found to outperform the parallelized MCDM techniques taken for study. The results also imply that the I-RIM is more consistent and stable towards the ranking process. It is also evident that the proposed framework which incorporates I-RIM outperforms RIM in terms of execution time, mean reciprocal rank and Spearman’s correlation coefficient which makes the framework more stable and reliable, thus, making it suitable for real-time web service selection.
Research on Small Target Pedestrian Detection Algorithm Based on Improved YOLOv3

Aiming at the problem that the target pedestrians’ environment in the intelligent driving system is complex and the detection accuracy is not high due to large size changes, the YOLOv3 algorithm is improved in this paper and the K-means clustering method to directly calculate the anchor frame size of the data set is used. Perform cluster analysis to reduce network training time.The multi-scale fusion of shallow features and deep features improves the feature extraction effect of the skeleton network; the SE module is introduced to enhance the spatial channel after the multi-level feature fusion, which promotes the ability of pedestrian multi-scale information mining, and integrates the DIoU loss function Introducing the training process speeds up the model convergence and running speed.The model was trained with VOC2007, VOC2012 and Caltech datasets, and the Caltech pedestrian dataset was used to verify the effectiveness of the improved algorithm. The results show that the improved YOLOv3 algorithm for small-scale pedestrian targets in the intelligent driving system has improved detection accuracy.