Reviewerly/exHarmony huggingface dataset:
Learning Recurrent Structure-Guided Attention Network for Multi-person Pose Estimation

Multi-person pose estimation aims to localize tens of human joints (e.g., elbow, wrist, etc.) from multiple human bodies in an image. Existing approaches mainly adopt a two stage pipeline, which usually consists of a human detector (i.e., generating a bounding box for each person) and a single person pose estimator (i.e., generating human joints from each bounding box). However, these approaches neglect the challenges of large pose variations and heavy occlusions in each bounding box, which often results in imprecise human joint localization. In this paper, we propose a structure-guided attention network (SGAN) for multi-person pose estimation. Specifically, a structured pose representation is encoded by learning a joint confidence map and a joint association map, which can be further refined by a structure-guided attention network (SGAN) in a recurrent way. Note that SGAN enables a deep neural network to take initial pose estimation as references, and to discover multi-scale pose features as completion, and thus the learning of pose structures can be reinforced. Extensive experiments show the best single-model results against the state-of-the-art approaches, with a relative 3.5% mAP gain in the challenging COCO Keypoint dataset.
Fictional Literature, Classification and Indexing

Fiction content analysis and retrieval are interesting specific topics for two major reasons: 1) the extensive use of fictional works; and, 2) the multimodality and interpretational nature of fiction. The primary challenge in the analysis of fictional content is that there is no single meaning to be analysed; the analysis is an ongoing process involving an interaction between the text produced by author, the reader and the society in which the interaction occurs. Furthermore, different audiences have specific needs to be taken into consideration. This article explores the topic of fiction knowledge organization, including both classification and indexing. It provides a broad and analytical overview of the literature as well as describing several experimental approaches and developmental projects for the analysis of fictional content. Traditional fiction indexing has been mainly based on the factual aspects of the work; this has then been expanded to handle different aspects of the fictional work. There have been attempts made to develop vocabularies for fiction indexing. All the major classification schemes use the genre and language/culture of fictional works when subdividing fictional works into subclasses. The evolution of shelf classification of fiction and the appearance of different types of digital tools have revolutionized the classification of fiction, making it possible to integrate both indexing and classification of fictional works.
Development of positioning system for Nuclear-fuel rod automated assembly

A heavy-water nuclear reactor requires five different types of nuclear-fuel rods assemble into bundles. Intelligent identification and positioning of these different types rods for automated bundle assembly remain a challenge owing to their extensive variations and minute distinctions. This paper presents an innovative machine-vision based nuclear-fuel rod positioning system that enables automated rod bundle assembly in a heavy-water nuclear reactor. The system is capable of identifying the type and polarity and adjusting the pose of rods by incorporating the following solutions: 1) a hypercentric imaging solution that is capable of capturing clear and complete images of the rod spacers, even for the rods with large straightness deviations; 2) a robust and noise-resistant multi-scale, multi-directional morphological edge extraction algorithm that features complete and accurate edge image extraction with minimum false edges; and 3) a rod body center at spacers deviation correction algorithm that contributes to a higher level of consistency with the true rod body center. The system is robust, as demonstrated by the following performance indices: a process capability Cpk of 1.14, which is translated into a process yield of 99.95%; 100% correct rod type identification; 100% correct rod polarity identification; and an average time of 8.92 s for positioning a single rod, meeting the required production cycle. The system has been successfully deployed in the North Nuclear Fuel Components Co., Ltd. (a subsidiary of China National Nuclear Corporation).
Learning to Remove Reflections for Text Images

Text images taken behind a piece of glass in the wild are largely contaminated by reflections. Directly applying existing reflection removal methods on text images with reflections cannot recover clear and correct text contents due to the ignorance of special characteristics of texts. This paper proposes a stacked framework to solve the text image reflection removal problem by specifically considering the regional properties of reflection and embedding the specific text priors into the estimation process in a unified manner. Experiment results on a newly collected dataset demonstrate that the proposed method outperforms state-of-the-art methods in recovering visually pleasant reflection-free images and recognizable text features.
Analysis of Cyber Related Issues by Implementing Data Mining Algorithm

Computer forensics has been developed from investigating small cyber attacks to exploring the complicated international cyber cases which have a tremendous impact on the organization. Even though organization knows the type of attacks but still it won't help them to take preventive measures across the attack unless they don't know the consequences of the attack or the area where the attack is going to affect the most. This paper has examined the various types of attacks and motives behind those attacks through a questionnaire which was randomly distributed to various participants working for any industry or organization. The result was analyzed through weka software to propose model which uses the consequences mining algorithm. Consequences Mining Algorithm will take the motive of attacks as input and will give its hidden and visible consequences as output. The proposed model is the combination of computer forensics investigation and data mining. Results from the model will help the organization to take proactive measures against the consequences of the attack.
A Bayesian approach to abrupt concept drift

This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propose an approximate procedure based on discretizing both the possible probability values and the parameter representing the probability of change. The result is a method which is quite efficient in time and space (with a complexity directly related to the number of points used in the discretization) and providing very accurate predictions as well. These benefits are checked with a detailed comparison with other standard procedures based on variable size windows or forgetting rates.
Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan

Abstract Tectonic tremor in Taiwan is known for its short duration and weak amplitude. Recent observations from the mountain area (Central Range) allows us to document such type of tectonic tremor recorded at a close distance and to investigate a single station technique for a minute‐long tremor. To effectively distinguish tremor from earthquakes and noise, we applied the Fisher's class separability criterion to automatically select the optimal feature subset from a set of commonly adopted feature candidates. During the study period of 1 January to 16 September 2016 when a local seismic array was deployed, we successfully differentiated tremor from local earthquakes and noise with high accuracy of 86.6% to 98.9% from three stations using a k ‐nearest neighbors classifier. Other than the maximum amplitude, number of peaks, and energy of the 2‐ to 8‐Hz passband, the spikiness of discrete Fourier transforms median in time is also found to be important to separate tremor from noise.
Pose-based multisource networks using convolutional neural network and long short-term memory for action recognition

Pose-based action recognition has aroused increasing attention for its broad application prospects and excellent performance. Though the pose-based action recognition methods have been significantly advanced, pose-based action recognition remains a challenging task for various human action categories and subtle changes in human poses. To solve those problems, we propose pose-based multisource networks. First, human pose features are extracted from the raw video, followed by a filtration. Then, using a convolutional neural network (CNN) and long short-term memory (LSTM), the extracted pose sequence is fed into the proposed multisource networks. Subsequently, the CNN-based spatial model processes the relative position in each frame, and the LSTM-based temporal model is built to learn the temporal correlation of pose sequence. Afterward, the temporal model contains three sublevels to fully exploit the subtle information in the temporal domain. Finally, the experimental results verify the effectiveness of the proposed approach on SUB-JHMDB, MPII Cooking Activities, SYSU 3D Human-Object Interaction, and NTU RGB+D.
Recommending Points of Interest in LBSNs Using Deep Learning Techniques

The representation of real-life problems by using k-partite graphs introduced a new era in Machine Learning. Moreover, the merge of virtual and physical layers through Location Based Social Networks (LBSN s) offers a different meaning into the constructed graphs. To this point, multiple models introduced in literature that aim to support users with personalized recommendations. These approaches represent the mathematical models that aim to understand users' behaviour by finding patterns on users' check-ins, reviews, ratings, friendships, etc. With this paper we describe and compare 20 of those state-of-the-art deep learning models to bring into the surface some of their strengths and shortcomings. First, we categorize them according to: data factors or features they use, data representation, methodologies used and recommendation types they support. Then, we highlight the existing limitations that tackles their performance. Finally, we introduce research trends and future directions.
Recursive Multi-Stage Upscaling Network with Discriminative Fusion for Super-Resolution

Since convolutional neural networks have fundamentally changed how computers learn features, many super-resolution (SR) methods focus on extracting informative features to improve performance by using more layers or more innovative skip-connections. However, using more layers in feature extraction while keeping the upscaling module unchanged will exacerbate structural imbalances. Besides, blindly fusing different stage features by concatenation may make them interfere with each other. To address these issues, we proposed a recursive multi-stage upscaling network (RMUN) with discriminative fusion module (DFM). Specifically, we construct multiple upscaling paths to produce various high-resolution features in the forward propagation and deliver error loss in the back propagation. Furthermore, we fuse and re-weight those features by DFM to avoid mutual interference and boost reconstruction quality. Experiments show that RMUN is superior to the state-of-the-art methods, especially for large scale SR tasks.
A Survey on Attacks of Bitcoin

Bitcoin is a crypto-currency. Bitcoin economy has been gaining immense popularity. Bitcoin mining involves putting new coins into circulation after creating them. Miners utilise electricity on solving cryptographic puzzles. The miners give a proof of bitcoin transactions of the other people. Miners are assumed, to be honest, and have motives to behave well. This paper looks at the strategies miners use and bring notice to troublemaking and deceitful strategies or the ones which could affect the bitcoin and the security of bitcoin. This paper studies about several recent attacks in which dishonest miners gain an upper reward compares to their corresponding contribution to the network. This paper describes Double Spending Attack, Finney Attack, Vector 76 Attack, >50% Hash-Power Attack, Selfish Mining Attack in detail.
Intelligent agents in games: Review with an open-source tool

The field of artificial intelligence has come a long way in the last 50 years, and studies of its methods soon expanded to a field in which they are of great practical value—computer games. The concept of intelligent agents provides a much needed theoretical background for the comparison of various different approaches to intelligent, rational behavior of computer-controlled characters in games. By combining rationality with certain limitations to the capabilities of our agents, we can achieve behavior resembling that of a human player, which is also desirable in games. The goal of this article is to introduce various types of agents that are used in games, show how to implement meaningful, reasonable limitations to agent capabilities into the game world, and provide a freely available, open-source application for the comparison of such agents. Additionally, in this article we show that even the simplest agents can succeed in their tasks in certain task environments, whereas more difficult task environments often require a more sophisticated agent architecture. Our application consists of two task environments with nine agents in total but could easily be extended with additional task environments and agent implementations. In the end, we find the addition of goals into the agent architecture has the biggest impact on the agent's behavior and performance, whereas the state-based approach helps keep our implementation simple and compact.
FirstVR

Hand gesture input has been of growing importance in 3D content and VR/AR. However, a hand gesture detection system by camera was small in scope. And EMG was affected by noise from other electronic devices. Muscle deformation sensor system could only detect large muscles deformation related to wrist gestures because the sensor is covered to prevent light leakage noise. In this research, a device that covers the only sensor side circularly as light noise reduction case and has 14ch photo reflector sensors can detect small muscles deformation: FirstVR is suggested (Fig1, 2). Results of our experiment shows that recognition accuracy average of more than 90% is achieved with three types of PIP joint angles. In the future, FirstVR is expected to be used not only for content inputting finger movements indoors and outdoors but also for muscle displacement recording system.
A Framework for Resource-aware Online Traffic Classification Using CNN

As a fundamental problem in network security and management, traffic classification has attracted more and more research interest. In existing work, machine learning based traffic classification methods are mainstream in recent years. With the development of deep learning, Convolutional Neural Network (CNN) is widely used in traffic classification, achieving promising results. However, prior work only focuses on how to improve the accuracy of classification tasks without considering the time efficiency. As we know, the deep learning models require a lot of computational overhead. Therefore it is necessary to realize realtime CNN-based traffic classification with limited computational resources. In this paper, we propose a new framework for online traffic classification using CNN. By detecting CPU occupancy in real time, the proposed framwork can seek optimal window sizes using a regression model of meta-parameters to achieve accuracy at a lower cost of resources. The simulation experiments with real trace data show that the proposed framework significantly reduces processing latency by about 77%, while achieving matchable accuracy of classification compared to the state-of-the-art method.
Are You Sitting Comfortably? How Current Self-driving Car Concepts Overlook Motion Sickness, and the Impact It Has on Comfort and Productivity

A proposed benefit of self-driving cars is that of increased comfort and productivity of the occupants. Self-driving vehicle concepts and published research show the desire for engagement in non-driving related tasks while traveling in such vehicles. Based on survey results and financial productivity estimations, it is likely that completing work activities within such vehicles will be desirable, even expected. These predictions, along with current concepts for self-driving vehicles, fail to consider motion sickness. This paper explores why motion sickness is likely to be a factor in these vehicles, and explicit implications with a range of in-car non-driving related activities is discussed. Through a critique of current concepts, a contrast between that which is advertised, and what may be possible is highlighted and discussed. The importance for inclusivity in future self-driving vehicles considering demographic differences in motion sickness susceptibility is highlighted, and design recommendations for future self-driving vehicles are made.
The Quantum Cost, Garbage Outputs and Constant Input Optimized Implementation of 2:4 Decoder Using Peres Gate

This paper presents the quantum cost (QC), garbage output (GO) and constant input (CI) optimized 2:4 decoder using reversible logic gate. The proposed reversible circuit is design using existing reversible logic gate, such as reversible NOT gate, Feynman gate and Peres gate. The proposed decoder can be design using one Peres gate, one NOT gate and four Feynman gate. The QC, GO and CI of the proposed 2:4 decoder is 9, 0 and 2. Which is better w.r.t previously reported work. This is the first reversible 2:4 decoder circuit, which has `0' garbage output. The improvement % of QC, GO and CI are 0 - 74.28 %, 100 % and 33.33 - 75 %.
A Web Service Matchmaking Approach based on Topic Models

With the fast development of service-oriented computing and Cloud computing. A rapid growing number of Web services have been published on Internet, which brings selection difficulties for developers in developing service-oriented systems. Towards this issue, in this paper, based on the topic modeling techniques, we propose a service matchmaking approach to find and match appropriate services according to users' functional and nonfunctional requests, with the ultimate objective of providing suitable candidate component services during the development process of service-oriented systems. Preliminary experiments conducted on a real world dataset exhibit that our proposed approach can fulfill user requests to a large extent.
Semi-Supervised Semantic-Preserving Hashing for Efficient Cross-Modal Retrieval

Cross-modal hashing has recently gained significant popularity to facilitate retrieval across different modalities. With limited label available, this paper presents a novel Semi-Supervised Semantic-Preserving Hashing (S3PH) for flexible cross-modal retrieval. In contrast to most semi-supervised cross-modal hashing works that need to predict the label of unlabeled data, our proposed approach groups the labeled and unlabeled data together, and integrates the relaxed latent subspace learning and semantic-preserving regularization across different modalities. Accordingly, an efficient relaxed objective function is proposed to learn the latent subspaces for both labeled and unlabeled data. Further, an orthogonal rotation matrix is efficiently learned to transform the latent subspace to hash space by minimizing the quantization error. Without sacrificing the retrieval performance, the proposed S3PH method can benefit various kinds of retrieval tasks, i.e., unsupervised, semi-supervised and supervised. Experimental results compared with several competitive algorithms show the effectiveness of the proposed method and its superiority over state-of-the-arts.
A Novel Feature Selection Method for Software Fault Prediction Model

Software fault prediction (SFP) is an active issue in software engineering (SE). At present, machine learning (ML) has been successfully applied to SFP classification problems. However, one of the challenges for building software fault prediction models (SFPM) is processing high dimensional datasets, which include many irrelevant and redundant features. To address this issue, feature selection techniques, mainly contain wrapper methods and filter methods, are used. In the paper, we report an empirical study aimed at providing a novel approach to select feature for SFP.
Revisiting Subnet Inference WISE-ly

Since the late 90's, the Internet topology discovery has been an attractive and important research topic, leading, among others, to multiple probing and data analysis tools developed by the research community. This paper looks at the particular problem of discovering subnets (i.e., a set of devices that are located on the same connection medium and that can communicate directly with each other at the link layer). In this paper, we first show that the use of traffic engineering policies may increase the difficulty of subnet inference. We carefully characterize those difficulties and quantify their prevalence in the wild. Next, we introduce WISE (Wide and lInear Subnet inferencE), a novel tool for subnet inference designed to deal with those issues and able to discover subnets on wide ranges of IP addresses in a linear time. Using two groundtruth networks, we demonstrate that WISE performs better than state-of-the-art tools while being competitive in terms of subnet accuracy. We also show, through large-scale measurements, that the selection of vantage point with WISE does not matter in terms of subnet accuracy. Finally, all our code (WISE, data processing, results plotting) and collected data are freely available.
Variational nonparametric discriminant analysis

Variable selection and classification are common objectives in the analysis of high-dimensional data. Most such methods make distributional assumptions that may not be compatible with the diverse families of distributions data can take. A novel Bayesian nonparametric discriminant analysis model that performs both variable selection and classification within a seamless framework is proposed. Pólya tree priors are assigned to the unknown group-conditional distributions to account for their uncertainty, and allow prior beliefs about the distributions to be incorporated simply as hyperparameters. The adoption of collapsed variational Bayes inference in combination with a chain of functional approximations led to an algorithm with low computational cost. The resultant decision rules carry heuristic interpretations and are related to an existing two-sample Bayesian nonparametric hypothesis test. By an application to some simulated and publicly available real datasets, the proposed method exhibits good performance when compared to current state-of-the-art approaches.