Reviewerly/exHarmony huggingface dataset:
The Study on the Improvement of Humanistic Tour Experience Based on VR Technology

Humanistic tourism attracts tourists from all over the world because of its rich cultural atmosphere and historical details. However, with the development of social economy and the continuous improvement of people's living standards, tourists are no longer satisfied with the traditional mode of humanistic tourism, where tourists just walk and glance over all the exhibits. More in-depth sense of participation, more comprehensive experience has become the urgent needs of tourists, and the development of VR (virtual reality) technology provides a feasible solution to this problem. From this point of view, this paper studies the possibility and method of applying VR technology to improve human tourism experience, and provides suggestions for the development of human tourism.
Detection of Railway Tunnel lining Based on Adaptive Background Learning

Tunnel is the absolutely necessary part of railway line. Its state of service is often influenced by linings. Once the lining has fallen on the concrete bed, it will directly threaten the safety of trains in the tunnel. So, one timely lining detection system is a key assistant to keep the tunnel a good operating condition. In this paper, the approach of lining fall-blocks detection by intelligent video analysis based on adaptive background modeling is proposed. Without a large number of labeled samples, the unsupervised method based on gaussian mixture model (GMM) is applied to monitor the fallen block in real time. The basic idea is to set the learning rate automatically, which is mainly according to the critical attributes including image intensity and feature point of current frame. Because of the strategy the method has the advantage of modeling the background in variable scenes such as illumination-changing, camera-shaking, train-passing, etc. Finally, in our experiment, the results demonstrated the effectiveness of the proposed approach.
3D Residual Networks with Channel-Spatial Attention Module for Action Recognition

Effectively modeling spatio-temporal information in the videos is the key to improving the performance of action recognition. In this work, we propose 3D residual networks with channel and spatial attention modules for action recognition. The proposed network architecture can directly extract spatio-temporal features. Channel attention module and spatial attention module can effectively assist the network to learn what and where to emphasize or suppress, at virtually negligible increase in computation cost. Specifically, we sequentially add channel attention module and spatial attention module to each slice tensor of the intermediate feature map to form channel and spatial attention maps. Then the attention maps are multiplied to the input feature map to reweight important features. We validate our network through extensive experiments and visualization method on the datasets of HMDB-51 and UCF-101.
Traffic Sign Recognition Based on ResNet-20 and Deep Mutual Learning

Traffic sign recognition has strong practical significance, such as driving assistance systems, urban planning and construction, automatic driving and even traffic monitoring content understanding. The traditional traffic sign recognition algorithm has the problems of low recognition accuracy and low robustness. In order to improve the classification accuracy and the self-adaptability of the network, this paper transfers the Deep Mutual Learning algorithm to the problem of traffic sign recognition. During the training process of the model, we use two ResNet-20 models to learn from each other. By training in this way, we find that the performance of each sub-network is better than when trained separately. Finally, we got two neural network models with higher robustness. In the prediction stage, we used the method of model fusion and obtained higher prediction accuracy. Through the verification of the test dataset, when only one model is used, Top-1 accuracy can reach 99.390%. After using the method of Deep Mutual Learning, the Top-1 accuracy of model one is 99.462% and Top-5 accuracy is 99.905%. The Top-1 accuracy of model two can reach 99.422% and the Top-5 accuracy is 99.881%, above the human performance of 98.81%. After using the method of model fusion, the Top-1 accuracy can reach 99.612% and Top-5 accuracy is 99.952%. And we do the comparative experiment of this article and the recent mainstream methods. It shows that this algorithm has higher classification accuracy and stronger generalization ability.
Evaluation of Re-identification Risks in Data Anonymization Techniques Based on Population Uniqueness

With the increasing appetite for publicly available personal data for various analytics and decision making, due care must be taken to preserve the privacy of data subjects before any disclosure of data. Though many data anonymization techniques are available, there is no holistic understanding of their risk of re-identification and the conditions under which they could be applied. Therefore, it is imperative to study the risk of re-identification of anonymization techniques across different types of datasets. In this paper, we assess the re-identification risk of four popular anonymization techniques against four different datasets. We use population uniqueness to evaluate the risk of re-identification. As per the analysis, k-anonymity shows the lowest re-identification risk for unbiased samples of the population datasets. Moreover, our findings also emphasize that the risk assessment methodology should depend on the chosen dataset. Furthermore, for the datasets with higher linkability, the risk of re-identification measured using the uniqueness is much lower than the real risk of re-identification.
TopicOcean: An Ever-Increasing Topic Model with Meta-Learning

Topic modeling has been intensively studied and widely applied in both academia and industry in the last decade. In the literature, topic models usually need to be trained from scratch for each individual corpus. Hence, the wisdom of the crowd (i.e., topic models previously trained based upon other corpora) is abandoned. Since a massive amount of in-domain data, considerable computational cost, and human labour are involved in obtaining a high-quality topic model, training from scratch for each new corpus is a huge waste of resources. In this paper, we propose the novel TopicOcean framework, which aims to integrate well-trained topic models and transfer the knowledge of accumulated topics to new corpora in order to improve the quality of their topic models. We first propose a method of constructing the ever-increasing TopicOcean, and then propose a meta-learning mechanism that transfers the meta-level knowledge (i.e., topics) in TopicOcean to the scenario of topic modeling on new corpora. Comprehensive experiments validate that the TopicOcean framework can significantly outperform the state-of-the-art (53.77% perplexity improvement on a temporal-shift corpus and 29.24% improvement on a domain-shift corpus). The well-trained high-quality topic models used to construct TopicOcean have been opensourced to promote further research. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The well-trained topic models can be accessed at Github (https://github.com/baidu/Familia/blob/master/model/download_model.sh).
Power Wheelchair Virtual Reality Simulator with Vestibular Feedback

Autonomy and the ability to maintain social activities can be challenging for people with disabilities experiencing reduced mobility. In the case of disabilities that impact mobility, power wheelchairs can help such people retain or regain autonomy. Nonetheless, driving a power wheelchair is a complex task that requires a combination of cognitive, visual and visuo-spatial abilities. In practice, people need to pass prior ability tests and driving training before being prescribed a power wheelchair by their therapist. Still, conventional training in occupational therapy can be insufficient for some people with severe cognitive and/or visio-spatial functions. As such, these people are often prevented from obtaining a power wheelchair prescription from their therapist due to safety concerns. In this context, driving simulators might be efficient and promising tools to provide alternative, adaptive, flexible, and safe training. In previous work, we proposed a Virtual Reality (VR) driving simula-integrating vestibular feedback to simulate wheelchair motion sensations. The performance and acceptability of a VR simulator rely on satisfying user Quality of Experience (QoE). Therefore, our simulator is designed to give the user a high Sense of Presence (SoP) and low Cyber-sickness. This paper presents a pilot study assessing the impact of the vestibular feedback provided on user QoE. Participants were asked to perform a driving task whilst in the simulator under two conditions: with and without vestibular feedback. User QoE is assessed through subjective questionnaires measuring user SoP and cyber-sickness. The results show that vestibular feedback activation increases SoP and decreases cyber-sickness. This study constitutes a mandatory step before clinical trials and, as such, only enrolled people without disabilities.
Deep Neural Network-Based Real-time Trajectory Planning for an Automatic Guided Vehicle with Obstacles

In this paper, a hybrid-intelligent real-time optimal control approach based on deep neural networks (DNNs) for an automatic guided vehicle (AGV) with obstacles is studied. The path planning problem of the AGV is formulated as an optimal control problem (OCP) and the numerical solution of the OCP can be obtained by discretizing the OCP into a nonlinear programming (NLP) problem. However, due to the change of the starting point and the high nonlinearity of the model, the solution process is usually difficult without good initial guesses. Multiple iterations are often needed and real-time performance cannot be easily achieved. To this end, we first use the Gauss pseudospectral method incorporated a smoothing technique to solve the AGV optimal control problem with high computational efficiency offline. Subsequently, a DNNs is developed to learn the functional relationship between the optimal state and action pairs obtained by the approximate indirect method, and thus the resulting DNNs can generate the optimal control instructions in real time for the AGV navigation. Numerical results are illustrated to verify the real-time performance, control optimality and robustness of the developed DNN-based controller.
Research on Recognition of Marine Ships under Complex Conditions

The maritime navigation environment is complicated and there are many interference factors. In order to improve the recognition effect of ships under complex weather conditions, a deep learning algorithm based on You Only Look Once (YOLO) V3 is proposed to identify ships, and an improved dark channel defogging algorithm is used to reduce the recognition of clouds and fog as soon as possibly. By the trained convolutional neural network to predict the attributes and types of cargo in this block diagram. Experimental results prove that it can meet the needs of ship identification.
Classification of Graduates Student on Entrance Selection Public Higher Education Through Report Card Grade Path Using Support Vector Machine Method

Entrance selection Public Higher Education through report card grades is one way of accepting new students at Public Higher Education, free of registration fees, and without written examinations. In Mojokerto, the students' participation in the selection process is still done manually, so the results have not been maximized. Students with good report cards are not necessarily accepted, and vice versa, students with average report cards can be one General Higher Education through this pathway. Therefore, a classification system is needed to predict student graduation at Public Higher Education through this path to obtain optimal results. In this study will make the classification of student graduation at SNMPTN, SPAN PTKIN, SNMPN, and not received/not passed by using the parameters of the average semester report card grades 1, average semester report card grades 2, average semester report card grades 3, grades average semester report card 4, and the average grade report card semester 5 using the Support Vector Machine (SVM) method. Based on the results of data testing to identify how well the classification performed by measuring levels of accuracy of data classification at SMAN Puri, SMAN Sooko, and MAN 1 Mojosari in 2018 and 2019 using SVM method obtained a good degree of accuracy, namely amounted with 82% for SMAN Puri, 81% for SMAN Sooko, and 90% for MAN 1 Mojosari. The results showed that the level of accuracy of the SVM method could be used as a classifier to predict student graduation in Public Higher Education Entrance Selection through a report card grade path.
A Successful Framework for the ABET Accreditation of an Information System Program

Scientific programs in higher educational institutes are measured by their performances in showing evidences for expected quality assurance levels and for obtaining the academic accreditation. However, obtaining an accreditat... | Find, read and cite all the research you need on Tech Science Press
Implementing Viewport Tile Extractor for Viewport-Adaptive 360-Degree Video Tiled Streaming

Because 360-degree video streaming has become significantly popular in the field of virtual reality, the viewport-adaptive tiled streaming technology for 360-degree video is emerging. This paper presents a viewport tile extractor (VTE) that is implemented on high-efficiency video coding (HEVC). The VTE extracts multiple tiles that represent the viewport of a user and merges them into one bitstream. The proposed system transmits the bitstream of high-quality tiles and the low-quality video bitstream of entire area to reduce both latency and bandwidth. The proposed method shows more than 16.98% of bjontegaard delta rate saving in terms of the luma peak signal-to-noise ratio, compared with the HEVC-compliant streaming method. Additionally, compared with the existing tiled streaming method, it achieves 66.16% and 69.79% saving of decoding memory and time consumption, respectively.
Enhancement of Nighttime Image Visibility Using Wavelet Fusion of Equalized Color Channels and Luminance with Kekre’s LUV Color Space

The usage of digital images is growing because of the benefits possessed by digital images in many ways in day to day life. But not all the images captured have a filmic appearance that pleases the human eye. Dissatisfaction is mainly because of noise addition, bad illumination where the captured image is either extra dark or bright, which leads to the need of enhancement in the quality of images. The motivation behind enhancement of image is to grasp the hidden information which is unavailable during image acquisition due to improper light conditions. One way to do so is enhancing the contrast of poorly illuminated images. In this paper, a new technique is presented which fuses HE enhanced image with proposed algorithm using wavelet transform. The results are tested on 240 images of 12 different categories ExDark dataset. Performance measures used are Entropy, NIQE and BRISQUE.
Language Adaptation for Entity Relation Classification via Adversarial Neural Networks

Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence, and plays a vital role in various natural language processing applications. However, existing studies focus on exploiting mono-lingual data in English, due to the lack of labeled data in other languages. How to effectively benefit from a richly-labeled language to help a poorly-labeled language is still an open problem. In this paper, we come up with a language adaptation framework for cross-lingual entity relation classification. The basic idea is to employ adversarial neural networks (AdvNN) to transfer feature representations from one language to another. Especially, such a language adaptation framework enables feature imitation via the competition between a sentence encoder and a rival language discriminator to generate effective representations. To verify the effectiveness of AdvNN, we introduce two kinds of adversarial structures, dual-channel AdvNN and single-channel AdvNN. Experimental results on the ACE 2005 multilingual training corpus show that our single-channel AdvNN achieves the best performance on both unsupervised and semi-supervised scenarios, yield- ing an improvement of 6.61% and 2.98% over the state-of-the-art, respectively. Compared with baselines which directly adopt a machine translation module, we find that both dual-channel and single-channel AdvNN significantly improve the performances (F1) of cross-lingual entity relation classification. Moreover, extensive analysis and discussion demonstrate the appropriateness and effectiveness of different parameter settings in our language adaptation framework.
Predicting the association between fine-grained attributes and black-boxed perceptual performance

Per-image performance of a certain visual perception algorithm is the combination of per-task performances in the image. Based on the black box test, we address to discover and explain the potential shortness of the evaluated intelligent algorithms/systems at the fine-grained task-level by human knowledge. By assuming the domain knowledge in visual tasks could be represented by a latent vector which is a sparse embedding of the catenated object-level and image-level features, we propose a latent dictionary learning framework for joint latent knowledge representation and knowledge-output regression at task level. In this way, so we can use semantic concepts to explain the relationship between test cases and test results. The experiments validate the idea of task-level explainable AI evaluation initially as well as the effectiveness of proposed method.
Decision-Making Rules and the Influence of Memory Data

The problems that decision-makers face can escalate under imbalances, turbulent development, risks, uncertainties, disasters, and other influences. The development of processes in technical and economic structures is generally considered complex and chaotic, and it usually expands into innumerable dynamic influences. The paper focuses on the evaluation of the decision criteria choice structure, such as the factual cause of the consequences (e.g., future threats, opportunities, chances, occasion). It offers a graphical vision of the future forecast. It draws attention to prevention and prophylaxis versus criterion-generated time–space (TS). The paper deals with the question: Is it possible to choose and recommend the right time and place of process activities? The paper formulates a positive answer and illustrates a range of consequences. Developed activities (investment, production, etc.) take place in a defined TS; over time, they create new time-series states and expand the space by defining processes as a time series of activities. In a broader context, the article deals with the issue of the lifecycle of decision rules (dynamic proposal of opportunities) as the first step of decision making, i.e., the decision about the existence of opportunity. On the one hand, it respects static applications based on equilibrium states, while on the other hand, it draws attention to the need for a dynamic view of turbulent, dynamic, chaotic, and nonlinear phenomena.
Influenced Citation Analysis Using Modified Word Movers Distance (MWMD)

Citation analysis is an essential part of the research to inculcate the potentiality of the work and giving credit to the researcher. Generally, citations are used by researchers for many purposes, however mainly it is used to show that the references influenced author's work. Now a day's most of the references influenced one way or other. The proposed model is used to find influenced citations in the scientific documents using a semantic approach rather than simple keyword matching. Generally, every scientific document contains references or a bibliography at the end of the document. The goal of the proposed technique is to discover the relevancy among scientific document and a list of reference papers in the same document in a semantic way. In this paper, the proposed work developed using the proposed semantic similarity measure called Modified Word Movers Distance (MWMD) applied to the deep learning model. Finally, the proposed work efficiently finds influenced citations percentage in the scientific documents.
People, Process and Technology for Cryptocurrencies Forensics: A Malaysia Case Study

Crimes related to cryptocurrencies are on the rise. In Malaysia, reported losses is increasing from USD2.67 million in 2017 to USD55.32 million in 2018. This raise a concern whether the current process, tools and trainings are sufficient for Malaysian investigators to conduct cryptocurrencies forensics. A survey questionnaire was conducted to answer four questions; is the current (i)process model (ii)tools, and (iii)trainings sufficient? Lastly (iv)is there a relationship between work experience years and the needs for process model, tools and trainings? The result shows respondents (i) agreed that process model is insufficient; (ii) neutral on the sufficiency of tool, however, they suggested a tool to collect suspected cryptowallets information, cryptowallets triage tool; and automated cryptocurrencies transfer tool; (iii) agreed that training program is insufficient, and lastly (iv) regardless of years of experience, investigators do need a process model, tools and trainings to conduct work. Surprisingly these findings echoed with worldwide view. The study highlights the need for a cryptocurrencies forensics process model, tools and trainings program in place.
l<sup>1/2</sup> Regularized RPCA Technique for 3D Human Action Recovery

Even though human action data is widely used for various applications, the inevitable occlusions and limitations of motion capture systems lead to loss of information. Different methods have been used for recovering the lost information in motion data, among which low rank approximation gives promising results. However, the convex approximation of low rank minimization problems which uses nuclear norm minimization completely ignores the importance of larger singular values compared to the smaller ones. In this paper, we propose a new method for the recovery of lost 3D human action data using weighted nuclear norm approximation, which is a non-convex low rank approximation technique. The recovery of human action data is made possible by minimizing the weighted nuclear norm of the clean motion matrix and the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> norm of the error matrix by 2 effectively utilizing the aspects of Robust Principle Component Analysis (RPCA). Experimental results show that our method is efficient in recovering clean human action data from the corrupted skeletal information.