Reviewerly/exHarmony huggingface dataset:
Path advisor

The shortest path in both the two dimensional (2D) plane and the three dimensional (3D) terrain is extensively used both in industry and academia. Although there are some map visualization tools for viewing the shortest path in 2D and 3D views, we find two limitations: (1) they are not applicable for map applications with obstacles (such as the wall in a building), and (2) they look unrealistic and strange when a road network approach is blindly adopted. Motivated by this, we developed a web-based multi-functional campus map tool called Path Advisor , which allows users to visualize the shortest path in the 2D view, the bird's eye view and the virtual reality view (VR view). Path Advisor uses Dijkstra's shortest path algorithm and breadth-first tree in the 2D view, and the weighted shortest surface path algorithm in the bird's eye view and the VR view. We shot a video for demonstrating Path Advisor at https://youtu.be/ZgdjyXXHwqg.
A Chatbot for Recipe Recommendation and Preference Modeling

This paper describes the main steps and challenges in building a chatbot for a nutritional recommendation system addressed to the elderly population. We identified 4 main components: Natural Language Understanding (NLU), Dialogue Management, Preference Modeling and Ingredient Matching and Extraction. To address the specific challenges of a chatbot for this domain we have tested transformer-based models both in the development of the NLU component and the Dialogue Management component. Moreover, we explored word embeddings and nutritional knowledge bases combined with sentiment analysis for user preferences modeling. The sentiment analysis algorithms used to model food preferences showed to correctly match the real feeling of the users. Each one of these components were evaluated individually using appropriate metrics. Moreover, the developed chatbot was successfully tested by users and its opinions were recorded by means of usability and user experience questionnaires. The results of usability tests show that the components were well integrated. The scores obtained were higher than the benchmark values for both the System Usability and the User Experience Questionnaires.
Detection of Sinusoids with Frequency Drift in White Gaussian Noise

In this study, 4 methods of detecting sinusoid targets in white Gaussian noise are investigated. Averaging FFTs coherently and non-coherently is examined. A brute force method is introduced and investigated. Using a single large FFT spanning the observation window is also considered. Detection performance is studied on targets whose frequencies drift over time. It is determined that averaging non-coherent FFTs is a safe choice in a detection system. The preferred size of the FFT used for tone detection as well as the number of spectra to average is driven by the nature of the target tone frequency drift. This information is especially useful when high sample rate detectors are used to find tones from low quality transmitters because making FFTs too large or taking too many averages may hinder detection.
Blockchain-Based Solution for Supply Chain Traceability: The Case of SmartLog Project

The digital transformation in the logistics sector is inevitable. New blockchain technology is changing how logistics sector industries and enterprise's function having a profound impact in future. The significance of blockchain technology is recognized but the number of functioning solutions is low, they are in early stages and not ready to be widely exploited. European Territorial Cooperation Program INTERREG project SmartLog (Smart Logistics and Freight Villages Initiative) with the goal to develop Hyperledger Fabric based blockchain solution to the logistics sector. The aim of the project was to test the developed technology in two TEN-T corridors measuring the potential of saving lead times in transporting goods. To achieve and measure the above-mentioned goal the following methodology was used: an IT-platform, online questionnaire, expert interviews, data and process analyses and modelling. The results showed the possibility to shorten lead times 3-8%. This paper describes the course of the research project and presents results and conclusions achieved, also suggestions for further development.
3D Reconstruction for the Operating Environment of the Robots in Distribution Hot-line Working Based on Mixed Reality

The hot-line work robot for distribution network, an important branch of intelligent robot, has been given more and more attention. In the operation of the hot-line work robot for distribution network, environmental inference such as weather, light and temperature could have a great impact on the robot’s performance. Therefore, it is of great significance to improve the anti-inference performance of robot in distribution-network operation. Firstly, this article studies about 3D vision recognition and reconstruction in terms of distribution network operation, and explores the principle of binocular stereo vision in measuring sparse parallax of conducting wires in live working environment of distribution network and the operator-for -contour-matching algorithm based on RGB image data. Secondly, this article builds up a system, where based on binocular vision and laser point cloud, the distance between wires and the robot is measured, parameters of the operating environment are identified, the target’s depth is estimated. Thus, the anti-disturbance performance and operating accuracy are effectively improved.
Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation.

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical con-sequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinical scans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance. Specifically, we designed a dual-path segmentation model with two novel connecting mechanisms, namely multi-input attention module (MAM) and attention fusion module (AFM) to fuse the information from two paths for accurate results. Experiments on two publicly available datasets show the effectiveness of the proposed BAGAU-Net. With only FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art method with T1-weighted images, paving the way for effective development of WMH segmentation. Availability: https://github.com/Ericzhang1/BAGAU-Net.
Non-cooperative MPSK Modulation Identification in SIMO Underwater Acoustic Multipath Channel

Due to the influence of underwater acoustic multipath channel and lack of prior information, the likelihood-based MPSK modulation identification method is facing great challenges. Therefore, we proposed an identification method combining the orthogonal matching pursuit (OMP) and expectation maximization (EM) in single input and multiple output (SIMO) underwater acoustic channel. First, the blind channel is estimated and equalized by the OMP. Then, the EM is used to compensate the recovered constellation. Finally, the likelihood-based method is applied to identify the MPSK modulation. Numerical results show the proposed method can achieve more than 95% identification rate while the SNR is higher than 7 dB.
Polar Codes for Quantum Reading

Quantum reading provides a general framework to formulate the statistical discrimination of quantum channels. Applying classical codes to this task gives at least two open questions: how to construct low complexity encoding schemes that are interesting for channel discrimination and, how to develop capacity-achieving protocols. The aim of this paper is to present a solution to these questions using polar codes. We characterize the rate and reliability of the channels under polar encoding. Additionally, it is shown that the error probability of the proposed scheme decays exponentially with respect to the code length. A full version of this paper is accessible at https://arxiv.org/abs/2012.07198v2
EDGY

Voice user interfaces and assistants are rapidly entering our lives and becoming singular touchpoints spanning our devices. Raw audio signals collected through these devices contain a host of sensitive paralinguistic information (e.g., emotional patterns) that is transmitted to service providers regardless of deliberate or false triggers. We thus encounter a new generation of privacy risks by using these services. To tackle this issue, we have developed EDGY; a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and selectively filter sensitive attributes at the edge prior to offloading to the cloud. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in ABX score and minimal performance penalties in learning linguistic representations from raw signals on a CPU and single-core ARM processor with no specialized hardware.
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image

In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous methods train a multi-object detection network through a procedure that directly assigns the ground truth bounding boxes to the specific locations of the network’s output. However, this procedure makes the training of a multi-object detection network too heuristic and complicated. In this paper, we reformulate the multi-object detection task as a problem of density estimation of bounding boxes. Instead of assigning each ground truth to specific locations of network’s output, we train a network by estimating the probability density of bounding boxes in an input image using a mixture model. For this purpose, we propose a novel network for object detection called Mixture Density Object Detector (MDOD), and the corresponding objective function for the density-estimation-based training. We applied MDOD to MS COCO dataset. Our proposed method not only deals with multi-object detection problems in a new approach, but also improves detection performances through MDOD. The code is available: https://github.com/yoojy31/MDOD.
lazyCoP: Lazy Paramodulation Meets Neurally Guided Search

State-of-the-art automated theorem provers explore large search spaces with carefully-engineered routines, but most do not learn from past experience as human mathematicians can. Unfortunately, machine-learned heuristics for theorem proving are typically either fast or accurate, not both. Therefore, systems must make a tradeoff between the quality of heuristic guidance and the reduction in inference rate required to use it. We present a system (lazyCoP) based on lazy paramodulation that is completely insulated from heuristic overhead, allowing the use of even deep neural networks with no measurable reduction in inference rate. Given 10 s to find proofs in a corpus of mathematics, the system improves from 64% to 70% when trained on its own proofs.
Urban activity monitoring using wireless geophones in Singapore

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsUrban activity monitoring using wireless geophones in SingaporeAuthors: Yunyue Elita LiEnhedelihai NilotYumin ZhaoGang FangYunyue Elita LiSustainability Geophysics Project, National University of SingaporeSearch for more papers by this author, Enhedelihai NilotSustainability Geophysics Project, National University of SingaporeSearch for more papers by this author, Yumin ZhaoSustainability Geophysics Project, National University of SingaporeSearch for more papers by this author, and Gang FangSustainability Geophysics Project, National University of SingaporeSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3583803.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractUrban activities in megacities around the world are not only the most severely impacted by the COVID-19 pandemic, but also the most consequential for the effectiveness of the mitigation measures. Therefore, monitoring and understanding the realtime changes of different sectors of an urban society is vital for policy making and dynamic pandemic management. By analyzing the high-frequency seismic signals, we separate the motor vehicle signal and the runner signal from the environment and background noise. We quantify the impact of the pandemic and its corresponding mitigation measures as traffic flow at nonessential workplaces, essential workplaces, and recreational areas. Enabled by the anonymity of seismic data, our study achieves an unprecedented spatial and temporal resolution that is pivotal to understand the heterogeneity and evolution of pandemic responses in different sectors of an urban society. The rich seismic-derived information provide a unique opportunity for realtime urban activity monitoring and dynamic policy making to ensure a successful pandemic mitigation while minimizing negative impacts on urban lifestyle.Keywords: monitoring, near surface, passive acquisitionPermalink: https://doi.org/10.1190/segam2021-3583803.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Yunyue Elita Li, Enhedelihai Nilot, Yumin Zhao, and Gang Fang, (2021), "Urban activity monitoring using wireless geophones in Singapore," SEG Technical Program Expanded Abstracts : 3209-3214. https://doi.org/10.1190/segam2021-3583803.1 Plain-Language Summary Keywordsmonitoringnear surfacepassive acquisitionPDF DownloadLoading ...
Effect of Principal Component Analysis in Feature based Uncalibrated Steganalysis using Block Dependency

Steganalysis is a domain where hidden information sent through a medium over the internet is detected. The medium can be text, audio, images or video. The steganalytic method is of two types- target and blind. In this paper, we utilize the blind steganalysis and extract the statistical changes that occur when an image is embedded. The statistical values are known as features. During the extraction of features, the irrelevant ones are also extracted, which may hamper the efficiency of the analysis. This paper checks the efficiency rate of steganalysis by eliminating irrelevant data. Principal component analysis is used for feature reduction. Discrete Cosine Transform is used. The features used here are a combination of different statistical features as first order, second order, and Markov. The features which include the inter-block dependency features, as well as the intra-block features are reduced.
Formal Techniques for Simulations of Distributed Web System Models

This work aims to develop models of different types of systems for the performance evaluation. From different models, we chose two types of formal models, such as Petri nets (PNs) and queuing nets (QNs), as well as tools to support model creation. This paper presents the class of distributed information systems. In these systems, the clients’ interaction time can be compared to the system offer change time. The simulations were the main method of analysis. This work aims to bring the model closer to reality and verifies its behavior in the case of parameter changes.
Challenges and Their Practices in Adoption of Hybrid Cloud Computing: An Analytical Hierarchy Approach

Cloud computing adoption provides various advantages for companies. In particular, hybrid cloud shares the advantages of both the public and private cloud technologies because it combines the private in-house cloud with the public on-demand cloud. In order to obtain benefits from the opportunities provided by the hybrid cloud, organizations want to adopt or develop novel capabilities. Maturity models have proved to be an exceptional and easily available method for evaluating and improving capabilities. However, there is a dire need for a robust framework that helps client organizations in the adoption and assessment of hybrid cloud. Therefore, this research paper aims to present a taxonomy of the challenging factors faced by client organizations in the adoption of hybrid cloud. Typically, such a taxonomy is presented on the basis of obtained results from the empirical analysis with the execution of analytical hierarchy process (AHP) method. From the review of literature and empirical study, in total 13 challenging factors are recognized and plotted into four groups: “Lack of Inclination,” “Lack of Readiness,” “Lack of Adoption,” and “Lack of Satisfaction.” The AHP technique is executed to prioritize the identified factors and their groups. By this way, we found that “Lack of Adoption” and “Lack of Satisfaction” are the most significant groups from the identified challenging factors. Findings from AHP also show that “public cloud security concern” and “achieving QoS” are the upper ranking factors confronted in the adoption of hybrid cloud mechanism by client organizations because their global weight (0.201) is greater than those of all the other reported challenging factors. We also found out 46 practices to address the identified challenges. The taxonomy developed in this study offers a comprehensive structure for dealing with hybrid cloud computing issues, which is essential for the success and advancement of client and vendor organizations in hybrid cloud computing relationships.
Development of a Lightweight Octocopter Drone for Monitoring Complex Indoor Environment

A lightweight octocopter drone for monitoring indoor environment is developed. The robot is designed with the weight of 486g, 308mm in width and 160mm in height so that it can get through narrow spaces under complex indoor environment. In addition, the drone is designed with 8 blades, and this octocopter is controlled by PID method. The high-flying ability is proven by hovering experiments.
Research Based on Visual Sensors and VR in the Field of Visual Culture

Visual sensors provide us with a wealth of multimedia data; one of the core parts of VR technology is to present people with a real and immersive simulation environment; the application of this technology makes the human narrative to achieve visual transformation and further strengthens the central position of visual culture. This paper studies the field of visual culture from two aspects of visual sensor and VR. In this paper, the HSV color model models task processing problems in visual sensor networks using separable load theory and studies ways to seek optimal task scheduling strategies to minimize the completion time of the visual task. This paper mainly uses literature research method, questionnaire survey method, and statistical analysis method to design a model image output process to explore the application of visual culture under the development of visual sensor and VR and analyzes the development status of film, game, and cultural tourism industry VR in the field of visual culture. Questionnaire data show that compared with traditional video games, more people chose games under the application of VR technology for entertainment; in VR game experience, more than 70% are satisfied with game color and style, operation form, scene design, and experience of VR game itself, which shows to some extent the role of visual sensor and VR in visual culture.
Research on Attention-guiding Methods in Cinematic Virtual Reality Based on Eye Tracking Analysis

Reproducing the virtual world is the development direction of film technology and film art. However, the current technology is not ready to build a true comprehensive cinema. However, the current technology is not ready to construct a truly total cinema. The viewer of a 360 degree virtual reality movie can choose viewing directions freely when watching a movie. Therefore, traditional language technique in filmmaking for guiding viewers’ attention is less effective to a great extent. Viewers are no longer guided by directors or film editors in a passive way, but have almost complete freedom in choosing what they want to watch. This brings new challenges for story-telling. In Cinematic Virtual Reality (CVR), the viewing process is not decided by the filmmaker, instead viewers are guided to watch. Guiding viewers to catch the preset plots effectively and accurately within fixed time can ensure complete viewing experience. Based on eye tracking in CVR, this paper makes an experiment with different attention guiding methods, explores the differences of effects of these methods and attention patterns in watching VR movies, and provides new basis and references for narrative mode in VR movies from the perspective of eye tracking.
An Approach to Identifying and Filling Data Gaps in Machine Learning Procedures

The article considers the methods of detecting and filling gaps in data sets at the stage of preliminary data processing in machine learning procedures. A multi-stage approach to identifying and filling gaps in data sets and combined statistical quality criteria is proposed. The method consists of three stages. At the first stage, the presence of gaps in the data is determined. In the second stage, the patterns of occurrence of gaps are investigated. Three approaches are used: matrix analysis, graphical analysis and correlation analysis. One of the mechanisms of formation of gaps in the data is identified: MCAR, MAR, MNAR. In the third stage, various methods of data generation without gaps are used. The methods used include deleting part of the data with gaps, various replacement methods, methods for predicting missing values. Consider separately the methods of overcoming gaps in time series. The effectiveness of the proposed approach is investigated numerically. Examples of application of methods on various data sets are given.
Water Surface Targets Recognition and Tracking Based on Improved YOLO and KCF Algorithms

Main problems in the recognition and tracking of water surface targets include the recognition omission or error of small targets, and the bigger tracking error of occluded targets etc., This paper uses an improved YOLO v3 and KCF algorithms to obtain the accurate recognition and real-time tracking of water surface multi-target. Firstly, the water surface targets data set is established and preprocessed; then the improved YOLO v3 network based on Inception module is used to extract and identify the fine feature information of water surface targets. Next, the KCF algorithm is improved by using confidence judgment mechanism to avoid big tracking error for blocked target. Finally, combined with the data association algorithm, improved KCF can complete multi-target tracking. The test results show that the proposed recognition and tracking algorithm can recognize and track multiple targets on water surface and in the air accurately and continuously.