Reviewerly/exHarmony huggingface dataset:
Towards Knowledge-Based Tourism Chinese Question Answering System

With the rapid development of the tourism industry, various travel websites are emerging. The tourism question answering system explores a large amount of information from these travel websites to answer tourism questions, which is critical for providing a competitive travel experience. In this paper, we propose a framework that automatically constructs a tourism knowledge graph from a series of travel websites with regard to tourist attractions in Zhejiang province, China. Backed by this domain-specific knowledge base, we developed a tourism question answering system that also incorporates the underlying knowledge from a large-scale language model such as BERT. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. We also show the effectiveness of each of the question answering components in detail, including the query intent recognition and the answer generation.
A WSN Framework for Privacy Aware Indoor Location

In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system.
Geophysical Mapping of Ghana Using Advanced Cartographic Tool GMT

Ghana is a country exceptionally rich in geologic mineral resources with contrasting topographic relief and varied geophysical setting. This paper evaluated the geological and geophysical setting of Ghana with a special focus on the impact of the geologic setting and topography on gravity. Specifically, it assessed how variations in geology, topography, landscapes and the environment control the geophysical parameters and how these vary among the major regions of the country – the Volta Basin, Northern Plains, Ashanti-Kwahu (Kumasi) and Coastal Plains in the Accra surroundings. Previous studies utilizing traditional Geographic Information System (GIS) approaches have documented the geologic evolution of Ghana evolved as a part of the West African Craton. As a contribution to the existing research, this paper presents a regional analysis of Ghana by integrated mapping of geology, geophysics and topography of the country. The technical approach of this research focuses on utilizing the console-based scripting cartographic toolset Generic Mapping Tools (GMT) integrated with QGIS for processing and mapping the datasets: General Bathymetric Chart of the Oceans (GEBCO), Earth Gravitational Model 2008 (EGM-2008), gravity grids. The theoretical background is based on the geologic research of West Africa supported by high-resolution data. The paper defines a conceptual cartographic framework for integrated geologic and geophysical visualization in a regional-scale mapping project on Ghana.
Guide Local Feature Matching by Overlap Estimation

Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee reliable matches. In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. OETR performs overlap estimation in a two step process of feature correlation and then overlap regression. As a preprocessing module, OETR can be plugged into any existing local feature detection and matching pipeline, to mitigate potential view angle or scale variance. Intensive experiments show that OETR can boost state of the art local feature matching performance substantially, especially for image pairs with small shared regions. The code will be publicly available at https://github.com/AbyssGaze/OETR.
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning

Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. However, due to the dynamic wireless environments and the resource limitations of edge devices, communication becomes a major bottleneck. In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation. By exploiting the temporal correlations among model parameters, we construct a global sparsification mask, which is identical across devices, and thus enables efficient model aggregation over-the-air. Each device further constructs a local sparse vector to explore its own important parameters, which are aggregated via digital communication with orthogonal multiple access. We further design device scheduling and power allocation algorithms for TCS-H. Experiment results show that, under limited communication resources, TCS-H can achieve significantly higher accuracy compared to the conventional top-K sparsification with orthogonal model aggregation, with both i.i.d. and non-i.i.d. data distributions.
Weakly-Supervised Salient Object Detection Using Point Supervision

Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations, but manually labeling pixels is time-consuming and labor-intensive. There are some weakly supervised methods developed for alleviating the problem, such as image label, bounding box label, and scribble label, while point label still has not been explored in this field. In this paper, we propose a novel weakly-supervised salient object detection method using point supervision. To infer the saliency map, we first design an adaptive masked flood filling algorithm to generate pseudo labels. Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps. However, due to the sparseness of the label, the weakly supervised model tends to degenerate into a general foreground detection model. To address this issue, we propose a Non-Salient Suppression (NSS) method to optimize the erroneous saliency maps generated in the first round and leverage them for the second round of training. Moreover, we build a new point-supervised dataset (P-DUTS) by relabeling the DUTS dataset. In P-DUTS, there is only one labeled point for each salient object. Comprehensive experiments on five largest benchmark datasets demonstrate our method outperforms the previous state-of-the-art methods trained with the stronger supervision and even surpass several fully supervised state-of-the-art models. The code is available at: https://github.com/shuyonggao/PSOD.
University Students’ Perceptions on the Use of Quizlet in Learning Vocabulary

Computer-assisted language learning (CALL) and mobile-assisted language learning (MALL) approaches have had positive effects on language education. Quizlet is an emerging tool which has helped many language learners to achieve their learning goals. The study aims to explore students’ perceptions on the use of Quizlet for their learning vocabulary. Both quantitative and qualitative approaches were employed to examine the frequency of use among students and their level of satisfaction toward Quizlet as well as its advantages and disadvantages it brought students. For data analysis, 148 students participated in the study through a questionnaire and a semi-structured interview. The results revealed that students sometimes used Quizlet for their learning vocabulary (M=3.073, SD=.6485) with the frequency of approximately 2 hours per week. In addition, students’ level of satisfaction was rated quite positive (M=3.515, SD=.8289). In particular, the feature Test was most noticeable and satisfactory since it helped students review vocabulary effectively. Many students preferred Quizlet because of its convenience and effectiveness while others were dissatisfied with some features such as Match as well as some spelling and definition errors, and unnatural pronunciation. It is to suggest that students would prefer Quizlet as a helpful tool for their vocabulary mastery. Besides, Quizlet developers could upgrade its features to make it an outstanding aid for language learners.
Evaluasi Satellite Precipitation Product (GSMaP, CHIRPS, dan IMERG) di Kabupaten Lampung Selatan

The availability of rainfall data is an important requirement in various activities. The limitations of observational data make the satellit precipitation product (SPPs) as an alternative. However, the data must be verified before being used. Verification methods are done by using matric statistic methods such as correlation, error and relative bias. Meanwhile, to see the ability of SPPs in detecting rainfall events, it uses a contingency table method. The purpose of this research is to evaluate the ability of SPPs against observation data. Evaluation of SPPs rainfall data is carried out based on a time scale, namely monthly, 10 daily, and daily. This research uses the data from 2018–2020. On a monthly and 10 daily scale, the CHIRPS data shows excellent linearity and rain detection ability. On a daily scale, IMERG shows better linearity than GSMaP and CHIRPS in every season, with moderate to strong correlation coefficients. However, these data tend to be underestimated with a very large bias. In terms of detecting daily rain, GSMaP tends to be better than CHIRPS and IMERG on every season based on the CSI index. However, in the dry season and transition II, the ability tends to be lower. In general, for the amount of rainfall intensity, the three SPPs data still has a fairly large error against the observation data eventhough the ability to detect rainfall is good.
HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics

Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in a variety of fields such as security, traffic and parking management, urban planning, logistics, and transportation, among many others. This paper presents the HAGDAVS dataset fusing RGB spectral channel and Digital Surface Model DSM for the detection and segmentation of vehicles from aerial drone images, including three vehicle classes: cars, motorcycles, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models to increase its accuracy. RGB orthomosaic, RG-DSM fusion, and multi-label mask are provided in Tag Image File Format. Geo-located vehicle bounding boxes are provided in GeoJSON vector format. We also describes the acquisition of drone data, the derived products, and the workflow to produce the dataset. Researchers would benefit from using the proposed dataset to improve results in the case of vehicle occlusion, geo-location, and the need for cleaning ghost vehicles. As far as we know, this is the first openly available dataset for vehicle detection and segmentation, comprising RG-DSM drone data fusion and different color masks for motorcycles, cars, and ghosts.
Sequence-to-Sequence Video Captioning with Residual Connected Gated Recurrent Units

Recurrent neural networks, have recently emerged as a useful tool in computer vision and language modeling tasks such as image and video captioning.The main limitation of these networks is preserving the gradient flow as the network gets deeper.We propose a residual connection based video captioning approach to overcome this limitation and maintain the gradient flow by carrying the information through layers from bottom to top with additive features.The experimental evaluations on the MSVD dataset indicate that the proposed approach achieves accurate caption generation compared to the state-of-the-art results.In addition, the proposed approach is integrated with our custom-designed Android application, WeCapV2, capable of generating captions without an internet connection.
An Optical Image Encryption Method Using Hopfield Neural Network

In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4f system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information's security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme.
Distributed Asymmetric Virtual Reality in Industrial Context: Enhancing the Collaboration of Geographically Dispersed Teams in the Pipeline of Maintenance Method Development and Technical Documentation Creation

Virtual Reality (VR) is a critical emerging technology in industrial contexts, as it facilitates collaboration and supports the product development lifecycle. However, its broad adoption is constrained by complex and high-cost integration. The use of VR among devices with various immersion and control levels may solve this obstacle, and increase the scalability of VR technologies. This article presents a case study on applying asymmetry between the COVE-VR platform and Microsoft Teams to enable distributed collaboration of multinational departments and enhance the maintenance method and documentation creation processes. Overall, five remote collaborative sessions were held with 20 experts from four countries. Our findings suggest that asymmetry between head-mounted display and Teams users enhances the quality of communication among geographically dispersed teams and their spatial understanding, which positively influences knowledge transfer and efficiency of industrial processes. Based on qualitative evaluation of the asymmetric VR setup, we further suggest a list of guidelines on how to enhance the collaboration efficiency for low-cost distributed asymmetric VR from three perspectives: organization, collaboration and technology.
Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.
Path Optimization of Agricultural Robot Based on Immune Ant Colony: B-Spline Interpolation Algorithm

This study presents a path planning algorithm based on immune ant colony B spline interpolation to eliminate path duplication and corner inflection points, increase the smoothness of the agricultural robot’s path trajectory, and enhance the path planning performance. Firstly, this study improves the convergence speed of the algorithm by introducing an adaptive factor that obeys the normal distribution to optimize the path heuristic function. Secondly, to strengthen the variety of the target path search, an immune algorithm was utilized to perform a global investigation of the appropriate routes and assign the initial pheromones of the ant colony. Then, the adaptive judgment factor is introduced based on the incentive degree function and the pheromone evaporation coefficient, and the update formula are adjusted to promote the algorithm’s flexibility while suppressing the optimal regional problem. Finally, the pathways are processed using a cubic B spline interpolation method to remove the edges and minimize the peak inflection points. To verify the specific optimization performance of the algorithm in this paper, we have selected the same type of improved algorithm as a comparison and tested it in a raster map model. The simulation results demonstrate that the values of the proposed algorithm paths and the standard deviation of the tracks are smaller than the comparison algorithm, which indicates the higher performance of the algorithm in searching the optimal routes and more flexible and accurate search. Compared with the comparison algorithm, the average time-consuming reduction rate of the algorithm in this paper is between 38% and 45% in different grid maps, and the number of turns is reduced by 33% to 58%. It meets the requirements of the actual work on the robot.
A Blockchain-Based Authentication Solution for 6G Communication Security in Tactile Networks

In this era, the ultimate vision is to transform current technologies into intelligent global environments to facilitate everyday transactions. The emerging Industry 4.0 has introduced promising potential technologies that have expedited the transition of Internet of Things (IoT) into the Internet of Everything (IoE), utilizing the advances in artificial intelligence. Such a transition implies that sensitive data can be effortlessly accessed via the open network used by various domains such as military, business, transportation, medical, and education, leading to potential security concerns. Although a blockchain, along with the above fields, already employs a fast network such as 5G, the explosive growth in the development and implementation of various Industry 4.0-related domains requires significantly faster networking speeds and a secure mechanism for data transfer. This exhibits the need for 6G to meet the requirements of real-time applications, as shown in the graphical representation of the abstract. The bulk of this work was performed on a blockchain, but some methodology was needed that fulfilled security at different levels, such as the process level, data level, and infrastructure level. Our contribution in this work was twofold: first, at the process level, a novel smart contract mechanism was described; and second, at the data level, a digital signature methodology was employed that allowed anonymization to authenticate and secure the blockchain without encryption.
Sensitive Pictures

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction
”Your Eyes Tell You Have Used This Password Before”: Identifying Password Reuse from Gaze and Keystroke Dynamics

A significant drawback of text passwords for end-user authentication is password reuse. We propose a novel approach to detect password reuse by leveraging gaze as well as typing behavior and study its accuracy. We collected gaze and typing behavior from 49 users while creating accounts for 1) a webmail client and 2) a news website. While most participants came up with a new password, 32% reported having reused an old password when setting up their accounts. We then compared different ML models to detect password reuse from the collected data. Our models achieve an accuracy of up to 87.7% in detecting password reuse from gaze, 75.8% accuracy from typing, and 88.75% when considering both types of behavior. We demonstrate that using gaze, password reuse can already be detected during the registration process, before users entered their password. Our work paves the road for developing novel interventions to prevent password reuse.
CO/DA: Live-Coding Movement-Sound Interactions for Dance Improvisation

We present a performance-led inquiry that involved a live coder programming movement-based interactive sound and two dance improvisers. During two years of collaboration, we developed a joint improvisation practice where the interactions between the dancers’ movement and the sound feedback are programmed on the fly through live coding and movement sensing. To that end, we designed a new live coding environment called CO/DA that facilitates the real-time manipulation of continuous streams of the dancers’ motion data for interactive sound synthesis. Through an autoethnographic inquiry, we describe our practice of sound and movement improvisation where live coding dynamically changes how the dancers’ movements generate sound, which in turn influences the dancers’ improvisation. We then discuss the value, potential and challenges of our dance/code improvisation practice, along with its implications as a design method.