Reviewerly/exHarmony huggingface dataset:
Study on Clustering Method of Driving Behavior Data Based on Variational Auto Encoder and Coupled-GP-HSMM

In Japan, where the population is aging, traffic accidents caused by elderly drivers have become a social problem. The main cause of the accident is a decline in cognitive ability, and there is an urgent need to develop technology for early detection of decline in the ability. The authors have been developing a technology to evaluate the cognitive ability of a driver from the driving behavior data. In the driving behavior data, there are some scenes where the cognitive ability can be evaluated and some scenes where the cognitive ability cannot be evaluated. Therefore, in this paper, we propose a clustering method for driving behavior data. Here, it is considered that the cognitive ability is the accuracy of the operation for the surrounding situation, and it is useful to cluster the relationship pattern between the situation and the operation for the driving behavior data as a group. In this method, Coupled-GP-HSMM and Convolutional Variational Auto Encoder are applied as a time series clustering method. This method realizes clustering of the relationship pattern between the time-series data representing the situation and the time-series data representing the operation.
Museums and Digital Mobile Technologies: Proposal Application for the Ducal Palace of the Bragança

Museums have long been trying to find ways to mediate their collections with their visitors. By using mobile digital technologies, museums can provide their visitors a more interactive access to the environment and their collections that, for conservation and security reasons, does not allow direct contact. This work aims to analyse which tools Portuguese museums provide to their visitors to help them in their visits, and which tools visitors to the Ducal Palace of Bragança would like to be made available to them. For this purpose, a survey by questionnaire was carried out in the museums of the Portuguese Museums Network (PMN), a presential survey to the visitors of the Ducal Palace of Bragança, and three mobile applications from three museums of the PMN were analysed. From the analysis of the data obtained, three proposals based on mobile digital technologies were developed, to implement portable and interactive solutions during the visit to the museum space of the Ducal Palace of Bragança, thus satisfying the diverse needs of visitors through QR Codes, a Native Mobile Application, and an Augmented Reality Application.
A Proposed Implementation of Internet of Things as a Teaching Aid for Learning Science Collaboratively

Collaboration is one of the competencies within the 21st century skills framework. To nurture this competency in schools, teachers design their lessons to include group work and activities. The COVID-19, however, has dramatically changed the way teachers design collaborative activities. The Internet of Things (IoT) is an example of technology that can play a role in collaborative learning. The use of IoT as a teaching aid might not be as widespread as mobile technologies, virtual reality, and augmented reality but its impact is equally beneficial. The impact of IoT in teaching and learning can be seen in two perspectives. Firstly, it could make learning more interesting as the use of the IoT devices provides more hands on to the students. Students get to use the IoT device, record the data and perform analysis on the data. Secondly, IoT allows for collaborative learning to happen as data captured by the IoT device is stored on the Internet. The aim of this paper is to explore the potential of IoT as a collaborative learning teaching aid for Science subject. This paper proposes two IoT implementations. This paper presents the design of the two IoT systems and the way that it can be used in the teaching and learning of science. The intention of adopting IoT as a teaching aid for science subject is to enhance student’s learning experience in the subject through hands on and collaboration activities.
A New Approach for Formal and Coherent Ontology Alignment

The ontology alignment process is a big challenge especially in open environments like the semantic web. In this process, the construction of the global ontology corresponds to the application of a succession of operations of change (add new mappings). This is obviously a critical task because the new implementation of changes can make the global ontology incoherent. Inevitably, some mappings will be a source of contradictions in terms of ontology. In order to handle these contradictions, the authors propose in this study a new formal solution based on CTL model checking for a consistent ontology alignment. This model uses the Kripke structure which satisfies the CTL logic formula to model the behaviors of the ontology of alignment. To test the functional utility of the proposed approach, they applied it to several examples and the results showed that the proposed framework is working correctly and that inconsistencies are detected in CTL models.
Research on improved A-star algorithm for global path planning of unmanned logistics vehicles

Global path planning process of unmanned logistics vehicles, such as difficulty in improving accuracy, long operation time, and poor driving stability, this paper improves map construction, acquisition node mode, and expansion node mode. Firstly, road nodes instead of obstacle nodes are used in map construction so that the search times and search time are reduced. Secondly, UTM nodes are collected in the field to construct scatter maps instead of grid maps, which solves the problem of long searching time under high precision caused by the positive correlation between grid accuracy and path accuracy in the traditional A-star algorithm. Finally, the k- nearest neighbor is used to expand the sub-nodes instead of the nine-grid method to improve the path accuracy of the Astar algorithm, reduce the search time, and improve the driving stability of unmanned logistics vehicles. Based on the unmanned logistics vehicle experimental platform of an intelligent vehicle research and development company in Tianjin, this paper uses the proposed algorithm to carry out global path planning in a specified area of 27,800 square meters. The experimental results show that the centimeter- level (10 significant digits of the path node) searches were reduced to 765. Compared with the A-Star algorithm under the grid map, it is reduced by 99.88% and the search time was reduced from 41884s to less than 2s. At the same time, by changing the value of k, several controlled experiments were conducted to compare the number and distance of nodes in each group. Finally, when k is 5, the number of nodes is 117, and the distance between nodes is 0.5 to 2m, the unmanned logistics vehicle can run continuously and stably.
Tone Image Processing with Discrete Data Structure

The article focuses on the problem of image processing using a discrete data structure for tone images. The problem of conversion of continuous image into discrete form is considered. Two procedures are described: selecting a discrete grid for representing an image, and quantization, which consists in displaying luminance and color values in integers. The solution to the problem of selecting the visual display resolution and the number of levels of gray tone or colors when processing one-dimensional data is presented. A review of the transformation methods was carried out, after which the sample for both the one-dimensional case and the two-dimensional one was considered. The basis of the transformation methods was the Fourier and Hadamard transformations. These transformations are used in the design of filters designed to improve the quality of images, as well as in the implementation of data compression methods. They form the basis of image recovery from projections.
Integrating Inquiry-Based Pedagogy with Mixed Reality: Theories and Practices

Mixed reality is virtual environment with varying levels of immersion and interaction, and it is often used as an umbrella term for both augmented reality (AR) and virtual reality (VR). Owing to its unique learning affordances, MR has been used to assist teaching and learning activities in various contexts. However, the literature of MR-based instruction focuses on its impact on teaching and learning, lacking systematic analysis of the underlying pedagogies, technological features, and their relationship. To address such limitation, this study uses two design cases of MR interventions to demonstrate effective integration of well-established inquiry-based pedagogy and technological features of MR. The research findings can extend our understanding of inquiry-based learning in MR context and inform the design and development of MR-based instruction.
Modal verbs through the years: Computational linguistics perspective

This diachronic study which implements computational linguistics approach investigates the linguistics events of modal verbs in English song lyrics across time. This study aims to identify the frequency distributions of modal verbs, and to analyze the language uses of modal verbs in the Diachronic Corpus of English Song Lyrics. Quantitative and qualitative methods of analysis were employed. First, quantitative data consisting of frequency distributions of modal verbs and collocations were produced via LancsBox. Collocational patterns are illustrated through collocation graph created via Lancsbox. Frequency distributions of modal verbs were compared to general reference corpus namely the Corpus of Contemporary American English, to generalize the findings from this study as representative of English language. The numerical data were submitted for four statistical tests of significance namely Chi-square, Mutual Information, Log-likelihood, and t-score. Second, qualitative data are consisting of corpus annotations. Corpus annotation was conducted via CLAWS for assigning part-of-speech C7 tagset to identify modal verbs. Semantic categories of modal verbs were identified via UCREL Semantic Analysis System. Findings uncovered the significantly high frequencies and uprising trend of semi modals verbs want to and wanna as compared to core modal verbs in English song lyrics from l960s towards l990s. These two semi modals reflect social actions, high preference for personal pronouns I and you, and carry meanings of obligation and necessity similar to core modal verb should. The aforementioned characteristics characterized English song lyrics as a spoken-like genre.
DPCN: Dual Path Convolutional Network for Single Image Deraining

The visual effect of images captured on rainy days is severely degraded, even making some computer vision or multimedia tasks fail to work. Therefore, image rain removal is crucial for these visions and multimedia tasks. However, most existing works cannot strike a good balance between removing rain streaks and restoring the corresponding background detail. To address this problem, this paper proposes an effective dual path convolutional network (DPCN) for single image rain removal. Specifically, we complete the positioning, extraction and separation of rain streaks through multiple dual path units. Firstly, considering the irregularity of the size, density and shape distribution of rain streaks, a pixel-wise attention mechanism is applied to pinpoint the position of rain streaks. Simultaneously, for these rain streaks distributed across regions, we propose a multi-scale aggregation method to extract and fuse features at different scales. Further, for some backgrounds with similar texture details as the rain streaks, we introduce a self-calibration operation that separates the rain streaks from these background details by adaptively constructing long-range spatial and internal channel dependencies at each spatial location. By cleverly combining multiple dual path units through a dual path topology, our network obtains rain removal results that are closer to the real background and largely remove rain streaks. The quantitative and qualitative results on synthetic and real datasets show that our proposed DPCN is superior to other state-of-the-art methods.
Understanding attention-based encoder-decoder networks: a case study with chess scoresheet recognition

Deep neural networks are largely used for complex prediction tasks. There is plenty of empirical evidence of their successful end-to-end training for a diversity of tasks. Success is often measured based solely on the final performance of the trained network, and explanations on when, why and how they work are less emphasized. In this paper we study encoder-decoder recurrent neural networks with attention mechanisms for the task of reading handwritten chess scoresheets. Rather than prediction performance, our concern is to better understand how learning occurs in these type of networks. We characterize the task in terms of three subtasks, namely input-output alignment, sequential pattern recognition, and handwriting recognition, and experimentally investigate which factors affect their learning. We identify competition, collaboration and dependence relations between the subtasks, and argue that such knowledge might help one to better balance factors to properly train a network.
SP-Net: Slowly Progressing Dynamic Inference Networks

Dynamic inference networks improve computational efficiency by executing a subset of network components, i.e., executing path, conditioned on input sample. Prevalent methods typically assign routers to computational blocks so that a computational block can be skipped or executed. However, such inference mechanisms are prone to suffer instability in the optimization of dynamic inference networks. First, a dynamic inference network is more sensitive to its routers than its computational blocks. Second, the components executed by the network vary with samples, resulting in unstable feature evolution throughout the network. To alleviate the problems above, we propose SP-Nets to slow down the progress from two aspects. First, we design a dynamic auxiliary module to slow down the progress in routers from the perspective of historical information. Moreover, we regularize the feature evolution directions across the network to smoothen the feature extraction in the aspect of information flow. As a result, we conduct extensive experiments on three widely used benchmarks and show that our proposed SP-Nets achieve state-of-the-art performance in terms of efficiency and accuracy.
Raising awareness of Poja traditional dance of the Bugis society using mobile application

Poja dance is an artistic heritage that can be found in the state of Selangor that was brought by the Bugis to Malaya a long time ago. The recent development of Poja Dance has begun to receive attention from the public, especially from performances at official functions organized by the government as well as individuals. However, only the adult generation is active in this art. The acceptance of the younger generation and children is still very sad. Therefore, certain efforts should be made to preserve this art from continuing to disappear in the mists of time. This study aims to create awareness and understanding about Poja dance in society, especially for the younger generation. To highlight the arts of Poja dance among the new generation a mobile application was created which contains infographic video and Augmented Reality (AR) on Poja dance. ADDIE model was adapted in this research comprises of Analysis, Design, Development, Implementation and Evaluation phases. At the end of this study, user testing is carried out to see if this project accomplished the objectives of this research. Based on the feedback it has shown that this application is effective in creating awareness and understanding about Poja dance to the society, especially to the younger generation.
Prior-Guided Contrastive Image Compression for Underwater Machine Vision

Machine analysis of underwater images is essential to most underwater applications. However, both the limitation of communication bandwidth and underwater degradation bring much difficulty to accurate machine recognition at the end system. Few existing underwater compression methods consider unique underwater prior knowledge to better serve for machine vision under low bit-rates. To address this problem, we propose a novel underwater image compression framework for machines, which utilizes underwater priors to contrastively enhance degraded features by contrastive learning and efficiently compress machine-friendly features under low bit-rates. A dataset is built to provide positive and negative samples for contrastive learning based on machine analysis performance. At the encoder side, a feature extractor and a feature encoder are employed to extract machine-related features and compress them into compact representations. To alleviate the effect of underwater degradation on machine vision, a prior-guided contrastive feature enhancement module is proposed to learn more machine-friendly features based on positive and negative samples from our dataset. Then a feature refinement block is designed to remove channel-wise redundancy and focus spatial-wise importance based on high similarity of machine-related features and characteristics of underwater images. More compact representations are obtained without degrading analysis performance under low bit-rates. At the decoder side, both machine-friendly features and image are reconstructed to support different types of analysis tasks. Experimental results demonstrate the superiority of our framework in machine vision tasks compared with traditional compression methods and learned-based methods. Besides, our method still preserves basic capability of human perception.
SwiftUI

SwiftUI is a modern way of building user interfaces. It appeared in iOS 13, macOS 10.15, tvOS 13, and watchOS 6. Nowadays, most apps don't need to support iOS 12, so it's safe to use SwiftUI in your project.
BurstSketch: Finding Bursts in Data Streams

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Burst</i> is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. To detect bursts accurately in real time, we propose a novel sketch, namely BurstSketch, which consists of two stages. Stage 1 uses the technique Running Track to select potential burst items efficiently. Stage 2 monitors the potential burst items and captures the key features of burst pattern by a technique called Snapshotting. We further propose an optimization, namely Dynamic Buckets, which can improve the accuracy of BurstSketch. We provide theoretical error bounds for Stage 1, Stage 2 and the optimized version. Experimental results show that, compared with the strawman solution, Burstsketch achieves 2.00 to 11.63 times higher F1 score, and 1.56 times higher throughput. We also integrate BurstSketch into Apache Flink, and show that using BurstSketch can be faster than simply using the built-in APIs provided by Apache Flink.
MILSA: Model Interpretation Based Label Sniffing Attack in Federated Learning

Federated learning allows multiple participants to come together and collaboratively train an intelligent model. It allows local model training, while keeping the data in-place to preserve privacy. In contrast, deep learning models learn by observing the training data. Consequently, local models produced by participants are not presumed to be secure and are susceptible to inference attacks. Existing inference attacks require training multiple shadow models, white-box knowledge of training models and auxiliary data preparation, which makes these attacks to be ineffective and infeasible. This paper proposes a model interpretation based label sniffing attack called MILSA, which does not interfere with learning of the main task but learns about the presence of a particular label in the target (participant's) training model. MILSA uses Shapley based value functions for interpreting the training models to frame inference attacks. MILSA is evaluated on different datasets and the results show its effectiveness. Further, MILSA is evaluated against differentially-private local model updates, and it is observed that MILSA could successfully perform the inference attacks. We propose a secure training strategy to address these issues.
CAM-Guided Spatially Denormalization for Lesion Weakly-Supervised Localization

Automatic localization of lesion regions is helpful to assist doctors in the diagnosis and treatment of diseases. In this paper, we propose a weakly-supervised 2D lung lesion localization method. Our method only requires image-level labels to perform cross-domain translation between normal and patient images through image style transfer. Finally realizes the conversion of the patient's image into the corresponding healthy person's image. The final lesion area is gained by comparing the difference between the patient image and the corresponding healthy version. On this basis, Class Activation Map (CAM) is used as additional semantic supervisory information incorporated into the generator through the SPADE layer to address the drawback of the current methods with blurred localization boundaries and noise in non-relevant areas. Experimental results on a real-world chest CT dataset demon-strate that the proposed method yields better performance than comparable methods.
Intentable: A Mixed-Initiative System for Intent-Based Chart Captioning

We present Intentable, a mixed-initiative caption authoring system that allows the author to steer an automatic caption generation pro-cess to reflect their intent, e.g., the finding that the author gained from visualization and thus wants to write a caption for. We first derive a grammar for specifying the intent, i.e., a caption recipe, and build a neural network that generates caption sentences given a recipe. Our quantitative evaluation revealed that our intent-based generation system not only allows the author to engage in the generation process but also produces more fluent captions than the previous end-to-end approaches without user intervention. Finally, we demonstrate the versatility of our system, such as context adaptation, unit conversion, and sentence reordering.
A Neural Expectation-Maximization Framework for Noisy Multi-Label Text Classification

Multi-label text classification (MLTC) has a wide range of real-world applications. Neural networks recently promoted the performance of MLTC models. Training these neural-network models relies on sufficient accurately labelled data. However, manually annotating large-scale multi-label text classification datasets is expensive and impractical for many applications. Weak supervision techniques have thus been developed to reduce the cost of annotating text corpus. However, these techniques introduce noisy labels into the training data and may degrade the model performance. This paper aims to deal with such noise-label problems in MLTC in both single-instance and multi-instance settings. We build a novel Neural Expectation-Maximization Framework (nEM) that combines neural networks with probabilistic modelling. The nEM framework produces text representations using neural-network text encoders and is optimized with the Expectation-Maximization algorithm. It naturally considers the noisy labels during learning by iteratively updating the model parameters and estimating the distribution of the ground-truth labels. We evaluate our nEM framework in multi-instance noisy MLTC on a benchmark relation extraction dataset constructed by distant supervision and in single-instance noisy MLTC on synthetic noisy datasets constructed by keywords supervision and label flipping. The experimental results demonstrate that nEM significantly improves upon baseline models in both single-instance and multi-instance noisy MLTC tasks. The experiment analysis suggests that our nEM framework efficiently reduces the noisy labels in MLTC datasets and significantly improves model performance.