Reviewerly/exHarmony huggingface dataset:
Public Sentiment Assessment of Coronavirus-Specific Tweets using a Transformer-based BERT Classifier

Worldwide, the (COVID-19) pandemic had also affected people's daily routines. In general also during lockdown periods, people around the world use social media to express their thoughts and feelings about the epidemic which has interrupted their daily lives. There has been a huge spike in tweets about coronavirus on Twitter in a short period of time, including both positive and negative messages. As a result of the wide range of content in the tweets, the researchers have turned to sentiment analysis in order to gauge how the general public feels about COVID-19. According to the findings of this study, the best way to examine COVID-19 is to look at how people use Twitter to share their thoughts and opinions. Sentiment categorization can be accomplished by utilising a variety of feature sets as well as classifiers in combination with the suggested approach. Tweets collected from people with COVID-19 perceptions can be used to better understand and manage the epidemic. Positive, negative, as well as neutral emotion classifications are being used to classify tweets. In this study, Tweets containing specific information about the Coronavirus epidemic are used as sentiment analysis packages. Bidirectional Encoder Representations from Transformers (BERT) are used to identify sentiment categories, whereas the TF-IDF (term frequency-inverse document frequency) prototype is used to summarise the topics of postings. Trend analysis and qualitative methods are being used to identify negative sentiment traits. In general, when it comes to sentiment classification, the fine-tuned BERT is very accurate. In addition, the COVID-19-related post features of TF-IDF themes are accurately conveyed. Coronavirus tweet sentiments are analysed using a BERT and TF-IDF hybrid classifier. Single-sentence classification is transformed into pair-sentence classification, which solves BERT's performance issue in text classification problems. Our evaluation measures (accuracy= 0.70; precision= 0.67; recall= 0.64; and F1-score= 0.65) are used to evaluate the effectiveness of the classifier.
Indoor and outdoor integrated path planning algorithm for multi-storey buildings

Starting from the form, image and significance of environmental design, the path planning algorithm in navigation algorithm has always been one of the core contents that need to be solved and optimized in automatic navigation research. Industrial technology and Internet, people have higher and higher requirements for the performance of path planning search, such as truthfulness, timeliness, efficiency and accuracy. Nowadays, many algorithms implemented on CPU or GPU can not meet people's requirements for the performance of the algorithm, so more and more algorithms are integrated into hardware to improve the performance of the algorithm. Integrating the algorithm into hardware can solve the problem of insufficient efficiency of path planning algorithm on industrial computer. At present, FPGA (field programmable gate array) has the characteristics of strong portability, repeated configuration of computing resources, and internal logic can be changed according to user needs. Many embedded navigation algorithms are suitable to be developed through FPGA platform to realize the synthesis of hardware, so as to accelerate the algorithm. This paper puts forward the method of integrated design, the shaping of place spirit and cultural intervention, and makes a case analysis combined with many years of design practice.
Design of a Tightly-Coupled RISC-V Physical Memory Protection Unit for Online Error Detection

While semiconductors are becoming more efficient generation after generation, the continuous technology scaling leads to numerous reliability issues due, amongst others, to variations in transistors characteristics, manufacturing defects, component wear-out, or interference from external and internal sources. Induced bit flips and stuck-at-faults can lead to a system failure. Security-critical systems often use Physical Memory Protection (PMP) modules to enforce memory isolation. The standard loosely-coupled approach eases the implementation but creates overhead in area and performance, limiting the number of protected areas and their size. While delivering great support against malicious software and induced faults, better performance would benefit safety tasks by preventing the program from jumping into an undesired region and giving wrong outputs.We propose a novel model-driven approach to resolve these limitations by generating a tightly-coupled RISC-V PMP, which reduces the impact of run-time reconfiguration. We also discuss guidelines on configuring a PMP to minimize the overhead on performance and memory, and provide an area estimation for each possible PMP design instance. We formally verified a RISC-V Core with a PMP and evaluated its performance with the Dhrystone Benchmark. The presented architecture shows a performance gain of about 3 times against the standard implementation. Furthermore, we observed that adding the PMP feature to a RISC-V SoC led to a negligible performance loss of less than 0.1% per thousand PMP reconfigurations.
Accurate real-time UAV flight-mode classification

Identifying flight patterns in unmanned aerial vehicle (UAV) operations is a critical function, especially when UAVs are used in safety-critical missions. UAVs are used in various applications and their increased penetration in the market over the recent past has motivated investigations of their safety and security. For instance, due to their highly-non-linear dynamics it is inherently quite difficult to monitor and quickly and reliably identify a change in their behaviour or a fault in their sensors. Clearly, not being able to detect changes in the operations could cause a failure or event the complete loss of a UAV.To avoid such events, in this work we propose a mechanism for detecting the operational flight modes of a flying UAV based on onboard sensor data. We study and analyze two complementary approaches to identify these flight modes and evaluated their performance on real data gathered from various flights performed by a multirotor UAV. We concentrated our study on developing models that are both fast and accurate so that they could be executed at run-time and produce reliable results.
Sonar Image Denoising Based on Anisotropic Guided filtering

The limitations of imaging principle and imaging environment distort the sonar images by various noises, which causes the sonar image to have the characteristics of low resolution and blurred edges. The existing physical models have certain limitations on the description of sonar noise. In order to adapt to a wider range of sonar images, a filter with blind denoising is the best choice; an edge-preserving filter effectively solves the problem of blurred edges; in real application scenarios, processing efficiency is one of the important factors, and a filter with a linear model has a high processing efficiency. In this paper, the anisotropic guided filter is applied to sonar image denoising. Firstly, the adaptive regularization parameters are obtained using the local variance and the approximate median variance of the natural image. Secondly, the local mean, local variance, and regularization parameters are used to obtain the parameters of the guided filter model. Thirdly, anisotropic weighting is applied to the guided filter model parameters. Finally, the output image is obtained by integrating the anisotropic weighted model parameters with the guidance image. The experiments show that the anisotropic guided filter outperforms the comparison algorithm in the removal of both synthetic and sonar image noise, and achieves efficient sonar image denoising and edge preservation.
A Channel Hopping LoRa Technology Based Emergency Communication System for Elderly People Living Alone

Nowadays, as more and more senior citizens have retired and lived alone at home, the population aging issue is becoming serious. For this elderly group, their health and safety deserve more attention. In this paper, we design an emergency communication system to facilitate the health and safety monitoring of the elderly people living alone. The system consists of both hardware and software parts. The hardware part includes Raspberry Pi, sensors and LoRa modules. Among them, Raspberry Pi 4B+ is the core control board, and the Sense-Hat is an integrated board of sensors to collect relevant data. The LoRa modules are used to transmit data between the terminal and the gateway. The software part includes communication protocol, channel hopping strategy, fall detection algorithm and WeChat applet. Among them, the communication protocol combined with the channel hopping strategy can improve the quality and efficiency of communication between the gateway and the terminals, and the fall detection algorithm can monitor in real-time whether elderly people are incurring falling danger. Once a fall occurs, a warning message will be reported to the gateway, and then reported to the OneNet cloud platform and WeChat applet, so that the family members (say, sons or daughters) who are far away from home can know the situation of the elderly people at home. The system is helpful to realize remote nursing of the elderly people living alone.
Domain-Adaptive Pre-training BERT Model for Test and Identification Domain NER Task

Name entity recognition (NER) is the foundation of a wide range of natural language processing (NLP) task in the domain of test identification. In this paper, we continue to train the pre-trained BERT model by unlabeled texts related to the domain of test identification, so as to inject domain knowledge into the pre-trained BERT model and realize the domain adaptation. The experiment results show that the proposed domain-adaptive pre-training method increases the F1 value by 1% compared with the baseline in the domain of test identification NER task.
Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user's device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user's mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.
Generating effective label description for label-aware sentiment classification

Sentiment classification aims to predict the sentiment label for a given text. Recently, several research efforts have been devoted to incorporate matching clues between text words and class labels into the learning process of text representation. However, these methods heavily rely on the availability of label content. Moreover, they simply capture the label-specific signals to measure each word’s contribution by either implicitly employing a learnable label representation or explicitly leveraging the interaction between text words and labels via the interaction mechanism. To deal with these issues, in this paper, we propose a novel framework called Label-Guided Dual-view Sentiment Classifier (LGDSC). We first introduce a new strategy for generating an effective label description and then design a novel Dual-Channel Label-guided Attention Network (DLAN) to learn a text representation via two different channels. DLAN will be further leveraged to learn label-guided text representations from two different views. Extensive experimental results on four real-world datasets demonstrate that LGDSC consistently outperforms the state-of-the-art baseline methods.
Expanding your institutional repository: Librarians working with faculty

Since a successful institutional repository will contain a higher percentage of the contributors' materials, we implemented a system to upload faculty publications more effectively to our academic library's institutional repository. This article acts as an explanation of that system, in the hopes that other scholars or libraries can implement similar systems to increase the popularity of their own institutional repositories. Our method enables a maximum level of materials inputted with minimal required effort from faculty or scholars. We utilize student workers and the resources of the institutional repository manager to get materials uploaded. The success of this method is indicated by the increase in articles that have been uploaded to our institutional repository; as a result of the implementation of this program, the number of publications in our university's institutional repository by these authors has increased 174 %.
Demand for and disposal of stolen goods in legitimate second-hand online markets: an explorative online survey

An exploratory survey of 1,276 legitimate second-hand online market users from the United States (US), United Kingdom (UK), and Australia was conducted to gauge the extent of stolen goods disposal in these markets; the willingness among users to receive suspected stolen goods; and the likely utility of hypothetical market reduction measures to disrupt disposal of stolen goods online. Chi-square analyses and binomial logistic regressions were conducted on responses to test whether perceptions and experiences of stolen goods disposal varied by demographic variables or browsing activity. Findings suggest that a high volume of stolen goods are perceived to be advertised in second-hand online markets; there is a willingness among a substantial minority of users to receive stolen goods online; and there is an appetite among the majority of users to disrupt the sale of stolen goods online. Theoretical and applied prevention and disruption implications of these findings are explored.
Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Abstract Current resolvable group targets tracking algorithms can only track a small number of group targets assuming the standard deviation vectors (SDVs) are known. Based on generalized labeled multi-Bernoulli filter, this paper first proposes a serial tracking algorithm, in order to save the computational cost of filter tracking large-batch and multi-structure group targets. Then, the least squares and the k -means algorithms are used to jointly estimate the SDVs and the group target states. The experimental simulation results verify the effectiveness of the proposed methods.
Rotorcraft aerial vehicle’s contact-based landing and vision-based localization research

Abstract A novel concept—the contact-based landing on a mobile platform—is proposed in this paper. An adaptive backstepping controller is designed to deal with the unknown disturbances in the interactive process, and the contact-based landing mission is implemented under the hybrid force/motion control framework. A rotorcraft aerial vehicle system and a ground mobile platform are designed to conduct flight experiments, evaluating the feasibility of the proposed landing scheme and control strategy. To the best of our knowledge, this is the first time a rotorcraft unmanned aerial vehicle has been implemented to conduct a contact-based landing. To improve system autonomy in future applications, vision-based recognition and localization methods are studied, contributing to the detection of a partially occluded cooperative object or at a close range. The proposed recognition algorithms are tested on a ground platform and evaluated in several simulated scenarios, indicating the algorithm’s effectiveness.
Canadian contributions to environmetrics

Abstract This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.
Modeling User Reviews through Bayesian Graph Attention Networks for Recommendation

Recommender systems relieve users from cognitive overloading by predicting preferred items for users. Due to the complexity of interactions between users and items, graph neural networks (GNN) use graph structures to effectively model user–item interactions. However, existing GNN approaches have the following limitations: (1) User reviews are not adequately modeled in graphs. Therefore, user preferences and item properties that are described in user reviews are lost for modeling users and items; and (2) GNNs assume deterministic relations between users and items, which lack the stochastic modeling to estimate the uncertainties in neighbor relations. To mitigate the limitations, we build tripartite graphs to model user reviews as nodes that connect with users and items. We estimate neighbor relations with stochastic variables and propose a Bayesian graph attention network (i.e., ContGraph) to accurately predict user ratings. ContGraph incorporates the prior knowledge of user preferences to regularize the posterior inference of attention weights. Our experimental results show that ContGraph significantly outperforms 13 state-of-the-art models and improves the best performing baseline (i.e., ANR) by 5.23% on 25 datasets in the five-core version. Moreover, we show that correctly modeling the semantics of user reviews in graphs can help express the semantics of users and items.
Co-designing the Affective City: Speculative Explorations of Affective Place-Based Experiences

The built environment has the capacity to generate affective responses and entice emotions. Spaces can be lively and cheerful, fearful or boring, and these emotions can be triggered by design elements, or by previous experience and memory. However, urban design and smart city approaches have often minimized the role of emotions in the built environment. In this workshop, we actively engage participants in co-mapping, ideating and speculating potential affective interactions in future cities. We curate a typology of urban spaces and emotional states, as well as a toolkit of strategies from urban design and HCI research and practice. We invite participants to contribute their own selection of places and emotional states, iterate on tools, and conceptualize speculations for the affective city. Our workshop will result in a more nuanced understanding of the relationships between people, places, technology and affect.
Learning About Plant Intelligence from a Flying Plum Tree: Music Recommenders and Posthuman User Experiences

Recommender Systems (RS) are used in many different applications such as ecommerce and for media streaming, including music. Recommenders not only help users discover new music, but they also help to create assemblages of songs into playlists. Intentionally or otherwise, playlists often manifest themes, that is, universal ideas that are expressed in particular songs, lyrics, or passages. In this paper we were interested to explore the capabilities of AI to introduce themes through generated playlists, them-selves seeded by the theme of plants. Taking a self-reflexive and user experience approach, we collaborated with AI to create four Plant Music playlists to subject ourselves to what came to refer to as a posthuman user experience.
Care Stories: Understanding People's Hopes and Fears for Technologies of Care through Story Elicitation

For a long-time HCI discourse has viewed ageing and elderly care through a medicalised lens in which care is broken down into distinct problems, such as activities of daily living and addressing accessibility problems. Increasingly, this approach is criticised within the HCI community. Adding to this critique, we asked non-experts to imagine the life of Lor, a 189 year old woman who needs support in her daily life, through an adapted story elicitation method The resulting stories present care as a network which is set against a backdrop of societal changes and environmental factors, such as climate change. In this paper, we distil the rich worlds developed in these stories into reflective questions to support designers to “stay with the trouble” of caregiving and reflect on their own work as well as creating novel grounds for the engagement with care technologies.
On Designing Affordances of Ambient Digital Artifacts through the Lens of Postphenomenology and Sociomateriality

Popular design approaches to develop ambient or artistic digital artifacts such as speculative design have revealed various serious problems hidden in our modern convenient life. Such digital artifacts developed based on speculative design appeal to audiences in appropriately curated museums but are difficult to deploy in our everyday lives. This is because the users of the artifacts may not understand the expected meaning of how to use the artifacts, and the design approach does not offer a guideline to explicitly design the roles of the artifact explicitly in our daily lives. This paper proposes a design concept for developing ambient digital artifacts based on the lens of postphenomenology and sociomateriality. The affordance concept in the proposed approach is defined on two levels: agential affordance and interactive affordance. Each affordance is examined through the key concepts of postphenomenology and sociomateriality: material agency, multistability, and transparency. We discuss the benefits of the proposed concept based on case studies to analyze three ambient digital artifacts. The analysis reveals some insights for developing the semantics of better future ambient digital artifacts.
Cell as Framework: Articulating interaction points through instinctive imaging practices

This work presents a critical design case that uses the metaphor of a cell in relation to its designed environment. It then unpacks a photo-taking sequence using a site-specific installation to analyze both mechanically observed and biologically-embodied interactions. Combining the biological with a systemic way of thinking affords a view of interaction with multiple epistemological consequences. Titled Tracing Spaces, the installation attempts to connect an experience of ‘nature’ to the tacit knowledge of being in a body while immersed within an environment. This reflection pays particular attention to ‘the unseen’ relationships enabled in the designed surroundings. To these, it proposes six points of interaction to reveal the technological, ready-to-hand habits needed for contemporary photo-taking. These points suggest an intricate entanglement between immediately physical embodied gestures made visible through visible-representational content pushed to an awkward extreme. Moreover, these relationships point back towards critical sensibilities for interactive technologies – imaging technologies in particular – embedded within the everyday.