Reviewerly/exHarmony huggingface dataset:
DRT: A Lightweight Single Image Deraining Recursive Transformer

Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only ~1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://github.com/YC-Liang/DRT.
Chatbots in the frontline: drivers of acceptance

Purpose By extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots. Design/methodology/approach By using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing. Findings Findings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements. Practical implications The study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots. Originality/value This is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.
Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance. However, most existing methods focus on building a more complex network with a large number of layers, which can entail heavy computational costs and memory storage. To address this problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET) network to solve this problem. The architecture of SCET mainly consists of the self-calibrated module and efficient transformer block, where the self-calibrated module adopts the pixel attention mechanism to extract image features effectively. To further exploit the contextual information from features, we employ an efficient transformer to help the network obtain similar features over long distances and thus recover sufficient texture details. We provide comprehensive results on different settings of the overall net-work. Our proposed method achieves more remarkable performance than baseline methods. The source code and pre-trained models are available at https://github.com/AlexZou14/SCET.
Downsampling for Binary Classification with a Highly Imbalanced Dataset Using Active Learning

In many industrial applications, classification tasks are often associated with imbalanced class labels in training datasets. Imbalanced datasets can severely affect the accuracy of class predictions, and thus they need to be handled by appropriate data processing before analyzing the data since most machine learning techniques assume that the input data is balanced. In general, the skewness between class labels is managed by either increasing the number of samples in minorities or decreasing the number of samples in majorities. In this research, we are seeking to find a better way of downsampling by selecting the most informative samples in the given imbalanced dataset through the active learning strategy to mitigate the effect of imbalanced class labels. The data selection is performed by the criterion used in optimal experimental designs, from which the generalization error of the trained model is minimized sequentially, under the penalized logistic regression as a classification model. It is important to note that the informative samples can be either minority or majority instead of selecting majority samples only. This paper also suggests that the performance is improved especially with the highly imbalanced dataset, if tuning hyper-parameter λ and cost weights are applied to the active downsampling technique. The proposed algorithm shows better performance compared to other resampling methods with smaller sample sizes.
Mixing in Reverse Optical Flow to Mitigate Vection and Simulation Sickness in Virtual Reality

Simulator sickness has been one of the major obstacles toward making virtual reality (VR) widely accepted and used. For example, virtual navigation produces vection, which is the illusion of self-motion as one perceives bodily motion despite no movement actually occurs. This, in turn, causes a sensory conflict between visual and actual (or vestibular) motion and sickness. In this study, we explore a method to reduce simulator sickness by visually mixing the optical flow patterns that are in the reverse direction of the virtual visual motion. As visual motion is mainly detected and perceived by the optical flow, artificial mixing in the reverse flow is hypothesized to induce a cancellation effect, thereby reducing the degree of the conflict with the vestibular sense and sickness. To validate our hypothesis, we developed a real-time algorithm to visualize the reverse optical flow and conducted experiments by comparing the before and after sickness levels in seven virtual navigation conditions. The experimental results confirmed the proposed method was effective for reducing the simulator sickness in a statistically significant manner. However, no dependency to the motion type or degrees of freedom were found. Significant distraction and negative influence to the sense of presence and immersion were observed only when the the artificially added reverse optical flow patterns were rather visually marked with high contrast to the background content.
VRception: Rapid Prototyping of Cross-Reality Systems in Virtual Reality

Cross-reality systems empower users to transition along the reality-virtuality continuum or collaborate with others experiencing different manifestations of it. However, prototyping these systems is challenging, as it requires sophisticated technical skills, time, and often expensive hardware. We present VRception, a concept and toolkit for quick and easy prototyping of cross-reality systems. By simulating all levels of the reality-virtuality continuum entirely in Virtual Reality, our concept overcomes the asynchronicity of realities, eliminating technical obstacles. Our VRception Toolkit leverages this concept to allow rapid prototyping of cross-reality systems and easy remixing of elements from all continuum levels. We replicated six cross-reality papers using our toolkit and presented them to their authors. Interviews with them revealed that our toolkit sufficiently replicates their core functionalities and allows quick iterations. Additionally, remote participants used our toolkit in pairs to collaboratively implement prototypes in about eight minutes that they would have otherwise expected to take days.
Hitting the Triple Bottom Line

Sustainable Development (SD) in its dimensions – environment, economy, and society – is a growing area of concern within the HCI community. This paper advances a systematic literature review on sustainability across the Sustainable Human-Computer Interaction (SHCI) body of work. The papers were classified according to the Triple Bottom Line (TBL) framework to understand how the pillars of SD play into the HCI discourse on sustainability. The economic angle was identified as a gap in SHCI literature. To meet the TBL of SD, however, a balance needs to be sought across all ‘lines’. In this paper, we propose that HCI can advance the discussion and the understanding of the economic concepts around sustainability through taking a sociology perspective on the economic angle of the TBL. We sustain this claim by discussing economic concepts and the role that digital can play in redefining the established foundations of our economic system.
Rethinking Menstrual Trackers Towards Period-Positive Ecologies

Menstrual tracking is a mechanism widely engaged towards preserving menstrual dignity, as natural birth control, for ensuring adequate preparation for an upcoming cycle, among other motivations. We investigate the design of digital menstrual trackers towards enabling period-positive ecologies in otherwise stigmatized contexts. We examine menstrual tracking practices across ages (12–65 yrs.) using a combination of methods—3 surveys (450+ responses), a cultural probe (10 adolescents), interviews (16 adults), and a review of (9) mobile applications. Our analysis highlights the diversity across menstrual tracking practices and the role of relationships in influencing these practices throughout the menstrual journey. We also identify menstrual tracking as an avenue towards the emancipation of those who menstruate. Finally, we draw on Martha Nussbaum's central human capabilities to discuss sociotechnical implications for redesigning digital menstrual trackers towards crafting just and period-positive futures.
Causality-preserving Asynchronous Reality

Mixed Reality is gaining interest as a platform for collaboration and focused work to a point where it may supersede current office settings in future workplaces. At the same time, we expect that interaction with physical objects and face-to-face communication will remain crucial for future work environments, which is a particular challenge in fully immersive Virtual Reality. In this work, we reconcile those requirements through a user's individual Asynchronous Reality, which enables seamless physical interaction across time. When a user is unavailable, e.g., focused on a task or in a call, our approach captures co-located or remote physical events in real-time, constructs a causality graph of co-dependent events, and lets immersed users revisit them at a suitable time in a causally accurate way. Enabled by our system AsyncReality, we present a workplace scenario that includes walk-in interruptions during a person's focused work, physical deliveries, and transient spoken messages. We then generalize our approach to a use-case agnostic concept and system architecture. We conclude by discussing the implications of Asynchronous Reality for future offices.
Tangible Globes for Data Visualisation in Augmented Reality

Head-mounted augmented reality (AR) displays allow for the seamless integration of virtual visualisation with contextual tangible references, such as physical (tangible) globes. We explore the design of immersive geospatial data visualisation with AR and tangible globes. We investigate the "tangible-virtual interplay" of tangible globes with virtual data visualisation, and propose a conceptual approach for designing immersive geospatial globes. We demonstrate a set of use cases, such as augmenting a tangible globe with virtual overlays, using a physical globe as a tangible input device for interacting with virtual globes and maps, and linking an augmented globe to an abstract data visualisation. We gathered qualitative feedback from experts about our use case visualisations, and compiled a summary of key takeaways as well as ideas for envisioned future improvements. The proposed design space, example visualisations and lessons learned aim to guide the design of tangible globes for data visualisation in AR.
Progressive privileged knowledge distillation for online action detection

Online Action Detection (OAD) in videos addresses the problem of real-time analysis for streaming videos, i.e., only the observed historical video frames are available at prediction time. Considering the future frames observable only at the training stage as a form of privileged information, this paper adopts the Learning Using Privileged Information (LUPI) paradigm. Knowledge distillation (KD) is employed to transfer the privileged information from the offline teacher to the online student. Note that this setting is different from conventional KD because the difference between the teacher and student models mostly lies in the input data rather than the network architecture. To relieves the input information gap for the LUPI, we propose a simple but effective Privileged Knowledge Distillation (PKD) method that enforce KD loss to partial hidden features of the student model. Moreover, we also schedules a curriculum learning procedure to gradually distill the privileged information. This approach is named as Progressive Privileged Knowledge Distillation (PPKD). Compared to some OAD methods that explicitly predict future frames or feature, our approach avoids predicting stage and achieves state-of-the-art accuracy on two popular OAD benchmarks, TVSeries and THUMOS14.
Research on the Cross-Chain Model of Rice Supply Chain Supervision Based on Parallel Blockchain and Smart Contracts

Rice is one of the three major staple foods in the world, and the quality and safety of rice are related to the development of human beings. The new crown epidemic, pesticide residues, insect pests, and heavy metal pollution have a certain security impact on the food supply chain. The rice supply chain is characterized by a long life cycle; complex roles in the main links; many types of hazards; and multidimensional, multisource, and heterogeneous information. To strengthen the rice supply chain's supervision ability under the epidemic situation, a supervision cross-chain model suitable for the complicated data of the rice supply chain based on parallel blockchain theory and smart contract technology was built. Firstly, the data collected in the rice supply chain and different types of data stored in different parallel blockchains were analyzed. Secondly, based on data analysis, a collection/supervision cross-chain mechanism based on "hash lock + smart contract + relay chain", a concurrency mechanism based on the K-means algorithm and a Bloom filter, and a consensus mechanism suitable for multichain consensus named the Supervision Practical Byzantine Fault Tolerance (SPBFT) were proposed. Furthermore, a cross-chain model of rice supply chain supervision was constructed. Finally, theoretical verification and simulation experiments were used to analyze the operation process, safety, cross-chain efficiency, and scalability of the model. The results showed that the application of parallel blockchains and smart contracts to supervision of rice supply chain information improved the convenience and security of information interaction between various links in the rice supply chain, the storage cost of supply chain data and the high latency of interaction was reduced, and the refined management of the rice supply chain data and personnel was realized. This research applied new information technology to the coordination and resource sharing of the food supply chain, and provides ideas for the digital transformation of the food industry.
Blockchain Technology Applied in IoV Demand Response Management: A Systematic Literature Review

Energy management in the Internet of Vehicles (IoV) is becoming more prevalent as the usage of distributed Electric Vehicles (EV) grows. As a result, Demand Response (DR) management has been introduced to achieve efficient energy management in IoV. Through DR management, EV drivers are allowed to adjust their energy consumption and generation based on a variety of parameters, such as cost, driving patterns and driving routes. Nonetheless, research in IoV DR management is still in its early stages, and the implementation of DR schemes faces a number of significant hurdles. Blockchain is used to solve some of them (e.g., incentivization, privacy and security issues, lack of interoperability and high mobility). For instance, blockchain enables the introduction of safe, reliable and decentralized Peer-to-Peer (P2P) energy trading. The combination of blockchain and IoV is a new promising approach to further improve/overcome the aforementioned limitations. However, there is limited literature in Demand Response Management (DRM) schemes designed for IoV. Therefore, there is a need for a systematic literature review (SLR) to collect and critically analyze the existing relevant literature, in an attempt to highlight open issues. Thus, in this article, we conduct a SLR, investigating how blockchain technology assists the area of DRM in IoV. We contribute to the body of knowledge by offering a set of observations and research challenges on blockchain-based DRM in IoV. In doing so, we allow other researchers to focus their work on them, and further contribute to this area.
Cryptocurrency: A panacea for economic growth and sustainability? A critical review of crypto innovation

Distributed ledger technology is highlighted as a solid tool to address social and ecological challenges. Cryptocurrency is seen as a network with a socio-technical structure since the systems are composed of technical infrastructure and the social relations between users of the crypto ecosystem. The sociopolitical ramifications of cryptocurrency are critically assessed to identify key features and conceptualize issues to unlock opportunities to guarantee a sociotechnical approach to blockchains. Despite progressive initiatives, a series of challenges, such as trust, cybersecurity, and scalability, have emerged as cryptocurrency begins to develop its full potential. With these issues, sociotechnical concerns have surfaced regarding how to respond to the possible consequences that such a policy choice would entail, how to govern the crypto economy, and how to create a sustainable crypto ecosystem. We argue that cryptocurrency is a socio-technical system that constitutes new and important objects of social inquiry that must be addressed beyond the myopic context of a financial asset.
Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction

Action segmentation aims to split videos into segments of different actions. Recent work focuses on dealing with long-range dependencies of long, untrimmed videos, but still suffers from over-segmentation and performance saturation due to increased model complexity. This paper addresses the aforementioned issues through a divide-and-conquer strategy that first maximizes the frame-wise classification accuracy of the model and then reduces the over-segmentation errors. This strategy is implemented with the Dilation Passing and Reconstruction Network, composed of the Dilation Passing Network, which primarily aims to increase accuracy by propagating information of different dilations, and the Temporal Reconstruction Network, which reduces over-segmentation errors by temporally encoding and decoding the output features from the Dilation Passing Network. We also propose a weighted temporal mean squared error loss that further reduces over-segmentation. Through evaluations on the 50Salads, GTEA, and Breakfast datasets, we show that our model achieves significant results compared to existing state-of-the-art models.
Enhanced COOT optimization algorithm for Dimensionality Reduction

COOT algorithm is a recent metaheuristic algorithm that simulates American coot birds when moving in the sea. However, the COOT algorithm like other metaheuristic techniques may be stuck in local regions. In this study, a modified COOT algorithm called (mCOOT) is presented which is based on 2 techniques: Opposition-based Learning (OBL) & Orthogonal Learning to overcome these limitations. Moreover, to test the novel algorithm called mCOOT, we apply it to the dimensionality reduction problem using 9 UCI datasets and compare it with the original algorithm and 3 other ones. Results prove the effectivness and superiority of the proposed algorithm in solving feature selection in terms of classification accuracy and selected features numbers.
How social media users perceive different forms of online hate speech: A qualitative multi-method study

Although many social media users have reported encountering hate speech, differences in the perception between different users remain unclear. Using a qualitative multi-method approach, we investigated how personal characteristics, the presentation form, and content-related characteristics influence social media users’ perceptions of hate speech, which we differentiated as first-level (i.e. recognizing hate speech) and second-level perceptions (i.e. attitude toward it). To that end, we first observed 23 German-speaking social media users as they scrolled through a fictitious social media feed featuring hate speech. Next, we conducted remote self-confrontation interviews to discuss the content and semi-structured interviews involving interactive tasks. Although it became apparent that perceptions are highly individual, some overarching tendencies emerged. The results suggest that the perception of and indignation toward hate speech decreases as social media use increases. Moreover, direct and prosecutable hate speech is perceived as being particularly negative, especially in visual presentation form.
State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods' performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.