Reviewerly/exHarmony huggingface dataset:
CPAD: Component Pixel-Aware Multispectral Image Destriping

Stripe noise removal of Multispectral images (MSIs) is a challenging topic and has attracted substantial research attention in remote sensing areas. Existing destriping methods mainly concentrate on patch-based model representation, which ignores the heterogeneous components across the degraded MSIs and poorly utilizes the prior knowledge of the image cubes of the MSIs on high-level perception. In this paper, we present a component pixel-aware MSIs destriping method. Intuitively, the image cube of the MSIs is divided into three different components (the jump, transition, and gentle components) based on the gradients of each pixel. Then, three different regularization operators are adopted adaptively for different components in a pixel-level manner. In addition, to properly depict the stripe cube of the MSIs, we exploit the non-convex non-smooth iteratively reweight nuclear norm. By incorporating both improved terms, our proposed method can estimate the component-variant linear representation coefficients and model the structural information of both the stripe cube and image cube. The alternating direction method of multipliers (ADMM) is adopted to solve the proposed method. Evaluating various destriping performance measurements, the algorithm proposed in this paper outperforms other state-of-the-art methods under extensive experiments on synthetic and real datasets.
Long-Horizon Planning and Execution With Functional Object-Oriented Networks

Following work on joint object-action representations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functional object-oriented networks</i> (FOON) were introduced as a knowledge graph representation for robots. A FOON contains symbolic concepts useful to a robot's understanding of tasks and its environment for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object-level planning</i> . Prior to this work, little has been done to show how plans acquired from FOON can be executed by a robot, as the concepts in a FOON are too abstract for execution. We thereby introduce the idea of exploiting object-level knowledge as a FOON for task planning and execution. Our approach automatically transforms FOON into PDDL and leverages off-the-shelf planners, action contexts, and robot skills in a hierarchical planning pipeline to generate executable task plans. We demonstrate our entire approach on long-horizon tasks in CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.
Blockchain-Enabled Precision Agricultural System Using IoT and Edge Computing

With the improvement of Internet of Things (IoT) and edge computing, the smart agricultural system is driven by data produced by the different sensors and smart computing devices in the agricultural land. A new methodological paradigm high-performance edge computing is incorporated with blockchain implemented precision agriculture system to improve the data processing operation related with resource management. Edge computing nodes collect and analyze the sensor data locally without transforming to the remote centralized cloud server, which rises data processing speed and reduce the network latency. The blockchain technology is incorporated with machine learning algorithm to maintain secured and protected distributed database for storing smart farm details like pH, soil moisture, temperature, crop management, humidity, and water irrigation level. The proposed system improves the productivity of food items and performance of the smart agricultural system by providing useful information to the farmers to make time-based decision about the land and increase the profit.
Noisy Supervised Deep Learning for Remote Sensing Image Segmentation Using Electronic Maps

Deep learning has made substantial progress in remote sensing image segmentation tasks. It usually requires a large number of high-quality annotation maps (i.e., clean labels), which are labor-intensive. In this paper, we propose to use abundant electronic maps (i.e., noisy labels) to supplement a small number of clean labels to solve the problem of massive label production. In addition, we propose a multi-stage noise supervised framework (NSDI) to prevent noise from deteriorating the performance of deep model. NSDI consists of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clean training</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weight initialization</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hybrid training</i> stages. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clean training</i> stage, we train a segmentation model using a small amount of clean labels and remote sensing images to compute the confusion probability matrix. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weight initialization</i> stage, we use the confusion probability matrix as well as the prediction probability to calculate the label reliability of the electronic map. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hybrid training</i> stage, an adaptive weighted loss function based on cross-entropy is used to dynamically update the label reliability. Then we train model further using electronic maps with the support of the adaptive weighted loss function and label reliability. Experiments were undertaken on 2,656 images of 512 × 512 pixels. Ablation studies show that NSDI improves the model robustness as well as the segmentation quality.
AI virtuous circle: preparing youth for the future of creative economy

extended-abstract Share on AI virtuous circle: preparing youth for the future of creative economy Author: Ariel Han Informatics, UC Irvine, United States Informatics, UC Irvine, United States 0000-0002-0047-0817View Profile Authors Info & Claims IDC '23: Proceedings of the 22nd Annual ACM Interaction Design and Children ConferenceJune 2023Pages 748–751https://doi.org/10.1145/3585088.3593919Published:19 June 2023Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
“We're Hinged. They're Not. It's in that Space that Creativity Happens:” Adult co-designers’ perspectives on designing technology with children

Children have been involved in technology co-design processes for several decades. While previous studies suggest potential benefits to child participants, research has not been conducted regarding the impact of these design methods on the adult designers involved in these processes. We conducted a retrospective online survey with 18 adults who have participated in co-design with children on technologies intended for children. Responses about their experiences, learnings, and recommendations for the process were synthesized. Overall, the participants perceived their experiences of partnering with children and hearing their perspectives to be valuable both personally and professionally as well as for the products’ usability for children. Participants also noted some challenges or areas for improvement for the co-design process. Areas for future work may include a more formal study of the impacts of these experiences on adults and on the technologies developed via co-design with children.
“Rosita Reads With My Family”: Developing A Bilingual Conversational Agent to Support Parent-Child Shared Reading

Bilingual children have unique needs for school readiness as they navigate between two languages and cultures. A supportive home language environment, where children are frequently exposed to language through conversation and reading, can positively impact their language development and prepare them for school. However, current conversational agents and e-books designed for children do not typically take into account the cultural and linguistic needs of bilingual children and do not involve parents. This project presents the development of a bilingual conversational agent and accompanying e-book, designed to support parent-child interactions and promote language development for Latinx Spanish-English bilingual children. Results from a user study indicate that the bilingual agent effectively engages children verbally and encourages parental involvement in reading processes. The study also provides design insights for creating conversational agents for bilingual children.
Big Buddy: Exploring Child Reactions and Parental Perceptions towards a Simulated Embodied Moderating System for Social Virtual Reality

Children experience new forms of harassment in Social Virtual Reality (VR), often inaccessible to parental oversight. We aimed to understand how an artificial intelligent moderator safeguarding children from harassment in social VR is perceived by children and parents, by introducing “Big Buddy”, a Wizard-of-Oz embodied AI-moderator. 43 children (aged 8-16) played a tower-block-construction game in a simulated Social VR classroom where fictitious competitors disrupted their game and, in experimental conditions where present, Big Buddy intervened. We measured children’s perceptions after the disruptions, towards Big Buddy, and the moderation actions it took. Children felt significantly less sad and safer when Big Buddy suspended the saboteur. Parents (n=17) noted Big Buddy’s usefulness and felt reassured but would remain in the supervision loop. We present the first empirical research of a VR-embodied AI-moderator with children’s and parents’ perspectives, and propose design directions for embodied AI-moderators in Social VR.
High-technology within the supply chain: a systematic review

Purpose This study systematically reviews the current state of research on the application of high technology in supply chain management (SCM). It identifies key topics, trends and influential scholars in this field, providing a knowledge structure for future research. This study contributes to advancing the understanding of how high technology can be leveraged to enhance SCM, guiding and informing future research endeavors. Design/methodology/approach A comprehensive bibliometric analysis was conducted on 1,523 published articles retrieved from Web of Science. Through co-occurrence analysis of the titles, abstracts and keywords, the authors investigated popular research trends and topics. Through co-citation and co-authorship analyses, the authors identified leading research clusters, productive researchers and countries of the research. Findings There is a significant increase in publications by scholars from the USA, China and India on the impact of high technology on supply chains, particularly on food supply chains. Most articles examine the barriers and challenges of applying blockchain technology to different aspects of supply chains. Active contributions predominantly originate from scholars in the USA and China. The top five largest clusters are “supply chain management,” “scoping review,” “blockchain technology”, “food supply chains” and “management perception.” Originality/value This study represents the first systematic review establishing a comprehensive framework of knowledge on high technology and supply chains. Highlighting key research areas, tracing the evolution of research and explaining the knowledge structures pertaining to the role of high technology in supply chains, this study contributes to the existing literature and its findings hold practical implications for scholars and practitioners.
Exploring Semantic Relations for Social Media Sentiment Analysis

With the massive social media data available online, the conventional single modality emotion classification has developed into more complex models of multimodal sentiment analysis. Most existing works simply extracted image features at a coarse level, resulting in the absence of partially detailed visual features. Besides, social media data usually contain multiple images, while existing works considered a single image case and used only one image for representing visual features. In fact, it is nontrivial to extend the single image case to the multiple images case, due to the complex relations among multiple images. To solve the above issues, in this paper, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> ated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> usion <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emantic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> elation (GFSR) network to explore semantic relations for social media sentiment analysis. In addition to inter-relations between visual and textual modalities, we also exploit intra-relations among multiple images, potentially improving the sentiment analysis performance. Specifically, we design a gated fusion network to fuse global image embeddings and the corresponding local Adjective Noun Pair (ANP) embeddings. Then, apart from textual relations and cross-modal relations, we employ the multi-head cross attention mechanism between images and ANPs to capture similar semantic contents. Eventually, the updated textual and visual representations are concatenated for the final sentiment prediction. Extensive experiments are conducted on real-world <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Yelp</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flickr30k</i> datasets, showing that our GFSR can improve about 0.10% to 3.66% in terms of accuracy on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Yelp</i> dataset with multiple images, and achieve the best accuracy for two classes and the best macro F1 for three classes on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flickr30k</i> dataset with a single image.
Feature Attention as a Control Mechanism for the Balance of Speed and Accuracy in Visual Search

Abstract Finding an object amongst a cluttered visual scene is an everyday task for humans but presents a fundamental challenge to computational models performing this feat. Previous attempts to model efficient visual search have focused on locating targets as swiftly as possible, but so far have not considered balancing the costs of lengthy searches against the costs of making errors. Here, we propose a neuro-inspired model of visual search that offers an attention-based control mechanism for this speed-accuracy trade-off. The model combines a goal-based fixation policy, which captures human-like behaviour on a simple visual search task, with a deep neural network that carries out the target detection step. The neural network is patched with a target-based feature attention model previously applied to standalone classification tasks. In contrast to image classification, visual search introduces a time component, which places an additional demand on the model to minimise the time cost of the search whilst also maintaining acceptable accuracy. The proposed model balances these two costs by modulating the attentional strength given to characteristic features of the target class, thereby minimising an associated cost function. The model offers a method for optimising the costs of visual search and demonstrates the value of a decision theoretic approach to modelling more complex visual tasks involving attention.
Knowledge Distillation for Autonomous Intelligent Unmanned System

The need for more advanced Unmanned Systems (US) is supported by the growing demand not only of the military industry but also of the civil community. The efforts of AI-enabled US developers are focused on increasing the level of autonomy of future systems. Known AI models can't comprehensively solve the problem of US autonomy due to gap between the two paradigms "processing data from sensor" and "decision-making based on domain expert knowledge". Through introduced universal models of external and internal meaning of Knowledge GranuleKnowledge granule (KG), approach proposed allows to move from the tasks of distilling data from sensorsData from sensors and distilling semantic knowledge to the single task of sense distilling. The distillate as a meaning of the spatio-temporal segment of data from the sensors is presented with the word sense of a high level of abstraction. The task of knowledge distillationKnowledge distillation divided into two subtasks: distillation the knowledge about the US environment by abstracting and about temporal events stream by convolution. Ways of using distillate in decision-making and control of the US are considered. An example of decision-making and control based on a traditional Fuzzy Logic SystemFuzzy Logic Systems (FLS) is given, the input variables of which are the meaning of the US data from sensors obtained by the sense distillation model. A new goal-driving controlGoal-driving control model based on distillate is discussed, too. The results of the experiments demonstrated an increase in US autonomy due to the ability to abstract and flexible to switch of action plans are given.
Measuring Collaboration Quality Through Audio Data and Learning Analytics

Collaboration is an important twenty-first-century skill. Collaboration quality detection can help to support collaboration. This chapter addresses the collaboration quality detection and measurement: (1) to define collaboration quality using audio data and unobtrusive learning analytics measures; (2) to explain the design of a sensor-based set up for automatic collaboration analytics; (3) to move toward quantifying the quality of collaboration by using this set up and show the analysis using meaningful visualizations. Furthermore, we address the challenges and issues at hand and how solutions can be built upon the work already done. To elaborate the different chapter's objectives, we use the terminology of indicators (i.e., the events) and indexes (i.e., the process) to define the components to detect collaboration quality. In one study, during collaborative brainstorming, higher was the equality (i.e., the index) of total speaking time (i.e., the indicator), lower was the dominance of each group member (in terms of total speaking time), and better was the quality of collaboration. However, quality of collaboration is dependent on the context of collaboration and the actual content of the discussion. During collaboration content analysis has been mostly on the surface level by using certain representative keywords to model different topic clusters. Therefore, we develop a sensor-based setup for automatic collaboration analytics to understand collaboration quality holistically in a learning context. Here, our aim is to understand "how" group members speak (i.e., speaking time indicator) and "what'" (i.e., the content of the conversations) group members speak to move toward collaboration quality measurement.
Multiqubit quantum logical gates between distant quantum modules in a network

Distributed quantum computation is deemed a viable pathway toward the realization of quantum computation at large scale. The authors demonstrate how to efficiently realize multiqubit quantum logical gates between distant quantum modules in a network, which are critical elements in distributed quantum computation.
Decentralized Autonomous Organization (DAO): The case of MakerDAO

The global adoption and diffusion of cryptocurrencies and blockchain technologies has given rise to a new form of organizing referred to as decentralized autonomous organizations (DAO). Many DAOs sit at the intersection between decentralized blockchain technologies and finance, so-called decentralized finance (DeFi). This teaching case focuses on one of the most prominent and established DeFi DAOs, MakerDAO, a community-driven initiative that leverages blockchain technology to create a stablecoin pegged to the US Dollar as a basis for decentralized financial services such as lending and yield generation. The case describes the delicate balancing act between centralization and decentralization in the evolution of MakerDAO. It can be used in graduate business programs to achieve the following learning objectives: (1) Understand the concept of decentralized autonomous organization (DAO) as a new form of organizing in which communities leverage blockchain technologies and community-based principles to work jointly on a common goal, (2) Be able to assess the benefits and risks of novel decentralized finance (DeFi) models, such as MakerDAO, which offer alternative financial tools and resources that are not controlled by traditional financial institutions, (3) Evaluate the potential and pathway for disruptive innovation in the era of blockchain, cryptocurrencies, and Web3, where decentralized applications (dapps) on blockchain networks enable decentralized and trustless transactions and interactions.
Expressions of emotion as perceptual media

Abstract Expressions of emotion pose a serious challenge to the view that we perceive other people’s emotions directly. If we must perceive expressions in order to perceive emotions, then it is only ever the expressions that we are directly aware of, not emotions themselves. This paper develops a new response to this challenge by drawing an analogy between expressions of emotion and perceptual media. It is through illumination and sound, the paradigmatic examples of perceptual media, that we can see and hear objects around us. Instead of screening these objects from view, however, they enable our perception while being transparent to us. With reference to perceptual constancy and transparency, I show how expressions show up in our experience of emotions in a surprisingly similar way. Given this, we can understand expressions as the media through which we perceive emotions and overcome the above challenge to an otherwise attractive view.