Reviewerly/exHarmony huggingface dataset:
Passive Wall Tracking for a Rotorcraft With Tilted and Ducted Propellers Using Proximity Effects

The letter proposes a passive wall following strategy for a multirotor robot based on the aerodynamic interaction between the propeller wake and the vertical surface. To reinforce the proximity effects, a vehicle with tilted and ducted propellers is introduced. Momentum theory is employed to elucidate the changes in horizontal and vertical components of the propelling thrust in the vicinity of a wall. The modeling and force measurements, when combined with the analysis of the flight dynamics, reveal the existence of a stable distance between the robot and a wall when only the attitude and altitude of the robot are controlled. Flight experiments were conducted to validate that the stable distance between the robot and the wall can be manipulated through the attitude setpoint or control gain, eliminating the need for position feedback. The outcomes enable a human operator to effortlessly fly the robot safely along a corridor without a collision in the absence of vision or other sensing instruments.
Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non-linear systems with unknown inputs. Specifically, the non-linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.
What will the future kitchen look like? An exploratory laboratory study of the future expectations of Chinese Generation Z

Understanding the expectations of future mainstream users will promote innovation in user studies and guide the process of producing original designs. With a focus on Chinese Generation Z, this paper focuses on the features of future kitchens as a case study with the aim of understanding the future expectations of the younger generation. An exploratory laboratory experiment was conducted to elicit users’ expectations via a setting the stage method involving 51 participants. The participants were asked to imagine the future kitchen and draw conceptual sketches of interactive interfaces. The results reveal a four-factor 17 index instrument for exploring the anticipated features of kitchens in terms of expansibility, sustainability, smart technology and interactivity, and emotional connection. The study develops a comprehensive model highlighting and describing the temporal aspects of future expectations. Valuable insights into the expectations of Chinese Generation Z are discussed, and important implications for the design of smart kitchens are proposed based on the results obtained.
The Usage of Emoji in Tourism-Related Instagram Posts: Suggestions from a Marketing Perspective

Abstract The relevance of emoji in social media marketing has attracted tremendous interest from academics and marketing professionals alike ever since emoji became a fixed component in user-to-user and business-to-user communication on online platforms such as Instagram and Facebook. Using a quantitative research approach in the form of a self-administered online survey in an experimental setting, the purpose of this study is to investigate what type of emoji positively impacts consumer behaviour, purchase intention, and user interaction in tourism-related Instagram posts. This research is novel in that it bridges the usage of emoji in the context of social media and tourism. The findings support tourism managers in the practical use of emoji for social media marketing campaigns on Instagram and show that (positive) face emoji evoke more positive emotions than non-face emoji.
A Transformer-based Network for Pathology Image Classification

Pathology image classification plays an important role in cancer diagnosis and precision treatment. Convolutional neural network has been widely employed in pathology image classification. Due to its convolution and pooling operation, it has a great advantage in extracting local features of small objects in images. However, it lacks the ability to extract the global contextual information contained in long-rang tissue structures in pathology images. Transformer, which adopts innate global self-attention mechanisms, has been obtained remarkable performance on large-scale datasets, but its localization abilities are limited because of insufficient low-level details. In this paper, we propose a transformer-based method which combines the advantages of CNN and Transformer for pathology image classification. CNN pays attention to extracting the local information of small objects, and Transformer focuses on digging out the global contextual information implied in the long-dependence tissue structures. The sparse interaction and weigh sharing inherited from CNN also allow the proposed method can be trained on small datasets. Experiments show that the proposed method achieves accuracy of 90.48 and 97.18 on PCam and NCT-CRC datasets, respectively, which is better than existing state-of-the-art methods.
Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies

High-utility sequential pattern mining techniques have demonstrated good performance in identifying associations between mRNA levels in microarray experiments taking into account both the biological context of each gene and the temporal characteristics of the dataset. However, these patterns do not provide information about how likely it is that the events in the pattern occur in the order indicated, therefore causal relationships cannot be established between of them. This reduces their predictive ability, making difficult its direct applicability to the field of gene expression dynamic modeling. An alternative to sequential patterns which takes the confidence of the forecast into account is the discovery of sequential rules. Their natural and seamless relation to human behavior makes them very suitable to understand complex models without missing the possibility of using the generated rules as a standalone prediction model. This contribution proposes an evolutionary algorithm optimizing multiple objectives for mining biologically relevant high average-utility sequential rules from longitudinal human gene expression data with a good compromise through average-utility and explainability. This proposal enhances the well-known NSGA-II to learn, by evolutionary optimization, the rules maximizing two objectives: Utility and Interestingness. Moreover, a restarting mechanism and an external population have been particularly designed and included in order to encourage diversity in the search process preserving all the rules found. The quality of our approaches has been analyzed using external biological resources, statistical analysis and comparing with other proposals from the literature.
Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language

Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of optimization. Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. We perform experiments on retargeting Chinese sign language, which involves two arms and two hands, with additional requirements on the relative relationships among joints. Experiments include retargeting various human demonstrations to YuMi, NAO, and Pepper in the simulation environment and to YuMi in the real-world environment. Both efficiency and accuracy of the proposed method are verified.
Fine-Grained Adversarial Semi-Supervised Learning

In this article, we exploit Semi-Supervised Learning ( SSL ) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization ( FGVC ). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art fine-grained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared.
Performance Impact of Error Correction Codes in RNS with Returning Methods and Base Extension

In this paper, we study techniques of correction codes for systems based on the Residue Number System (RNS). Self-correcting and error detection are important properties of RNS that allow the development of distributed data storage systems. However, the complexity of several approaches have a significant influence on system performance, it mostly depends on two methods: expanding the number base in the residual class and converting numbers from the residual system to the positional system. In this work, we review the state-of-the-art methods in the field and show that the most efficient error correction method combines the syndrome method and our method to expand the number base.
Density-Based Clustering for Adaptive Density Variation

Cluster analysis plays a crucial role in data mining and knowledge discovery. Although many researchers have investigated clustering algorithms over the past few decades, most of the well-known algorithms have shortcomings when dealing with clusters of arbitrary shapes and varying sizes and in the presence of noise and outliers. Density-based methods partially solve these issues but fail to discover clusters with varying densities. In this paper, we propose a novel Density-Based clustering algorithm for Adaptive Density Variation (DBADV), which is based on the classic clustering algorithm DBSCAN. To address the problem of density variation, we define the local density information, which not only reflects the individual property of each object but also describes the density distribution of clusters, and finds the adaptive search range of each object by collecting information from its neighbors. Moreover, we design a new metric to obtain the mutual nearest neighbors of each object to better detect the objects around the boundaries between clusters. We show the effectiveness of our method in extensive experiments on synthetic and realworld data sets, which demonstrate that the performance of the proposed algorithm DBADV is superior to other competitive clustering algorithms.
Aconcagua Fablab: Learning to <i>Become with</i> the World through Design and Digital Fabrication Technologies

Abstract We reflect about the role of creative and innovative design/fabrication technologies in processes of learning to become with the world and understanding our place as humanity within nature. In particular, we present part of the work of Aconcagua Fablab, a mobile design and digital fabrication laboratory. We situate it in relation to the Fablab movement, highlighting its focus in generating processes of ecological understanding, immersion and literacy, working in association with BioGeoArt, an action‐research project inquiring about humanity‐nature relationships in Chile. Unfolding this discussion, we present two workshops developed by Aconcagua Fablab with children and young people, focused in designing / fabricating artefacts and occasions for the exploration of more‐than‐human languages in territorial expressions and immersion in sensitive landscapes – one of them focused on biomimesis and filotaxis; the other one on soundscapes. In doing so, we discuss digital fabrication technologies from a convivial conceptualization, in relation to a more‐than‐human geographical understanding of nature, and concepts such as relational agency, intra‐action, more‐than‐human languages and representation, and situated knowledge, arguing for the role that these technologies and arts in general can play, when used within creative and collaborative learning processes, in awakening ‘the curiosity about life on earth that we will need to limit the destruction we call Anthropocene and protect the Holocene entanglements that we need to survive’.
Task-RM: A Resource Manager for Energy Reduction in Task-Parallel Applications under Quality of Service Constraints

Improving energy efficiency is an important goal of computer system design. This article focuses on a general model of task-parallel applications under quality-of-service requirements on the completion time. Our technique, called Task-RM , exploits the variance in task execution-times and imbalance between tasks to allocate just enough resources in terms of voltage-frequency and core-allocation so that the application completes before the deadline. Moreover, we provide a solution that can harness additional energy savings with the availability of additional processors. We observe that, for the proposed run-time resource manager to allocate resources, it requires specification of the soft deadlines to the tasks. This is accomplished by analyzing the energy-saving scenarios offline and by providing Task-RM with the performance requirements of the tasks. The evaluation shows an energy saving of 33% compared to race-to-idle and 22% compared to dynamic slack allocation (DSA) with an overhead of less than 1%.
Heterogeneous Graph Neural Architecture Search

Heterogeneous Graph Neural networks (HGNNs) have been popularly used in processing complicated networks such as academic networks, social networks, and knowledge graphs. Despite their success, the design of the neural architectures of HGNNs still requires rich domain knowledge and heavy manual work. In this paper, we propose a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which enables automatic design of the best neural architectures with minimal human effort. Specifically, HGNAS first defines a general HGNN framework on top of existing popular HGNNs. A search space of HGNAS is designed based on the general framework that includes multiple groups of message encoding and aggregation functions. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a validation dataset. Moreover, we introduce effective methods to improve HGNAS from three aspects, i.e., the optimization of hyper-parameters, the improvement of search space, and the selection of message receptive fields. Experiments on public datasets show that HGNAS is capable of designing novel HGNNs that rival the best human-invented HGNNs. More interestingly, HGNAS finds some sparse yet powerful neural architectures for HGNNs on the benchmark datasets.
Visual Analytics for the Coronavirus COVID-19 Pandemic

The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard. The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.
Online Education Patterns, Part 1

In this paper, the core pattern and 12 patterns for LINKING SEPARATE WORLDS from Online Education Patterns are presented. Online Education Patterns consists of 37 patterns for creating great online classes. Instead of mimicking the ways of teaching in a real classroom to online, the most important thing to keep in mind is Redesigning for Online Learning, and this can be realized by LINKING SEPARATE WORLDS, CREATING A NEW FORM OF LEARNING and BUILDING A SENSE OF BELONGING. This paper focuses on patterns for LINKING SEPARATE WORLDS, which are patterns to link students and teachers who can feel physically and mentally distant as they participate in online classes remotely. By mining the experiences of 25 teachers who have already made efforts to initiate great online learning at university, high school, junior high school, and elementary school, we came to the following findings; Think of online classes as a VISUAL RADIO where students are listeners who are listening from far away; In order to make the class more attractive, DRAWING STUDENTS INTO THE CLASSROOM ATMOSPHERE helps them switch their focus from daily life activities at home to the class in front of them; Although students and teachers are distant, FEELING EACH OTHER'S PRESENCE will make everyone feel that they are taking the class together; Creating opportunities for INTERACTION BETWEEN STUDENTS can then further link students together as well as deepen their learning. To realize the above, this paper presents the following 13 patterns: Redesigning for Online Learning, Radio Host Spirit, Asynchronous Interaction, Operation Team, Inviting Opening, Sharing Excitement, Reaction Members, Encouraging Cameras On, Written Voices, Priming Water for Chat Flow, Casual Talk Time, Conversation Starter, and Same Members.
X-Reality (XR) and Immersive Learning: Theories, Use Cases, and Future Development

Cross-Reality, or XR, has the great potential to enhance teaching and learning, but it is not commonly used in the classroom. Educators may not know how to use the technology or understand how it can help their students reach their learning objectives. This paper will discuss XR use cases, learning theories, and the benefits and challenges of implementing XR in an educational environment.
SinLU: Sinu-Sigmoidal Linear Unit

Non-linear activation functions are integral parts of deep neural architectures. Given the large and complex dataset of a neural network, its computational complexity and approximation capability can differ significantly based on what activation function is used. Parameterizing an activation function with the introduction of learnable parameters generally improves the performance. Herein, a novel activation function called Sinu-sigmoidal Linear Unit (or SinLU) is proposed. SinLU is formulated as SinLU(x)=(x+asinbx)·σ(x), where σ(x) is the sigmoid function. The proposed function incorporates the sine wave, allowing new functionalities over traditional linear unit activations. Two trainable parameters of this function control the participation of the sinusoidal nature in the function, and help to achieve an easily trainable, and fast converging function. The performance of the proposed SinLU is compared against widely used activation functions, such as ReLU, GELU and SiLU. We showed the robustness of the proposed activation function by conducting experiments in a wide array of domains, using multiple types of neural network-based models on some standard datasets. The use of sine wave with trainable parameters results in a better performance of SinLU than commonly used activation functions.
The Kullback–Leibler Divergence Between Lattice Gaussian Distributions

A lattice Gaussian distribution of given mean and covariance matrix is a discrete distribution supported on a lattice maximizing Shannon’s entropy under these mean and covariance constraints. Lattice Gaussian distributions find applications in cryptography and in machine learning. The set of Gaussian distributions on a given lattice can be handled as a discrete exponential family whose partition function is related to the Riemann theta function. In this paper, we first report a formula for the Kullback–Leibler divergence between two lattice Gaussian distributions and then show how to efficiently approximate it numerically either via Rényi’s $$\alpha $$ -divergences or via the projective $$\gamma $$ -divergences. We illustrate how to use the Kullback-Leibler divergence to calculate the Chernoff information on the dually flat structure of the manifold of lattice Gaussian distributions.
Blockchain-Based Mechanism for Robotic Cooperation Through Incentives: Prototype Application in Warehouse Automation

The use of blockchain in cyber-physical systems, such as robotics, is an area with immense potential to address many shortcomings in robotic coordination and control. In traditional swarm robotic applications, where homogeneous robots are utilized, it is possible to replace a robot if it malfunctions, and it can be assumed that all robots are interchangeable. However, in many real-world applications spanning from search and rescue missions to future household robotic appliances, heterogeneous robots will need to work together with the other robots and human agents to achieve specific tasks. Nevertheless, no such system exists. Therefore, we propose a system that utilizes a token economy for robotic agents that makes agents responsive to token acquisition as an incentive for collaboration in achieving a given task. The economy enables the system to self-govern, even under Byzantine and adversarial settings. We further incorporate a novel subcontracting framework within a blockchain environment to allow the robotic agents to efficiently and cost-effectively perform complex jobs requiring multiple agents with various capabilities. We conducted a thorough evaluation of the system in a prototype warehouse application scenario, and the results are promising.