Reviewerly/exHarmony huggingface dataset:
Feature Detection and Matching for Low-Resolution Hyperspectral Images

Hyperspectral imaging has become an emerging research topic recently due to reduced form factor and low initial cost. However, these sensors and the optics still can’t be compared with traditional imaging solutions in terms of resolution, sharpness, and Field of View. These limitations present challenges in the process of image stitching that is required for creating the orthomosaic image. This paper introduces the problem of identifying a low number of features using traditional feature detection methods in low-resolution hyperspectral imaging. Then, a state-of-the-art learning-based detector-free feature identification and matching algorithm is compared with the conventional methods for its ability to identify and match features in low-contrast and high-contrast hyperspectral images. Results indicate that the leaning-based approach outperformed the traditional methods and it is usable for image orthomosaic generation tasks in hyperspectral imaging.
FedH2L: A Federated Learning Approach with Model and Statistical Heterogeneity

Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches require each participant to share a common network architecture and further assume that data are sampled IID across participants. However, in real-world deployments, participants may require heterogeneous network architectures; and the data distribution is almost non-uniform. To address these issues we introduce FedH2L, which is agnostic to the model architecture and robust to different data distributions across participants. In contrast to approaches sharing parameters or gradients, FedH2L relies on mutual distillation, exchanging only posteriors on a shared seed set between participants in a decentralized manner. This makes it extremely bandwidth efficient, model agnostic, and crucially produces models capable of performing well on the whole data distribution when learning from heterogeneous silos.
Dynamic path planning in human-shared environments for low-resource mobile agents

Mobile agents are widely employed in industrial applications. However, in an environment where humans and robots coexist, the robot should be able to navigate autonomously while safely avoiding moving obstacles, especially human operators.In this paper, we propose a dynamic path planner providing an additional costmap layer in which the area around the detected human is inflated by a Gaussian cost. The latter is proportional to the obstacle’s speed and orientation, leading to a safer avoidance behaviour during navigation. The algorithm, suitable for low-resource mobile agents, has been developed in ROS1 and then experimentally validated on the Locobot mobile manipulator in a laboratory environment.
Physical Anomaly Detection in EV Charging Stations: Physics-based vs ResNet AE

The number of electric car users has grown in recent years, increasing the demand for reliable electric vehicle charging stations (EVCS). The safety of EVCSs is very important as compromised charging stations can disrupt the grid, injure the end-users, and damage the vehicle. In this paper, we will focus on the physical security of EVCS, because physical attacks tend to be more harmful to the end user. Two anomaly detection approaches were presented for detecting physical anomalies: physics-based anomaly detection and deep learning-based anomaly detection (ResNet Autoencoder). The presented approaches were trained and tested using data collected from the EV Charging Station System testbed of the Idaho National Laboratory. Anomaly detection performance was evaluated on three different attack scenarios, targeting various parts of the system including power transfer subsystems and the cooling subsystem of the charger. The presented approaches were compared against two widely used unsupervised anomaly detection algorithms: OCSVM and LOF. Moreover, we evaluated the advantages and limitations of the physics-based vs ResNet Autoencoder approaches for each of the three attack scenarios. The ResNet Autoencoder approach showed the highest performance in terms of accuracy, F1, recall, and precision. Furthermore, this approach demonstrated a number of advantages including automated non-linear feature extraction and unsupervised learning.
Construction of Traffic Moving Object Detection System Based on Improved YOLOv5 Algorithm

The detection and recognition technology of moving target is the core technology of video analysis and processing steps in intelligent transportation system, and it is also the premise of technical research on moving target recognition, understanding and behavior analysis in the later stage. In the intelligent transportation system, only by obtaining the parameters such as traffic flow, speed, traffic density, etc., can the traffic automatically maintain the best running state through the adjustment and control of these parameters. In this paper, the construction of traffic moving target detection system based on improved YOLOv5 algorithm is studied. In this paper, the traffic moving target detection scheme firstly inputs the picture data, uses the improved YOLOv5 detection algorithm, and gets the trained detection model by training the vehicle training model. Then, the apparent features are introduced on the basis of DeepSort, which can better deal with the occlusion problem. The tracking performance of the algorithm on pedestrian tracking data set is improved by improving the detector. The research results show that the performance of this algorithm is better than that of YOLOv5 and YOLOv3 before improvement, and its mAP reaches 97.696%. This shows that the improvement of this research is effective, and the improved YOLOv5 target detection algorithm has certain application value in complex real scenes.
Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.
Grouped Contrastive Learning of Self-Supervised Sentence Representation

This paper proposes a method called Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors to be the objective of contrastive learning, suffering from the high-dimensionality of the vectors. In addition, most previous works have adopted discrete data augmentation to obtain positive samples and have directly employed a contrastive framework from computer vision to perform contrastive training, which could hamper contrastive training because text data are discrete and sparse compared with image data. To solve these issues, we design a novel framework of contrastive learning, i.e., GCLSR, which divides the high-dimensional feature vector into several groups and respectively computes the groups’ contrastive losses to make use of more local information, eventually obtaining a more fine-grained sentence representation. In addition, in GCLSR, we design a new self-attention mechanism and both a continuous and a partial-word vector augmentation (PWVA). For the discrete and sparse text data, the use of self-attention could help the model focus on the informative words by measuring the importance of every word in a sentence. By using the PWVA, GCLSR can obtain high-quality positive samples used for contrastive learning. Experimental results demonstrate that our proposed GCLSR achieves an encouraging result on the challenging datasets of the semantic textual similarity (STS) task and transfer task.
Teacher, Think Twice: About the Importance and Pedagogical Value of Blended Learning Design in VET

The integration of technology into educational institutions is transforming education and fostering innovative approaches to learning. Blended learning, an increasingly popular approach, integrates technology with traditional teaching approaches. Blended learning can overcome the limitations associated with using technology purely as a tool, facilitating its full integration into the educational process. The present study involved 106 students enrolled in a vocational education program (VET) in Slovenia. The students were subjected to a pilot implementation of blended learning, and their experiences were subsequently analyzed and evaluated. The results showed that the use of different teaching approaches led to different outcomes in terms of student workload, learning outcomes, and motivation. The different teaching approaches were found to present different levels of difficulty for students. For example, live sessions and the integration of virtual reality/augmented reality (VR /AR) technology presented greater challenges. Conversely, collaborative group work and online courses focused on developing professional skills were found to be beneficial. The study also highlighted the importance of feedback and instructor support in online learning environments. In general, the results of our study suggest that the adoption of blended learning in education can lead to positive outcomes. However, it is important to emphasize the importance of careful planning and thoughtful consideration of optimal pedagogical strategies.
Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter

This study provides novel and accurate symbolic regression-based solutions for the calculation of pipe diameter when flow rate and pressure drop (head loss) are known, together with the length of the pipe, absolute inner roughness of the pipe, and kinematic viscosity of the fluid. PySR and Eureqa, free and open-source symbolic regression tools, are used for discovering simple and accurate approximate formulas. Three approaches are used: (1) brute force of computing power, which provides results based on raw input data; (2) an improved method where input parameters are transformed through the Lambert W-function; (3) a method where the results are based on inputs and the Colebrook equation transformed through new suitable dimensionless groups. The discovered models were simplified by the WolframAlpha simplify tool and/or the equivalent Matlab Symbolic toolbox. Novel models make iterative calculus redundant; they are simple for computer coding while the relative error remains lower compared with the solution through nomograms. The symbolic-regression solutions discovered by brute force computing power discard the kinematic viscosity of the fluid as an input parameter, implying that it has the least influence.
Based on cross-scale fusion attention mechanism network for semantic segmentation for street scenes

Semantic segmentation, which is a fundamental task in computer vision. Every pixel will have a specific semantic class assigned to it through semantic segmentation methods. Embedded systems and mobile devices are difficult to deploy high-accuracy segmentation algorithms. Despite the rapid development of semantic segmentation, the balance between speed and accuracy must be improved. As a solution to the above problems, we created a cross-scale fusion attention mechanism network called CFANet, which fuses feature maps from different scales. We first design a novel efficient residual module (ERM), which applies both dilation convolution and factorized convolution. Our CFANet is mainly constructed from ERM. Subsequently, we designed a new multi-branch channel attention mechanism (MCAM) to refine the feature maps at different levels. Experiment results show that CFANet achieved 70.6% mean intersection over union (mIoU) and 67.7% mIoU on Cityscapes and CamVid datasets, respectively, with inference speeds of 118 FPS and 105 FPS on NVIDIA RTX2080Ti GPU cards with 0.84M parameters.
Vivid and Engaging: Effects of Interactive Data Visualization on Perceptions and Attitudes about Social Issues

AbstractWith the rising availability and volume of data, journalists are finding ways to integrate data-rich information into news content and make them accessible to everyday news readers by accompanying them with exemplifying cases. However, these exemplars do not always fully capture the complexity of the data patterns, leading to significant biases in readers’ issue perceptions. Drawing on the theoretical frameworks of exemplification theory and interactivity, this study examines the effectiveness of interactivity in counteracting the exemplification effect through perceived vividness of and user engagement with data-rich information. A mixed-design experiment revealed that, compared to textual description and static visualization, interactive data visualization is effective in motivating systematic processing of baserate information, which positively influences accuracy of issue perception as well as personal attitudes toward two controversial issues—climate change and same-sex marriage legalization. These findings have theoretical and practical implications for journalistic practice and interface design for communicating data information.Keywords: Data journalisminteractive visualizationexemplificationinteractivityuser engagementissue impression AcknowledgementsThis research is supported by the U.S. National Science Foundation (NSF) via Standard Grant No. IIS-0916944.Disclosure StatementNo potential conflict of interest was reported by the author(s).
Mind the Gap: Gender Disparity in Online Learning Platform Interactions

Problem definition: Education technology innovations, such as massive open online course (MOOC) platforms, could potentially enable a more inclusive learning environment by delivering education to traditionally disadvantaged learners, like women. However, inclusivity does not necessarily translate into equal treatment on the platform. We investigate whether female and male learners benefit equally from forum discussions—typically the only form of interaction available—in online learning platforms. Methodology/results: Utilizing a large-scale, interaction-level data set on 174 courses on Coursera, we uncover an economically sizable and statistically significant disparity between male and female learners in receiving responses to their posts in MOOC discussion forums. On average, female learners’ questions are 3.11 percentage points less likely to receive responses from teaching staff than male learners’ questions, which equals 15.2% of the female group average. We also find significant gender disparity in staff response quality and sentiments. We investigate possible mechanisms behind the gender disparity using new techniques, including textual analysis tools. We show that the disparity is not because of content differences in male and female learners’ posts, nor is it attributable to their linguistic styles or the reputation of the posters. Instead, our results are most consistent with a male-driven gender homophily mechanism; although female staff members are gender neutral in their interactions with learners, male staff members systemically prefer responding to posts from male learners. We additionally show that receiving staff response leads to significant improvement in course passing rates, particularly for female learners. Therefore, the unequal access to information through course forums unfavorably hinders female learners’ performance. Managerial implications: Our results provide operational and organizational suggestions to platforms and content providers, including degendering user identifiers, implementing a content-focused post recommendation system, incorporating a gender-neutral user reputation system, promoting the recruiting of female teaching staff, and providing staff training that highlights the importance of gender-neutral interactions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0426 .
A quantum “black box” for entropy calculation

Abstract A significant part of global quantum computing research has been conducted based on quantum mechanics, which can now be used with quantum computers. However, designing a quantum algorithm requires a deep understanding of quantum mechanics and physics procedures. This work presents a generic quantum “black box” for entropy calculation. It does not depend on the data type and can be applied to building and maintaining machine learning models. The method has two main advantages. First, it is accessible to those without preliminary knowledge of quantum computing. Second, it is based on the quantum circuit with a constant depth of three, which is equivalent to three operations the circuit would perform to achieve the same result. We implemented our method using the IBM simulator and tested it over different types of input. The results showed a high correspondence between the classical and quantum computations that raised an error of up to 8.8e−16 for different lengths and types of information.
The Power of a 😉: Emojis Reducing Ambiguity in Digitally Mediated Romantic Encounters

Digital technology has long provided new ways of initiating romantic relationships as people communicate through text messages, social media, and dating applications. Emojis have been widely adopted as a means of conveying nonverbal cues in digital communication. However, what role do platform‐provided social cues, such as emojis, play in fostering or impeding clear communication and shared romantic expectations from a flirtatious text message conversation? In this study, 713 college students were randomly assigned to read a Snapchat conversation with or without emojis and, they were subsequently asked to infer the characters' thoughts and feelings, clarity of the characters' intentions, and indicate their own discomfort with receiving a similar Snapchat message. The results showed that emojis increase the clarity of the main character's intentions. Moreover, the participants' cognitive efforts, the extent to which they were emotionally affected by the conversation, and the presence of emojis reduced comfort level with receiving a similar Snapchat message. These findings suggest that emojis provide clarity to romantic conversations, which can amplify the interpersonal discomfort of receiving text‐based sexual overtures.
Learning Foams: Towards an Atmospheric Ethics of Education

Abstract Where is the air in the design of learning futures? The Covid-19 pandemic and the unfolding climate crisis have rendered air as an explicit life support infrastructure of learning and as an agential force in design. As air becomes more contested and access to air is constricted, we need to design for atmospherically entangled learning futures. Existing discussion of atmospheres in educational literature focuses on their affective dimensions. We advocate for biometeorological dimensions of atmospheres to also be included by introducing air into the discourse around leaning futures. Addressing a networked learning context, we further ask, what constitutes digital air? By applying Peter Sloterdijk’s ontology of foams to educational situations, we develop ‘learning foams’ as a novel thought-image to aerate the existing concept of networked learning and provide a guide for thinking through atmospheric entanglements in education. We argue that being attuned to the air and atmospheres is necessary to design for breathable learning futures. Following Sloterdijk’s spatial and relational ontology, we show how the frame of learning foams surfaces the need for an atmospheric ethics of education that is centred on human and more-than-human relationality. We speculate on some emerging approaches to support this ethics, such as ‘planty’ pedagogies and more-than-human-centred design.
Information and communication technologies in physical education: Exploring the association between role modeling and digital literacy

Teacher educators should serve as role models in terms of information and communication technologies (ICTs) use to promote digital literacy of future teachers. To analyze the association between role modeling by teacher educators and preservice teachers’ digital literacy and ICT integration intention in their classrooms, 185 physical education (PE) preservice teachers in the German federal state of Baden-Württemberg completed an online questionnaire of self-assessed technological, pedagogical, and content knowledge (TPACK) and ICT integration. The results of regression models revealed a positive association between content knowledge and pedagogical knowledge (PK) and overall intent to integrate ICT into teaching. The results further indicated that the impact of role modeling on preservice teachers varies depending on the chosen ICT. In this paper, we discuss the implications of these findings for higher education in general and for PE in particular.
Assessing Synthetic Voices for Mental Health Chatbots

Considering the possibility to exploit information and communication technologies (ICT) and specifically speaking chatbots, in the mental health domain, a study is proposed aimed at testing the perceptual features of different synthetic voices considering some fundamental aspects of human–computer interaction, namely users’ acceptance and expectations. More specifically, the effect of synthetic voices’ gender and quality on user’s preferences were investigated. The study involved 40 participants, recruited in Northern Ireland, split into two groups: mental health experts and participants living with depression and/or anxiety. Six synthetic voices, three females and three males, characterized by different quality levels were developed for the experiment, exploiting free online synthesizers. The Virtual Agent Voice Acceptance Questionnaire (VAVAQ) was used to collect data regarding preferences toward the different synthetic voices. Results showed that participants’ preferences seem to be affected by both the gender and the quality of a synthetic voice. In particular, participants preferred female voices and high-quality voices. Results also seem to suggest that the quality of a synthetic voice could have a stronger impact on users’ evaluations compared to voice’s gender.