Reviewerly/exHarmony huggingface dataset:
Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection

In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the computational cost. Secondly, each sensor runs a probabilistic hypothesis density filter separately and floods the filtering posterior with the neighboring sensors so that each sensor obtains the posterior information of the neighboring sensors. Subsequently, Gaussian components are correlated by distance division, and Gaussian components corresponding to the same target are correlated into the same subset. GCI fusion is performed on each correlated subset to complete the fusion state estimation. Simulation experiments show that the proposed method can effectively perform multitarget tracking in a distributed sensor network with a limited sensing range.
Alienated Serendipity and Reflective Failure: Exploring Queer Game Mechanics and Queerness in Games via Queer Temporality

Queerness can help redefine interactive technologies and re-conceptualize their design beyond cisheteronormativity. Recently, games have emerged as promising avenues for exploring queerness. In this research-through-design (RtD) effort, we advance queer explorations in games by leveraging queer temporality to evoke and explore feelings of alienation and isolation in a horror game called You're Going To Be Late. We further engage with queer individuals who playtest our game and participate in focus groups to discuss how queer they felt our game was and what they regard as queer game mechanics. Participants had varying experiences while playing our game, ranging from serendipity and wonder to alienation and confusion. The focus groups described queer game mechanics as queer modes of play and often tapped into gender representation. Finally, we show how including and amplifying queer voices in discursive efforts around interactive technology design has implications for transformative and critical reflections that can broadly benefit game design and HCI research.
LightPlay: Using an External Ambient Lighting Strip for Video Game Indicators

Visual indicators provide important in-game information, but displaying them on screen consumes space and increases clutter. In addition, the option to increase indicator brightness above levels possible in standard monitors can substantially improve accessibility. To address both aspects, we developed an ambient lighting system for video game indicators using an LED strip attached around the back border of a monitor. A controlled study compares the speed, error rate, and perceived workload of this approach with an on-screen baseline method. Results show that ambient lights can capture attention 17.5% faster compared to on-screen indicators, and ambient lights performed at least as well as on-screen across all other metrics. Our work provides empirical evidence that ambient lights can be an adequate replacement for on-screen methods when displaying indicators in games, allowing people to reclaim screen real estate and adjust brightness for accessibility, without sacrificing performance.
NCAlt: Alternatives and Difference Visualizations for Behavior Trees in Game Development Learning

When learning how to develop AI behavior it is common for students to test different ideas before settling on a desired outcome. This functionality is not available in modern behavior tree systems beyond the traditional methods of duplication and conditional execution. We present NCAlt - a visual behavior authoring framework for exploring multiple game AI behaviors. The system also allows selective merging of nodes between multiple behavior tree alternatives and their visual differencing in the game scene and behavior tree views. We present two worked examples which demonstrate how NCAlt can improve the workflow of game development students. We have conducted a user study where NCAlt was compared against NodeCanvas for creating and using alternatives with moderately skilled game development students. Participants rated NCAlt 15% higher than NodeCanvas on the System Usability Scale (SUS). NCAlt was also rated higher on a self-developed questionnaire. We also conducted a semi-structured interview and a detailed longitudinal follow-up and obtained mostly positive feedback for NCAlt. The results suggest NCAlt has a potential to improve the types of workflows with behavior trees that involve exploration for game development students and facilitate learning, as a result. NCAlt was built on top of NodeCanvas - a plugin for the Unity Engine.
Human-Computer Interaction in Mobility Systems

People’s mobility is constantly increasing all over the world, and more and more transport systems are available. This is especially evident in urban transport systems that link individual transport with carCars and bikeBikes sharing or autonomous shuttlesShuttles. This creates completely new challenges for humanHumans-computer interaction, as the complexity of mobility information and the management of intermodal travel becomes more and more sophisticated. A broad spectrum of travelersTraveler with different needs must be taken into account. To achieve the acceptance of these systems, humanHumans-computer interaction must be completely redesigned. In order to structure this complexity for HCI research, small modules for a theoretical basis have been developed step by step over the last 10 years, most of them were published for the first time at the HCI conferences. In many case studies, method sets and classifications were testedTested in order to get to know the challenges for HCI better and better. On the basis of case studies method sets and theoretical concepts have been developed. The contribution shows how these results were transferred into the holistic concept of the Mobility Experience.
Multimodal Interactions Within Augmented Reality Operational Support Tools for Shipboard Maintenance

Large shipsShips, both military and commercial, are dependent on many complex and interdependent systems, necessitating notoriously intensive maintenance regimens. For instance, low event rates, ambiguous problem presentation, temporal stressors, and high working memory demands are common challenges for shipboard maintenance and repair personnelPersonnel. Operational support systems are often employed in this context to supplement or fill training gaps at the point-of-need. Such support, however, has traditionally been heavily dependent on insight and input from subject matter experts, which places a steep premium on personal experience. Recent advancesAdvances in augmentedAugmented reality (AR), especially head-worn displays (HWDs), present a promising avenue for improving the efficacy of operational support by leveraging multimodal interactions and displays. Multimodal solutions provide an opportunity to present support information, in a more veridical form, and, if carefully designed, without increasing demand on operatorsOperators. Real-world spatialization of such information sources via AR is one technique that can be used to increase the level of operational support while simultaneously reducing short-term memory demands imposed by traditional operational support tools, the latter of which require mental transformation from the medium of the support tool (e.g., a technical manual) to the operator’sOperators environment. The broad range of capabilities of AR HWDs also affords tailoring operational support tools to the unique sensory needs of each use case and, in conjunction with adaptive training techniques and advancesAdvances in AI, to the needs of each operatorOperators. Herein, we provide an overview of how multimodal AR can be implemented within operational support tools; best practices for the design and development of multimodal interactions and displays within AR are discussed.
An Integrated Graph-of-Words with Tensor Graph Neural Network Learning Paradigm for Text Classification

In recent years, with tremendous progresses of deep learning in multiple disciplines, there are several advanced sequential neural-network (NN) based architectures (e.g., recurrent neural network—RNN, Auto-Encoding—AE, transformer, etc.) have been proposed. Recently, there are several well-known GNN-based architectures like as graph convolutional network (GNN) have been proposed to deal with challenges related to the global representation preservation of text. However, most of recent proposed GNN-based text-embedding models still be unable to integrate the global structure with the semantic sequential representations of words/sentences into the unified textual embedding space. Moreover, they are also considered as unable to learn the rich context-varied representations of words. In order to tackle aforementioned challenges, in this paper we proposed a novel integrated text graph representation learning approach, named as: GOWSeqGCN. Our proposed GOWSeqGCN is an integrated semantic graph-of-words sequential textual representation under the graph convolutional network framework. In order to demonstrate for the effectiveness of our proposed GOWSeqGCN model in comparing with recent state-of-the-art text representation learning baselines, we conducted extensive experiments in benchmark textual datasets. The experimental outputs showed the outperformances and necessary of our proposed ideas in this paper.
Players chatter and dice clatter: exploring sonic power relations in posthuman game-based learning ecologies

Responding to both recent interest in sound within qualitative education research and sound studies literature that conceptualizes sound as a posthuman technology, we use this paper to explore the following research questions: How does sound both enact and unveil posthuman learning ecologies? And how can education scholars engage sound within posthuman research? Through a posthuman framework, we position noise as an analytical tool for exploring and unveiling more-than-human relations. We then draw parallels between posthuman qualitative research into sound (via noise) and the ideological foundation of experimental music, a musical tradition deeply invested in working with sound as an agentic actor. Within this alignment, we propose using graphic scores to transcribe sonic data without reinscribing humanist research aims. To illustrate, we provide a micro-analysis of preservice teachers engaged in a role-playing game activity and uncover the ways sound asserts its agency within learning ecologies.
Self-representative kernel concept factorization

Kernel concept factorization (KCF) has successfully utilized kernel trick to conduct matrix factorization in the kernel space. However, conventional KCF methods usually define kernel in advance, which limits their ability to exploit the power of kernel. This paper proposes a semi-supervised self-representative kernel concept factorization (S3RKCF) method to integrate adaptive kernel learning and low-dimensional data representation learning into a unified model. Technically, an adaptive local geometric structure is acquired in the KCF-induced self-representation space, and then it facilitates data representation learning simultaneously. Furthermore, to enhance the discriminability of data representation, limited supervisory information is imposed in the formulated optimization problem as constraints. In this way, our model can learn kernel and discriminative low-dimensional representation adaptively and iteratively. To solve the optimization problem, an alternating iterative algorithm is designed with convergence guarantee. The performance of our proposed S3RKCF is evaluated through clustering and classification tasks, and the experimental results on eight real-world data sets demonstrate its effectiveness compared to state-of-the-art methods.
Decentralizing components of electronic markets to prevent gatekeeping and manipulation

The landscape of electronic marketplaces has been monopolized by a handful of market operators that have accumulated tremendous power during the last decades. This trend raises concerns about fairness and market manipulation by these operators acting as gatekeepers. These concerns have recently been outlined in the EU Digital Markets Act (DMA). In this work, we highlight how technological logic of separation understood in the framework of decentralization can address manipulation concerns. As a first step, we devise a reference model of electronic marketplaces, containing six functional components, and outline how control over these components enables different manipulative practices by gatekeepers. We identify two dimensions of decentralization that can counterbalance monopolistic abuse of marketplace components. We then present a software implementation of our reference model and demonstrate how decentralization and unbundling of market components can alleviate manipulation and fairness concerns. We end our work with a review of related approaches and conclude that modular and interoperable marketplaces can enable an open ecosystem of fair electronic markets envisioned by the DMA.
Cascaded multi-point regression Network for high-quality generic lesion detection

With such a high incidence of cancer, research on high-quality lesion detection is extremely significant to improve diagnostic efficiency. With the ability to correctly recognize the lesion, it is even more significant to accurately locate the lesion, which can contribute effectively to the subsequent segmentation and radiation of the lesion. In this paper, we propose a cascaded multi-point regression detection network, which can predict offsets of multiple local points of proposals. Different from traditional regression and location based on keypoints, Multi-point regression (MPR) predicts multi offsets for local points that are position sensitive instead of regressing the whole object proposal. In order to reduce the influence of background region on the final box regression, we set the corresponding regression weight for each local point according to different IoU thresholds. At low IoU threshold, the category score is used as the regression weight for each point. At high IoU threshold, a binary weight prediction is proposed to filter out the background area. For improving the transmission and extraction of the underlying geometric location information, we build a bottom-up information propagation path in FPN as an augmented feature pyramid network (AFPN). We use the dataset DeepLesion to evaluate our network, and the experimental results show that our method can perform as expected. The detection network can improve the quality of lesion detection in terms of localization accuracy compared to most mainstream models.
Meta-learning the invariant representation for domain generalization

Domain generalization studies how to generalize a machine learning model to unseen distributions. Learning invariant representation across different source distributions has been shown high effectiveness for domain generalization. However, the intrinsic possibility of overfitting in source domains can limit the generalization of invariance when faced with a target domain with large discrepancy to the source domains. To address this problem, we propose a meta-learning algorithm via bilevel optimization for domain generalization, where the inner-loop objective aims to minimize the discrepancy across different source domains while the outer-loop objective aims to minimize the discrepancy between source domains and a potential target domain. We show from a geometric perspective that the proposed algorithm can improve out-of-domain robustness for invariance learning. Empirically, we evaluate on five datasets and achieve the best results among a range of strong domain generalization baselines.
Research and application of automatic mineralogical detection of geological prospecting core section

As an important component of geological work, rock and mineral appraisal plays an invisible role in the development of geological work. The detection means mainly rely on artificial and optical microscope to detect and analyze, because of the researcher's result level and knowledge level, the research results are affected greatly, and the workload is large, the research cycle is long. In order to further optimize the detection method of geological rock and mineral, this study mainly aims at the sample slice of geological prospecting sample, carries on the regional detection by using the electron scanning electron microscope, carries on the picture splicing by using the BPMA software, and realizes the measurement of the geological sample. By means of Sample Mosaic Images, the rock structure and structure, the degree of mineral crystallization, the relation between joints and crushing, the relation between inclusion and insertion, the sequence of mineral formation and the automatic naming of rocks and minerals are described, thus the automatic mineralogical detection of rock and mineral is realized.
Wildcards need witness protection

In this paper, we show that the unsoundness discovered by Amin and Tate (2016) in Java’s wildcards is avoidable, even in the absence of a nullness-aware type system. The key insight of this paper is that soundness in type systems that implicitly introduce existential types through subtyping hinges on still making sure there are suitable witness types when introducing existentially quantified type variables. To show that this approach is viable, this paper formalizes a core calculus and proves it sound. We used a static analysis based on our approach to look for potential issues in a vast corpus of Java code and found none (with 1 false positive). This confirms both that Java's unsoundness has minimal practical consequence, and that our approach can avoid it entirely with minimal false positives.
Quantitative analysis of the effect of the damp heat test on optical imaging system under different scene usage conditions

As a widely used sensing device, the optical imaging system mainly includes products used in outdoor and indoor scenarios. However, there are few studies on the effect of environmental aging test on the imaging performance of the two types of products. In this paper, typical products used in two different scenarios, outdoor and indoor, are selected to investigate the quantitative impact of the damp heat test on their imaging quality. The investigated parameters used include imaging resolution, color revent and gray scale value. The study found that after about 10 cycles of damp heat test, the imaging quality of optical imaging products used indoors decreased significantly, with a decrease of more than 20% in key parameter. Outdoor use products have good tolerance to environmental testing, and there is basically no big difference. This paper will provide some reference for the rational use of optical imaging systems and the compilation of test standards.
Factors Affecting Proficiency Outcomes in a Foreign Language Program Over 20 Years

This chapter describes a longitudinal program evaluation examining the performance of a two-year Japanese university English as a Foreign Language (EFL) program over a 20-year period. Separate time series analyses were conducted using the Test of English as a Foreign Language Institutional Testing Program (TOEFL ITP) subsection results (listening, grammar, and reading) as repeated proficiency measures for the 20 student cohorts included in this study. The time series analysis allowed for investigations of emerging overall proficiency trends across the 20 years, cohort-specific proficiency gain patterns, and the impact of institutional and external events leading to gradual shifts in student demographics, which in turn contributed to changes in program proficiency outcomes.
Multilayer Fisher extreme learning machine for classification

Abstract As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM).