Reviewerly/exHarmony huggingface dataset:
Domain Adaptation Support Tensor Machine: An Extended STM for Object Recognition Using Cross-Source Heterogeneous Remote Sensing Data

Multisource remote sensing data observed from sensors with different resolutions and physical properties will present heterogeneous tensor structures and diverse feature distributions, thus posing a significant challenge for building an effective classifier for cross-source object recognition. The representative support tensor machine classifier can inherently preserve tensor structure information of remote sensing data and obtain effective recognition ability, while it can only handle same-source and same-distributed homogeneous data and fail to deal with cross-source heterogeneous remote sensing data with complex structures and various distributions. Therefore, the domain adaptation support tensor machine (DA-STM) is proposed to learn a uniform model for cross-source object recognition. To process heterogeneous tensor from different sources, multiple factor matrices with different modes are constructed to eliminate the structural differences and reduce distribution discrepancies for multisource heterogeneous data. To excavate shared classification information across sources, the shared core tensor is established to learn the classification hyperplane jointly using multisource data, and the adaptive sample labels are then embedded into the model to recover the class information during model training. To ensure efficient training, the decomposition algorithm is developed to accelerate the solving of dual problem of DA-STM. In addition, to improve classification performance as the acquirement of sequential samples, the proposed DA-STM is further upgraded to an online version to update the classification parameters dynamically. Using multi-resolution and multi-angle optical images as well as multi-angle SAR images, experimental results demonstrate that the proposed DA-STM can obtain better recognition results than typical domain adaptation methods.
Learning Spelling From Meaning

According to the instance-based approach, each novel word encounter is encoded as an episodic trace, including different aspects of word knowledge (orthography, semantics, phonology) and context. Experiencing the novel word again leads to reactivating the previous instances to support word identification. Accordingly, once a link between orthography and meaning is established through several instances of co-occurrence, presenting the novel word form enhances semantic learning even if the contexts are uninformative about the meaning (Eskenazi et al., 2018). Here, we investigated whether informative contexts enhance orthographic learning in the absence of the novel word form. Participants read pseudowords in three definition-like sentences, followed by three unrelated filler sentences (baseline condition), three uninformative sentences (orthographic condition), or three informative sentences with synonyms replacing the pseudoword (semantic condition). After reading, participants were better at spelling pseudowords exposed in the semantic than in the baseline condition and recalled more definitions of the pseudowords exposed in the orthographic than in the baseline condition. Such results indicate that both semantic and orthographic learning benefit from the contexts where the target information is absent. Overall, this supports the instance-based approach and contributes to the understanding of the interplay between orthography and semantics in contextual word learning.
The Fourth Industrial Revolution in South Africa’s basic education: a search for cogent curriculum justice

This study sought to examine how the Fourth Industrial Revolution (4IR) has promulgated curriculum (in) justices within basic education contexts in South Africa. Utilizing qualitative methods, we interviewed fifteen students and three teachers from three selected schools and one ICT district manager representing the Gauteng Education Zone in Johannesburg. We also deployed critical interpretivism to analyze the documents and the responses from the research subjects in order to explain how the ideologies related to use of 4IR and its roles in education had helped create and sustain curriculum (in) justices in South Africa. We mainly used Ideal Utilitarianism by George Moore, Justice as Fairness by John Rawls, and the Technology Acceptance Model by Fred Davis as key theoretical frameworks for analyzing 4IR within educational contexts. Key findings showed that 4IR has hugely sorted, stratified and unequalized rural-poor students more than it has done with the rich students especially at the basic education level. Essentially, the benefits highlighted by some respondents, particularly teachers, could not help offset the injustices and damages 4IR had unleashed upon the marginalized groups of students at this level. While acknowledging that 4IR was irreversible at this point – since it was now part of human life – we recommended that DBE should judiciously revisit the 4IR policy governing the Operation Phakisa Initiative (OPI), by instituting different support systems that will ensure provision of an equitable and just 4IR aided education for everybody. One such supports, but not limited to that, was provision of consistent capacity building trainings for the schools and society.
Encapsulation, inference and utterance interpretation

While utterance interpretation is standardly understood as context-sensitive inference, there are prima facie reasons to doubt it is fully unencapsulated.First, utterance interpretation is normally fast and automatic, while it has been argued that unencapsulated processes are slow and reflective.Secondly, certain illusions appear to show that the processing of utterances is unrevisable in light of further information.I argue that these reasons are not conclusive, and utterance interpretation relies on indefinite tracts of background knowledge and contextual information.Examples show that interpretation requires the use of information that is not in the input and is neither purely linguistic nor purely stereotypical knowledge.I show that this applies to arriving at the proposition expressed (e.g. the proposition asserted) as well as implicatures.'Grammatical' and 'semantic' illusions show less than one might think about encapsulation of utterance interpretation.Some do not depend on reaching any stable interpretation.The inversion illusion is due in part to background knowledge.While the Moses illusion is revisable, such revision may be performed by a distinct capacity, so is not clear evidence for cognitive penetrability of the normal utterance interpretation process.
Types of Innovation and Sustainable Development: Evidence from Medium- and Large-sized Firms

In the last few decades, the connection between innovation and sustainable development has been brought to the surface due to the concerns of academics, scientists, and policymakers regarding environmental and social issues. Although numerous studies have discussed the significance of innovation in achieving corporate sustainable development (CSD), the relationship between these two concepts remains vague. Under this background, the aim of this study was to examine which types of innovation have a significant impact on CSD. In particular, four types of innovation were tested — incremental, radical, product, and process innovation — with the three dimensions of CSD: the social, environmental, and economic dimensions. The data collected from medium- and large-sized firms revealed that incremental, radical, and process innovation had a significant positive impact on CSD. These results have substantial theoretical and managerial implications, as they indicate what types of innovation managers should focus on in order to improve their sustainable business performance.
Investigating trust and perceived value in cryptocurrencies: do optimism, FinTech literacy and perceived financial and security risks matter?

Purpose This study aims to empirically examine the impact of perceived risks, optimism and financial literacy on trust and the perceived value of cryptocurrencies. It will also examine the impact of trust on the perceived value of cryptocurrencies. Design/methodology/approach A quantitative approach is followed. A questionnaire was designed to collect data from 308 respondents in Jordan. The Structural Equation Modeling – Partial Least Squares (SEM-PLS) method was used to evaluate the research model and test hypotheses. Findings The results of PLS algorithm analysis showed that perceived risks negatively impact the optimism and trust in cryptocurrencies. This study revealed that while financial literacy minimizes the perceived risks, it serves to enhance optimism and improve the perception of the value of cryptocurrencies. Furthermore, the findings of this study show that optimism plays a significant role in trust and perceived value. Originality/value This study provides new insights into the literature on cryptocurrencies adoption, blockchain theory, the theory of trust in financial systems, the role of the optimism factor and the perception of the value of cryptocurrencies. It also provides important practical implications for different stakeholders.
A filter-based feature selection approach in multilabel classification

Abstract Multi-label classification is a fast-growing field of machine learning. Recent developments have shown several applications, including social media, healthcare, bio-molecular analysis, scene, and music classification associated with the multilabel classification. In classification problems, multiple labels (multilabel or more than one class label) are assigned to an unseen record instead of a single-label class assignment. Feature selection is a preprocessing phase used to identify the most relevant features that could improve the accuracy of the multilabel classifiers. The focus of this study is the feature selection method in multilabel classification. The study used a feature selection filter method involving the Fisher score, analysis of variance test, mutual information, Chi-Square, and ensembles of these statistical methods. An extensive range of machine learning algorithms is applied in the modelling phase of a multilabel classification model that includes binary relevance, classifier chain, label powerset, binary relevance KNN, multi-label twin support vector machine, multi-label KNN. Besides, label space partitioning and majority voting of ensemble methods are used and Random Forest is the base learner. The experiments are carried out over five different multilabel benchmarking datasets. The evaluation of the classification model is measured using accuracy, precision, recall, F1 score, and hamming loss. The study demonstrated that the filter methods (i.e. mutual information) having top weighted <?CDATA $80\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>80</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <?CDATA $20\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>20</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> features provided significant outcomes.
Co-Designing User Personas and Risk Scenarios for Evaluating Adolescent Online Safety Interventions

Adolescent online safety research has largely focused on designing interventions for teens, with few evaluations that provide effective online safety solutions. It is challenging to evaluate such solutions without simulating an environment that mimics teens online risks. To overcome this gap, we conducted focus groups with 14 teens to co-design realistic online risk scenarios and their associated user personas, which can be implemented for an ecologically valid evaluation of interventions. We found that teens considered the characteristics of the risky user to be important and designed personas to have traits that align with the risk type, were more believable and authentic, and attracted teens through materialistic content. Teens also redesigned the risky scenarios to be subtle in information breaching, harsher in cyberbullying, and convincing in tricking the teen. Overall, this work provides an in-depth understanding of the types of bad actors and risky scenarios teens design for realistic research experimentation.
ConvXAI : Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing

Despite a surge collection of XAI methods, users still struggle to obtain required AI explanations. Previous research suggests chatbots as dynamic solutions, but the effective design of conversational XAI agents for practical human needs remains under-explored. This paper focuses on Conversational XAI for AI-assisted scientific writing tasks. Drawing from human linguistic theories and formative studies, we identify four design rationales: “multifaceted”, “controllability”, “mix-initiative”, “context-aware drill-down”. We incorporate them into an interactive prototype, ConvXAI 1, which facilitates heterogeneous AI explanations for scientific writing through dialogue. In two studies with 21 users, ConvXAI outperforms a GUI-based baseline on improving human-perceived understanding and writing improvement. The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing2.
Algorithm for Selecting Optimal Features in Face Recognition Systems

Biometric methods are divided into methods based on physical and habitual movements. Physical methods are based on the anatomical uniqueness of each person. Anatomical features include facial features, eye pupils, fingerprints, hand geometry, ear lobes, and other biometric features. Face recognition systems are based on anatomical features, the quality of which is related to the efficiency of feature extraction. Methods for generating face image features are usually based on the separation of local and global features. Algorithms for extracting local features extract individual parts of the face image and perform recognition based on them. Algorithms for extracting global features perform the formation of features on the entire face image. At the same time, the recognition of these features based on optimality allows to reduce costs. This article is devoted to the analysis of algorithms for the formation of the space of optimal features of a face image, which proposes the task of identifying optimal features, as well as a method and algorithm for extracting optimal features of a face image.
Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research.
Unsupervised Learning of Sampling Distributions for Particle Filters

Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on their importance weight. For particle filters to be computationally tractable, it must be feasible to simulate the trajectories by drawing from the sampling distribution. Simultaneously, these trajectories need to reflect the reality of the nonlinear dynamical system so that the resulting estimators are accurate. Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators. In this work, we propose to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> the sampling distributions. We put forward four methods for learning sampling distributions from observed measurements. Three of the methods are parametric methods in which we learn the mean and covariance matrix of a multivariate Gaussian distribution; each method exploits a different aspect of the data (generic, time structure, graph structure). The fourth method is a nonparametric alternative in which we directly learn a transform of a uniform random variable. All four methods are trained in an unsupervised manner by maximizing the likelihood that the states may have produced the observed measurements. Our computational experiments demonstrate that learned sampling distributions exhibit better performance than designed, minimum-degeneracy sampling distributions.
Learning convolutional multi-level transformers for image-based person re-identification

Abstract As a vital vision task, person re-identification (Re-ID) aims to retrieve the same person under non-overlapping cameras. It is a very challenging task due to the presence of complex backgrounds, diverse illuminations and different perspectives. In this work, we integrate the advantages of convolutional neural networks (CNNs) and transformers, and propose a novel learning framework named convolutional multi-level transformer (CMT) for image-based person Re-ID. More specifically, we first propose a scale-aware feature enhancement (SFE) module to extract multi-scale local features from a pre-trained CNN backbone. Then, we introduce a part-aware transformer encoder (PTE) to further mine discriminative local information guided by global semantics. Finally, a deeply-supervised learning (DSL) technique is adopted to optimize the proposed CMT and improve its training efficiency. Extensive experiments on four large-scale Re-ID benchmarks demonstrate that our method performs favorably against several state-of-the-art methods.
Synchronizing chaos using reservoir computing

We attempt to achieve complete synchronization between a drive system unidirectionally coupled with a response system, under the assumption that limited knowledge on the states of the drive is available at the response. Machine-learning techniques have been previously implemented to estimate the states of a dynamical system from limited measurements. We consider situations in which knowledge of the non-measurable states of the drive system is needed in order for the response system to synchronize with the drive. We use a reservoir computer to estimate the non-measurable states of the drive system from its measured states and then employ these measured states to achieve complete synchronization of the response system with the drive.
Swarm Manipulation in Virtual Reality

The theory of swarm control shows promise for controlling multiple objects, however, its scalability is limited by costs, such as hardware and infrastructure needs. Virtual Reality (VR) can overcome these limitations, but research on swarm interaction in VR is limited. This paper introduces a novel Swarm Manipulation interaction technique and compares it with two baseline techniques: Virtual Hand and Controller (ray-casting). We evaluated these techniques in a user study (N = 12) in three tasks (selection, rotation, and resizing) across five conditions. Overall, our results show that the swarm manipulation technique did result in good performance, exhibiting significantly faster speeds compared with at least one of the other two techniques across most conditions for these three tasks. Furthermore, this technique notably reduced resizing size deviations in the resizing task. However, we also observed a trade-off between speed and accuracy in the rotation task. The results demonstrate the potential of the Swarm Manipulation technique to enhance the usability and user experience in VR compared to conventional manipulation techniques. In future studies, we aim to understand and improve swarm interaction, user control, and internal particle cooperation.
PalmGazer: Unimanual Eye-hand Menus in Augmented Reality

How can we design the user interfaces for augmented reality (AR) so that we can interact as simple, flexible and expressive as we can with smartphones in one hand? To explore this question, we propose PalmGazer as an interaction concept integrating eye-hand interaction to establish a singlehandedly operable menu system. In particular, PalmGazer is designed to support quick and spontaneous digital commands– such as to play a music track, check notifications or browse visual media – through our devised three-way interaction model: hand opening to summon the menu UI, eye-hand input for selection of items, and dragging gesture for navigation. A key aspect is that it remains always-accessible and movable to the user, as the menu supports meaningful hand and head based reference frames. We demonstrate the concept in practice through a prototypical mobile UI with application probes, and describe technique designs specifically-tailored to the application UI. A qualitative evaluation highlights the system's interaction benefits and drawbacks, e.g., that common 2D scroll and selection tasks are simple to operate, but higher degrees of freedom may be reserved for two hands. Our work contributes interaction techniques and design insights to expand AR's uni-manual capabilities.
Proxemic Cursor Interactions for Touchless Widget Control

Touchless gesture interfaces often use cursor-based interactions, where widgets are targeted by a movable cursor and activated with a mid-air gesture (e.g., push or Pinch). Continuous interactions like slider manipulation can be challenging in mid-air because users need to precisely target widgets and then maintain an ‘activated’ state whilst moving the cursor. We investigated proxemic cursor interactions as a novel alternative, where cursor proximity allows users to acquire and keep control of user interface widgets without precisely targeting them. Users took advantage of proxemic targeting, though gravitated towards widgets when negotiating the boundaries between multiple elements. This allowed users to gain control more quickly than with non-proxemic behaviour, and made it easier to move between user interface elements. We find that proxemic cursor interactions can improve the usability of touchless user interfaces, especially for slider interactions, paving the way to more comfortable and efficient use of touchless displays.
Guiding Visual Attention on 2D Screens: Effects of Gaze Cues from Avatars and Humans

Guiding visual attention to specific parts of an interface is essential. One powerful tool for guiding attention is gaze cues, that direct visual attention in the same direction as a presented gaze. In this paper, we explored how to direct users' visual attention on 2D screens using gaze cues from avatars and humans. For this, we conducted a lab experiment (N = 30) based on three independent variables: (1) stimulus shown either as avatars or human faces, (2) target direction with a target appearing left or right from a stimulus, and (3) gaze validity indicating whether a stimulus' gaze was directed towards a target (valid gaze) or not (invalid gaze). Our results show that participants' total and average fixation on a target lasted longer in the presence of the human image than the avatar stimulus when a target appeared on the right side and when a stimulus' gaze was towards the target. Moreover, participants' average fixation was longer on the human than avatar stimulus gazing in the opposite direction from a target than towards it.