Reviewerly/exHarmony huggingface dataset:
Fuzzy Analogical Reasoning

Automatically understanding the meaning of words is a powerful technique for interpreting perceptions expressed in natural language. However, in order to correctly react to the understood information, reasoning comes into play, enabling the inference of causes or consequences associated with expressed perceptions. This process holds great potential for artificial entities to better adapt interactions according to user needs and desires. Indeed, by mapping known situations (base) to unknown ones (target), artificial entities can extrapolate knowledge about expected reactions from users in specific contexts to react in analogous ways in unfamiliar situations where user expectations are unknown. In this chapter, an automated approach to reasoning by analogies, building on the theory of computing with words, is presented. This approach promotes a robust interaction, aligned with the core principles of phenotropics, emphasizing the importance of being an ever-improving guesser rather than a perfect decoder. Concretely, a framework for fuzzy analogical reasoning is introduced, followed by an implementation allowing to showcase its potential. An evaluation of the proposed fuzzy analogical reasoning framework is represented by an analysis of the Strengths, Weaknesses, Opportunities, and Threats (SWOT) identified in a workshop with experts, showcasing the great potential of this method.
Semantic Similarity Measures

To enable artificial systems to converse and adapt to users’ needs and desires, which is a fundamental aspect of phenotropic interaction, understanding the semantics of exchanged information is vital. Semantic similarity enables the estimation of the closeness of meaning between various elements. This is key for the processing of information in a human-like manner, allowing to automatically extend knowledge to unknown concepts and perceptions. Semantic similarity measures play a key role in the computing with words pipeline’s precisiation phase and the identification of relationships between concepts in approximate reasoning. Modeling perceived commonalities between elements relies on similarity estimation, which inherently possesses a subjective component. Therefore, similarity cannot be defined crisply but requires a fuzzy nuance to account for the subjectivity and imprecision of natural language. In this chapter, a novel semantic similarity measure taking into account these aspects is defined, covering the specific case of similarity between scalar terms, that is generally not well handled by state-of-the-art methods. In a final evaluation task, the developed measure not only results more accurate than state-of-the-art methods but reaches also a performance similar to that of humans in a semantic sorting task.
Automatic Precisiation of Meaning

Semantic similarity measures play a crucial role in providing a level of understanding of word semantics, essential for adaptive and robust interactions in phenotropic interfaces. However, semantic similarity only represent the meaning of a word related to another, lacking thus a general understanding of the semantics of the word itself. To address this limitation and achieve a broader comprehension of word meaning in language, the computing with words (CWW) pipeline is employed. This offers a practical framework for interpreting and treating human perceptions in a machine-understandable way. The precisiation phase of the CWW pipeline involves translating the semantics of words into their mathematical representation by means of fuzzy sets, on which computations can be performed to retrieve meaningful answers. However, the existing methodology for precisiation necessitates manual interventions and crowdsourcing. This hinders the flexibility, adaptivity, and robustness of any phenotropic interface building on CWW. To overcome these limitations, this chapter presents two iterations of an automated method for precisiating the meaning of scalar adjectives and adverbs. Building on a spectral semantic similarity measure, this approach reduces the reliance on manual interventions for the precisiation of meaning. An evaluation of the proposed algorithms proves their efficacy at performing precisiation of meaning with an accuracy similar to that obtained by means of manual precisiation.
Finding a needle in a haystack: insights on feature selection for classification tasks

Abstract The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding irrelevant ones, has been successfully used to reduce the dimensionality of datasets. However, with numerous feature selection approaches in the literature, determining the best strategy for a specific problem is not straightforward. In this study, we compare the performance of various feature selection approaches to a random selection to identify the most effective strategy for a given type of problem. We use a large number of datasets to cover a broad range of real-world challenges. We evaluate the performance of seven popular feature selection approaches and five classifiers. Our findings show that feature selection is a valuable tool in machine learning and that correlation-based feature selection is the most effective strategy regardless of the scenario. Additionally, we found that using improper thresholds with ranker approaches produces results as poor as randomly selecting a subset of features.
A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.
Enhancing Self-Explanation Learning through a Real-Time Feedback System: An Empirical Evaluation Study

This research introduces the self-explanation-based automated feedback (SEAF) system, aimed at alleviating the teaching burden through real-time, automated feedback while aligning with SDG 4’s sustainability goals for quality education. The system specifically targets the enhancement of self-explanation, a proven but challenging cognitive strategy that bolsters both conceptual and procedural knowledge. Utilizing a triad of core feedback mechanisms—customized messages, quality assessments, and peer-generated exemplars—SEAF aims to fill the gap left by traditional and computer-aided self-explanation methods, which often require extensive preparation and may not provide effective scaffolding for all students. In a pilot study involving 50 junior high students, those with initially limited self-explanation skills showed significant improvement after using SEAF, achieving a moderate learning effect. A resounding 91.7% of participants acknowledged the system’s positive impact on their learning. SEAF’s automated capabilities serve dual purposes: they offer a more personalized and scalable approach to student learning while simultaneously reducing the educators’ workload related to feedback provision.
Maximizing photovoltaic potential and minimizing costs in a future warmer climate: The role of atmospheric aerosols and greenhouse gas emissions

Climate change in the upcoming decades will affect solar resources and impact photovoltaic (PV) energy generation, which is key for the global energy transition. The climate-change associated costs for future solar plants need to be assessed, considering different climate futures and PV technologies. Here we show PV power potential increases up to 6% (12% at the end of century) in Europe, eastern America, and south Asia, while reductions predominate in the rest of the world. While temperature and radiation changes dominate the PV changes, atmospheric aerosols also play an important role in future PV production, which has not been thoroughly evaluated before. In future high-emission scenarios, the best outcomes are achieved with thin-film modules that are more resilient to a warmer climate, highlighting the importance of improving thermal management in market-dominant silicon solar cells. In a future low-emissions scenario, the climate impacts on PV and the associated costs are the lowest, potentially saving up to 12 billion US$/year.
Online Learning and Experiences in Higher Education during COVID-19: A Systematic Review

This paper aims to learn about Chilean teachers and students’ experiences and positions to understand how they coped with online learning during the COVID-19 pandemic. The article focuses on student engagement, virtual professional practices, and the difficulties teachers perceive. A review of articles published in Web of Science, Scopus, and SciELO is carried out to meet the objective. The results show different levels of behavioral, cognitive, and affective engagement of students. Different complications were also identified in the realization of professional practices in virtual modality. The profound impacts that this area had on teacher training were mainly highlighted. Finally, university professors raised difficulties in five aspects: acceptance and use, domestic, technological, organizational, and with students. These findings provide several recommendations for the post-COVID-19 era, which can serve as a guide for policymakers, university managers, and academics.
Taking inspiration from becoming “one with a bike” to design human-computer integration

Click to increase image sizeClick to decrease image size AcknowledgmentsFlorian ‘Floyd’ Mueller thanks the Australian Research Council, especially DP190102068, DP200102612 and LP210200656.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Australian Research Council [DP190102068, DP20102612, LP210200656].Notes on contributorsFlorian ‘Floyd’ MuellerDr Florian ‘Floyd’ Mueller is a Professor in the department of Human-Centred Computing at Monash University in Melbourne, Australia, directing the Exertion Games Lab that investigates the coming together of technology, the human body and play. Previously, he was at RMIT, Stanford, University of Melbourne, Microsoft Research, MIT Media Lab, Fuji-Xerox Palo Alto Labs, Xerox Parc, and the Australian CSIRO. Floyd was appointed to be general co-chair for CHI 2020 and 2024, the ACM SIGCHI Conference on Human Factors in Computing Systems, the premier publication outlet for the Human-Computer Interaction (HCI) discipline.Juerg von KaenelDr Jürg von Känel is Director of the Centre for Industrial AI Research & Innovation at RMIT University. Previously, he was Associate Director of IBM Research - Australia. In November 2015 he was appointed as an honorary Enterprise Professor at the University Melbourne, School of Engineering. In his scarce spare time he and his wife invent, design and make mechanical puzzles.Jonathan DuckworthDr Jonathan Duckworth is an Associate Professor in Digital Design and co-director of CiART (Creative interventions, Art and Rehabilitative Technology), School of Design at Royal Melbourne Institute of Technology. Dr Duckworth has established a strong reputation for his interdisciplinary practice-based design research that forges synergies between interaction design, digital media art, health science, disability, and game technology. His research relates to design innovation within acquired brain injury rehabilitation, the arts and human computer interaction technology field.Josh AndresDr Josh Andres is a human-computer interaction (HCI) and user experience researcher and educator at the School of Cybernetics at the Australian National University (ANU). His work investigates the design, experiential dimensions, and futures enabled by intelligent computation such as AI, ubiquitous environments, human-machine co-pilot experiences, and experiences to support wellbeing. Before the ANU, he was at IBM Research, where he coinvented over 20 patents; the decade prior, he worked across various industries, leading the design of multi-device experiences enjoyed by millions of users.
DeepFake detection against adversarial examples based on D‐VAEGAN

Abstract Recent years, the development of DeepFake has raise a lot of security problems. Therefore, detection of DeepFake is critical. However, the existing DeepFake detection methods are often vulnerable to adversarial attacks, i.e. adding carefully crafted imperceptible perturbations into forged images is possible to evade detection. In this paper, a DeepFake detection method based on image denoising is proposed by combining variational autoencoder (VAE) and generative adversarial network (GAN), namely D‐VAEGAN. Firstly, an encoder is designed to extract the features of the image in a low‐dimensional latent space. Then, a decoder reconstructs the original clean image using the features in this low‐dimensional latent space. Secondly, an auxiliary discriminative network is introduced to further improve the performance of the model, which improves the quality of the reconstructed images. Furthermore, feature similarity loss is added as a penalty term to the reconstruction optimization function to improve the adversarial robustness. Experimental results on the FaceForensics++ dataset show that the proposed approach significantly outperforms the five adversarial training‐based defence methods. The approach achieves 96% in accuracy, which is on average about 50% higher than other comparison methods.
Employing convolution-enhanced attention mechanisms for earthquake detection and phase picking models

In response to the challenge of improving the performance of deep learning models for earthquake detection in low signal-to-noise ratio environments, this article introduces a new earthquake detection model called ECPickNet. Drawing inspiration from the EQTransformer, this model leverages Convolution-Enhanced Transformer technology, Conformer architecture, and incorporates the Residual Stacking Block Unit with Channel-Skipping (RSBU-CS) module. The manuscript provides a detailed overview of the model’s network architecture, parameter settings used during the training process, and compares it with several similar methods through a series of experiments. The experimental results highlight ECPickNet’s well performance on both the STEAD and Gansu datasets, particularly performing exceptionally well in the processing of low signal-to-noise ratio data. Interested readers can access and download the proposed method from the following website address: https://github.com/20041170036/EcPick .
More efficient constant-round secure multi-party computation based on optimized Tiny-OT and half-gate

Constand-round secure multi-party computation enables a group of mutually distrustful parties to jointly compute a function of their inputs in a constant-round of communication. In recent years, the constant-round secure multi-party computation protocol has improved the efficiency of the online phase by completing the multiplication computation in advance of the online phase through the Beaver triples generated in the preprocessing phase. However, the preprocessing phase of these protocols is extremely overhead-intensive and there is room for improvement. In this paper, we propose an active, constant-round secure multi-party computation (MPC) protocol which can tolerate arbitrary n − 1 corruptions. At the high level, we follow the Tiny-OT protocol and the unauthenticated garbling for garbled circuits in the preprocessing phase presented by Carmit Hazay and Peter Scholl (ASIACRYPT 2017). Particularly, we first present a new optimized Tiny-OT protocol for generating Beaver (AND) triples which is more efficient than the previous protocol, and then combine the information generated by Tiny-OT to construct an unauthenticated garbled circuit. To be precise, we also reduce the number of commitments and hash function calls in the correctness check of AND triples, and avoid using additional OT operations to compute the circuit, thus further improving communication efficiency. Our new constant-round MPC protocol has a communication complexity O(|C|Bnk) per party. Moreover, we further provide solutions to the open problem of introducing the half-gate technique into multi-party distributed garbled, which will reduce half size of the garbled table per gate.
Research on the Operation Strategy of Dongman App, An Excellent Chinese Online Comics Platform in the Era of Mobile Reading

With the popularity of mobile internet and smartphones, the era of mobile reading has arrived. Webcomics, as a popular form of entertainment, have gained the favor of a large number of users. This paper takes the excellent Chinese webcomics platform Dongman APP as the research object, and discusses its operation strategy by comparing it with other platforms such as Kuai Kan APP, Tencent Animation APP, and Korean webtoon. Through research on the operation strategy of Dongman APP and from the perspective of innovative industries, this study provides concrete case comparisons and practical suggestions for the development of other online comic platforms.
Hey inspecta! :

and conducting the restoration efforts. This paper describes some of the highlights and distinctive features of the HEIS. The benefits of this multidisciplined, technologically challenging project are already being seen and felt within the Hanford scientific and operations community. It has fostered a greater awareness and appreciation of the scientific disciplines involved and cooperation among those involved in the Hanford cleanup.
Algebraic concordance order of almost classical knots

Torsion in the concordance group [Formula: see text] of knots in [Formula: see text] can be studied with the algebraic concordance group [Formula: see text]. Here, [Formula: see text] is a field of characteristic [Formula: see text]. The group [Formula: see text] was defined by Levine, who also obtained an algebraic classification when [Formula: see text]. While the concordance group [Formula: see text] is abelian, it embeds into the non-abelian virtual knot concordance group [Formula: see text]. It is unknown if [Formula: see text] admits non-classical finite torsion. Here, we define the virtual algebraic concordance group [Formula: see text] for Seifert surfaces of almost classical knots. This is an analogue of [Formula: see text] for homologically trivial knots in thickened surfaces [Formula: see text], where [Formula: see text] is closed and oriented. The main result is an algebraic classification of [Formula: see text]. A consequence of the classification is that [Formula: see text] embeds into [Formula: see text] and [Formula: see text] contains many nontrivial finite-order elements that are not algebraically concordant to any classical Seifert matrix. For [Formula: see text], we give a generalization of the Arf invariant.
Human behavior recognition based on sparse transformer with channel attention mechanism

Human activity recognition (HAR) has recently become a popular research field in the wearable sensor technology scene. By analyzing the human behavior data, some disease risks or potential health issues can be detected, and patients' rehabilitation progress can be evaluated. With the excellent performance of Transformer in natural language processing and visual tasks, researchers have begun to focus on its application in time series. The Transformer model models long-term dependencies between sequences through self-attention mechanisms, capturing contextual information over extended periods. In this paper, we propose a hybrid model based on the channel attention mechanism and Transformer model to improve the feature representation ability of sensor-based HAR tasks. Extensive experiments were conducted on three public HAR datasets, and the results show that our network achieved accuracies of 98.10%, 97.21%, and 98.82% on the HARTH, PAMAP2, and UCI-HAR datasets, respectively, The overall performance is at the level of the most advanced methods.
An Efficient Transfer Learning-Based OBS Seismic Phase Picker (OBSPD) Trained on Cascadia Subduction Zone Earthquake Dataset

Abstract Earthquake monitoring and many seismological studies depend on seismic phase arrivals. Thus, detecting seismic events and picking the phase arrival times are fundamentally important. In the recent years, seismic phase picking models based on deep learning approaches have been widely developed. These deep learning models can achieve better performances than traditional phase picking methods and improve the quality of phase picking for land-based earthquake monitoring. However, these models might not perform well on data from ocean-bottom seismometers (OBSs), because they are trained exclusively using onshore seismic data and have limited out-of-distribution generalization ability. Nevertheless, there are insufficient labeled OBS phase arrivals dataset to train a deep learning model from scratch. In this study, we developed an automatic phase detection model for OBS data (OBS phase detection [OBSPD]) using the transfer learning approach based on an existing U-GPD model with pretrained weights from a generalized phase detection model feature extraction system. We developed OBSPD with a limited amount of training data (2784 three-component event waveforms) from the Cascadia subduction zone (CSZ) OBS deployments. Our results show that transfer learning can achieve lower model loss with less overfitting compared to when training a model from scratch. Our new OBSPD model outperforms four existing deep learning pickers in terms of phase detection accuracy with smaller arrival time residuals on a test OBS dataset at CSZ, especially for P phases.