Reviewerly/exHarmony huggingface dataset:
Impact of oxymoron features and deep learning techniques in the detection of sarcastic contents

Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms (SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model.
Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

Abstract With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.
Empowering Micro-Credentials Using Blockchain and Artificial Intelligence

The recognition and transfer of credits is becoming essential for students, as an increasing number of them are studying at different institutions, often at the same time online, in both traditional and unconventional settings. Micro-credentials can aid in this process by providing easily accessible and transparent evidence of skills or knowledge, certified by an authority, based on small units of learning. The development of blockchain technology holds promise of becoming a useful enabler for supporting the storage and dissemination of micro-credentials on a global scale. Because of its immutability, blockchain can be used to attest to students' accomplishments securely and privately. Artificial intelligence (AI) can facilitate the maintenance and dissemination of micro-credentials, while ensuring that access is readily available for students under their control. So, AI can play a role in supporting blockchain-enabled micro-credentials. For educators, a basic understanding of the development of all three technologies is becoming essential.
Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR

Abstract The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model’s significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset’s SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.
Markovian noise modelling and parameter extraction framework for quantum devices

Abstract In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.
Estimating many properties of a quantum state via quantum reservoir processing

Estimating properties of a quantum state is an indispensable task in various applications of quantum information processing. To predict properties in the postprocessing stage, it is inherent to first perceive the quantum state with a measurement protocol and store the information acquired. In this paper, we propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties, while most of the previous methods need an exponentially increasing number of measurement settings. To estimate $M$ properties simultaneously, the size of the classical approximation scales as $lnM$. Moreover, this estimation scheme is extendable to higher-dimensional systems and hybrid systems with nonidentical local dimensions, which makes it exceptionally generic. We support our theoretical findings with extensive numerical simulations.
Collective writing as survival tool: Mechanisms of reflexivity against neoliberal academia

This paper introduces an innovative method for enhanced researcher reflexivity: the use of synchronous collective writing as a space to collaboratively reflect on experiences of subjectification within the contemporary academy. We explore how, despite its apparent importance to contemporary research, the neoliberalisation of academia leaves little room for meaningful reflexivity. The authors in this paper – ranging from Master’s student to postdoctoral researcher – wrote collaboratively in real-time to organically develop a method of collective reflexivity. Through auto-ethnographic vignettes that act as raw data, and a critical analysis of how we came to experience the events showcased in these vignettes, we analyse how our positionalities shape both our subjection to, and perpetuation of, systems of symbolic violence in neoliberal academic institutions. Through this method, we explore experiences of the contemporary university as patriarchal, intensively marketised, and as a space where the prevalence of ‘weak’ reflexivity has negative impacts on research ethics. We argue that the affect of collaborative writing spaces acts as a resistance against our experiences of loneliness, competition and individualism. We also argue our new approach fosters research that is more responsive to the socio-material conditions to which it attends, and enables a deeper engagement with affect-led methodologies and slow-research.
Emojis and Radiology: The Power of Shared Language

HomeRadiologyVol. 310, No. 2 PreviousNext Reviews and CommentaryEditorialEmojis and Radiology: The Power of Shared LanguageSanjeev Bhalla Sanjeev Bhalla Author AffiliationsFrom the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110.Address correspondence to the author (email: [email protected]).Sanjeev Bhalla Published Online:Feb 27 2024https://doi.org/10.1148/radiol.233089MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Yow WQ, Lim TZM. Sharing the same languages helps us work better together. Palgrave Commun 2019;5(1):154. Crossref, Google Scholar2. Bankier AA, MacMahon H, Colby T, et al. Fleischner Society: glossary of terms for thoracic imaging. Radiology 2024;310(2):e232558. Google Scholar3. Curtis AM, Smith GJ, Ravin CE. Air crescent sign of invasive aspergillosis. Radiology 1979;133(1):17–21. Link, Google Scholar4. Kim SJ, Lee KS, Ryu YH, et al. Reversed halo sign on high-resolution CT of cryptogenic organizing pneumonia: diagnostic implications. AJR Am J Roentgenol 2003;180(5):1251–1254. Crossref, Medline, Google Scholar5. Paredes A. The WIRED Guide to Emoji. https://www.wired.com/story/guide-emoji/. Published February 1, 2018. Accessed July 1, 2023. Google ScholarArticle HistoryReceived: Nov 12 2023Revision requested: Dec 1 2023Revision received: Dec 4 2023Accepted: Dec 6 2023Published online: Feb 27 2024 FiguresReferencesRelatedDetailsRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 310, No. 2 Metrics Altmetric Score PDF download
Character sums over sparse elements of finite fields

Abstract We estimate mixed character sums of polynomial values over elements of a finite field with sparse representations in a fixed ordered basis over the subfield . First we use a combination of the inclusion–exclusion principle with bounds on character sums over linear subspaces to get nontrivial bounds for large . Then we focus on the particular case , which is more intricate. The bounds depend on certain natural restrictions. We also provide families of examples for which the conditions of our bounds are fulfilled. In particular, we completely classify all monomials as argument of the additive character for which our bound is applicable. Moreover, we also show that it is applicable for a large family of rational functions, which includes all reciprocal monomials.
Design for performability: a proposal for democratic redesign of the classical guitar

Mastery of a musical instrument, including an ability to reach virtuosity in expression and performance, is commonly achieved through a lifelong commitment to practise. The specific instrument played has a crucial role in this journey. This paper reports on design research that aimed to determine the potential for redesigning the classical guitar, whether iteratively or radically, from the perspective of key stakeholder groups: luthiers, composers, and performers. A total of 25 interviews were conducted and analysed to identify shared and unique concerns amongst the stakeholder groups. The findings are used to conceive and develop a new design-related musical term ‘performability’, which builds upon the established term ‘playability’ but has much stronger interactional and experiential dimensions. Furthermore, the paper provides insights into ways in which an industrial designer can contribute to classical guitar design as a fourth stakeholder. Industrial design expertise is proposed to provide a mediating and catalysing role for democratic (i.e. multi-stakeholder, multi-perspective) instrument design or redesign. The paper concludes with a proposal for a ‘design for performability’ framework suited to co-design sessions aiming to achieve a highly performable classical guitar.
Instance-level Adversarial Source-free Domain Adaptive Person Re-identification

Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this paper, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.
A heuristic for the non‐unicost set covering problem using local branching

Abstract In this paper, we present a heuristic for the non‐unicost set covering problem using local branching. Local branching eliminates the need to define a problem specific search neighbourhood for any particular (zero‐one) optimisation problem. It does this by incorporating a generalised Hamming distance neighbourhood into the problem, and this leads naturally to an appropriate neighbourhood search procedure. We apply our approach to the non‐unicost set covering problem. Computational results are presented for 65 test problems that have been widely considered in the literature. Our results indicate that our heuristic is better than six of the eight other heuristics we examined, slightly worse than that of one heuristic, but that there is a single heuristic that outperforms all others. We believe that the work described here illustrates that the potential for using local branching, operating as a stand‐alone matheuristic, has not been fully exploited in the literature.
Characterising Modal Formulas with Examples

We study the existence of finite characterisations for modal formulas. A finite characterisation of a modal formula φ is a finite collection of positive and negative examples that distinguishes φ from every other, non-equivalent modal formula, where an example is a finite pointed Kripke structure. This definition can be restricted to specific frame classes and to fragments of the modal language: a modal fragment \(\mathcal {L} \) admits finite characterisations with respect to a frame class \(\mathcal {F} \) if every formula \(\varphi \in \mathcal {L} \) has a finite characterisation with respect to \(\mathcal {L} \) consisting of examples that are based on frames in \(\mathcal {F} \) . Finite characterisations are useful for illustration, interactive specification, and debugging of formal specifications, and their existence is a precondition for exact learnability with membership queries. We show that the full modal language admits finite characterisations with respect to a frame class \(\mathcal {F} \) only when the modal logic of \(\mathcal {F} \) is locally tabular. We then study which modal fragments, freely generated by some set of connectives, admit finite characterisations. Our main result is that the positive modal language without the truth-constants ⊤ and ⊥ admits finite characterisations w.r.t. the class of all frames. This result is essentially optimal: finite characterizability fails when the language is extended with the truth constant ⊤ or ⊥ or with all but very limited forms of negation.
Greater usage and positive mood change for users of a dynamic VR app before and after the COVID-19 pandemic onset

Americans reported an increase in stress during the novel coronavirus disease 2019 (COVID-19). Virtual reality (VR) apps have been shown to distract users from stressors in the environment, but little is known about the efficacy of specific content features to reduce stress or improve mood for consumer users during a pandemic. The present study investigated secondary archival data to explore how mood and usage behavior changed before and after the onset of COVID-19 for consumer users of a VR app with dynamic, interactive content. Study findings indicate that the COVID-19 pandemic had significant effects on user behavior and mood. Users created more accounts and used app content more often during the pandemic, while reporting increased negative mood states. This suggests that users were motivated to use the content to cope with pandemic stressors. Users also experienced a greater positive mood change after using the content during the pandemic than before, which implies that elements related to the VR app content met users’ psychological needs. Passive content with less interactivity resulted in a greater positive mood state after the COVID-19 onset, likely related to its capacity to reduce stress, facilitate restoration, and improve persistent affective states in stressful environments. This study offers a vital window into how consumer users respond to psychosocial pandemic stressors outside of a controlled environment as well as the prospective for VR app content to serve as a valuable mental health intervention during similar stressful events.
FPGA-Based Acceleration of K-Nearest Neighbor Algorithm on Fully Homomorphic Encrypted Data

The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datasets and the findings indicate that the FPGA-based acceleration method provides significant performance improvements over software implementation. The Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme is used for the computation of ciphertext. After extensive simulation in Python and implementation in FPGA, it was found that the proposed architecture brings down the computational time of KNN on ciphertext to a realistic value in the order of the KNN classification algorithm over plaintext. For the FPGA implementation, we used the Intel Agilex7 FPGA (AGFB014R24B2E2V) development board and validated the speed of computation, latency, throughput, and logic utilization. It was observed that the KNN on encrypted data has a computational time of 41.72 ms which is 80 times slower than the KNN on plaintext whose computational time is of 0.518 ms. The main computation time for CKKS FHE schemes is 41.72 ms. With our architecture, we were able to reduce the calculation time of the CKKS-based KNN to 0.85 ms by using 32 parallel encryption hardware and reaching 300 MHz speed.
Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence

Abstract On 18 September 2022, the M W 6.9 Chihshang earthquake struck the south half of the Longitudinal Valley, Taiwan, and caused severe damage. A precise and rapid report for the distribution of aftershock sequence after a devastating earthquake provides key information for deciphering the seismogenic structure in the source region. The utilization of deep-learning methodologies for earthquake event detection offers a significant acceleration in data analysis. In this study, we use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence from September to October 2022, including the M W 6.5 Guanshan foreshock, the M W 6.9 mainshock, over 14,000 aftershocks, and 866 foal mechanisms from two sets of broadband networks. After applying hypoDD for earthquakes, the distribution of aftershock sequence clearly depicts not only the Central Range Fault and the Longitudinal Valley Fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.