Reviewerly/exHarmony huggingface dataset:
Structure-aware Table-to-Text Generation with Prefix-tuning

Table-to-text generation is designed to generate descriptive natural language for structured tables that conforms to objective facts and follows the source data. The current challenge in this field is to capture the structural information of the table and improve the quality of the generated text. The existing sequence-to-sequence approach is to linearize the table, which leads to miss captured structure information and is not conducive to the model learning contextual semantics. In this paper, we introduce structural-aware self-attention, which focuses on table structure to capture cell relationships between the same row or column. In this way, the generated descriptive text can more accurately reflect the correlation between the cells in the table, discarding irrelevant information. In order to adapt the pre-trained language model to the table-to-text generation task, we introduce prefix-tuning. Traditional fine-tuning methods update all model parameters, which leads to increased training costs. In contrast, using prefix-tuning for a more lightweight approach can improve model performance considerably. Attaching continuous prompts to tokens helps the model better understand the structure and semantics of the input sequence. All of our models are extended based on T5 and have strong competitiveness in the ToTTo dataset and Hitab dataset compared with several classical baselines.
Playing the Malay Gamelan Bonang in the Air: A Pilot Study

Virtualization of traditional musical instruments is gaining popularity due to the rapid advancement of technology. Due to the limited access to traditional musical instruments, technology is seen as an enabler to preserve these musical instruments along with making them more attractive and accessible. This paper focuses on a pilot study of exploring the interaction with a Digital Musical Instrument (DMI) based on the Malay bonang. A prototype called Air Bonang was developed on a virtual reality system for users to experience playing the bonang in an immersive platform to facilitate musical learning, performance, and composition. Findings from the study were used to further improve the research protocol for a larger-scale study.
Improving Multi-Domain Learning by Balancing Batches With Domain Information

Collections of data obtained or generated under similar conditions are referred to as domains or data sources. The distinct conditions of data acquisition or generation are often neglected, but understanding them is vital to address any phenomena emerging from these differences that might hinder model generalization. Multi-domain learning seeks to find the best way to train a model so that it performs adequately in all domains used during training. This paper explores multi-domain learning techniques that use explicit information about the domain of an example, in addition to its class. This study evaluates a general approach (Stew) by mixing all available data and also proposes two novel batch domain-regularization methods: Balanced Domains and Loss Sum. We train machine learning models with the listed approaches using datasets with multiple sources of data for image and audio classification tasks. The results suggest training a model using the Loss Sum method improves the results of models otherwise trained in a mix of all available data.
Research on Multi-Domain Sample Classification Method Based on Baidu API

This paper proposes a multi-domain sample classification method based on Baidu API's general object recognition function. we used three datasets in the experiment,including CIFAR-10, CIFAR-100, and Mini-ImageNet. For an unknown sample belonging to these three datasets, we first predict which domain it may belong to by using the output results of Baidu API, and then obtain the label of the sample by training a model on that domain. Compared with existing methods, our method reduces the number of high-performance models that need to be trained and reduces the computational difficulty. Experimental results show that our method is more convenient and accurate.
“这个真的不好说.” (It is Hard to Say): Positioning, Graphicons, and Culture: A Multimodal Discourse Analysis of a WeChat Discussion

This article examined the intersection and interaction among positioning, communication modes, and culture by taking positioning theory as a theoretical framework. Data were collected from a WeChat discussion group where three Chinese international students engaged in a community of English as a second language (L2) literacies. A multimodal discourse analysis reveals that three WeChat group members creatively, freely, and deliberately used verbal language and graphicons along with their cultural beliefs and situational contexts to construct, negotiate, and sometimes reject positions. Findings also show that three types of self-other positioning were frequently constructed and negotiated through their discussions, such as the “ Self-Agreed-to-Other,” “ Self-Opposed-to-Other,” and “ Self-Complained-to-Other” positioning. The “ Self-Agreed-with-Other” positioning was explicitly conveyed, but the “ Self-Opposed-to-Other” positioning was implicitly expressed, which can be attributed to one of the Chinese cultural values: face ( mianzi). In addition, graphicons, such as emojis and stickers, were used to challenge first-order positioning and negotiate second-order positioning. Also, the semiotic sign @ used to specify WeChat message recipients performed illocutionary acts as including someone or excluding them from the discussion based on a specific interactional discourse. Graphicons, collectively and sometimes independently, were utilized to contribute to positions that not only provided or limited opportunities for L2 literacies practice but also invited or sometimes rejected community memberships. Future research on the incongruent verbal and nonverbal expression for different types of positioning is needed, especially when verbal language and graphicons are used collaboratively to design meaning.
Dashboards to support rehabilitation in orientation and mobility for people who are blind

In Brazil, there are over 7 million individuals with visual disabilities (PDVs), including at least 500,000 blind individuals, according to IBGE 2019 data. Orientation and Mobility (OM) training is crucial for PDVs to navigate, identify objects, determine directions, and reach destinations. Existing literature offers virtual OM environments using gamification and Virtual Reality (VR) techniques. However, there is a lack of customizable maps and detailed monitoring of student interaction in these environments. This article proposes integrating game analytics and customization methods in OM teaching for PDVs. The main goal is to assist OM teachers in utilizing gamification and VR for safe and engaging practices. The integration aims to enhance customization, gamification, and monitoring, enabling better assessment of student progress and personalized activities to maximize engagement and learning.
An accurate real-time method to detect the smile facial expression

The rapid advancement of technology has significantly improved our ability to interact with electronic devices and derive meaning from this interaction. One crucial aspect of human-environment interaction is emotion recognition, which allows us to understand and appropriately respond to emotional cues. Among these cues, a smiling facial expression represents contentment and a positive response to situations, usually indicating happiness. Facial expression recognition techniques have been extensively utilized by researchers and scientists in both academic and commercial settings because the analysis of users’ facial expressions can provide valuable insights into computer system behavior and usability, making it an important area of research. In this paper, we propose a method for detecting smile facial expressions by focusing on the mouth region, which plays a vital role in smile characterization and identification. Our approach innovates utilizing normalized facial reference points, commonly known as landmarks, as input for machine learning classifiers. These normalized landmarks are two-dimensional coordinates, enabling low-cost and real-time processing, particularly suitable for devices with limited processing capabilities, such as cellphones. Remarkably, our method exhibits a real-time performance accuracy exceeding 95%. Our research contributes to producing more consistent and accurate outcomes in smile detection, surpassing existing results from the literature.
Evaluating the Limits of the Current Evaluation Metrics for Topic Modeling

Topic Modeling (TM) is a popular approach to extracting and organizing information from large amounts of textual data, by discovering and representing semantic topics from documents. In this paper, we investigate an important challenge in the TM context, namely Topic evaluation, responsible for driving the advances in the field and assessing the overall quality of the topic generation process. Traditional TM metrics capture the quality of topics by strictly evaluating the words that built the topics syntactically (i.e., NPMI, TF-IDF Coherence) or semantically (i.e., WEP). In here, we investigate whether we are approaching the limits of what the current evaluation metrics can assess regarding topic quality for TM. We performed a comprehensive experiment, considering three data collections widely used in automatic classification, for which each document’s topic (class) is known (i.e., ACM, 20News and WebKb). We contrast the quality of topics generated by four of the main TM techniques (i.e., LDA, NMF, CluWords and BerTopic) with the previous topic structure of each collection. Our results show that, despite the importance of the current metrics, they could not capture some important idiosyncratic aspects of TM, indicating the need to propose new metrics that consider, for example, the structure and organization of the documents that comprise the topics.
MSTCNet: Parallel Multi-Scale Network For Medical Image Segmentation

Transformer-like architectures, which are the model of choice in the field of natural language processing, have recently been adapted to computer vision (CV) fields and demonstrated remarkable effectiveness on various CV tasks. However, current transformer-based methods require large-scale datasets, which are usually unavailable in medical image analysis, thus resulting in adverse achievement. To this end, we propose a novel segmentation model, named MSTCNet, which constructs a parallel multi-scale transformer (MST) encoder in U-Net. In MST, we devise multi-scale patch partition and multi-scale mix attention to perform multi-scale long-range dependencies modeling. The U-Net encoder paralleled with MST alleviates the burden of large-scale datasets and extract local features supplementarily. We also propose Feature Fusion Head to narrow the gap between convolutional features and transformer features. Sufficient experiments demonstrate that our MSTCNet outperforms state-of-the-art methods on GlaS and ISIC18 datasets and is more suitable for medical image segmentation with small-scale datasets.
A User Study on Explanations with Different Levels of Detail in Recommender Systems

Explanations are a useful feature to improve the transparency and reliability of recommender systems. However, the amount of information presented to the user regarding a recommendation is an important parameter that is not usually considered. On the one hand, large explanations may carry more information than the users would like, which could be a potential case of the information overload problem. Similarly, if short explanations are provided, some users may find insufficient information to make decisions. In this context, our main objective is to analyze perceptions of users that were presented to explanations with varying level of detail. In our validation process, we conducted user experiments and identified a correlation between users reporting that the ability to choose the level of detail in the explanation aids in finding a recommendation that aligns with their interests, and their subsequent acceptance of the recommendation.
The Effectiveness of Historical Memes on High School Students' Interest in Learning History

This research aims to: (1) Understand the history learning process for class in history learning at SMA Jakenan Pati (a High School); (2) Analyzing the effectiveness of history learning using historical memes at SMA Jakenan Pati; and (3) Knowing the influence of the use of historical memes on students' interest in learning history at SMA Jakenan Pati. This research uses a quantitative research method that uses an experimental research design. The research results show (1) teachers are good at teaching history but teachers do not use the facilities provided by the school such as wifi facilities, LCD projectors in each class. Teachers still use the lecture method; (2) The first indicator of respondents was doubtful at 20.75%, strongly agreed and agreed at 76.85%, and a small portion disagreed and strongly disagreed at 2.4%. The second indicator which discusses learning more quickly, effectively and efficiently shows that 29.44% are unsure, 67.56% strongly agree and 3% disagree; and (3) Based on the summery model table which has been analyzed using the SPSS 25 program, it shows that the R Square value is 0.228= 22.8%. These results show that the independent or non-free variable (historical memes) influences the independent variable (interest in learning) in history learning for class Meanwhile, the remaining 77.2% is explained by other factors not covered in this study.
Analyzing the performance of persistent storage for fault-tolerant stateful fog applications

In this paper, we analyze the scalability and performance of a persistent, fault-tolerant storage approach that provides data availability and consistency in a distributed container-based architecture with intended use in industrial control applications. We use simulation to evaluate the performance of this storage system in terms of scalability and failures. As the industrial applications considered have timing constraints, the simulation results show that for certain failure patterns, it is possible to determine whether the storage solution can meet critical deadlines. The presented approach is applicable for evaluating timing constraints also of other container-based critical applications that require persistent storage.
SPASE metadata as a building block of a heliophysics science-enabling framework

Heliophysics and space weather research encompass the effects of solar output on practically the entire Solar System and are fundamentally cross-disciplinary. Cross-domain science investigations, such as in Sun-heliosphere interactions, solar wind-magnetosphere interactions, or magnetosphere-ionosphere coupling, often require the use of data, models, and other digital resources pertaining to different heliophysical domains: the Sun, the solar wind, the magnetosphere, the ionosphere, the thermosphere and the mesosphere. Due to differences in measurement platforms, techniques and instruments, heliophysics data obtained from different domains are diverse and complex, making the resource landscape difficult for untrained users to navigate. Without proper and adequate guidance from domain experts, it is often difficult for early-career scientists and non-domain experts to discover useful datasets and to know from where and how to obtain and understand the data they need to support their research. This paper describes the roles of metadata in providing the identification, location, access protocol, and detailed content description of a digital resource. More specifically, we point out that metadata written according to the Space Physics Archive Search and Extract (SPASE) metadata model are fully compatible with the FAIR principles so that digital resources described using the SPASE model can be uniformly Findable, Accessible, Interoperable, and Reusable. SPASE metadata can thus be the key element, the lingua franca so to speak, that enables unfettered information flow between data systems and services throughout the heliophysics data environment and lowers the understandability barrier of the resources to ensure their independent usability. After describing various components of the heliophysics data environment, their metadata requirements for effective operations, and some essential features of the SPASE metadata model, we then illustrate how metadata in SPASE can enable or facilitate the performance of different science tasks. The current status and future outlook of SPASE are also presented.
Development of a visual recurrence prevention tool

ABSTRACTLower education levels among addiction treatment service users poses a significant communication barrier in addiction treatment. We sought to develop a new visual educational resource to support recurrence prevention among those seeking alcohol dependency treatment. Eighty-six Canadian addiction counseling professionals provided feedback on the model developed based upon five principles of evidence-based visuals: content, cognitive load, writing style, organization, and color choice. Thematic analysis revealed overwhelmingly positive feedback for the infographic as a resource that could benefit service users with poor educational backgrounds or for whom English was not their first language. Specific feedback and critique were used to generate an enhanced visual resource that combines science communication theory with clinical expertise of addiction counselor in order to reinforce complex ideas in a simpler manner.KEYWORDS: Addiction treatment educationalcohol misusehealth informationinfographicrecurrencevisual communication Disclosure statementNo potential conflict of interest was reported by the author(s).
A fractional-order visual neural network for collision sensing in noisy and dynamic scenes

Autonomously sensing collision threats in intelligent mobile machines is a significant challenge. Inspired by the perfect collision sensing system of locusts, various neural networks based on lobular giant motion detectors (LGMD) have been proposed. These networks use integer-order differential operators to simulate the simple and efficient visual neural system of locusts. However, some biological studies have shown that the neurodynamics of neuronal response should be fractional-order, and fractional-order differential operators can accurately describe the biological logic brain neural response process. In view of this, firstly, this paper uses a fractional-order differential operator to simulate the membrane potential response of neurons, and a new fractional-order LGMD collision sensing visual neural network is proposed. This network is designed to accurately detect looming objects and generate reliable response spikes. Then, through experimental analysis, a range [0.36, 0.4] of order in the fractional-order system is given, which is considered to be a reasonable interval for modeling the behavior of biological nervous systems. This maybe contribute to the study of the locust’s fractional-order biological nervous system. Finally, the network was systematically tested in noisy and complex dynamic environments. The experimental results demonstrated that the fractional-order LGMD visual neural network generated responses with high adaptability and reliability, showcasing significantly improved collision detection performance.
Covariance models and Gaussian process regression for the wave equation. Application to related inverse problems

In this article, we consider the general task of performing Gaussian process regression (GPR) on pointwise observations of solutions of the 3 dimensional homogeneous free space wave equation. In a recent article, we obtained promising covariance expressions tailored to this equation: we now explore the potential applications of these formulas. We first study the particular cases of stationarity and radial symmetry, for which significant simplifications arise. We next show that the true-angle multilateration method for point source localization, as used in GPS systems, is naturally recovered by our GPR formulas in the limit of the small source radius. Additionally, we show that this GPR framework provides a new answer to the ill-posed inverse problem of reconstructing initial conditions for the wave equation from a limited number of sensors, and simultaneously enables the inference of physical parameters from these data. We finish by illustrating this “physics informed” GPR on a number of practical examples.
An empirical technique to predict monthly mean global solar radiation for <scp>PV</scp> applications in Indian context

Abstract Global radiation is a significant input in the design and simulation of solar PV as well as low temperature solar thermal systems. Moreover, meteorological conditions of any location also greatly influence the design, simulation, and output of the solar energy system. The considerable importance of global radiation leads to a dire need for global radiation models in the lack of measurement. Consequently, to enhance the predictability of solar resources over India, models for global radiation simulation through meteorological parameters (temperature, relative humidity, and wind speed) are proposed. Data of nine stations from NASA for 10 years (2008–2017) are used in the study to calibrate the models and performance of developed models is assessed through different statistical indicators (MPE, MBE, RMSE, R 2 , RMSD, NSE, and TS). The investigation reveals that out of the three proposed models, the model correlating total solar radiation with relative humidity, temperature, and wind speed is found to provide the highest accuracy in global radiation simulation with least statistical errors and is selected as best fit model. Moreover, validation of the proposed models is approved through monthly average daily total solar radiation predication for the remaining four testing cities of India and also through comparison with previously available models in the literature.
Phase retrieval via Lagrange programming neural network

In this paper, the problem of phase retrieval is addressed. Its solution is based on the Lagrange programming neural network (LPNN), which is an analog neural computational technique for solving nonlinear constrained optimization problems according to the Lagrange multiplier theory. The local stability of the proposed algorithm is also investigated. Furthermore, we extend the LPNN based approach to more challenging array signal processing applications, namely, when multiple vector signals with full column rank or other constraints are required to be recovered from measured magnitudes. One of the key difficulties for these challenges is pairing the separately estimated parameters, while the parameters estimated by the extended method are automatically paired. The performance of the developed algorithms is demonstrated via computer simulations.
An area-efficient and low-latency elliptic curve scalar multiplication accelerator over prime field

For the elliptic curve cryptography (ECC) over prime field, this paper presents an area-efficient and low-latency elliptic curve scalar multiplication (ECSM) accelerator, which supports to process any prime number p ≤ 256 bits. The proposed accelerator is based on a parallel hardware implementation of iterative digit-digit Montgomery multiplication and an optimized data flow architecture for elliptic curve point operations. The proposed design is implemented in Xilinx Virtex-7 FPGA. The experimental results show that the proposed architecture occupies 23.7k look-up tables (LUTs) and performs single 256-bit scalar multiplication in 0.56 ms. The area-time product of the proposed architecture is only 13.36. Compared with other state-of-the-art ECSM implementations, the proposed architecture has obvious advantage in terms of the area-time product.