Reviewerly/exHarmony huggingface dataset:
A Survey of Perception-Based Visualization Studies by Task

Knowledge of human perception has long been incorporated into visualizations to enhance their quality and effectiveness. The last decade, in particular, has shown an increase in perception-based visualization research studies. With all of this recent progress, the visualization community lacks a comprehensive guide to contextualize their results. In this report, we provide a systematic and comprehensive review of research studies on perception related to visualization. This survey reviews perception-focused visualization studies since 1980 and summarizes their research developments focusing on low-level tasks, further breaking techniques down by visual encoding and visualization type. In particular, we focus on how perception is used to evaluate the effectiveness of visualizations, to help readers understand and apply the principles of perception of their visualization designs through a task-optimized approach. We concluded our report with a summary of the weaknesses and open research questions in the area.
Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.
Extending a Brainiac Prover to Lambda-Free Higher-Order Logic

Decades of work have gone into developing efficient proof calculi, data structures, algorithms, and heuristics for first-order automatic theorem proving. Higher-order provers lag behind in terms of efficiency. Instead of developing a new higher-order prover from the ground up, we propose to start with the state-of-the-art superposition-based prover E and gradually enrich it with higher-order features. We explain how to extend the prover’s data structures, algorithms, and heuristics to $$\lambda $$ -free higher-order logic, a formalism that supports partial application and applied variables. Our extension outperforms the traditional encoding and appears promising as a stepping stone towards full higher-order logic.
Design of True Random Number Generator Based on Multi-Stage Feedback Ring Oscillator

True random number generators (TRNGs) play an important role in encryption systems. In this brief, a novel method of generating true random numbers on a field-programmable gate array (FPGA) is proposed based on the random jitter of a multi-stage feedback ring oscillator (MSFRO) as the entropy source. Based on the traditional ring oscillator, a multi-stage feedback structure is added to enlarge the range of clock jitter, and improve the frequency of clock sampling and the randomness of the entropy source. Different from the traditional clock sampling structure, we use the clock jitter signal generated by the MSFRO to sample the clock signal generated by the phase-locked loop (PLL) of the FPGA. The obtained output value is operated by XOR to reduce the deviation of the output value and improve its randomness. The TRNG is implemented in Xilinx Virtex-6 FPGA, which has low hardware resource consumption and high throughput. The entropy source classification, hardware resources and throughput are compared with those of existing TRNGs. The results showed that the proposed TRNG only consumed 24 LUTs and 2 DFFs. Compared with other TRNGs, this design has very low hardware resource consumption and its throughput is up to 290 Mbps. The random bit sequence generated by this TRNG passes the NIST SP800-22 test and the NIST SP800-90B test.
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition

One crucial challenge of real-world multilingual speech recognition is the long-tailed distribution problem, where some resource-rich languages like English have abundant training data, but a long tail of low-resource languages have varying amounts of limited training data. To overcome the long-tail problem, in this paper, we propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference. Extensive experiments on the CommonVoice corpus show that A2 significantly outperforms conventional approaches.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution

Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting.In this paper, we seek to fill the gap through comprehensive studies on the behavior of neural text classifiers trained with various defense methods against representative adversarial attacks.In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks, and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets, outperforming existing methods by a significant margin.We hope this study could provide useful clues for future research on text adversarial defense.
Multiplex Graph Neural Network for Extractive Text Summarization

Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.To extract a good summary from a long text document, sentence embedding plays an important role.Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) to learn contextual sentence embedding.However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words).To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words.Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization.Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effectiveness of our method.
X-modaler

With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the state-of-the-art of cross-modal analytics between vision and language in multimedia field. Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion. In this work, we propose X-modaler --- a versatile and high-performance codebase that encapsulates the state-of-the-art cross-modal analytics into several general-purpose stages (e.g., pre-processing, encoder, cross-modal interaction, decoder, and decode strategy). Each stage is empowered with the functionality that covers a series of modules widely adopted in state-of-the-arts and allows seamless switching in between. This way naturally enables a flexible implementation of state-of-the-art algorithms for image captioning, video captioning, and vision-language pre-training, aiming to facilitate the rapid development of research community. Meanwhile, since the effective modular designs in several stages (e.g., cross-modal interaction) are shared across different vision-language tasks, X-modaler can be simply extended to power startup prototypes for other tasks in cross-modal analytics, including visual question answering, visual commonsense reasoning, and cross-modal retrieval. X-modaler is an Apache-licensed codebase, and its source codes, sample projects and pre-trained models are available on-line: https://github.com/YehLi/xmodaler.
Sliding window approach with first-order differencing for very short-term solar irradiance forecasting using deep learning models

The intermittent behavior of solar is usually responsible for creating uncertainty while generating power. By implementing suitable forecasting techniques for solar irradiance (SI), we can overcome this intermittency which can be helpful in the economic load dispatch as well as to control, manage and optimize the power generation in the microgrid. This paper presents three deep learning (DL) models to forecast SI from 1 step (15-minute) to 6 steps (1 h 30 min) ahead. By implementing the sliding window technique, the input variables are converted into 12 steps lag datasets to train the model whereas outputs are transformed by first-order differencing. A total dataset of 18,277 from average 15-min interval global SI collected during January 1, 2016, to January 6, 2017, from the Asian Institute of Technology (AIT) Metrological station is used to train and evaluate the performance of the DL models. Based on the obtained results from different evaluation parameters such as maximum absolute error, confidence interval (CI), linear regression plot, mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R squared, we found that the deep hybrid model consists of convolutional neural network-long short term memory (CNN-LSTM) can outperform during multistep forecasting. The findings of the present work suggest that the proposed deep hybrid LSTM–CNN model is a reliable alternative for very short-term SI prediction due to its high predictive accuracy.
Temporal network embedding framework with causal anonymous walks representations

Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning for graph machine learning problems involving node classification and link prediction in every available setting. The proposed model outperforms state-of-the-art baseline models. The work also justifies their difference based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.
Balancing consumer and business value of recommender systems: A simulation-based analysis

Automated recommendations can nowadays be found on many e-commerce platforms, and such recommendations can create substantial value for consumers and providers. Often, however, not all recommendable items have the same profit margin, and providers might thus be tempted to promote items that maximize their profit. In the short run, consumers might accept non-optimal recommendations, but they may lose their trust in the long run. Ultimately, this leads to the problem of designing balanced recommendation strategies, which consider both consumer and provider value and lead to sustained business success. This work proposes a simulation framework based on agent-based modeling designed to help providers explore longitudinal dynamics of different recommendation strategies. In our model, consumer agents receive recommendations from providers, and the perceived quality of the recommendations influences the consumers’ trust over time. We design several recommendation strategies which either give more weight on provider profit or on consumer utility. Our simulations show that a hybrid strategy that puts more weight on consumer utility but without ignoring profitability considerations leads to the highest cumulative profit in the long run. This hybrid strategy results in a profit increase of about 20% compared to pure consumer or profit oriented strategies. We also find that social media can reinforce the observed phenomena. In case when consumers heavily rely on social media, the cumulative profit of the best strategy further increases. To ensure reproducibility and foster future research, we publicly share our flexible simulation framework.
An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks

Speech representations learned from Self-supervised learning (SSL) models can benefit various speech processing tasks. However, utilizing SSL representations usually requires fine-tuning the pre-trained models or designing task-specific downstream models and loss functions, causing much memory usage and human labor. Recently, prompting in Natural Language Processing (NLP) has been found to be an efficient technique to leverage pre-trained language models (LMs). Specifically, prompt tuning optimizes a limited number of task-specific parameters with a fixed pre-trained model; as a result, only a small set of parameters is needed to be stored for each task. Prompt tuning improves computation and memory efficiency by leveraging the pre-trained LM's prediction ability. Nevertheless, such a paradigm is little studied in the speech community. We report in this paper the first exploration of the prompt tuning paradigm for speech processing tasks based on Generative Spoken Language Model (GSLM). Experiment results show that the prompt tuning technique achieves competitive performance in speech classification tasks with fewer trainable parameters than fine-tuning specialized downstream models. We further study the technique in challenging sequence generation tasks. Prompt tuning also demonstrates its potential, while the limitation and possible research directions are discussed in this paper. The source code is available on https://github.com/ga642381/SpeechPrompt.
Inferring Meaning from Indirect Answers to Polar Questions: the Contribution of the Rise-Fall-Rise Contour

Polar questions can be given direct answers (e.g., Do you want to eat? – No) and indirect answers (e.g., Do you want to eat? – I’m not hungry). Listeners infer positive or negative responses from indirect answers to polar questions with varying degrees of confidence (e.g., Clark 1979, Hirschberg 1985, Green & Carberry 1992, 1994, de Marneffe et al. 2009). For spoken language, the prosodic realization of the indirect answer has been speculated to provide a cue to the intended meaning of the indirect answer (Green & Carberry 1999, fn. 34). This paper presents an experiment designed to identify whether and how the prosodic realization of an indirect answer to a polar question influences the response that listeners infer from the indirect answer. The experiment explored American English listeners’ interpretations of indirect answers with scalar adjectives (e.g., She’s attractive) realized with a neutral contour (H* L-L%) or the rise-fall-rise contour (L*+H L-H%) in response to polar questions with semantically stronger adjectives (e.g., Is your sister beautiful?). Listeners inferred significantly more negative responses to the polar questions when the indirect answer was realized with the rise-fall-rise contour than with the neutral contour. These findings show that the prosodic realization of an indirect answer can provide a cue to the speaker’s intended meaning. The paper also discusses implications of our findings for scalar implicature generation and the meaning of the rise-fall-rise contour.
Leveraging clinical data across healthcare institutions for continual learning of predictive risk models

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems. We tested the hypothesis, that the proposed continual learning algorithm can maintain higher predictive performance than competing methods on previous cohorts once it has been trained on a new patient cohort. In the sepsis prediction task, after incremental training of a deep learning model across four hospital systems (namely hospitals H-A, H-B, H-C, and H-D), WUPERR maintained the highest positive predictive value across the first three hospitals compared to a baseline transfer learning approach (H-A: 39.27% vs. 31.27%, H-B: 25.34% vs. 22.34%, H-C: 30.33% vs. 28.33%). The proposed approach has the potential to construct more generalizable models that can learn from cross-institutional clinical big data in a privacy-preserving manner.
Towards Metaverse Manufacturing: A Blockchain-based Trusted Collaborative Governance System

This paper explores how the new metaverse concept can be validated, applied, and scaled up to enhance, transform and revitalize traditional manufacturing industries. Researchers have widely discussed the advantages of the metaverse. Still, the necessity of its application in manufacturing systems has yet to be studied, and there is a lack of a comprehensive system framework and collaborative governance system to support the implementation of metaverse manufacturing. In order to meet these challenges, this paper proposes a blockchain-based trusted collaborative governance system for metaverse manufacturing, establishes a value-oriented metaverse trusted manufacturing framework in terms of both value-added and cost-reduction, and constructs a method for the essential elements of metaverse collaborative governance. The research will provide theoretical support and practical ideas for applying metaverse in the manufacturing field, thus promoting the actual arrival of the metaverse generation.
Goal-Conditioned Reinforcement Learning: Problems and Solutions

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the standard RL solutions that learn a policy solely depending on the states or observations, GCRL additionally requires the agent to make decisions according to different goals. In this survey, we provide a comprehensive overview of the challenges and algorithms for GCRL. Firstly, we answer what the basic problems are studied in this field. Then, we explain how goals are represented and present how existing solutions are designed from different points of view. Finally, we make the conclusion and discuss potential future prospects that recent researches focus on.
LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [ eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.
Knowledge management practices on innovation performance in the hotel industry: mediated by organizational learning and organizational creativity

Purpose The purpose of this study is to investigate the role of knowledge management (KM) practices on innovation performance. It also examines whether organisational creativity and organizational learning mediate the relationship between KM practices and innovation performance. Design/methodology/approach The study uses a quantitative method by surveying ( n = 291) hotel employees from Malaysia using self-administered questionnaires. Data collected were analysed by using partial least squares structural equation modelling. Findings Findings reveal that KM positively impacts innovation performance among Malaysian hospitality workers. This study further reveals that organisational learning and organisational creativity significantly mediate the relationship between KM and innovation performance. Originality/value Theoretically, this study establishes how KM practices influence innovation performance. Organizations that practice organizational learning and creativity are more likely to benefit from increased innovation outcomes through KM practices.