Reviewerly/exHarmony huggingface dataset:
The perceptual span is dynamically adjusted in response to foveal load by beginning readers.

The perceptual span describes the size of the visual field from which information is obtained during a fixation in reading. Its size depends on characteristics of writing system and reader, but-according to the foveal load hypothesis-it is also adjusted dynamically as a function of lexical processing difficulty. Using the moving window paradigm to manipulate the amount of preview, here we directly test whether the perceptual span shrinks as foveal word difficulty increases. We computed the momentary size of the span from word-based eye-movement measures as a function of foveal word frequency, allowing us to separately describe the perceptual span for information affecting spatial saccade targeting and temporal saccade execution. First fixation duration and gaze duration on the upcoming (parafoveal) word N + 1 were significantly shorter when the current (foveal) word N was more frequent. We show that the word frequency effect is modulated by window size. Fixation durations on word N + 1 decreased with high-frequency words N, but only for large windows, that is, when sufficient parafoveal preview was available. This provides strong support for the foveal load hypothesis. To investigate the development of the foveal load effect, we analyzed data from three waves of a longitudinal study on the perceptual span with German children in Grades 1 to 6. Perceptual span adjustment emerged early in development at around second grade and remained stable in later grades. We conclude that the local modulation of the perceptual span indicates a general cognitive process, perhaps an attentional gradient with rapid readjustment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Collaborative Storytelling with Social Robots

Storytelling plays a central role in human socializing and entertainment, and research on conducting storytelling with robots is gaining interest. However, much of this research assumes that story content is curated. In this paper, we expand the recently-proposed task of collaborative storytelling, where an intelligent agent and a person collaborate to create a unique story by taking turns adding to it, for application to social robot and consider the design implications that arise. Since latency can be detrimental to human-robot interaction, we examine the performance-latency trade-offs of an existing generate-and-rank-based approach to collaborative storytelling by finding the optimal ranker’s sample size that strikes the best balance between quality and computational cost. We improve on existing evaluation that was previously based on system-generated stories by having human participants play the collaborative storytelling game with our system and comparing the stories they create with our system to a naive baseline. Finally, we conduct a pilot elicitation survey that sheds light on issues to consider when adapting our collaborative storytelling system to a social robot. Our evaluation shows that participants have a positive view of collaborative storytelling with a social robot and consider rich, emoting capabilities to be key to enjoyment.
Digital Design of Smart Museum Based on Artificial Intelligence

Today, as the soft power of culture is becoming more and more important, it is very important to pay attention to the learning and dissemination of culture. As the carrier of this process, the use of advanced technology to improve the museum is of great significance. This paper studies the digital design of smart museum based on artificial intelligence in order to explore the application of smart museum in artificial intelligence, analyze the spatial design of smart museum by using digital technology, explore a feasible method to give full play to the function of smart museum, and put forward some suggestions on the spatial design of smart museum. The design of the smart museum is no longer restricted by time and space and uses digital technology to double use virtual things and dynamic space. Through the detailed analysis of the application of artificial intelligence and digitization in the spatial design of the smart museum, combined with the information decision tree algorithm and data heterogeneous network algorithm, this study constructs the model of the information processing architecture of smart museum and the requirements of digital museum and makes a decision-making analysis of the comparison results of existing data. It includes the digital design of smart museum display technology, display effect, and other display-related contents. Analyzing the impact of smart museum on the object can provide data support for the feasibility of digital space design of smart museum based on artificial intelligence. The results of regression data processing show that the spatial visual sense of digital design wisdom museum is very strong, reaching the level of 5.0, and the picture aesthetic effect is up to 4.8.
Consistent Training and Decoding for End-to-End Speech Recognition Using Lattice-Free MMI

Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to introduce LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1% / 4.4% on Aishell-1 dev/test set; significant error reduction is also achieved on Aishell-2 and Librispeech datasets over strong baselines. Code is released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Multi-strategy-based Pre-training Method for Cold-start Recommendation

The cold-start issue is a fundamental challenge in Recommender Systems. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and has shown great potential for cold-start recommendation. However, due to the over-smoothing problem, PT-GNN can only capture up to 3-order relation, which cannot provide much useful auxiliary information to depict the target cold-start user or item. Besides, the embedding reconstruction task only considers the intra-correlations within the subgraph of users and items, while ignoring the inter-correlations across different subgraphs. To solve the above challenges, we propose a multi-strategy-based pre-training method for cold-start recommendation (MPT), which extends PT-GNN from the perspective of model architecture and pretext tasks to improve the cold-start recommendation performance. 1 Specifically, in terms of the model architecture, in addition to the short-range dependencies of users and items captured by the GNN encoder, we introduce a Transformer encoder to capture long-range dependencies. In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add an embedding contrastive learning task to capture inter-correlations of users and items. We train the GNN and Transformer encoders on these pretext tasks under the meta-learning setting to simulate the real cold-start scenario, making the model able to be easily and rapidly adapted to new cold-start users and items. Experiments on three public recommendation datasets show the superiority of the proposed MPT model against the vanilla GNN models, the pre-training GNN model on user/item embedding inference, and the recommendation task.
PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset

As the COVID-19 pandemic rampages across the world, the demands of video conferencing surge. To this end, real-time portrait segmentation becomes a popular feature to replace backgrounds of conferencing participants. While feature-rich datasets, models and algorithms have been offered for segmentation that extract body postures from life scenes, portrait segmentation has yet not been well covered in a video conferencing context. To facilitate the progress in this field, we introduce an open-source solution named PP-HumanSeg. This work is the first to construct a large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K fine-labeled frames and extensions to multi-camera teleconferencing. Furthermore, we propose a novel Self-supervised Connectivity-aware Learning (SCL) for semantic segmentation, which introduces a self-supervised connectivity-aware loss to improve the quality of segmentation results from the perspective of connectivity. And we propose an ultra-lightweight model with SCL for practical portrait segmentation, which achieves the best trade-off between IoU and the speed of inference. Extensive evaluations on our dataset demonstrate the superiority of SCL and our model. The source code is available at https://github.com/PaddlePaddle/PaddleSeg.
High-Accuracy Calibration of High-Speed Fringe Projection Profilometry Using a Checkerboard

Calibration is a fundamental step in fringe projection profilometry development. Among various methods, hybrid calibration is a promising solution given its high flexibility and sensing speed. However, existing approaches suffer the problem of limited accuracy and convenience. For example, customized high-accuracy artifacts with complex operations are usually required. This article reports a hybrid method with improved accuracy and operability, based on a unique back-projection and nonlinear epipolar sampling analysis. This method is performed in three steps: stereo calibration, residual distortion analysis, and phase 3-D mapping tabulation; the last two are pure computations without manual operations. The method is straightforward and cost-effective since only a checkerboard-based stereo calibrating operation is required. It is flexible without checkerboard poses to be known and also highly accurate because systematic distortions are almost compensated. The eventually obtained phase 3-D mapping table is computationally efficient for high-speed sensing. Our experiments confirm that a relative measurement error of 0.01% relative to the field-of-view has been finally achieved.
Is a Wizard-of-Oz Required for Robot-Led Conversation Practice in a Second Language?

Abstract The large majority of previous work on human-robot conversations in a second language has been performed with a human wizard-of-Oz. The reasons are that automatic speech recognition of non-native conversational speech is considered to be unreliable and that the dialogue management task of selecting robot utterances that are adequate at a given turn is complex in social conversations. This study therefore investigates if robot-led conversation practice in a second language with pairs of adult learners could potentially be managed by an autonomous robot. We first investigate how correct and understandable transcriptions of second language learner utterances are when made by a state-of-the-art speech recogniser. We find both a relatively high word error rate (41%) and that a substantial share (42%) of the utterances are judged to be incomprehensible or only partially understandable by a human reader. We then evaluate how adequate the robot utterance selection is, when performed manually based on the speech recognition transcriptions or autonomously using (a) predefined sequences of robot utterances, (b) a general state-of-the-art language model that selects utterances based on learner input or the preceding robot utterance, or (c) a custom-made statistical method that is trained on observations of the wizard’s choices in previous conversations. It is shown that adequate or at least acceptable robot utterances are selected by the human wizard in most cases (96%), even though the ASR transcriptions have a high word error rate. Further, the custom-made statistical method performs as well as manual selection of robot utterances based on ASR transcriptions. It was also found that the interaction strategy that the robot employed, which differed regarding how much the robot maintained the initiative in the conversation and if the focus of the conversation was on the robot or the learners, had marginal effects on the word error rate and understandability of the transcriptions but larger effects on the adequateness of the utterance selection. Autonomous robot-led conversations may hence work better with some robot interaction strategies.
Optimal strategies on pricing and resource allocation for cloud services with service guarantees

The emergence of cloud computing has brought challenges to pricing and resource allocation in cloud services. In particular, overloading cloud servers caused by peak demand may paralyze cloud-based services. Therefore, cloud service providers (CSPs) always use service guarantees to ensure the quality of the service, which affects the providers’ profit. This paper studies a CSP’s pricing and resource allocation strategies considering service guarantees under overload protection. Considering the influence of service guarantees on users’ perceived service value, we propose an economic model in which the provider adjusts both the price and resource allocation to maximize profit. We use queuing theory to model the user-service interaction and seek to protect servers from overloading and derive the provider’s optimal pricing and resource allocation decisions. We further examine the impact of service guarantee, characterized by basic compensation factor and additional compensation factor, on CSP’s profit. It is demonstrated that when the market environment requires the provider to offer a higher basic compensation factor, it does not always hurt the provider’s profit. We also find that as the additional compensation factor becomes larger, the CSP’s profit trend varies under different conditions of the promised service response time. Besides, we examine the CSP’s service fulfillment rate, which influences the users’ benefits. These findings can guide CSPs to develop virtuous profitability modes in cloud industrial practice.
Arabic Sign Language Recognition Using Deep Machine Learning

In this work, we present an effective method for automatic Arabic Sign Language recognition that uses a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) for classification. AlexNet, a CNN architecture, is used to extract deep features from the input image while the LSTM is used to preserve the sequential structure of the video frames. The method was tested on a data set consisting of 50 repetitions of 150 signs commonly used in daily activities performed by three signers. The proposed method achieved an overall recognition accuracy of 95.9% for the signer-dependent case, and 43.62% for the more difficult signer-independent case.
A Transformer-based CTDNN Structure for Automatic Modulation Recognition

The application of deep learning enhances the ability of Automatic Modulation Recognition (AMR) to process a variety of complex types of signals, and improves the accuracy and speed of recognition. In order to solve the local dependency constraints in CNN and RNN, we propose a Transformer- based CTDNN structure for AMR to further improve the accuracy of recognition. First, the time-domain signal sequence is projected into a high-dimensional continuous space through embedding with a convolutional layer, and the local features of the signal are captured. What’s more, the encoder of Transformer is used to extract global features of the signal. Finally, the recognition result of the signal is output after the fully connected layer. In the simulation, the RML2016.10b dataset was used to analyze the structure and recognition results of CTDNN. And the comparison with existing methods shows that the structure has higher recognition accuracy, especially at low SNR.
A Machine Learning Approach for Sentiment Analysis to Nurture Mental Health Amidst COVID-19

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a reader.
The impact of national culture on overinvestment

The purpose of this paper is to explore the link between national culture and investment efficiency through a cross-country study. Based on a sample of 3,216 firms operating in 23 countries and observed over the 2009-2018 period, the study uses national culture Hofstede dimensions and overinvestment as operationalised in Biddle et al. (2009) model. To this end, a logit model and a GMM regression are applied. The results indicate that individualism and masculinity dimensions reinforce the likelihood that managers overinvest and tend to increase overinvestment levels since people from these cultures are known by their individual and money-oriented aspirations. However, managers from power distance cultures and high uncertainty avoidance engage less in overinvestment. This study provides additional cross-country factors that explain overinvestment not determined by agency theory. Manager's investment decisions are not only perceived through an objective financial perspective but also through beliefs and perceptions which are inherited from a manager's culture.
Voice Controlled Artificial Intelligent Smart Wheelchair

An artificially intelligent wheelchair controlled by voice has been presented in this paper. A smartphone is used as an interface for implementing and automating the voice recognition process by the movement control system. The presented voice-controlled gadget is well suited for some disabled people who are unable to employ the customary joystick-operated wheelchair. The Arduino utilizes DC motors and the microcontroller circuit for supervising the wheelchair’s movement. The voice of the patient that controls the movement of the chair is recognized using graphical commands. The key attribute of this graphical commands control method is that it enables the handicapped patient to steer the wheelchair with a varying speed much the same as a standard joystick. This voice-controlled artificial intelligent wheelchair has shown good results when tried out on actual patients and also has low-performance costs.
Design of Distance Learning System for Dance Movement Based on Wireless Network Communication Technology

The traditional teaching system uses local area network to realize online teaching, which leads to high stability and hardware load of the teaching system and makes it difficult to meet the teaching demand. To solve the above problems, a dance movement distance learning system based on wireless network communication technology is designed. On the B/S architecture, the hardware control module and communication module of the system are designed. The fuzzy set principle is used to evaluate the students’ dance cognitive ability, so as to personalize the recommended teaching contents. The teaching video is compressed according to H.264/AV compression standard to reduce the system transmission and processing load. The system functionality test results show that the maximum transmission packet loss rate of the designed system is 8.3%, and the lost data does not interfere with teaching, has low computer memory consumption, and has superior performance.
Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.
A Vision-Based Measure of Environmental Effects on Inferring Human Intention During Human Robot Interaction

In this work, we consider the problem of sensing environmental influence on the recognition of human intention in human robot interaction (HRI). We propose a novel framework to analyze the mechanism and the characteristics of the environment in all aspects of HRI. A sensor-based deep learning method is proposed to model the conditional probability of interaction between humans and robots in different environments. Having collected RGB-D videos with 3D human skeletons and environmental features by a Kinect azure RGBD sensor, people’s intentions to interact with a robot is inferred by a discriminant sensor network, trained jointly with a LSTM and a MLP. We collect a HRI dataset with various possible environments to train models and then conduct experiments of predicting the intention of participants. Our method outperforms the traditional environment-free approach in training results (98.8% and 77.0% in testing accuracy). Experimental validations of real-time human prediction also prove the higher speed and precision of our method compared with the baseline.
UltaNet: An Antithesis Neural Network for Recognizing Human Activity Using Inertial Sensors Signals

Human activity recognition (HAR) is an essential component of ambient assistive living. HAR has traditionally relied on computer vision techniques. However, it has several drawbacks, including lack of privacy, higher operational costs, and being constrained by the number of spaces available for cameras, so it cannot be used for applications that require long-term monitoring of people. The use of initial sensors is proving to be a vital solution for HAR. Smartphones and smartwatches have embedded accelerometers and gyroscope sensors that help deep neural networks for reliable activity recognition and classification problems. In this letter, we have presented a novel deep neural network model, which is opposite to the traditional models. It has a gated recurrent unit layer followed by different kernel-sized convolutional layers. Our model has been evaluated on an openly available dataset by the wireless sensor data mining lab, and comparison with previously existing models proves that it outperforms them.