Reviewerly/exHarmony huggingface dataset:
Centralized Space Learning for open-set computer-aided diagnosis

In computer-aided diagnosis (CAD), diagnosing untrained diseases as known categories will cause serious medical accidents, which makes it crucial to distinguish the new class (open set) meanwhile preserving the known classes (closed set) performance so as to enhance the robustness. However, how to accurately define the decision boundary between known and unknown classes is still an open problem, as unknown classes are never seen during the training process, especially in medical area. Moreover, manipulating the latent distribution of known classes further influences the unknown's and makes it even harder. In this paper, we propose the Centralized Space Learning (CSL) method to address the open-set recognition problem in CADs by learning a centralized space to separate the known and unknown classes with the assistance of proxy images generated by a generative adversarial network (GAN). With three steps, including known space initialization, unknown anchor generation and centralized space refinement, CSL learns the optimized space distribution with unknown samples cluster around the center while the known spread away from the center, achieving a significant identification between the known and the unknown. Extensive experiments on multiple datasets and tasks illustrate the proposed CSL's practicability in CAD and the state-of-the-art open-set recognition performance.
A federated learning scheme meets dynamic differential privacy

Abstract Federated learning is a widely used distributed learning approach in recent years, however, despite model training from collecting data become to gathering parameters, privacy violations may occur when publishing and sharing models. A dynamic approach is proposed to add Gaussian noise more effectively and apply differential privacy to federal deep learning. Concretely, it is abandoning the traditional way of equally distributing the privacy budget and adjusting the privacy budget to accommodate gradient descent federation learning dynamically, where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually. It also incorporates adaptive threshold cropping to control the sensitivity, and finally, moments accountant is used to counting the consumed on the privacy‐preserving, and learning is stopped only if the by clients setting is reached, this allows the privacy budget to be adequately explored for model training. The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy, which is significantly better than the differential privacy method used by TensorFlow.
Typology of Teaching Actions during COVID-19 Lockdown in the Valencian Community (Spain)

The COVID-19 coronavirus affected all countries, changing living practices and patterns of social structures. Spain confined all citizens to their homes from 14 March until 21 June 2020. All schools were closed, and education was suddenly converted to an online format. This study is part of wider research and analyzes the ways in which teachers in the Valencian Community (Spain) have approached School at Home (SH) The approach of the study is based on mixed methods (quantitative surveys and qualitative focus groups) and has an exploratory aim. Multivariate profiles of the gaps (represented by composite indicators) in both groups are analyzed based on k-means cluster analysis, as well as the variables associated with each profile, using non-parametric tests. The results show three groups of teachers, established according to four types of gaps, with different perceptions of the situation. ICT proficiency was very important, as well as family situation and support during confinement. The main lines of research derived from this study in relation to CSE and teacher outcomes are proposed.
Inclusive Active Methodologies in Spanish Higher Education during the Pandemic

The period of pandemic caused by the arrival of COVID-19 had a series of repercussions at the personal, social, cultural and educational levels. The confinement declared by government agencies caused a shift from face-to-face to virtual learning, which led to certain adaptations and the use of digital tools in order to carry out the teaching-learning process. This technological proliferation became a challenge for the educational community and for the development of pedagogical and inclusive models that could ensure pedagogical continuity. Therefore, the aim of this paper was to analyse the scientific production on active and innovative methodologies that were used during the COVID-19 pandemic period and their effect on the personal, academic and social performance of Higher Education students in the Spanish context. A systematic review of the scientific literature was carried out in accordance with the criteria established in the PRISMA declaration. Among the results obtained, flipped learning stands out as the most used methodology, which increases motivation among other aspects. In terms of knowledge areas, Social Sciences prevails. Finally, it should be noted that the use of active and innovative methodologies improves performance at academic, personal and social levels.
PointMapNet: Point Cloud Feature Map Network for 3D Human Action Recognition

3D human action recognition is crucial in broad industrial application scenarios such as robotics, video surveillance, autonomous driving, or intellectual education, etc. In this paper, we present a new point cloud sequence network called PointMapNet for 3D human action recognition. In PointMapNet, two point cloud feature maps symmetrical to depth feature maps are proposed to summarize appearance and motion representations from point cloud sequences. Specifically, we first convert the point cloud frames to virtual action frames using static point cloud techniques. The virtual action frame is a 1D vector used to characterize the structural details in the point cloud frame. Then, inspired by feature map-based human action recognition on depth sequences, two point cloud feature maps are symmetrically constructed to recognize human action from the point cloud sequence, i.e., Point Cloud Appearance Map (PCAM) and Point Cloud Motion Map (PCMM). To construct PCAM, an MLP-like network architecture is designed and used to capture the spatio-temporal appearance feature of the human action in a virtual action sequence. To construct PCMM, the MLP-like network architecture is used to capture the motion feature of the human action in a virtual action difference sequence. Finally, the two point cloud feature map descriptors are concatenated and fed to a fully connected classifier for human action recognition. In order to evaluate the performance of the proposed approach, extensive experiments are conducted. The proposed method achieves impressive results on three benchmark datasets, namely NTU RGB+D 60 (89.4% cross-subject and 96.7% cross-view), UTD-MHAD (91.61%), and MSR Action3D (91.91%). The experimental results outperform existing state-of-the-art point cloud sequence classification networks, demonstrating the effectiveness of our method.
pMATE: A Privacy-Preserving Map Retrieval Task Assignment Scheme in Spatial Crowdsourcing

Spatial crowdsourcing (SC) task assignment is to find the optimal worker for the task from abundant alternative workers based on the information of the task and workers, such as location, time, and ability. This information will undoubtedly reveal the privacy of both the task and workers. The disclosure of private information is a crucial issue constraining the development of SC. To this end, various privacy-preserving task assignments have been proposed to protect privacy by obfuscating or encrypting information. Fuzzy processing will limit matching accuracy, while encrypted information will increase the time cost of data computation. Therefore, this paper proposes a privacy-preserving map retrieval task assignment scheme (pMATE), which can divide the map and accurately retrieve the optimal workers according to this division. In pMATE, relevant information about tasks and workers is encrypted, and neighboring workers are searched based on the task presence partition. The task location can also be hidden in that partition. Partitioned retrieval reduces the amount of encrypted data needed to be matched. Furthermore, to reduce the problem of multiple communications during encrypted data comparison, we propose the Find MinNumber (FMN) algorithm, which can determine the optimal worker or top-k optimal workers need only two communications. Experimental evaluations of real-world data show that pMATE is efficient and accurate.
An Automatic Question Generator for Chinese Comprehension

Question generation (QG) is a natural language processing (NLP) problem that aims to generate natural questions from a given sentence or paragraph. QG has many applications, especially in education. For example, QG can complement teachers’ efforts in creating assessment materials by automatically generating many related questions. QG can also be used to generate frequently asked question (FAQ) sets for business. Question answering (QA) can benefit from QG, where the training dataset of QA can be enriched using QG to improve the learning and performance of QA algorithms. However, most of the existing works and tools in QG are designed for English text. This paper presents the design of a web-based question generator for Chinese comprehension. The generator provides a user-friendly web interface for users to generate a set of wh-questions (i.e., what, who, when, where, why, and how) based on a Chinese text conditioned on a corresponding set of answer phrases. The web interface allows users to easily refine the answer phrases that are automatically generated by the web generator. The underlying question generation is based on the transformer approach, which was trained on a dataset combined from three publicly available Chinese reading comprehension datasets, namely, DRUD, CMRC2017, and CMRC2018. Linguistic features such as parts of speech (POS) and named-entity recognition (NER) are extracted from the text, which together with the original text and the answer phrases, are then fed into a machine learning algorithm based on a pre-trained mT5 model. The generated questions with answers are displayed in a user-friendly format, supplemented with the source sentences in the text used for generating each question. We expect the design of this web tool to provide insight into how Chinese question generation can be made easily accessible to users with low computer literacy.
Analyzing the Effect of COVID-19 on Education by Processing Users’ Sentiments

COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall.
Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

In this paper, we propose a framework that constructs two types of image aesthetic assessment (IAA) models with different CNN architectures and improves the performance of image aesthetic score (AS) prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. The average F1 of the ensemble improves 5.4% over the model of type A, and 33.1% over the model of type B. Moreover, it is found that the AS classification models trained on the XiheAA dataset seem to learn the latent photography principles, although it cannot be said that they learn the aesthetic sense.
Efficient Binary Weight Convolutional Network Accelerator for Speech Recognition

Speech recognition has progressed tremendously in the area of artificial intelligence (AI). However, the performance of the real-time offline Chinese speech recognition neural network accelerator for edge AI needs to be improved. This paper proposes a configurable convolutional neural network accelerator based on a lightweight speech recognition model, which can dramatically reduce hardware resource consumption while guaranteeing an acceptable error rate. For convolutional layers, the weights are binarized to reduce the number of model parameters and improve computational and storage efficiency. A multichannel shared computation (MCSC) architecture is proposed to maximize the reuse of weight and feature map data. The binary weight-sharing processing engine (PE) is designed to avoid limiting the number of multipliers. A custom instruction set is established according to the variable length of voice input to configure parameters for adapting to different network structures. Finally, the ping-pong storage method is used when the feature map is an input. We implemented this accelerator on Xilinx ZYNQ XC7Z035 under the working frequency of 150 MHz. The processing time for 2.24 s and 8 s of speech was 69.8 ms and 189.51 ms, respectively, and the convolution performance reached 35.66 GOPS/W. Compared with other computing platforms, accelerators perform better in terms of energy efficiency, power consumption and hardware resource consumption.
Tablet-Based Praxis Developed for Children in Primary Education Studying Natural Sciences and Mathematics

Information and Communication Technologies are now a common feature in classroom activities. The aim of this study was to present praxis developed for the tablet for use by primary education students (aged 6-12) studying the natural sciences and mathematics. This research is qualitative and follows the narrative-ethnographic approach. The study sample consisted of 120 primary education students and 52 educational blogs. The results and conclusions reveal praxis that is rarely innovative or ludic. The bulk of tablet-based activities were for natural sciences classes rather than mathematics, and the most common practice with the tablet in the natural sciences was information searching and content exploration. The most widely used apps were the Google search engine, YouTube and the tablet's default apps (camera, image and video editor). Course content in the natural sciences focused on living beings and states of matter, and the activities developed for children to do on the tablet aimed to foster learning through discovery, exploration and enquiry. In mathematics, a traditional methodological approach was apparent in children's use of the tablet for typical activities related to units of measurement.
Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm

Using traditional tea frost hazard risk assessment results as sample data, the four indicators of minimum temperature, altitude, tea planting area, and tea yield were selected to consider the risk of hazard-causing factors, the exposure of hazard-bearing bodies, and the vulnerability of hazard-bearing bodies. The random forest algorithm was used to construct the frost hazard risk assessment model of Hangzhou tea, and hazard risk assessment was carried out on tea with different cold resistances in Hangzhou. The model’s accuracy reached 93% after training, and the interpretation reached more than 0.937. According to the risk assessment results of tea with different cold resistance, the high-risk areas of weak cold resistance tea were the most, followed by medium cold resistance and the least strong cold resistance. Compared with the traditional method, the prediction result of the random forest model has a deviation of only 1.57%. Using the random forest model to replace the artificial setting of the weight factor in the traditional method has the advantages of simple operation, high time efficiency, and high result accuracy. The prediction results have been verified by the existing hazard data. The model conforms to the actual situation and has certain guiding for local agricultural production and early warning of hazards.
A Survey About the Application of Artificial Intelligence in Search Engines

Artificial intelligence (AI) mimics or stimulates human behaviors or thinking to solve specific problems. It has been applied in the analysis of huge datasets and provides reliable outputs without human supervision in various online platforms, for example, information retrieval in search engines, digital assistants, voice assistants, digital marketing, personalized learning, social media, etc. This technology has provided many opportunities and challenges in line with strengthening the authenticity of the information provided via different search engines. This chapter reviews the current pieces of literature about the different AI algorithms used in the most popular metasearch engines and the application of artificial intelligence in these search engine contexts.
The Rising Trend of Artificial Intelligence in Social Media

Artificial intelligence (AI) is a branch of cognitive science concerned with intelligent machines capable of doing tasks formerly accomplished by humans. It focuses on using computers to perform activities that require knowledge, perception, reasoning, comprehension, and cognitive talents. AI algorithms can be trained to exploit individual actions, preferences, opinions, and interests. They can educate machines to behave in human-like ways. Furthermore, AI can learn these habits much faster than humans. Artificial intelligence is used in various industries to automate and increase the efficacy of specific processes and excessively in social media. Organizations use social media to reach many people by assessing their general perception and learning about their feelings and reactions to brands and products through AI However, there is a knowledge gap in the literature when holistically exploring AI's role in social media, its application, challenges, and opportunities.
Multiclass Mask Classification with a New Convolutional Neural Model and Its Real-Time Implementation

The world has been greatly affected by the COVID-19 pandemic, causing people to remain isolated and decreasing the interaction between people. Accordingly, various measures have been taken to continue with a new normal way of life, which is why there is a need to implement the use of technologies and systems to decrease the spread of the virus. This research proposes a real-time system to identify the region of the face using preprocessing techniques and then classify the people who are using the mask, through a new convolutional neural network (CNN) model. The approach considers three different classes, assigning a different color to identify the corresponding class: green for persons using the mask correctly, yellow when used incorrectly, and red when people do not have a mask. This study validates that CNN models can be very effective in carrying out these types of tasks, identifying faces, and classifying them according to the class. The real-time system is developed using a Raspberry Pi 4, which can be used for the monitoring and alarm of humans who do not use the mask. This study mainly benefits society by decreasing the spread of the virus between people. The proposed model achieves 99.69% accuracy with the MaskedFace-Net dataset, which is very good when compared to other works in the current literature.
Differential Privacy-Enabled Multi-Party Learning with Dynamic Privacy Budget Allocating Strategy

As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party learning enables participants to train their model locally without uploading private data to a server. However, recent studies have shown that adversaries may launch a series of attacks on learning models and extract private information about participants by analyzing the shared parameters. Moreover, existing privacy-preserving multi-party learning approaches consume higher total privacy budgets, which poses a considerable challenge to the compromise between privacy guarantees and model utility. To address this issue, this paper explores an adaptive differentially private multi-party learning framework, which incorporates zero-concentrated differential privacy technique into multi-party learning to get rid of privacy threats, and offers sharper quantitative results. We further design a dynamic privacy budget allocating strategy to alleviate the high accumulation of total privacy budgets and provide better privacy guarantees, without compromising the model’s utility. We inject more noise into model parameters in the early stages of model training and gradually reduce the volume of noise as the direction of gradient descent becomes more accurate. Theoretical analysis and extensive experiments on benchmark datasets validated that our approach could effectively improve the model’s performance with less privacy loss.
AdvSCOD: Bayesian-Based Out-Of-Distribution Detection via Curvature Sketching and Adversarial Sample Enrichment

Detecting out-of-distribution (OOD) samples is critical for the deployment of deep neural networks (DNN) in real-world scenarios. An appealing direction in which to conduct OOD detection is to measure the epistemic uncertainty in DNNs using the Bayesian model, since it is much more explainable. SCOD sketches the curvature of DNN classifiers based on Bayesian posterior estimation and decomposes the OOD measurement into the uncertainty of the model parameters and the influence of input samples on the DNN models. However, since lots of approximation is applied, and the influence of the input samples on DNN models can be hardly measured stably, as demonstrated in adversarial attacks, the detection is not robust. In this paper, we propose a novel AdvSCOD framework that enriches the input sample with a small set of its neighborhoods generated by applying adversarial perturbation, which we believe can better reflect the influence on model predictions, and then we average their uncertainties, measured by SCOD. Extensive experiments with different settings of in-distribution and OOD datasets validate the effectiveness of AdvSCOD in OOD detection and its superiority to state-of-the-art Bayesian-based methods. We also evaluate the influence of different types of perturbation.