Reviewerly/exHarmony huggingface dataset:
Activity Recognition for Elderly Using Machine Learning Algorithms

Human activity recognition is one of the major research problems in computer science and it has a wide range of applications including healthcare applications. The goal of this research project is to design and apply machine learning methods that can be used to analyze publicly available datasets collected by wearable sensors of elderly and identify different activities such as setting and walking. Feature selection and ranking algorithms were used to select the most relevant feature and reduce the number of collected and used data from sensors. Several classification algorithms were used in this chapter for activity recognition and several experiments were conducted to compare between these different algorithms using different number of features based on the ranking of features. The experimental results show a high accuracy in recognizing activities can be achieved using a fewer number of features. This can help in providing the medical services required for elderly based on the detected activity using a small number of collected features from sensors. The experimental results show that random forest algorithm gave the highest accuracy compared to the other algorithms using only three features of sensors data.
Study of application of composite sampling and improved LightGBM algorithm to the diagnosis of unbalanced transformer fault samples

A new diagnosis method for unbalanced transformer fault samples was proposed in this study to solve the problems of low accuracy and long training time existing in the traditional transformer state evaluation and diagnosis algorithms under un-balanced sample distribution. First, the improved SMOTE alg-orithm was used to collect the minority among the unbalanced transformer fault samples, which were then normalized. Next, the ENN algorithm was adopted to solve the data noise problem generated after sampling, followed by the under-sampling pro-cessing, and the samples of different fault types were made to reach the balanced state through the composite sampling. In the end, the overfitting problem was solved through the improved LightGBM algorithm, and the balanced transformer fault sam-ples were diagnosed. The experimental results showed that under unbalanced samples, the diagnostic accuracy rate of the proposed method reached 95.1%, which was 24.7% higher than that reached by other traditional fault diagnosis methods. Moreover, the proposed method improved the indexes like recall rate, F1-score value and precision ratio by 20%, thus solving the low fault diagnosis accuracy and long training time of traditional artificial intelligence (AI) algorithms under unbalanced data distribution of transformer fault samples.
Exploring Children’s Engagement in Monitoring Indoor Air Quality: Longitudinal Study

Indoor air pollution is harmful to everyone, but children are of particular concern, as they are more vulnerable to its adverse health effects from air pollutants. Although mobile technology is increasingly being designed to support monitoring and improving air quality indoors, little attention has been paid to its use by and for children. Previously, we created inAirKids, a child-friendly device to promote children's engagement with monitoring indoor air quality through a participatory design process. The next step is to evaluate its usability in the real world.The aim of this study is to investigate how inAirKids affects children's understanding of and engagement with indoor air quality through a longitudinal field deployment study.We deployed inAirKids in the homes of 9 children aged between 6 and 7 years, and investigated their use for up to 16 weeks by conducting semistructured, biweekly interviews.The results show that participants promptly engaged with inAirKids but quickly lost interest in it owing to the lack of engaging factors to sustain engagement. In addition, we identified 2 design considerations that can foster sustained engagement of children with monitoring indoor air quality: design interactivity for engaging in continuity and corporate hands-on activities as part of indoor air quality monitoring for experiential learning.Our findings shed light on the potential to promote the engagement of children in indoor air quality as well as considerations for designing a child-friendly digital device. To the best of our knowledge, this is the first longitudinal field deployment to investigate how to engage children in monitoring indoor air quality.
Distance Education in the COVID-19 Pandemic

As the COVID-19 disease rapidly transmitted through interpersonal contact, it required people to avoid physical contact. Almost all countries in the world have suspended the education of children and youth, a highly active network, and switched to online education to reduce the spread of the virus. Distance education, unlike the traditional education system, has a structure that does not require students to be on a campus or be in the same environment with instructors. As the global epidemic broke out, the distance education system was reviewed, and a new education order started to be established. Students, parents, and teachers found themselves in a situation they were not used to before. In this study, the advantages and disadvantages of distance education are analyzed, hybrid and hybrid-flexible (hyflex) models are examined, the educational changes during the pandemic period as well as the anticipations of the post-pandemic period are reviewed, and lastly, future educational solutions are proposed.
Metric Learning for 2D Image Patch and 3D Point Cloud Volume Matching

Similarity measure of cross-domain descriptors (2D descriptors and 3D descriptors) between 2D image patches and 3D point cloud volumes provides stable retrieval performance and establishes the spatial relationship between 2D and 3D space, which plays the potential applications in geospatial space, such as 2D and 3D interaction of remote sensing, Augmented Reality (AR) and robot navigation. However, the mature handcrafted descriptors of 2D image patches and 3D point cloud volumes are extremely different, resulting in the huge challenge for 2D image patch and 3D point cloud volume matching. In this paper, we propose a novel network which combines both unified descriptor training and descriptor comparison function training for 2D image patch and 3D point cloud volume matching. First, two feature extraction networks are applied for jointly learning the local descriptors for 2D image patches and 3D point cloud volumes, respectively. Second, a fully connected network is introduced to compute the similarity between 2D descriptors and 3D descriptors. Motivated by the successful indicator system on evaluating 2D patch feature representation, we use the false positive rate at 95% recall (FPR95) and precision based on cross-domain descriptors as the measured metric. The experimental results show that our proposed network achieve state-of-the-art performance in the matching of 2D image patches and 3D point cloud volumes.
Classification of Multi-Resolution Hyperspectral Data by Convolutional Neural Networks

To realize the pixel-based classification of hyperspectral data, wavelet transform is applied to produce two-dimensional data, which makes it possible to combine convolutional neural networks (CNNs). Although wavelet transform is usually used for time series data, it can also be applied to hyperspectral data which consist of many bands. By analyzing hyperspectral data with multiple resolutions, the characteristics of the spectrum can be captured on various scales. The features generated by the wavelet transform are used as input data to the CNN classifier to identify class-specific patterns. We verified this approach using two data sets and confirmed its effectiveness. Futhermore, the proposed method performed well even with a small number of training samples.
Heterogeneous Identity Unified Management System Based on Trustworthiness Evaluation Model

With the popularity of the Internet, increasing personal private information is exposed in the network. Real name network systems have been introduced for identity authentication management and private protection. However, there are still vulnerabilities and risks of identity authentication in these systems. In this paper, a new Heterogeneous Identity Unified Management System (HID-UMS) Based on Trustworthiness Evaluation Model is proposed, which solves the problem of unified identity authentication and its management in the cross-domain scenario by introducing trust rating mechanism. Furthermore, consortium blockchain technology and SM Cryptographic Algorism is applied in HID-UMS to protect information data from malicious attacks and privacy leakage.
An Immersive Model of User Trust in Conversational Agents in Virtual Reality

Conversational agents (CAs) have been widely adopted for various purposes, ranging from personal assistants to health information providers. While the research on CAs is growing rapidly, less attention is paid to CAs in virtual reality (VR) environments with respect to how the design of these agents influences their trustworthiness as perceived by users, which is key to the adoption and use of VR products featuring CAs. Accordingly, this position paper conceptualizes an immersive model of user trust in CAs in VR. The model is centered around users’ sense of co-presence with CAs in VR, which is influenced by the agents’ embodiment, expressiveness, and responsiveness.
Topic Extraction from A Cancer Health Forum

This paper presents a methodology for topic extraction from a corpus of unstructured posts submitted to the online health forum r/Cancer. Topic extraction is important in many fields. It can provide an understanding of how patients and their caregivers manage the disease and related treatments. Reduced vector embeddings are generated for each post using a combination of a pre-trained language model and dimensionality reduction. These embeddings are then clustered with particle swarm optimization (PSO). An embedding size of 300 produced a topic model with a quality of 0.44. This quality level was obtained without biasing the words in the vocabulary towards a specific topic as currently practiced.
Attention Mechanism for Land Cover Mapping with Image-Level Labels

In this paper, we address the problem of land cover mapping where only the image labels are available in a training set. Here, a training sample is a pair of a remote sensing image and a class indicator of whether a land cover class is present or absent on the entire image. It is more economical to only identify what classes are present in an image than to label every pixel in an image. In this way, a large number of remote sensing images can be labeled quickly. To achieve this goal, we employed the attention layer to create the attention map. The attention map is then further segmented to produce the final land cover map where every pixel in an image will be labeled. We have tested the performance of our proposed algorithm with the UC Merced Dataset and achieved more than 85% overall accuracy with only 0.4% labels in a training set when comparing with the UNET [1].
On Detecting Cloud Container Failures from Computing Utility Sequences

As the popularity of cloud platforms and container grows rapidly, managing clouds has become an important issue. For example, failed containers on cloud platforms would trigger automatic restart mechanism. However, the failed containers caused by user error are not fixable by restart, and may lead to the loop between failure and restart. Therefore, the looping failure will harm the overall performance of cloud. In this paper, we propose to identify possible container failures, where the utility behavior of containers (e.g., CPU usage, GPU usage, I/O throughput, etc) are factored in, in a machine learning approach. We propose a light-weight neural network EEGNet-SE to support fast inference in real-time. In addition, EEGNet-SE is able to distinguish dynamic relations between each utility for different tasks. We conduct a real cloud container dataset from Taiwan Cloud Computing (TWCC) platform. Experimental results manifest that EEGNet-SE boosts the performance and efficiency simultaneously, and outperforms the other state-of-the-art methods in terms of accuracy.
Voice Creator: Giving Customized Voice to the Voiceless for Online Communication

Voice plays an important role in online communications by increasing intimacy among people. Despite the advantages of using voice in computer-mediated communications (CMC), it is difficult for people with speech or hearing impairments to participate in such communication methods. In this study, we investigate different attributes of voices and how it affects users’ preference. We also deployed a website called ‘Voice Creator’ for people who want to create an online voice by specifying the levels of different voice attributes: gender, age group, breathiness, smoothness, hoarseness, and variation. We plan to conduct a user study on the target users and study the behavior of voice customization in future work.
Estimating Fuzzy Possibility Functions for E-Commerce Decision Support

An automatic methodology is proposed for extracting valuable information from customer experience and comments after they purchase products, including the prediction of negative opinions from written phrases and the estimation of potential savings from attending unsatisfied customers. The complete methodology consists in natural language pre-processing, the calculation of the Term Frequency - Inverse Document Frequency (TF-IDF) index, the construction of binary classification models for negative opinions, and the estimation of the potential savings from using the knowledge extracted by the predictive models. The function for estimating the savings makes use of soft measures, based on fuzzy possibility functions and the estimation of client churning (given that they are unsatisfied with the delivery). The prediction algorithms for negative emotions are shown to achieve good results when tested with out-of-sample comments coming from social networks, and the complete methodology obtains significant savings when applied for e-commerce customer support.
A 3D Graphic Score Space and the Creative Techniques and Performance Practices that Emerge From It

The 3D graphic score space created for this study is a non-immersive virtual reality space for creating musical scores utilizing graphic notation. This project involved two main aspects; developing the graphic score space followed by a two-part user study. Two groups participated during the user study; one group of participants created graphic scores, and the second group of participants performed these graphic scores. The first question guiding this study is: what creative practices and techniques come about while designing a 3D graphic score with a screen-based non-immersive VR system? The second question is: what performance practices emerge when performing from different vantage points while viewing a screen-based non-immersive VR graphic score? This paper will present further details about the overall design and implementation of this particular 3D graphic score space and the common themes between participant groups revealed during the user study.
Prototype Selection and Generation with Minority Classes Preservation

Instance-based classifiers become inefficient when the size of their training dataset or model is large. Therefore, they are usually applied in conjunction with a Data Reduction Technique that collects prototypes from the available training data. The set of prototypes is called the condensing set and has the benefit of low computational cost during classification, while, at the same time, accuracy is not negatively affected. In case of imbalanced training data, the number of prototypes collected for the minority (rare) classes may be insufficient. Even worse, the rare classes may be eliminated. This paper presents three methods that preserve the rare classes when data reduction is applied. Two of the methods apply data reduction only on the instances that belong to common classes and avoid costly under-sampling or over-sampling procedures that deal with class imbalances. The third method utilizes SMOTE over-sampling before data reduction. The three methods were tested by conducting experiments on twelve imbalanced datasets. Experimental results reveal high recall and very good reduction rates.
HTRM: A Hybrid Neural Network Algorithm based on Tag-aware

Tag information is of great significance to improve the performance for rating prediction, but the tag extraction in the recommendation system suffers from heterogeneous information fusion. This paper proposes a hybrid tag-aware recommender model (HTRM). First, the word embedding model embeds the score and the label respectively; then, the autoencoder extracts the text feature of the item label and uses the Long Short-Term Memory (LSTM) to extract the feature of the user label behaviors. The experiments are carried out on the three recognized datasets, namely Last.Fm, Delicious and MovieLens- 20m. Compared with the four algorithms, i.e., TCF, CCF, ACF, and DSPR, the experimental results demonstrate that HTRM significantly outperforms all the baselines.
Deep Staging: An Interpretable Deep Learning Framework for Disease Staging

Disease staging aims to measure the development of disease that uses clinical criteria to qualitatively classify the course of illness. Staging is a critical task in many clinical scenarios, for example, it could be used to guide the therapy and care, predict the clinical outcomes, optimize the utilization of resources, etc. Recently, data-driven disease staging using massive observational data has attracted significant attentions in literature. However, it is a technically challenging task not only because it is an unsupervised job without professional guidance as traditional ways do, but also it is crucial to generate clinically meaningful explanations in addition to stage prediction. In this work, we propose an interpretable deep learning framework, named Deep Staging, for data-driven explainable disease staging. The proposed approach could not only predict the disease stages based on observational medical data, but also generate clinically relevant characterizations of the disease stage outputs. Experiments on a real-world healthcare dataset demonstrate the effectiveness of the proposed framework.
Peculiarities of the University’s Educational Environment Implementation in the Organization of Future Teachers’ Pedagogical Practice

The article addresses contradictions between the existing and predicted tendencies in the organization and content of pedagogical practices. Through the analysis of teacher training programs, the need for student educational practice has also been established, which provides a solution to a wider range of professional tasks. The revealed contradictory nature made it possible to formulate the problem: what can be a trigger for the initiative acquisition by a future teacher of the experience in solving professional problems? Primary attention in solving this problem is paid to the conditions for the manifestation of personal student initiatives: the creation of platforms for dialogue, the provision of a periodic process of transition from practice to theory and vice versa, the use of operators of semiotic diagnostics of changes in students’ personal interests. The article presents the experience of organizing the educational environment, which provides future teachers with a modern range of professional probation. The options for the organization of official student practice are presented, including the development of online classes of educational and leisure activities of schoolchildren. This model contributes to the manifestation of students’ personal initiatives, both within the training profile and in the sphere of interdisciplinary intersections. The authors adduce the results of the questionnaire survey of students after the practice, showing their satisfaction with the chosen profession, a high level of motivation for mastering professional actions. The main methods and directions of organizing practices have been determined. They make it possible to ensure the predicted level of solving professional tasks by graduates.
A new item recommendation algorithm based on convolutional neural network

Due to the lack of label information for new items, the recommendation effect of traditional recommendation algorithms will be reduced. To solve this problem, this paper proposes a recommendation algorithm based on Word2Vec and convolutional neural network. First extract the keywords of the item text description information, use the Word2Vec model to convert the keywords into word vectors, calculate the similarity matrix of each keyword, and then use the similarity matrix as the input layer of the convolutional neural network to obtain the similarity between the items. Calculate the prediction score of the new item, and finally consider the user's preference for attribute information to generate recommendations. The experimental results show that the algorithm has a good effect on the hit rate.
Global Context Guided Multi-scale Feature Network for Salient Object Detection

Currently, fully convolutional network based salient object detection approaches have some challenging problems. This paper proposes a novel salient object detection approach using global context and multi-scale feature representation to estimate saliency maps in a pixel-wise manner. Firstly, we explore and design a multi-scale feature enhancement module to improve the capability of feature representation and learning of multi-level side-output features. Moreover, we use global features to guide side-output multi-scale features to focus on the useful information, which could help the network effectively locate salient objects and suppress background noises. Finally, the feature pyramid network structure is utilized to refine the estimated results in a coarse-to-fine manner, and then obtain the final predicted results. The comparisons of our approach and 15 state-of-the-art methods demonstrate the effictiveness and robustness of the proposed approach on various scenarios.
Universal Adversarial Attack via Conditional Sampling for Text Classification

Despite deep neural networks (DNNs) having achieved impressive performance in various domains, it has been revealed that DNNs are vulnerable in the face of adversarial examples, which are maliciously crafted by adding human-imperceptible perturbations to an original sample to cause the wrong output by the DNNs. Encouraged by numerous researches on adversarial examples for computer vision, there has been growing interest in designing adversarial attacks for Natural Language Processing (NLP) tasks. However, the adversarial attacking for NLP is challenging because text is discrete data and a small perturbation can bring a notable shift to the original input. In this paper, we propose a novel method, based on conditional BERT sampling with multiple standards, for generating universal adversarial perturbations: input-agnostic of words that can be concatenated to any input in order to produce a specific prediction. Our universal adversarial attack can create an appearance closer to natural phrases and yet fool sentiment classifiers when added to benign inputs. Based on automatic detection metrics and human evaluations, the adversarial attack we developed dramatically reduces the accuracy of the model on classification tasks, and the trigger is less easily distinguished from natural text. Experimental results demonstrate that our method crafts more high-quality adversarial examples as compared to baseline methods. Further experiments show that our method has high transferability. Our goal is to prove that adversarial attacks are more difficult to detect than previously thought and enable appropriate defenses.