Reviewerly/exHarmony huggingface dataset:
Multi-Level Indoor Path Planning and Clearance-Based Path Optimization for Search and Rescue Operations

In this study a multi-level path planning system is proposed for indoor search and rescue operations. Requirements for the path planning system are derived based on the operational concept of the integrated indoor navigation system. Different aspects of various path planning algorithms are assessed, and their suitability to search and rescue operations in structured indoor environments is investigated. A five-step path planning system is proposed, which consists of map pre-processing, segment path planning, graph processing, route optimization, and path post-processing. The proposed method addresses a multi-goal path planning problem in a multi-story building in a computationally efficient way by adopting a graph-based approach while satisfying such requirements as clearance conditions in the pre- and post-processing steps. Furthermore, a multi-query approach is adopted to exploit the response time and earn flexibility with respect to environmental changes. The effectiveness of the proposed path planning system is demonstrated through numerical simulations.
Design and Application of English Vocabulary Computer Aided Learning System Based on Associative Memory

The improvement of students' English and application largely depends on words. Based on similar association law, comparative thinking method, and similar association method, this study is carried out to analyze the mechanism of English word associative memory and design an auxiliary system of English word associative memory using computer software. The experimental teaching results show that the system can effectively improve the students' word memory effect and learning efficiency by 45%.
NLI-based Filtering for Data Augmentation in Topic Classification

As manually labelling data can be labour intensive, some research proposes to automatically expand the training data in the hope of improving model's generalization ability, known as data augmentation (DA). In Natural Language Processing (NLP), some recent studies leverage pre-trained language models (PLMs) to generate new training samples and then fine-tune the underlying model to fit for downstream tasks. However, a direct use of such models tend to trigger large discrepancies and most existing post-processing methods do not work well in real-world applications. In this paper, we meet this challenge by adopting the generate-and-filter framework that consists of two phases: a generation phase which uses PLMs to develop new candidate samples without fine-tuning, and a filtering phase that determines which samples are of a better quality and could be included in the augmentation dataset. Another key contributions of our work is to formulate the filtering phase as a natural language inference (NLI) task, which makes it possible to employ sophisticated methods such as auto-encoder-based PLMs to evaluate the importance of the candidate sample. In addition, we investigate the particular choices of models used in this framework, and discuss optimization options. Experiments for the task of topic classification on three public datasets prove the effectiveness of our proposal.
Enhancing Health Training Education Experience with Volumetric Video and Wearable Mixed Reality

Physical exercise is essential for maintaining a healthy life, and regular health training is critical for patients with chronic diseases. Although the patient has access to a wide range of exercise prescriptions that combine aerobic, muscle-strengthening, stretching, and neuromuscular activities, exercising effectively and regularly is difficult without motivation and guidance at home. Therefore, we suggest a system design to enhance the health training education experience at home with volumetric video and wearable mixed reality. We track the cycling data on an indoor cycle trainer equipped with sensors, and then we employ IoT protocols to deliver the riding data to an application server. The server evaluates the training scenario and provides the HMD with the information for correction suggestions. The HMD, then, displays the instructions as a volumetric movie in the right place to facilitate the training. We discuss the design of volumetric video recording and replaying as well as early user responses.
A Cross-Corpus Deep Learning Approach to Social Media Emotion Classification

Emotion Detection refers to the identification of emotions from contextual data in the form of written text, such as comments, posts, reviews, publications, articles, recommendations, conversations, and so on. Because of the Internet’s exponential uptake and the recent coronavirus outbreak, social media platforms have become a crucial means of sharing thoughts and ideas throughout the entire globe, creating rapid data growth through users’ contributions on various platforms. The necessity to acquire knowledge of their behaviors is a matter of great concern for both internet safety and privacy. In this study, we categorize emotional sentiments using deep learning models along with hybrid approaches such as LSTM, Bi-LSTM, and CNN+LSTM. When compared to existing state-of-the-art methods, the experiments showed that the suggested strategy is more robust and achieves an expressively higher quality of emotion detection with an accuracy rate of 94.16%, including strong F1-scores on complex and difficult emotion categories such as Fear (93.85%) and Anger (94.66%) through CNN+LSTM.
An Entire Space Multi-gate Mixture-of-Experts Model for Recommender Systems

With the development of e-commerce, both advertisers and platforms pay more and more attention to the effectiveness of ads recommendation. In recent years, deep learning approaches with a mulit-task learning framework have shown to be effective in such recommendation systems. One main goal of these systems is to estimate the post-click conversion rate(CVR) accurately. However, higher click-through rate(CTR) for a product does not always lead to higher conversion rate(CVR) due to many reasons (e.g. lower rating). In addition, the overall performance of the recommendation system may not be optimal, since the usage of multi-task models (the CTR and CVR tasks) is often sensitive to the relationships of the tasks. In this paper, we propose a deep neural model under the Mixture-of-Experts framework (MoE), call ES-MMOE, in which a sub-network is used to promote samples with high CVR. The model can also be trained with the entire space by taking advantage of the Entire Space Multi-task Model (ESMM) model. Extensive experiments on a large-scale dataset gathered from traffic logs of Taobao’s recommender system demonstrate that ES-MMOE outperforms a number of the state-of-the-art models, including ESMM, with a relatively large margin.
Tell Me A Story: The Effects That Narratives Exert on Meaningful-Engagement Outcomes in Antiphishing Training

Antiphishing training is a critical element of an organization’s security posture. Yet, its effectiveness is often limited by trainees’ perception that it is boring and unengaging, which weakens their attention and thus diminishes learning outcomes. We examine whether antiphishing training that uses narratives—stories—can make training more engaging and improve training outcomes. To that end, we conduct an experiment to compare the effects of a narrative antiphishing training against an existing non-narrative training as well as a no-training control group. Comparing against the control group, we found both training models to be effective. However, comparing the two training models, the experiment showed that the narrative training engenders higher levels of curiosity among trainees and higher levels of phishing-detection self-efficacy and phishing-detection ability. Taken together, these findings show that antiphishing training in specific, and perhaps security training in general, can be improved using narratives.
Mixture of Linear Additive Markov Processes: A Probabilistic Model for Joint Clustering and History-Dependent Transition Estimation

In this paper, we propose a new probabilistic model called mixture of linear additive Markov processes (MoL) that can analyze sequential data such as histories of people's visit locations, songs sampled, and web-browsing tracks. The proposed method is constructed by combining linear additive Markov process (LAMP) and mixture models. By combining their benefits, the proposed model can capture long-range history-dependency of sequences as well as individual preference for each user. In addition, the steady state distribution (or equivalently, PageRank vector) of MoL can be obtained by power iteration similar to Markov Chain (MC). We also develop the MM algorithm for parameter estimation and give proof of its convergence. The algorithm enables us to use an exponential family as a component of LAMP and so, by using the distribution whose support lies in positive integers, we can also deal with infinite order LAMP and infinite order MoL. The effectiveness of MoL is confirmed by experiments on synthetic and real data.
Class Attendance System using Unimodal Face Recognition System based on Internet of Educational Things

This study presents the design and implementation of an IoT-based class attendance system with face recognition for the high school. The system includes the Open-Computer-Vision (OpenCV) library, Python programming language, and a Raspberry Pi as the main processing unit. The system employs a combination of Haar-Cascades for face detection and Eigenfaces, Fisher faces, and Local Binary Pattern Histograms for face recognition. The methodology for the system is described in detail, including flowcharts for each stage of the system. The experiment results of the system are analyzed and presented, including plots and screenshots. We discussed the challenges encountered during the project, the system's potential applications, and future developments. The system was developed to automate the attendance-taking process, increase the accuracy and security of attendance records, and provide data to improve student performance and progress by recording class attendance in Google Sheets.
Viewing Bias Matters in 360<sup>°</sup> Videos Visual Saliency Prediction

360° video has been applied to many areas such as immersive contents, virtual tours, and surveillance systems. Compared to the field of view prediction on planar videos, the explosive amount of information contained in the omni-directional view on the entire sphere poses an additional challenge in predicting high-salient regions in 360° videos. In this work, we propose a visual saliency prediction model that directly takes 360° video in the equirectangular format. Unlike previous works that often adopted recurrent neural network (RNN) architecture for the saliency detection task, in this work, we utilize 3D convolution to a spatial-temporal encoder and generalize SphereNet kernels to construct a spatial-temporal decoder. We further study the statistical properties of viewing biases present in 360° datasets across various video types, which provides us with insights into the design of a fusing mechanism that incorporates the predicted saliency map with the viewing bias in an adaptive manner. The proposed model yields state-of-the-art performance, as evidenced by empirical results over renowned 360° visual saliency datasets such as Salient360!, PVS, and Sport360.
Learning to Rank using Fuzzy-Enhanced LSTM Model

Underlying information in re-ranking models plays a major role in personalization systems. In these systems, user profiles improve the quality of results created by search engines. A combination of the extracted underlying information and deep learning methods has yielded significant results recently. Models based on Long Short-Term Memory(LSTM) have been considered in this field. In this paper, a fuzzy-enhanced model based on the topic model is proposed to integrate the topic-based user profile into the LSTM model. The topic-based user profiles are created using fuzzy logic to handle the uncertainty of the occurrence of all topics in a document. This model is used in an LSTM-based framework to re-rank search results on a input query using the sequence of queries and click history. To evaluate the proposed model, the results are compared between the model and the baseline model created using Latent Dirichlet Allocation (LDA). The result shows an improvement in the Fuzzy-enhanced LSTM model compared to the base model.
Object Detection and Tracking: Deep Learning based Novel Tools to Generate Robust Human and Machine-Annotated Ground Truth Data for Training AI Models

Object detection and tracking is one of the most emerging fields of computer vision that facilitates various fields including robotics, healthcare, security, autonomous vehicle systems, machine inspection, surveillance, and logistics. In object detection, many factors need to be considered including intrinsic and extrinsic factors, camera motion, deformation, occlusion, and motion blur. Machine learning (ML) and deep learning (DL) approaches are being adopted in object detection and tracking, and training these models is the key challenge achieving robust accuracy in the automated detection and tracking of objects. Data annotation paves the way to training ML and DL models; however, training models with inaccurate data jeopardizes the robustness of actual object detection and tracking. Towards generating 100% accurate datasets, human intervention is crucial for assigning identities to the correspondent objects throughout frames. In this paper, we utilize the OpenCV-based deep learning technique and introduce a framework that allows users to assign identities to detected objects towards generating flawless human-annotated ground truth data. The proposed framework allows the users to assign correspondence ids for bounding boxes on Tkinter GUI to help organizations prepare robust annotated datasets to train large-scale object-tracking models for object detection. In an extension of our study, we introduce a novel tool that will learn from human-annotated datasets and generate identities for the detected objects accurately. We evaluate our models on roughly 100 and 1000 human-annotated ground-truth datasets and later 5000 machine-generated ground-truth datasets. According to our demonstration, we achieved an accuracy of 97.55% and 96.68% respectively for human-annotated ground truth datasets. We also achieved an accuracy of 96.33% using a machine-automated ground truth dataset which indicates the robustness of our model. In future studies, we will extend our research to optimize proposed models to achieve an ultimate accuracy of 100%.
Two-Hand Gesture Recognition for User Information Interaction based on Internet of Educational Things

With the increasing prevalence of computer technology, interaction through the computer has become a significant challenge for certain groups, such as the elderly, disabled, and students. Hand sign recognition has emerged as a promising solution in recent years, as it offers a natural and adaptable means of human-machine interaction, particularly in educational contexts. However, real-time hand gesture recognition is a complex system development task that requires advanced technology and expertise. To address this issue, we propose a system architecture and software configuration for developing hand sign recognition based on the internet of things (IoT). In the experiment, a Raspberry Pi with a camera, Python programming, and Open-Source Computer Vision (OpenCV) software were used to develop an accurate system for detecting, recognizing, and interpreting two-hand gesture recognition in the context of human-IoT interaction. The project's primary focus is improving the accuracy of hand sign gesture recognition in real-time systems. The proposed system contributes to facilitating friendly and adaptable human-computer interaction, especially in educational services. In addition, the research result enables better computer interactions for the elderly and disabled, thus promoting greater inclusivity and accessibility in the technology industry.
Reform of English Listening Course Teaching Mode in Vocational Colleges Based on Artificial Intelligence

This paper explores how to use artificial intelligence to innovate the teaching mode of English Listening Course, and how to apply artificial intelligence to teaching sessions, that is listening-driven, listening-facilitating and listening evaluation. To take Chongqing Vocational Institute of Tourism as a research project, from listening achievements, it analyzes the effect of teaching mode in terms of listening ability and listening motivation. The results show that the teaching mode of English listening course based on artificial intelligence is useful to improve students' enthusiasm and listening ability in English listening course.
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-the-art Graph Transformer Networks to analyze all conversations that follow an initial post. This framework also supports adapting to future comments as the conversation unfolds. In addition, we study whether a community-specific analysis of hate speech leads to more effective detection of hateful discussions. We evaluate our approach on 333,487 Reddit discussions from various communities. We find that community-specific modeling improves performance two-fold and that models which capture wider-discussion context improve accuracy by 28% (35% for the most hateful content) compared to limited context models.
A Noise-enhanced Fuse Model for Passage Ranking

Since the rapid progress in deep learning in recent years, many language models have achieved significant results in various information retrieval (IR) tasks. Passage ranking plays a vital role in this field, and the neural network models significantly outperform the traditional method. However, fine-tuning the pre-trained model to the downstream task may be influenced by the fact that there are differences between the two tasks. And traditional methods also have their advantages. In some cases, the performance of BM25 is obviously better than the deep learning model. This paper discusses the results of the deep learning model linearly combining with BM25 and adds noise to the model for enhancing the finetune performance. We conduct experiments on the MS MARCO dataset to show convincing results.
Research on Influence of Teaching Assistance Technology on Students' Learning Process and Learning Result Based on Structural Equation Model

With the in-depth development of information technology, educational information technology has been applied more in teaching and plays a complementary, auxiliary and even leading role. Thus, this study was carried out to understand the influence of barrage quality control on college students' learning process and results by taking barrage assisted teaching technology. A total of 204 valid questionnaires were received from students in a university under the guidance of teachers through the questionnaire Star platform. The structural equation model (SEM) was established to verify the relationship between the influence of teaching assistive technology on the learning process and the learning results. The results verified that in the classroom teaching assisted by barrage, barrage quality control positively affected the learning process, and the learning process positively affected the learning result. Chi-square freedom ratio =1.85 (<3), GFI=0.942 (>0.9), RMSEA=0.065 (< 0.1), RMR=0.035 (< 0.05), CFI=0.975 (> 0.9), NFI =0.947 (> 0.9), NNFI=0.965 (> 0.9), It indicates that the fitting validity of the whole model is good. This study verifies that the path of barrage quality control - learning process - learning result is established, and the model shows that technical controls such as barrage quality, course content, and teacher management can alleviate learning burnout, increase learning interaction and learning process satisfaction, and improve social ability. In turn, these can promote students' learning enthusiasm, knowledge memorization, and application ability.
An Article Recommendation Technique from a Multi-Layer Reference Article Graph for Facilitating Chronological Learning

With the rapid growth of scientific publications, researchers often find difficulty in discovering appropriate articles that can mitigate the knowledge gaps to understand a target article (a.k.a., base article in this paper). In this case, reference articles can play an important role. It may happen that a researcher may have to read several levels of references, which is challenging since it increases exponentially over levels. This kind of learning method could be considered as the chronological learning. In this paper, a chronological learning supported recommender system is proposed, which utilizes the reference articles of multiple levels for generating a multi-level weighted graph. The weights of the various nodes in this graph are calculated considering three scores, namely lexical similarity, time-aware influence, and node centrality. Among them, the equation for calculating the time-aware influence score is improved by taking citation counts into consideration so that the articles with the higher citation counts receive higher influence scores, which is more practical. From this graph, a chronological path is selected considering a weight-based selection process envisioning mitigating the knowledge gaps to understand a base article. Since the keyphrase extraction plays an important role in this system, various unsupervised keyphrase extraction techniques are evaluated to discover the most suitable relevant technique for the proposed system.
Multimodal Attention Networks for Human Activity Recognition From Earable Devices

Earables (a.k.a ear-worn wearable devices) are gaining traction in the wearables ecosystem for monitoring user health. Human activity recognition (HAR) is a promising use case of earables due to their placement on the head and the combination of sensors. In this paper, we explore using multimodal attention-based neural networks for HAR from the ear. Attention networks have had a large impact on other disciplines’ machine learning tasks and we believe they present opportunities in HAR from earable data. Different methods of utilising attention mechanisms in the literature are discussed as well as the benefits and challenges of using such networks in the context of HAR on real systems.
A New Perspective of Improving Initialization for Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a pretty efficient algorithm to optimize functions. PSO can be used when traditional optimization algorithms, like Gradient Descent, fail. However, initializing the initial population of the PSO algorithm with random initialization does not yield good results. The primary target of this research is to find a unique way of initializing the initial population, so that PSO performs better than the traditional PSO algorithm. This proposal implemented techniques from Genetic Algorithms and used a novel approach to solve the issue of initialization. Researchers have introduced various initialization techniques in recent years to improve the performance of PSO. Low discrepant sequences like Sobol Sequence, Halton Sequence and Faure sequences have been used to initialize the initial population. But limitation with those initialization technique is that their performance degrades as the search space gets larger. In this research we were able to find better initialization methods, as well as a novel approach. Result comparisons on various scales proves the superiority of the proposed initialization method.