Reviewerly/exHarmony huggingface dataset:
On The Use of Gestures in Dialogue Breakdown Detection

In this paper, we verified the use of gestures in dialogue breakdown detection. The data is 30 real human-robot conversations annotated with body, hand and head gestures. We used as features the ratio of the duration of each gesture within the 3000 msec interval from the start of the robot&#x0027;s utterance, and applied Support Vector Machine to build a model to predict whether the robot&#x0027;s utterance had caused dialogue breakdown. The results showed that the accuracy of our model was higher than the chance level in multiple metrics. Moreover, it is suggested that in some cases the users reacted to the robot&#x0027;s response with gestures in the same way as in human-human interaction.
Programming an Educational Chatbot to Support Virtual Learning

With the educational hardships presented by the COVID-19 pandemic, the connections between teachers and their students as well as club leaders and their members have been compromised. These circumstances prompted the creation of a Discord education management chatbot to serve as an efficient communication assistant to help schools and clubs overcome the barriers of virtual and/or hybrid learning. This chatbot supports diverse features including reminders, stopwatches, polls, and breakout rooms that aim to streamline and enhance the virtual learning experience, but could also appeal to a variety of interests and audiences outside of the classroom. Its features also remain helpful for in-person schooling due to the wide variation of commands.
Integrated Path Planning and Lateral-Longitudinal Control for Autonomous Electric Vehicles

In this paper, an integrated path planning and tracking algorithm are presented. The proposed cascade structure consists of two parts. The outer loop is a predictive, optimization-based yaw rate reference planner that uses a path-based coordinate system handling the reference track as boundary constraints, seeking to find the maneuver with the possible minimal control effort. The inner loop is responsible for the lateral and longitudinal movement ensuring robustness against parameter changes and disturbances, using the Internal Model Control structure. The algorithm includes a minimal number of tuning parameters as the lateral planner path uses a physical phenomenon as a cost function. The effectiveness of the proposed structure is illustrated by simulation results. The algorithm was implemented in a real-time environment, and the tests performed on the Lotus Evora test vehicle developed by ThyssenKrupp are examined and evaluated.
Design of Wavelet Denoising and Image Enhancement Algorithm Based on MATLAB

In the process of visual positioning, the collected image needs to be processed first. Image denoising and enhancement belong to the preprocessing phase of digital image, which can effectively improve the overall or local characteristics of the image. In order to extract the key information of the image better, this paper first denoises the collected image through the wavelet soft threshold, then enhances the image. Wavelet threshold denoising includes soft threshold denoising and hard threshold denoising. Through experimental comparison, soft threshold denoising is selected finally. Image enhancement includes both spatial and frequency domain enhancement. Due to the advantages and disadvantages of both methods, a combination of spatial histogram equalization and frequency domain Butterworth low-pass filter algorithm is used to enhance the image. The combination of the two techniques can make the detailed features of the image more obvious and sharper, and the image restoration effect is better than that of either technique alone.
Scale-Aware Cascading for Semantic Segmentation

Abstract Semantic segmentation is an important technology commonly used in medical imaging, autonomous driving vehicles, and backgrounds for virtual meetings. Scale Aware approaches have become the standard when it comes to the semantic segmentation domain of Machine Learning. Multiple image scales are passed through the network allowing the result to use the regular CNN layers such as max-pooling as well as convolution layers. Also, a cascading hierarchy of attention has been shown to improve the accuracy of models for such segmentation tasks. The combination of both these approaches has been shown to greatly improve the accuracy of such models. A side effect of using the cascading approach is that the model turns out to use less memory in comparison to previous approaches. Auto-labelling engines are also helpful in generalizing the model further. The cityscapes dataset used here is a useful data bank as it consists of a myriad of situations where the model can be trained and tested on. This paper presents the tested results of such a segmentation model and incremental modifications to the model pipeline to understand and improve upon the existing architecture.
Visibility Prediction Based On XGBoost And Markov Chain Combined Model

Improving the accuracy of visibility prediction is conducive to taking corresponding measures for weather with different visibility to achieve the purpose of ensuring the safety of life and property. Meteorological factors, environmental factors, terrain and human activities all have an impact on visibility. At present, visibility prediction mainly includes meteorological map analysis, numerical model forecasting, statistical forecasting and other methods. This paper proposes a visibility prediction based on Extreme Gradient Boosting and Markov chain combined model (MC-XGBoost). Firstly, build the MC-XGBoost model, the visibility related feature parameters are selected as the input of the model, and the corresponding visibility is taken as the output of the model. Then perform model training, adjust the model parameters to make the model achieve the optimal prediction effect. Finally, the trained model is applied to visibility prediction. Experimental results show that MC-XGBoost model can effectively predict visibility with high accuracy. This model can be used to predict visibility, which provides a certain reference value for the construction of a visibility prediction model in the future.
Deep Learning Models Principles Applied to Biomedical Engineering

The underlying principle of “ Deep Learning ” is the compositional nature of “ neural networks ” inspired by the biological elements that form the “ human brain ,” with a collection of “ nodes ” emulating “ brain neurons ,” and their “ neuron synapses connections as primary elements of a net, that combined form midlevel elements identified as “ Artificial Neural Networks ( ANN ).” In turn the ANNs are combined with different architectures to form more complex networks. In this book “ ANN ” are organized based on their architectural type, and the way their different components are connected to one another to define the specific learning goal, into different types: “ Feed Forward Neural Network ,” “ Backpropagation Neural Networks ,” “ Recurrent Neural Networks ,” “ Memory Augmented Neural Networks ,” “ Modular Neural Networks ,” and “ Evolutionary Neural Networks .” The first two “ ANN types ” are studied in this chapter, along with a general net “ Shallow Neural Network ” and a special kind of learning known as “ Transfer Learning from pretrained Deep Learning Networks .” All are based on examples and practical research on biomedical engineering using existing “ AI tools ” from: “MATLAB®” and “IBM Watson Studio.”
Data sharing and privacy issues arising with COVID-19 data and applications

The coronavirus disease 2019 (COVID-19) (2019-nCov), which was first detected in Wuhan/China in December 2019 and spread to the whole world in a short time, was explained as a new coronavirus by the World Health Organization on February 11, 2020. Countries are developing various strategies against the spread of epidemic threat. The main ones are to develop web-based or mobile applications to reduce the spread and economic damage of the epidemic by making use of COVID-19 datasets. It is seen that the existing applications developed within the framework of these expectations contain absolute location information (direct), relative location information (indirect), and characteristic data defining people. Even if these data mean a lot to the world's struggle with COVID-19, it is necessary to foresee the risks that may occur after the epidemic when the relations of the information are considered. In order to measure the privacy risk of this kind of applications containing personal data, privacy metrics have been defined in the literature. In this chapter, we give a perspective about the sharing and privacy of medical data within the scope of COVID-19. Within this context, privacy models, metrics, and approaches for selecting the appropriate model are described, in particular for COVID-19 applications, and we also propose a new metric with the entropy approach to metrics defined in the literature and effective in determining the privacy score.
Improving Accessibility of an XML-based Conference Proceedings Web Site

This paper describes ongoing work to make the Web site containing the proceedings for the Balisage conference be more accessible. Like many older Web sites, the site may have followed best practices when the framework was built, but times and the Web have moved on. The site is built from static XML documents using XSLT, which was modified as needed. Difficulties encountered included those common to updating any project’s production code that has been in use for a long time and has undergone periodic tweaks and adjustments; changes specific to accessibility and to the XML input are highlighted. The work took only a few days, albeit spread over several months, accompanied by considerably more research and testing, also described in the paper. Limitations of the work, especially including dealing with author-submitted content, are also described.
Twitter Sentiments and Stock Market Indexes: A COVID-19 Analysis

In this study, we employ the COVID-19 Twitter sentiment of seven countries to examine the stock market indexes. We conduct our analysis on a sample of 1,616,007 tweets over the period January to June 2021. We process the tweets based on the VADER analyzer, thereby producing both positive and negative sentiment measures. Particularly, we prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English spoken countries. Notably, our results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes.
Jointly Modeling Fact Triples and Text Information for Knowledge Base Completion

Knowledge bases become essential resources for many data mining and information retrieval tasks, but they remain far from complete. Knowledge base completion has attracted extensive research efforts from researchers and prac-titioners in diverse areas, which aims to infer missing facts from existing ones in a knowledge base. Quantities of knowledge base completion methods have been developed by regarding each relation as a translation from head entity to tail entity. However, existing methods merely concentrate on fact triples in the knowledge base or co-occurrence of words in the text, while supplementary semantic information expressed via related entities in the text has not been fully exploited. Meanwhile, the representation ability of current methods encounters bottlenecks due to the structure sparseness of knowledge base. In this paper, we propose a novel knowledge base representation learning method by taking advantage of the rich semantic information expressed via related entities in the textual corpus to expand the semantic structure of knowledge base. In this way, our model can break through the limitation of structure sparseness and promote the performance of knowledge base completion. Extensive experiments on two real-world datasets show that the proposed method successfully addresses the above issues and significantly outperforms the state-of-the-art methods on the benchmark task of link prediction.
Merging 360°-videos and Game-Based Virtual Environments for Phlebotomy Training: Teachers and Students View

This paper presents a Virtual Reality (VR) game aimed at phlebotomy learning in real-life contexts. 360&#x00B0;-images and -videos of existing laboratories were used as the basis of the virtual environment. The game was designed with a focus on problem- and inquiry-based learning through exploration of laboratories with VR mini-games and interaction opportunities for essential procedural learning tasks. The results are based on usability and user experience evaluations from 6 students and 5 educators. Aspects related to games&#x2019; attractiveness, engagement and enjoyment were highly rated, though several improvements were deemed necessary before the application should be used in practice. The result contrasts the different opinions of teachers and students. While teachers were more critical regarding usability problems, they were positive towards using gamified experiences for procedural learning. Students wished to have even more gamified tasks and a better flow between tasks or environments.
Squeeze-Excitation Based Communication Behavior Recognition Approach for Spectrum Sensing Data

Communication behavior recognition under electronic countermeasures conditions has become an increasingly important topic for communication security and military application. This paper proposes a squeeze-excitation-based communication behavior recognition approach to adapt to the demand for rapid response under electronic countermeasures conditions. Unlike previous studies, a more practical communication scene is constructed to acquire convincing data. To speed up the recognition process and realize better performance on recognition, the squeeze-excitation network is utilized to apply the attention mechanism in the recognition approach to speed up the learning process. The comparative experiments show that the proposed approach can realize ideal recognition performance much faster compared with the traditional method, which is adaptive to the electronic countermeasure environment.
Goal Generation and Management in NARS

AGI systems should be able to pursue their many goals autonomously while operating in realistic environments which are complex, dynamic, and often novel. This paper discusses the theory and mechanisms for goal generation and management in Non-Axiomatic Reasoning System (NARS). NARS works to accomplish its goals by performing executable actions while integrating feedback from its experience to build subjective, but useful, predictive and meaningful models. The system’s ever-changing knowledge allows it to adaptively derive new goals from its existing goals. Derived goals not only serve to accomplish their parent goals but also represent independent motivation. The system determines how and when to pursue its many goals based on priority, context, and knowledge acquired from its experience and reasoning capabilities.
A Fusion Framework to Enhance sEMG-Based Gesture Recognition Using TD and FD Features

Gesture recognition based on surface electromyographic (sEMG) is one of the most critical technologies in the field of human–computer interactions(HCI). The previous sEMG-based gesture recognition frameworks have poor effect due to the inappropriate feature processing. In this paper, we propose a sEMG-based gesture recognition framework called TF-LSTMCNN(LSTM and CNN based on TD and FD features). We formulated the feature combination from time domain (TD) and frequency domain (FD) as the diversified feature representation, as well as an advanced feature representation learned from a fusion technique that integrates a variety of discriminative sEMG signal patterns. To generate diversity feature representations, we considered the redundancy and uniqueness among features and chose a combination of TD and FD features. The accuracy of the proposed method achieved 85.13%, and is 8.58% higher than SVM which showed higher performance in the machine learning (ML) methods.
Face Recognition System with Feature Fusion for Rehabilitation Robots in Healthcare

Face recognition is a research problem across multiple disciplines such as computer vision, pattern recognition, artificial intelligence, psychology, healthcare etc. At present, the rehabilitation centers in hospitals are dependent on the nursing staff with no implementation of rehabilitation automation. In this study of face detection, a facial expression recognition system was developed which can be useful for robots of rehabilitation nursing beds. This article mainly researches two aspects: face image feature extraction and classifier design. Since many low-resolution face images are often encountered in actual environments (such as surveillance), if you use traditional methods to directly extract image features, the results are often not satisfactory. For the influence of face recognition results, this paper proposes a feature fusion extraction method based on KNN with LDA(Fisherfaces) and PCA (eigenfaces). First, the original image (low resolution) is decomposed into three different resolution images, and then the three images are used as the basis for the position to extract facial features, then LDA and PCA are used to perform preliminary analysis to obtain reduced dimensional reduction features. Finally use the K-nearest neighbor classifier to perform classification prediction. Study compares KNN-LDA with KNN-PCA with different distances of KNN algorithm.
Multi-player dynamic game model for Bitcoin transaction bidding prediction

With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity.
Designing Privacy Filters for Hidden Markov Processes

We address the problem of releasing a utility process correlated with a hidden sensitive source (both modeled through a hidden Markov model) by designing a privacy filter hiding the sensitive data while maintaining a fidelity criterion on the utility process. The problem is formulated as a constrained minimization of a variant of relative entropy between the sensitive hidden process and the output of the privacy filter. We first explain that in its initial form, the problem is suffering from the curse of dimensionality. Then, we propose a relaxation of it taking into account the information structure of the decoder policies. Such relaxation leads to tractable privacy filtering policies that are illustrated via a simulation study.
Privacy-Preserved Social Distancing System Using Low-Resolution Thermal Sensors and Deep Learning

The COVID-19 pandemic brought drastic changes to daily routines and abiding by the guideline for social distancing is necessary to prevent the spread of the virus while easing back to normality. While camera-based social distancing solutions are widely available, they do not preserve the privacy of the user. To the best of our knowledge, there is no practical privacy-preserved social distancing technology in the market. This research employs IoT and Deep Learning to implement an indoor privacy-preserved social distancing wireless sensor network-based system that uses 8×8 low-resolution infrared sensors (AMG8833) to promote social distancing within companies and organizations. This research uses four top view wireless sensor nodes to collect a total of 6,606 low-resolution infrared images, creating a dataset that covers 200 cases of varying numbers of people in diverse locations. The YOLOv4-tiny model achieved a mAP@0.5 of 95.4% and an inference time of 5.16ms when trained on the collected and labeled dataset to detect people. Thus, we conclude that our proposed novel social distancing system is as effective as other solutions that use high-resolution images while maintaining privacy.
Exploration of SVD for Image Compression and Time Series Processing

This paper explores the block-based singular-value-decomposition (SVD) with applications to image compression and processing in both static and dynamic cases. Results include a comprehensive performance study of rank-1 asymmetric block sizes in comparison to conventional uniform square blocks for static image compression. In addition, a method of movement detection for video streaming applications are presented and discussed in this paper. We show that by tracking the Frobenius norm of the derivatives of sequential images, represented as a time series matrix, allows one to infer change points in varying lighting conditions and foreground movement in video sequences. We show that by combining SVD background subtraction we can create clearer distinction between movements and changes in lighting intensity. It is also demonstrated that one can maintain the original derivative feature after re-scaling the images of the video stream to a fraction of their original resolution via local averaging.
A Novel Visual Indoor Positioning Method With Efficient Image Deblurring

Aiming to improve the accuracy of visual indoor positioning, an efficient deep multi-patch network based image deblurring algorithm (DMPID) is proposed to eliminate the effect of blurred images on the positioning accuracy. Meanwhile, the self-sorting visual word based image retrieval algorithm and the block based improved eight-point method are also proposed in this paper to improve the retrieval accuracy and positioning accuracy. The proposed image deblurring algorithm adopts the deep multi-patch network and the weight selective sharing scheme to acquire the deblurred images. Then, we propose the self-sorting visual word and add two additional elements representing the relative spatial information into every obtained feature point to utilize the intrinsic relationships between extracted features and physical positions in order to improve the retrieval accuracy. Meanwhile, the visual word filtering is also employed to eliminate redundant visual words and reduce the time consumption of image retrieval. Finally, the position estimation of query camera can be achieved by the epipolar constraint based on the improved eight-point method. Simulation results and performance analysis show that the proposed method can restore the image details effectively and improve the accuracy of visual indoor positioning.