Reviewerly/exHarmony huggingface dataset:
Chronological text similarity with pretrained embedding and edit distance

Text similarity algorithms are the core building blocks for several natural language processing (NLP) applications. Question-Answering, Search Engines, Plagiarism check, and Summarization are a few of the several applications with text similarity as the building block. Over the last two decades, immense research has been conducted by the NLP and computer science community to develop more accurate algorithms to detect semantic and syntactic similarities. But, more than often than not these works have overlooked the importance of chronological order of text, which is very essential in cases like a manufacturing-process text where we try to find the order in which ingredients are added, processed, etc. When we additionally capture word order similarity, the final text similarity tends to be more accurate. In this chapter, we present a simple ensemble method to capture semantic, syntactic, and chronological similarities between two sentences. The ensemble consists of a proportioned modified edit distance score and cosine similarity score from pretrained state-of-the-art embedding. The embedding captures the semantic similarity and the modified edit distance helps capture the word order, thus making the model more robust. Further, we have studied the effects of various ensemble ratios to help achieve maximum accuracy. The proposed model was demonstrated on the standard STS-B dataset and custom dataset. The results show that the ensemble achieves far better accuracy than the individual models to measure similarity in sentences.
Topology Design and Algorithm Analysis of VBCC in Hybrid Data Center Network

With the development of cloud technology and people’s needs, many applications need to be supported by data centers. Therefore, excellent network topology is needed to meet the requirements of high bandwidth, low delay and stable network transmission.Data center network is divided into wired network and wireless network. This paper presents a topology design method for the hybrid data center network VBCC by setting up wireless VLC links in the BCCC of wired data center network. It combines the advantages of wired and wireless data centers. Through property analysis, it has more link selection and fewer routing hops, and has stronger scalability. Finally, a unicast and multicast algorithm based on VBCC is presented.
Generalized Net Model of the Upper Limb Arterial Supply System

In a lot of papers, a different medical processes, different organs and systems in the human body are described by Generalized Nets. This models can be used for simulation of real processes and objects in the human body with the aim of using the information obtained in the process of medical diagnosis and research. In the present investigation a Generalized Net model of the arterial supply of the upper limb as a part of the vascular system is presented. The vascular system ensures the circulation of blood and lymph in the body. It is composed of a CardioVascular System (CVS) which distributes blood, and a lymphatic system, which distributes lymph. The transitions of the Generalized Net model of the arterial supply of the upper limb proposed in this paper have maximal simple form. The purpose of the construction of the model is to complement and enrich the knowledge about the CVS, or at least about a part of it.
Application of Dunkl Wavelets to solve the Integral Equations

Abstract The Integral Equation appears in different fields of science and engineering mathematics, for example image processing and the concept of electromagnetic. The technique of wavelet transform to find the solution of integral equations allows creating an algorithm after comparing with known algorithms. This paper is about the uses of the Dunkl Wavelet transforms and associated convolution to establish some suppositions for solution of these equations on distribution spaces. This work defined the method to solve various integral equations by using the Dunkl wavelet transforms.
Skin-Colour-Based Hand Segmentation Techniques

People have been continuously mesmerized by the sci-fi notion of controlling things based on hand gestures. However, only a few ponder upon the working of these gesture-based systems. This chapter explains how these gesture-based systems generally use skin-colour-based hand segmentation techniques. This chapter sheds light on and explains various colour models, various methods to use these colour models and perform skin-colour-based hand segmentation, various real-life applications of segmenting areas using skin-colour with multiple examples of hand gesture recognition, as well as other applications where skin-colour based segmentation is used.
English Education Online Platform Based on Artificial Intelligence

Over the years, AIT has begun to present to people's lives, making people's lives more comfortable. With the progress of intelligent scientific method, online education is developing in the field of education. In the face of the pandemic, millions of students around the world have turned to online education platforms to learn. This paper takes English teachers and students as examples to study the innovation and development of online education. This paper compares the influence of new teachers and students through experiential models and traditional teaching models, and focuses on analyzing the benefits of reformed advanced technologies for online English teachers and students. The results showed that 67% of the students in the experimental group were satisfied or very satisfied with the new teacher-student model, while only 46% of the students in the control group were satisfied or very satisfied with the traditional teaching model. Artificial intelligence scientific method (AIT) can promote the reform platform of Online English education.
PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors

We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single-task setting to the meta-learning multiple-task setting to upper-bound the error evaluated on any, even unseen, tasks and samples. We also propose a generative-based approach to estimate the posterior of task-specific model parameters more expressively compared to the usual assumption based on a multivariate normal distribution with a diagonal covariance matrix. We show that the models trained with our proposed meta-learning algorithm are well-calibrated and accurate, with state-of-the-art calibration errors while still being competitive on classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.
Gesture Based Confidence Assessment System for Visually Impaired People Using Deep Learning

Deep learning is used in gesture recognition, but researchers explore sign language identification, human emotions like sadness, happiness, anger, fear, joy without context. In this paper, we describe a gesture-based confidence assessment (GCA) system for hand gesture recognition to identify the mental state from their hand movements during an interview as a context. The system also useful for visually impaired people, whether they conduct or appear for an interview. The GCA system generates the audio alert about the confidence level of the target users. In the past, there is no work done to detect a person’s mental state using hand gestures. Our approach is based on a CNN with LSTM to capture the temporal information. The dataset used for training purposes is a set of frames extracted from open source videos, like YouTube. The data is annotated taken into account four mental conditions that are presented during interviews. The accuracy of our model is 86%.
Semi-Supervised Transfer Learning for Language Expansion of End-to-End Speech Recognition Models to Low-Resource Languages

In this paper, we propose a three-stage training methodology to improve the speech recognition accuracy of low-resource languages. We explore and propose an effective combination of techniques such as transfer learning, encoder freezing, data augmentation using Text-To-Speech (TTS), and Semi-Supervised Learning (SSL). To improve the accuracy of a low-resource Italian ASR, we leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively. In the first stage, we use transfer learning from a well-trained English model. This primarily helps in learning the acoustic information from a resource-rich language. This stage achieves around 24% relative Word Error Rate (WER) reduction over the baseline. In stage two, We utilize unlabeled text data via TTS data-augmentation to incorporate language information into the model. We also explore freezing the acoustic encoder at this stage. TTS data augmentation helps us further reduce the WER by ∼ 21 % relatively. Finally, In stage three we reduce the WER by another 4% relative by using SSL from unlabeled audio data. Overall, our two-pass speech recognition system with a Monotonic Chunkwise Attention (MoChA) in the first pass and a full-attention in the second pass achieves a WER reduction of ∼ 42% relative to the baseline.
Using Annotation for Transparent Inquiry (ATI) to Teach Qualitative Research Methods

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SolutionTailor: Scientific Paper Recommendation Based on Fine-Grained Abstract Analysis

Locating specific scientific content from a large corpora is crucial to researchers. This paper presents SolutionTailor (The demo video is available at: https://mm.doshisha.ac.jp/sci2/SolutionTailor.html ), a novel system that recommends papers that provide diverse solutions for a specific research objective. The proposed system does not require any prior information from a user; it only requires the user to specify the target research field and enter a research abstract representing the user's interests. Our approach uses a neural language model to divide abstract sentences into "Background/Objective" and "Methodologies" and defines a new similarity measure between papers. Our current experiments indicate that the proposed system can recommend literature in a specific objective beyond a query paper's citations compared with a baseline system.
Distributed File System on Medical Data Using Machine Learning Techniques for Healthcare Surveillance

The tremendous research into the medical health system provides a lot of opportunities for computing systems to come up with new ideas. These advancements are critical to the well-organized deployments of remedial systems that aid in the automated identification of health-related issues. The majority of essential health research is available on cancer prediction, which comes in a variety of forms and can impact different regions of the body. Pancreatic cancer is one of the most commonly afflicted cancers that are expected to be incurable. Once diagnosed, it cannot be treated properly. Machine learning and neural networks are providing promising findings for accurate pancreatic image segmentation in real-time early detection these days. The deep learning-based Hadoop distributed convolutional neural network (HdiHCNN) and Hadoop distributed recurrent neural network (HdiRNN) are used in this study to identify pancreatic tumors. The tentative results show that the suggested technique can improve the classifier’s performance for early identification of pancreatic cancer.
Depth-Perception Based Geometry Compression Method of Dynamic Point Clouds

Point cloud technologies have been used in many applications, such as virtual reality, automatic driving, and so on. However, huge amount of data in point clouds will seriously affect their application. Thus, how to compress point clouds effectively, especially dynamic point clouds, is an urgent problem to be solved. Based on the video point cloud coding (V-PCC) standard from MPEG, this paper proposes a new depth-perception based geometry compression method of dynamic point clouds. Firstly, with the idea of point cloud's perpendicular projection, a concept of relative depth of point cloud is defined and a scheme is given to calculate the relative depth. At different rotation angles, the depth of point cloud's each point to the observation/projection planes is calculated, and the depth at different rotation angles can be weighted to represent the relative depth of the point cloud. Secondly, based on the relative depth of different points, a point cloud can be divided into four regions, including close shot, medium and close shot, medium and long shot, and long shot. Finally, a rate-distortion optimization strategy based on depth perception is proposed to achieve bitrate control of geometry compression in different regions of the point cloud. Experimental results show that compared with the V-PCC anchor, the proposed method can achieve bitrate saving of 11% for dynamic point cloud's geometry compression, under the premise of ensuring visual quality.
Simulation of Three-person Cooperation - Effect of Mutual Beliefs on Team Performance

Many human factor studies have explored the cognitive and behavioral factors that affect team performance by verbal protocol analysis and behavioral analysis. However, since the measurements used in these studies focused only on observable and recordable data, there is a fundamental limitation for understanding cognitive mechanisms behind those data. Computer simulation is an alternative method for exploring the cognitive aspects of human factors in team cooperation. In our previous study, therefore we developed an agent-based simulation of team cooperation using a team cognition model based on the concept of mutual beliefs and investigated how mutual beliefs affect better team performance. While the previous study modeled and focused only on a two-person team, the actual team in the real world often consists of three or more people, where more complicated cognitive processes are required than those in two-person cooperation. In this study, we developed an agent-based simulation of three-person team cooperation, employing the extended team cognition model considering mutual beliefs and mental subgrouping. This extended model describes cognitive processes in a team of three or larger with three different layers namely self-cognition, direct belief, and projected belief. We applied this model and simulation to the cooperative diagnosis problem of car failures and observed how a team collaboratively collects information on car parts, shares with other members, and identifies broken car parts. The results showed that the teams could share information effectively and identify the correct failures. The results also suggested that the communication generated by the mutual beliefs worked effectively and enhanced the team performance. We believe that our simulation is a promising method to compensate for the limitations of conventional human factor methods and explore cognitive aspects of team cooperation.
A Novel Smart Aging Approach for Monitor the Lifestyle of Elderlies and Identifying Anomalies

For the past two decades, there is a paradigm shift in the field of automation (IoT), and it is affecting several aspects of life. “Activity Modeling” is a part of such automation, and it has several applications. One such application of “activity modeling” is in the field of distant well-being investigation and nursing. In proposed study, modeling and analysis of everyday activities of elderly persons are done by “recording of periodic behavior pattern of elderly people” on daily basis. Today, the challenge is to develop such a device that requires least human touch and provides automation in irregularity detection along with alarming arrangement in it. Presently, the investigation focuses on the diagnosis of elderly people’s day-to-day behavioral patterns by observing their daily basis routine tasks from time, context, and location point of view. For the sake of precision, numeral sensing and actuator components are installed at retirement homely places. Through literature review, an exclusive sensing blend technique has been recommended in order to record physical, social, seasonal, and well-being-related notes about humdrum activities for elderly persons. As a part of research methodology, an innovative routine-based activity-learning system is recommended that can monitor relative data annotations of countless positions at an elderly home and can aware the custodians if any case of glitch recognition happens. Observations for 20 activities have been recorded for one month in two of elderly smart homes situated in the city having sample size of more than 5000. Analysis leads to the results that legalize the precision and the proficiency of the projected structure.
The Combination of Searching Algorithms with Sample-Based Planner in Solving 2D Robot Navigation Problems

A* search or other nonuniform-cost searching algorithms are always combined with samplebased planners, such as probabilistic roadmap planner (PRM), to solve 2D navigation problems. However, the combination of uniform-cost searching algorithms and sample-based planners in solving these problems was not fully explored in the past. In this study, programs were implemented to allow the combination of PRM with A* search, breadth-first search, and depth-first search. After the searching algorithms were implemented, experiments were done to test the completeness, optimality, and time of the combinations of the algorithms. In the experiments, the programs were required to find a path for a robot to navigate from the start point to the endpoint in a 2D map with obstacles. This work found that the combination of PRM with A* search was not always complete, but the combinations of PRM with breadth-first search and depth-first search were always complete. After comparing the path planned by three different algorithms, it was determined that the combinations of PRM with breadth-first search and depth-first search were not always optimal, especially when the number of sample points was large. Consequently, A* search is still the most suitable searching algorithm for sample-based planners. This study well explained the shortcomings of combining uniform-cost search algorithms with sample-based planners.
The Determinants of Express Cabinet Interface’s Cognitive Efficiency: An Eye-Tracking Study

Express Cabinet has become a ubiquitous equipment in China. With an extensive population of users, it is necessary to comprehend how Express Cabinet users view different area of its interface. Based on an eye tracking study of 26 subjects on 6 interfaces from a certain Express Cabinet, the aim of this research is to investigate the determinants of ocular behavior on the cabinet interface: whether it is determined by gender of the subjects, or different positions of its main area. The analysis of the data of the total fixation time (TFT), the duration of the first fixation on the Task-area (DFT), the average duration of the fixation on the Advertising-area (AFA), and the ratio of the fixation duration of Advertising-area to the total fixation duration, the results indicate that gender has no influences on the cabinet interface’s cognitive efficiency, yet the position of its main area affected the cognitive efficiency significantly. eye-movement-traces map analysis revealed that the variation of the position of interface’s main area influences the distribution of fixation points and its scan path. The advantages and shortcomings of this research, future research directions are also discussed, then some suggestions are given.
Balancing of an imbalanced dataset by applying SMOTE variants and predicting neonatal mortality using ensemble learning techniques

Dynamic environment and imbalanced datasets are unavoidable challenges in developing medical diagnostic tools where incremental learning is a necessity. The prediction tools upon imbalanced data normally work with majority class bias, and it is not easy to recognize faulty classes. This work aims to solve the class imbalance problem by generating synthetic data using SMOTE variants to balance the dataset and predict the neonatal mortality by adopting different ensemble classification methods. This system will be applied to diagnose newborns, vulnerable to die in the initial period of 28 days after birth.
Measuring Developers' Software Security Skills, Usage, and Training Needs

Software security does not emerge fully formed by divine intervention in deserving software development organizations; it requires that developers have the required theoretical background and practical skills to enable them to write secure software, and that the software security activities are actually performed, not just documented procedures that sit gathering dust on a shelf. In this chapter, the authors present a survey instrument that can be used to investigate software security usage, competence, and training needs in agile organizations. They present results of using this instrument in two organizations. They find that regardless of cost or benefit, skill drives the kind of activities that are performed, and secure design may be the most important training need.
Agent-Based Approach for Monitoring Risks in Software Development Projects

According to various surveys conducted, regardless of how many studies in software development projects have been done, the chance that software development projects may fail remains very high. A relatively new approach to the problem of failure is using the concept of artificial intelligence (AI) to help automate a certain part(s) of the projects in order to minimize the issue. Unfortunately, most of the works proposed to date use AI as a standalone system, which leads to limiting the degree of automation that the overall system can benefit from the technology. This chapter discusses a preliminary work on a novel risk monitoring, which utilizes a number of agent-based systems that cooperate with each other in minimizing risks for the projects. The proposed model not only leads to a high degree of automation in risk management, but this extensible model also allows additional tasks in risk monitoring to be easily added and automated if required.
Unified Approach to Risk Assessment at Railway Crossings

Abstract – This conference paper discusses a new analytical approach to the evaluation of traffic safety at the railway crossings. The new method is based on the use of ontological principles. The ontological tools present a comprehensive decision-making tool, will can be applied to any potential hazardous railway crossing on a road or rail network. Keywords – railway crossing, Traffic safety, Ontology