Reviewerly/exHarmony huggingface dataset:
3D depth sensing of active structured light field based on EPI

In this paper, a three-dimensional (3D) depth sensing system based on active structured light field imaging (ALF) is proposed. In light field imaging, one of most commonly used method for depth estimation is based on its Epipolar Plane Image (EPI), in which the slope of line features is related to parallax and is inversely proportional to the depth of the measured object. However, it is difficult to extract the line features accurately only according to the captured texture information of the object, especially in the case of weak texture, repeated texture and noise. Therefore, active phase feature provided by a phase-shifting fringe projection is introduced for this system, with which the line features in EPI can be extracted by simply searching correspondence points with the same phase value. In order to obtain depth map with measuring accuracy, a metric calibration method is proposed to establish the quantitative relationship between the slope of lines and depth. Besides that, due to the existence of distortions in the light field camera (LFC), the correspondence points in EPI cannot fit well enough with linear distribution, another calibration based on the LFC imaging model and Bundle Adjustment (BA) was implemented to correct distortions in the EPI, which can reduce the fitting errors of line features. experiment results proved that calibration method described above is effective, and the built ALF system sensor can work well for 3D depth estimation.
Garden Visitor Detection Based on Improved Vibe Algorithm

If a certain group of people have been gathered in public places, in the event of an accident, the loss may be huge. Nowadays, there are more and more emergencies in some crowded public places such as scenic spots. The difficulty of monitoring is gradually increasing, and the need for monitoring is gradually emphasized. In the current situation, most video surveillance systems only replace the human eye to record the results as the basis for handling accidents. Therefore, they cannot detect potential actual hazards in time, and cannot perform real-time functions to actively analyze and monitor. This article mainly introduces the research of garden visitor detection based on the improved ViBe algorithm, and intends to provide ideas and directions for the improvement of garden visitor detection technology. This paper proposes an improved ViBe algorithm, including fixed threshold improvement and ghost elimination methods to improve the problems in traditional ViBe. The experimental results of this paper show that the average accuracy of the garden visitor detection method designed in this paper is 93.78%, which fully demonstrates the superiority of the improved ViBe algorithm.
Sememe-based Topic-to-Essay Generation with Neural Networks

Abstract Topic-to-Essay generation task aims to generate topic related and coherence text based on user input. Previous research generates text solely based on the input words and the generation results in not satisfactory. The traditional methods using semantic relationships of words from corpus to guide the model, which made the model rely heavily on the corpus. In this paper, we propose a sememe-based topic-to-essay generation model(S-TEG), which integrates sememes from external knowledge graph HowNet with input topic words to guide the model. In order to prevent introducing noise, we elaborately devise measuring the similarity of the non-current topic words to filter sememes information. The experiment results demonstrate that our approach achieved 4.10 average score in subjective evaluation and a 3.60 BLEU score, which shows that our model is able to generate text that is more coherence, topic-related and fits the daily logic.
Lightweight BIM Architecture Design System Based on AutoCAD

Low pollution, low energy consumption and sustainable development have become the development direction of China's construction industry. The traditional two-dimensional design environment is not suitable for architectural design requirements. Based on the concept of BIM, this paper presents a lightweight architecture design system based on database and Auto CAD. Firstly, this paper summarizes the related technologies involved in the lightweight architectural design system based on Auto CAD, mainly including Auto CAD extended development technology and SQL Server database; secondly, it analyzes the business process of architectural design, and carries out the system requirement modeling and data modeling; secondly, combining with the hierarchical software design idea, the system application architecture is given, and the system function module design and database design are carried out, By setting the plane, elevation and section view of the model, the multi view generation based on unified data is realized, and the statistics of component information is realized by querying the model database.
Quo vadis modularity in the 2020s?

This chapter focuses on the key ideas at the heart of Annette Karmiloff-Smith's thinking, the modularity. Modularity is the idea that the cognition is largely composed of independent, domain-specific cognitive components, as revealed by the possibility of selective damage in adulthood. Annette felt strongly that modularity had been inappropriately applied to the study of developmental disorders through the 1980s and 1990s. The chapter argues that advances in neuroscience have caused modularity to be recast from an a priori design principle, which it would make good sense for potential cognitive systems to employ, to a data-driven concept based on how the brain is actually working. It illustrates the latter point by presenting data from Annette’s final project on infant development in Down syndrome, which uses graph theoretical analyses of electrophysiological brain imaging data to explore functional network structure in the brain activity of the young children.
A Brief Survey on Recent Advances of Object Detection with Deep Learning

Object detection is innovatively combined with deep learning and makes significant progress in recent years. In this paper, we first summarize some significant achievements of object detection in the last few years, then introduce two mainstream detection frameworks, integrate five significant object detection challenges and corresponding solutions, then list four well-known object detection datasets and evaluation metrics, and enumerate some applications related to object detection. Finally, we put forward some of our views and future research directions according to the status quo of this domain to facilitate research for object detection based on deep learning.
Neural Network for Predicting the Thermal Conductivity of Steel with the Bayesian Method Using Matlab Software

The article offers a solution of prediction of thermal conductivity for different grades of steel at different temperatures. To solve this problem, the authors consider the possibility of using a neural network based on the Bayesian method. The neural network was built using the Matlab software. The aim of the article is to determine the optimal network structure to ensure better prediction accuracy, both on the training set and on the test set. Much attention is given to analyze the obtained results for different numbers of hidden neurons and compare them with the results of other authors. It was found that the greatest influence on the value of thermal conductivity has the value of temperature and the ratio of alloyed elements. The findings are of direct practical relevance.
Emotion Detection for Traveling Services Using Rule-Based Fuzzy Inference System

Emotion analysis has been reviewed in numerous fields like behavior, product ratings and industry. In recent years, it has shown an essential aspect in emotional analysis or sentiment analysis. This study uses tweets about services of Indian Railways provided to the travelers during Deepawali week. A methodology based on fuzzy logic has been introduced for solving the problem of emotion detection. Several different features extracted from each tweets have been used as inputs in the proposed fuzzy logic-based model. A number of fuzzy rules are formulated, and a defuzzification method is used in order to achieve required classification results. The analysis shows that the overall positivity depends upon the morning and evening situations faced by the users from the services offered.
Toward a New Information Architecture: The Rise and Fall and Rise of a Necessary Discipline

When the internet was first becoming a thing, it was very different than it is now. It wasn’t very interactive. To be honest, it barely had any interface design either. The great bulk of websites were just walls of text arranged into a semblance of order by tables with the borders turned off. Interactivity was clicking “bookmark,” “set as homepage” or submitting a “contact us” form. But what the Internet did have was information. Everybody put everything they had up on the web, from help pages to marketing brochures.
Smoothing Approximation to the New Exact Penalty Function with Two Parameters

In this paper, we propose a new non-smooth penalty function with two parameters for nonlinear inequality constrained optimization problems. And we propose a twice continuously differentiable function which is smoothing approximation to the non-smooth penalty function and define the corresponding smoothed penalty problem. A global solution of the smoothed penalty problem is proved to be an approximation global solution of the non-smooth penalty problem. Based on the smoothed penalty function, we develop an algorithm and prove that the sequence generated by the algorithm can converge to the optimal solution of the original problem.
Affinity Derivation for Accurate Instance Segmentation

Affinity, which represents whether two pixels belong to a same instance, is an equivalent representation to the instance segmentation labels. Conventional works do not make an explicit exploration on the affinity. In this article, we present two instance segmentation schemes based on pixel affinity information and show the effectiveness of affinity in both aspects. For proposal-free method, we predict pixel affinity for each image and then propose a simple yet effective graph merge algorithm to cluster pixels into instances. It shows that the affinity is powerful as an instance-relevant information to guide the clustering procedure in proposal-free instance segmentation. For proposal-based methods, we extend conventional framework with affinity head and introduce affinity as attached supervision in training phase. Without any additional inference cost, we can improve the performance of existing proposal-based instance segmentation methods, which shows that the affinity can also be applied as an auxiliary loss and training with such extra loss is beneficial to the training progress. Experimental results show that our schemes achieve comparable performance to other state-of-the-art instance segmentation methods. With Cityscapes training data, the proposed proposal-free method achieves 28.8 AP and the proposal-based method gets 27.2 AP both on test sets.
Scrum to support application development project for online learning

Good health services require adequate human resources, especially nursing personnel during a pandemic. To produce quality human resources in the health sector, a quality learning process is needed in health education institutions. One of the steps that can be taken to achieve this is to transform online and mobile-based nursing education. Technological developments have changed the way applications developed. Application users have also experienced changes by prioritizing mobile-based applications. This change makes application developers look for new methods in developing applications that can be used effectively. This article presented a study that applies the principles of agile mobile application development. The agile methodology used in this study is SCRUM for application development. This study prepared a mobile-based learning application that will be used by nursing students. The development of this application uses an agile methodology. This paper discusses Scrum's use in developing mobile-based applications that will be used in the nursing department's learning environment. The requirements are deliberately modified to provoke changes to the software being developed. Our findings revealed that agile practices were capable of assisting mobile software development. The advantage of this method was the ease of managing the project. control, and speed of development. However, aspects of user interface and user experience, different development platforms, and user expectations still represented the challenges in developing mobile applications.
Potential of Volunteering Activity in Prevention of Social Escapism among the Student Youth

The purpose of the study is to actualise the potential of volunteering activity in prevention of social escapism among the student youth. The article analyses pedagogical functions of volunteering activity and also presents results of an experimental study of social escapism among the student youth. Scientific novelty of the study lies in analysing scientific approaches to the definition of escapism and identifying the pedagogical potential of various types of volunteering activity. As a result, the researchers have presented a model of organising student volunteering activity for preventing social escapism, which can be used in educational and extracurricular activity at university by teachers, organisers, supervisors of student groups and youth policy coordinators.
ELECTRONIC EDUCATIONAL COURSES IN PHYSICS IN THE SYSTEM OF PRE-HIGH PROFESSIONAL EDUCATION

The article defines the essence of the concept of “electronic educational course”, defines its characteristics and qualities, reveals the requirements for its creation and application in institutions of professional pre-high education in Ukraine. The essence of the electronic educational course concept as a didactic computer environment containing is systematized material on the relevant discipline, united by a single software shell. The description of such a course is revealed from two positions: as a structured educational material and as a computer program. The functional subsystems of the electronic educational course are characterized. They are information and navigation, informative and diagnostic. The stages of development and implementation of the electronic educational course in educational institutions are disclosed. The functional capabilities of educational resources management systems, where the electronic educational course can be located, are shown. The authors describe the peculiarities of the process of the electronic educational course creating and introducing in institutions of professional pre-high education, which are determined by peculiarities of the educational process, and are shown in the semantic content and methods of using the electronic course. The requirements for the creation of electronic educational course, which provides opportunities for students’ skills development of the self-educational activity, are considered. One of the principles, which is worth using in the development of the electronic educational course intended for the study of educational physics in institutions of professional pre-high education. The structural composition of mentioned the electronic educational course is described and some recommendations for its application in the study of physics are given.
High-Performance Object Recognition by Employing a Transfer Learned Deep Convolutional Neural Network

Researchers have been conveying and producing various models for the precise identification of objects for a long period now. Distinguished contributions are notable in the field of object identification. Numerous benchmark datasets are now available for composing an effective model that can discover a large number of target classes without a tremendous loss in performance. In this study, we began with a benchmark dataset Caltech-101 which consists of 102 classes. Being an extremely imbalanced dataset and having a blend of RGB and Gray images, the recognition is extremely challenging. We proposed and implemented a modified DenseNet-201 design after the preprocessing steps and produced an overall accuracy of 99.73% which exceeded all the previous results or achievements by a noteworthy boundary.
Collision-free Trajectory Planning for Cable-driven Robot in Dynamic Environment

This paper presents a collision-free trajectory planning algorithm for cable-driven robots (CDPRs) in dynamic environment. As the basis, the wrench feasible workspaces (WFW) of CDPRs are obtained. The tradition artificial potential field (APF) method can quickly plan a trajectory. However, APF is not time-efficient with dynamic obstacles. In order to solve this problem, a new scope of APF based on kinematic model is used to generate a trajectory in dynamic environment. Then, Bezier curve is employed to optimize the initial trajectory making motors run smoothly. To validate the proposed improved APF method, kinematic simulations are carried out. The results demonstrate the trajectory generated by improved APF can avoid obstacles in different motion states smoothly.
Unsupervised Learning from Data and Learners

In the collaborative clustering framework, the hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution. The goal is that each local computation, quite possibly applied to distinct data sets, benefits from the work done by the other collaborators. This article is dedicated to collaborative clustering based on the Learning Using Privileged Information paradigm. Local algorithms weight incoming information at the level of each observation, depending on the confidence level of the classification of that observation. A comparison between our algorithm and state of the art implementations shows improvement of the collaboration process using the proposed approach.
A Battery-Free Sensor Based Fall Monitoring System for the Elderly on a Staircase

This paper proposes a battery-free sensor-based monitoring system on a staircase to distinguish fall and non-fall events in the elderly without invading individual privacy. The system distinguishes three events on a staircase such as neutral, walking, and falling with the use of passive RFID sensor tags, which are capable of pressure detection, and machine learning-based on pressure values and the Received Signal Strength Indicator (RSSI). This research will focus on expanding the monitoring fields on the staircases since there are more than 19% of the elderly die from accidental falls on a staircase and develop a system with a high detection rate.
Eye Gaze Map as an Efficient State Encoder for Underwater Task Automation

ROVs are the primary tool used in marine environments for conducting dexterous manipulation tasks and the teleoperation of which requires experienced operators. In an attempt to ease the fatigue of the operators and to potentially improve task efficiency, we propose an image based framework to automate underwater routine tasks via imitation learning. In particular, we utilize the gaze information of the operator for extracting task-relevant information from the raw image input by an encoding network. We further demonstrate that the incoporation of the eye gaze encoder facilitates the training of the task policy which includes both maneuver and decision making.
Impact of gender on information literacy skills of students of library and information science Department, University of Maiduguri

The skills to proficiently explore or navigate the world of information by male and female undergraduate students are very tremendously remarkable. Conversely, the determining factors of their art of navigating the world of information available to them are unclear. It is therefore, imperious to ascertain those essential determinants that influence the information literacy skills of the undergraduate students. The study used descriptive survey design, which aims at determining the influence of level of study on information literacy skills Possessed by undergraduate students in Library and Information Science Department, University of Maiduguri. One thousand students were sampled from the population for the study. One research question which was raised to guide the study was subsumed in a hypothesis. Questionnaire was used to collect the data which were analysed using mean and standard deviation while t-test statistic and ANOVA was used to test the hypothesis at 0.05 level of significance. The outcome of the findings revealed that gender does not influence the information literacy skills of undergraduate students in Library and Information Science Department, University of Maiduguri.Keywords: Gender, Information, information Literacy, Information Literacy Skills, Literacy skills
Dimensionality Reduction Using Principal Component Analysis for Lecture Attendance Management System

Student class attendance record plays an important role in most of the universities of Asia particularly countries like India. Keeping this in mind, an efficient attendance management system is proposed in this paper. This system makes use of dimensionality reduction using PCA-based face recognition algorithm incorporating Euclidean distance as the distance classifier. Faces of the students are segmented using Viola–Jones algorithm. Based on the recognized faces, attendance is updated into an Excel database. The algorithm has been tested using two different student databases. Training database is created using 50 subjects including male and female with different facial variations (15 instances per subject). Statistical data shows that an accuracy of the algorithm is greater than 99% with normal lighting conditions.
Information Architecture in the Anthropocene

Today’s information architecture (IA) practitioners work in a morally and politically challenging climate where pervasive, systemic problems demand that we consider the consequences of our work for social justice and sustainability. Using “Information Architecture in the Anthropocene” as a framing device, and drawing from critical perspectives in design scholarship, this chapter explores what these systemic problems mean for everyday information architecture practice, and it asks what methodological, theoretical, and paradigmatic qualities would enable information architecture to respond adequately to social and environmental challenges. Both design and information architecture practitioners are deeply involved in ongoing sociopolitical problems, which highlights the need for awareness of their limitations and their situatedness within the systems that are traditionally treated as objects for detached research and design. Reflexivity, informed by a systemic epistemology, is identified as a critical attribute for information architecture in the Anthropocene. Three proposals are offered as ways to achieve this: information architecture as a developmental process, information architecture as ethical practice, and information architecture as a network. These approaches apply processual and relational interpretations, along with biological theory, to the practice of information architecture, challenging our field to include ourselves in the systems we study and to rethink information architecture as a responsible practice.