Reviewerly/exHarmony huggingface dataset:
Spatial Co-Location Pattern Mining Based on Hesitant Fuzzy Location

Spatial Co-location pattern mining is an important research content of data mining. Traditional spatial co-location pattern mining and those based on fuzzy sets can only deal with certain data and uncertain data expressed by a fuzzy membership degree, which the data that there are many possible values of fuzzy membership degree cannot be processed. To this, combined with the theory and method of hesitant fuzzy sets, a method of spatial co-location pattern mining based on hesitant fuzzy location is proposed, which the hesitant fuzzy location of spatial feature instances is defined by using the characteristic that hesitant fuzzy sets can express multiple possible fuzzy membership degrees at the same time. Hesitation fuzzy score space proximity is defined by score function of hesitant fuzzy sets, and then the participation ratio and participation index of hesitant fuzzy location are defined by combined with the hesitant fuzzy location and hesitant fuzzy score function. The algorithm flow of the proposed method is given. The experimental results show that the proposed method is effective and it can find more frequent spatial co-location patterns compared with the existing method.
Deep Photo Relighting by Integrating Both 2D and 3D Lighting Information

In this paper, we propose a novel framework called “deep photo relighting” (DPR) that can transform the lighting condition of an image for a virtual test of image detection/classification algorithm, city environment design, and data augmentation for machine learning. Our framework employs the deep neural network (DNN) approach based on U-Net. Specifically, DPR has two keypoints for transforming one lighting condition to another one by DNN. One is that we can support all factors that affect the lighting conditions (e.g., viewpoint, object materials/geometry, light position) by using 2D and 3D information such as omnidirectional image, omnidirectional depth image, and region segmentation image. The other keypoint is that we can reproduce indirect influences from outside the frame such as shadow by grasping the whole lighting environment with omnidirectional image/depth. As a result, DPR can generate relighting image without fatal artifacts such an unnatural shading/shadows of objects. In experiments, we confirmed that a generated image is well reproduced compared with the ground truth image. We also confirmed that shadows, which occur inside and outside the frame through obstacles, are properly added/deleted in the generated image compared with the ground truth image.
Public opinion analysis about The coronavirus COVID-19 based on micro-blog data

It is known to all that Coronavirus disease 2019 (COVID-19) is an infectious disease, which has spread to more than 180 countries and territories around the world since the end of 2019.lt is clear that this disease results in a moment of panic. However, some people used the data of Tieba to analyze public opinion and describe the intensity of people's emotions, but Tieba is not representative. Because whether it is in terms of popularity, number of users or age spread, the data of Weibo is more representative compared to Tieba. Therefore, this article carries out the public opinion analysis by using Python crawler to obtain data on the topic of COVID-19 in Weibo. The methods we use include word frequency analysis tools, plug-ins in the ROST content mining system, and analysis interfaces in the Baidu Brain Al open platform. The purpose of this article is to analyze the mental health level of the public, such as nervousness, fear, panic, and anxiety. By experimental results on the relevant Weibo in February and March, we find that positive emotions stood at 14.64%,neutral emotion blog posts still accounted for 66.64% and negative emotions accounted for 18.72%.In the negative segment, the general negative emotions accounted for 12.485%,the moderate negative emotions accounted for 4.9%,and the users showed a high degree of negative emotions accounted for 0.455%.And then we roughly give the relevant strategies and suggestions for some groups which have the intense negative emotions. The limitation is that we can't give them some specific suggestions for different users for the time being, which needs further research in the future.
FieldSweep: A 2D Tracking System With Embedded Magnets and a Smartphone

In this paper, we propose FieldSweep, a tracking method on a plane using only permanent magnets and a smartphone. This method estimates the position of a magnetic sensor on a plane using the magnetic fields of permanent magnets placed at an appropriate pattern. The magnetic sensor is a built-in one in a smartphone. Since the planar side consists of only magnets and a plate for fixing the magnet, no power supply or electronic components are required for the system. In this paper, we report the necessary conditions for tracking, present the implemented prototype, and discuss possible future developments and applications.
PDSR: Optimization of SNIP Pre-Pruning Algorithm Based On Dynamic Sparsity Rate

As a key parameter of network pruning, the sparsity rate determines the sparsity effect after network pruning, and is closely related to the complexity, accuracy and application of neural network. Therefore, the determination of neural network sparsity rate has become one of the research hotspots. After reading a large number of relevant literatures, it is found that in the process of model training, the value of sparsity rate is often set artificially according to experience. So that the sparsity rate cannot change dynamically with the change of experimental environment and data, and the accuracy of its value is difficult to determine. To solve the above problems, this paper introduces dynamic sparsity rate, optimizes the SNIP pre-pruning algorithm, and proposes the PDSR algorithm. It calculates the sparsity rate dynamically according to the connection sensitivity of weights and realizes the pre-pruning of neural networks. Experimental results on various convolutional neural networks show that compared with SNIP algorithm, the PDSR algorithm has obvious improvement in accuracy rate and operation efficiency.
Design of the Introduction Part of Information Processing and Machine Translation Course for Students Majoring in Computer Science and Technology

Machine translation is the process that the computer transforms one natural language (source language) to another (target language). It is a branch of Computational Linguistics and one of the ultimate goals of the research in Artificial Intelligence. Machine translation is of applicable importance as well as scientific importance. Due to the rapid development of Internet and globalization of economic, machine translation plays an increasingly important role in promoting an open exchange of politics, economy and culture. Therefore, the course of information processing and machine translation has been set up as an elective course for the students of computer science in our university. Around 100 students attend this course each year. The course has demanded considerable investment of time and effort. Introduction is a very important part for a course. It can make the students understand the objectives, tasks, connotation and characteristics of the course. Moreover, the introduction part can provide the students the outline of the course and arouse the students' enthusiasm. This paper introduced the design of the introduction of this course, including subject depending, problem posing, tortuous process, problem of ambiguity, revival of natural language processing, the motive force of modem social progress, perception and cognition, valuable cognitive knowledge, the contribution of natural language processing to knowledge acquisition.
A GPU memory leakage code defect detection method based on the API calling feature

The General-Purpose Graphics Processing Unit (GPGPU) programming has been widely used in artificial intelligence and deep learning, and the GPGPU programming framework, represented by the CUDA framework launched by NIVIDIA Corporation, can apply the powerful parallel computing power of Graphics Processing unit (GPU) to non-graphics tasks. The gradually open computing power of GPU also brings related security risks, but the industry is still mainly concerned with how to dig into the potential security risks of GPU rather than the protection of known problems. In this paper, we propose an API calling feature (ACF) based method for detecting memory leaks in GPU codes and programmatically implement a prototype method to detect the risk of memory data residue in GPU codes written in CUDA framework. The prototype detection method is implemented using the Pass module development capability provided by the LLVM compiler project, and the method is tested to have good accuracy an effectiveness, which can provide a basis for subsequent GPU codes' security research.
Knowledge-based Adequacy assessment Approach to support AI adoption

Adopting AI as a technology that solves a particular problem (i.e., meets an architectural driver) is a significant architectural decision. Existing techniques for adequacy assessment of architectural decisions often fail to predict effects of adopting complex technologies such is AI. In this paper, we argue that the reason for this is that they fail to capture the level of knowledge that architects have about a complex technology that they aim to adopt, making the discussion about it difficult and adequacy check prone to mistakes. To solve these issues, we introduce an approach that instructs architects to decompose complex drivers for adopting new technologies according to properties of the technology, and to explicitly assess knowledge that architects have about each of those properties. In order to do so, we present a template that explicitly captures the level of knowledge that architects have about important AI properties, which serve as new requirements exposing the influence of adopting AI on software system. Through evaluation, we have demonstrated that our approach successfully complements existing adequacy assessment techniques and is able to expose influences of adopting new complex technologies on underlying software architecture.
Anomaly object detection in urban management surveillance video based on deep learning

In modem city management, the detection and identification of abnormal objects is particularly important. Random parking of non-motor vehicles is one of the common important problems in the process of smart city management. The traditional processing method is mainly to find abnormal targets manually and equipment, and then report to the urban management center for the next step. These methods are time consuming, laborious, and inefficient. In order to further improve the efficiency of urban management, this paper proposes an improved YOLOv3 urban management surveillance video anomaly object detection algorithm. On the basis of the original YOLOv3 framework, we use depthwise separable convolution to reduce the amount of model calculations and increase the detection speed. At the same time, the SE module is added to improve the network structure complexity and detection accuracy. Based on the actual surveillance video data, the detection speed of this algorithm is 4 fps faster than the traditional YOLOv3, and the recognition accuracy is improved by 3%.
Our Theory: De-Anthropocentrized Externalism

Abstract This chapter introduces the central claim of the book about AI communication. It argues first that we should understand AI communication in terms of externalism, the thought that the semantic content an entity can express is determined to a large extent by the environment it finds itself in, rather than by its internal states. It then argues that existing externalist theories are too human-centric: they concentrate on peculiarities of human beings not shared by AI systems. It accordingly proposes to abstract from those human peculiarities when developing theories of communication for AI. The chapter ends by discussing how to decide between competing metasemantic frameworks such as externalism and internalism.
A New Method for Measuring Leveling Heights Using Computer Vision Techniques

The levels and leveling techniques have developed over the last decades. The development of level instruments started with the appearance of precise levels, they are precise instruments used for precise leveling. The operation of digital levels is based on the digital processing of video indications of a coded staff. A new method for measuring leveling heights using computer vision techniques is proposed in this paper. The methodology implementation comprises capturing an image of a metric staff using a digital camera, then the height is computed by processing the image using a developed algorithm. The algorithm consists of two major steps. In the first step, the implemented software computes the meter and tenth of meter height values through recognizing numbers in the image. Then, it computes the rest of the decimal part digits from the coordinate difference between the image's middle row (reading line) and E shape's (or its mirror image) end row. The proposed algorithm works well for the tested precisions (1, 0.1, and 0.01milimeters), also it can measure the heights from a distance up to more than 100m. The testing results indicate that the measurement algorithm is workable and can be used as a low-cost alternative to the barcoded staff digital levels.
Dialogue on Calculus: Key Concepts and the Foundation

This relatively short dialogue is intended for beginners in calculus who are not satisfied with incomplete stories or "sound right'' overview on calculus, but look for comprehensive coverage of the foundation and "logically correct" understanding. The dialogue starts from scratch and prerequisite knowledge is limited to some basic algebra and linear/quadratic functions. For each major concept, the discussion generally begins with some demonstrative question such that the concept can be explained and relevant theory can be developed in the solution seeking process. Intuitive explanation on initial idea is generally followed with algebraic description to avoid ambiguity. All covered results, including the fundamental theorem of calculus, are proved rigorously to help readers get comfortable with the analytical skills that are typically used in the development of the foundation. Readers are expected to be able to dependent on themselves for the rest of study of calculus.
Indicators and Predictors of the Suspension of Disbelief: Children’s Individual Presence Tendencies

Presence is a phenomenon where a person distributes his/her attention to internal (mental) or distal (sensory) cues. Research shows, that not only technological immersion, but also person-specific factors, influence the sense of presence. These factors may cause an individual presence tendency (IPT) that affects how presence is experienced. This paper investigates if an IPT exists, how it can be calculated, and what factors influence it. A study is presented with 78 participants, aged 13-16, who experienced three different environments within different immersive settings. The results show that level of technological immersion has a strong effect on presence.
ON-AFN: Generating Image Caption based on the Fusion of Residual Attention and Ordered Memory Module

In this paper, we propose a novel and effective Fused Network, which is based on the residual attention mechanism and ordered memory module, a framework for image captioning that enables computer to produce the more accurate natural language description. Inspired by the recent advanced studies, our approach creatively divided our caption survey into two subtasks, one is how to efficiently extract visual features in the image, and the other is how to accurately express the semantic information in the features, and then propose excellent solutions for these tasks. In other words, Ordered Memory Attention Fusion Network is a comprehensive captioning model with the support of residual attention network and ordered memory module. Specially in this model, we innovatively employ the residual attention learning mechanism to optimize the extraction of features, and generate a more accurate and comprehensive natural language caption by applying the ordered memory module. We validated our model through extensive experiments on COCO Captioning benchmark, experiments show that it is conducive to generate the caption with higher accuracy and sets the new state-of-the-art by a significant margin.
A Hologram Prism Glass Visualization to Attract Young People's Interest to Poetry

The current rapid of development in technology has driven literary field studies to be intertwined with the new media art to attract the present generation. This research study was conducted to observe the implementation of digital hologram projection using prism glass in visualizing poetry titled “Lock In” by Jhoanna Andersson and to find out whether this digital visualization application in poetry able to attract the interest of the young audience. This study was conducted through an online survey distributed to 80 youths as the targeted participants. The study has shown a promising result where the implementation of holographic prism glass hologram is indicated to be an effectively helpful method to increase the interest of the young generation to enjoy and explore poetry.
Application of Blockchain in Construction Inspection Activities

Construction management is a complex and time-consuming process, and effective quality management is mandatory to ensure overall project quality. Unfortunately, current methods are cumbersome and rife with error. Therefore, the application of electronic and blockchain-based technology for autonomous inspection operations is proposed, and its advantages to auxiliary management operations are demonstrated. The application of blockchain technology in the construction industry using appropriate electronic aids can improve operational efficiency over traditional paper-based methods, thus reducing the time required by engineers and improving quality management.
Research on Acceleration Methods of Semi-training Color Stripping DehazeNet

This paper discussed the influence of the network size of the color stripping module on the dehazing performance of STCSDN through analysis and studies of the color stripping module structure in semi training color stripping DehazeNet (STCSDN). After a large number of tests, this paper verified that the performance of simplified network is close to the original network in image dehazing by removing redundant parameters and simplifying the model structure. Furthermore, the simplified network processed images faster, made the network can conduct real-time image dehazing tasks, and improved the practicality of the network.
Prediction of Students' GPA Using Levenberg–Marquardt Backpropagation Algorithm

Courses marks are the main factors that contribute to the Grade Point Average, commonly called GPA. Besides, GPA describes the academic success of the student. Predicting the students' GPA becomes essential for the student in preparing for their studies in the next semester. However, the prediction itself needs to be reviewed by students to get the proper weighted value. The purpose of this research is to predict the students' GPA as an information exchange study using the Levenberg-Marquardt Backpropagation algorithm. This study applies to the Higher Secondary Certificate Students' GPA. The required dataset is downloaded from the Kaggle website. The results show that the best architecture for solving the problem is a 9-12-1 pattern. It implies that there are nine units in the input layer, 12 neurons in the hidden layer, and one unit in the output layer which results are in a high predictive level.
LibGuides: 2020 Shanghai Middle School Battle of the Books: Home

On March 6, 2020, middle school students will compete in the Battle of the Books at Dulwich College Puxi.
Modern Pedagogy and the Modern Student

The chapter elaborates on how the practice of teaching seems to be changing towards more seminar-type discussions of key dilemmas, with students coming from diverse backgrounds, that they would draw on. This means a new role for the professor (now a catalyst, discussion leader, synthesizer), and with the classroom simply being an ordinary flat-floored room with round tables. The physical space, in general would be open, to facilitate discussions and reflective learning
A Campus-wide Survey of Consciousness on Research Datasets

To obtain the basic information necessary for planning a research data management (RDM) system in Kyoto University, Japan, we conducted a campus-wide survey of research datasets twice with different topics. Although only a part of campus members responded, some clear tendencies were observed. That is, (1) considerable portion of researchers are not very positive to make their data open because of a confidentiality obligation or for holding their predominance in their research fields, (2) strength of consciousness on open data is considerably different depending on their discipline, (3) the above difference may come not only from inherency of each discipline but also from the requirement of open data by the journals. (4) There exist more than fifty open data repositories, and more than half of them are managed by the institutions established for each specific field of research or by the organization outside of the campus. From the survey, it is suggested that the researchers are still conservative to make their data open, and a current practical necessity to RDM system in the campus seems to be a system for keeping and making the data open within each research group. By preparing a convenient RDM system as well as the requirement of open data by the journals in wider discipline may change the consciousness of the researchers on open data.
The Effects of the COVID-19 Outbreak on Social Service-Providing Organizations in the Migration Sector in Turkey and the Digital Measures

The COVID-19 pandemic has had a massive impact on everyone on the world since early 2020. The situation in Turkey is not different than the rest of the world. In the time of a pandemic, it is important, for everyone's safety, to identify ways to deliver social services without in-person contact; therefore, organizations must adopt models for remote and digitalized social service provision which can minimize risks to the health and safety of both for social service-receivers and providers. In this respect, this chapter has got three foci: the effects of COVID-19 outbreak on the social service-providing non-governmental organizations in the context of migration in Turkey, the needs of the asylum seekers as social service-receivers, and the digital measures of the non-governmental organizations in social service provision as a response to COVID-19 outbreak.
Identifying the Offensive Words in the Twitter Using Latent Semantic Analysis

The technologies around social media has changed how people connect with others, how they access information even how they organize their political point of view. The vast technologies has made the message can be sent quickly, become widespread and even viral. The drawback of this technology is people can post anything, from the positive content to negative content and it can be viral. Information contains negative content or known as hate speech can encourage conflicts between groups in society. In Indonesia, there is no automated mechanism to detect whether the information in social media is hate speech or not. Therefore, it could takes time to identify a hate speech. A starting point to identify hate speech is the use of offensive words and slurs the in feature. Once the offensive words are identified, we can classified the tweet into hate speech or not. Machine learning can be used to identify potential hate speech in collection of text. This paper aims to identify the offensive words using machine learning approach.