Reviewerly/exHarmony huggingface dataset:
Prototype of porcelain safety display based on Augmented Reality Technology

The popularity of hand-held augmented reality devices provides a novel and immersive method for the protection of porcelain cultural heritage. In the augmented reality technology, through the real restoration of various existing porcelain, provide users with a high degree of immersion experience, reduce the probability of permanent damage caused by the improper behavior of users. A porcelain display model based on augmented reality is proposed. In this paper, a 3D mathematical modeling scheme is proposed, which takes into account the different patterns and textures of porcelain in different periods, and presents a binary lattice feature matching algorithm suitable for mobile augmented reality porcelain display. In order to evaluate whether augmented reality porcelain display can reduce the loss of porcelain itself on the premise of ensuring high user experience, this paper studies the differences between virtual and real porcelain display for the audience. The experimental results verify the effectiveness of the method, overcome the shortcomings of traditional methods such as poor reality, limited interaction, lack of investigation and evaluation of participants, and provide a new way for the protection of porcelain cultural heritage.
Automated mapping of fault logs to SRS requirements using key-phrase extraction

Software requirement specification (SRS) document contains faults due to the inherent ambiguous nature of natural language (NL). These faults are identified and reported (using fault logs) through inspections and are handed back to the requirements author for fixations. This process is very manual, time consuming and a lot of efforts is spent on re-inspection of the SRS document while fault fixations. An automated approach is needed that can map fault logs to faulty requirements and to other similar requirements. The automated approach could enable large fault coverage and can reduce significant manual re-inspection time and efforts. Our proposed approach extracts the key-phrases to identify key problems from fault-logs, and then maps them back to group of similar requirements in an SRS document to inspect requirements that may contain a similar types of faults. Our approach uses key-phrase extraction algorithms, semantic analysis models and clustering approaches to map faults to requirements. We evaluated the mapping of faults to requirements in our approach using two widely used semantic analysis models (i.e., Latent Semantic Analysis and Latent Dirichlet Allocation) with the evaluation performed by the domain expert. Our results have been promising and have showed a large potential to support additional decision making during fault fixations.
Survey of Visual Crowdsensing

Abstract In recent years, Visual Crowdsensing (VCS) that sensed through images and video, has become a predominant sensing paradigm of Mobile Crowdsensing (MCS), which is one of the current research hotspots. VCS requires people to capture the details of sensing objects in the real world in the form of pictures or video, which is widely used in various fields. However, there is no article summarizing the development and current situation of VCS. To this end, this paper summarized the latest applications of VCS, including floor plan generation, indoor scene reconstruction, outdoor scene reconstruction, event reconstruction, indoor localization, indoor navigation and disaster relief, and summarized some unique problems of VCS at present.
A Traffic Anomaly Detection and Identification Approach Based on Multi-instance Learning

Traffic anomaly detection plays an important role in effective prevention and timely handling of traffic accidents. However, currently traffic anomaly detection is still in its infancy, mainly depending on manual intervention, which not only consumes a lot of manpower, but also is unfavorable in timeliness. According to the characteristics of urban road traffic scenes, this paper proposes a MFnet network structure based on multiple fiber modules, aiming to realize fast extraction and real-time calculation of video streaming features via group convolution and sparse connectivity. In addition, the weakly-supervised multi-instance learning method is introduced for the training on application of traffic anomaly detection model, which reduces the difficulty of labeling sample videos and improves the capacity of traffic anomaly detection in complex scenarios. Experimental results based on real traffic video data show that this method proposed herein, compared to the existing traffic anomaly detection methods, is good in terms of detection accuracy and recall rate, and is efficient in traffic anomaly detection in actual traffic scenarios.
Sanne Berends. Acquiring Dutch quantitative ER

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
Optimization of Neural Network Input Through Genetic Algorithm

Big data is utilized and developed across a range of fields, such as; engineering, pharmaceutical, medical, ...etc. In the past decade, Reliable data acquisition has become increasingly in demand, and recent developments and advancements made with regard to Machine Learning (ML) technologies have served to only increase the interest in the proper acquisition, processing, and utilization of big data. These components are almost certainly dependent on the quality of available data. Accordingly, in this paper we introduce a new approach towards the utilization of the Genetic Algorithm (GA) systems and auto encoders alongside neural networks to aid in the tasks of machine vision and decision-making processes. The presented approach aims at improving neural network training parameters through the exploitation of real-coded GAs in the enhancement of the imagery and sensory inputs used in training the network. As such, The GA is utilized to adapt to several parameters of an input imagery and conforms to a local enhancement technique similar to statistical scaling. This would produce an image with enhanced sharpness, contrast and less noise represented in the choice of fitness criterion.
Real-time adaptable resource allocation for distributed data-intensive applications over cloud and edge environments

Applications performance is strongly linked with the total load, the application deployment architecture and the amount of resources allocated by the cloud or edge computing environments. Considering that the majority of the applications tends to be data intensive, the load becomes quite dynamic and depends on the data aspects, such as the data sources locations, their distribution and the data processing aspects within an application that consists of micro-services. In this paper we introduce an analysis and prediction model that takes into account the characteristics of an application in terms of data aspects and the edge computing resources attributes, such as utilization and concurrency, in order to propose optimized resources allocation during runtime.
Composite G-codes over formal power series rings and finite chain rings

In this paper, we extend the work done on $G$-codes over formal power series rings and finite chain rings $\mathbb{F}_q[t]/(t^i)$, to composite $G$-codes over the same alphabets. We define composite $G$-codes over the infinite ring $R_\infty$ as ideals in the group ring $R_\infty G.$ We show that the dual of a composite $G$-code is again a composite $G$-code in this setting. We extend the known results on projections and lifts of $G$-codes over the finite chain rings and over the formal power series rings to composite $G$-codes. Additionally, we extend some known results on $\gamma$-adic $G$-codes over $R_\infty$ to composite $G$-codes and study these codes over principal ideal rings.
Study On The Proportion Of Paired Classroom Teaching Method To Different Undergraduate Students Based On Spss Software

With the deepening of China's higher education reform process, the student-centered concept has been recognized by all sectors of society. The segmented classroom teaching method is designed based on China's local experience, and has been gradually developed and achieved remarkable results in colleges and universities. However, there are differences in the learning status of students in different grades, including learning consciousness, learning habits, cultural background, and autonomous consciousness. This study uses SPSS software, using one-way analysis of variance, according to the differences of students in different grades, choosing different proportions and clarifying the responsibilities of teachers and students can better achieve the teaching goal of individualized teaching and achieve better learning results.
Construction technology of large scale knowledge maps in customer service field of Power Grid

In the power customer service field question answering system, knowledge map is one of the key technologies, which can significantly reduce the cost of customer service, promote the intelligent customer service, and provide customers with a good and convenient service experience. This paper studies the construction technology of building knowledge maps in the electric power field. First, we complete the construction of the technical terminology dictionary, and use it as the basic corpus of the knowledge maps. Then, build the necessary algorithm model by mining the entities and entity relationships from the power customer service data. Finally, finish the mass of the service knowledge base construction.
Classification of Surgical Devices with Artificial Neural Network Approach

In an operation, large number of surgical devices are generally used by surgeons. After they have been used, a special cleaning protocol is required to make sure that they will be disinfected and safe and to use in subsequent operations. In hospitals, the used devices will return to be treated at CSSD (Central Sterile Supply Department). The device needs to be classified and treated separately according to the types and models. Traditionally manual classification process has become an issue when the number of the returned devices increases. In this research, robotic and vision systems are used to classify the surgical devices. Object recognition and detection are developed with Machine Learning (ML) approach. Artificial Neural Networks, YOLO (You Only Look Once) algorithm, is applied to solve this problem. Five classes of surgical devices - i.e., scissor, blade holder, clamp, suction, retractor- are trained and demonstrated.
Computational intelligence paradigm for job shop scheduling and routing in an uncertain environment

Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and sufficient solutions for problems. Therefore, new techniques are necessary to efficiently solve these issues seriously. New techniques, such as Emergent Intelligence (EI), Multi-Agent System (MAS), etc., provide robust, generic, flexible, and self-organised to solve complex real-world problems. In this paper, we discuss Emergent Intelligence (EI) and its uniqueness in solving problems in an uncertain environment. We also discuss EI, Swarm Intelligence (SI) and MultiAgent System (MAS)-based problem-solving in an uncertain environment and compared their performance. We have considered two different problems: job shop scheduling using EI and MAS and route establishment for routing using MAS, SI and EI in an uncertain environment. Each problem is categorically analysed and solved step by step using MAS, SI and EI in a dynamic environment. We measure the performance of these three methods by varying the number of agents, tasks and time. Performance measures are compared and shown to demonstrate the importance of EI over MAS and SI for solving problems in an uncertain environment.
Collaborative Gaussian mixture model for background subtraction

Gaussian mixture per-pixel model cannot handle complex background motion and needs different parameters setting for variant target motion speed scenario. In this paper, a Collaborative Gaussian mixture model for background subtraction is proposed. In which, each pixel was modeled by a background Gaussian mixture model or a foreground Gaussian mixture model. The foreground Gaussian mixture model is respond for pixel value statistics and prepare new background model for the background Gaussian mixture model. The background Gaussian mixture model implement the background Gaussian models update procedure. Furthermore, A periodic control parameter and new parameter update method are proposed to improve the robustness of the algorithm. Evaluation results based on the Cdnet 2012 database are presented in this paper. The results indicate that the proposed algorithm work well on various scenario.
한국과 아시아권 고등학교 1학년 영어 교과서 코퍼스 대조 분석: 지문 난이도와 유형을 중심으로

Based on the text type list and CohMetrix analysis, this study aims to compare text difficulty and genres of reading passages in 10th Grade English textbooks which are being used in Asian countries (Korea, China, Singapore, and India). Each textbook is looked through 14 indices under 7 categories in CohMetrix. These indices indicated that Singaporean textbooks had the top scores in text difficulty, followed by Korean textbooks. Second, according to the 7 categories of text genres, Korean textbooks contained only 3 text genres; the least among 4 countries. The most frequently presented genre in Korean textbooks was expository texts. Third, although the countries in the same circle (Kachru, 1992) had similarities in number of word, number of sentences, and LSA (Latent Semantic Analysis) indices, they were not directly related to text difficulty. In text types, textbooks in the Expanding Circle countries (Korea and China) contained expository texts the most while the Outer Circle countries (Singapore and India) contained descriptive and narrative texts each. Results of this study suggest that Korean textbooks include more words and sentences to promote learners’ exposure to English, providing more various text genres.
Unsupervised Image Segmentation with Spatial Triplet Markov Trees

Hidden Markov Trees (HMTs) are successful probabilistic models [1] [2] [3] in image segmentation or genetic analysis for example. They offer a good compromise between the random variables that can be modeled and the tractability of the inference within these models. Indeed, the inference procedures can be conducted in a direct, exact fashion [4]. However the conditional independence restrictions in HMTs can be too simplistic with respect to the interactions we wish to model, particularly in the task of natural image segmentation. In this article we study an extension of HMTs called Spatial Triplet Markov Trees (STMTs) which is designed to greatly increase the correlations of the random variables while keeping the possibility of direct and exact inference procedures. The STMT model is tested and compared to other unsupervised segmentation techniques in the case of unsupervised image segmentation. Numerical experiments show that STMTs outperform HMTs and present rich spatial dependencies which are crucial for image segmentation.
Advancement in Digital Flight Control System

In a commercial aircraft, the control surfaces are controlled and actuated using hydraulic or pneumatic or servo actuators. These actuators may fail to give the expected actuation due to aerodynamic loading and presence of any other obstacles. So, it is required to develop an aircraft which gives the expected actuation by overcoming aerodynamic loading and presence of any other obstacles. In the analysis, digital control system is used and merged with the light intensity in the control surface actuation. The main control console unit includes microcontroller, stepper motor, DC gear motor, relays and voltage regulators light source, and receiver. Comparator is used in the feedback unit. The required actuation is not obtained due to the aerodynamic disturbances, and so, the actual actuation in the control surfaces introduces overloading in the stepper motor resulting in the requirement of additional voltage for actuation. This additional voltage is obtained from the comparator and feedback to the stepper motor. The entire system is interfaced with computer so that the entire control unit is managed by a special VB program written on .NET platform.
Multiple Instance Learning for Unilateral Data

Multi-instance learning (MIL) is a popular learning paradigm rooted in real-world applications. Recent studies have achieved prominent performance with sufficient annotation data. Nevertheless, acquisition of enough labeled data is often hard and only a little or partially labeled data is available. For example, in web text mining, the concerning bags (positive) is often rare compared with the unrelated ones (negative) and unlabeled ones. This leads to a new learning scenario with little negative bags and many unlabeled bags, which we name it as unilateral data. It is a new learning problem and has received little attention. In this paper, we propose a new method called Multiple Instance Learning for Unilateral Data (MILUD) to tackle this problem. To utilize the information of bags fully, we consider statistics characters and discriminative mapping information simultaneously. The key instances of bags are determined by the distinguishability of mapped samples based on fake labels. Besides, we also employed an empirical risk minimization loss function based on the mapping results to learn the optimal classifier and analyze its generalization error bound. The experimental results show that method outperforms other existing state-of-art methods.
Practice and experience of online course construction—taking Food Chemistry as an example

At present, mobile phones, computers and other digital devices are very popular among college students. With the popularization of modem information technology, the traditional teaching methods must be reformed and innovated. The construction and implementation of online open courses meet this requirement. Based on the experience of “Food Chemistry ” online open course construction, this paper summarizes and suggests the role of online open course in the development of education and teaching, the content and standard of online open course construction, teachers' online teaching and supporting services, so as to provide a reference for online teaching and education.
Domain-Specific Chinese Transformer-XL Language Model with Part-of-Speech Information

Language models hope to use more context to predict the next word. However, not all words in the context are related to the next word and are effective for prediction. The language model based on the attention mechanism can select more useful word representations from the context and efficiently use long-term historical information. In this paper, we will apply Transformer-XL language model to Chinese automatic speech recognition in a specific domain. We add part-of-speech information for domain adaptation. First, we construct a Chinese corpus dataset in a specific domain. And by collecting common vocabulary and extracting new words in the domain, we also construct a domain vocabulary. Then, the Chinese word boundary information is added to the Transformer-XL language model to make the model can better adapt to the characteristics of the domain. Finally, our experimental results show that the method is effective on the dataset we provided. It can further reduce the Character Error Rate (CER) in speech recognition.
Application of the Waiting Line Method for Evaluation of the IAIN Batusangkar Information Technology Helpdesk Information System

Information Technology and Database (TIPD) IAIN Batusangkar is a unit engaged in the field of IT services, namely software and network maintenance software application services so that students / lecturers / units have to go to the TIPD office and fill out a complaint form after which repairs are made by officers or staff TIPD, because the procedures and services provided took a long time so that the TIPD IT services at IAIN Batusangkar had not been able to provide maximum service. This study aims to produce a system that can facilitate TIPD IT services by using a helpdesk information system with the waiting line method. This type of research is the collection of library research data through field research (Field Research), library research, laboratory research, system analysis, application design and application testing. Data collection techniques in this study were using interviews and collecting documents. The results showed the helpdesk application that was built to provide convenience to TIPD IT services and can improve the performance of TIPD officers. Each service with the waiting line method, the technician service facility level has a busy 37% level from the previous 61%, the expected time for each customer arrival to wait on the Waiting Line is 0.21 minutes from the previous one, 1.8 minutes, and time Expected by each customer arrival while in the system or waiting in service is 3.6 minutes from the previous 7.2 minutes, with this it can be said that the helpdesk information system application can provide maximum service.
A Continuous Learning System for Face Clustering and Recognition

This work is to design a high performance and light weight system that can be trained and work well on practical real- world face images. It can run on the mobile phones in the market, and it can automatically identify and group photos in a personal digital album. One target of the research is to produce a system that can be given in the hands of users for long-term. To achieve this goal, we employ the face recognition and object clustering techniques to build a system that can update the number of classes (faces) with a continuously growing number of input photos. This system can be used together with the personal storage (such as hard disk drives); therefore, it has the advantage of privacy. Hence, we propose a system pipeline to create a smart album that will automatically cluster photos and recognize previously seen faces when the user is adding pictures to it. We have done experiments on the public and self-collected datasets and the system is able to perform well even on the challenging images that are difficult to cluster by using the conventional techniques.
SaGCN: Structure-Aware Graph Convolution Network for Document-Level Relation Extraction

Document-level Relation Extraction(DocRE) aims at extracting semantic relations among entities in documents. However, current models lack long-range dependency information and the reasoning ability to extract essential structure information from the text. In this paper, we propose SaGCN, a Structure-aware Graph Convolution Network, extracting relation with explicit and implicit dependency structure. Specifically, we generate the implicit graph by sampling from a discrete and continuous distribution, then dynamically fuse the implicit soft structure with the dependent hard structure. Experimental results of SaGCN outperform the performance achieved by current state-of-the-art various baseline models on the DocRED dataset.