Reviewerly/exHarmony huggingface dataset:
High-precision Position Measurement for Linear Motor Mover Based on PCSAC

To achieve high-precision and real-time measurement of the mover position of a permanent magnet linear synchronous motor (PMLSM), a PCSAC-based visual measurement method for the mover position of a linear motor is proposed. First, take the non-periodic fence fringe image as the target to take the image and collect it by the linear CCD camera on the linear motor mover. Then, the phase correlation algorithm is used to calculate the phase angle vector in the frequency domain of the two pictures. Finally, the least truncated squares (LTS) are used for linear fitting to find the slope in the frequency domain and calculate the distance in the spatial domain, to realize the sub-pixel measurement of the displacement of the linear motor mover. Simulations and experiments prove that the proposed method can effectively achieve high-precision and rapid measurement of mover position.
An Empirical Study of Picture Password Composition on Smartwatches

Recent research works suggest that human cognitive differences affect security and usability of picture passwords within a variety of interaction contexts, such as conventional desktops, smartphones, and extended reality. However, the interplay of human cognition towards users’ interaction behavior and security of picture passwords on smartwatch devices has not been investigated so far. In this paper, we report on such a research attempt that embraced a between-subjects in-lab user study (n = 50) in which users were classified according to their cognitive processing characteristics (i.e., Field Dependence-Independence cognitive differences), and further composed a picture password on a smartwatch device. Analysis of results reveal that already known effects of human cognition towards interaction behavior and security of picture passwords within conventional interaction contexts, do not necessarily replicate when these are deployed on smartwatch devices. Findings point towards the need to design for diversity and device-aware picture password schemes.
Deep Learning-Based Body Mass Index (BMI) Prediction Using Pre-trained CNN Models

Mana, Nur Alifah Megat Abd Fook, Chong Yen Chin, Lim Chee Vijean, Vikneswaran Ardeenawatie, Saidatul Muthusamy, HariharanAutomatically predicting Body Mass Index (BMI) from face images is an interesting and challenging problem in the field of computer vision. Extracted facial features are the most important requirement for estimating BMI readings. The experiment was conducted on a visual BMI database containing 4206 face images. This work studies how to detect and crop face images through image processing methods for BMI prediction. The face detector used in the image pre-processing stage is Multi-Task Convolutional Neural Network (MTCNN). This method was based on Convolutional Neural Network (CNN) and Keras framework. Throughout this research, deep pre-trained CNN models were implemented to go through the training task and to evaluate the performance of those models for the BMI prediction system. Also, the aim of this research is to predict BMI scores and BMI classes from detected face images using three pre-trained CNN models. This research provides some deep insights which are image pre-processing method using MTCNN algorithm performed better than traditional methods such as Haar classifier or Adaboost algorithm and also pre-trained CNN models resulting in better accuracy with less computation time.
ICT Literacy among High School Students Based on E-Learning Effectivity in Surakarta

Skills for living in the world are 21st century skills based on information, information, and communicate literacy. ICT literacy supports education successfully due to the fact that ICT literacy on education is lower than general ICT usage. E-learning engages ICT tools to increase transfer of knowledge, then e-learning usage is expected to improve ICT literacy especially on education. The purpose of this study is to determine ICT literacy through the assessment of school e-learning effectiveness. The questionnaire consisted of students' ICT literacy tests based on PISA (2012) and the assessment of e-learning effectiveness according to The European Association of Distance Teaching Universities. The results showed students' ICT literacy of high level school is 34.95%, middle level school is 34.95%, and lower level school is 34.95% while the assessment of school e-learning effectiveness is around 43.07%. We determined the impression of the students' ICT literacy with the assessment of school e-learning effectiveness. This study shows that there is an influence between ICT literacy of students towards the assessment of e-learning effectivity at school (sig <0.05). The results of the research have shown that the effectiveness of using e-learning in the learning process needs to be optimized to improve students' ICT literacy.
Rescaling CNN Through Learnable Repetition of Network Parameters

Deeper and wider CNNs are known to provide improved performance for deep learning tasks. However, most such networks have poor performance gain per parameter increase. In this paper, we investigate whether the gain observed in deeper models is purely due to the addition of more optimization parameters or whether the physical size of the network as well plays a role. Further, we present a novel rescaling strategy for CNNs based on learnable repetition of its parameters. Based on this strategy, we rescale CNNs without changing their parameter count, and show that learnable sharing of weights itself can provide significant boost in the performance of any given model without changing its parameter count. We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one. The relevance of weight sharing is further highlighted through the example of group-equivariant CNNs. We show that the significant improvements obtained with group-equivariant CNNs over the regular CNNs on classification problems are only partly due to the added equivariance property, and part of it comes from the learnable repetition of network weights. For rot-MNIST dataset, we show that up to 40% of the relative gain reported by state-of-the-art methods for rotation equivariance could actually be due to just the learnt repetition of weights. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is publicly available at https://github.com/transmuteAI/RepeatNet
Design of a Low Cost Coin Acceptor for Vending Machine

Coin acceptor is a device that receive and identifies the validity of the coin. It works by analyzing the coin it received based on its mass, size, diameter, thickness, metal composition, and then sends an appropriate signal through the output connection. However, the cost of purchasing the device is rather high compare to self-fabricate where the current surveys on online shows that the device will cost around RM150-300. This cost is consider high for SME who just planning to start their vending machine business. Therefore, the alternative to come out with a design for a low cost coin acceptor for the vending machine had been done. To make sure the cost will not be high the scope of the project has been determine where the device is designed to only accept the older version of RM0.50 which is 26mm in diameter. In this project, the design is produced by using Solidworks software and the coin identification mechanism was designed by integrating the like of sensors, actuators and microcontroller which is the basic component of a coin acceptor. To further reduce the cost a new mechanism of a sorting hole were introduced to replace and eliminate number of sensor used. The expected cost to fabricate the design was expected to be around RM139 and further cost reduction can be done with further research on reducing the product component.
Archival, anonymization and presentation of HTCondor logs with GlideinMonitor

GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.
Machine Learning-Based Imaging in Connected Vehicles Environment

Intelligent algorithms greatly influence imaging. Machine learning techniques find applicability in correcting and highlighting medical images generated by X-rays, Computed Tomography (CT) scan, Positron Emission Tomography (PET) scan, Magnetic Resonance Imaging (MRI). Such techniques increase the reliability and quality of diagnosis to aid the doctors in devising an effective treatment. These systems have found wide applicability under clinical settings. Imaging is also an essential task in autonomous vehicle development and Connected Vehicles. Research on Connected Vehicles is evolving at a staggering rate with an objective to reduce road accidents significantly and replace drivers with fully autonomous self-driving vehicles. Driver monitoring system (DMS) is a new area of research where drivers are monitored using cameras and other medical sensor networks to detect the drivers’ medical state, mental state as well as cognitive state. Objective biomarkers allow such systems to predict these states. Imaging plays an essential role in the diagnosis aided by the state of the art machine learning algorithms. This paper addresses the challenges posed by imaging under driving environments for diagnosis of medical and cognition of drivers.
Extraction Automatic Abstract Method Based on Bert and Graph Model

research-article Extraction Automatic Abstract Method Based on Bert and Graph Model Share on Authors: Weidong Zhao Shandong University of Science and Technology, China Shandong University of Science and Technology, ChinaView Profile , Xiaolu Chen Shandong University of Science and Technology, China Shandong University of Science and Technology, ChinaView Profile , Ming Wang Qingdao Customs Distruct P.R.China, China Qingdao Customs Distruct P.R.China, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 137Pages 1–7https://doi.org/10.1145/3469213.3470340Online:18 August 2021Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-scale Multi-block Covariance Descriptor for a Compact Face Texture Representation: Application to Kinship Verification

Division-based strategies for face representation are common methods for capturing local and global features and have proven to be effective and highly discriminative. However, most of these methods have been mainly considered for descriptors based only on one single type of features. In this chapter, we introduce an effective approach for face representation with application to kinship verification that relies on pyramid multi-level (PML) face representation, and which exploits second order statistics of several local texture features such as Local Binary Pattern (LBP), quaternionic local ranking binary pattern (QLRBP), gradients, and different color spaces. The proposed approach consists of two main components. First, we model the face image using a PML representation that seeks a multi-block-multi-scale representation where several local texture features are extracted from different blocks at each scale. Second, to achieve a global context information, we compute the covariance between local features characterizing each individual block in the PML representation. The resulting face descriptor has two interesting properties: (i) thanks to the PML representation, scales and face parts are explicitly encoded in the final descriptor without having to detect the facial landmarks, (ii) the covariance descriptor (second order statistics) encodes spatial features of any type allowing the fusion of several state-of-the art texture features. Experiments conducted on two challenging kinship databases provide results that outperform state-of-the-art Kinship verification algorithms.
Design and Implementation of English Online Translation Software Based on Intelligent Proofreading System

With the continuous development of modern information technology, the ways and means of learning English have become more diversified. Especially, the emergence of English electronic dictionary makes English learning more and more simple. This paper puts forward the design idea of English online translation software based on intelligent proofreading system. Firstly, the semantic ontology model is created by creating semantic ontology model and phrase translation combination translation algorithm. In addition, the overall system is designed. The hardware and software of the system are designed through the overall architecture of the system, and the hardware design is mainly the search module, so as to improve the search accuracy. In the process of software module, it mainly includes information transmission model, system network topology design and system function design, and can support translation and proofreading of multiple languages. Finally, the designed system is tested. The test results show that this method is accurate and intelligent in English automatic translation.
More Than an Instrument: Improvising with failing playback media

The adoption of playback media – such as vinyl and the compact disc – by performers as a means of sound creation and manipulation sees our perception of these media ‘elevated’ to the status of an instrument. As this practice develops, improvisers push these devices further in pursuit of new sounds, including actively exploring the sonic potential of failure and destruction. This article argues that when pushed to the point of failure, playback media begin to function as an improviser, exhibiting musical agency over the performance that mirrors that of human improvisers. This article will identify how failing playback functions within a performance, and explore the ways in which it can be understood to improvise, discussing how failing playback media can engage in a dialogical back and forth with human improvisers as well as the ways it can directly influence and determine the structure and musical content of a performance.
Creating Recommender Systems Datasets in Scientific Fields

Recommender systems (RS) have been successfully explored in a vast number of domains, e.g. movies and tv shows, music, or e-commerce. In these domains we have a large number of datasets freely available for testing and evaluating new recommender algorithms. For example, Movielens and Netflix datasets for movies, Spotify for music, and Amazon for e-commerce, which translates into a large number of algorithms applied to these fields. In scientific fields, such as Health and Chemistry, standard and open access datasets with the information about the preferences of the users are scarce. First, it is important to understand the application domain, i.e. "what the recommended item is". Second, who are the end users: researchers, pharmacists, clinicians or policy makers. Third, the availability of data. Thus, if we wish to develop an algorithm for recommending scientific items, we do not have access to datasets with information about the past preferences of a group of users. Given this limitation, we developed a methodology, called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is the creation of <user, item, rating> datasets, related with scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. More info about the tutorial at https://lasigebiotm.github.io/RecSys.Scifi/.
Building of online evaluation system based on socket protocol

As an important part of the evaluation reform, online evaluation system can effectively improve the efficiency of evaluation work, which has been paid attention by teaching institutions. The online evaluation system needs to support the safe and stable transmission of information between the client and the server, and socket protocol establishes the connection through the listening port, which can easily carry out the message transmission and process control. Because it can well meet the construction requirements of online evaluation system, it is applied in our study. The building of online evaluation system based on socket protocol includes the function design of students and teachers, data flow design, evaluation difficulty grading design and system implementation. The system uses Java language and MVC mode for development, which has good scalability and platform-independence. It realizes the paperless examination process and greatly reduces the workload of teachers. The contribution of this paper is mainly reflected in two aspects. One is to explore the construction of an online evaluation system based on the socket protocol, and it provide an Asynchronous IO technical solution for the network communication between the student and the server, which provides a reference for the development of similar systems. The second is to give the realization method of the difficulty classification of the evaluation, and classify the difficulty of the test questions, which lays the foundation for carrying out personalized testing and evaluation.
The Challenges of Open Source Software Alternatives

Developing an Open Source Software application is a challenge. Mainly because there are commercial alternatives that have an army of expert developers behind them, experienced supporters and wellestablished business processes in their development and promotion. Nevertheless, web-based applications, that securely handle the users’ personal data are an area of freedom and ease of use, features that make such applications very attractive. The “ease-of-use” part is very hard to achieve, for the developers and the end-users. Dependencies change often in OSS packages, so the fear that something breaks is always around the corner. If the application looks attractive, additional user requirements fall like rain. This poses a problem of continuity, maintenance and operational quality of the packages. In this paper and presentation we shall share our experience in building such a tool, using https://cern.ch/slides , as a showcase and a learning exercise. We shall describe what was available, what was missing, how it was put together, how much effort it took, and what was achieved.
Tourism English Based on Association Rules Mining Algorithm

research-article Tourism English Based on Association Rules Mining Algorithm Share on Author: Jianzhou Cui Wuxi City College of Vocational Technology, China Wuxi City College of Vocational Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 366Pages 1–5https://doi.org/10.1145/3469213.3472785Online:18 August 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multiagent Based Spam Filtering System

The rapid spread and the easy availability of a free e-mail service have made it the medium of choice for the sendingof unsolicited advertising and bulk e-mail in general. These messages are called spam e-mail and are an increasing problem toboth Internet users and Internet Service Providers (ISPs). In this paper, a multi-agent based architecture is adopted to allow forefficient on-line application of the developed filter while maintain the scalable design that lend itself to openness, a multi-agentsystem (MAS) designed to implement a spam filter system based on mobile agent technology with SVM-filter that worksbackground, at client side, to improve classification accuracy of Gmail and to allow for distant classification.
Design of information sy stem model management system based on micro-service

In order to solve the problems of high coupling degree and poor flexibility of the traditional information system model management system in the process of use and update, a system architecture design method based on micro-service was proposed. Firstly, a solution based on micro-service technology architecture is proposed. Then, according to the reality of the system, the requirements of various aspects are analyzed, and the overall architecture of the system and the system architecture based on micro-service are proposed. At last, the function modules of the system and the key technologies of the system are introduced. This system design method can well solve the related problems of model management in the process of information system construction, and it is of great significance to the improvement of information system function.