Reviewerly/exHarmony huggingface dataset:
Research on improving social service ability of local university libraries based on Wuhan City Circle

As an important platform for gathering documentary and information resources, local university libraries naturally play an important role in serving local social and economic development. Local university libraries in Wuhan city circle should be based on locality, take the construction of information resources as the basis, take innovative services as the fulcrum, promote the construction of Wuhan city circle and help the construction of Wuhan national central city as its main purpose, improve various social information service systems, take the initiative to serve local governments and enterprises in the city circle, provide information services and support for the conversion of technical achievements of the universities in which they are located, and serve the economic and social development of the city circle.
An Operating System Identification Method Based on Active Learning

In operating system (OS) identification, machine learning algorithms are widely adopted, which can receive reasonable accuracy even under encrypted traffic. However, machine learning algorithms require large amounts of labeled data for train. In addition, such algorithms have difficulty dealing with data imbalances and predicting the types of OSes that account for a small percentage of traffic. To solve the above challenges, we propose an OS identification algorithm based on active learning (AL) in this paper for the first time. In the algorithm, a query strategy is designed. We test the performance of the proposed algorithm using an unbalanced dataset. The results show that the proposed algorithm can achieve similar performance only using 0.4% of labeled data for training compared with the existing machine learning algorithms. Compared with existing algorithms, the proposed AL-based algorithm only needs 32% of the training time and 3.2% of the training samples to achieve the same accuracy under the full data set. In addition, it also performs better on multi-classification problems than existing algorithms.
Enhanced Performance of Finitie Boundary Isolation Forest (FBIF) for Datasets with Standard Distribution Properties

We show superior performance of Isolation Forest (IF) algorithm with Finite Boundary (FB) or Finite Boundary Isolation Forest (FBIF) algorithm compared to IF and Extended Isolation Forest (EIF), when the dataset consists of continuous variables that fit standard distribution properties. The experiments performed on a public dataset show distinct advantage by FBIF over IF and EIF. EIF demonstrates better performance than IF on the same public dataset consistently but FBIF outperforms EIF. EIF proposes improved speed of computation over SciForest algorithm resulting from the use of randomly generated slanted hyperplanes without incurring the additional processing burden to find optimum slanted hyperplanes for each split for branching decision boundary by SciForest but empirical evidence of limitations of slanted hyperplanes as branching boundary in SciForest and EIF in the form of inconsistent anomaly scores for a set of synthetic datasets consisting of spirally distributed in 2-D, single blob of Gaussian in 2-D and two diagonally placed blobs of Gaussian in 2-D have been demonstrated previously. FBIF uses hypersphere as branching boundary to eliminate the inconsistency. We show that input features consisting of standard distribution properties are sufficient to get improved anomaly detection by FBIF, implying that test for standard distribution property is a simple and effective dimensionality reduction technique for anomaly detection in conjunction with FBIF. The combination of hypersphere as a generalized branching decision boundary and the test for standard distribution of input features are presented here as an effective approach by comparing results of FBIF, EIF and IF with a public dataset as input. The results show that FBIF, with only a small number of features displaying standard distribution profile, can outperform IF and EIF, even when the input to IF and EIF consists of greater number of input features.
Digital transformation in higher education in Vietnam and its challenges nowadays

In June 2020, the Prime Minister has approved the plan for National Digital Transformation Program to 2025, vision to 2030, in which education is one of the most prioritized domains. In this program, higher education takes the pioneer mission to accelerate innovation process, enhance education quality, actively contribute to the nationwide digital transformation. This article researches the meaning of the term “digital transformation” and its impacts, as well as analyzes the application of digital transformation in higher education, in relation to the traditional higher education in Vietnam, in order to understand the insufficiencies of the current model of education and the needs of applying digitalization. The article also names the challenges that education institutions must face when bringing this plan to reality. Defining these challenges is a prerequisite of providing solutions and building the strategy, to ensure the success of digital transformation mission in Vietnam higher education.
Evaluation study of an IT security measurement method for micro-enterprises

This paper presents the evaluation of a multidimensional IT security measurement method. The underlying solution was developed for the target group of handicraft business as micro-enterprises, i.e. 1 to 9 persons in the context of German industry and IT security requirements. The study aims to test hypotheses. Furthermore, it is to be determined which staged IT security status the subject groups have and which differences arise after a second measurement. In the result of the study the status quo of the target group and an increase of the IT security by application of the results of the measuring instrument after the 1. measurement is to be seen.
Multi-modalities Analysis In Profiled Learning

The advances in information technologies offer a promising approach to leveraging effective and engaging learning experiences. The diversity of sensor-based technologies, such as facial expression analysis and gaze tracking, has introduced the opportunity to capture students’ interactions with learning activities or assessments. In such context, captured data hold meaningful promise for gathering a deeper understanding of students’ learning experience and informing an adaptive frame to support individualized learning needs. This paper introduces an analytic approach that incorporates students’ eye tracking, facial expression, and mouse movement data to predict students’ performance while resolving an English exam. The proposed system examined the degree to which different modalities captured from 53 students (aged 18 to 22) in an authentic learning environment were predictive of the students’ exam scores. The analysis of the collected dataset shows that adding student features has effectively predicted his assessment score. The adoption of different regression models revealed that multi-modal data could accurately predict students’ exam scores and hold significant potential for guiding a real-time adaptive environment.
Image Demosaicking using Super Resolution Techniques

Limitations do exist on capturing the full color information in a scene, apart from the resolution of captured images. Therefore mosaic images are the preferred format in digital cameras, where incomplete set of color information is acquired. In this paper, a super-resolution demosaicking (SRD) approach is proposed to reconstruct an enhanced-resolution full-color image from the observed samples, robustly and without the need (a) for a training process. The acquisition model assumes degraded observations using known blur and noise. The reconstruction approach iteratively estimates the unknown registration parameters and the demosaic image simultaneously. Qualitative and quantitative experiments performed (b) on synthetic observations reveal high performance images.
Automated Personnel Digital Twinning in Industrial Workplaces

Digital twin technology is designed to use real world data together with simulation programs to understand or predict how a process or activity is being performed. This technology arose in the last decade in several industrial contexts, mostly focused on devices, facilities and manufacturing, and only very recently to twinning human behavior. This paper describes an applied study on digital twinning of workers within complex and varying industrial settings, using the recently introduced VIBE neural network. VIBE comprises a two-stage pipeline of neural network blocks (NNB), where the first stage determines detection and tracking, while the second stage performs 2D-to-3D human model mapping. The first stage allows using of three different NNB combinations, namely Mask R-CNN & SORT, YOLO & SORT, and STAF. In this work, we focus on the influence of each NNB on the second stage and the final output using state-of-the-art performance metrics. Mask R-CNN & SORT provided a multi object tracking accuracy (MOTA) of 87,2%, while STAF obtained a MOTA 88,5%, and the best performance with occlusions in the final 2D-to-3D human rendering. These results pave the road for new developments in real-time complex digital twinning and workplace virtualization, in which human activities are an essential part.
Proposed Automated Wheelchair with Voice Recognition System

This research is established for those patients who are physically disabled and cannot even move their hands. So, we generally designed an automated wheelchair with voice control through simulations in the Proteus design suite software. This wheelchair also detects obstacles through sensors, balances the chair on critical roads with the help of sensors, and sends a notification to a responsible person through a global system for mobile communication (GSM) if the patient is in a dangerous situation. Some small devices, such as Arduino, VR3 module, GSM, and sensors, will be used to design the automated wheelchair. The user doesn’t need to wear any external device or sensor to control the movement of the automatic wheelchair. The user only needs to command clearly with their voice (Left or Right) then it will move automatically to left or right. The novelty of the research is to help certain people live a life with less dependence on others for their movement in daily life. Where the user only needs to command clearly with their voice.
Grasshopper Algorithm based Fuzzy system for Trajectory Tracking of Robot Manipulator

This work presents a grass hopper optimization algorithm based fuzzy system for trajectory following in robot manipulators. The idea is to optimize the parameters of the fuzzy control scheme The modified copy of the algorithm is presented and adopted in the optimization process of parameters of fuzzy controllers. The developed modified grasshopper based fuzzy scheme is applied for trajectory tracking in two link robot manipulator and compared with that of using the basic grasshopper optimization algorithm. Simulation. Results show good tracking performance for the new developed scheme.
OCR and Levenshtein distance as a measure of image quality accuracy for identification documents

Optical Character Recognition (OCR) is a technology used to distinguish printed or handwritten text characters inside digital images. The areas that it is applied differ from text editors where the scanned images are converted to text, to text recognition where license plates are identified through a camera. The proposed model in this paper uses this technology with the integration of a text-matching algorithm to decide if an image has good quality and clear readability. The sample dataset is based on identification documents, such as a health insurance card. The main objective of the designed model is to enhance the pre-processing phase of dataset creation used from the training models for document classification based on artificial intelligence. It can be used in the pre-processing phase as a boundary for the processed images, to clean the input data from low quality images.
The current situation of students’ Pedagogical practice of preschool education at Quang Binh University

Pedagogical practice is one of the core contents in preschool education curriculum. It aims to develop pedagogical students’s professional competencies. This article presents the results of a survey of students, lecturers, instructors, and administrators about the pedagogical practice activities of students majoring in Preschool Education at Quang Binh University in the two years 2019 and 2020. Surveyed aspects include students' perceptions of pedagogical practice, their achievements and difficulties in the practice process. From there, we propose solutions to improve the efficiency of pedagogical practice activities for students.
Sign Language Interpreter Using Inception V2 and Faster R-CNN

Sign language is used for communication by many people who have speech or hearing impairment. People with knowledge in particular sign language can easily understand this language but it is difficult for people who don't know that language. So, there is a need for sign language interpreter to make the communication easier between a person using sign language and a normal person who can't understand sign language. Most of existing research were able to identify only the static symbols of American Sign Language with considerable accuracy but failed to identify the motion symbols 'J' and 'Z'. The proposed system tries to do an accurate translation of American Sign Language alphabet symbols using deep learning techniques called inception V2 and Faster RCNN. The novelty of the proposed system is identification of all alphabet symbols including motion symbols with overall model accuracy of 98% by employing inception V2 combined with Faster RCNN technique. The model was tested by displaying signs continuously on webcam.
Machine Translation for Indian Languages Utilizing Recurrent Neural Networks and Attention

Neural Machine Translation better known as NMT is an end-to-end approach for autonomous language translation that utilizes neural models. This is an effort to bridge the gap between the multinational and multilingual people to understand their views. The NMT systems involves models to learn directly through mapping of input–output which has proven to generate increased accuracy outputs. This technique has made remarkable accomplishments and has overcome the weakness of the conventional translations models. The paper implements the RNN, Attention based mechanism and transformer on Indian-English language pairs. So far there are no specific benchmarks for Indian languages. There are companies such as Facebook, Bing, Google whose translators supports few Indian languages. In this research work models have been trained on two set of Indian language pairs which have been retrieved from open source platform Tatoeba.
Email Spam Detection Using Machine Learning and Feature Optimization Method

Email communication has been stated to be the most affordable, economical, and quickest communication way nowadays. Although there are significant perks of emails, sadly, its practice has been baffled by the vast amount of unsolicited and often deceitful emails. And these extreme amounts of spam are decreasing the essence of information present on the Internet and causing concern among users. Many spam detection models have been suggested and experimented with within the literature; however, the listed accuracy showed that more could be done in this area to improve accuracy. This work describes how to detect spam emails using machine learning based on words, numbers, and characters in the emails’ content. We have used some prevalent machine learning models (Naive Bayes’, Neural Network, K-NN, Tree, Logistic Regression) and compared them to classify emails. This paper’s primary focus is obtaining optimal accuracy with the help of limited features out of 57.
Blockchain Implementation Process Model for Supply Chains - From Technology Awareness to Scaling

This contribution proposes a new blockchain implementation process model which closes identified gaps of existing models such as the lack of supply chain management focus, guidance on scaling or performance measurement. In particular, existing models do not provide a sufficiently detailed approach. To prove this, we develop criteria for a blockchain implementation process model in supply chains and systematically compare existing process models in this context. In order to identify a suitable use case for companies, to plan its implementation and to measure its impact on the company performance, we develop a new implementation process model. Furthermore, this process model supports the roll-out of the blockchain use case to other partners along the value chain, thereby increasing scalability.
Instance Based Authorship Attribution for Kannada Text Using Amalgamation of Character and Word N-grams Technique

Authorship Attribution is the task of identifying a true author of a given text from a set of suspected authors stylometry features play a vital role in recognizing the right author, it includes lexical and syntactic features. N-gram is one of the popular techniques used to extract syntactic features from the text. The main objective of this work is to use both lexical and syntactic features on a Kannada text and compare the performance of both approaches using different machine learning algorithms. The Kannada language is spoken by the Indian southern state Karnataka. Even though we can see major works in text processing, Authorship Attribution is in a tender state. Researches have been carried out on handwritten Kannada documents but not on digital text. Char n-gram, word n-gram and the combination of these two known as Amalgamation technique are used as syntactic features to extract the writing style of an author. The results show that Support Vector Machine algorithm outperform with 94% and 60% accuracy using N-grams and lexical features respectively.
Collaborative Management of Traffic Accidents Data for Social Impact Analytics

Traffic accidents have a devastating effect on society, and despite the measures taken by transport authorities, numbers still are of concern. As such, various studies emphasize the need for investments in the road infrastructure, vehicle safety, and enforcement measures. However, traffic and accident data are scattered among several stakeholders. Police authorities, emergency agencies, hospitals, road concessions, and the national road safety authorities all hold partial data about accidents and their consequences related to human lives. If such data could become widely available on production time, e.g., when an emergency doctor reports injuries or deaths, a police officer registers the scenario, etc., intelligent analytics could be used on such data towards helpful decision support. To cope with the wide diversity of data sources and ownership, more than data integration, this requires an approach for collaborative data management. Based on previous work on strategies to structure computing and communication artifacts and data science management, we present and discuss a collaborative traffic data management strategy considering the data producers as part of an intelligent traffic collaborative network. The challenge is thus to rethink traffic and accident data collection and management under the responsibility of diverse organizations, keeping their processes and technology culture, but promoting sharing and collaboration. Therefore, the proposed approach considers data analysis performed through business processes executed in the context of virtual organizations.
Web Vulnerability Detection: The Case of Cross-Site Request Forgery Using Classification and Regression Trees

In the world of hacking, one of the simple and human-understandable web attacks is Cross-Site request forgery, but this is the most top priority attack that needed to handle with keen observation because of the very small difference in their appearance. A hacker develops a cross-site application that looks similar to the original site and embeds this URL in either back transactions or E-commerce applications, it misleads the user to deviate from the original website without his notice by disabling all the authentication mechanisms. The hackers develop this type of cross website by embedding the SQL injection queries either in HTML navigation pages or while executing the external Java Scripts. To develop a solution for this type of attack, the system needs a tool that balances both scalability and usability because of millions of users working with the internet to performs various tasks on social media platforms. This tool can also isolate web attacks by designing robust web applications which include advanced encryption algorithms, which require a lot of effort to decrypt the source code. The developers of the website have to work a lot to take care of the security functionalities, negligence of which may cost the website source code to be hacked. Accidentally, Cross-Site request forgery (CSRF) attacks can be left behind, which motivated recent research on asynchronous Cross-Site request forgery detection. The proposed paper uses the Classification and Regression Trees (CART) Algorithm to detect phishing websites based on the URL posted.
A Semantic-Based Collaborative Ambient-Assisted Working Framework

Over the last two decades an utmost interest has been shown to Ambient Intelligence (AmI), with most of the related applications focusing on home settings. However, considering the ever-increasing number of ageing people occupied in the workforce, the Ambient-Assisted Working (AAW) is arguably at the beginning of its development. For an effective development and integration of AAW systems, cooperation among and shared knowledge from different stakeholders are required. This work proposes an AmI framework leveraging on Semantic Web technologies to foster employees’ wellbeing. The AAW framework makes use of a domain ontology, outcome of the cooperation between different stakeholders (employer, employees and environment) to adjust, modify and correct indoor comfort metrics in workplaces. In this paper, the proposed framework’s architecture and its underlying ontology are described, along with a use case scenario that illustrates how collaboratively modelled data can actively support ageing workers.
Malaria Detection from Blood Cell Images Using Convolutional Neural Network Model

Malaria disease is the major and worldwide reason for death in the human community. To identify the malaria, sample of red blood cell is examined under the microscopic instrument by the technician. But, due to the varying structural nature of malaria parasite in the blood cell; relying on the single method may endanger the diagnosis of this disease. With the advancement of advanced technique including machine and deep learning mechanism, this solution can be enhanced to verify the severity of this diseases and make them to properly diagnose the infected case. In this context, an artificial intelligence based convolutional neural network model has been developed to categorize the blood cell into malaria infected and uninfected type of classes. Performance of the model has been evaluated using standard evaluation measures on the public dataset of malaria blood cell images. Obtained quantitative scores confirms its performance which outperforms the available state-of-the-art techniques.