Reviewerly/exHarmony huggingface dataset:
Digital reading lists as a teaching and learning tool for the Divinity School of the Chinese University of Hong Kong

In the past few years, CUHK Library has been in search of a course resource tool for creating, fulfilling, and viewing course reading lists for faculty, librarians, and students. Among the commerci...
Verification of a Model of the Isolated Program Environment of Subjects Using the Lamport's Temporal Logic of Actions

The article considers a modern approach to the creation of formal computer system security models, which consists in describing a model in some formal language suitable for its verification for compliance with the expected properties. The article provides an example of such a description in the form of a specification of a formal model of the isolated program environment in the language of the Lamport's temporal logic of actions. The specification is formed as an initial state of the system, a list of possible further actions and a set of invariants and temporal properties to which the system's states must correspond. The initial state is described by some entities that must exist in each system implementation. The system's actions are given in the form of predicates of pre- and postconditions, with some model's variables changing in the latter. Invariants and temporal properties are described in the form of predicates, whose truth must be checked in each possible state of the system or depending on the conditions occurring in previous or future states. The article considers the features of forming a security model specification in TLA+ notation and verifying it using special tools. In its conclusion, the article describes the results of verifying the specification of the formal model of the isolated program environment of subjects, the existing problems and directions for further research on this topic.
Evaluation of the Accuracy of Spatial Data in Detecting the Rate of Land Change in Sinjai District

Abstract Aerial photo interpretation can make it easier to detect, identify and delineate the presence of an area so that it can save time and effort and costs to observe directly the area in question. With the presence of satellite images, we can retrieve data from an area without having to visit that area. In this research, the authors use 3 (three) types of satellite image data, namely Landsat 8 satellite image data with an accuracy of 30 meters,. The objectives of this study is to evaluate the rate of accuracy of spatial data in detecting the rate of land change between 2014 and 2019 in Sinjai Regency. This research is closely related to the enumerative induction process (calculation based induction). To find out the level of accuracy of the data from the results of image interpretation, a field check was carried out by taking a sample point of about 20% of the total pixels of 911111 pixels, so that 191 points were obtained which were distribution points for the accuracy test of using image data to evaluate the accuracy level of land cover based on 2019 image data. The overall accuracy test uses the Kappa formula.
New Probe into English Network Language Features Value in the Context of Online Education based on Character Recognition

Network language is the product of network culture. Its emergence and development conform to the law of language development. English network language is a part of network language. Deep learning is a special machine learning algorithm, which belongs to the field of machine learning. In recent years, breakthroughs have been made in image classification, object detection, image segmentation, speech recognition and other fields. Convolutional neural network is an important network structure in deep learning. Convolutional neural network is a kind of bionic network, which imitates biological neural network. It has excellent characteristics that traditional detection and classification algorithms can not achieve. Character recognition has been widely used in recent years. English handwritten character recognition is a difficult and complex recognition problem in the field of character recognition. Based on the background of network education, this paper uses character recognition technology to study the network language feature model of English.
Few-Shot Semantic Segmentation via Prototype Augmentation with Image-Level Annotations

Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-network-based methods typically suffer from a shortage of large amounts of pixel-level annotations. Recent progress in few-shot semantic segmentation tackles the issue by only a few pixel-level annotated examples. However, these few-shot approaches cannot easily be applied to multi-way or weak an-notation settings. In this paper, we advance the few-shot segmentation paradigm towards a scenario where image-level an-notations are available to help the training process of a few pixel-level annotations. Our key idea is to learn a better prototype representation of the class by fusing the knowledge from the image-level labeled data. Specifically, we propose a new framework, called PAIA, to learn the class prototype representation in a metric space by integrating image-level annotations. Furthermore, by considering the uncertainty of pseudo-masks, a distilled soft masked average pooling strategy is designed to handle distractions in image-level annotations. Extensive empirical results on two datasets show superior performance of PAIA.
The Combination of Technologies Supporting Interactivity and Identification of Students at the Level of Architectural Blocks of the Educational System Using Information Technology

The article describes the basic elements of a unified landscape of educational technologies implementing ways to support interactivity and identification of students in the educational complex. Modern educational complexes using information technology (IT) should form a controlled environment with the ability to systematically collect and process information about the educational process. A permanent analysis of information about students, as well as educational materials, will allow us to identify the classes of students and educational content that are most suitable for each other in terms of time and success in learning . In the architecture of the educational system using IT, technological solutions have been identified that provide the formation of a database and a knowledge base required to solve this problem. On the basis of the specific features of the educational process implementation, it is possible to use various sets of technologies to solve certain didactic tasks, but it is the integrated application of the technologies described in the article that reveals the potential in the field of the education management quality.
Anomaly Detection in Real-Time Surveillance Videos Using Deep Learning

The real-time events are fast and occurring at highly dynamical moments. Hence, the important challenges are identifying the anomaly incidents properly. The specified methods and techniques are to be quick in identification for control and other measures of the events. In the proposed method, the anomalies are detected from the surveillance videos using the multiple instance learning and ID3 for extracting the features. The extracted features are then used as input to a deep neural network where the classification of the videos to anomalous and normal videos is done. The investigated dataset is with 128 hours of videos with ten percent of different realistic anomaly videos. The AUC of the proposed approach is 81. The proposed approach is most beneficial for the real-world anomaly recognition in surveillance videos.
Vocational awareness and aspiration of seafaring students in Taiwan

Purpose This study aims to examine variables and factors in vocational awareness and attitudes that affect the vocational aspiration of seafaring course students who are potential future seafaring employees. Design/methodology/approach A literature review was conducted to identify factors that prompt students in seafaring courses to pursue seafaring careers, followed by a design of preliminary questionnaire questions to survey students’ perception on the conditions of seafaring careers (i.e. vocational awareness) and their intention to pursue a seafaring career (i.e., vocational aspiration). A total of 744 seafaring course students in four Taiwanese universities completed a questionnaire developed according to the previous studies. A structured equation modelling was conducted in this research including model validity, goodness of fit, model correction and mediation effects. Findings With a model consisting of four factors for vocational awareness, three factors for overall attitudes and vocational aspiration as a dependent variable, the results showed that factors of vocational awareness and students’ overall attitudes were significantly correlated, and the factors of overall attitudes exhibit strong mediation effects on vocational aspiration from vocational awareness. The results confirmed that the factors are important determinants for the vocational aspiration of students. Originality/value The findings of this study provide a comprehensive approach to understand students’ seafaring aspiration for the universities, governments and shipping companies. Seafaring policies and management are discussed from the research findings.
Analysis on Data Migration Strategies in Heterogeneous Databases

Data required for a new application are frequently come from other existing application systems. If data required for the new application are available from existing systems and the volume of data is large, the necessary data should be migrated from the existing systems (source systems) to the new application (target system) instead of recreating those data for the target system. The Transformation of data is generally a necessary step in data migration because the data requirements and the architecture of the target system are different from that of the source systems. This paper surveys the data migration techniques which focus on improving the data quality between different types of databases.
Analysis and Application of Precise Teaching Design Based on Clustering Algorithm Analysis

The accurate teaching mode is the long-term education practice in our country to create "according to their aptitude" the principle of integration with modern education technology, through the measurement process to track the students' academic performance and learning effects, according to different learners, design different target, selection of different teaching contents, teaching to provide data for subsequent reference, accurate guide teaching.
Improvement of Engineer Ethics Education Using Resilience Engineering Concept

The purpose of engineering ethics education is to understand the effects and impacts of technology on society and nature and the responsibilities that engineers have to fulfill for society. There are many cases used in the educational method for students to understand the problems surrounding the engineers. However, most of the cases correspond to event scenarios where engineers have failed to maintain safety. Resilience engineering was born from the criticism of safety measures for the purpose of preventing recurrence by seeking human error and organizational culture as the cause of accidents in the field of ergonomics. Its features are that people are considered as beings that realize safety in dangerous systems, and that they focus on good practices. This paper describes the improvement of engineering ethics education by utilizing resilience engineering concept.
Evaluating and propagating uncertainty in digital fringe projection systems

Fringe projection is a measurement technique able to produce high-density data measurements of Lambertian surfaces using a camera and projector. Evaluating the uncertainty of a fringe projection system is challenging because of the highdensity and the interdependence of data involved in both characterising the instrument parameters and conducting the measurement. Fringe projection systems undergo a characterisation process to define the parameters that triangulate the surface points from camera images. The accuracy of the parameters defined during the characterisation process will partly define the accuracy of the final measurement. The characterisation process is often called “calibration” – yet does not provide a value of uncertainty of the parameter values found during characterisation. This paper outlines a method to allow the evaluation of uncertainty in a fringe projection system caused by imperfect characterisation procedures before applying this method to a flat plane artefact and a sphere plate artefact.
PEMODELAN REGRESI WLS UNTUK DATA DALAM STUDI KECERDASAN EMOSIONAL

Regression analysis is frequently used to study forecasting and relation between some variables. In studying forecasting and relation between some variables using regression, needed the best model. This research aimed WLS regression modelling for data in emotional intelligence studies and applied to the case for identified of factors influence the student emotional intelligence in Institut Agama Islam Negeri Bone. The research objective was knowledge the best regression linear model and factors influence to the emotional intelligence. The results showed the best regression linear model to know influence factors of the student emotional intelligence in Institut Agama Islam Negeri Bone is, i = 27.9 0.7 X 1 +0.5 X 2 +1.87 X 3 . Based on this model, imagination, cognition, and personality is significant influence to the emotional intelligence. Keywords:  WLS regression, emotional intelligence, imagination, cognition, personality .
An Effective Proguard: Identifying Malicious User-Accounts in Social Networks Based on Online Voting and Ranking System

Online social networks gradually integrate financial capabilities by enabling the usage of real and virtual currency. They serve as new platforms to host a variety of business activities such as online promotion events, where users can possibly get virtual currency as rewards by participating such events. Both OSNs and business partners are significantly concerned when attackers instrument a set of accounts to collect virtual currency from these events, which make these events ineffective and result in significant financial loss. It becomes of great importance to proactively detecting these malicious accounts before the online promotion activities and subsequently decrease their priority to be rewarded. In this paper, we propose a novel system, namely ProGuard, to accomplish this objective by systematically integrating features that characterize accounts from three perspectives including their general behaviors, their recharging patterns, and the usage of their currency. We have performed extensive experiments based on data collected from Tencent QQ, a global leading OSN with built-in financial management activities. Experimental results have demonstrated that our system can accomplish a high detection rate of 96.67% at a very low false positive rate of 0.3%
Degraded document image binarization based on U-Net and transfer learning

Datasets of degraded document image are small, making the network unable to be fully trained or easily over-fitting. And single-convolution network has poor generalization ability. These factors lead to an unsatisfactory binarization performance. This paper proposes a degraded document image binarization method based on U-Net and transfer learning to solve these problems. U-Net is used as our model’s backbone for its good performance in small datasets. The common transfer learning network models ResNet is utilized as the pre-training encoder to improve the generalization ability of our model. Then we establish different decoder network structures for the characteristics of different encoders. In addition, different from conventional U-Net, the convolutional layer output of downsampling is made as the skip connection object to be superimposed with the input of upsampling in our models, so the upsampling layers can better restore the details of document images. In this way, we improve the convergence and generalization ability to get a better binarization performance.
Energy Consumption Estimation Framework at Source Level for Data Centers

In a cloud computing environment, you can use source-level energy consumption (EC) estimation to estimate the amount of energy consumed by the cloud before the cloud computing tasks are performed.EC estimation of tasks is important for source code improvement in the aspect of task scheduling and EC optimization. We propose a cloud data centre. Here to perform a task on our cloud data centre and calculate the energy. We use energy awareness algorithm to calculate the energy of the task. Finally, we compare the energy required to perform the task on local machine and cloud. We show that our proposed system is more efficient than the existing system.
Literal is not always easier

Abstract “Literal translation” is a popular construct in Translation Studies. Research from computational approaches has consistently shown that non-literal translations, i.e., renderings semantically and syntactically different or not close to the source text, are more difficult or effortful to produce than literal ones. This paper researches whether literal translations are systematically less effortful to process than non-literal ones using comparable corpus data. The effort incurred in processing literal translations from a parallel corpus is compared to that of processing the most frequent non-literal renderings found in previous comparable corpus studies. Ten professional translators edited a text using a mock translation environment setup using the keylogger Inputlog. The task was presented as a regular editing process with a full cohesive text presented segment pair by segment pair. Time served as a proxy for overall cognitive effort. We analyzed time from presentation to type (TTP) and time to completion of segment edit (TC), or complete editing events. Results showed that processing efforts are indistinguishable between categories, suggesting that cognitive effort to edit non-literal default translation candidates is not always higher when compared to the most frequent literal translations from a parallel corpus.
Toward Automatic Mobile Application Fingerprint Extraction from Non-encrypted HTTP Traffic

The ever-increasing number of mobile applications and polymorphic variants create a pressing need for automatic approaches to infer distinguishable fingerprints for dissecting the collected network traces into the corresponding categories. In this paper, we present LightPrint, a lightweight method that given a large pool of HTTP network traffic, generates app fingerprints. LightPrint produces these fingerprints by constructing a series of domains, and every domain is a set of weighted keywords, to characterize HTTP request/response pairs. We have implemented LightPrint and evaluated it on a recently collected traffic dataset with ground truth. Our experimental results show that the proposed approach performs mobile app identification task with high precision (over 96 %) and recall (over 93 %).
Creating a Private Cloud using FTP Server

Data plays an indispensable role in our day-to-day lives. Also, in this digital era, the amount of data is increasing at an unprecedented rate, which in turn requires specific storage devices to store different types of data. Despite the hype, maintaining these devices require high cost. Sometimes these devices may also get corrupted and also it require a lot of devices to store data. To reduce these problem, this research work maintains a cloud space, which can be accessed through a FTP login(admin), where user need to register/subscribe to access the cloud. After successful registration they will get links through mobile number, and then they need to login to upload their data. Here, the Data is protected by using ROTA algorithm. Also, the poposed research work provides bitcoin transaction as an additional feature. A private cloud has been created for storage and later it was implemented and created by using NetBeans IDE. The user can upload and download the data into the cloud by using FTP server. The bitcoin transaction shows the graph with different tokens entered by the user.
Construction and analysis of foreign language online guiding model based on classroom network environment and face recognition

Because network teaching is different from traditional classroom teaching and other forms of teaching, some new theories and methods need to be adopted in network teaching. Network teaching goes beyond the limitation of time, space and region, which makes it difficult to supervise the effectiveness of network teaching. The old method of testing the effectiveness of network teaching only through the stage test and the test after the end of learning can not effectively supervise the effectiveness of network teaching. New methods are needed to supervise the effectiveness of network teaching. At the same time, the maturity of face perception theory, technology and application makes it possible to supervise the effectiveness of online teaching based on face perception. Face perception includes face detection, face feature extraction, face contour detection, face recognition and so on. This paper will combine the relevant theories of educational ecology and online education to study the necessity of carrying out ecological classroom teaching, and explore the construction strategy of ecological classroom of intelligent teaching in online live classroom. In this paper, combined with face recognition verification, online instruction model is used to build online classroom platform.
Fuzzy-based Integration of Sentinel-2 MSI Data for Locating Potential Hydrocarbon Microseepag

Abstract The vertical migration of hydrocarbons from oil and gas reservoirs to the surface and/or subsurface (i.e., microseepage) can induce mineral alteration that results in concentration of ferrous iron, clay, and carbonate minerals at the Earth’s surface. These altered zones may be detected in remote sensing imagery based on their specific reflectance spectral characteristics; zones where three types of mineral alteration occur simultaneously reflect possible hydrocarbon microseepage locations with the potential for underlying oil and gas reservoirs. In this study, a fuzzy set based approach was used to integrate the results of principal component analysis (PCA) and band ratios (BRs). First, each selected indicative principal component (PC) and BR was treated as a fuzzy set, and a corresponding fuzzy membership function defined. The membership degree of each pixel, which indicates the possibility of the presence of specific altered minerals, was then calculated. Subsequently, the Gamma operator was used to fuse all fuzzy sets to create a new fuzzy set, which was then defuzzied and used to indicate the possible locations of hydrocarbon microseepages. The Sentinel-2 MSI data were used to illustrate the approach and the zones of hydrocarbon microseepage mapped.
Review Process on URL Phishing

Phishing is that the most typical and most dangerous attack among cybercrimes. The aim of these attacks is to steal the data that’s utilized by people and organizations to perform transactions or any vital info. The goal of this is often to perform an Extreme Learning Machine (ELM) primarily based upon the classification of options together with Phishing Websites information among the UC Irvine Machine Learning Repository information. For results assessment, ELM was compared with different machine learning (SVM), Naive Thomas Bayes (NB) strategies and detected to possess the best possible accuracy.