Reviewerly/exHarmony huggingface dataset:
Perceptual image quality assessment based on gradient similarity and Ruderman operator

In this work, a new metric for image quality assessment is suggested, which provides more suppleness than previous measures in using Ruderman operator, visual region of interest and gradient similarity. Firstly, the luminance distortion between the reference and test images is determined. Secondly, the gradient similarity is computed by using canny filter and proposed gradient mask. Thirdly, the test and reference images are transformed using Ruderman operator. Fourthly, the visual region of interest is calculated by employing entropy operator. Lastly, the dissimilarity between the reference and test images is obtained, by combining all previous metrics: luminance distortion measure, gradient similarity measures, Ruderman measure and visual region of interest measure. Experimental comparison demonstrates the effectiveness of the proposed method.
THE USE OF PIE TECHNIQUE IN TEACHING WRITING SKILL

The aim of this study is to investigate differences of students’ writing skill in analytical exposition text between the eleventh grade students of SMAN 11 Palembang who are taught by using PIE Technique and those who are not. The population of this study is the eleventh grade students of  SMAN 11 Palembang in academic year of 2019/2020 wih the total number of students is 515 comprising 12 classes. The sample consists of two groups, experimental and control groups for each group consists of 40 students with the total numbers of 80 students. In collecting the data, pretest, treatment and posttest are used to obtain the data. The results show that the result of paired sample t-test in experimental group showed that the t- obtained (11.902) was higher that t- table (1.684). The result of paired sample t-test in control group showed that the t- obtained (5.609) was higher than t- table (1.684). The result of independent t- obtained (10.718) was higher than t- table (1.664). The data used to analyze by using t-test find that there is a significant difference in writing skill between the students who are taught by using PIE Technique and those who are not. Keywords : PIE technique , teaching, writing skill ,
Distance Measures in Mineral Potential Mapping: Squared L2, Shannon and Combination Families

As a complementary work to the first part of the non-Euclidean distance measures in the spatial data decision analysis for mineral potential mapping (MPM), three families of squared L2, Shannon and combination are examined in this research to evaluate the performance of fifteen distances in geospatial data integration. The TOPSIS method as a well-known outranking method in the multi-criteria decision making (MCDM) problem was utilized in the MPM, where its distance kernel was substituted by these new families to investigate the efficiency of different distances in final preparation of the synthesized indicator layers. For consistency with the previous work (i.e. distance measures in spatial data decision analysis), the North Narbaghi porphyry copper deposit in the Saveh, Iran, was revisited as a deposit-scale prospecting zone. The ultimate goal was to prioritize the favorable zones for borehole drilling. The geospatial datasets were derived from a multi-disciplinary survey comprising of geological, geochemical and geophysical criteria. It is indicated that some distances could partially promote the performance of the mineral potential maps in comparison to the traditional Euclidian-based TOPSIS outranking method in the studied case, which is of considerable importance that deserves more investigation in the future.
Guides @ UF: The Harold and Mary Jean Hanson Rare Book Collection: Digital Holdings

A guide to the holdings of UF's Rare Book Collection with tips for conducting research in similar collections Information about our digital collections and advice for using digital books.
Research on Error Compensation Method of Encoder Signal Based on Cloud Computing

In order to improve the performance of encoder signal error compensation, this paper proposes a method of encoder signal error compensation based on cloud computing. This method uses near-field sou...
DISTANCE LEARNING: CURRENT MODEL OF LEARNING IN THE EDUCATIONAL SPACE

Distance learning is widespread in the modern educational space. This form of education opens up limitless opportunities for students: getting education at the world's leading universities remotely, a rich selection of distance education programs, saving time and money, flexible schedules and individual study planning. The purpose of this work is to study distance learning as an innovative method of learning and to identify the extent to which remote learning is widespread both in Kazakhstan and abroad. This paper examines the main characteristics of distance learning, highlights the advantages and possible disadvantages of remote learning, and reveals the concept of mixed learning.
Pengembangan Teknologi ERP Untuk Modul Human Resource Management Studi Kasus PT Raja Digital Media

Human Resources Management is an important instrument for a company in achieving its goals. The use of an integrated information system is an important element for managing business processes in company management. ERP is an integrated information system that is meant in an effort to improve company quality. Especially for online news companies, conventional business process management can affect the quality of performance of the company itself. Focusing on the HRM of the company Berita Online, the study discusses the implementation of human resource management business processes using ERP software, namely Odoo. The research was conducted by analyzing existing business processes and then designing an integrated proposal system and then implementing it into Odoo. The modeling system is designed to optimize data management which is still being processed conventionally. Optimization in every business process improves the quality of security by setting user access rights and the accuracy of data obtained by real-time data transactions, as well as time efficiency, namely reducing the time duration required for recording and storing documents compared to manually.
A hierarchical method for assessing cyber security situation based on ontology and fuzzy cognitive maps

The hierarchical analysis method is widely used in the field of cyber security situation assessment. It is a key research topic. However, lots of them have paid less attention to the analysis of interrelationships among cyber security situation elements, and still have no effective cyber security events tracking capability. In this work, we proposed a hierarchical cyber security situation assessment method based on ontology and fuzzy cognitive maps (FCM). Firstly, we collected cyber security events from multiple ways and created a general cyber security risk events according to structured description of events based on ontology. Secondly, we generated semi-automatically the FCM structure according to general cyber security risk events using our FCM build method. Thirdly, we assessed and quantified cyber security situation based on ontology and FCM, and then determined the cyber security situation level according to relevant cyber security risk level table. At last, the cyber security events tracking capability was introduced. In our experiment, we used DARPA2000 dataset to verify and analyse our cyber security assessment method and explained tracing the high-risk events in target network. The result shows that our method can reflect the cyber security situation accurately and has the cyber security tracing capability.
Training Deep Architectures Without End-to-End Backpropagation: A Brief  Survey

This tutorial paper surveys training alternatives to end-to-end backpropagation (E2EBP) -- the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully global E2EBP and the strictly local modular training, there are "weakly modular" hybrids performing training without the backward pass only. These alternatives can match or surpass the performance of E2EBP on challenging datasets such as ImageNet, and are gaining increased attention primarily because they offer practical advantages over E2EBP, which will be enumerated herein. In particular, they allow for greater modularity and transparency in deep learning workflows, aligning deep learning with the mainstream computer science engineering that heavily exploits modularization for scalability. Modular training has also revealed novel insights about learning and has further implications on other important research domains. Specifically, it induces natural and effective solutions to some important practical problems such as data efficiency and transferability estimation.
임진왜란 시 청주성 전투에 관한 실증적 고찰

The Battle of Cheongju Castle, the first battle to recapture the town among the ground battles during the Imjin Invasion, was accomplished by united forces of civil soldiers, monks and the royal army. However, most of the studies have been focused on the progress of combat or the activities of civil soldiers and monks using existing historical sources, and objective analysis based on facts has been insufficient. Therefore, in this study, based on Delbruck''s empirical criticism analysis, each axes of attack that carried out the cooperative attack of the Battle of Cheongju Castle were classified and analyzed, and based on this, the characteristics of the tactics and victory factors that appeared in the Battle were derived.As a result of the study, it was found that the number of Korean troops in the battle was three times more than Japanese troops. In addition, for combat organization and tactics, the direction of attack and the form of maneuvering were set through prior cooperation, and the tactics that made the most of the advantage of the terrain were selected. In addition, the weapon system was very poor, but it used a frontal attack through concealment and cover. They also lured Japanese army and launched a pincer movement.The factors of victory are the first, the selection of a suitable combat method from the perspective of military geography. Second, the success of the civilian-military operation, which performed an integrated operation in cooperation with the joint efforts of the civil, public, and military. Third, the leadership was secured based on the conviction and determination of victory, centered on the commander. Fourth, the enemy''s weak point was selected through constant collection information and surveillance of the battlefield, and the combat power was concentrated.
Effort estimation in software development using story point: a machine learning approach

Agile methodologies are besieged with problems and potential solutions around predictive insights on a project. These problems range from estimation, quality, to effort and duration requirements. Despite of having innumerable predictive models not a single reliable solution is available to estimate the duration and effort required to complete an agile project on an ongoing basis. This is due subjective nature of 'story point', and progressive elaboration of 'user story'. This paper analyses the relationship between story points and effort, across a sample of software development projects, in an organisation. A novel machine learning predictive model has been developed and is implemented across agile projects that infer relationship between 'effort' and 'story points', directly in contrast with agile literature. This predictive model was tested and worked accurately, reliably and effectively across various agile projects. This research can be extended to agile projects having sprints of less than fifty story points.
Automatic Word Association Norms (AWAN)

Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.
RISKS AND BENEFITS OF ADOPTING CLOUD ACCOUNTING

The current business environment is extremely dynamic and competitive. Elements such as access to information, its transmission speed, quick decision making, mobility, and flexibility have become more and more important for companies operating in the field. Given that information technology has evolved incredibly fast over the past decade, digital technologies such as cloud computing, e-commerce, and mobile applications provide opportunities to improve networking with partners and customers, and new ways to generate value. Cloud applications have gradually gained ground to the detriment of locally operated applications. There are numerous advantages of cloud technology which can no longer be overlooked, especially by small- and medium-sized companies. At company level, regardless of its size or activity, the decision to change the accounting software is critical and may have a major positive impact on the business, nevertheless involving a series of risks that can be largely mitigated.
Residual-Based Nodewise Regression in Factor Models with Ultra-High Dimensions: Analysis of Mean-Variance Portfolio Efficiency and Estimation of Out-of-Sample and Constrained Maximum Sharpe Ratios

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of the maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a new hybrid model where factor models are combined with feasible nodewise regression. Returns are generated from an increasing number of factors plus idiosyncratic components (errors). The precision matrix of the idiosyncratic terms is assumed to be sparse, but the respective covariance matrix can be non-sparse. Since the nodewise regression is not feasible due to the unknown nature of errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix of errors as a new method. Next, we show that the residual-based nodewise regression provides a consistent estimate for the precision matrix of errors. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors. Benefiting from the consistency of the precision matrix estimate of returns, we show that: (1) the portfolios in high dimensions are mean-variance efficient; (2) maximum out-of-sample Sharpe ratio estimator is consistent and the number of assets slows the convergence up to a logarithmic factor; (3) the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (4) the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios.
The impacts of integrated STEM education on Students' learning motivation

The purpose of this study was to determine the effect of STEM (Science, Technology, Engineering, Math) activities on learning motivation of the first-year students and the studentsâ€™ view on STEM. A mixed research method was used. In the quantitative section of the study, the pre-test and post-test experimental single group design were used, whereas the qualitative research was conducted with the interview. The survey questionnaire and interview form were used as data collection instruments. Forty-first-year students attending at Cao thang Technical College participated in this study. A 6-week program was conducted with STEM education through an industrial project. The paired sample T-test was used to determine the significant difference between pre-test and post-test motivation scores of students participating in STEM activities. The studentsâ€™ view on STEM education demonstrated that STEM improved their learning motivation. Furthermore, the students also consolidate students' careers before entering the job market.
Blockchain and the Law: A Critical Evaluation

It is a high-risk, high-reward enterprise to write a scholarly monograph on an emerging technology when its societal use, economic worth, and even its technical design are still in flux. With little empirical material with which to work, one often has to resort to extrapolating the future developments from the myriad seed of possibilities of the present. Yet, there are moments in time when undertaking such an enterprise seems inevitable, because there is a rough consensus that the emerging technology represents more than just an incremental improvement of already existing routines, and promises—or threatens—a disruption of the status quo. Such is the case of blockchain or distributed ledger technologies. In that light, Primavera De Filippi’s and Aaron Wright’s Blockchain and the Law is a timely and valuable contribution.
The economic impacts of learning losses

The worldwide school closures in early 2020 led to losses in learning that will not easily be made up for even if schools quickly return to their prior performance levels. These losses will have lasting economic impacts both on the affected students and on each nation unless they are effectively remediated.While the precise learning losses are not yet known, existing research suggests that the students in grades 1-12 affected by the closures might expect some 3 percent lower income over their entire lifetimes. For nations, the lower long-term growth related to such losses might yield an average of 1.5 percent lower annual GDP for the remainder of the century. These economic losses would grow if schools are unable to re-start quickly.The economic losses will be more deeply felt by disadvantaged students. All indications are that students whose families are less able to support out-of-school learning will face larger learning losses than their more advantaged peers, which in turn will translate into deeper losses of lifetime earnings.The present value of the economic losses to nations reach huge proportions. Just returning schools to where they were in 2019 will not avoid such losses. Only making them better can. While a variety of approaches might be attempted, existing research indicates that close attention to the modified re-opening of schools offers strategies that could ameliorate the losses. Specifically, with the expected increase in video-based instruction, matching the skills of the teaching force to the new range of tasks and activities could quickly move schools to heightened performance. Additionally, because the prior disruptions are likely to increase the variations in learning levels within individual classrooms, pivoting to more individualised instruction could leave all students better off as schools resume.As schools move to re-establish their programmes even as the pandemic continues, it is natural to focus considerable attention on the mechanics and logistics of safe re-opening. But the long-term economic impacts also require serious attention, because the losses already suffered demand more than the best of currently considered re-opening approaches.
Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualised Embeddings.

This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides. In this specific task, given the contents of a slide we are asked to predict the degree of emphasis to be laid on each word in the slide. We propose 2 approaches to this problem including a BiLSTM-ELMo approach and a transformers based approach based on RoBERTa and XLNet architectures. We achieve a score of 0.518 on the evaluation leaderboard which ranks us 3rd and 0.543 on the post-evaluation leaderboard which ranks us 1st at the time of writing the paper.
Digital forensic standards and digital evidence in Polish criminal proceedings. An updated definition of digital evidence in forensic science.

Digital evidence is increasingly popular in criminal proceedings - not only to those commonly referred to as 'cybercrimes'. The credibility criteria of such evidence are in theory governed by a set...
A model-based approach for multi-level privacy policies derivation for cloud services

To benefit from the advantages offered by the cloud services, a data owner must move his private data to external servers to be accessed and handled by unknown parties. This brings about several concerns such as lack of user control, data leakage, improper access and use, unauthorised data storage location and retention, etc. Although extensive research efforts have been carried out to address data privacy issues in the cloud, this still requires many more efforts. Particularly, the privacy policy specification which presents the first step to ensure fine-grained data protection in such an untrusted environment. In this paper, we introduce a model-based approach that couples access, usage and storage policies specification in the context of cloud services. The approach stands on a high-level abstract model and a low-level concrete model to design the policies. Models refinement from top to bottom and automatic derivation of the final policies are introduced. The approach is demonstrated and evaluated through a case study.
More on <i>apply</i> family of functions - avoid loops to get more speed.

<title>Abstract</title> This chapter focuses on the 'apply' set of functions. These functions are for those who need to process very large datasets, or who need to perform loop-type operations on largish datasets but perhaps in a nested fashion.
SWARM INTELLIGENCE BASED TASK SCHEDULING ALGORITHM FOR LOAD BALANCING IN CLOUD SYSTEM

As on miniature devices to military applications, the cloud computing plays a vital role. Building efficient cloud management systems will lead to improve extraordinary features in the applications of cloud services. In cloud atmosphere, enormous tasks are performed simultaneously; an effectual task scheduling is very important role to get better performance of the cloud system. An assortment of cloud-based task scheduling algorithms is offered that schedule the user's task to resources for implementation. The innovation of cloud computing, conventional scheduling algorithms cannot gratify the cloud's requirements, the researchers are frustrating to modify conventional algorithms that can accomplish the cloud needs similar to rapid elasticity, resource pooling and on-demand self-service. Also, the priority becomes an important task when dealing with critical functionality systems. Real world invocations are needed to make an efficient selection in cloud services through a collection of functionally equivalent services. This research aims to detect a novel method to predict the system functionality without consuming more time and less expensive for implementation. Investigation on swarm intelligence-based task scheduling is presented. This will improve the power consumption by reducing overloads when more services opt for a single load. Experiments were carried out to test the effectiveness of the method.
METHODS FOR FORGERY DETECTION IN DIGITAL FORENSICS

The information present in the footage/video clip is one of the solid evidence of the event at the incidents. Therefore, visual media crime-scene investigation has risen as a crucial research field...