Reviewerly/exHarmony huggingface dataset:
B(u)y the book: evaluation of a university initiative to provide students with funds to buy books

Purpose In the era of high student fees and intense market competition, many universities now buy books for their new students, and recently have incorporated student choice into the offer, enabling students to choose how to spend funds. Teesside University has successfully piloted such an approach with one academic School, the School of Social Sciences, Humanities and Law. The pilot has now been extended to all academic Schools, with all students receiving £100 per academic year to spend on reading list books. The scheme covers new full-time undergraduate students at the University, and is operated in collaboration with an external company, John Smiths. The purpose of this paper is to evaluate the Teesside University Advance scheme against baseline data of book borrowing and reservation patterns of reading list titles. The paper explores the impact upon the student experience and student perceptions of the Library. Design/methodology/approach The project used a mixed methods approach. The quantitative strand analysed book borrowing and reservation patterns data from library systems and from book purchasing patterns data provided by the online store supporting the scheme. Students were also surveyed about the scheme. The qualitative strand, via one-to-one interviews conducted by the student researcher, gained an insight into why students select certain titles to purchase; and what their expectations of the university library are for the supply of reading list titles. Findings Analysis revealed an overall decline in book borrowing from the library of the titles selected for purchase by students via the scheme. Student perceptions of the library were positive and demonstrated a strategic use of library resources alongside book purchases and open web resources. At early stages of university undergraduate study, students need guidance on most appropriate resources to use and why, from either reading lists or book bundles. Originality/value Teesside University scheme is unique in the UK in covering all new full-time undergraduates and letting them choose which reading list titles to buy with the university funds provided.
Possibilistic vs Evidential Valuation Algebra Networks

Realistic reasoning applications typically involve many interrelated variables and require the interpretation of data that is both heterogeneous in nature and affected by various types of uncertainty. Accordingly, in this paper we investigate the performance of valuation based algebra networks for reasoning in uncertain multivariate systems. Specifically, we consider networks built from two different approaches to modelling uncertainty: possibility theory and Dempster-Shafer evidence theory. To compare these differing networks, we propose a new possibilistic counterpart to the uncertain implication rule that exists in evidential networks. Using the Captain's decision problem, we analyse the performance of these networks when estimating the number of days a ship will be delayed based on a mixture of uncertain knowledge. We demonstrate that the evidential network is more cautious to changes in uncertainty whereas the possibilistic network is more sensitive. This characteristic could allow the possibilistic network to be used to perform sensitivity analysis on a system.
Activity Analysis in Software Visualization Systems

The goal of this paper is to identify the task for researching the activity of software engineers using software visualization systems based on virtual reality. It discusses the activity theory statements developed in our country as far back as in the past century. The paper describes possible tasks to analyse the activity of software engineers using such systems. It provides examples illustrating the use of the systems based on virtual reality for the purposes of software complexes representation and visual programming. The activity of the user of such systems is analysed.
Multi-Stage Mixed Attribute Outlier Detection Algorithm Based on Neighborhood Density Difference

With the development of big data, an increasing number of mixed attribute dataset has become ubiquitous. It is also extremely important for decision analysis in the processing of a mixed attribute dataset. The existing outlier detection algorithm does not process a mixed attribute dataset and some special objects, because of too high computational time complexity and unsatisfactory detection results. In this paper, we present a multi-stage mixed attribute outlier detection algorithm. Firstly, with data set being divided, the neighborhood information was constructed based on the heterogeneous similarity metric to generate the core point. Then, the primitive clusters can be formed on the basis of the definition. Finally, a neighborhood density difference metric-based outlier detection algorithm was designed to construct neighborhood outlier factor (NOF). Extensive experimental results show the advantages of the proposed method, which could improve the outlier detection accuracy and reduce the time complexity on mixed attributed.
Development of general transportation applications in Garut City web-based

Abstract Garut city is one of the cities that is densely packed with transportation, especially on public transportation. The problem is that there are still many people who do not understand the path that the public transportation passes, both in the city itself, outside the city and even foreign tourists. The purpose of developing this application is to find out where to go. In this study, researchers designed and built public transport transportation applications in the web-based city of Garut with the name Garut Anyar utilizing GIS technology in it. The application is useful for searching city transportation routes (Angkot) in Garut City so that users no longer feel difficulties in finding public transport to get to their destination, especially for migrants in Garut City. In addition, this application can display information about angkot routes so that it can add to the user’s insight. The conclusion obtained from this study is the Garut Anyar application was successfully built and useful for every user who has used this Garut Anyar application, which has been tested 100 times a trial from the RSU route to Banyuresmi as evidenced by an application testing questionnaire that gets good grades.
Detecting Similarity in Multi-procedure Student Programs Using only Static Code Structure

Plagiarism is prevalent in most undergraduate programming courses, including those where more advanced programming is taught. Typical strategies used to avoid detection include changing variable names and adding empty spaces or comments to the code. Although these changes affect the visual components of the source code, the underlying structure of the code remains the same. This similarity in structure can indicate the presence of plagiarism. A system has been developed to detect the similarity in the structure of student programs. The detection system works in two phases: The first phase parses the source code and creates a syntax tree, representing the syntactical structure of each of the programs, while the second takes as inputs two program syntax trees and applies various comparison algorithms to detect their similarity. The outcome of the comparison allows the system to report a result from one of four similarity categories: identical structure, isomorphic structure, containing many structural similarities, and containing few structural similarities. Empirical tests on small sample programs show that the prototype implementation is effective in detecting plagiarism in source code, although in some cases manual checking is needed to confirm the presence of plagiarism.
Search Engine Optimization with Google-Web

Search engine optimization is a technique to take a web document in top search results of a search engine. Web presence Companies is not only an easy way to reach among the target users but it may be profitable for Business is exactly find the target users as of the reason that most of the time user search out with the keywords of their use rather than searching the Company name, and if the Company Website page come in the top positions then the page may be profitable. This work describes the tweaks of taking the page on top position in Google by increasing the Page rank which may result in the improved visibility and profitable deal for a Business. Google is most user-friendly search engine to prove for the all users which give user-oriented results. In addition ,most of other search engines use Google search patterns so we have concentrated on it. So, if a page is Register on Google it Is Display on most of the search engines.
Design of Adaptive Camouflage System Based on SOPC

Abstract With the development of electrochromic devices, the requirements for the accuracy and real-time performance of control devices are constantly increasing. For the reliability problems of single-chip microcomputer control technology, through the SOPC development platform, based on the Cydone IV core chip, the hardware design is completed by itself. The Quartus II integrated development environment is used to tailor the NIOSII real-time operating system, to achieve data collection and control. The signal is filtered using an FIR filter. The designed system has the characteristics of real-time, stability, scalability, low power consumption, etc., and can adaptively control the MOFs-based flexible electrochromic device.
Research on Blind Forensics of Large Data Compression Traces in Power System

In order to better carry out quasi-blind forensics of large data compression traces in communication, a blind forensics method for large data compression traces in power communication under the Internet of Things of magnitude is proposed. Firstly, the compressed trace features of power communication big data are collected and analyzed, and data noise reduction processing is carried out according to the characteristic collection results. After noise reduction is completed, the compressed trace features are subjected to blind forensics so as to complete comparison and correction of compressed data and realize blind forensics of compressed trace of power communication big data. Finally, the experiment proves that the blind evidence collection method of large data compression traces in power communication under the lightweight Internet of Things has better practicability than the traditional method.
Segmentation of Relevant Region in Breast Histopathology Images using FCM with Guided Initialization

Abstract This study proposes a modified initialization approach for the conventional FCM, namely FCM with guided initialization. The FCM with guided initialization was implemented to segment the relevant regions in the breast histopathology images. The initialization method to select initial centers is based on the Cyan (C) channel histogram. Area Overlap Measure ( AOM ) and Combined Equal Importance ( CEI ) were used to evaluate the performance of the proposed FCM with guided initialization. The obtained AOM and CEI for the overall dataset achieved promising results: 0.89 in AOM and 0.88 in CEI . When comparing the number of iterations required to complete the proposed FCM clustering algorithm, the FCM with guided initialization is found to be effective in reducing the search space by showing a lower number of iterations.
Image quality assessment (IQA) using high-frequency and image variance (HFIV) for colour image

Abstract Image quality is often lost during image acquisition, transmission, and compression. Therefore, image quality assessment (IQA) is crucial in image processing. Currently, image quality can be measured from the frequency domain features, but it only applicable to blurred grayscale images. Nevertheless, noise distortion is also a common problem in digital images, and colour also affects the perception of image quality. Therefore, this paper proposes an enhanced blur and noise specific colour image quality assessment that measures high-frequency components and image variance. The number of high-frequency components is related to the edge and noise. In order to distinguish the distortion of the image, the image variance estimation is included. Experiments on public databases have shown that this method outperforms PSNR and SSIM in terms of noise and blur distortion and has low processing time of 0.0941 s/img.
A Bayesian Nonparametric Model for Integrative Clustering of Omics Data

Cancer is a complex disease, drivenPeneva, Iliana by a range of genetic and environmental factors. Many integrative clustering methods aim to provide insight into theSavage, Richard S. mechanisms underlying cancer but few of them are computationally efficient and able to estimate the number of subtypes. We have developed a Bayesian nonparametric model for combined data integration and clustering called BayesCluster, which aims to identify cancer subtypes and addresses many of the issues faced by the existing integrative methods. The proposed method can integrate and use the information from multiple different datasets, and offers better cluster interpretability by using nonlocal priors. We incorporate feature learning because of the large number of predictors, and use a Dirichlet process mixture model approach to produce the patient subgroups. We ensure tractable inference with simulated annealing. We apply the model to datasets from the Cancer Genome Atlas project of glioblastoma multiforme, which contains clinical and biological data about cancer patients with extremely poor prognosis of survival. By combining all available information we are able to be better identify clinically meaningful subtypes of glioblastoma.
Analysis and Design Information System of Recapitulation Jam Minus P5M

Pertemuan 5 Menit (P5M) is a routine activity that is always carried out by instructor in Politeknik Manufaktur Astra (Polman Astra) to students in each Unit Pelaksana Teknis (UPT) before or after lectures begin. Students who do not follow or violate the rules at the time of P5M will get a penalty in the form of minus hours. Minus hours are penalties given to students. Data minus hours of P5M are recorded using a form that is recapitulated one by one for every week and inputted into academic information system Polman Astra. The input time for minus P5M clock data is 565 minutes. Therefore we need an information system that can help overcome or automate it. In this research an analysis and design will be carried out using the System Development Life Cycle (SDLC) stages with extreme programming methodology which will reduce the recapitulation time from 565 to 280 minutes.
2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique

A technique for 2D shape recognition and retrieval is proposed. The proposed technique is based on the 8-neighborhood pattern which represents each point or pixel on the contour of the shape. These patterns are used as a framework in matching the shape of the object. The recognition and retrieval process are conducted by traversing through the contour of the shape and analyzes each point on the contour by considering the 8-neighborhood pattern. The 8-neighborhood patterns are assigned unique labels which are computed on their every occurrence during contour traversal. The cost of the best match between the shapes is evaluated by comparing the hit value obtained by the contour traversal of the shapes to be matched. The recognition and retrieval are carried out using the leave-one-out strategy and standard bull eye score, respectively. The proposed method is experimented on the MPEG-7 data set and the chicken piece data set. The results both for recognition and retrieval outperform most of the previously proposed methods.
Quality of Modern Learning Environment and Health-Saving Discourses

Abstract This paper dwells upon the environmental health as the key feature of a “quality city” that must be prioritized by any educational institution in its development. The research identifies the factors of creating a health-saving environment as well as the specifics of creating learning space at a school. Emphasis is made on designing comfortable space of high aesthetic value, preserving a health-saving environment, implementing ecological resources and natural motifs, creating a unique environment and a positive image, as well as making unique artistic designs psychologically safer. It is important that designs make use of salutogenesis. The presented designs focus on modernizing the learning environment in Irkutsk to improve its quality, to make it help preserve and boost students’ and teachers’ health. Special emphasis is made on the importance of creative environment and nature-aligned lifestyles.
Design Issues of a Hybrid Wrapping Attack Protecting Scheme in Cloud Computing Environment

In the cloud era, cloud security and user privacy have become important issues. Since cloud users often use web browsers to request services from the cloud service providers, when a signed message request is sent to the service receiver from the service provider, attackers can use wrapping attacks to tamper with the SOAP message transferred through the internet in order to avoid legal verifications and access web services without being detected to implement wrapping attacks. This paper is to integrate and improve Node Counting mechanism to propose a hybrid wrapping attack protection scheme called HWRAP. This HWRAP is a continuation on the Node Counting method and the determining conditions were improved and divided into three modules interception, detection and logging; it performs analysis to the incoming SOAP requests. Not only does it compare the number of times the child node appears, it also performs detection to the elements on the path between the root node to the final node in order to enhance the verification procedure on the detection module. The simulation results of KPIs indicate that the Detection rate can be increased by 2%, 3.8%, 3.7%, and 2.8%; the Accuracy rate can be increased by 8%, 7%, 10.5%, and 9.5% in 50, 100, 200, and 500 packet requests, respectively. Consequently, the HWRAP scheme has a higher detection rate and better accuracy rate than Node Counting for detecting wrapping attacks. The final results show that the proposed HWRAP can identify attackers more accurately and enhance the security and quality of cloud services.
Lossless Audio Coding using Extended Activity Level Classification Model

In this paper a lossless audio compression encoder using backward adaptation that maintains the degree of compression while significantly reducing computational complexity among other solutions is presented. The solution uses an adaptive arithmetic binary encoder with the our proposition extended method of adaptive correction of prediction coefficients and a method for correcting a context-dependent cumulate prediction error.
Accelerating Public Key Cryptography in Android Platforms

Cryptography technology has been considered as one of the core technology of information security in mobile applications. Public Key cryptography, as a major component of cryptography plays an important role in mobile security applications. This paper studies the multiplication of large integers, and explores the methods and techniques of accelerating public key cryptosystem. Taking full advantage of the parallel computing mechanism of Android platform, we design a special storage structure and calculation method. By decomposing large integers and calculating them in a special two-dimensional array, the time optimization of addition and multiplication is realized, so that they can be executed in parallel, and at the same time, the computational tasks of multiple units can be completed. Experiment results show that, compared to the original Java built-in library for big integers in the Android platform, the proposed method has distinctive advantage in execution time.
Thresholding Neural Network Image Enhancement Based on 2-D Non-separable Quaternionic Filter Bank

The thresholding neural network with a 2-D non-separable paraunitary filter bank based on quaternion multipliers (2-D NSQ-PUFB) for image enhancement is proposed. Due to the high characteristics of the multi-bands 2-D NSQ-PUFB (structure “64in-64out”, $$CG_{2D} = {{17,15\,\mathrm{\text {dB}}}}$$ , prototype filter bank ( $$ 8 \times 24 $$ ) Q-PUFB), which forms the basis of the TNN, the results of noise editing in comparison with the approaches based on the two-channel wavelet transform in terms of PSNR are $${{1\,\mathrm{\text {dB}}}}$$ – $${{1.5\,\mathrm{\text {dB}}}}$$ higher.
Medical image prediction using artificial neural networks

Artificial Neural Networks (ANN) have been applied to solve a large number of real-world problems, considerable complexity. Solving problems that are too complex for conventional technologies is the main advantage of ANN. In general, these problems include pattern recognition and forecasting. ANN have been used in the medical imaging, in computer aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration. In this paper we use ANN as a prediction method in medical images to complete the missing data in MRI and CT images. By using these methods, we can eliminate artifacts of image and visualize the new image which is much closer to the desired one. This image can be used for diagnostic purposes or radiotherapy.
Attention Relational Network for Few-Shot Learning

Few-shot learning aims to learn a model which can quickly generalize with only a small number of labeled samples per class. The situation we consider is how to use the information of the test set to generate the better prototype representation of the training set. In this paper, based on attention mechanism we propose a flexible and efficient framework for few-shot feature fusion, called Attention Relational Network (ARN) which is a three-branch structure of embedding module, weight module and matching module. Specifically, with attention mechanism, the proposed ARN can model adaptively the constribution weights of sample features from embedding module and then generate the prototype representations by weighted fusion of the sample features. Finally, the matching module identify target sample by calculating the matching scores. We evaluated this method on the MiniImageNet and Omniglot dataset, and the experiment proved that our method is very attractive.
ACS-WAM: An Access Control Scheme Supporting Write Authority Management in Cloud-assisted Cyber-Physical Systems

In Cloud-assisted Cyber-Physical Systems, data is produced on monitoring devices, collected and aggregated on controllers, and uploaded on cloud storage system. There is undoubtedly a need for encryption in each stage of data processing. The judgement of ciphertext aggregating permission, which is usually done on controllers, becomes difficult due to the heterogeneity of monitoring devices. Besides, controllers can also transfer the data aggregating to cloud because of their limited resources. Due to data aggregating, data write control must be considered. However, ABE (Attribute Based Encryption), as a fine-grained scalable access control method, is used to realize the judgement of data read permission, but not the one of data write permission. In this scene, a controller needs cloud to aid him/her to judge whether or not a device's request writing data is accepted, and the less cloud knows, the better. In this work, we propose an Access Control Scheme supporting Write Authority Management (ACS-WAM): 1) It supports write privilege control on the same data written by multiple devices. In other words, it prevents an attacker from participating in the data aggregation as a device by injecting false data. 2) It provides reusability of the semi-hidden write access control policy in the cloud-assisted write authority management, thus cloud can aid an controller to realize write privilege control with the leakage of policy as little as possible.