Reviewerly/exHarmony huggingface dataset:
Biogeography optimization algorithm based next web page prediction using weblog and web content features

&lt;span lang="EN-US"&gt;Recommendation of web page as per users’ interest is a broad and important area of research. Researcher adopts user behavior from actions present in cookies, logs and search queries. This paper has utilized a prior webpage fetching model using web page prediction. For this purpose, web content in form of text and weblog features are analyzed. As per dynamic user behavior, proposed model LWPP-BOA (Logistic Web Page Prediction By Biogeography Optimization Algorithm) predict page by using genetic algorithm. Based on user actions, weblog feature are developed in form of association rules, while web content gives a set of relevant text patterns. Page prediction as per random user behavior is enhanced by means of Biogeography Optimization Algorithm where crossover operation is performed as per immigration and emigration values. Here population updation depends on other parameters of chromosome except fitness value. Experiments are conducted on real dataset having web content and weblogs. Results are compared using precision, coverage, M-Metric, MAE and RMSE parameters and it indicates that the proposed work is better than other approaches already in use.&lt;/span&gt;
[Progress in biomedical data analysis based on deep learning].

Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.传统生物医学数据分析技术在大数据时代背景下，面临着巨大挑战，而深度学习技术在生物医学分析领域的应用，迎来了巨大的发展机遇。本文综述了深度学习在生物医学数据分析领域的最新研究进展。首先阐述了深度学习方法及工具，随后以时间为主线，围绕生物医学问题的提出、数据预处理方法、模型建立方法、训练算法重点总结了近五年深度学习在生物医学数据分析中的具体应用，并重点强调了医疗辅助诊断中的深度学习应用。最后给出了未来深度学习在生物医学数据分析领域可能的发展方向。.
Super Resolution Approach Using Generative Adversarial Network Models for Improving Satellite Image Resolution

Recently, the number of satellite imaging sensors deployed in space has experienced a considerable increase, but most of these sensors provide low spatial resolution images, and only a small proportion contribute with images at higher resolutions. This work proposes an alternative to improve the spatial resolution of Landsat-8 images to the reference of Sentinel-2 images, by applying a Super Resolution (SR) approach based on the use of Generative Adversarial Network (GAN) models for image processing, as an alternative to traditional methods to achieve higher resolution images, hence, remote sensing applications could take advantage of this new information and improve its outcomes. We used two datasets to train and validate our approach, the first composed by images from the DIV2K open access dataset and the second by images from Sentinel-2 satellite. The experimental results are based on the comparison of the similarity between the Landsat-8 images obtained by the super resolution processing by our approach (for both datasets), against its corresponding reference from Sentinel-2 satellite image, computing the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity (SSIM) as metrics for this purpose. In addition, we present a visual report in order to compare the performance of each trained model, analysis that shows interesting improvements of the resolution of Landsat-8 satellite images.
The Motives for Applying Creative Accounting Methods and Their Impact on the Credibility of Financial Statements in Jordanian Hotels

This study explored the motives for applying creative accounting methods and their impact on the credibility of financial statements in Jordanian hotels. This study is an attempt to shed light on the motives for creative accounting in hotel management and their impact on the credibility of financial statements in hotels. To promote scientific and intellectual awareness of the creative accounting practices of investors and business owners. This could be done through holding seminars in trade unions and accounting associations for the purpose of raising awareness of investors and hotel management about these practices and attention by the accounting professionals of the Association of Jordanian Accountants and Auditors, the indicators of the practice of creative accounting methods. Tightening procedures on auditing the financial statements that can contain those indicators.
Privacy-Aware Distributed Mobility Choice Modelling over Blockchain

A generalized distributed tool for mobility choice modelling is presented, where participants do not share personal raw data and computations are done locally. Participants use Blockchain based Smart Mobility Data-market (BSMD), where all transactions are secure and private. Nodes in blockchain can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. A case study is presented where a mode choice model is distributed and estimated over BSMD. As an example, the parameter estimation problem is solved on a distributed version of simulated annealing. It is demonstrated that the estimated model parameters are consistent and reproducible.
Dual Image Fusion Technique for Underwater Image Contrast Enhancement

Underwater imaging is receiving attentions throughout these years. Attenuation of light causes the underwater images to have poor contrast and deteriorated color. Furthermore, these images usually appear foggy and hazy. In this paper, a new approach to enhance underwater images is proposed, which implements the integration of dehazing method, homomorphic filtering and image fusion. The dehazing method consists of multi-scale fusion technique, which applies weight maps in the pre-processing step. Homomorphic filtering and image fusion are then applied to the resultant image for contrast and color enhancement. Qualitative and quantitative evaluations are performed to analyze the performance of the proposed method. The results show the superiority of the proposed method in terms of contrast, image details, colors, and entropy. Moreover, implementation of Raspberry Pi with Picamera as standalone underwater image processing device is also successfully implemented.
Solving machine learning optimization problems using quantum computers

Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms. We also apply quantum machine learning and quantum parallelism to a 3-dimensional image that vary with time as well as tracking speed in object identification.
Tingkat pengetahuan mahasiswa semester VI, Pogram Studi Pendidikan Dokter, Fakultas Kedokteran, Universitas Udayana terhadap konjungtivitis bakteri tahun 2017

Bacterial conjunctivitis is an infection of the eye caused by bacteria, such as Staphylococcus, Streptococcus, or Haemophilus. Based on 30% of visits in the Department of Eye Disease in the United States, 15% are conjunctivitis complaints caused by bacteria and virus, and 15% are conjunctivitis complaints caused by allergy. A common complication of conjunctivitis is the decreased visual acuity that greatly affects the patient in performing daily activities.Aim: This study aimed to determine the knowledge level of semester VI students of Doctor Education Study Program, Faculty of Medicine, Udayana University, toward conjunctivitis.Method: The samples were 70 people with 10% relative accuracy. Sampling was done by random sampling technique. The data were collected by using questionnaire technique. Data analysis was performed using descriptive statistics.Result: The result of the students' knowledge level on bacterial conjunctivitis was 62 respondents (89%) were in good category, 8 respondents (11%) were in moderate category, and none for poor category (0) as well. The knowledge level of the respondent toward bacterial conjunctivitis at the age of 21 years was quite good, that were 31 people (44.5%). Respondents with moderate knowledge level at the age of 21 years were 5 people.Conclusion: The result of the test shows that the level of students' knowledge on bacterial conjunctivitis is that 62 respondents (89%) are in good category. Therefore, to the management of Faculty of Medicine, Udayana University, this research is expected to be used as a reference in formulating various learning strategies, so the learning achievement can be improved.
Parallel color image watermarking scheme for multiple picture object based on multithreading coding

In this paper, a novel parallel color image watermarking scheme in the frequency domain for copyright protection of Multiple Picture Object (MPO) based on multithreading coding is proposed. Designed technique consists of three steps: firstly, the color watermark is codified using the Curvelet Transform (CvT), reducing quantity of information that represents the watermark; the second step represents the embedding process of a color watermark. In the embedding process, theMPO file contains two images where each image is divided in 8x8 pixels blocks applying DCT to any block. A color watermark is inserted in the medium frequency sub-bands of each DCT block, because the modifications performed in this sub-band are less perceptible by Human Visual System (HVS). Finally, during third step, the recovering process for color watermark is performed. The proposed scheme appears to demonstrate significant improvement in processing time though possible parallelization process when moving from serial programming to the use of threads in multicore CPUs. According to numerous experiments, where the Peak Signal-to-Noise Ratio (PSNR) and Similarity Structural Index Measure (SSIM) were used as quality criteria for watermarked MPO image and the recovered color watermark, novel method is not intrusive, since it does not degrade the quality of the watermarked MPO image. Additionally, the proposed framework is resistant to the most common image processing attacks.
The effectiveness of library information literacy instructions given to undergraduate students at the University of Dar es Salaam

The purpose of this study was to assess the effectiveness of library information literacy instructions offered at the University of Dar es Salaam. The study population consisted of undergraduate students and library staff at the Mwalimu Nyerere Campus. The study employed a mixed methods approach under which a survey design was used to collect data with the use of questionnaire, observation, and interviews methods. Whereas quantitative data were processed through Microsoft Excel program to simplify analysis, qualitative data were analysed using content analysis. The key findings of the study indicate that the library’s information literacy instructions had moderately impact on majority of respondents’ ability to articulate and locate information they require, use information ethically, and evaluate online information for credibility. Based on these findings, respondents recommend extension of library opening hours, integrating information literacy in the university’s curricula, increasing the number of computers, and deploying more library staff to oversee information literacy instruction.
Analysis of the Influence Factors of Transformer Substation Project Cost Based on the Random Forest Model

There are many factors affecting the cost of transformer substation project. The project cost difference is large and the data is discrete. It brings great difficulties to the classification analysis, index preparation and control management of project cost. Based on the project division of transformer substation project, this paper analyzes and identifies the main factors affecting the construction cost of transformer substation project, and uses the Random Forest model to measure 25 of the influencing factors, selects the key influencing factors to provide reference for the cost prediction of transformer substation project.
Research progress of 3D measurement technology based on phase coding

Structured light has developed rapidly as a non-contact active three-dimensional measurement method. The phase coding method based on the principle of structured light embeds the codeword into the intensity of the fringe in the form of phase, which is insensitive to surface contrast, ambient light and camera noise due to the use of phase instead of intensity to determine the code word, so it has stronger robustness. The research progress of three dimensional measurement methods of phase-coding was reviewed, and the three-dimensional measurement principles based on phase encoding was summarized, including the design method of initial phase, the generation method of encoded fringes, phase calculation methods, acquisition and correction of fringe order, and acquisition of absolute phase. Some experimental results were given, and the advantages and disadvantages of the phase coding method were pointed out. Finally, the challenges faced by the technology were discussed, and the future development direction of the field was pointed out.
Measuring and Reducing Non-Multifact Reasoning in Multi-hop Question Answering.

The measurement of true progress in multihop question-answering has been muddled by the strong ability of models to exploit artifacts and other reasoning shortcuts. Models can produce the correct answer, and even independently identify the supporting facts, without necessarily connecting the information between the facts. This defeats the purpose of building multihop QA datasets. We make three contributions towards addressing this issue. First, we formalize this form of disconnected reasoning and propose contrastive support sufficiency as a better test of multifact reasoning. To this end, we introduce an automated sufficiency-based dataset transformation that considers all possible partitions of supporting facts, capturing disconnected reasoning. Second, we develop a probe to measure how much can a model cheat (via non-multifact reasoning) on existing tests and our sufficiency test. Third, we conduct experiments using a transformer based model (XLNet), demonstrating that the sufficiency transform not only reduces the amount of non-multifact reasoning in this model by 6.5% but is also harder to cheat -- a non-multifact model sees a 20.8% (absolute) reduction in score compared to previous metrics.
Akabeko Ensemble: Cultural multimodal helical computer music installation

In this study, we made “Aka-beko” choral ensemble. It comprises an octave of a chromatic musical scale, arranged in a helix and populated by animated oxen, instances of a regional mascot, who lift their heads and sing when triggered by events from a realtime MIDI keyboard controller. The application installation in the University of Aizu UBIC 3D Theater features stereographic display and spatialized sound.
Perencanaan lanskap Villa Cloud Nine Estate dengan pendekatan permakultur di Kecamatan Tegallalang, Kabupaten Gianyar, Bali

Villa Cloud Nine Estate is one of the tourism accommodations that cares about environmental problems in Bali. This villa has the potential for permaculture planning. A permaculture is a place that can be used for the management of agriculture and livestock to improve the quality of land, provide results and income, and remain sustainable into the future. This study aims to determine the condition of the site and its potential so as to produce a landscape plan that fits the characteristics of the villa landscape. The stages of this research are preparation, inventory, analysis, synthesis and site planning. The results of the data analysis show that the site has low land characteristics with high rainfall throughout the year. The character of the landscape on the site is natural with the main landscape elements in the form of grass and trees. The basic concept produced in this study is The Journey of Permaculture. Landscape planning is designed based on the concept of space, the concept of circulation, the concept of vegetation, and the concept of hydrology. The layout is divided to create zones that can save the time and effort needed. The vegetation is divided into production plants, conservation plants and supporting plants. Villa Cloud Nine Estate is one of the tourist accommodations that applies permaculture on the site.
Image Classification with Hierarchical Multigraph Networks.

Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data. Despite being general, GCNs are admittedly inferior to convolutional neural networks (CNNs) when applied to vision tasks, mainly due to the lack of domain knowledge that is hardcoded into CNNs, such as spatially oriented translation invariant filters. However, a great advantage of GCNs is the ability to work on irregular inputs, such as superpixels of images. This could significantly reduce the computational cost of image reasoning tasks. Another key advantage inherent to GCNs is the natural ability to model multirelational data. Building upon these two promising properties, in this work, we show best practices for designing GCNs for image classification; in some cases even outperforming CNNs on the MNIST, CIFAR-10 and PASCAL image datasets.
Evaluation of Sentiment Databases

Nowadays it happens more and more frequently that people get their opinions about products in social media or online reviews. For this purpose, product reviews themselves or channels on social media are searched in order to get an impression of what other users think about products before buying. For companies, these Social Listening Statements (SLS) can be business critical. A sentiment analysis is part of Natural Language Processing (NLP). The analysis of text tries to identify and extract sentiments in a text. In this paper, two supervised machine learning algorithms for text analysis were combined with four sentiment databases to increase accuracy in recognition. For better comparability, basic sentiment databases without subject-specific terms were used. Experiments were conducted with manually and automatically generated data sets, ranging in size from 300 to 16,000 entries. The combination of these two methods resulted in an accuracy of up to 94% in detecting positive or negative sentiment in texts. The combination of sentiment databases (SD) and machine learning significantly increased the recognition.
Transfer Learning and Domain Adaptation for Named-Entity Recognition

Transfer learning is a technique that is widely used in machine learning tasks when the availability of data for a task is limited and not enough to train a model. Transfer learning consists of primarily two major phases: pre-training and fine-tuning. During pre-training, a model is trained on a different but similar corpus which has a large amount of data in it. This model is further fine-tuned on the target task using the limited data available. Selecting the appropriate pre-training corpus is vital to avoid negative transfer learning that can harm a target task and impede the transfer of knowledge. Transfer learning is being used widely in industrial applications where the amount of labelled data is limited. This paper applies transfer learning to the task of named-entity recognition and investigates the effectiveness of transfer learning in a stacked bi-LSTM architecture with fastText word embedding.
Application research on service innovation and entrepreneurship education in university libraries and archives

College students' innovation and entrepreneurship education is an education to cultivate college students' innovative spirit, entrepreneurial awareness and ability to improve innovation and entrepreneurship. At present, the actual development effect of innovation and entrepreneurship education in China is not obvious. The innovation ability of college students needs to be improved, the proportion of innovation and entrepreneurship is still relatively low, and the success rate of entrepreneurship is not high. Therefore, we suggest that university libraries need to make full use of big data technology to better serve innovation and entrepreneurship education, thus effectively improving the effectiveness of innovation and entrepreneurship education, and facilitating the development of more innovative and entrepreneurial talents. Firstly, it expounds the connotation of innovation and entrepreneurship education, analyses the application of big data in university libraries, and secondly proposes the mechanism of cross-functional collaboration. Then, by integrating the independent data resources of university functional departments, the mechanism overcomes the problem of 'fragmentation'; finally, and innovatively proposed the establishment of new archives, and then through the means of big data for innovation and entrepreneurship education, and ultimately improve the quality of innovation and entrepreneurship education.
RNR: A Generic Bayesian-based Framework for Enhancing Top-N Recommender Systems

In personalized top-N recommender systems, a core task is to design effective methods to measure user-item preference scores and then to suggest, for each user, a small set of personalized items with high scores. However, little attention was paid to the recommendation of low-score user-item links. In this work, based on the Bayesian estimation theory, we propose a novel metric RNR (Recall-to-Noise Ratio) to characterize the ability to recommend both high-score and low-score user-item links. Then we propose a generic framework that leverages RNR to transfer the link scores of the state-of-the-art recommendation methods. Empirical experiments show that the proposed framework could optimally improve the recommendation accuracy.
Towards Identification of Patterns Aligning Security and Usability

Academic research and existing implementations of various systems and services identify instances of conflict between security and usability. Engineering the right trade-offs between security and usability is often not an easy task. Engineering of such trade-offs is mainly reliant on developers’ skills, who are either experts in security or usability. This research aims to assist the developers in engineering the right trade-offs by proposing the use of patterns. Patterns provide benefits like means of common vocabulary, shared documentation, reuse, among others. The use of patterns can assist security and usability developers by influencing their decision-making abilities when dealing with conflicts in other but similar context of use. For the identification of such patterns, the paper presents a three-stage methodology. To instantiate the methodology, a case study was conducted whose results are also presented in the paper.