Reviewerly/exHarmony huggingface dataset:
A Comprehensive Analysis of Deep Learning Techniques for Documentation Classification

The continuously increasing volume of documents in different fields has rendered document classification by manual labor infeasible. This has led to the genesis of automatic classification with the help of a myriad of techniques like Data Mining, Machine learning, and Deep Learning (N.L.P). This automation prevents human error and, more importantly, builds up speed, which is critical given the volubility of the data. Document classification has seen many developments in the past decade. This paper presents a comprehensive analysis of various state-of-the-art deep-learning algorithms for document classification and perform extensive experiments on these techniques. The performance of these algorithms is analyzed quantitatively using established metrics.
Assessment of Pe Female Teacher's Training Programs on Capacity Building According to Trainee's Perspectives in Ksa

The objective of this paper is to test the design of female PE teachers training program to improve their capabilities to execute physical activities in girls’ schools in KSA. The results showed that the feedback about the PE in girl schools improve the inputs of the programs. Also, the proper inputs improved the female teachers’ capabilities in PE and improve their capabilities to design PE programs and to be influential in their schools to convey students and principals of PE important in education and life.
PERANCANGAN SISTEM INFORMASI CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB UNTUK MENINGKATKAN LOYALITAS PELANGGAN

With more laundry outlets being opened, the competition is getting tighter where customer satisfaction is a top priority as a strategy to retain old customer and get new customer. Today’s customer satisfaction does not only cover the quality of the laundry results but also covers the service from beginning to the end of the transaction, including the information on laundry time and information about the information on laundry completiontime. Customer Relationship Management (CRM) is a strategy used by business people in managing relationships with customers so that they can maximize communication in order to gain customer trust and loyalty. Web E-CRM has been successfully built using the waterfall system development method through 5 stages, namely planning, analysis, desing, implementation and system stages. The E-CRM web implementation phase is done by testing the system. Testing e-crm using black box testing where the method test the input and output functionality of a software.
Influence of Participation and Tardiness to Synchronous Learning Sessions as a Motivation Factor for E-Learning

A study was made on the attitudes of 76 students of the Food Technology subjects of the Universidad Nacional de Frontera [], during the 32 virtual sessions that covered the 2020-I academic semester, Covid 19 world pandemic period; in which it was determined that their own inner motivation, which has no weight in the subject, affects the final grade obtained at the end of the semester. The hypothesis asks whether there is a significant relationship between the independent variable called “Intrinsic attitudes” (Number of participations minus number of tardiness), obtaining three levels of variable: positive, neutral or negative, in comparison with the dependent variable called “Final grade”, with two levels of variable: passed or failed. It was determined that the asymptotic sig. value of the pearson chi square of 0.002 less than 0.05 (5% significance level), means that there is dependence between the two variables.
Extending a re-identification risk-based anonymisation framework and evaluating its impact on data mining classifiers

Preserving sensitive information in data mining processes is one of the major issues in the context of big data. Handling huge volumes of data demands techniques to assure that private data is not accessible to non-authorised users. One of these techniques is data anonymisation, which aims to avoid individual identification. However, even when anonymised, data may be subject to re-identification through privacy attacks. This paper presents a two-stage policy-based anonymisation framework, which applies anonymisation techniques in ETL process and before exporting data analytic results. We extended part of this framework - the k-anonymity-based component - to help minimising the risk of data re-identification. Experiments evaluated the impact of applying this two-stage anonymisation on data mining regarding accuracy, performance, re-identification risk and information loss. Results showed that, when applied carefully, the anonymisation barely affect classifier results, improving accuracy in some cases.
LibGuides: Environmental Studies: Multimedia

This guide is intended for undergraduate students pursuing a Bachelor of Arts in Environmental Studies at the University of Alberta.
Research guides: Troubleshooting e-resources: Permalinks in desLibris

Tips for accessing Camosun Library e-resources such as e-journals, e-books, databases, streaming videos on or off campus.
일 지역 여대생의 월경전증후군 대처, 음식갈망이 월경전증후군에 미치는 영향

Purpose: This study was performed to measure the influence of premenstrual syndrome coping strategies and food cravings on premenstrual syndrome symptoms among female college students. Methods: A cross-sectional survey was done with the participation of 509 female college students in D city. Data were analyzed using a t-test, ANOVA, Scheffe test, Pearson’s correlation coefficients, and Hierarchical multiple regression. Results: The average scores for PMS coping mechanisms, food cravings, and premenstrual syndrome symptoms were 1.58, 2.32, and 2.82, respectively. There were significant differences in PMS symptoms according to food, menarcheal age, menstrual amount, menstrual pain and whether their mother or sister has dysmenorrhea. The factors influencing premenstrual syndrome symptoms of female college students were identified as avoidance (β=-.16, p<.001), menstrual cycle specific (β=-.11, p=.013), and cognitive restraint on eating (β=.13, p=.026). This factor explained 35% of female college students' premenstrual syndrome symptoms. Conclusion: Therefore, it is necessary to take appropriate stress coping measures for the subjects.
Kejiwaan Tokoh Utama Dalam Novel Sabtu Bersama Bapak Karya Adhitya Mulya Serta Relevansinya Dengan Pembelajaran Sastra Di SMA

This study aims to describe (1) structural elements in the SATURDAY JOINT BAPAK novel by Adhitya Mulya, (2) the psychological aspects of the main characters contained in the SATURDAY TOGETHER BAPAK novel by Adhitya Mulya, and (3) the relevance of structural and psychological aspects to literary learning in senior high school. This research is a qualitative descriptive study. The subject of this research is the SATURDAY TOGETHER BAPAK novel by Adhitya Mulya. The object of this research is the structural elements and the psychological aspects of the main character in the SATURDAY JOINT BAPAK novel by Adhitya Mulya. Data obtained by recording documents or archives. The data analysis technique used in this study is a flow model of analysis that includes three components, namely (1) data reduction; (2) data presentation (display power); (3) drawing conclusion. The validity of the data is obtained through data triangulation techniquesThe results of this study are as follows. First, the Structural Elements in the Saturday Novel Together with Mr. Karya Adhitya Mulya that there is integration between the elements in the novel which includes themes, characterizations, settings /pe rspectives, perspectives, and mandates. These elements are interrelated with one another so as to create a beautiful unity. Second, the psychological characteristics of the characters in the Saturday Together with Mr. by Adhitya Mulya include: Id, Ego, and Superego. Third, the relevance of the Saturday Novel Together with Mr. Karya Adhitya Mulya if used in learning in high school, especially in class XII, namely in KD: understanding the structural and legal norms of historical stories, news, advertisements, editorials / opinions, and novels both through oral and written.
Research Guides: INF 6120 - Access to Information: Search Strategies & Boolean Logic

This guide includes helpful information for SIS students learning about reference services and resources!
Directed Evolution of Human Facial Images

In a criminal investigation, situations are quite often where no evidence but a witness is available. In these cases, facial composite is a tool applied in search for a perpetrator of a crime. Facial composites are images of faces drafted by a forensic technician requiring a precise description provided by a witness. Despites deploying the computational technique into the process, the naming rates remain very low (not reaching 5%). In this paper, we present a system developed according to the latest research on facial perception. Incorporating the interactive evolutionary algorithm, whole face images are automatically generated based on the witness’s selection. The system is presented in the latest tested version, providing information on the selected EA as well as several image manipulation algorithms inclusive of method for age progression and hair manipulation. Besides describing applied methods and in-office testing, testing in simulated field conditions is part of the paper.
Reaction Document to Responsible AI: A Policy Framework by the International Technology Law Association

The Cyberjustice Laboratory is an award-winning research institute that examines and harnesses emerging technology to improve access to the justice system in Canada and beyond. Founded in 2010 by Professor Karim Benyekhlef (Faculty of Law, University of Montreal), the Laboratory brings together an interdisciplinary team of experts who specialize in law, computer science, sociology, science & technology studies, data science, and software engineering in order to reimagine and improve justice systems through technology. The Laboratory’s dual focus aims to analyze the impact of technology on access to justice and develop concrete technological tools that adapt to the reality of the justice community.We believe it is important to recognize the rich potential of artificial intelligence. Indeed, many tout the problem-solving promises of AI and use it as a marketing tool for commercial purposes. While there have been significant advancements in the use of AI for certain functions (e.g. image and audio recognition), there is need for increased improvement in and analysis of the use of AI in other tasks (e.g. analysis of legal texts, legal reasoning, judging). It is critical for legal experts to distinguish between AI-powered software in use that requires legal response and mere AI vaporware, the forward-looking promises of which have yet to be realized. Pulling apart AI initiatives that require normative reflection from those best understood as science fiction is precisely one of the objectives of the ACT Project.For these reasons, the Cyberjustice Laboratory (“Laboratory” or “Lab”) considers it highly beneficial to offer input on the development of Responsible AI: A Global Policy Framework (“the Framework”) published by the International Technology Association (“ITechLaw”).In sum, the Laboratory’s response to the Framework prioritizes the need to concretely apply these principles to national laws. We offer ways forward for the authors of the second iteration of this document to encourage Canadian lawmakers, in particular, to pragmatically implement these principles through such actions as creating an Office of Technological Assessment, enacting or amending legislation in various industries to account for AI, proactively accounting for possibilities of discrimination, and facilitating government regulation rather than industry self-regulation.
KEANDALAN SISTEM DAN PENANGANAN GANGGUAN UNTUK MENURUNKAN ANGKA TRIP PMT DI ULP BALEENDAH

Electricity distribution system is defined as part of an electric power system that connects the power station / power station with consumers. While the distribution network is a means of electric power distribution system in delivering energy to consumers. An electric power distribution system cannot be separated from various kinds of electrical disturbances that can disrupt the quality and continuity of electricity supply services. Such as interference with the feeder network that makes protection devices, namely PMT (Power Disconnector) must often trip on ULP Baleendah. The impact of feeder disturbances is the cessation of electricity supply, making PT. PLN (Persero) feels a big loss and makes the system reliability decreases. Therefore, in this final project, the handling of the feeder network disturbance will be carried out to reduce the trip number in the PMT (Power Disconnector) at PT. PLN (Persero) Distribution West Java UP3 Majalaya ULP Baleendah to prevent interference with the feeder network.
Research Guides: SIFT: Quick Source & Claim Checking to Spot Fake or Misleading Information: Tools to Use for Lateral Reading

Learn to use the SIFT method to spot fake and misleading information You can use the tools on this page to conduct lateral reading to fact check claims and learn about the source of those claims.
Mode-Assisted Joint Training of Deep Boltzmann Machines

The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions. However, jointly training DBMs in the unsupervised setting has proven to be a formidable task. A recent technique we have proposed, called mode-assisted training, has shown great success in improving the unsupervised training of RBMs. Here, we show that the performance gains of the mode-assisted training are even more dramatic for DBMs. In fact, DBMs jointly trained with the mode-assisted algorithm can represent the same data set with orders of magnitude lower number of total parameters compared to state-of-the-art training procedures and even with respect to RBMs, provided a fan-in network topology is also introduced. This substantial saving in number of parameters makes this training method very appealing also for hardware implementations.
The Prediction Model of College English Performance Based on Data Mining Under the Concept of OBE

With the penetration of the concept of Outcome Based Education (OBE) and the rapid development and implementation of information technology in the field of education, how to combine the OBE concept with data mining technology to obtain a prediction of college English performance is a research topic worth exploring. In this paper, firstly, an overview is given of the OBE concept and education data mining, and then the focus will shift to the construction of a college English score prediction model based on data mining. Specifically, the â€œModeler+ neural network modelâ€ will be used to complete data mining and prediction analysis. Finally, based on the analysis results, several teaching suggestions are offered.Keywords: OBE concept; Education data mining; College English; â€œSPSS Modelerâ€ + â€œneural network modelâ€Cite AsY. Yue, The Prediction Model of College English Performance Based on Data Mining Under the Concept of OBE,Engineering Intelligent Systems, vol. 29 no. 2, pp. 89-96, 2021.
Analysis of Prediction Based Algorithm for Hand Gesture Recognition

Gesture recognition is a way for the computer to understand the body language of humans. Through gesture recognition, a human can communicate with machines. This is a technique that is used to identify the hand, monitoring the hand movement, analyzing the different hand gestures and find the correct gestures in the last. There are many technologies developed and discussed but no technology ensured 100% accuracy. In my survey, I found that when applying DTW (dynamic time wrapping) algorithm they achieved 75% accuracy and the LCS (Longest common subsequence) method achieved 85% accuracy in recognizing gestures.
Evaluation of artificial neural network and support vector machine methods in estimating total solar radiation at Kerman and Yazd

The value of radiation energy is considered to be the main component of the design of clean energy devices. Due to the fact that this amount is not measured or in some cases the radiation station may not be available in Iran, so it is necessary to estimate this component. In this study, Kerman and Yazd were selected as the regions with the maximum potential of solar energy. Then Artificial Neural Network (ANN) and Support Vector Machine (SVM) capabilities were evaluated in solar energy estimation. For this purpose, the daily data of 25 years (1992-2017) including maximum temperature, mean temperature, relative humidity, sunshine hours and solar radiation were collected at the synoptic stations of these regions. To evaluate the performance of models, common evaluation statistics were used. The results showed that at the Yazd station, in the ANN method, the lowest values of RMSE, MAE and the highest values of IA and R2 were 2.381, 1.760, 0.869, and 0.962 , respectively. These values at Kerman Station are equal to 2.708, 2.050, 0.945 and 0.810 , respectively.In the SVM method, the lowest values of RMSE, MAE and the highest values of IA and R2 at the Yazd station were 2.028, 1.540, 0.901 and 0.973, respectively, and at Kerman station were 2.407 , 1.896, 0.956 and 0.846 , resp ectively. Overall, the efficiency of the SVM method in both regions was more accurate than the ANN method.
ANALYSIS OF FAKE DATA ON SOCIAL MEDIA

Fake news is continuously spreading through social media. One such forum is Facebook. Identifying the originality of news has been the focus of this research article. In this paper, we are going to propose LSTM based model for the authenticity of the news. We have used ANN for classification. We have made a satisfactory improvement against the methods used before and found the accuracy of our model as 93%.
Creating Secured Multi-Clouds By Continuous Automated Auditing Using Ternary Hash Tree In AWS

There are countless choices for the way to store and access your data. There’s the disc drive on your portable computer, the external disc drive you utilize for backing up and transferring information, network file shares, USB drives, and a lot of. With numerous storage choices accessible, what makes cloud storage unique? what makes cloud storage more captivating is that files are accessed and altered with ease. All you would require is a web affiliation and you'll access your files from any device, anywhere. Most of the organizations are moving to cloud storage to acquire advantage of the larger information availability, vital value savings, and information redundancy compared to conventional infrastructure. An important element of cloud data security is data integrity — preventing unauthorized modification or deletion and guaranteeing that knowledge remains unchanged since originally uploaded. This can be sometimes monitored by Third-party auditors appointed by the organizations. However, it can lead to high risks associated related to knowledge integrity which incorporates human errors(procrastination), corporate executive threats (the corruption of files), malicious intruders, compromised hardware, and configuration error, etc. Our resolution aims at resolving the preceding issues. During this work, we tend to plan a unique integrity verification framework in a multi-cloud atmosphere for securing cloud storage with the help of Ternary Hash Tree (THT) and Replica-based Ternary Hash Tree (R-THT). This helps to perform continuous data auditing at fixed time intervals which will be set by the admin. Differing from existing work, the auditing occurs at 3 levels-Block- level, File-level, and Replica-level with tree block ordering, storage block ordering for preserving data-integrity and guaranteeing data availableness within the cloud. The auditing processes have a flow, initial the parent block checking. If the parent block has any corrupted file, then the child node auditing happens. If the child nodes have any corrupted files, then the File recovery process will be carried out by the checker automatically if the information gets corrupted while checking. Users will cavil the cloud for file recovery. The outcomes depict that the planned secure cloud auditing framework is very secure and economical in storage, communication, and computation prices.
Klasifikasi Suara Paru Menggunakan Transformasi Wavelet dan Recurrent Neural Networks

AbstrakSuara paru mengacu pada bunyi spesifik yang dihasilkan oleh pergerakan udara melalui sistem pernapasan. Ada dua tipe suara paru yaitu normal dan abnormal. Contoh suara paru abnormal adalah crackles, wheezes. Crackles adalah suara paru yang berderak atau bergelembung yang berasal dari pangkal paru, akibat penundaan pembukaan kembali jalan napas yang menutup. Wheezes adalah bunyi seperi bersiul yang diakibatkan udara yang melewati jalan napas yang menutup sebagian. Pada penelitian ini, penulis membangun model klasifikasi suara paru abnormal wheezes, crackles dan suara paru normal dengan menggunakan metode wavelet haar dan wavelet daubechies 3 untuk mengekstrak vektor ciri dan menggunakan algoritma Gate Recurrent Unit (GRU) untuk mengklasifikasikan suara paru. Pada Proses klasifikasi dengan model GRU beberapa skenario pengujian ditambahkan untuk menganalisa hasil akurasi yang didapat. Hasil akurasi tertinggi didapatkan pada scenario pengujian wavelet daubechies 3, optimizer adam dan nilai dropout 0,6 mendapatkan nilai akurasi 88,81%.Kata Kunci: Suara paru, normal, abnormal, wavelet, GRU, akkurasiAbstractLung sounds refer to the specific sounds produced by the movement of air through the respiratory system. There are two types of lung sounds, namely normal and abnormal. Examples of abnormal lung sounds are crackles, wheezes. Crackles are the sound of the lungs crackling or bubbling that originates from the base of the lungs, as a result of delays in reopening the closed airway. Wheezes are whistling sounds caused by air passing through the airway that is partially closed. In this study, the authors built a classification model for abnormal wheezes, crackles and normal lung sounds using the wavelet haar and wavelet daubechies 3 methods to extract feature vectors and using the Gate Recurrent Unit (GRU) algorithm to classify lung sounds. In the classification process with the GRU model several test scenarios are added to analyze the accuracy results obtained. The highest accuracy results were obtained in the wavelet testing scenario Daubechies 3, Adam's optimizer and the dropout value of 0,6 got an accuracy value of 88,81%.Keywords: Lung sounds, normal, abnormal, wavelets, GRU, accuracy
LibGuides: LLILAS Benson Digital Collections and Content: Social Media

This guide is aimed to provide users with a comprehensive resource to help them navigate and access the Benson's digital content which stems from a variety of projects and departments.
Subject guides: Open Content: Sound and music

This guide brings together information about Open Access Resources and Open Educational Resources. It aims to make Open Access resources easier to find and use for study, teaching and research.