Reviewerly/exHarmony huggingface dataset:
Bayesian variable selection via a benchmark in normal linear models

With increasing appearances of high-dimensional data over the past two decades, variable selections through frequentist likelihood penalisation approaches and their Bayesian counterparts becomes a popular yet challenging research area in statistics. Under a normal linear model with shrinkage priors, we propose a benchmark variable approach for Bayesian variable selection. The benchmark variable serves as a standard and helps us to assess and rank the importance of each covariate based on the posterior distribution of the corresponding regression coefficient. For a sparse Bayesian analysis, we use the benchmark in conjunction with a modified BIC. We also develop our benchmark approach to accommodate models with covariates exhibiting group structures. Two simulation studies are carried out to assess and compare the performances among the proposed approach and other methods. Three real datasets are also analysed by using these methods for illustration.
Methodology and Techniques of Digital Image Processing

The entire procedure for any image processing technique using blocks Simulink and Xilinx system goesthrough different phases. IP technique use XSG, Image post processing block, many applications in IP. Thisstudy approachestoclassifyspatialrelationships,includingtopology(setofrelationships,adjacency)andmetrics(distance, relative directional position). There is usually no exact symmetry in real artifacts, and it is necessary toaddress approximate symmetries. Many works quantify the degree of symmetry, often using a symmetry metricbasedondistance. Inthefuzzysituation,theissueisfarless important. Inreality,there isno morerigidmembership; Fluidity helps us cope with a gradual transition between objects and meaning. Therefore, we shouldclaim to be robust when analyzing the interactions between two objects. In this paper, the software package ismoved to hardware for faster segmentation, so that the device component processing speed is bound within adefinitetiminglimit.Therefore,imagesegmentationmethodsbasedonFPGAsaretheidealchoiceforimplementing.
TEACHERS’ PERCEPTION AND UNDERSTANDING OF SCHOOL LEADERSHIP IN RURAL NIGERIA

This study focused on teachers’ perception and understanding of school leadership in rural Nigeria. A qualitative research design was used. Two strategies were used; interaction (exclusion strategy) and interview (inclusion strategy). The interview instrument used was ‘open-ended’. The rural schools of Kogi State made up the study’s universe. For the purpose of the study, inclusive and exclusive criteria adopted. Based on the interview, data were gathered for qualitative analysis. The data were organized on the basis of responses. The data were systematically scrutinized and are organized based on similarity in the responses of participants. Findings revealed that his leaders are inspirational and are conscious of the goals of the individual teachers. Finding further showed that teachers have positive perception about their school leaders. The study concluded that leadership of schools in the rural areas of Kogi State is deduced satisfactory, and that majority of principals in the rural schools possess the common qualities of transformational leaders. The study recommended that stakeholders of education in Kogi State including the government should boost the principals’ leadership practices towards achieving effective teaching and quality learning in the rural schools, and that principals should adopt ‘transformational leadership practice’ in the rural schools of Kogi State.
IMPLEMENTASI ALGORITMA C4.5 UNTUK MENENTUKAN UANG KULIAH TUNGGAL (UKT) MAHASISWA (Studi Kasus : Mahasiswa UMRAH Tahun 2018)

Lecture is an advanced learning process to increase insight, knowledge, interests and talents as well as the ability of a person with the desired choice of majors or expertise. However, higher education is also needed for financial planning for tuition. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. In this UKT study has 4 attributes such as father's occupation, mother's occupation, gender number of siblings. Data from UKT students are divided into 5 groups in the form of parental income. The results of this study obtained 4 patterns of UKT classification obtained from the C4.5 algorithm. This system produces an accuracy of 50,00% with 360 testing data. However, this system cannot cross validation because all data are as testing data.
A Hybrid Ensemble Method for Accurate Fuzzy and Support Vector Machine for Gene Expression in Data Mining

Malignancy is a bunch of infection which spreads all through the human body. Since it is an exceptionally deceptive illness its determination is of vital importance. Information mining innovation helps in arranging and bunching the malignancy information and this procedure assists with distinguishing potential disease patients by investigating the data alone. In this examination we analyze three information mining calculations, namely PCA, Genetic calculation and Hierarchical Fuzzy C Means (HFCM). The hereditary calculation is done using the Quantum-enhanced Support Vector Machine (QSVM). The outcome demonstrates that the proposed calculation accomplishes a better outcome when contrasted to the other two calculations.
Usable Authentication Methods for Seniors

Standard authentication methods require user to remember long texts or random numbers when authenticating. The elderly face problems of remembering these passwords maybe due to cognitive decline or simply because they are unfamiliar with current technological devices. This paper compares available authentication methods that are more usable for the elderly and summarises the effectiveness of these systems
Research Guides: Teaching Online with Primary Sources: Library Guides

A resource for those who wish to teach online using special collections and archival materials
Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. The model achieved 0.846 area under the receiver operating characteristic curve in predicting patients' outcomes averaged over 5-fold cross validation. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission.
Development of Kodály Hand Sign Educational Tools using Multi-Layer Perceptron

Hand signs have been recognized to be useful in enhancing the effectiveness of music learning in music education. In particular, Kodaly hand signs based on the Kodaly teaching method are widely used around the world because their effectiveness has been recognized. The purpose of this study is to implement a hand sign recognition system using a neural network and to present a method that can systematically learn hand signs. Device-based systems were considered for portability, activity, and economy. In addition, the introduction of additional filtering to the neural network input was proposed to improve portability and recognition rate to small devices.
Performance Analysis of Machine Learning Classifiers for Detecting Phishing Websites

Phishing attacks are now a significant threat to people’s personal lives and their networking environment. The idea is to build a classification methodology and predict whether a website is a phishing website or a legitimate website based on a given set of predictions. The data consists of 5000 phishing URLs obtained from the PhishTank website, while another 5000 legitimate URLs contain Alexa websites provided by the University of New Brunswick. The paper proposes to apply different machine learning algorithms and identifying the best model for phishing detection.  Machine learning classifiers are tuned with hyperparameter values and select the models that achieve exceptional performance with high true positives(TP) and low false positives(FP)., CatBoost classifier performed well among various other classifiers by achieving an F1-Score of 87.6 percent and the best detection accuracy of 88.4 percent.
Offline Meta Learning of Exploration

Consider the following problem: given the complete training histories of N conventional RL agents, trained on N different tasks, design a meta-agent that can quickly maximize reward in a new, unseen task from the same task distribution. In particular, while each conventional RL agent explored and exploited its own different task, the meta-agent must identify regularities in the data that lead to effective exploration/exploitation in the unseen task. This meta-learning problem is an instance of a setting we term Offline Meta Reinforcement Learning (OMRL). To solve our challenge, we take a Bayesian RL (BRL) view, and seek to learn a Bayes-optimal policy from the offline data. We extend the recently proposed VariBAD BRL algorithm to the off-policy setting, and demonstrate learning of approximately Bayes-optimal exploration strategies from offline data using deep neural networks. For the particular problem described above, our method learns effective exploration behavior that is qualitatively different from the exploration used by any RL agent in the data. Furthermore, we find that when applied to the online meta-RL setting (agent simultaneously collects data and improves its meta-RL policy), our method is significantly more sample efficient than the state-of-the-art VariBAD.
Offline Handwritten Kannada Sentence Recognition Using Knn With Convolutional Neural Network

Multiple algorithms have been proposed for character segmentation, recognition of multilingual Indian document images of Devanagari scripts. This document is suffering from its outer layout organizations, different handwriting styles, local skews, bad print quality, and has overlapped texts. There are three levels of segmentation namely line, word, character segmentation in the suggested segmentation algorithm. Using the structural character property segmentation paths are obtained, and graph distance theory is using joined and overlapped characters are divided. The highly accurate KNN classifier is used to certify the segmentation consequences. In the Proposed character recognition algorithm, the CNN is used to train and test the handwritten dataset with the help of the KNN classifier increases the accuracy for testing the model and an accuracy of 92.49 % is achieved.   Several experiments have been made on various databases including printed just as manual texts. Benchmarking results tell that proposed methods for recognition of Kannada handwritten sentence recognition have better performances in terms of segmentation of lines, words, and characters related to different methodologies, where the most elevated segmentation and recognition rates are achieved.
MODELING METHODS AIMED AT THE FORMATION OF UNIVERSAL TRAINING IN THE INITIAL EDUCATION

Modeling in the form of essentially, organizational and targeted methodological innovations of methodical innovations for primary education. Theory and practice required to systematically update primary education, primary education standard education, was studied.
Next generation Open Access analytics: A case study

A critical component in the development of sustainable funding models for Open Access (OA) is the ability to communicate impact in ways that are meaningful to a diverse range of internal and external stakeholders, including institutional partners, funders, and authors. While traditional paywall publishers can take advantage of industry standard COUNTER reports to communicate usage to subscribing libraries, no similar standard exists for OA content. Instead, many organizations are stuck with proxy metrics like sessions and page views that struggle to discriminate between robotic access and genuine engagement. This paper presents the results of an innovative project that builds on existing COUNTER metrics to develop more flexible reporting. Reporting goals include surfacing third party engagement with OA content, the use of graphical report formats to improve accessibility, the ability to assemble custom data dashboards, and configurations that support the variant needs of diverse stakeholders. We’ll be sharing our understanding of who the stakeholders are, their differing needs for analytics, feedback on the reports shared, lessons learned, and areas for future research in this evolving area.
To study the concept of Artificial Intelligence and its impact on business processes

With the increasing competition in today’s scenario, it has made it important for the business sectors to make use of intensive machines so as to reduce the number of human efforts involved in processing any activity. The use of machine processing in replacement of the human processing is in turn known as artificial intelligence. Therefore, artificial intelligence means the simulation of human intelligence in machines so that the machines think and act like humans. Artificial intelligence and its very promise to have improved approach towards the efficiency of the businesses has been a popular topic of debate these days. Many of the companies are still fidgeting to find the right tools and applications that would further drive their business towards a successful aim. It should be noted that however companies aim to use the artificial intelligence, but they do not aim to have reduced the available workforce at their service in any which way. Employees generally fear the loss of their jobs due to adoption of the artificial intelligence in the company. Thus, they reject and oppose the adoption and implementation of artificial intelligence in the business processes. Artificial intelligence also involves the study of how to make computer do things which now people do it better.it is related to similar tasks of using computers to understand human intelligence but it must be remembered that it cannot simply replace the human factor out. Artificial intelligence does not use the objectivity behind every activity that is performed. However further to add on there are generally two issues that need to be addressed with regards to artificial intelligence. The two issues are firstly, knowledge that is represented by the humans in different forms can never be done by a machine, secondly knowledge can be very easily manipulated in the sense that the knowledge contained in one’s mind can never be used for deception. Whereas the knowledge stored in a machine can be easily manipulated. This might be a cause of major concern. This study would help the researchers in their further studies.
Protecting Ownership Rights of Videos Against Digital Piracy: An Efficient Digital Watermarking Scheme

Violation of one’s intellectual ownership rights by the others is a common problem which entertainment industry frequently faces now-a-days. Sharing of information over social media platforms such as Instagram, WhatsApp and twitter without giving credit the owner causes huge financial losses to the owner and hence needs an immediate attention. Digital watermarking is a promising technique to protect owners’ right against digital piracy. Most of the state-of-the-art techniques does not provides adequate level of resilience against majority of video specific attacks and other commonly applied attacks. Therefore, this paper proposes a highly transparent and robust video watermarking solution to protect the owners rights by first convert each video frame into YCbCr color components and then select twenty five strongest speeded-up robust features (SURF) points of the normalized luminance component as points for both watermark embedding and extraction. After applying variety of geometric, simple signal processing and video specific attacks on the watermarked video meticulous analysis is performed using popular metrics which reveals that the proposed scheme possesses high correlation value which makes it superior for practical applications against these attacks. The scheme also proposes a novel three-level impairment scale for subjective analysis which gives stable results to derive correct conclusions.
Research Guides: Profiles in Teaching and Learning with Student Media Projects: Communicating the Relationship Between People, Community and Spaces

This online gallery highlights a diversity of student created projects that Library Media Services has supported over the years. More in-depth description of each project can be found on secondary pages.
LibGuides: Course Clearances For Instructors: Video and Audio Use

Navigate copyright requirements related to the use of resources in online and campus classrooms
Application of Hybrid Metaheuristic Optimization Algorithm (SAGAC) in Beef Cattle Logistics

The study objective was to evaluate the performance of SAGAC in optimizing a linear mathematical model in whole variables to determine the most cost-effective solution in transporting cattle for slaughter. The model determines the choice of refrigerator truck, road (route), and an open-truck in a scripting process. The tests performed with the SAGAC algorithm for optimizing the proposed model were compared with the results obtained, under similar conditions, by the branch-and-bound method for solving entire problems and solving a problem optimally. After the first twenty-two experimental trials, for comparison between the two methods, nine more experimental trials were carried out, with an increase in the degree of complexity, only with the SAGAC algorithm. The results obtained in the first twenty-two experimental trials demonstrate an equivalent performance between the two methods, showing that the SAGAC algorithm, even though it is not a technique that guarantees optimal results, in this case, was also able to find them. The nine final experiments performed only by SAGAC showed satisfactory results, with an evolutionary curve of exponential behavior.
Investigation and Validation of Distributed PEP-PDP Authorization Architecture in Insider Access Control Mechanism in Scyther

Cloud authorization techniques have helped in numerous applications where user authorization management is required. Authorization Framework Policies were implemented by using PEP-PDP Architecture.  PEP receives user authorization requests, and PDP helps in making required Decisions based on authorization rules. Various Authorization languages use this model. The limitation of this model is that it uses a single PEP-PDP. It can handle a limited number of Requests. Various Research Scholars have proposed PEP-PDP Model applications. Yaseen et al. [24] have also suggested the Multiple PEP-PDP Model. This model, too, suffers from various Limitations. This paper investigates and validates our Proposed Distributed PEP-PDP Architecture in Scyther Formal Verification Tool. This paper also proposes to uses Asymmetric Encryption in Distributed PEP-PDP Architecture. In this Proposed Architecture, 4PDPs, along with PEPs, are connected. In Scyther, all required claims were achieved using asymmetric encryption, which suggests the proposed system is robust
Tingkat Pemahaman Siswa dalam Pembelajaran Daring dan Tatap Muka Langsung dalam Masa Pandemi Covid-19

The Covid-19 pandemic has a major impact on the world of education. Government policies to implement Distance Learning (PJJ) have an impact on learning in schools. Increasing ICT competence is needed to support the smooth running of PJJ. One of them is through ICT guidance activities during the Covid-19 Pandemic. SMP Negeri 1 Lengayang carried out online and face-to-face ICT guidance activities during the Covid-19 Pandemic. However, student learning outcomes in online and face-to-face learning have not shown maximum results. Various obstacles arise that affect student learning outcomes. Teachers have difficulty measuring the level of students' understanding of ICT guidance. Predicting the level of understanding of students is important as a measure of learning success during the Covid-19 Pandemic. This study aims to predict the level of understanding of students in online and face-to-face learning during the Covid-19 period, so that it can also help schools to take the right policies to improve the quality of learning for the future. This study uses the Backpropagation method of Artificial Neural Network (ANN). ANN is a part of artificial intelligence that can be used to predict. The data that is managed is a recap of the value of student cognitive learning outcomes during ICT guidance in online and face-to-face learning during the Covid-19 Pandemic. The results of calculations using the Backpropagation method with the Matlab application produce a percentage value for the level of student understanding, so that the accuracy value in prediction is obtained. With the results of testing the predictive accuracy of the level of understanding online and face-to-face with the 3-10-1 pattern, the best accuracy value is 95%. The prediction results can measure the level of students' understanding of learning during the Covid 19 Pandemic towards ICT guidance
Building 3D scene representations from different viewpoints: A contextual cueing study

Observers can build representations of panoramic scenes from snapshots (Robertson et al., 2016) and can form scene memories incidentally during visual search (Utochkin & Wolfe, 2018). Here, we used contextual cueing (Chun & Jiang, 1998) to test whether observers could build and exploit a 3D scene representation, acquired from exposure to varied viewpoints of a naturalistic scene, during a visual search task. We tested 29 observers (25 females; Mean = 19.34 years) in an online study. On each trial, observers saw a snapshot of the same computer-generated room, but from a different viewpoint (8 viewpoints total, varied trial-to-trial). In the Same-place condition, the target (a tablet) always appeared in the same relative location (e.g., on the table), no matter the viewpoint. In the Different-place condition, the target appeared in a different location in each viewpoint. (In both conditions, the target’s absolute location on the screen varied, due to the changes in viewpoint.) We collected reaction time data based on two redundant measures: keypress (participants reported the left-right orientation of a small probe that appeared on the target) and gaze latency. Overall, search times improved, nearing asymptote within the first third of the block of 48 trials. We performed a nonlinear (exponential decay) regression, and found a significant effect of condition (keypress: F(3,84)=25.5, p<0.001; gaze: F(3,88)=12.4, p<0.001). For keypress data, the Same-place condition had a significantly lower asymptotic search time (1092 ms) than the Different-place condition (1240 ms), a difference of 148 ms. Gaze latency data showed the same pattern, with significantly lower search time in the Same-place (778 ms) versus Different-place (913 ms) condition, a difference of 135 ms. These results are consistent with contextual cueing, and provide evidence that 3D scene representations (acquired from varied viewpoints) are built incidentally and used to facilitate search.