Reviewerly/exHarmony huggingface dataset:
Bayesian Image Segmentation using HMRF Algorithm

Abstract Image segmentation is a process of dividing the image in to some distinct regions. These regions shave specially coherent in nature and have similar attributes. This technique is widely used for image analyses and to interpret the desired feature. In this present paper, we will study about the hidden Markov random fields (HMRF) and find its expectation maximization algorithm. The main idea behind developing HMRF is to adjoin the “data faithfulness” and “model smoothness” that show very similar nature with the active contours, GVF, graph cuts, and random walks. Here we also use the HMRF-EM along with the Gaussian mixture models, and then we use it on color image segmentation process. These algorithms are implemented in MATLAB. In color image segmentation experiments, we observe that the result obtain from HMRF segmentation are much smoother then the direct k-means clustering. The segmented object is much closer to the original shape than clustering. The segmentation time for Bacteria 1, Bacteria 2, SAR and brain images are 0.35, 0.43, 0.12 and 0.12, respectively. The accuracy for Bacteria, Bacteria 2, SAR and brain images are 97.70, 98.06, 98.89 and 97.35%, respectively. Keywords: Bayesian methods, convex optimization, image segmentation, spatial mixture models, Potts Markov random field .
On the Inherent Regularization Effects of Noise Injection During Training

Randomly perturbing networks during the training process is a commonly used approach to improving generalization performance. In this paper, we present a theoretical study of one particular way of random perturbation, which corresponds to injecting artificial noise to the training data. We provide a precise asymptotic characterization of the training and generalization errors of such randomly perturbed learning problems on a random feature model. Our analysis shows that Gaussian noise injection in the training process is equivalent to introducing a weighted ridge regularization, when the number of noise injections tends to infinity. The explicit form of the regularization is also given. Numerical results corroborate our asymptotic predictions, showing that they are accurate even in moderate problem dimensions. Our theoretical predictions are based on a new correlated Gaussian equivalence conjecture that generalizes recent results in the study of random feature models.
Over financial regulation and stability: Does it matters for bank efficiency in Nigeria?

Abstract. Banks play a key and critical role in society, mediating resources and assets around the economy between surpluses and household spending on deficits. Therefore, they have a great responsibility for sustainability and prosperity. This study is motivated by the need to investigate whether competition, regulation and stability are important for the efficiency of the Nigerian banking system. Data sourced from the World Development Indicators between 1996 and 2017 was used. The results show a negative and statistically significant relationship exists between financial regulation and efficiency of the banking sector. However, further results show a positive and statistically significant relationship exists between financial regulation and stability in the system. Thus, the study concludes by emphasizing the importance of stability and bank performance matters in Nigeria. Keywords. Bank efficiency, Financial regulation, Stability. JEL. D81, D91, E71, G01, G41, H11, I18, Z18.
Υλοποίηση σε FPGA συστήματος αναγνώρισης προσώπου, με εφαρμογές στη δικανική υπολογιστών

Face recognition systems are a very important tool for researchers of the Digital Forensics sector, because these systems help them solve criminal cases easier and faster. Many such systems based on software, achieve quite good performance regarding the recognition rate. However, the recognition time is not satisfactory. For this reason, there is a need of research and study on the hardware-based face recognition systems, and specifically on Field Programmable Gate Arrays (FPGAs) in order to reduce the execution time. The purpose of this diploma thesis is the study and implementation of an FPGA-based face recognition system. The algorithm used for the feature extraction stage is the Local Binary Pattern Histograms (LBPH) and the Manhattan distance is used for the calculation of the distances between the database’s face images. The system is implemented on the Artix-7 xc7a75tcsg324-3 FPGA and the achieved recognition rate is 80% for 30 consecutive trials. The maximum frequency is 66.23 MHz. Regarding the allocated hardware resources, a big percentage of the FPGA is occupied for the calculations, because of the repeated parallel feature extraction and Manhattan distance units. Finally, the recognition time of the system is almost 128 ms, when the face database that needs to be checked consists of 165 images. On-line training process is also included in this time.
Research Guides: Understanding Open Access: Frequently Asked Questions

This guide is intended to help you understand open access-related policies, the various routes you may use to make work open, and the OA-related resources available to you through Lane Library.
Analysis Of WPA2 Security Test At PT. Andaglos Global Teknologi Using Fluxion

PT. Andaglos Global Technology uses internet service providers and is equipped with wifi facilities. The location of the office is in the terminal and is adjacent to the shop and other buildings. This resulted in the wifi network at PT. Andaglos Global Technology is often exploited by illegal users around the office, so that the impact on the internet speed decreases. Even the employees of PT. Andaglos Global Teknologi claimed to have been a victim of hacking in its social media after accessing the network at PT. Andaglos Global Technology. To solve this problem, penetration testing is done using FLUXION on PT. Andaglos Global Technology with the method (Action Research) which consists of several stages, namely Diagnosing, Action Planing, Action Taking, Evaluating and Specifyng Learning, to determine the level of security on the network of PT. Andaglos Global Technology. Penetration testing with FLUXION proves that network security at PT. Andaglos Global Technology works well in securing passwords, but FLUXION is able to get passwords from these networks, not by breaking into security but by deceiving network users by creating the same login form as the routers used by PT. Andaglos Global Technology, so that unknowingly network users have given the password to the fake login page, which resulted in the network being accessed by users illegally. Keywords: Wireless Network Penetration Testing, Fluxion
Implementation of adaptive learning at higher education institutions by means of Moodle LMS

E-learning courses have become popular means of delivering knowledge to students in higher education institutions. Most participants of learning process note that they benefit from the possibility to gain knowledge regardless of time, location and device they use. Among other advantages possibility to return to learnt material several times, divide material into parts, consume information through different types of educational materials (video, infographics, presentations, text, quizzes etc.) are mentioned. At the same time most of the surveyed students chose that they lack personalization both of materials and studying process, limited in terms of fulfillment and would like to have a choice of the level of study. The educational trend that is able to put into practice the above mentioned requirements is blended learning as it has a range of advantages such as usability, consideration of individual abilities, additional materials introduction, activities monitoring. Although it combines offline and online learning,effectiveness of e-learning courses designed for its implementation play a crucial role. To make a learning process correspondent to the students’ needs adaptive learning can be introduced in higher education institutions. Adaptive learning is a methodology that allows to identify level of students’ knowledge and their learning styles and transform materials, tasks and ways of their delivery according to the needs of learning process participants. LMS Moodle offers different solutions for adaptive learning. They provide administrators and teachers with tools to vary allstages of a learning process starting with delivery of information and ending with assessment.
LibGuides: Research Data Support Services for Researchers: Home

This guide supports the University’s Policy on Research Data Management contained within the Research Integrity Policy Framework. The guide provides guidance for Swansea University researchers on expectations, obligations, advice and support for research
PIGan: Pose Invariant face reenactment for game character using a GAN-based face rotation technique

The recent emergence of generative adversarial networks (GANs) accelerate the research on face reenactment. Face reenactment is a technique for synthesizing a face image of desired expressions from a source face using a target image of desired expressions or a vector with facial expression information. In this paper, we propose a GAN architecture-based method for reenacting the expressions of game characters with various angles through a rotation module and action unit (AU) vectors. In the first step, we devise a rotate module that synthesizes the frontalized face images from the face of a game character with arbitrary poses. This prevents the side effects of the face poses in reenacting the facial expressions. We feed the frontalized image as well as a vector with facial expression information to the generator in order to synthesize a face with the desired expression. The generator generates an attention mask indicating a region of interest of facial expression and a color mask indicating color information for the expression. This allows us to synthesize a reenacted image that generates facial expressions while preserving the identity and other attachments of a game character. The attention mask and color mask incorporate to synthesize a reenacted image with target expression. Finally, we recover the original pose of a character from the frontalized reenacted image.
Galveston College Library: Online Library Resources: Find Media

This guide is designed to help online students locate and use the GC Library's online resources.
Crowdsourced-Data Normalization with Python and Pandas

Pandas is a popular and powerful package used in Python communities for data handling and analysis. This lesson describes crowdsourcing as a form of data creation as well as how pandas can be used to prepare a crowdsourced dataset for analysis. This lesson covers managing duplicate and missing data and explains the difficulties of dealing with dates.
English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time.
Probabilistic user modelling methods for improving human-in-the-loop machine learning for prediction

In many machine learning applications and in particular those with only few training data, human involvement in the form of data provider or expert of the task is crucial. However, human interaction with a machine learning model is constrained by (i) the interaction channels, i.e., how human knowledge can be applied in the model, and (ii) the interaction budget, i.e., how much the user is willing to interact with the model. This thesis presents new methods to improve these constraints in human-in-the-loop machine learning. The core idea of the thesis is to jointly model the available data with a model of the human user, i.e., the user model, in a unified probabilistic model and then perform sequential probabilistic inference on the joint model to design improved interaction. The thesis contributes on two types of prediction tasks. The first task is expert knowledge elicitation for high-dimensional prediction. Experts in a field usually have information beyond training data which can help to improve the prediction performance. User models, as priors and likelihood functions, are proposed to directly connect expert knowledge about the relevance of parameters to a model responsible for prediction. The user model can account for complex user behaviour such as users updating their knowledge during the interaction. Furthermore, sequential experimental design on the joint model is employed to query the most informative expert knowledge earlier to minimize the amount of interaction. The second task is personalized recommendation where the goal is to predict the most relevant item for a user with as few interactions as possible. The interactions are based on user relevance feedback on the recommendations. The thesis proposes user models that are able to receive and integrate feedback on multiple domains and sources by providing a joint probabilistic model connecting all feedback types. Sequential inference on the joint model, using Thompson sampling, was employed to find the targeted recommendation with minimum interaction. Simulated experiments and user studies in both tasks demonstrate improved prediction performance only after few interactions with the users. The research highlights the benefits of joint probabilistic modelling of the user and prediction model in interactive tasks.
Library: How to use the Reading List Tool (Leganto): Students

This guide gives you step-by-step instructions on how to access and create your course reading lists in the Reading List Tool through D2L
Visión extensionista de apoyo didáctico a la educación a distancia. Intercambio académico entre la Cujae y la UPM

This article shows an experience arising from an academic exchange between the Higher Polytechnic University of Madrid and the Technological University of Havana, Cujae as an extension of university education during the exceptional situation that the COVID-19pandemic imposed to the face-to-face teaching modality in 2020. The isolation conditions caused by COVID-19, officially recognized worldwide brought about new teaching forms and methods to be used or the use of the existing ones in different ways. The impossibility of carrying out the programmed exchange between students and professors of the UPMand Cujae, led to seeking other alternatives to carry out this exchange. This experience, derived specifically from the training process at Cujae Architecture career through an academic exchange with the Higher Polytechnic University of Madrid.University cultural promotion (university Extension methodology) was the main methodology proposed. Key results were obtained such as the distance interuniversity communication process; a “cujaena” university extension activity that enriches the didactics of the teaching-learning process of a teacher-student group at the UPM, through the cultural promotion of Havana architecture.It is concluded that the distance modality favored the implementation of new ways for the academic exchange with renewing glances in the management of university substantive processes, benefiting the integrality of the university extension.
Learning-space compass: workshop summary with the School of Computer Science, Cardiff University

This is the summary of workshops conducted with the School of Computer Science, Cardiff University to pilot application of the Learning-Space Compass. More information about the project, visit https://futurelearningenvironments.org/
The implementation of Total Quality Management (TQM) in the hotel industry

This study sets out to explore the Critical Success Factors (CSFs) necessary for Total Quality Management (TQM) implementation in hotels. It also aims to classify participating hotels into groups based on their TQM adoption by using cluster analysis. A quantitative survey method was applied. Data were collected from a sample of managers from four- and five-star hotels in Jordan, 170 questionnaires were distributed to managers and 104 usable questionnaires were returned. The findings revealed that TQM is existed and implemented in the hotel industry. The researcher then confirmed that four- and five-star hotels can be classified into two groups, namely, “high TQM adopters” and “low TQM adopters”.
Multi-Functional Privacy-Preserving Data Aggregation: With Malicious User Detection

In practice, many applications like traffic monitoring and smart grids rely on computing functions on privacy-sensitive data. In order to protect privacy-sensitive data and still keep the ability to compute any arbitrary function, multi-functional privacy-preserving data aggregation schemes have been created. These schemes, however, can be abused by malicious users, leading to incorrect results. Existing literature mostly provides aggregation schemes which are either multi-functional or support malicious user detection, but to the best of our knowledge, there is only one scheme that provides both. This scheme requires each user to send a number of ciphertexts linear in the size of the aggregation function's domain. Furthermore, that scheme is not collusion-resistant. In this thesis, we design a multi-functional privacy-preserving data aggregation scheme with malicious user detection. In contrast to existing schemes in literature, the amount of messages is independent of the size of the aggregation function's domain, it does not rely on a trusted authority and it is collusion-resistant as long as at least two users are honest-but-curious.
Penerapan Model Pembelajaran Kontekstual Untuk Meningkatkan Prestasi Belajar Mata Pelajaran Ekonomi Siswa Kelas XI Di SMA Negeri 4 Surakarta

The purpose of this study was to describe the increase in learning achievement of students in class XI IPS 3 at SMA N 4 Surakarta by applying the contextual learning model. The type of research used is classroom action research (PTK) which is carried out collaboratively between researchers, teachers, and class XI IPS 3 SMA Negeri 4 Surakarta. This research was carried out in two cycles. Subjects in this study were teachers and students of class XI IPS 3 SMA Negeri 4 Surakarta, totaling 36 students. The data collection methods used were observation, tests, field notes, and documents. The results showed that in the pre-cycle research action the percentage of student learning achievement was 58.33%, from the pre-cycle action to cycle I student learning achievement had increased by 13.89% so that the percentage of student achievement in class XI IPS 3 in economic cycle I amounted to 72.22%. In the action cycle II student achievement also increased to 80.56%, thus the increase in student achievement from cycle I to cycle II was 8.34%. Based on the research data, it can be concluded that the application of the contextual learning model can improve the learning achievement of students in class XI IPS 3 in SMA Negeri 4 Surakarta.
KESALAHAN POSISI (DISPLACEMENT) DATA HASIL PENGUKURAN ALAT GPS GARMIN 78s TERHADAP DATA PENGUKURAN ALAT TOTAL STATION PADA TITIK - ITIK TOWER SUTT 150 KV AMUNTAI - TAMIANG LAYANG

The government has committed to build a power plant of 35,000 Megawatts (MW) in stages by providing infrastructure in the form of transmission, substations and generating sources. One of the transmissions that can function to distribute from the generator source to the load centers is the 150 kV High Voltage Air Line (SUTT) transmission. The purpose of this study was to analyze the Displacement generated by the Garmin 78s GPS Tool against the Total Station Measurement Data. SUTT 150 kV Amuntai - Tamiang Layang consists of 120 tower points along the transmission line and tower points using Garmin 78s GPS to determine the position of the Total Station data. Position errors in the search for tower points can occur, so the analysis method uses descriptive statistics to find out the real position error on the average of the two coordinate points using Total Station and Garmin 78s GPS. Total Station is a tool used to project tower points. Based on the results of the study, the coordinate position error generated from the 120 tower points is 427.57 meters and the average calibration error is 0.23 (23%) on a weighted average of 3.56 meters.Keywords: displacement; total station; GPS Garmin 78s.
Egyptian Colloquialism a semantic study of the electronic novel (Zicula Land) as a model.

The subject of Egyptian Colloquialism relates to the semantic study of the electronic novel (The Land of Zecula) as a model, by enumerating the Colloquialism contained in the electronic novel, classifying those words and placing them in their correct semantic fields, then explaining the origin of those words and the semantic relationships between them. Therefore, the research was divided into three sections, namely: the first topic - Colloquialism in language and idiomatically, then came the second topic entitled: Colloquialism, a study in light of the semantic field theory, and finally the third topic entitled: Colloquialism in the novel and its connotations, and the semantic relations between them. The research revealed: Colloquialism in the novel, and the multiplicity of semantic fields in it, and also showed that most of the Egyptian terms Colloquialism, which we thought were related to formal language (al-Fusha) ; It is, in fact, of eloquent origin in our Arabic language. He also explained the diversity of semantic relationships between the Egyptian terms Colloquialism (Synonymy, and Contradiction).
Automatic Identification of Authors’ Stylistics and Gender on the Basis of the Corpus of Russian Fiction Using Extended Set-theoretic Model with Collocation Extraction

We present a novel quantitative approach for classification of authors' stylistics and gender differences based on extraction of word collocation. The proposed algorithm attenuates previously described issues of text processing using the vector models. We demonstrate the approach by analyzing a corpus of Russian prose. We discuss different approaches for classification and identification of the author's style implemented by currently-available software solutions and libraries of morphological analysis, methods of parameterization, indexing of texts, artificial intelligence algorithms and knowledge extraction. Our results demonstrate the efficiency and relative advantage of regression decision tree methods in identifying informative frequency indexes in a way that lends itself to their logical interpretation. We develop a toolkit for conducting comparative experiments to assess the effectiveness of classification of natural language text data, using vector, set-theoretic and the author's set-theoretic with collocation extraction models of text representation. Comparing the ability of different methods to identify the style and gender differences of authors of fiction works, we find that the proposed approach incorporating collocation information alleviates some of the previously identified deficiencies and yields overall improvements in the classification accuracy.