Reviewerly/exHarmony huggingface dataset:
Detection of Fraud Patterns in Accounting Accounts Using Data Mining Techniques

Accounting databases with fraudulent transactions inside was used to detect fraud patterns by data mining tool.The object was accomplished by the following method: first, inside data, fraudulent transactions according to three fraud patterns were settled, over it the algorithms, Euclidian distance and local outlier factors were run using Rapidminer program.As a result the fraud patterns were shown in different ways according to the specific graphics contributed by the program.In conclusion, clusters grouping by Euclidian distance with k Means algorithm (k = 4) allowed an adequate visualization of the values' distribution, as consequence was detected the first and third fraud patterns.The application of the outlier's detection algorithm (LOF) detected the three fraud patterns in a clear way as a consequence of the insolate outliers in different graphics, shown by Rapidminer program, with different variables correlated.
Chapter 10 Case Study from the Energy Domain

Information systems are most often the main focus when considering applications of Big Data technology. However, the energy domain is more than suitable also given the worldwide coverage of electrification. Additionally, the energy sector has been recognized to be in dire need of modernization, which would include tackling (i.e. processing, storing and interpreting) a vast amount of data. The motivation for including a case study on the applications of big data technologies in the energy domain is clear, and is thus the purpose of this chapter. An application of linked data and post-processing energy data has been covered, whilst a special focus has been put on the analytical services involved, concrete methodologies and their exploitation.
Framework Information Technology Infrastructure Library (Itil V3) : Audit Teknologi Informasi Sistem Informasi Akademik (Siakad) Perguruan Tinggi

Purpose of this study is to provide convenience to XYZ University to determine performance or quality of information technology services Academic Information System information used by comparing the level of maturity of information technology current conditions (Current Maturity) and conditions to come (Expected Maturity), the results is a gap analysis and recommendations for improving quality of information technology services.Information technology audits at universities are used to determine the capability of information technology used.This information technology audit uses the ITIL V.3 framework, because the method is easier to use and provides recommendations to improve the performance of information technology to increase stakeholder satisfaction, we made the object of information technology audit at XYZ University an Academic Information System (SIAKAD).The data in this study obtained through direct observation to XYZ University by interviewing the developer and users of information technology and distributing questionnaires
Perancangan Sistem Keamanan Brangkas Menggunakan Pengenalan Wajah Berbasis Android

The level of security in terms of access is one of the main priorities of everyone to improve the security system that feels the need for improvement following the development of modern technology. This study discusses a security system using Android-based face recognition. The aim of this research is that the safe safety system has a better level of security than the previous system. The initial stage to build this system, the authors do the literature data collection stage as a basis for the theory and system development methods used by software designers before is the waterfall method, in general this method is divided into several stages, including: Analysis, Design, Program Code and Unit Testing. For the method used in the research of this system is the eigenfaces algorithm method for the detection of facial objects in the initial process of image training. As well as the Local Binary Patterns algorithm method and Histrogram Equalization at the stage of reading the user's face recognition image accurately which has an accuracy of face reading up to 95.56%. The results of the user's face data will be processed in Wemos D1 and the data will be sent and stored in a database. The results of data from face recognition data will be used again as user data to open the safe. The conclusion, the system can read the user's face in real time and can work well for safe security systems
Temporal Refinements for Guarded Recursive Types

Abstract We propose a logic for temporal properties of higher-order programs that handle infinite objects like streams or infinite trees, represented via coinductive types. Specifications of programs use safety and liveness properties. Programs can then be proven to satisfy their specification in a compositional way, our logic being based on a type system. The logic is presented as a refinement type system over the guarded $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> -calculus, a $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> -calculus with guarded recursive types. The refinements are formulae of a modal $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>μ</mml:mi></mml:math> -calculus which embeds usual temporal modal logics such as and . The semantics of our system is given within a rich structure, the topos of trees, in which we build a realizability model of the temporal refinement type system.
On a combinatorial generation problem of Knuth

The well-known middle levels conjecture asserts that for every integer n ≥ 1, all binary strings of length 2(n + 1) with exactly n + 1 many 0s and 1s can be ordered cyclically so that any two consecutive strings differ in swapping the first bit with a complementary bit at some later position. In his book ‘The Art of Computer Programming Vol. 4A’ Knuth raised a stronger form of this conjecture (Problem 56 in Section 7.2.1.3), which requires that the sequence of positions with which the first bit is swapped in each step of such an ordering has 2n + 1 blocks of the same length, and each block is obtained by adding s = 1 (modulo 2n + 1) to the previous block. In this work, we prove Knuth's conjecture in a more general form, allowing for arbitrary shifts s ≥ 1 that are coprime to 2n + 1. We also present an algorithm to compute this ordering, generating each new bitstring in (n) time, using (n) memory in total.
Diagnosa Penyakit Ginjal Kronis Menggunakan Metode Klasifikasi Naïve

Chronic Kidney Disease is a very dangerous disease that is often not seriously considered with the effects of this disease which leads to death. More than 26 million people in the United States are not aware of their kidney disease, only 8% of them begin to realize the disease, each body must be known early whether or not the body condition / / by diagnosing it, in this study classifications will be carried out on the diagnostic data attributes Chronic kidney disease aims to simplify the process of classifying symptoms and making decisions on the diagnosis of kidney disease, this process is carried out using a data mining classification approach using the Naïve Bayes Classifier method
Humanitats digitals i pedagogies culturals: mirades creuades als entorns educatius híbrids.

The Arts and the Humanities are experiencing a rapid process of evolution and change thanks to the growth of the digital universe. Education is also undergoing profound transformations, also motivated by the impact of this virtual reality which is already both local and global, ubiquitous, and affects us all. As teacher trainers, we analyse the hybridizations of the two scenarios of digital humanities and cultural pedagogies which can either create an integrative climate geared towards achievement, or, in the worst case, foster a new model of abuse and excess towards teachers and students at different stages of education. Looking at the situation, we observe that the digital dimension can help to break down the traditional barriers that have been imposed by specific areas of knowledge, and can help to achieve connected scenarios. Here we are interested in the different hybrid models, involving artists, arts educators, historians, linguists and designers, who show us their particular visions of this new model of connected educational contexts.
The need for Digital Library Resources in the Distance Education System in India

Open and Distance Learning is widely known in the field of education.Its impact on education field is greatly appreciated.It opens all the way to learn through the development of technologies.With the growth of the internet and its viability, there are palpable opportunities for students pursuing distance education to gain access to valuable resources.Digital libraries can offer invaluable services to distance learners.However, it is of the utmost importance to ensure that various web-based technologies are in place in order to support Open and Distance Learners' needs and propensities.Digital libraries play a vital role in the expansion of Distance and Open Learning system in India.In present scenario, Digital Libraries are one of the essential information resource for the students who registered in the distance education system.This paper discuss the importance, ease, need and flexibility of digital libraries for distance learners.The purpose of the proposed study is to analyze the role played by a digital library in the life of students pursuing distance education in India.
Breast Cancer Classification using Local Directional Ternary Patterns

Local texture descriptors have outperformed holistic texture descriptors in various pattern recognition applications.However, the local descriptors have limitations that can compromise the data in an image.For instance, the Local Binary Patterns (LBP) are sensitive to noise, Local Ternary Patterns (LTP) use a static threshold for all images making it a challenge to select an optimum threshold for all images in a dataset, and the Local Directional Patterns (LDP) use orientation responses to derive an image gradient disregarding the central pixel and 8-k responses.These limitations lead to the loss of subtle texture features while encoding an image.This paper proposes a Local Directional Ternary Patterns (LDTP) texture descriptor, which not only considers the central pixel in encoding image gradient but also takes into account all directional responses and an adaptive threshold.Findings from empirical records for the MIAS breast cancer dataset and using different classifiers show that LDTP attains a higher accuracy level for both normal/abnormal and benign/malignant classification compared to the other local texture descriptors.
Graph Regularized Constrained Non-Negative Matrix Factorization With Lₚ Smoothness for Image Representation

Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years. In this paper, we propose a graph regularized constrained nonnegative matrix factorization with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> Smoothing (GCNMFS) for image representation. Specifically, the main contributions of the proposed GCNMFS method include as follows: firstly, the geometric manifold structure hidden in data is effectively exploited by adopting a graph regularizer. Secondly, the label information of labeled samples is incorporated into the model of NMF without additional parameters. Finally, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> smoothness constraint is used to constrain the basis matrix, and thus a smooth and more accurate solution is produced. Moreover, an effective optimization scheme is presented to solve the proposed model. Extensive experiments on several image datasets show the proposed GCNMFS method can achieve better performance than other state-of-the-art methods in clustering.
Reference model for academic results certification in student mobility scenarios : Position paper

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context.
Penerapan keterampilan bertanya mahasiswa untuk stimulasi keterampilan berpikir tingkat tinggi (HOTs) anak TK

Questioning skills need to be mastered by the student-teacher of kindergarten. Questioning skills consist of asking advanced questions (high-level questioning skills) and basic questioning questions. High-level thinking is a way of thinking based on facts and concepts to direct the mindset of children, giving value, finding, and interpreting solutions to problems. The purpose of this research is to describe the basic questioning skills (LOTIs) and advanced questioning skills (HOTs) of the student-teacher of kindergarten in micro-teaching courses. This type of research is qualitative descriptive using case studies. The research subjects consisted of 4 students who took the micro-teaching course in the Department of Early Childhood Education, STKIP Adzkia Padang. The object of the study consisted of further questioning skills used by kindergarten students when teaching practice in lectures for 3 months consisted of 1) cognitive level questions, 2) ordering order arrangement, 3) using of tracking questions, 4) increasing interaction. Data collection techniques were carried out with the study of documentation and direct recordings of data analysis techniques with simple percentages and were converted according to the scale of interpretation so that the research conclusions were obtained. The application of students' questioning skills during teaching practice was dominated by LOTs questions, which were limited to questions of knowledge level just to remember.
The privacy paradigm : An overview of privacy in Business Analytics and Big Data

In this New Age where information has an indispensable value for companies and data mining technologies are growing in the area of Information Technology, privacy remains a sensitive issue in the approach to the exploitation of the large volume of data generated and processed by companies. The way data is collected, handled and destined is not yet clearly defined and has been the subject of constant debate by several areas of activity. This literature review gives an overview of privacy in the era of Business Analytics and Big Data in different timelines, the opportunities and challenges faced, aiming to broaden discussions on a subject that deserves extreme attention and aims to show that, despite measures for data protection have been created, there is still a need to discuss the subject among the different parties involved in the process to achieve a positive ideal for both users and companies.
Multiway dynamic mask attention networks for natural language inference

Attention mechanisms are widely used on NLP tasks and show strong performance in modeling local/global dependencies. Directional self-attention network shows the competitive performance on various datasets, but it not considers the reverse information of a sentence. In this paper, we propose the Multiway Dynamic Mask attention Network (MDMAN). The model has two modules: a dynamic mask selector and a multi-attention encoder. The dynamic mask selector chooses high-quality reverse information with reinforcement learning and feeds reverse information to multi-attention encoder, the multi-attention encoder uses four attention functions to match the word in the same sentence at different token level, then combine the information from all functions to obtain the final representation. Our experiments performed on two publicly available NLI datasets show that MDMAN achieves significant improvement over DSAN.
Efficiency Analysis of Binary Search and Quadratic Search in Big and Small Data

When doing a searching process, Binary Search is one of the classic algorithm used in sorted data. The characteristic of this algorithm is to make a comparison of the keywords you want to find with the start, middle, and end values of a data series. Keyword search is done by reducing the range of start and end points to finally find the keyword you want to search. The time complexity of the binary search algorithm is O(log2n) while the memory capacity needed is O(1) for iterative implementation and O(log2n) for recursive implementation. This research will develop a level of comparison in binary search in order to get optimal performance in accordance with the amount of data available.
DESIGN OF VIDEO AIDED RETENTION TOOL FOR THE HEALTH CARE PROFESSIONALS IN SELF-DIRECTED VIDEO-BASED LEARNING

Health Care Professionals (HCPs) depend on self-directed learning by watching medical videos. In the traditional video learning system, it is difficult to identify the important videos from the huge data set and to find the essential inside parts of a long video. In addition, it is hard to know learners’ preferences inside the video parts, including duration and repetition of watching. If the system could know the attention and retention process of each learner, it could change the way to show the video. Accordingly, this research proposes to design the Video Aided Retention Tool (VART) system for analyzing video content to improve self-directed video-based learning among HCPs. The VART consists of a combination of video tracking, analyzing, and filtering tools, with the integration of domain model, learners’ model, and e-teaching strategy model to aid in self-directed learning. The proposed VART will pick important videos on a single topic and put automatic indexes to represent the essential parts of video content. It will also track the learner’s ID, content preference, monitor watching duration, and repetition of the content. Using such kind of data, attention, and retention will be determined and filtered reels, recommendations, interactive videos will be provided to the learners.
Speed Limit Camera Monitoring/Tracking System Using SaaS Cloud Computing Module and GPS

The number of accidents is rising daily along with the expanding driver and passenger safety concerns. The countries that have successfully reduced road traffic risk have embraced a 'systems approach' to road safety. Speed is at the core of the road safety problem. There is a strong relationship between speed and both the number of crashes and the severity of the consequences of a crash. This paper suggests a solution to increase safety with a speed limit camera monitoring/tracking system using Global Positioning System (GPS) and cloud computing with the Software-as-a-Service (SaaS) module which provides useful information about roads. Critical information like driver position is collected by the GPS build-in the device for Android or iOS systems, then this information is sent to a cloud server where it is kept over the Internet. This system provides precise location services of the driver, driving speed, road allowed speed and speed limit camera position all to provide the driver with a street guide to improve speed limit enforcement aiding in road safety and preventing more accidents. The proposed system was tested by implementing it by many drivers at Duhok and Zakho provinces using the System Usability Scale (SUS) method with the subscription of 30 potential system users. Results of the SUS showed a relatively high rate of user satisfaction at around 75.58%.
Human Parsing with Discriminant Feature Learning for Person Re-identification

It is urgent key research for person re-identification (ReID) to extract discriminant distinguishing features from pedestrian pictures, optimize the feature extraction ability of the network, and obtain better pedestrian feature representation. By combining the global and detailed features of pedestrians, based on the multilevel attention map generated by human parsing model and soft target obtained by self- teaching way, we design a multi-branch deep network model to distinguish pedestrian key features from different levels for ReID, in particular, each branch is guided by different information to jointly learning complementary global and local feature representation, then a feature selection module used to emphasize important features to further improve discriminative power. Through experimenting on datasets, we verify that human parsing with discriminant feature learning contributes to the performance of ReID. Compared with current state-of-the-art methods, our method achieves promising results.
Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization

Abstract. We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-and-conquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data.
Mouse Cursor Control Using Hand Gesture Recognition Based on PHOG-Improved LBP and K-NN Classification

Abstract A real-time mouse cursor control system using static gestures was designed to achieve the goal of human-computer interaction (HCI) in this paper. The system uses the computer’s own camera or external USB camera to collect video data, detect and recognize gestures of people in the video, and control the cursor movement or click in real time based on the gesture. To improve the recognition accuracy of hand gesture images detection, a method of hand gesture images detection based on PHOG + Improved LBP + K-NN was proposed in this paper. In order to improve the real-time performance of the system, the system determines whether the current frame has human hands by skin color detection. When human hands are detected, Pyramid Histogram of Oriented Gradients (PHOG) features and the improved Local Binary Pattern (LBP) features are further extracted. After fusing PHOG features and improved LBP features, k-nearest neighbor classification (K-NN) is used to implement gesture recognition. Six different gestures were tested 50 times with different angles, different lights and no skin tone in the background. The experimental results show that the system has good recognition performance.