Reviewerly/exHarmony huggingface dataset:
Reducing Computational Complexity of New Modified Hausdorff Distance Method for Face Recognition Using Local Start Search

Average Hausdorff distance that is an efficient measurement is widely used in face recognition method for measuring the dissimilarity between two sets of features. The New modified Hausdorff distance (MMHD) is a face recognition method, which uses average Hausdorff distance for measuring the dissimilarity between two sets of dominant points, which are features of face image. However, the disadvantage of the average Hausdorff distance is high computational complexity. Various methods have been proposed in recent decade with the purpose of reducing the complexity of Hausdorff distance computing. Local start search (LSS) is a state-of-art method for reducing the complexity of the Hausdorff distance computing. In this paper, we present how to use the LSS method for reducing the complexity of the computing the average Hausdorff distance. Firstly, a modification of the MMHD method, namely Least Trimmed New Modified Hausdorff distance (LT-MMHD) is proposed. The LT-MMHD method uses average Hausdorff distance of largest values for measuring the distance between two sets of dominant points. The proposed method gives higher recognition rate than the MMHD method for all conditions of face image. Finally, the LSS method is used for reducing the computational complexity of the proposed method. Experimental results show that by using the LSS method, the proposed method could reduce the computational complexity of 17%.
Modeling Time Series Forecasting Using Evolutionary Techniques

In recent years, usage of time series forecasting has been increasing day by day for prediction like, share market, weather forecasting and data analysis. Forecasting of Mackey Glass chaotic time series has been carried out in this paper. It is considered that prediction of a chaotic time series system is a nonlinear, multi-variable and multimodal optimization problem. To get an optimum output of times series, global optimization techniques are required in order to minimize the effect of local optima. Application of recent evolutionary techniques have been considered as pervasive technology for Optimization. In this paper, Fuzzy Logic System (FLS) deals with nonlinearity and generates the rule base from training data used for time series forecasting. Further, application of five recent evolutionary techniques have been considered for optimization like Genetic Algorithm (GA) and Gravitational Search Algorithm Particle Swarm Optimization (GSA-PSO),. A comparison for bench mark data of time series forecasting is done using above discussed techniques. it is observed that GA performs better as compared to GSAPSO in both terms, i.e. accuracy and time
Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems

In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, partial state observations, and a multiagent structure. We discuss and compare algorithms that simultaneously or sequentially optimize the agents' controls by using multistep lookahead, truncated rollout with a known base policy, and a terminal cost function approximation. Our methods specifically address the computational challenges of partially observable multiagent problems. In particular: 1) We consider rollout algorithms that dramatically reduce required computation while preserving the key cost improvement property of the standard rollout method. The per-step computational requirements for our methods are on the order of $O(Cm)$ as compared with $O(C^m)$ for standard rollout, where $C$ is the maximum cardinality of the constraint set for the control component of each agent, and $m$ is the number of agents. 2) We show that our methods can be applied to challenging problems with a graph structure, including a class of robot repair problems whereby multiple robots collaboratively inspect and repair a system under partial information. 3) We provide a simulation study that compares our methods with existing methods, and demonstrate that our methods can handle larger and more complex partially observable multiagent problems (state space size $10^{37}$ and control space size $10^{7}$, respectively). Finally, we incorporate our multiagent rollout algorithms as building blocks in an approximate policy iteration scheme, where successive rollout policies are approximated by using neural network classifiers. While this scheme requires a strictly off-line implementation, it works well in our computational experiments and produces additional significant performance improvement over the single online rollout iteration method.
Studi Keterbacaan dan Nilai Karakter pada Buku Siswa Kelas IV SD (Edisi Revisi 2017) Kurikulum 2013

The readability and value of the character are important factors of the preparation of teaching materials. The method used is a qualitative method with a documentation study approach. The subjects used were the reading texts contained in the fourth-grade elementary school as many as 40 reading texts from 9 themes with the form of a Raygor graph formula. There are 23 reading texts or (57.5%) as appropriate and 17 reading texts or (42.5%) as inappropriate for the level of fourth-grade elementary school. Then, there are 7 reading texts or a small portion (17.5%) containing the value of religious, 21 reading texts or most (52.5%) have Nationalist values, 25 reading texts or most of them (62.5%), contain Mandiri values, 15 reading texts or almost half of them (37.5%) have the value of the Gotong Royong. Lastly, 9 reading texts or a small portion (27.5%) containing the value of the Integrity.
Deteksi Foto Manipulasi Dengan Tools Forensicallybeta dan Imageforensic.org Dengan Metode Error Level Analysis (ELA)

The development of the current digital era, makes a photo easily manipulated so that it can change the information conveyed to be different and make it vulnerable to being used for crime. Digital image forensics is one of the scientific ways to prove the authenticity of photos. The purpose of this research is to analyze a digital image that has been manipulated or engineered using certain software or programs. While the analysis tool uses the Error Level Analysis (ELA) method introduced by Krawetz. The tools used are web-based Imageforensics (http://imageforensic.org//) and ForensicallyBeta (https://29a.ch/photo-forensics/#forensicmagnifier). The results obtained from this study are the detection of differences in noise contrast between the original photo and the manipulated photo, which indicates a change in the photo. Photo manipulation by means of image splicing generally produces darker spots, while the original photo produces lighter spots
SPIRIT: A Microservice-Based Framework for Interactive Cloud Infrastructure Planning

The IaaS model provides elastic infrastructure that enables the migration of legacy applications to cloud environments. Many cloud computing vendors such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform offer a pay-per-use policy that allows for a sustainable reduction in costs compared to on-premise hosting, as well as enable users to choose various geographically distributed data centers. Using state-of-the-art planning algorithms can help application owners to estimate the size and characteristics of the underlying cloud inveterate. However, it’s not always clear which is the optimal solution especially in multi-cloud environments with complex application requirements and QoS constraints. In this paper, we propose an open framework named SPIRIT, which allows a user to include cloud infrastructure planning algorithms and to evaluate and compare their solutions. SPIRIT achieves this by allowing users to interactively study infrastructure planning algorithms by adjusting parameters via a graphical user interface, which visualizes the results of these algorithms. In the current prototype, we have included from the IaaS Partial Critical Path algorithm. By taking advantage of SPIRIT’s microservice-based architecture and its generic interfaces a user can add to the framework, new planning algorithms. SPIRIT can transform an abstract workflow described using the CWL to a concrete infrastructure described using the TOSCA specification. This way the infrastructure descriptions can be ranked on various key performance indicators.
High-Dimensional Explainable AI for Cancer Detection

The real industrial problems are often influenced by many uncertain factors, leading to computing issues by AI solutions, such as using medical data to predict cancer. As a new field, explainable AI still faces many challenges, such as the high-dimension of features to ex- plain. This paper proposes two possible solutions to the high-dimension XAI problem. The first solution is the Bi-level approach based on the method from cooperative coalitionalgame theory. In order to solve the high-dimension problem, the features are clustered using the historical input data set. In particular, the euclidean metric and size-constrained k-means algorithm are used. The second approach is based on the sampling algorithm for calculating the Shapley value, which makes it possible to compute the approximation of the Shapley value without using the coalitional game theory and clustering approaches. This paper implements the XAI solution in cancer prediction algorithm based on a Sampling approach and Bi-level approach, which is new in XAI field.
Use Of Microsoft Teams Add-Ons in High Schools: The Effect on Student Learning Outcomes

Online learning is an essential thing during the covid-19 pandemic. Especially the education sector, whereas almost everything must be put on the online system. Online learning implementation can harm the achievement of student learning outcomes. The SMA Katholik 2 Kabanjahe experienced a decline in their learning outcomes using online learning, far below the standard built from minimum competence for the test scores of mathematics subjects. This study aims to increase student learning outcomes with optimized Microsoft Teams Add-ons in mathematics subjects based on these problems. The type of this research is Classroom Action Research, and the subject of this research is students class X SMA Katholik 2 Kabanjahe with 21 students as participants. Observation student evaluation, an evaluation test, and a questionnaire are used to collect data. The result has shown that students' learning outcomes were significantly improved from the first cycle by 42.85% on average increased to 72.16% in the second cycle. Student's activity also significantly changed from the first cycle by 32.14%, increasing to 72.00% on average from the second cycle. 79.87 % of students gave positive responses and 20.13% for negative responses.
Measuring Cybercrime as a Service (CaaS) Offerings in a Cybercrime Forum

The emergence of Cybercrime-as-a-Service (CaaS) is a critical evolution in the cybercrime landscape. A key area of research on CaaS is where and how the supply of CaaS is being matched with demand. Next to underground marketplaces and custom websites, cybercrime forums provide an important channel for CaaS suppliers to attract customers. Our study presents the first comprehensive and longitudinal analysis of types of CaaS supply and demand on a cybercrime forum. We develop a classifier to identify supply and demand for each type and measure their relative prevalence and apply this to a dataset spanning 11 years of posts on Hack Forums, one of the largest and oldest ongoing English-language cybercrime forum on the surface web. Of 28 known CaaS types, we only found evidence for only 9 of these in the forum.We saw no dramatic shifts in these offerings over time, not even after major underground marketplaces were being seized by law enforcement. Around 16% of first posts of the threads in the ‘Market’ section of the forum offers CaaS, whereas only 3% is focused on product-type criminal offerings. Within the types of CaaS, ‘bot/botnet as a service’, ‘reputation escalation as a service’ and ‘traffic as a service’ categories make up the majority (over 60%) for whole period in terms of both supply and demand. At least half of each CaaS offerings directs potential buyers to an instant messaging app or private message for transacting privately. In sum, we find that forums do in fact provide a channel for CaaS supply and demand to meet, but we see only a fraction of the CaaS landscape and there is no evidence in our data for the supposed growth of CaaS over time. We reflect on the implications of our findings for developing effective disruption strategies by law enforcement.
Perancangan Sistem Informasi Pengajuan Kredit Kendaraan Bermotor (Studi Kasus : FIF Group Kedaton Bandar Lampung)

AbstractOne type of vehicle that is in great demand by the public is a motorized vehicle. Motorized vehicle is any vehicle that is driven by mechanical equipment in the form of a machine other than a vehicle that runs on rails. In the past, motorbikes were a type of vehicle that was quite expensive but over time, now motorbikes are not a type of vehicle that is difficult to own. This is due to the presence of a motorcycle loan program held by the leasing party, one of which is the FIF Group. FIF Group is one of the financing or leasing companies in Indonesia that has motor vehicle credit facilities. Currently, FIF GROUP is still in the process of applying for credit using the conventional method, namely by coming directly to the dealer or via WhatsApp for data requirements. In addition, the credit application process is too time-consuming and expensive. Based on this, the researcher proposes an information system for applying for a motor vehicle loan based on a website. From the results of the analysis and testing, the application made can be used to apply for a motorcycle loan and runs according to the original purpose of this study. In addition, the application of this motorized vehicle information system can save time and costs, because the application created has a centralized data source.
Eggs Martabak Training With Shirataki to Students SMKN 7 Tangerang

Published The Covid-19 condition in the world has had a bad impact on everyone, which is detrimental to all parties in terms of health and the economy. The Covid-19 virus is deadly, so people with low immunity will be vulnerable to contracting it. Due to the fairly rapid transmission factor, this has caused some schools to have to do independent online learning, and some employees have to do work from home. Some of the preventions that we can do are to maintain our health by consuming healthy and low-calorie foods. Community Service is one of the student activities of the Faculty of Tourism, Pelita Harapan University, as evidence of caring for the community by providing knowledge and skills that can be done at home. Through this PkM activity, we will provide a menu of Indonesian dishes that can be tried and processed while at home, making shirataki egg martabak. Egg martabak, which generally contains meat, this time the team will replace the meat filling by including shirataki in it. The use of shirataki as a substitute for this meat will make a difference in terms of variety. Besides having a more chewy texture, this shirataki martabak is also healthy because the calorie content of shirataki is quite low.Shirataki egg martabak is expected to help partners provide new healthy flavor variants because some Indonesian people eat egg martabak using rice. The purpose of this community service activity is (1) To share knowledge and practice skills, (2) to Provide cooking methods and steps to make martabak with Shirataki basic ingredients (3) to provide input to the community to stay healthy. Martabak is unique in its taste and can be easily found. KeywordsMartabak shirataki
Towards Making Fusion Data FAIR.

The concept of FAIR data is being increasingly adopted at national and global levels as a way of maximising the impact and transparency of publicly-funded research outcomes and data. In this paper we introduce the FAIR requirements of the Fusion community as well as technological directions proposed by the Fair4Fusion project aiming at increasing the accessibility to fusion data.
The Decision Support System of the selection of tourist attractions of Bali Island is used Preference method of Organization For Enrichment Evaluation

Tourism today is very potential to be developed as one source of local revenue by providing information both online and offline and online for the community so that increased regional revenue desired by the government. The number of tourist attractions in Bali to make many tourists from abroad and in the country feel confusion in determining the destination, the most beautiful and easy to reach the place. There are many criteria to be considered, then through this recommender system, tourists can find out what tours they will visit while in Bali. One of the problems of decision making with many criteria and attributes in choosing a tourist attraction is to provide detailed decisions that refer to the weight scale they have. The decision support system gives priority result of tourist attraction suitable for every tourist from abroad and in the country. This research is focused on applying multi-attribute decision making, for decision support system using the preferred method of organization for enrichment evaluation. This research uses the descriptive analytical method to present a summary of survey results made by spreading quizzers to domestic, foreign and foreign tourists, from tourists who want to choose the island of Bali tourism in accordance with cost, security, natural beauty, facilities and infrastructure and location the island of Bali
PENINGKATAN EFFEKTIFITAS INFRASTRUKTUR AKADEMI MILITER DENGAN PENERAPAN METODE ALGORITMA REGRESI LINIER

Abstract— In processing data to obtain useful information for the world of education, for example in predicting data on learning infrastructure for Akmil cadets by predicting the number of cadets per year. In fact, if done using the right programming, it can estimate the number of needs for study rooms, classes, laboratories, halls and facilities to support the teaching and learning process at the Military Academy. The stages of the simple Linear Regression process are by calculating the square of the independent variables X2, XY1, XY2, XY3, XY4, XY5, and the total of each variable and after knowing the total of each variable, the next step is to calculate the value of the constant a and the regression coefficient b based on the formula . To find out the results of the experimental analysis used to predict the correlation between the two variables, it is necessary to test its significance using PHP and MY SQL applications. Significance testing is carried out using hypothesis testing. The data used in predicting facilities and infrastructure is taken from the number of cadets from 2010 to 2020 to find out the projected needs for facilities and infrastructure to get 100% accuracy.
Sana Al-Tajweed

Sana AI-Tajweed is an Automated system based on a classification approach. It uses classification methods and various acoustic features to evaluate the points of articulation (Makhrai) and characteristics ( Sifaat) of the Quranic letters.
Clinical named entity recognition from Chinese electronic medical records using a double-layer annotation model combining a domain dictionary with CRF

As a document recorded by professional medical personnel, electronic medical records contain a large and important clinical resource. How to use a large amount of potential information in electronic medical records has become one of the major research directions. Chinese electronic medical records are knowledge-intensive, in which the data has considerable research value. However,they have more complex entities because of the language features of Chinese, and the composite entity is long. These sentences components in the text are missing. Moreover, the boundaries of clinical entities are often unclear. Labeling corpus is a job that requires a great deal of manpower because of the technical language used in a given text. Therefore, the recognition of Chinese clinical named entities is a hard problem. Considering these characteristics of Chinese electronic medical records, this paper proposed a double-layer annotation model that combined with a domain dictionary and conditional random field(CRF). A medical domain dictionary was constructed by statistical analysis method, and combined with CRF to mark two different granularity labeling operations. The manually constructed medical domain dictionary has extremely high accuracy for the recognition of registered words, and machine learning could automatically recognize unregistered words. This work integrated the two aspects based on these advantages. With the proposed method, diseases, symptoms, drugs, and operations could be recognized from Chinese electronic medical records. Using the test dataset, the Macro-P with 96.7%,the Macro-R with 97.7%and the Macro-F1 with 97.2%were obtained.The recognition performance of the proposed method was greatly improved compared with that of a single-layer model.The recognition effect of deep neural network with attention was also analyzed,which did not perform well due to the size of the domain dataset.The experimental results show the efficiency of the double-layer annotation model for the named entity recognition of Chinese electronic medical records.
Групповые защитные механизмы в психотической организации

The study presents modern approaches to the analysis of group processes in the framework of the psychoanalytic approach to the analysis of organizations. The history of the development of the conceptual apparatus and ideas about groups of the basic assumption is considered in detail. The existing approaches to the analysis of organizational groups and human capital in organizations are also studied.
Network traffic performance analysis from passive measurements using gradient boosting machine learning

Effective monitoring and analysis of network traffic are vital for scientific computing, since scientific applications often require moving massive data from one site to another. A body of statisti...
Difficulty Analysis of Physics Test Questions in Senior High School Entrance Examination Based on Bao Jiansheng's Test Question Difficulty Quantification Tool——Take the 2016-2020 Chengdu High School Entrance Examination as an example

The difficulty of test paper is an important index of test paper quality.Most existing research methods can only determine the relative difficulty of a topic, but can not explain the factors that affect the difficulty of the test paper.Therefore, Bao Jiansheng's quantitative model of difficulty is selected as a research tool, and the physics test paper of the senior high school entrance examination in Chengdu, Sichuan Province from 2016 to 2020 is taken as an example to analyze its difficulty.Get the characteristics of physics difficulty changes in recent five years, and on this basis, put forward corresponding suggestions on the test paper life system, in order to provide new ideas for scientific proposition, teachers and students preparing for exams.
К вопросу об определении гибридного новостного формата в цифровой среде

This article provides the author's definition of a hybrid news format in digital media. A classification of such formats is carried out with the analysis of elements of media texts from modern editorial practices. In addition, the material provides a brief overview of the genre-format concepts of journalism, as well as the preconditions of the media industry for the hybridization of news content.