Reviewerly/exHarmony huggingface dataset:
The Exact Asymptotic Form of Bayesian Generalization Error in Latent Dirichlet Allocation

Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clarified since it is a singular statistical model where there is no one-to-one mapping from parameters to probability distributions. In this paper, we give the exact asymptotic form of its generalization error and marginal likelihood, by theoretical analysis of its learning coefficient using algebraic geometry. The theoretical result shows that the Bayesian generalization error in LDA is expressed in terms of that in matrix factorization and a penalty from the simplex restriction of LDA's parameter region. A numerical experiment is consistent to the theoretical result.
SISSLE in consensus-based Ripple: Some Improvements in Speed, Security, Last Mile Connectivity and Ease of Use.

Cryptocurrencies are rapidly finding wide application in areas such as Real Time Gross Settlements and Payments Systems. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General's Problem with it's Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called Unique Node List (UNL) that represents the network for the server, and will not collectively defraud it. The server believes that the network has come to a consensus when members of the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security, last mile connectivity and ease of use. We implement guidelines and automated systems for building and maintaining UNLs for resilience, robustness, improved security, and efficient information propagation. We enhance the system so as to ensure that each server receives information from across the whole network rather than just from the UNL members. We also introduce the paradigm of UNL overlap as a function of information propagation and the trust a server assigns to its own UNL. Our design not only reduces vulnerabilities such as eclipse attacks, but also makes it easier to identify malicious behaviour and entities attempting to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe $\geq 4.97x$ and $98.22x$ in speedup and success rate for information propagation respectively, and $\geq 3.16x$ and $51.70x$ in speedup and success rate in consensus.
Instructional Materials’ Effect on Learners’ Literacy Among Public Pre-Primary II in Webuye West Sub-County, Kenya

Unavailability of instructional materials inhibits the acquisition of literacy competencies among learners at pre-primary level. This study sought to find out how the availability of instructional materials influences learners’ literacy among Pre-Primary II learners in public primary schools in Webuye West Sub-County. Descriptive survey research design that adopted mixed methods approach was used. A population of 3,216 respondents that comprised 55 headteachers and their deputies, 109 pre-primary II teachers and 2,997 pre-primary II learners was engaged in the study. From these, a sample of 86 pre-primary II teachers and 341 pre-primary II learners were randomly drawn while 48 head teachers and 48 deputies were sampled using purposive sampling technique. The researcher collected data using questionnaires from deputy headteachers and pre-primary II teachers, interview guide from headteachers and observation schedule from pre-primary II learners. The questionnaires were piloted using test re-test method and a reliable Cronbach correlation alpha index of 0.79 computed. The instruments were adequately validated. Quantitative data was analysed and presented on tables while qualitative data was analysed thematically as guided by the research objectives. The study found out that schools in the study area did not provide adequate and appropriate instructional materials that would equip learners with literacy skills. It further found out that inadequacy of instructional materials affected the acquisition of literacy skills in pre-school learners. The study recommends that the National and County government should provide adequate teaching and learning materials to enhance the acquisition of literacy skills among pre-primary II learners in public primary schools in Webuye West Sub-County. It also recommends that a similar study is carried out in other areas and consider using a larger population and include more variables such as the role of the teacher in influencing learners’ acquisition of literacy skills in pre-primary II level.
Asymmetric Key Blum-Goldwasser Cryptography for Cloud Services Communication Security

Cloud computing is a promising technology that provides different types of services such as data sharing and distribution. The Asymmetric Key Blum–Goldwasser Cryptography (AKBGC) technique is proposed to enhance the communication security of cloud services with minimum overhead. The users transmit requests to a cloud server to get required cloud services. The cloud server provides desired services to users in the cloud with aid of Blum-Goldwasser Cryptography (BGC) to attain higher confidentiality and data security. The AKBGC technique used Probabilistic encryption algorithm of BGC in order to encrypt the user needed cloud data with a public key of the receiver before transmission. This encryption process results in the generation of ciphertext and sent to users in a cloud. At the receiver side, AKBGC technique performs a key authentication process in order to access the original cloud data. The users can reconstruct original data with aid of deterministic decryption process in BGC when public key of both sender and receiver is identical. This helps for AKBGC technique to get higher communication security for cloud service provisioning and performs experimental evaluation using metrics such as data confidentiality, communication overhead, space complexity, and throughput. The result shows that the AKBGC technique is able to increases the data confidentiality and reduce the communication overhead of cloud services.
Active Object Search

To address the problem of data inconsistencies among different facial expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs) have been extensively devised in recent years. Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors. In this work, we first analyze the performance effect caused by these inconsistent choices, and then re-implement some well-performing CD-FER and recently published domain adaptation algorithms. We ensure that all these algorithms adopt the same source datasets and feature extractors for fair CD-FER evaluations. We find that most of the current leading algorithms use adversarial learning to learn holistic domain-invariant features to mitigate domain shifts. However, these algorithms ignore local features, which are more transferable across different datasets and carry more detailed content for fine-grained adaptation. To address these issues, we integrate graph representation propagation with adversarial learning for cross-domain holistic-local feature co-adaptation by developing a novel adversarial graph representation adaptation (AGRA) framework. Specifically, it first builds two graphs to correlate holistic and local regions within each domain and across different domains, respectively. Then, it extracts holistic-local features from the input image and uses learnable per-class statistical distributions to initialize the corresponding graph nodes. Finally, two stacked graph convolution networks (GCNs) are adopted to propagate holistic-local features within each domain to explore their interaction and across different domains for holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
SOP of Information System Security on Koperasi Simpan Pinjam Using ISO/IEC 27002:2013

Information security problems always increase every year. One way to minimize problems related to information system security is to establish an SOP. This study was conducted in koperasi simpan pinjam for several reasons that there has never been an assessment related to the level of security of the information system used, there are threatshave occured, and there do not have documented information system security procedures. The SOPs compiled in this study are based on the ISO/IEC 27002:2013 framework. The method used is qualitative with the OCTAVE framework to process the information obtained. Meanwhile, to calculate the value of each risk, FMEA is used. This study shows that 22% of the risks invloved in koperasi simpan pinjam have low categories, 59% medium categories and 19% high categories. The final result of the stiff research is the proposed 8 policies and 12 information system security procedures for koperasi simpan pinjam.
Pervasive Ideals

Introduces the article presented in this issue of the publication, “Enabling the Internet of Things” (Want, R., et al), which was published in Computer Magazine, vol. 48, no. 1, pp. 28–35, January 2015.
Sistem Informasi Evaluasi Strategi Kualitas Layanan Berbasis Android di Griya Bandung Trade Center

Griya Bandung Trade Center is a branch of YOGYA Group is a retail company with a format Supermaket and Departement Store which has served the people's needs for daily necessities. So Griya Bandung Trade Center must be able to provide satisfaction and make consumers become loyal to increase profits so that the company always maintains good relations with customers, But the system that runs is still not good. Griya Bandung Trade Center cannot manage feedback data yet obtained because there is no media or place for consumers to express disappointment, complaints about the services provided, or other feedback. Using the android mobile application because the use of cellphones is increasing every time. Delivering feedback through mobile media is considered effective because it is mobile and easy to use. The system development method used is OOAD (Object Oriented Analisis Design) which is a software engineering approach of a system consisting of a group of interacting real-world objects.
Effectiveness of cardio pulmonary resuscitation in identifying end of life based on the data of RS Juwita Bekasi from 2013-2017 and the overview according to Islamic view

Background: Cardio Pulmonary Resuscitation is used in basic life support procedure consisting of compression, ventilation and defibrillation. Cardio Pulmonary Resuscitation is recommended as an emergency intervention for respiratory or cardiac arrests. Method: This study will use non-probability sampling technique. Nonprobability sampling technique will be determined purposively, the sample is taken according to the established criteria. Result: The result of the study conducted for 26 days taken from the medical record of patients at RS Juwita Bekasi, showed that 94 patients were unsuccessful after Cardio Pulmonary Resuscitation and 4 patients were successful after Cardio Pulmonary Resuscitation. Regarding the diagnosis of traumatic brain injury and severe head injury was found that the most were not successful at Cardio Pulmonary Resuscitation. Conclusion: The use of Cardio Pulmonary Resuscitation in the medical record of RS Juwita Bekasi from 2013-2017 on the indication of respiratory and cardiac arrestsis found to be ineffective because the success rate is 1%.
IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF

Diabetes mellitus is the result of changes in the body caused by a decrease of insulin performance which is characterized by an increase of blood sugar level. Detection of blood sugar can be done with Invasive methods or non-invasive methods. However, non-invasive methods are considered better because they can check early, faster and accurate. The prototype output is values of intensity in the time domain, thus fourier transformation is very much needed to transform into the frequency domain. In this study, Fourier transformation methods used are Discrete Fourier Transform (DFT), Fast Fourier Transform Radix-2, and Fast Fourier Transform Radix-4. Evaluation for the best method is done by comparing the processing speed of each method. The FFT Radix-4 method is more effective to perform the transformation into the frequency domain. The average processing speed with the FFT Radix-4 method reaches 2.67×105 nanoseconds, and this is much faster 5.06×106 nanoseconds than the FFT Radix-2 method and 2.40×107 nanoseconds faster than the DFT method.
Effectiveness of the public health measures to prevent the spread of COVID-19

COVID-19 which has become a global pandemic, recently, has spread to hundreds of countries and territories. This pandemic spreads rapidly through human transmission. In order to reduce the spread of COVID-19, the government emerged several policies. Numerous public health measures can be implemented to counter the risk of an emerging outbreak with pandemic potential. Meanwhile, Jakarta and West Java are the regions with the most confirmed cases in Indonesia, the government announced Large-Scale Social Restrictions (PSBB) policy in both provinces. Many researchers conducted forecasting methods for modeling or predict the further number of cases of this pandemic. Forecasting is slightly hard because of those interventions. In this study, we involved some of neural network forecasting methods, including Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine meanwhile neural networks become well-known at this time for forecasting the number of active, confirmed, recovered, death, and daily new cases in Jakarta and West Java. These methods are undertaking automatically without considering any factors that will be impacted the result as the reason that we assumed those factors have pursued the pattern of each case. The best model for all of the cases is the MLP (10,10) model. This intervention carried out by the government, namely PSBB, proved effective in reducing the spread of this pandemic in Jakarta and West Java. This can be seen from the results of the daily new cases which show a downward trend for both although still fluctuating.
A Systematic Review of the Literature of the Techniques to Perform Transformations in Software Engineering

Along with software evolution, developers may do repetitive edits.These edits can be identical or similar to different codebase locations, which may occur as developers add features, refactor, or fix a bug.Since some of these edits are not present in Integrated Development Environments (IDEs), they are often performed manually, which is time-consuming and error-prone.In order to help developers to apply repetitive edits, some techniques were proposed.In this work, we present a systematic review of the literature of the techniques to do transformations in software engineering.As a result, this systematic review returned 51 works ranging from the domains programming-byexamples, linked editing, API usage, bug fixing, complex refactoring, and complex transformations, which can be used to help tools' designer in the proposition of new approaches.
Group Activity Prediction with Sequential Relational Anticipation Model

In this paper, we propose a novel approach to predict group activities given the beginning frames with incomplete activity executions. Existing action prediction approaches learn to enhance the representation power of the partial observation. However, for group activity prediction, the relation evolution of people's activity and their positions over time is an important cue for predicting group activity. To this end, we propose a sequential relational anticipation model (SRAM) that summarizes the relational dynamics in the partial observation and progressively anticipates the group representations with rich discriminative information. Our model explicitly anticipates both activity features and positions by two graph auto-encoders, aiming to learn a discriminative group representation for group activity prediction. Experimental results on two popularly used datasets demonstrate that our approach significantly outperforms the state-of-the-art activity prediction methods.
Status of Central Libraries at Iranian Universities of Medical Sciences for Joining an Integrated Network of Medical Libraries

The aim of the study is to offer integrated access to information and data sources in the libraries of Iranian universities of medical sciences (UMS) as an important feature enhancing the quality and quantity of research. Thus, it is essential to study the status of Iranian libraries of UMS in terms of integration features. Accordingly, the main objective of the present study was the identification of integration in libraries of Iranian UMS in terms of metadata, digital contents, software, hardware, and human force. The quantitative study was conducted with an analytic survey method. The statistical population consists of 66 Iranian UMS, of this, 59 libraries completed the researcher-made questionnaire. To ensure the accuracy of data, interviews and, in some cases, observations were also performed. Statistical estimates of frequency, percentage, cumulative frequency, and diagrams were utilized for data analysis. The results demonstrated that the library software programs of UMS do not have a desirable status in terms of content, human force, software infrastructure, and integration features. These libraries enjoyed an optimal status in terms of hardware which was, however, not properly used. To create integrated access to the contents of libraries of Iranian UMS, it is essential to provide the required infrastructure, including integration features, human force, and appropriate contents such as the metadata and digital contents of data sources
CRATER DETECTION USING TEXTURE FEATURE AND RANDOM PROJECTION DEPTH FUNCTION

Abstract. In this paper, a novel automatic crater detection algorithm (CDA) based on traditional texture feature and random projection depth function has been proposed. By using traditional texture feature, mathematical morphology is used to identify crater initially. To further reduce the false detection rate, random projection depth function is used. For this purpose, firstly, gray level co-occurrence matrix and a novel grade level co-occurrence matrix are both used to further obtain the texture features of these candidate craters. Secondly, based on the above collected features, random projection depth function is used to refine the crater candidate detection results. LRO Narrow Angle Camera (NAC) mosaic images (1 m/pixel) and Wide-angle Camera (WAC) mosaic images (100 m/pixel) are used to test the accuracy of proposed method. The experimental results indicate our proposed method is robust to detect craters located in different terrains.
Comparison and improvement of time aware collaborative filtering techniques : Recommender systems

Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify rec ...
Two-Layer Perceptron for Voice Recognition of Speaker’s Identity

The article is devoted to the problem of ensuring reliable authentication of users of information systems for various purposes. The prospects of solving this problem through the use of voice signal analysis tools to recognize the speaker’s personality are shown. The main advantages of such tools include the increased durability of the biometric access code, the use of common registration tools, as well as the possibility of implementation of hidden monitoring of the user’s identity. The relevance of research in the direction of developing low-resource means of recognizing the speaker’s personality by voice fragments of a fixed duration, using only available computing power on the spot, is substantiated. Based on the analysis of literary works, the prospects of using neural network solutions are shown, the creation of which is complicated by the existing uncertainty in choosing the type of neural network model, as well as in determining the set of input parameters. As a result of the studies, it was determined that in the task of recognizing the speaker’s identity by voice fragments of a fixed duration, it is advisable to use a type of neural network model such as a two-layer perceptron, the input parameters of which are associated with small-cepstral coefficients characterizing each of the quasi-stationary fragments of the analyzed voice signal, and the output parameters match of recognizable speakers. By computing experiments, it is proved that each of the quasistationary fragments should be described using 20 chalk-cepstral coefficients. At the same time, the recognition accuracy of the speaker using a two-layer perceptron is at the level of the best modern means of this purpose and is 8% higher than the recognition accuracy using a convolutional neural network such as LeNet. The need for further research in the direction of adapting the parameters of the two-layer perceptron to the recognition conditions under the influence of various kinds of interference was also established.
Expert System to Determine Characteristics of Students With Special Needs With Forward Chaining Method

Pandeglang is one of the disadvantaged areas in Banten Province.The regional original income in this city is very small compared to other cities in the Banten region.Here there are rarely human resources who do community service.This is due to the low regional minimum wage value.Therefore, an expert system was made to determine the character of special needs children.This requires a method-based, method step so that the completion step is proceeding properly.Forward chaining method is a method based on fitting rules in determining the character of students with special needs.This research contributes to teacher training in the field of counseling and computer science, especially in the field of expert systems.
A Practice of Educational Program to Instruct Collaborative Education in Small Classes of Multiple Schools and Its Effect

Recently, developing human resources with problem-solving skills and communication skills is required in educational institutions. Therefore, collaborative learning becomes increasingly important for these institutions. In collaborative learning, “new awareness” and “reflection” tend to occur from learners’ conversations and learning activities in which they teach each other. For this reason, collaborative learning can improve the learning effect compared to individual learning. However, in small schools in the depopulated area, the number of students is low. Therefore, it is difficult for students to learn through sufficient dialogue if the number of students is small in class. So, we developed an educational program that encourages knowledge sharing between deepens students’ learning in multiple classes.This paper describes the practice of the educational program and its effects.
HOMOGRAPHY MATRIX AND RANSAC BASED PANORAMIC IMAGE FOR MULTIPLE STITCHING IMAGES

Invariant-local feature can be discussed by several image stitchings. Parting inliers and outliers have scatteredsequence for multiple pictures for more panoramas identifing techniques. Atleast 8 feature points utilize the homographymatrixes. The feature-points that kept on corresponding epipolar-lines have chosen to obtain efficient homography matrix. Ifhomography matrix is evaluated among several images, a panoramic view of the multiple images is observed. Rectilinearprojectionsare taken to view panoramic picture. Here same things are visible in 2-D planes. We have also used Harris-CornerDetection technique for detecting-corners in the images. RANSAC gives desirable image stitching.