Reviewerly/exHarmony huggingface dataset:
HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Abstract For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.
Text Analysis of Applicants for Personality Classification Using Multinomial Naïve Bayes and Decision Tree

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.
Exploring relationships between effort, motion, and sound in new musical instruments

We investigated how the action–sound relationships found in electric guitar performance can be used in the design of new instruments. Thirty-one trained guitarists performed a set of basic sound-producing actions (impulsive, sustained, and iterative) and free improvisations on an electric guitar. We performed a statistical analysis of the muscle activation data (EMG) and audio recordings from the experiment. Then we trained a long short-term memory network with nine different configurations to map EMG signal to sound. We found that the preliminary models were able to predict audio energy features of free improvisations on the guitar, based on the dataset of raw EMG from the basic sound-producing actions. The results provide evidence of similarities between body motion and sound in music performance, compatible with embodied music cognition theories. They also show the potential of using machine learning on recorded performance data in the design of new musical instruments.
Analysis of Blokchain Technology Recommendations to be Applied to Medical Record Data Storage Applications in Indonesia

Personal Health Record or we know as the medical record in Indonesia has its regulations relating to ownership, confidentiality and authorization from the authorities to provide medical record entries to their ownership.Not many health facilities in Indonesia that digitize medical record data storage and there are still many health facilities that use third parties to manage medical record data.This raises problems such as data access, data exchange, privacy, security and approval among the people involved in it.In this case, the doctor is authorized to provide the patient's medical record, according to the examination results that have been carried out by the patient.Blockchain technology or distributed ledger technology seems to offer a solution to some of the problems encountered.Blockchain is a digital ledger of verified transactions that are locked chronologically in an encrypted chain.This platform uses a decentralized approach that allows the information to be distributed and that each piece of distributed information or commonly known as data have shared ownership.Based on these functional needs, Blockchain technology Ethereum can be a solution.Ethereum blockchain provides smart contract features that are stateful and Turing-completeness, so that it can be used to store data and execute complex operations.This study provides an overview of how blockchain technology can be a solution to problems that arise related to the storage of patient medical record data in Indonesia.While the InterPlanetary File System (IPFS) is used to accommodate file sharing requirements.
Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence

This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.
Unpacking the black box

Today there are large databases (Big Data) of electronic records and digital images that allow pattern recognition in large volumes of information in a short period of time, contributing to the creation of a personalized Medicine with the aid of artificial intelligence.(,) The algorithms used in medical diagnosis software are formed by a deep neural network, consisting of several layers that resemble the biological processes of the human cortex. Each neuron corresponds to a specific stimulus for a region within [...]
The Application of Genetic Algorithm in Solving Traveling Salesman Problem

Traveling Salesman Problem (TSP) is one form of optimization problem with easy concept, but complicated if solved conventionally.The purpose of TSP is to build an optimal routes, with the rules of each city to be visited by salesmen and the cities are visited only exactly once, the trip begins and ends in the city early.To build the optimal routes, in this study using genetic algorithm.In the example case there are 4 cities that must be traversed by the salesman, that city A, B, C, and D with the trip starts from the city A and ends in city A as well.So obtained the optimal route that is [A D B C] with minimum distance that is 19 km.
Improving <i>k</i> Nearest Neighbors and Naïve Bayes Classifiers Through Space Transformations and Model Selection

Improving classifiers' performance is the goal of techniques like prototype selection, normalization, and feature mapping; these techniques aim to reduce the complexity and improve the accuracy of models. In this manuscript, we present a boosting artifact for the well-known single-label k-Nearest Neighbors (k-NN) classifier and the Naïve Bayes (NB) classifier. The improvement comes as a pipeline that includes several data transformations orchestrated by a model selection scheme. The construction of these classifiers relies on the composition of simpler parts found in several open-source libraries and can be effortlessly put together to replicate our proposal. We also explore ensembling and the effect of preprocessing and normalizing the data. We compare our approach experimentally with 17 popular classifiers using raw and rank-based scores on 34 different benchmarks; statistical tests support our results. For instance, our results regarding average performance ranks under balanced error rate show that the models created with our proposal achieve first, third, and fourth-best ranks, compared with 10th position of raw k-NN and 14th of raw NB.
Financial intelligence: Financial statement fraud in Indonesia

Indonesia is currently in an honesty crisis, especially in financial governance, bothin government and private institutions. Our study uses the concept of financial intelligence toidentify and collect information related to financial affairs in an organization. We use theopinions of 76 auditors regarding various fraudulent attempts, both with fraudulent financialstatements and other corrupt practices in organizations in Indonesia. Our important finding isthat small companies are more likely to commit fraud due to weak supervisors than listed publiccompanies. This is also more likely than family-owned companies and government levelorganizations. It was indicated by some respondents that local government level organizationswith weak supervision are more likely to commit fraud than local governments with closesupervision from urban communities. The results of the non-parametric relationship analysisshow that although there is a possibility that the more experienced the auditor is, the more ablethey are to detect fraud and manipulation in the organization, the relationship is relativelyweak. Other findings also show that auditors who have a CFE certificate find it easier to findfraud in the company.
Making data playable: A game co-creation method to promote creative data literacy

This article explores how making data playable, i.e. developing exploratory co-creation techniques that use elements of play and games to interpret small to mid-sized datasets beyond the current focus on visual evidence, can help a) promote creative data literacy in higher education, and b) expand existing definitions of data literacy.The article briefly investigates playful characteristics in existing data practices, and discusses how this perspective compares to existing frameworks that define data literacy.In a second step, we present a Discursive Game Design technique to promote creative data literacy.The article reports on findings from a sample workshop, during which students explored how modifying small, hybrid games based on real-world datasets can alter players' interpretation of the data, but also their perception of how the games operate as epistemic objects within data analysis.Finally, we formulate recommendations on how to adapt the technique to different educational settings.
Robust large image steganography using LSB algorithm and 5D hyper-chaotic system

This paper contains a robust hiding system proposed to hide and reconstruct efficiently a large size secret image based on merging encrypting and hiding. This approach deals with a video utilized as a cover of frames to hide the blocks of large image inside it. Frames are selected according to 5D hyperchaotic algorithms and consequently change value of the pixel in the original image depends on it. Secret image will divide into many blocks and hide each block in one frame selected by 5D chaotic. This method will support and enhance the techniques of information security with less time and morecapacity of transmission. The results obtained are analyzed under criteria including: Peak signal to noise ratio, Mean square error, key space and sensitivity, correlation coefficient and capacity of hiding. The results demonstrate both of the secret color image and cover video, where retaining its explicitness and properties after reconstruction in the receiver. However, the results prove stability and reliability of the proposed system under several conditions and can avoid attacks.
A Systematic Literature Review of Key Performance Indicators (KPIs) Implementation

Management performance measurement based on Key Performance Indicators (KPIs) has been widely applied in any organization. This paper aims to identify Key Performance Indicators (KPIs) and classify them based on performance measurements to improve optimal management organizational performance. This research is based on a literature review of 50 journals based on performance management with the weight of Key Performance Indicators (KPIs) for the years 2011-2020. Performance measurement using this Key Performance Indicator can be implemented in many institutional organizations such as industry company sector, services, academics, hospitals, and government. KPIs can change behavior in an organization and provide results in broad contexts such as improving business results or driving improved operational performance. KPIs also help in implementing performance measurement on an ongoing basis. So that KPIs become a better way of managing the organization.
Influence of the COVID-19 Coronavirus Distribution on the Organization of Higher Education in Russia

The pandemic of the coronavirus infection COVID-19 has made great adjustments in all aspects of the life in the global community, and education has not been left aside.The purpose of the present article is to reveal the psychological and pedagogical consequences of the spread of the coronavirus on the organization of higher education in Russia, the readiness of participants of the educational process to switch to distance learning.The article shows the results of a survey of students of Russian universities on the organization of distance learning in the context of the coronavirus pandemic.A comparative analysis of the possibilities and advantages of traditional and distance learning, of the distance learning methods used by university teachers, as well as the psychological and pedagogical readiness of participants of the educational process of Russian universities for the transition from classroom to on-line and off-line training is carried.The authors conclude that modern living conditions require adjustments, revision and rethinking of the educational process, partial transition to distance learning, which in the future may become prevalent.Today, the educational environment is not yet ready to switch to full distance learning, due to the lack of new and proven distance learning methods aimed at developing practical skills and abilities.Nowadays, there are no research results regarding the attitude of university students, especially Russian, to the urgent transition to distance learning in the context of the coronavirus pandemic.The present study is a pioneer in this field of study.
A Comparative Evaluation of Feature Extraction and Similarity Measurement Methods for Content-based Image Retrieval

Content-based image retrieval is the popular approach for image data searching because in this case, the searching process analyses the actual contents of the image rather than the metadata associated with the image.It is not clear from prior research which feature or which similarity measure performs better among the many available alternatives as well as what are the best combinations of them in content-based image retrieval.We performed a systematic and comprehensive evaluation of several visual feature extraction methods as well as several similarity measurement methods for this case.A feature vector is created after color and/or texture and/or shape features extraction.Then similar images are retrieved using different similarity measures.From experimental results, we found that color moment and wavelet packet entropy features are most effective whereas color autocorrelogram, wavelet moment, and invariant moment features show narrow result.As a similarity measure, cosine and correlation measures are robust in maximum cases; Standardized L 2 in few cases and on average, city block measure retrieves more similar images whereas L 1 and Mahalanobis measures are less effective in maximum cases.This is the first such system to be informed by a rigorous comparative analysis of the total six features and twelve similarity measures.
A Type-Sensitive Service Identification Approach for Legacy-to-SOA Migration

A common strategy for modernizing legacy systems is to migrate them to service-oriented architecture (SOA). A key step in the migration process is the identification of reusable functionalities in the system that qualify as candidate services in the target architecture. We propose ServiceMiner, a bottom-up service identification approach that relies on source code analysis, because other sources of information may be unavailable or out of sync with the actual code. Our bottom-up, code-based approach uses service-type specific functional-clustering criteria. We use a categorization of service types that builds on published service taxonomies and describes the code-level patterns characterizing types of services. We evaluate ServiceMiner on an open-source, enterprise-scale legacy ERP system and compare our results to those of two state-of-the-art approaches. We show that ServiceMiner automates one of the main labor-intensive steps for migrating legacy systems to SOA. It identifies architecturally-significant services with 77.9% of precision, 66.4% of recall, and 71.7% of F-measure. Also, we show that it could be used to assist practitioners in the identification of candidate services in existing systems and thus to support the migration process of legacy systems to SOA.
Implementation of Augmented Reality for Introduction To Android Based Mammalian Animals Using The Marker Based Tracking Method

Augmented Reality is a technology on 2-dimensional and 3-dimensional virtual objects that are combined into the real environment that is around us. With the ability of Augmented Reality that is able to change the atmosphere of children’s learning that can be used as a medium of learning in the introduction of mammals for kindergarten children. This AR can provide interesting facilities such as displaying 3-dimensional objects of these mammals along with animal sounds and animations using Smartphones, so that children can interact and be more creative in recognizing these mammals, because children experience their golden age at the age of 4 to 7 years which is a time when children begin to receive stimuli, so that children will be faster to receive and catch on learning from the introduction of these mammals by using Augmented Reality Technology. The results of testing on the Vuforia plugin and making the AR application on Unity can provide a good information result, where the use of AR can bring up mammalian objects by pointing the Smartphone at the marker, so that all mammals can be recognized properly. This shows that children’s interest around 85% in terms of UI/UX appearance, and 70% of children have no problems in running the Mammal Animal AR application
Modified distance regularized level set evolution for brain ventricles segmentation

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.
Fast Implementation of NIST P-256 Elliptic Curve Cryptography on 8-Bit AVR Processor

In this paper, we present a highly optimized implementation of elliptic curve cryptography (ECC) over NIST P-256 curve for an 8-bit AVR microcontroller. For improving the performance of ECC implementation, we focus on optimizing field arithmetics. In particular, we optimize the modular multiplication and squaring method exploiting the state-of-the-art optimization technique, namely range shifted representation (RSR). With optimized field arithmetics, we significantly improve the performance of scalar multiplication and set the speed record for execution time of variable base scalar multiplication over NIST P-256 curve. When compared with previous works, we achieve a performance gain of 17.3% over the best previous result on the same platform. Moreover, the execution time of our result is even faster than that over the NIST P-192 curve of the well-known TinyECC library. Our result shows that RSR can be applied to all field arithmetics and evaluate the impact of the adoption of RSR over the performance of scalar multiplication. Additionally, our implementation provides a high degree of regularity to withstand side-channel attacks.
Partial Identification of Latent Correlations with Binary Data

The tetrachoric correlation is a popular measure of association for binary data and estimates the correlation of an underlying normal latent vector. However, when the underlying vector is not normal, the tetrachoric correlation will be different from the underlying correlation. Since assuming underlying normality is often done on pragmatic and not substantial grounds, the estimated tetrachoric correlation may therefore be quite different from the true underlying correlation that is modeled in structural equation modeling. This motivates studying the range of latent correlations that are compatible with given binary data, when the distribution of the latent vector is partly or completely unknown. We show that nothing can be said about the latent correlations unless we know more than what can be derived from the data. We identify an interval constituting all latent correlations compatible with observed data when the marginals of the latent variables are known. Also, we quantify how partial knowledge of the dependence structure of the latent variables affect the range of compatible latent correlations. Implications for tests of underlying normality are briefly discussed.