Reviewerly/exHarmony huggingface dataset:
i Effects of meteorological factors on the prediction of malaria incidences AI-based approaches i

This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers including Quadratic Integrate and Fire neuron (QIFN) model as a benchmark. As per research studies, parasite transmission is highly dependent on the (i) stagnant water, (ii) population of area and the (iii) greenery of the locality. Considering these factors, three more attributes were added to the existing novel dataset and comparison on the results is presented. For four feature dataset, QIFN exhibited an accuracy of 97.08% in K10 protocol, and with extended dataset; QIFN yields an accuracy of 99.58% in K10 protocol. The benchmarking results showed reliability and stability. There is 12.47% improvement against multilayer perceptron (MLP) and 5.39% against integrate-and-fire neuron (IFN) model. The QIFN model performed the best over the conventional classifiers for deciphering the risk of acquiring malaria in different geographical regions worldwide.
Sistem Informasi Geografis Letak Puskesmas di Wilayah Kabupaten Tabanan Berbasis Web

Technology is used as a tool in everyday life to accelerate the resolution of a problem one ofwhich is the design of Geographic Information Systems (GIS).The purpose of this study is tohelp collect and store data and analyze objects and geographical data to support decisionmaking in planning a mapping of the route search location to the nearest clinic that can helpthe community in Tabanan Regency. Using a Geographic Information System (GIS) allows itwill help the community in solving the problem of finding the location of the nearest clinicbecause in the presence of a GIS it will illustrate the location of the Clinic where the actualconditions are. The system built can display map data and data from the map can also bechanged dynamically. Here you will get a visualization of the location of the location in a webform so that it can be accessed anywhere and the information can be used as a reference fordecision makers, especially in the search for transportation routes that can be traversed to theclinic location is located. In addition, the information contained in the GeographicInformation System can be processed to provide a new discourse in the presentation ofgeographic information online that is equipped with data analysis in the form of images,descriptions and other attribute analysis. This research resulted in a program for Web-BasedGeographic Information System for Clinical Location Search in Tabanan Regency, which isable to display maps effectively with information. This process makes use of existing librarieson Google Maps combined with the Web Mapping process.
An Implementation of ElGamal Scheme for Laplace Transform Cryptosystem

ElGamal algorithm is public key cryptosystem and a signature scheme in the speed of the procedures for generating and verifying signatures.An implementation of ElGamal scheme for Laplace Transform Cryptosystem is proposed and it is executed using a .NET program.The time analysis is, compared with existing algorithms.The comparison reveals that the proposed cryptosystem enhances the data security and password security.The statistical tools are use for the planned scheme and accessible algorithms.They are, analyzed graphically.
The Application of Let's Read! in Extensive Reading Class: Integrating MALL and Task-based Learning

Doing national program so called School Literacy Movement (GLS), classes are expected to do 15-minute regular reading activity every day; including English classes in elementary schools. Witnessing that some classes are having limited English story books to read as well as ready-to-use follow-up activities, this article proposes reading activities using e-book application Let’s Read! that can be applied after the regular reading activities. Ranging from the low leveled-books to the advanced ones, the proposed activities in this extensive reading class are integrated; combining tasks that require children to experience Mobile Assisted Language Learning (MALL) as well as having both individual and group offline work. This type of reading activity promotes L2 learner language development, increases their motivation in reading, and helps them build reading habits. At the same time, the alpha generation maximizes the use of technology to get themselves updated with the changing use of online applications such as Let’s Read!
Theory of Quantum Path Entanglement and Interference with Multiplane Diffraction of Classical Light Sources

Quantum history states were recently formulated by extending the consistent histories approach of Griffiths to the entangled superposition of evolution paths and were then experimented with Greenberger–Horne–Zeilinger states. Tensor product structure of history-dependent correlations was also recently exploited as a quantum computing resource in simple linear optical setups performing multiplane diffraction (MPD) of fermionic and bosonic particles with remarkable promises. This significantly motivates the definition of quantum histories of MPD as entanglement resources with the inherent capability of generating an exponentially increasing number of Feynman paths through diffraction planes in a scalable manner and experimental low complexity combining the utilization of coherent light sources and photon-counting detection. In this article, quantum temporal correlation and interference among MPD paths are denoted with quantum path entanglement (QPE) and interference (QPI), respectively, as novel quantum resources. Operator theory modeling of QPE and counterintuitive properties of QPI are presented by combining history-based formulations with Feynman’s path integral approach. Leggett–Garg inequality as temporal analog of Bell’s inequality is violated for MPD with all signaling constraints in the ambiguous form recently formulated by Emary. The proposed theory for MPD-based histories is highly promising for exploiting QPE and QPI as important resources for quantum computation and communications in future architectures.
Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier

In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied that speaker recognition systems with the new auditory feature has better robustness and recognition performance compared to Mel-Frequency Cepstral Coefficients(MFCC), Relative Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and gammatone Frequency Cepstral Coefficeints (GFCC)
Estimasi Debit Puncak Menggunakan Hidrograf Satuan Sintetik Pada Daerah Aliran Sungai Kayan Kabupaten Bulungan

The Kayan Watershed in Bulungan Regency is the largest river in North Kalimantan which has the potential for flooding in several irrigated points. Kayan River is a flood-prone area and prioritized to be handled immediately because there is already a smooth life for the community. Potentially need to know the great potential of flooding in the Kayan River. To find out the relationship between flood discharge and flood time, the Synthetic Unit Hydrograph calculation method is used. The aim is to determine the shape and results of peak discharge in Synthetic Unit Hydrograph Y (SUH). This study uses a debit calculation in the Kayan watershed using Snyder HSS a nd Nakayasu HSS. From the calculation results obtained Snyder SUH peak discharge of 118.0 m3/ sec at 73.85 hours, and Nakayasu SUH has a peak discharge of 109.35 m3 / sec at 54.09 hours.
Multi-scale superpatch matching using dual superpixel descriptors

Over-segmentation into superpixels is a very effective dimensionality reduction strategy, enabling fast dense image processing. The main issue of this approach is the inherent irregularity of the image decomposition compared to standard hierarchical multi-resolution schemes, especially when searching for similar neighboring patterns. Several works have attended to overcome this issue by taking into account the region irregularity into their comparison model. Nevertheless, they remain sub-optimal to provide robust and accurate superpixel neighborhood descriptors, since they only compute features within each region, poorly capturing contour information at superpixel borders. In this work, we address these limitations by introducing the dual superpatch, a novel superpixel neighborhood descriptor. This structure contains features computed in reduced superpixel regions, as well as at the interfaces of multiple superpixels to explicitly capture contour structure information. A fast multi-scale non-local matching framework is also introduced for the search of similar descriptors at different resolution levels in an image dataset. The proposed dual superpatch enables to more accurately capture similar structured patterns at different scales, and we demonstrate the robustness and performance of this new strategy on matching and supervised labeling applications.
ICT FOR LEARNING AND INCLUSION IN FINLAND

reason or another have limitations for access to ICT, is among the main objectives of Cuban society. The present chapter ventures into this issue and systematically exposes achievements, projections and barriers still existing for the gradual digital inclusion of the disabled, elderly, women, children and young people from rural schools in remote areas. Educational experiences that have been obtained are addressed, as well as pedagogical and technological challenges that still need to be resolved in order to reduce the digital divide in Cuba.
WPQA: A Gaming Support System Based on Machine Learning and Knowledge Graph

Honor of Kings is a multiplayer online battle arena game in which two teams fight with each other with five players controlling five different heroes on each side. By 2017, Honor of Kings has over 80 million daily active players and 200 million monthly active players and was both the world's most popular and highest-grossing game of all time as well as the most downloaded gaming app globally. In this paper, we will introduce a prediction model based on a machine learning algorithm to forecast the victory of Honor of Kings 5V5 game by considering the heroes formation on each side using a gaming history dataset.
Cost-aware orchestration of applications over heterogeneous clouds

The orchestration of applications and their components over heterogeneous clouds is recognized as being critical in solving the problem of vendor lock-in with regards to distributed and cloud computing. There have been recent strides made in the area of cloud application orchestration with emergence of the TOSCA standard being a definitive one. Although orchestration by itself provides a considerable amount of benefit to consumers of cloud computing services, it remains impractical without a compelling reason to ensure its utilization by cloud computing consumers. If there is no measurable benefit in using orchestration, then it is likely that clients may opt out of using it altogether. In this paper, we present an approach to cloud orchestration that aims to combine an orchestration model with a cost and policy model in order to allow for cost-aware application orchestration across heterogeneous clouds. Our approach takes into consideration the operating cost of the application on each provider, while performing a forward projection of the operating cost over a period of time to ensure that cost constraints remain unviolated. This allows us to leverage the existing state of the art with regards to orchestration and model-driven approaches as well as tie it to the operations of cloud clients in order to improve utility. Through this study, we were able to show that our approach was capable of providing not only scaling features but also orchestration features of application components distributed across heterogeneous cloud platforms.
Toward Image Privacy Classification and Spatial Attribution of Private Content

Machine labeling of image content as private or public is a notoriously difficult problem, with the usual image processing challenges compounded by the highly personal, subjective, and contextual nature of access control decision making. In general, a user’s privacy expectation for a given image is consequential to specific contents therein and the presence of sensitive content somewhere in the image is sufficient to warrant a private label. In this work, we extend the problem of determining a single privacy label for a given image to jointly inferring a privacy label and detecting the specific areas of sensitive content within a privately labeled image. We propose a stochastic spatial attribution model which exploits sophisticated (deep neural net derived) image features over randomly selected image patches, as well as image saliency quantification. We validate our detected private regions through extensive user study experiments. This effort to achieve spatial attribution of private image content helps to lay a foundation for warning mechanisms which may serve to aid both social media sites and their users.
Forward Secure Dynamic Searchable Symmetric Encryption with Lighter Storage

Users with limited computing and storage capabilities can access large amounts of data easily by uploading the database to cloud server. But this leads to security issues. Dynamic searchable encryption scheme is a solution, providing not only data privacy for users, but also the ability of searching for keywords on the ciphertext and inserting or deleting documents. However, most of the existing efficient dynamic searchable encryption schemes do not satisfy forward security, considering file-injection attack. In this paper, we design and implement a new forward secure dynamic symmetric searchable encryption (DSSE) scheme. We also use a new index structure inspired by linked list to achieve forward security instead of using ORAM. Our storage overhead is greatly reduced compared to previous works, without losing efficiency. Finally, we implement, evaluate and analyze our scheme.
The Subjectivity Study on Participant Recognition for Improving Tourism English Education Program at Cyber Universities

English is the most widely used language for international travel. Many universities recognize the importance of tourism English as ESP(English for Specific Purpose) and offers the classes in the department of tourism. Teaching tourism English should be practical and planned to meet the expectations of learners. To do so, students’ learning motivation should be identified based on their subjectivity. Although several researches have studied learning motivations, most of them are quantitative researches for general off-line university students with similar and limited variables from previous studies. However, it is necessary to approach from the phenomenological perspectives to analyze true motivations of learners. This study aims to analyze online learners’ motivations based on their subjectivity applying Q-methodology developed by Stephenson (1953). As a result, the learning motivations of cyber university students for tourism English were categorized into four types including Type 1: Overseas trip, Type 2: Intrinsic motivation, Type 3: Utilization in business, and Type 3: English proficiency improvement.
Gaussian-process-based robot learning from demonstration

Abstract Learning from demonstration allows to encode task constraints from observing the motion executed by a human teacher. We present a Gaussian-process-based learning from demonstration (LfD) approach that allows robots to learn manipulation skills from demonstrations of a human teacher. By exploiting the potential that Gaussian process (GP) models offer, we unify in a single, entirely GP-based framework, the main features required for a state-of-the-art LfD approach. We address how GP can be used to effectively learn a policy from trajectories in task space. To achieve an effective generalization across demonstrations, we propose the novel Task Completion Index (TCI) for temporal alignment of task trajectories. Also, our probabilistic GP-based representation allows encoding variability throughout the different phases of the task. Finally, we present a method to efficiently adapt the policy to fulfill new requirements and modulate the robot behavior as a function of task variability. This approach has been successfully tested in a real-world application, namely teaching a TIAGo robot to open different types of doors.
Mechanisms of ensuring security in Keystone service

User authentication is one of the most important aspects in the area of cloud services, followed by the storing of sensitive information about customers. A number of solutions exist for authentication, security, and privacy provisioning in cloud, while cloud identity management systems aim to simplify and harmonise access. This paper presents an investigation into the security problems associated with cloud identity and access management system (IAMS), using the Keystone identity service within OpenStack as an example. In order to analyse the existing challenges, the paper expands security provisioning into authentication management, authorization management, personal data protection, privacy and confidentiality, as well as logging and auditing and considers the security mechanisms required for any cloud IAMS for each one of these categories. The paper also investigates some of the existing and potential attacks against the Keystone service, then follows with recommendations and mechanisms for enhancing the security. The vulnerabilities in cloud IAMS show that most systems support at most a subset of security provisioning mechanisms or have their own flaws; in addition, there are no unified international standards in this cloud identity systems area for cloud and service providers. The identified list of attacks and the associated mitigation mechanisms will help to provide the identity and access management system with the protection of identity credentials in the cloud system. The provided results can help with further researching mechanisms aiming to ensure personal data confidentiality and integrity.
Relationship Between Organizational Ethics and Employee Performance in Airlines Industry: Evidence from Nigeria

The need for organizations to achieve set goals and employees' desire for equity have affected etiquette, integrity, self-discipline and by extension the level of performance within the framework of employment relationship.This study therefore examines the effect of organizational ethics on employee performance in Airlines Industry.A survey research design was used based on population of 23 registered domestic airline carriers in Nigeria.Using purposive sampling technique, the scope was limited to the 6 busiest Airline.Judgmental random was used in administering 96 questionnaires to the employees of the 6 selected airline carriers, i.e. 16 employees from each airline.Multiple regression analysis was used in analyzing the data obtained.The study revealed that Organizational discipline, Commitment, and Work attitude positively affect employee's performance in the Airline industry.However, the results suggests that Organizational Teamwork has a negative impact on the performance of Employees, signifying that a unit increase in Teamwork on the average lead to a corresponding unit decrease on employee performance by -0.067 units.Though, this impact is negative it is also statistically insignificant at conventional level.This study concludes that ethics serves as organizations guide and encourage employees to practice good behavior for the sake of improving their performance.The study recommends that management of Airline businesses should strive to see the implementation of organizational ethics that can help them to achieve superior performance in terms of Organizational discipline, Commitment and Work attitude.
Vocabulary Learning Strategies Preference Used By The Seventh Grade Students of SMP Negeri 1 Mlati

This research investigated English vocabulary learning strategies employed by the seventh-grade students of SMP Negeri 1 Mlati.This research aimed to find the most and least frequently vocabulary learning strategies (VLS) employed by the seventh-grade students of SMP Negeri 1 Mlati.The participants of this research were one hundred and twenty-eight students in SMP Negeri 1 Mlati.A 20-item questionnaire was adapted from Schmitt's Vocabulary Learning Strategies Taxonomy.The research data were analyzed utilizing descriptive and inferential statistics through SPSS program version 23.The result of this survey revealed that the students belonged to low users of VLS.Social strategies were the most frequently used strategies.Memory strategies were the least frequently utilized strategies.The finding also revealed that there was no significant difference in the use of VLS among male and female students.
Employee performance in Jeddah national hospital in light of visual management

The aim of this study was to explore the degree of employee performance in light of visual management in Jeddah National Hospital and to identify if there were any statistical differences among respondents' estimations of their performance in terms of their work experience and training.The population of the study consisted of 820 employees, from which a sample, comprised 350 randomly chosen employees.Data were collected using a valid and reliable questionnaire and analyzed using IBM SPSS.The results underlined that the total degree of employee performance considering visual management was moderate.Particularly, employee performance on all dimensions; rules and regulations of visual management, as well as change, improvement and innovation, and work continuous follow-up, were moderate.Further, the results highlighted statistically significant differences between respondents due to work experience in favor of employees who have 15 years of experience or more and those who have 10 to less than 15 years of experience in favor of the former group.Additional differences were detected between employees with 10 to less than 15 years and those with less than 5 years of experience in favor of the group with 10 to less than 15 years.On the other hand, there were no statistically significant differences between respondents due to training courses.
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift

A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift. We use Auto Encoding Variational Bayes to find a latent representation of the data, on which a Variational Gaussian Mixture Model is applied to deliberately create distribution shift by dividing the dataset into different clusters. Wasserstein distance is used to characterize the extent of distribution shift between the generated data splits. In experiments using the Fashion- MNIST data, we assess several popular image classification Convolutional Neural Network (CNN) architectures and Bayesian CNN models with respect to their robustness and generalization behavior under the deliberately created distribution shift, which is analyzed in contrast to random Cross Validation. Our method of creating artificial domain splits of a single dataset may also be used to establish novel model selection criteria and assessment tools in machine learning, as well as for benchmark methods in the areas of domain adaptation and domain generalization.