Reviewerly/exHarmony huggingface dataset:
Human Capital Development and Growth in Africa: Does Youth Unemployment Matter?

The rates of youth unemployment and low human capital development in Africa have been worrisome. This study empirically investigated the role that youth unemployment played in the relationship between human capital and growth in Africa. The effects of both male and female youth unemployment on growth were examined. A system generalized method of moment (GMM) was used to estimate the model employed in the study. It was found that youth unemployment had resulted to a detrimental effect on growth in Africa in diverse ways. While, male youth unemployment was found to have a negative and statistically significant effect on growth, female youth unemployment shows no statistically significant effect on GDP per capita in Africa. Among the human capital development indices used in the regression, only primary school enrollment was found to have a positive and statistically significant effect on GDP per capita. Secondary and tertiary enrollments were found to be statistically insignificant. Appropriate policies geared towards job creation for the youths and human capital development would reduce the detrimental effect of youth unemployment and low human capacity building on long run growth.
Media Pembelajaran Video Tutorial Interaktif Berbasis Adobe Flash Pada Mata Kuliah Pemrograman Dasar

feasible to be applied as learning media for C ++ Basic Programming,and knowing the feasibility of interactive video tutorial learning media to be applied as basic programming learning media for the University Hamzanwadi Program Study Education Informatics. This research is a research and development. The development model used is the ADDIE development model. The type of data used is quantitative and data collection techniques using a questionnaire. The data analysis technique in this research is quantitative descriptive. The results showed that the results of interactive tutorial video learning media products stated that it was appropriate to be used according to material experts with a percentage of 85% while according to media experts it was declared very feasible with a percentage of 89% and the results of user responses were stated to be Very High with a percentage of 83%. Thus the interactive video tutorial learning media is very feasible to be used as a learning media.
Indonesian Sign Language (SIBI) Vocabulary Learning Media Design Based on Augmented Reality for Hearing-Impaired Children

The lack of mastery of spoken and written vocabulary makes hearing-impaired children have poor vocabulary designations. A case study in a kindergarten school for hearing-impaired children in Surabaya, the students are taught to understand vocabulary through media recognition with original objects, artificial objects, and through images. For media recognition through images usually use media in the flashcards form. The rapid development of Information and Communication Technologies (ICT) such as the Augmented Reality (AR) should also be utilized to support the learning media for hearing-impaired children. In this study an application based on AR technology has been integrated with flashcards as a learning tool for children with hearing impairment in learning the vocabulary of the Indonesian sign language (SIBI) In designing the learning media, we use Luther's version of multimedia development method, include concept, design, material collecting, assembly, testing, and distribution. Based on the test results, it would be concluded that the AR application system would run well as evidenced by the buttons on this AR application 100% running according to its function. Markers on the flashcard 97% are detected by the system and would be integrated with appropriate video displays. As for this AR application that runs offline in which all assets and videos are stored in the application database installed on the device, the RAM and processor capacity is very influential on the success and speed of detection of markers and access to display video. So it is recommended to use a device with a minimum of 1.5 GB RAM and a 1.4GHz Quad-core processor. With this AR application, we hope hearing-impaired children become more interested in learning and increasing their vocabulary independently.
On the Use of Hyperparameter Optimization in Big Data Processing Pipelines: A Case Study

The term sparks a wide range of multidisciplinary fields since it requires a high analytical expertise in different domains. Data Analytics applications include many successive steps, such as data collection, outlier detection, missing data imputation, feature selection, clustering analysis, classification model selection and result interpretation. The whole procedure can be seen as a chain of steps, organized in a pipeline manner, where the output of the upper layer is the input of the lower layer. Thus, the tasks of every step depend directly on the results of the previous steps. Moreover, there are alternative algorithmic methods for each step in Data Analytics. Opting for one methodology over the other requires high-skilled data scientists with a huge technical background. The key point of such a decision is optimizing the parameters of every layer in the pipeline. In this study, we develop an automated pipeline with different layers. Every layer contains several methods. We investigate the implementation of a suitable hyperparameter optimization algorithm, which allows the pipeline to be autonomous and select wisely the best algorithm for every layer. We discuss the specifics of the proposed prototype and the details of the used frameworks. We evaluate the prototype with an experimental study. The results are pertinent.
Keystroke Dynamics Algorithm for Securing Web-based Password Driven Systems

Web security is a critical aspect for many web-based applications, along its research track, keystroke dynamics techniques have attracted broad interests due to their high efficiency in security. In this paper, the aim was to come out with a keystroke login system that overcomes the typical challenges associated with keystroke dynamics and improves on password security but with focus on irritability nature of keystroke dynamics based systems. Specifically, we proposed two stages user matching method, training/enrolment phase of users and authenticating registered users with previously stored data. Furthermore, the proposed algorithm added dwell, flight times and multiplied by the locate time to get the upper and lower bounds. Moreover, the uniform differences between the bound timings were calculated to further enhance security. Experimental results show that the proposed keystroke dynamics approach used in augmenting password security emerged to be superior as compared to existing customary distance metrics.
Generating Dialogue Responses from a Semantic Latent Space

Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary.However, a good response does not need to resemble the gold response, since there are multiple possible responses to a given prompt.In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses.To address this issue, we propose an alternative to the end-to-end classification on vocabulary.We learn the pair relationship between the prompts and responses as a regression task on a latent space instead.In our novel dialog generation model, the representations of semantically related sentences are close to each other on the latent space.Human evaluation showed that learning the task on a continuous space can generate responses that are both relevant and informative.
Structure of a Guide for Usability Evaluation in Virtual Learning Environments

Usability is an important factor in determining software quality, therefore, its evaluation is very important for the design process of usable software systems, as well as for the development of tests about its final performance to suggest improvements in different stages of the development life cycle. The usability evaluation of an interactive system is an important step in the User-Centered Design since it allows obtaining the usability characteristics of the system and the measurement in which usability principles are being applied, besides that it implies several activities that depend on the evaluation method to be implemented. To make this evaluation there are different methods, which present methodological defects and whose application depends on many variables, which makes it unreliable its use and it more complicated choosing methods to evaluate a Virtual Learning Environment (VLE). For this reason, this paper proposes a guide to be applied a usability evaluation in these contexts, in whose construction process a literature analysis was made to identify and select the usability evaluation methods that were subsequently applied in a VLE, to choose the ones that best adapted to the context and with them generate the guide. In addition, a case study was conducted to validate the usefulness, applicability, and complexity of the guide. According to the case study results, it can be considered that the guide is useful and applicable despite considering a lot of information that makes it complex and difficult to use.
Multi-Level Coupling Network for Non-IID Sequential Recommendation

Sequential recommendation has been recently attracting a lot attention to suggest users with next items to interact. However, most of the traditional studies implicitly assume that users and items are independent and identically distributed (IID) and ignore the couplings within and between users and items. Although deep learning techniques allow the model to learn coupling relationships, they only capture a partial picture of the underlying non-IID problem. We argue that it is essential to distinguish the interactions between multiple aspects at different levels explicitly for users’ dynamic preference modeling. On the other hand, existing non-IID recommendation methods are not well designed for the sequential recommendation task since users’ interaction sequence causes more complicated couplings within the system. Hence, to systematically exploit the non-IID theory for coupling learning of sequential recommendation, we propose a non-IID sequential recommendation framework, which extracts users’ dynamic preferences from complex couplings between three aspects, including users, interacted items and target items at feature level, entity level and preference level. Furthermore, to capture the couplings effectively, we implement the framework on the base of capsule network. We indicate that the basic ideas of capsule seamlessly suit our purpose of modeling sophisticated couplings. Finally, we conduct extensive experiments to demonstrate the effectiveness of our approach. The results on three public datasets show that our model consistently outperforms six state-of-the-art methods over three ranking metrics.
The ABCs of Assured Autonomy

Each passing day seems to bring new instances of automation of routine tasks and the addition of artificial intelligence or machine learning algorithms to new domains. Autonomous systems are a diverse class of technologies ranging from AI driven natural language processing and image recognition to closed-form control systems for aircraft autopiloting. Coincident with this explosion in autonomy is a related drumbeat of stories of AI failures. These failures have ranged from tragedies resulting in bystander deaths to humorous examples of video game flaws being exploited. Widespread deployment and use of autonomous systems will depend on society trusting that these systems will perform as expected. While bias from incomplete training data is a well-trod area for improvement toward reducing AI failures, it is unclear if this bias is a sufficient or merely a necessary condition for loss of trust. What else may be needed for public assurance in autonomous systems? Here we identify three features of diverse autonomous systems that serve as a foundation for assured autonomy. These features are: the accuracy with which the algorithm senses and perceives the environment in a manner relatable to humans; a reduction in bias driven by the training data and algorithmic bias; and the complexity of the algorithmic process in terms of the ability to reverse engineer the decision-making processes. Building from this foundation, future autonomous systems can begin to reverse the loss of trust starting to be seen with respect to these technologies.
Critical success factors of continuous practices in a DevOps context

Context: Software companies try to achieve adaptive near to real-time software delivery and apply continuous practices in a DevOps context. While continuous practices may create new business opportunities, continuous practices also present new challenges. Objective: This study aims to aid in adopting continuous practices and performance improvements by increasing our understanding of these practices in a DevOps context. Method: By conducting a systematic literature review we identified critical success factors on continuous practices and grouped the found factors. This led to the construction of our initial framework. We started to validate the critical success factors in this framework in a DevOps context by conducting a first pilot interview. Results: We developed an initial framework of critical success factors and conducted a pilot interview to make a first step to validate the framework. Some factors were confirmed and clarified i.e., enriched, on the basis of the retrieved information. In future work we will strive at further validation of the framework. Conclusions: We took a first step to validate our framework and retrieved valuable information, which is promising to take the next steps for further development of the framework.
A Joint Transform Correlator Encryption System Based on Binary Encoding for Grayscale Images

A binary encoding method for grayscale images is proposed to address their unsatisfactory decryption results from joint transform correlator (JTC) encryption systems. The method converts the encryption and decryption of grayscale images into that of binary images, and effectively improves decrypted-image quality. In the simulation, we replaced unencoded grayscale images with their binary encoded counterparts in the JTC encryption and decryption processes, then adopted a median filter to suppress saturation noise while keeping other settings unchanged. Accordingly, decrypted-image quality was clearly enhanced as the correlation coefficient (CC) between a decrypted image and its original rose from 0.8237 to 0.9473 initially, and then further to 0.9937, following the above two steps respectively. Finally, optical experimental results confirmed that the proposed encryption system works correctly.
Holistic Power Side-Channel Leakage Assessment: Towards a Robust Multidimensional Metric

For many devices, power side-channel attacks are an effective means of obtaining secret keys from cryptographic algorithms. Recently, methods have been proposed to assess the vulnerability of devices to these attacks. While existing approaches effectively evaluate device vulnerability to attacks at specific points during execution, they do not consider the power measurement vectors holistically, using all time points in the measurement. This is necessary in order to accurately assess resistance to multi-target attacks. In this work, we identify characteristics of an ideal holistic side-channel security metric and develop a metric under these criteria. We demonstrate that our approach correctly ranks different FPGA implementations of AES with respect to attack difficulty.
Comparison of Magnetic Resonance Angiography and Computed Tomography Angiography Stereoscopic Cerebral Vascular Models

In this paper, we will discuss and compare the stereoscopic models developed from two types of radiographic data, Magnetic Resonance Angiography (MRA) images and Computed Tomography Angiography (CTA) images. Stereoscopic models were created using surface or volume segmentation and semi-auto combined segmentation techniques. Although, the CTA data were found to improve the speed and quality of constructing virtual vascular models compared to conventional CT data, small blood vessels were difficult to capture during the imaging and reconstruction process thereby limiting the fidelity of the stereoscopic models. Thus, high contrast Magnetic Resonance Angiography (MRA) images offer better resolution to visualize and capture the smaller branches of the cerebral vasculature than CTA images.
Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation

In the constellations approach to probabilistic argumentation, there is a probability distribution over the subgraphs of an argument graph, and this can be used to represent the uncertainty in the structure of the argument graph. In this paper, we consider how we can construct this probability distribution from data. We provide a language for data based on perspectives (opinions) on the structure of the graph, and we introduce a framework (based on general properties and some specific proposals) for aggregating these perspectives, and as a result obtaining a probability distribution that best reflects these perspectives. This can be used in applications such as summarizing collections of online reviews and combining conflicting reports.
Detection of Fake Accounts in Social Networks Based on One Class Classification

Detection of fake accounts on social networks is a challenging process. The previous methods in identification of fake accounts have not considered the strength of the users’ communications, hence reducing their efficiency. In this work, we are going to present a detection method based on the users’ similarities considering the network communications of the users. In the first step, similarity measures somethings such as common neighbors, common neighbors graph edges, cosine, and the Jaccard similarity coefficient are calculated based on adjacency matrix of the corresponding graph of the social network. In the next step, in order to reduce the complexity of data, Principal Component Analysis is applied to each computed similarity matrix to provide a set of informative features. then, a set of highly informative eigenvectors are selected using elbow-method. Extracted features are employed to train a One Class Classification (OCC) algorithm. Finally, this trained model is employed to identify fake accounts. As our experimental results indicate the promising performance of the proposed method a detection accuracy and false negative rates are 99.6% and 0%, respectively. We conclude that bringing similarity measures and One Class Classification algorithms into play, rather than the multi-class algorithms, provide better results.
Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk

Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery &amp; Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829
Development and Effect of a Mobile Education Program for Nursing Students on High-Risk Infant

Purpose: This study, as presented by Kern, Thomas, and Hughes , aims to verify the effectiveness of clinical inference education through digital storytelling programs based on Smith and Mann’s (2002) mobile education model in accordance with the six stages of the medical course development model. Based on the mobile education model of Smith and Mann (2002), the effectiveness of clinical reasoning education is verified through the mobile education program. Method: A single group pre-post test design that evaluates the clinical reasoning education effects of the mobile education program was used. Results: A statistically significant improvement was observed in clinical inference capacity from before intervention (t=-9.81, p<0.001); there was also a greater difference in the average value after intervention than before intervention in all of the results. Conclusion: This study suggests that while scenarios were constructed around five cases of high-risk Infant with respiratory and mechanical problems, in the future, additional scenarios should be developed to include different body systems.
Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel’s Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies
Peningkatan Kemampuan Mengenal Lima Huruf Vokal Melalui Media Bola Huruf Pada Anak Usia 3-4 Tahun

This research is motivated by the difficulty of children aged 3-4 years in recognizing five vowels that is often children fnd it difficult to mention and distinguish vowels such as the letter “e”, “u” and “o”. While the letters that are always emembered by children are the letters “i” and the letter “a”. This study aims to describe the learning process by using letter ball media in order to improve the ability to recognize five vowels in children aged 3-4 years and to find out the increased ability to recognize five vowel letters the use of lette ball media in children aged 3-4 years. The method used in this research is classroom action research. This research consisted of pre cycle, cycle I, cyle II, cyle III. Data collection techniques in this study used observation, interviews, non-test assessment, and documentation. The analysis technique in this study uses quantitative descriptive analysis, the results of this study are the se of letter ball media developed very well in cycle III by using letter ball media plus media decorating plastic cups.