Reviewerly/exHarmony huggingface dataset:
Diphtheria Case Number Forecasting using Radial Basis Function Neural Network

In Indonesia, diphtheria ranks fourth as a deadly disease after cardiovascular, tuberculosis, and pneumonia. The death rate of diphtheria is estimated to 21% with symptoms of malaise, anorexia, sore throat, and increased body temperature. The diphtheria cases which was reported in 2014 showed that East Java occupied the highest number for diphtheria cases which reached until 295, contributed to 74% cases of 22 provinces in Indonesia. In the mid-2017 until mid-2018, the Ministry of Health of the Republic of Indonesia announced that there has been an ongoing diphtheria outbreak in Indonesia. The number of diphtheria cases in East Java were highly raising up at the end of 2018. Forecasting is needed to reduce the number of diphtheria cases. The method used for forecasting is the Radial Basis Function Neural Network. Several variables are involved, including Immunization Coverage, Population Density, and Number of Cases. It is observed from the experimental results that the best model indicates only one variable involved, which is Number of Cases. This model is used to forecast the number of cases of diphtheria in Malang Regency, Surabaya City, and Sumenep Regency. The results showed that RBFNN method has a good performance for forecasting in Malang with MASE value of 0.84, Surabaya with MASE value of 0.817, and Sumenep with MASE value of 0.820, which all MASE values are less than 1.
Learning Sparse Patterns in Deep Neural Networks

Acceleration in deeper neural networks is hindered by high demand for computation and memory storage in resource constrained devices. In this paper, we propose a novel regularization method to learn hardware-friendly sparse structures for deep convolutional neural networks. A limited number of fixed sparse patterns are learned through the regularization and the filters are represented by these fixed sparse patterns. Our regularization method can produce compact models with higher degree of sparsity than the state-of-the-art structured sparsity learning method. The limited number of fixed sparse patterns also facilitate the hardware implementation. Our experiments on several deep neural networks with image classification tasks show that our proposed approach can achieve remarkably higher degree of sparsity with acceptable loss of accuracy.
Genetic Design of Topology for Neural Network

Neural networks (NNs) are widely used and show their values in many fields with the assistance of open source software (OSS) nowadays. Most of these modern applications emphasize the practices and achievements of using NNs. In this paper, we try to evolve the topology of a backpropagation neural network (BPNN) by genetic algorithm (GA): GABPNN. There are two decision variables needed to be decided in this evolution. The first one is the number of hidden layers, and the second one is the number of neurons in each layer. We propose a unique scheme of a gene to solve the problem. Compared to previous researches, this genetic design presented here is simple, general, and can find the appropriate topology of BPNN efficiently. Finally, we use an image classification problem in artificial intelligence (AI) to validate our idea. The handwritten digits from MINST database are used for Convolutional Neural Network (CNN) and GABPNN, and their computational results are compared. Although CNN performs better than GABPNN, GABPNN gives an acceptable level within a shorter time.
Application of Artificial Intelligence on Behavioral Finance

Nowadays there is a concern in everyone's mind regarding the changes that are going to happen very soon or we can say the changes that are already happening in today's world. Yes, I am talking about the 4th industrial revolution that is going to take place very soon due to which many new jobs will be created whereas, on the other hand many existing jobs will get disappeared. Everyone is talking about Artificial Intelligence and its pros and cons but we wanted to think in other aspects too regarding artificial intelligence. So, in this research paper, we put the spotlight on Application of Artificial Intelligence on Behavioral Finance. By this we not only get to know about artificial intelligence and its merits and demerits but also, get to know about its applications on behavioral finance. In this paper we tried to explain the meaning of artificial intelligence as well as its advantages and disadvantages and thereafter, the meaning of behavioral finance is considered so as to put clarity over the topic. Artificial intelligence is also known as machine learning as it is related to work which is going to be done by the machines rather than manually by human beings. Further, we included the interaction of behavioral finance and artificial intelligence (AI) in the future when AI will be full-fledged used for the working and its working for the economy and future developments. With the use of artificial intelligence in behavioral finance, the outcomes will be more accurate as with the use of machines the psychological biasness can be reduced and accuracy will be increased. This paper helps to understand the working of artificial intelligence with behavioral finance and its applications so as to get ready for the future beforehand.
Semantic Oriented Data Modeling Based on RDF, RDFS and OWL

The original aim of RDF and OWL data models was to encourage the semantic web to get a web of interlinked data. Many efforts have also been done to make data of existing enterprise applications available to the semantic web by converting associated data models to equivalent RDF and OWL ones. The majority of such applications are however developed using classical relational modeling methodologies. In this paper we propose the use of RDF and OWL frameworks as a basis for data modeling for the development of enterprise applications. Furthermore we propose an approach for an efficient semantic data modeling and show its effectiveness by giving a comparison of our approach with the classical relational data modeling.
Risk Control System of Construction Engineering Based on Data Mining and Artificial Intelligence Technology

Cloud computing provides a displaying and sharing platform for data mining technology. In order to prevent privacy disclosure, these data often contain artificially added risk control data, which makes the process of data sharing face the problem of deep mining of data. In terms of privacy protection, construction engineering risk control system is usually through the generalization way to complete the sharing of the accurate data, providing a condition for the follow-up accurate query process. At the same time, it also ensures that these data can be effectively predicted. According to the possible intersection or inclusion relationship between the generalized values, the generalized values are stratified and clustered in order to save the related important information of the construction project risk control. On this basis, this paper uses data mining technology (DMT) to design a construction project risk control system (CPRCS) based on data mining technology. Finally, through theoretical analysis and experimental comparison, this paper demonstrates the feasibility and effectiveness of the system.
A Hybrid Deep Neural Network for Urdu Text Recognition in Natural Images

In this work, we present a benchmark and a hybrid deep neural network for Urdu Text Recognition in natural scene images. Recognizing text in natural scene images is a challenging task, which has attracted the attention of computer vision and pattern recognition communities. In recent years, scene text recognition has widely been studied where; state-of-the-art results are achieved by using deep neural network models. However, most of the research works are performed for English text and a less concentration is given to other languages. In this paper, we investigate the problem of Urdu text recognition in natural scene images. Urdu is a type of cursive text written from right to left direction where, two or more characters are joined to form a word. Recognizing cursive text in natural images is considered an open problem due to variations in its representation. A hybrid deep neural network architecture with skip connections, which combines convolutional and recurrent neural network, is proposed to recognize the Urdu scene text. We introduce a new dataset of 11500 manually cropped Urdu word images from natural scenes and show the baseline results. The network is trained on the whole word image avoiding the traditional character based classification. Data augmentation technique with contrast stretching and histogram equalizer is used to further enhance the size of the dataset. The experimental results on original and augmented word images show state-of-the-art performance of the network.
Generating Diverse and Accurate Captions Based on Generative Adversarial Network

Benefiting from recent advances in deep learning, image captioning has achieved substantial progress. However, the diversity of human descriptions, which is closely related to the quality of caption, is overlooked in most existing works. In this paper, in order to generate diverse captions, we use the framework based on generative adversarial network (GAN), which jointly learns a generator to produce image-based descriptions and a discriminator to distinguish whether captions are machine-generated or human describes. We show how to use policy gradient method, a strategy from reinforcement learning, to guide the training of the generator. By directly optimizing the language evaluation metrics, we solve the problem of reduced fidelity when generating diverse captions. The experiments are conducted on MSCOCO dataset, the results show that our model is capable of producing diverse captions and improve accuracy.
Approaches in Creating Meta-requirement: A Systematic Literature Review

Abstract Common development team will spend less resources on User Requirement Specification (URS) validation, in the case of development project that provides this URS in the phase of competing for tender. In order to overcome this, the use of meta-requirement in evaluation of requirements is being introduce. The purpose of this article is to present the finding of a systematic literature review of meta-requirements. A methodology that involve defining a systematic way of finding literature and presenting it in a meaningful manner has been adopted. Meta-requirement is an area that is station within the field of information system design theory which is contain in the design research body of knowledge. Based on the result of the analysis of the systematic literature review, it is shown that there is still advancement in the field of meta-requirement and there still improvement that can be done to further the knowledge and contribution in this area. It is suggested that further research that will define a highly comprehensive model of creating meta-requirement should be undergo and the issue of meta-requirement traceability against real-world requirements should be tackle.
Elderly Care Monitoring System with IoT Application

Falls among elderly can pose serious consequences such as injury or even fatal ones. Therefore, it is essential that fall are detected early and a way to that is by using IoT platform. The authors have been developing a wearable device for elderly monitoring system utilizing accelerometer. The data from accelerometer is connected to an Internet-of-Things (IoT) platform called ThingSpeakTM. Based on IoT platform, elderly patients can be remotely monitored as long as the care providers have good internet access. The paper presents the experimental results of determining the sensitivity and specificity of the accelerometer used in the proposed system. This is the first step for developing an accurate data acquisition for monitoring purposes. Based on the experimental results, the average percentage for sensitivity obtained for this device is 73.3%, while the average for specificity obtained is 89.3%. Both sensitivity and specificity tests shows promising results which indicates that the device only has a fail rate of 26.7% and error rate of 10.7%.
Censored quantile regression model with time‐varying covariates under length‐biased sampling

Quantile regression is a flexible and effective tool for modeling survival data and its relationship with important covariates, which often vary over time. Informative right censoring of data from the prevalent cohort within the population often results in length-biased observations. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest based on length-biased observations with time-dependent covariates. In addition, inspired by Zeng and Lin 2008, we also develop a more numerically stable procedure for variance estimation. Large sample properties including consistency and asymptotic normality of the proposed estimator are established. Numerical studies presented demonstrate convincing performance of the proposed estimator under various settings. The application of the proposed method is demonstrated using the Oscar dataset.
NVR Guess: Automated Question Generation for Honing NonVerbal Reasoning Skills

Non-verbal reasoning tests allow evaluators to test a diverse set of abilities in students without relying upon, or being limited by, language skills. In this paper, we present an automated system to generate a variety of non-verbal reasoning questions spanning abstract reasoning, non-verbal analogy and spatial reasoning question types with granular control over difficulty levels. Our design also generates meaningful distractors as this is an important aspect of generating better quality multiple-choice questions. We present experimental results through analytics on question feedback and performance that demonstrate that the system-generated questions indeed serve to assess and hone nonverbal reasoning skills.
High Parallel VLSI Architecture Design of BPC in JPEG2000

Embedded block coding with optimized truncation (EBCOT) is the most computationally intensive part of JPEG2000 image coding standard. In order to improve the encoding efficiency, in this paper, we design a high parallel VLSI architecture of bit plane coding (BPC). Based on this proposed architecture, each encoding pass and each bit plane are encoded in parallel. Moreover, this proposed BPC can be connected with multiple MQ encoders during a coding session, which increase the encoding throughput furthermore. Logic synthesis results based on SIMC 0.25μm technology library and the throughput analysis results based on several standard test images show that the proposed architecture can work at a higher frequency and complete the encoding process with lower clock cycles compared with the available designs.
Automated Question Generation and Answer Verification Using Visual Data

This paper delineates the automation of question generation as an extension to existing Visual Question Answering (VQA) systems. Through our research, we have been able to build a system that can generate questions and answer pairs on images. It consists of two separate modules—Visual Question Generation (VQG) which generates questions based on the image, and a Visual Question Answering (VQA) module that produces a befitting answer that the VQG module generates. Through our approach, we not only generate questions but evaluate the questions generated by using a question answering system. Moreover, with our methodology, we can generate question-answer pairs as well as improve the performance of VQA models. It eliminates the need for human intervention in dataset annotation and also finds applications in the field of the educational sector, where the requirement of human input for textual questions has been essential till now. Using our system, we aim to provide an interactive interface which helps the learning process among young children.
"Was It Real?" - The Effects of Virtual Reality Communication Skills Training Among University Students in India

Virtual Reality (VR) technology enables students to be trained in new behaviour in an authentic and situated learning context. The pilot study investigated the effects of a scaffolded five-minutes 360-degree video-based VR training on communication skills, achievement emotions and motivation among university students in India. The results of an experimental trial with repeated measures show, except for a slight rise in positive emotions for the control group, no significant differences in the VR-condition. Nevertheless, students are highly convinced that VR-training has the potential to improve their academic skills and performance. High acceptance of VR-based education and the potential to replace real education settings at university level were observed.
Hierarchical Mind Map Generation from Video Lectures

With a steep increase in awareness and advancement in technologies over the past decade the standards and content of curriculum receives constant upgradation. In the current scenario, students often find it exhausting to learn the voluminous matter presented to them. In these cases, it is essential to find an approach beyond conventional means to prove as an aid while studying. One such tool is a Mind Map. Although the tool is proven to be effective, the process of creating a Mind Map is found to be laborious. Our inventive software is designed to obtain useful information from a video lecture. This is done by utilizing the content obtained by crowd sourcing, splitting the video into multiple subtopics and then transcribing the same. The tool then processes this data and extracts keywords relevant to the area of discussion. Following this, the tool organizes these key phrases so that it can be represented as a hierarchical Mind Map. Each node contains necessary phrases and all nodes and these nodes are carefully ordered. Additionally, the Map is made interactive and dynamic in order to enhance the students' experience.
Design of the Innovative Teaching Platform for College English Linguistics Teaching Based on the Network Technology

As a compulsory course for English majors in colleges and universities, English linguistics is of great significance to English majors in colleges and universities. However, as the English linguistics curriculum itself belongs to theoretical teaching, the teaching process requires the two-way interaction between teachers and students, so the teaching effect is not good. College English linguistics takes the cultivation of students’ comprehensive abilities to use English as the premise, and emphasizes the cultivation of students’ listening and speaking abilities to help students transmit the accurate written and oral information in the process of the social work and communication, thus improving students’ English levels and preparing for their future international communication.
Implementation of an Interactive Information Sharing System for Disaster Measure Operation

This study develops an interactive information sharing system for disaster measure operations, called “HyperRem.” It consists of the HyperRem-Master and the HyperRem-Node. The HyperRem-Master aggregates information held by meeting participants and visualizes it on a large display. The HyperRem-Node transmits information to the HyperRem-Master by flicking operation from mobile terminals and laptops owned by meeting participants. HyperRem saves screen states visualized on a large display to a database in chronological order, and the saved screen states can be reused. A reuse function and a playback function are implemented to reuse past screen states. The reuse function enables users to resume meetings by restoring past meeting contents to the large display. The playback function provides a review function for past meetings to absentees via a dedicated web page. The operability, functionality, effectiveness, and applicability of HyperRem were confirmed in an evaluation experiment with 10 subjects. The survey results of all five measures revealed an overwhelmingly positive response.
Applications of Smartphone Seismic Data for Rapid Structural Health Assessment

The citizen science app, MyShake, represents a new source of global seismic data that can be provided with much higher density sampling than a traditional seismic network, and at a relatively low cost. Because phones are most frequently located in buildings, the signals they record include structural response information in addition to ground shaking. This work explores the capabilities of MyShake as a remote damage detection tool within the field of structural health monitoring. There are several common, empirically determined parameters, such as modal frequency and inter-story drift, which characterize buildings and their response to shaking. Structural damage causes these values to deviate from their ‘healthy’ baseline. Using data collected during smaller excitations, we aim to generate a database of healthy values for buildings in which MyShake users live and work. Then, when a significant earthquake occurs, data from the mainshock and aftershock coda seismic records can be analyzed to detect deviation from this baseline, permitting a rapid, remote diagnosis of damage that could provide critical information to engineers on the ground. To gauge the expected capabilities of MyShake while the system is still relatively new, we compile a database of waveforms collected by the Center for Engineering Strong Motion Data (CESMD), which has instrumented buildings across California. Available CESMD data spans several years, building types, and earthquake magnitudes. After ‘downgrading’ these data to emulate the quality of waveforms collected by MyShake, we determine how big and how close an earthquake needs to be to excite buildings so that key structural health parameters can be determined. These data also inform our expectations for the range of values that typify each building type and size, permitting a gauge for the reasonableness of our results from true phone data. Demonstrative examples from this CESMD-based database in addition to real MyShake examples will be presented.
Soft and Hard Clustering for Abstract Scientific Paper in Indonesian

For ease in grouping research papers is by doing clustering. Clustering is a method to classify the objects into subsets with similar attributes. Clustering method divided into two categories ie hard and soft clustering. Hard clustering is method to grouping the data items such that each item is only assigned to one cluster, K-Means is one of them. While Soft clustering is method to grouping the data items such that an item can exist in multiple clusters, Fuzzy C-Means (FCM) is an example. Most research papers are documented into groups that are associated with the area of expertise of the researcher, even though there is also research whose contents relate to other fields outside the area of expertise of the researcher, so it should also be documented in the group of other fields so that its contribution in other fields can be known. Here in this paper we analyse the abstract of papers written in Indonesian as data set. Data samples were taken from 3 fields, namely information technology, health and economics. Clustering process using k-means and FCM to find out whether scientific paper's abstracts from different fields of research can be in the same group / cluster as a whole, not whole or different groups. As unstructured data, abstracts must be processed through a text mining procedure first to become vector data.