Reviewerly/exHarmony huggingface dataset:
Diabetes Mellitus Diagnosis Expert System With Web-Based Forward Chaining

Diabetes Mellitus Diagnosis Expert System with Web-Based Forward Chaining Method. An expert system is one branch of artificial intelligence technology that combines the knowledge of an expert with tracing data to solve problems that are normally carried out by an expert. The development of information technology now makes it possible to access information from anywhere and anytime. So the role of information technology is increasingly useful to be able to develop in various fields including in the health sector. One of them is the system used to diagnose diabetes mellitus. Diabetes Mellitus is one of the most common diseases in the world. The inability of the pancreas to produce insulin normally becomes one of the reasons a person has this disease. In this research, the method used is Forward Chaining which sorts all data obtained before getting the final conclusion. The final results obtained from this study is an expert system application for diagnosing diabetes with a forward chaining method, where the user answers questions based on perceived symptoms, then the results obtained in the form of disease and explanation of the disease.
Blockchain in Supply Chain: Case Study of Lottemart Ciputat

Current supply chain methods are having difficulties in resolving problems arising from the lack of trust in supply chains. The root reason lies in two challenges brought to the traditional mechanism: self-interests of supply chain members and information asymmetry in production processes. Blockchain is a promising technology to address these problems. The key objective of this paper is to present qualitative analysis for blockchain in supply chain as the decision-making framework to implement this new technology. The analysis method used Val IT business case framework, validated by the expert judgements. The further study needs to be elaborated by either the existing organization that use blockchain or assessment by the organization that will use blockchain to improve their supply chain management.
Digital Dualism and the “Speech as Thought” Paradox

This chapter critiques the view, expressed in the 1996 Barlow Declaration and elsewhere, that the digital realm—“cyberspace”—is a disembodied space for pure thought. This chapter shows that the view that speech online is disconnected from the material realm echoes the same idea in traditional free speech theory, which has long considered speech to be something nonmaterial. Given the agent-driven nature of online communications, the materiality of internet technology, and the very real, often physical, effects of online speech on users and audiences, the chapter argues that the view that the digital realm has its own ontological status, distinct from that of the material world, is unsupportable. The chapter concludes that it is incorrect to hold that online communications are, in their causal capacity, more akin to thought than to non-speech conduct, just as it is incorrect to hold that offline communications are, in their causal capacity, more akin to thought than to non-speech conduct.
Cloud Computing: Securing without Losing Control

Computing virtualization is the key technology in cloud computing as it can reduce the investment in the hardware component drastically. The present research work carefully analyse some of the major security threats to the cloud services based on the security threat analysis produced by Cloud Security Alliance (CSA). The physical network components are not necessarily located on the same geographical area but yet are doing the necessary processing and storage. As such, “cloud computing” will be the driving force behind most of today's promising computing technologies. Security will be always an issue which needs to be addresses at two different levels: front-end and back-end. In a nutshell, cloud computing is hugely beneficial for the enterprise and while still evolving, will be around for the long-term. It is crucially important for those enterprises which will adopt the cloud computing model to put long-term security strategy in place. Knowledge of cloud architecture, technology, process, services, and deployment models is vital in specifying security models and identifying security concerns in cloud computing. As the providers and end users increase, standardization and security techniques will play an important role in helping organizations to reduce risks involved. This survey also illustrates the view of cloud providers’ actions and assessment of the cloud-computing services provided by them to the IT users.
INOVASI KEPEMIMPINAN KEPALA MADRASAH KREATIF DALAM MENINGKATKAN MUTU PENDIDIKAN DI SEKOLAH

This knowledge has a purpose to know the problems of the headmaster to make the innovation and creation to increase the education quality. The method of knowledge that used is qualitative, the presentation method is descriptive Analysis, also designed with the literature phenomenology that allow some literature related to the study of headmaster problems to make the innovation and creation to increase the education quality such as low support from the parents and society about the finance like the parents feel exception when the school fee increase, the minimally finance a facility, the inappropriate teacher with the qualification also minimally teachers commitment. So, it can be the problem of the headmaster to make innovation and creation to increase education quality. The solution of those problems such as keep on giving the understanding to the parents and society that what headmaster and teachers do have a purpose to their students. They use school finance efficiently. For better facilities, the headmaster tried to get the assist from the government to complete the school facilities. And for the inappropriate teacher with the qualification, the institute must allow the teachers to participate in the training or study at university. And for lower commitment, the headmaster can give the motivation so that the teacher will have the motivation to be better.
Traditional SETA No More: Investigating the Intersection Between Cybersecurity and Cognitive Neuroscience

We investigated the role automated behavior plays in contributing to security breaches.Using different forms of phishing, combined with multiple neurophysiological tools, we were able to more fully understand the approaches participants took when they engaged with a phishing campaign.The four participants of this pilot study ranged in their individual characteristics of gender and IT experience while controlling for age.It seems the biggest factor for awareness and successfully resisting a phishing campaign may be proximity of security training to engagement with that campaign.Neurophysiological tools helped illustrate the thought processes behind participants' statements and actions; combined with consideration of individual characteristics, these tools help shed more light on human behavior.In the future, we plan to further enhance our testing environment by incorporating an emergent model that considers work task complexity and incorporate more industry participants with a range of IT experience.
Salient region detection through salient and non-salient dictionaries

Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value.
Dimensional Reduction Analysis for Constellation-Based DNA Fingerprinting to Improve Industrial IoT Wireless Security

The Industrial Internet of Things (IIoT) market is skyrocketing towards 100 billion deployed devices and cybersecurity remains a top priority. This includes security of ZigBee communication devices that are widely used in industrial control system applications. IIoT device security is addressed using Constellation-Based Distinct Native Attribute (CB-DNA) Fingerprinting to augment conventional bit-level security mechanisms. This work expands upon recent CB-DNA “discovery” activity by identifying reduced dimensional fingerprints that increase the computational efficiency and effectiveness of device discrimination methods. The methods considered include Multiple Discriminant Analysis (MDA) and Random Forest (RndF) classification. RndF deficiencies in classification and post-classification feature selection are highlighted and addressed using a pre-classification feature selection method based on a Wilcoxon Rank Sum (WRS) test. Feature down-selection based on WRS testing proves to very reliable, with reduced feature subsets yielding cross-device discrimination performance consistent with full-dimensional feature sets, while being more computationally efficient.
Critical Success Factors of Total Quality Management in Software Development

Due to the considerable competition and fast growth in the software industry, software development has become essential.Thus, software quality is a vital instrument in software development field as well as in the determination of the most important Critical Success Factors (CSFs) in software development.The theory of Total Quality Management (TQM) adopted by organizations to achieve quality and improved organizations' performance while meeting and exceeding customers' expectations.This paper discusses the CSFs that can be applied to software development to implement the TQM for the software development field.The result presented the impact of the progress in time and technology on the rank of the essential CSFs while discovering the new CSFs that did not exist before.
Advanced Charting Techniques of Microsoft Excel 2016 Aiming Visualization

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Malayalam Questions Classification in Question Answering Systems using Support Vector Machine

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
KLASIFIKASI PENERIMA DANA BANTUAN DESA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR)

Determining the status of poor families as recipients of assistance is very important so that poverty reduction assistance from the government can be channeled on target. Data mining utilizes experience or even mistakes in the past to improve the quality of the model and the results of its analysis, one of which is the ability possessed by data mining techniques, namely classification. The purpose of this study was to test K-Fold Cross Validation in the K-Nearst Neighbors algorithm in predicting receipt of village aid funds. In the beneficiary dataset used in this study, there were 159 records or tuples with four attributes (house condition, income, employment and number of dependents). The new data category prediction is done by using the Euclidean Distance manual calculation stage of five different K values. While using the Rapidminer application aims to test the accuracy of the dataset in five different K values. The results show that with K=15 and K=30 the new data (D160) has a "Not Eligible" category with an accuracy of 100%. Then with K=45, K=60 and K=75, the new data (D160) has the category "Eligible" with an accuracy rate of 81.25%.
Social Media and Indian Youth

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Transfer Learning from Automatically Annotated Data for Recognizing Named Entities in Recent Generated Texts

In recent years, the machine learning based systems make an impressive performance only the same distribution of training data and test data. It means that the systems sometimes do not perform well on newly generated data because new data have different distribution with existing training data. In particular, named entity recognition (NER) task often face this problem due to numerous newly created named entities over time. However it is challenging to annotate for every newly generated text data manually. Therefore we propose the method of reducing the cost of manual annotation for recently generated texts by using a large amount of unlabeled data. We first automatically recognize named entities in unlabeled data with a knowledge-base (KB). Automatic annotation for unstructured data costs less, but it has considerable noise because they are not a golden-standard data. To overcome this problem, we next apply a transfer learning approach that reduces the influence of noise from automatically annotated data. In our transfer learning approach, the automatically annotated data are used as data for pre-training the NER model, and then the model is finetuned with the existing manually annotated data. We evaluate our proposed method with three different distributions. Experimental results demonstrate that our approach improves the performance of NER in recent texts.
Predicting Paediatric Malaria Occurrence Using Classification Algorithm in Data Mining

This paper gives the current overview of the application of data mining techniques on the haematological and biochemical dataset to predict the occurrence of malaria in children between age zero (0) and five (5). Malaria has been eradicated from the developed countries but still affecting a large part of the world negatively. A larger percentage of malaria is estimated to affect young children in sub-Sahara Africa. In order to reduce mortality from paediatric malaria, there should be an efficient and effective prediction method. In healthcare, data mining is one of the most vital and motivating areas of research with the objective of finding meaningful information from huge data sets and provides an efficient analytical approach for detecting unknown and valuable information in healthcare data. In this study, a model was built to predict the occurrence of malaria in children between age zero (0) and five (5) years, using decision tree classification algorithms on WEKA workbench tool. The classification algorithms used are LMT, REPTree, Hoeffding tree and J48. A J48 algorithm was used for building the decision tree model since it has higher accuracy for performance with least error margin.
Natural image clutter degrades overt search performance independently of set size

Although studies of visual search have repeatedly demonstrated that visual clutter impairs search performance in natural scenes, these studies have not attempted to disentangle the effects of search set size from those of clutter per se. Here, we investigate the effect of natural image clutter on performance in an overt search for categorical targets when the search set size is controlled. Observers completed a search task that required detecting and localizing common objects in a set of natural images. The images were sorted into high- and low-clutter conditions based on the clutter metric by Bravo and Farid (2008). The search set size was varied independently by fixing the number and positions of potential targets across set size conditions within a block of trials. Within each fixed set size condition, search times increased as a function of increasing clutter, suggesting that clutter degrades overt search performance independently of set size.
Multi-agent Neural Reinforcement-Learning System with Communication

Deep learning models have as of late risen as popular function approximators for single-agent reinforcement learning challenges, by accurately estimating the value function of complex environments and being able to generalize to new unseen states. For multi-agent fields, agents must cope with the non-stationarity of the environment, due to the presence of other agents, and can take advantage of information sharing techniques for improved coordination. We propose an neural-based actor-critic algorithm, which learns communication protocols between agents and implicitly shares information during the learning phase. Large numbers of agents communicate with a self-learned protocol during distributed execution, and reliably learn complex strategies and protocols for partially observable multi-agent environments.
Journey Towards Agility – A Retro- and Prospective Review

After more than 15 years since the Agile Manifesto and extensive research on agile software development (ASD) for nearly three decades, a comprehensive body of knowledge is available and is constantly growing.ASD is considered an effective way for managing software development projects in environments characterized by rapidly changing requirements.This study aims to shed light on the existing knowledge on ASD by applying a structured literature review and computer aided analysis consisting of distinct text mining techniques.We analyzed a sample of 1,376 papers and provide results from articles among relevant information systems research as well as computer science conferences and journals.Based on our approach, we are able to (1) evaluate key articles and journals, (2) analyze the development of ASD research in the last three decades and, most importantly, (3) identify research foci of the past as well as gaps in our knowledge on ASD.
An Innovative Approach for Risk Identification and Management in Software Projects

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Building a FIWARE Smart City Platform

This paper describes the architecture of a comprehensive IoT solution entirely based on the FIWARE platform.The application is designed to record data from environmental sensors and to eventually visualize them on a Smart City Dashboard.Besides solving certain architectural and technical issues, one particular challenge arose from the fact that some of the sensors were assumed to be mounted on public transportation vehicles like buses and trams.It could be shown that the FIWARE platform provides a range of components that allows for building such an IoT platform in a very efficient way.
Augmenting Authentication with Context-Specific Behavioral Biometrics

Behavioral biometrics, being non-intrusive and cost-efficient, have the potential to assist user identification and authentication.However, user behaviors can vary significantly for different hardware, software, and applications.Research of behavioral biometrics is needed in the context of a specific application.Moreover, it is hard to collect user data in real world settings to assess how well behavioral biometrics can discriminate users.This work aims to improving authentication by behavioral biometrics obtained for user groups.User data of a webmail application are collected in a large-scale user experiment conducted on Amazon Mechanical Turk.Used in a continuous authentication scheme based on user groups, off-line identity attribution and online authentication analytic schemes are proposed to study the applicability of application-specific behavioral biometrics.Our results suggest that the useful user group identity can be effectively inferred from users' operational interaction with the email application.