bluuebunny/crossref_metadata_2025 huggingface dataset:
Corporate Social Responsibility
Authors: Dikeocha Chijioke
Published: March 2020

This chapter seeks to find out whether energy corporations that operate in Nigeria provide adequate information about their corporate social responsibility, and whether these programmes are available on their managed online media platforms. For this reason, the study investigates how two energy corporations have engaged the online newsletter platform to share information concerning their CSR performance. The research employs a content analysis method. The population of the study comprises all online newsletters published by these corporations from January 2013 to August 2014. There are recognized similarities and difference in their use of online newsletters to share information concerning their corporate social responsibility to their various stakeholders. The study recommended that ExxonMobil (Nigeria) and SPDC (Nigeria) should make an effort to be consistent in their online newsletter publication. SPDC (Nigeria) should de-emphasise the frightening account of oil theft and spillage and focus more on human capacity development of persons within their host communities.
Analysing Ethical Issues of a Patient Information Systems Using the PAPA Model
Authors: Goundar Sam , Pillai Alvish , Bhardwaj Akashdeep
Published: March 2020

Healthcare is a vital portion of today's medical environment, and it is necessary for medical providers to do their work in an efficient and effective manner. Everyday, hundreds of thousands of patients visit medical amenities stimulating the administration to run smoothly. Almost all hospitals and the health centers in Fiji are now heavily dependent on a patient information system (PATISplus) that helps the employees to manage all the medical and administrative information. In this chapter, the authors analyse the ethical issues of a patient information systems (PATIS) using the PAPA model. This is in terms of privacy, accessibility, accuracy, and property. This chapter reviews current policies within the Ministry of Health and Medical Services in Fiji and also if there is a need of development of standard operating procedures in view of the PAPA model.
Consequent Formation in Security With Blockchain in Digital Transformation
Authors: Makka Shanthi , Arora Gagandeep , Sagar B. B.
Published: March 2020

Blockchain technology makes use of a centralized, peer-to-peer (P2P) network of databases, also called nodes, to validate and record digital transactions between individual users located anywhere across the globe. These transactions often take place through the exchange of cryptocurrencies such as bitcoins, Ethereum, and Ripple, etc. The security and transparency that is inherently present in digital transactions place blockchain technology in high demand across various industrial applications. Each node updates its database in real-time as and when transactions occur. The transaction gets authorized only when a majority of the nodes in the network validate the transaction. Once the verification is complete, a block, consisting of hash and keys, is generated for each new transaction and is linked to previous transactions in every database. Every node updates its database with the new block. A hacker would have to break down every node in the system to commit fraud. Blockchain could play a major role in maintaining the cyber security of digital transactions in the future.
Efficient and Secure Data Access Control in the Cloud Environment
Authors: Chunduru Anilkumar , Mamidisetti Gowtham
Published: March 2020

Cloud computing is a highly demanding zone in the present IT enterprise. The key characteristics are adaptability, productivity, pay-per-utilize, and cost viability. Access control and information security are the significant issues in cloud computing. Various access control strategies are present. The major problems of storing data in expandable access control and access rights from users are not used in this scheme. This chapter presents a generic survey on scalable and secure access control systems and schemes in cloud computing with a key focus on cloud security. Research gaps in the existing literature on cloud security are presented.
Machine Learning in Healthcare, Introduction and Real World Application Considerations
Authors: Pitoglou Stavros
Published: March 2020

Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.
Data Mining Problems Classification and Techniques
Authors: Rahman Nayem
Published: March 2020

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.
Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN
Authors: Prakash S.P. Shiva , Nagabhushan T.N. , Krinkin Kirill
Published: March 2020

Minimization of delay in collecting the data at any base stations is one of the major concerns in cluster based Wireless Mesh Networks. several researches have proposed algorithms to control congestion considering static nature of a node. Mobility of a node results in high congestion due to frequent link breakages and high energy consumption due to re-establishment of route during routing process. Hence, the authors consider dynamic nodes with single hop inside the static cluster. The proposed model includes four modules namely, Cluster head selection, slot allocation, slot scheduling and data collection process. the cluster head selection is based on the maximum energy, number of links and link duration. Slot allocation is based on the available energy () and the required energy (). Slot scheduling is carried out based on the link duration. Data at the base station will be collected as they are scheduled. Model is tested using Network Simulator-3 (NS3) and results indicate that the proposed model achieves least delay besides reducing the congestion compared to the existing methods.
Extracting Rules for Decreasing Body Fat Mass Using Various Classifiers from Daily Lifestyle Habits Data
Authors: Ushikubo Sho , Kanamori Katsutoshi , Ohwada Hayato
Published: March 2020

This study was performed to extract rules and factors for reducing body fat mass and to compare Inductive Logic Programming (ILP) and common classifiers to provide necessary steps for the healthcare system. Many researchers have focused on lifestyle-related diseases; however, few have attempted to extract rules and factors for decreasing body fat mass. The authors obtained lifestyle habits data. This data includes a variety of features (e.g., sleep, exercise, and nutrient intake). These features are easier for patients to understand. ILP and common classifiers are applied to this data. In terms of accuracy, random forest outperformed all other methods, and random forest is suitable for extracting factors among common classifiers. However, in terms of rules, ILP is more suitable than others, because ILP can extract rules covering many positive and negative examples, and it is easy to apply to the healthcare system because these rules cover a range of features.
Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework
Authors: Shirazi Hossein , Haefner Kyle , Ray Indrakshi
Published: March 2020

Denizens of the Internet are under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever-evolving form of social engineering. There is an improved framework that was previously implemented called “Fresh-Phish”, for creating current machine-learning data for phishing websites. The improved framework uses a total of 28 different website features that query using python, then a large labeled dataset is built and analyze over several machine learning classifiers against this dataset to determine which is the most accurate. This modified framework improves the accuracy of modeling those features by using integer rather than binary values where possible. This article analyzes not just the accuracy of the technique, but also how long it takes to train the model.
Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features
Authors: G. Manju , V. Kavitha , T.V. Geetha
Published: March 2020

Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.
Brave New Digital Tools for Action Research in Education
Authors: Bauer Reinhard , Himpsl-Gutermann Klaus , Sankofi Martin , Szucsich Petra , Petz Ruth
Published: March 2020

Due to the rapid development of digital media, the work of researchers in all scientific disciplines has dramatically changed. The objective of this chapter is to give a brief overview of digital tools that can be used for action or practice research in the context of seamless learning. It is the intention of the authors to, on the one hand, provide some initial orientation and deeper insight into the complex subject matter of digital science. On the other hand, researchers shall be equipped with a user guide that encourages them to try out various digital tools for searching, collecting, annotating, analyzing, visualizing, interpreting as well as publishing information. Owing to the dynamic nature of the issue under review this chapter will undoubtedly only offer a snapshot.
A Taxonomy of Data Mining Problems
Authors: Rahman Nayem
Published: March 2020

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techniques and then classify them in terms of data mining problems taxonomy to help assist practitioners in using appropriate data mining techniques that solve business problems. This will allow researchers to expand the body of knowledge in this discipline. This article proposes a data mining problems taxonomy based on data mining techniques being used. Prominent data mining problems include classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns and anomaly detection. The data mining techniques that are used to solve these data mining problems in general fall under top 10 data mining algorithms.
Inquiry-Based Learning on the Cloud
Authors: Mikroyannidis Alexander , Okada Alexandra , Correa Andre , Scott Peter
Published: March 2020

Cloud Learning Environments (CLEs) have recently emerged as a novel approach to learning, putting learners in the spotlight and providing them with the cloud-based tools for building their own learning environments according to their specific learning needs and aspirations. Although CLEs bring significant benefits to educators and learners, there is still little evidence of CLEs being actively and effectively used in the teaching and learning process. This chapter addresses this issue by introducing a European initiative called weSPOT (Working Environment with Social, Personal and Open Technologies for Inquiry-based Learning) for supporting and enhancing inquiry-based learning in STEM education via a cloud-based inquiry toolkit. The chapter presents evidence of using this toolkit within a case study that investigates how a secondary education community of students / co-learners selects information sources on the web and identifies factors associated with the reliability of information sources during their collaborative inquiry (co-inquiry) project in online environments.
AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement
Authors: Del Río Susana Ferrerio , Fernández Santiago , Bravo-Imaz Iñaki , Konde Egoitz , Irigaray Aitor Arnaiz
Published: March 2020

The development and the implementation of advanced actuation systems has increased in recent years, as many factors are driving the migration from hydraulic actuators to electromechanical actuators (EMAs) in aeronautics. But not only do we have to consider the right design to customize the system from the requirements oriented to the final application, also additional functions that can provide the system with additional value, to make it more competitive in this market. This is the case of the Health Monitoring (HM) systems. The development, implementation and integration of predictive algorithms into the environment of the EMA provide the system with an additional functionality, from which it is possible to detect failures at an early stage in order to avoid catastrophic accidents and improve maintenance activities. This work shows how to develop HM algorithms based on AI and Statistical technologies to detect and predict early stages of failure in a gearbox, which can directly affect to the transmission of power in EMAs.
A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus
Authors: Priyadarshini Rojalina , Barik Rabindra Kumar , Dash Nilamadhab , Mishra Brojo Kishore , Misra Rachita
Published: March 2020

Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who are non-diabetic). In K-means algorithm instead of using random samples as initial cluster head, the optimized centroids from GSA are used as the cluster centers. The inherent problem associated with k-means algorithm is the initial placement of cluster centers, which may cause convergence delay thereby degrading the overall performance. This problem is tried to overcome by using a combined GSA and K-means.
Managing Seven Dimensions of ICT4D Projects to Address Project Challenges
Authors: Potnis Devendra
Published: March 2020

A large number of ICT for development (ICT4D) projects experience a variety of challenges, especially when conducting field research with disadvantaged communities in developing nations. Using cluster analysis, this chapter identifies the six most common factors associated with a majority of ICT4D project challenges, and depicts the inter-relationship between these factors and over 100 distinct challenges reported by existing literature. In addition, based on the secondary analysis of 380 research artifacts in the ICT4D literature, this chapter proposes ways to manage the scope, time, costs, quality, human resources, communication, and risks for addressing ICT4D project challenges. Findings inform researchers of best practices for conducting ICT4D research with disadvantaged communities in developing nations.
A Novel Machine Learning Algorithm for Cognitive Concept Elicitation by Cognitive Robots
Authors: Wang Yingxu , Zatarain Omar A.
Published: March 2020

Cognitive knowledge learning (CKL) is a fundamental methodology for cognitive robots and machine learning. Traditional technologies for machine learning deal with object identification, cluster classification, pattern recognition, functional regression and behavior acquisition. A new category of CKL is presented in this paper embodied by the Algorithm of Cognitive Concept Elicitation (ACCE). Formal concepts are autonomously generated based on collective intension (attributes) and extension (objects) elicited from informal descriptions in dictionaries. A system of formal concept generation by cognitive robots is implemented based on the ACCE algorithm. Experiments on machine learning for knowledge acquisition reveal that a cognitive robot is able to learn synergized concepts in human knowledge in order to build its own knowledge base. The machine–generated knowledge base demonstrates that the ACCE algorithm can outperform human knowledge expressions in terms of relevance, accuracy, quantification and cohesiveness.
Using Hearing Assistance Technology to Improve School Success for All Children
Authors: Scott Diane M.
Published: March 2020

The acoustical conditions in the classroom play an important role in the learning process of children. Most daily instruction is verbal; therefore, all children in the classroom and other educational settings need access to auditory information. This chapter will provide information to teachers and administrators about hearing assistance technology that can facilitate classroom learning for typically developing children, second language learners, children who are hearing impaired, and children with normal hearing thresholds but significantly poorer auditory performance, such as children who are diagnosed with auditory processing disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, and language disorder. Teachers and educational audiologists can collaborate on the use of technology to ensure children have access to auditory information in the classroom.
Mobile + Cloud
Authors: Singh Pushpendra
Published: March 2020

A mobile phones provides portability and personalized computing with ubiquitous connectivity. This combination makes them an ideal choice to use for various applications of personal use. The portability of mobile devices is the most important and useful feature of mobile devices. However, portability is achieved at the high cost of limited power and computation ability of the mobile device. Cloud computing fulfills the need of providing more computation power to complete the tasks that cannot be done on a mobile platform. The cloud provides an always available platform and do not have typical limitations, e.g. limited battery and computation power, of mobile platforms. Therefore combining cloud computing with mobile provides us best of both worlds i.e. we have a computing platform available for us all the time which we move, and yet we can access services and perform tasks that require high-power computation.
Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques
Authors: Kumar Ravinder
Published: March 2020

The objective of this article is to propose rotation invariant fingerprint descriptor, and a faster and better generalized performance classifier. The author proposes a new multi-resolution analysis based fingerprint descriptor, computed from fingerprint orientation pattern called as orientation local binary pattern (OLBP). The feature vector is constructed by concatenating the OLBP histograms obtained from tessellated ROI of distorted fingerprint images. Secondly, the author proposes a hybrid classifier, which combines a powerful extreme learning machine (ELM) and a well generalized resilient propagation (RPROP). Finally, they propose two hybrid training algorithms using ELM and RPROP. The matching accuracy of 99.9% validates the performance of the proposed OLBP features and the proposed hybrid classification algorithms perform better as compared to the original ELM.
A Cyber-Physical Photovoltaic Array Monitoring and Control System
Authors: Muniraju Gowtham , Rao Sunil , Katoch Sameeksha , Spanias Andreas , Tepedelenlioglu Cihan , Turaga Pavan , Banavar Mahesh K , Srinivasan Devarajan
Published: March 2020

A cyber physical system approach for a utility-scale photovoltaic (PV) array monitoring and control is presented in this article. This system consists of sensors that capture voltage, current, temperature, and irradiance parameters for each solar panel which are then used to detect, predict and control the performance of the array. More specifically the article describes a customized machine-learning method for remote fault detection and a computer vision framework for cloud movement prediction. In addition, a consensus-based distributed approach is proposed for resource optimization, and a secure authentication protocol that can detect intrusions and cyber threats is presented. The proposed system leverages video analysis of skyline imagery that is used along with other measured parameters to reconfigure the solar panel connection topology and optimize power output. Additional benefits of this cyber physical approach are associated with the control of inverter transients. Preliminary results demonstrate improved efficiency and robustness in renewable energy systems using advanced cyber enabled sensory analysis and fusion devices and algorithms.
Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies
Authors: Bai Junjie , Luo Kan , Peng Jun , Shi Jinliang , Wu Ying , Feng Lixiao , Li Jianqing , Wang Yingxu
Published: March 2020

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.
Speech Enhancement Using Heterogeneous Information
Authors: Xiong Yan , Xu Fang , Chen Qiang , Zhang Jun
Published: March 2020

This article describes how to use heterogeneous information in speech enhancement. In most of the current speech enhancement systems, clean speeches are recovered only from the signals collected by acoustic microphones, which will be greatly affected by the acoustic noises. However, heterogeneous information from different kinds of sensors, which is usually called the “multi-stream,” are seldom used in speech enhancement because the speech waveforms cannot be recovered from the signals provided by many kinds of sensors. In this article, the authors propose a new model-based multi-stream speech enhancement framework that can make use of the heterogeneous information provided by the signals from different kinds of sensors even when some of them are not directly related to the speech waveform. Then a new speech enhancement scheme using the acoustic and throat microphone recordings is also proposed based on the new speech enhancement framework. Experimental results show that the proposed scheme outperforms several single-stream speech enhancement methods in different noisy environments.
The Diagnosis of Dengue Disease
Authors: Gambhir Shalini , Malik Sanjay Kumar , Kumar Yugal
Published: March 2020

This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.
Machine Learning Classification of Tree Cover Type and Application to Forest Management
Authors: MacMichael Duncan , Si Dong
Published: March 2020

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.
Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning
Authors: Serpen Alexander Arman
Published: March 2020

This research study employed a machine learning algorithm on actual patient data to extract decision making rules that can be used to diagnose chronic kidney disease. The patient data set entails a number of health-related attributes or indicators and contains 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.
Data Mining for Multicriteria Single Facility Location Problems
Authors: Tolun Seda , Tayalı Halit Alper
Published: March 2020

This chapter focuses on available data analysis and data mining techniques to find the optimal location of the Multicriteria Single Facility Location Problem (MSFLP) at diverse business settings. Solving for the optimal of an MSFLP, there exists numerous multicriteria decision analysis techniques. Mainstream models are mentioned in this chapter, while presenting a general classification of the MSFLP and its framework. Besides, topics from machine learning with respect to decision analysis are covered: Unsupervised Principal Components Analysis ranking (PCA-rank) and supervised Support Vector Machines ranking (SVM-rank). This chapter proposes a data mining perspective for the multicriteria single facility location problem and proposes a new approach to the facility location problem with the combination of the PCA-rank and ranking SVMs.
Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload
Authors: Wu Zhiming , Lin Tao , Tang Ningjiu
Published: March 2020

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.
Inferring Intent and Action from Gaze in Naturalistic Behavior
Authors: Lukander Kristian , Toivanen Miika , Puolamäki Kai
Published: March 2020

We constantly move our gaze to gather acute visual information from our environment. Conversely, as originally shown by Yarbus in his seminal work, the elicited gaze patterns hold information over our changing attentional focus while performing a task. Recently, the proliferation of machine learning algorithms has allowed the research community to test the idea of inferring, or even predicting action and intent from gaze behaviour. The on-going miniaturization of gaze tracking technologies toward pervasive wearable solutions allows studying inference also in everyday activities outside research laboratories. This paper scopes the emerging field and reviews studies focusing on the inference of intent and action in naturalistic behaviour. While the task-specific nature of gaze behavior, and the variability in naturalistic setups present challenges, gaze-based inference holds a clear promise for machine-based understanding of human intent and future interactive solutions.
Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text
Authors: Biradar Usha B. , Gurulingappa Harsha , Khamari Lokanath , Giriyan Shashikala
Published: March 2020

Identification of chemical named entities in text and subsequent linkage of information to biological events is of immense value to fulfill the knowledge needs of pharmaceutical and chemical R&D. A significant amount of investigation has been carried out since a decade for identifying chemical named entities at morphological level. However, a barrier still remains in terms of value proposition to scientists at chemistry level. Therefore, the work described here aims to circumvent the information barrier by adaptation of a Conditional Random Fields-based approach for identifying chemical named entities at various levels namely generic chemical level, morphological level, and chemistry level. Substantial effort has been invested on generation of suitable multi-level annotated corpora. Recommended machine learning practices such as active learning-based training corpus generation and feature optimization have been systematically performed. Evaluation of system performance and benchmarking against the other state-of-the-approaches showed improved results.
Social Media in Accelerating Mobile Apps
Authors: Bäck Asta , Jaring Päivi
Published: March 2020

Mobile application stores have become very popular, and the two most popular, Google Play and Apple App Store, both have over a million applications (apps) available. Social media is extensively used for marketing products and services; but, its true potential, in service and product acceleration, has not been researched much. This paper studies the differences in actions between successful and less successful app developers and especially their social media use in accelerating applications and its impact on success. In this study, a longitudinal analysis is performed on 682 applications, from four Google Play categories, at three data points. This study concludes that almost 50% of the analyzed applications use some form of social media to promote their app, and that successful apps use social media more actively than less successful ones. The qualitative analysis of the apps sheds some light as to why some apps succeed without social media use, and why some fail while using it.
Blind Image Source Device Identification
Authors: Venkata Udaya Sameer , Naskar Ruchira
Published: March 2020

This article describes how digital forensic techniques for source investigation and identification enable forensic analysts to map an image under question to its source device, in a completely blind way, with no a-priori information about the storage and processing. Such techniques operate based on blind image fingerprinting or machine learning based modelling using appropriate image features. Although researchers till date have succeeded to achieve extremely high accuracy, more than 99% with 10-12 candidate cameras, as far as source device prediction is concerned, the practical application of the existing techniques is still doubtful. This is due to the existence of some critical open challenges in this domain, such as exact device linking, open-set challenge, classifier overfitting and counter forensics. In this article, the authors identify those open challenges, with an insight into possible solution strategies.
A Novel Anti-Obfuscation Model for Detecting Malicious Code
Authors: Wang Yuehan , Li Tong , Cai Yongquan , Ning Zhenhu , Xue Fei , Jiao Di
Published: March 2020

In this article, the authors present a new malicious code detection model. The detection model improves typical n-gram feature extraction algorithms that are easy to be obfuscated. Specifically, the proposed model can dynamically determine obfuscation features and then adjust the selection of meaningful features to improve corresponding machine learning analysis. The experimental results show that the feature database, which is built based on the proposed feature selection and cleaning method, contains a stable number of features and can automatically get rid of obfuscation features. Overall, the proposed detection model has features of long timeliness, high applicability and high accuracy of identification.
Introduction to HR Research Using Statistics
Authors: Bhattacharyya Dipak Kumar
Published: March 2020

HR research through collection, collation and analysis of data, using statistics, immensely help. Even in day to day HR decision making, we need statistics. HR managers can take decisions, and so also a HR researcher can frame suitable hypothesis for getting deeper insights on specific research issue. In all HR functions, we see use of statistics can help. While for some HR researcher statistics help in finding answer to a research issue or a problem; for HR manager statistics can help in taking critical HR decisions with minimum chances of error. Thus, statistics promotes fact based approach in HR research and HR decision making. With statistics, it is also possible to take predictive HR decisions, when we simultaneously make use of HR analytics. Predictive HR decisions help in understanding the future implications of HR decisions, which help HR managers to initiate corrective actions well in advance, before the catastrophic failure in organizations.
Investigation of Software Reliability Prediction Using Statistical and Machine Learning Methods
Authors: Kumar Pradeep , Wahid Abdul
Published: March 2020

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter investigates performance of some classical and intelligent machine learning techniques such as Linear regression (LR), Radial basis function network (RBFN), Generalized regression neural network (GRNN), Support vector machine (SVM), to predict software reliability. The effectiveness of LR and machine learning methods is demonstrated with the help of sixteen datasets taken from Data & Analysis Centre for Software (DACS). Two performance measures, root mean squared error (RMSE) and mean absolute percentage error (MAPE) is compared quantitatively obtained from rigorous experiments.
Unleashing Artificial Intelligence onto Big Data: A Review
Authors: Mahanty Rupa , Mahanti Prabhat Kumar
Published: March 2020

We live in an ocean of data. Big Data is characterized by vast amounts of data sized in the order of petabytes or even exabytes. Though Big Data has great potential, Big data by itself has no value unless one can derive meaningful results from it. That is where Artificial Intelligence pitches in. Artificial Intelligence's most common application is about finding patterns in enormous quantities of data. The confluence of Big Data and Artificial Intelligence allows companies to automate and improve complex descriptive, predictive and prescriptive analytical tasks. In other words, Big Data can offer great insights with the help of Artificial Intelligence (AI). Artificial Intelligence can act as a catalyst to derive tangible value from Big data and serve as key to unlocking Big data. This review article focuses on applications of artificial intelligence to Big Data, its Limitations and issues.
Analytical Study on Bug Triaging Practices
Authors: Goyal Anjali , Sardana Neetu
Published: March 2020

Software bugs are inevitable and fixing these bugs is a difficult and time consuming task. Bug report assignment is the activity of designating a developer who makes source code changes in order to fix the bug. Many bug assignment techniques have been proposed in the existing studies. These studies use different datasets, varied input and evaluation parameters to validate their work. This diversification in bug triaging results in perplexity among researchers. Hence, this paper organizes the work performed in bug triaging in a structured manner. This paper aims to present current state of the art to provide a structured consolidation of bug triaging approaches. The paper has identified six research questions under five dimensions to address the various aspects of bug triaging. 60 articles from 36 venues have been reviewed and categorized in order to organize and substructure existing work in the field of bug report assignment. This study will help researchers to wisely decide the weapons for bug triaging. Also, it will act as a ready reference for the bug triaging practitioners.
Customer Satisfaction through Technological Integration
Authors: Seng Kah Phooi , Ang Li-Minn , Shing Ooi Chien
Published: March 2020

This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction levels. The paper also includes a comparison of the common resources that are used to measure customer satisfaction. The main part of the paper subsequently describes the related concerns and challenges that are faced by the business company to realize customer satisfactions. This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction. These components can be integrated into communication tools to solve the existing problems efficiently, and improve the way of assessing customer satisfaction. The limitations or challenges of current approaches in technology related ways to realize the satisfactions are also discussed. The end of the paper gives recommendations and solutions to show the possible ways in solving the existing problems and improving the way of assessing customer satisfaction by integrating the appropriate technology.
A Social Constructionist Model for Human-Machine Ecosystems
Authors: Galés Neus Lorenzo , Gallon Ray
Published: March 2020

Future learning experiences will be shared in hybrid communities that include humans and non-human agents. Students will need to be protagonists of their social learning situations, together with interactive “smart” tools, and teachers will share planning responsibilities with high-tech assessment tools. Personal engagement will be a major factor for educational success, and collective constructionism will represent the unified model for understanding human-machine interaction. The authors analyze the factors that influence how this will happen through three specific domains of socio-cognitive development: explicit information acquisition, implicit knowledge development, abstract meta-reflection. Humans will experience unpredictable cognitive changes just by merging their goals and actions with artificial intelligence agents. A social constructionist educational system needs to take this into account, plan for the unknown, and work with these evolutions with the goal of developing a new ethos based on a society that is global, networked, collective, ethical, and inclusive.
Directional Location Verification and Routing in Vehicular Ad-Hoc Network
Authors: Rana Kamlesh Kumar , Sharma Vishnu , Jain Vishal , Das Sanjoy , Tiwari Gagan , Bali Vikram
Published: March 2020

Vehicular ad-hoc network (VANET) is an autonomous system of mobile vehicles in which vehicles are a source of information. In VANET, direct communication between vehicles provides high-level safety and hassle-free drive. Large moving vehicles such as trucks or buses may affect direct communication of vehicles as a nonline of sight (NLOS) may occur. NLOS restricts direct communication of vehicles. Even the corresponding vehicle is within the communication range of the communicating vehicle. To overcome the NLOS problem and verify the location of the vehicles, this chapter has presented a routing mechanism, namely Directional Location Verification and Routing (DLVR) in Vehicular Ad-hoc Network. DLVR model prevents the false location information of the nodes by reduced packet drop and increased packet delivery ratio. Before transmitting data packets DLVR verifies data packets through reliability check. Through simulation work, it has shown the proposed DLVR model reduced packet drop and increased packet delivery ratio which increases the network performance.
Relative Position Estimation in Vehicle Ad-Hoc Network
Authors: Afifi Walaa Abd el aal , Hefny Hesham Ahmed , Darwish Nagy Ramadan , Fahmy Imane
Published: March 2020

Position is a vital element for ITS applications. Its accuracy helps to deliver services quickly to drivers to increase their satisfaction. GPS is a well-known position system but it suffers from multipath effect and non-line of sight in tunnel environments. Relative Position or sometimes called cooperative localization is an alternative position estimation. It utilizes different forms of v2x communication to exchange position, distance, direction, and velocity parameters. It will benefit in collecting a large amount of data to increase the accuracy of position estimation. However, the dependency of radio range communication methods has drawbacks such as poor-received signal, multipath, lose packets, delay, and overhead communication that will have inverse impact on position accuracy. In addition, safety applications require fewer seconds to make quick response. This chapter provides the latest related papers, the state art of radio range, the well-known localization algorithms, and current challenges and future direction.
Efficient Encryption Techniques for Data Transmission Through the Internet of Things Devices
Authors: Gupta Deena Nath , Kumar Rajendra , Kumar Ashwani
Published: March 2020

A secure environment is needed to communicate without any information leakage. From large devices having UPS to small devices having a battery, the parameter about security changes over time. Researchers need to work in three basics of security: (1) Mutual authentication between devices, (2) Strong encryption methodology for transmission, and (3) Secure storage environment with anytime availability. The IoT-enabled devices demand a lightweight secure environment. In this chapter, authors are concerning on all three points, i.e. Mutual authentication between devices, Strong encryption methodology for transmission, and Secure storage environment with anytime availability. Authors study some of the methods related to lightweight mutual authentication, lightweight cryptography, and local storage techniques; will talk about different issues in the field of secure communication, secure transmission, and secure storage; and will try to find out some research gap with a possible countermeasure.