Reviewerly/exHarmony huggingface dataset:
Performing Software Defect Prediction Using Deep Learning

Traditional approaches for defect prediction generally begin with a feature construction step to encode the characteristics of programs, followed by a defect modeling stage that involves training a classification algorithm. However, the feature construction stage in these approaches is carried out without considering known defect labels, potentially leading to suboptimal learned features. Hence, the paper proposes a new deep learning approach called deep discriminative autoencoder (DDA), which provides an end-to-end learning scheme to construct discriminative embedding features and an accurate defect classification model in one go. The paper, then, constructs a novel deep learning technique for anomaly prediction in software. It is trained through a joint loss function that simultaneously takes into account the defect prediction quality and reconstruction quality of the embedding features. The paper probes four popular software built with Java. It is evident from the outcomes that the approach significantly improve traditional defect prediction methods by 8.6% and 5.4% in terms of F1 score.
EAP Based Certificateless Authentication Technique to Access Cloud Services in Openstack

Nowadays cloud services have gained more interest. The main advantage of the cloud is, it reduces management costs and efficient usage of resources. The major role of efficient authentication means not only providing authentication but it should also reduce the authentication traffic and provide security. There are many authentication mechanisms most of them were only for a web application. Here in this paper, we focus on both web and non-web applications authentication in cloud environment to access SaaS service. For non-web application services of cloud, ABFAB has created an architecture. ABFAB also defines how to use existing EAP/AAA and GSS-EAP for both web and non-web-based applications. In this paper, we mainly focus on efficient AAA to access cloud services. To demonstrate the proposed system, we use the moonshot from Github, freeradius an opensource, and Openstack for cloud service with our proposed authentication method. We have done the performance analysis of our authentication method compared with the other authentication mechanism to access cloud services. This analysis shows a significant reduction in the computation time required for authentication and reduction in the authentication traffic.
Comparative Study of Workflow Modeling Services Based on Activity Complexity

Serverless computing has been widely used in several applications, where tasks are represented through an independent stateless functions. These stateless functions can be orchestrated in the form of workflow and can be deployed and executed in serverless frameworks. A number of serverless frameworks have emerged recently to provide orchestration services for serverless workflow. It is necessary to explore the potential of serverless frameworks which can be helpful for developers in taking business decisions. The performance of a serverless framework depends on several complexities associated with the serverless workflow. In this paper, we have focused on the effect of activity complexity associated with the workflow. The activity complexity of a workflow refers to the number of functions in the serverless application. A comparative analysis of various serverless workflow services based on change in performance parameters based on activity complexity is presented. The performance parameters such as overall function time, overhead time, and total response time are considered as the evaluation parameters. The extensive comparison has been done by considering both sequential and parallel workflow, which have been generated by various workflow services. Some of the findings are also presented for comparative analysis. This could be a potential resource for the application developers who have focused on serverless computing.
Dual Image Watermarking Technique Using IWT-SVD and Diffie Hellman Key Exchange Algorithm

One of the most serious issues in today's information systems is data security when communicating over an open network. Digital watermarking is a technology which is rapidly evolving from the recognition of ownership to the recovery of altered information. This work presents dual image watermarking approach formulated on Diffie-Hellman key Exchange (DHKE) algorithm to enhance the secure technique such as: confidentiality, authenticity and non-repudiation. The present dual watermarking technique is intended for safe keeping of copyright by utilizing Integer wavelet transform (IWT) and Singular Value Decomposition (SVD). Before embedding both logos, host images are transformed by IWT. Again, SVD has been applied on selected bands of both the transformed logos. Singular Values (SVs) of both the converted watermark logos are embedded to the sub bands which are transformed by SVD of the original image. The proficiency of present technique has been analyzed in respect of performance parameters such as MSE, SSIM, PSNR, NC. Also, simulation results signify the high embedding capacity of the proposed work.
Towards an Ontology of Explanations

The ability of an agent to explain how it arrived at a decision is important for garnering trust in the agent, understanding its operation, and possibly gaining new and more generalizable knowledge. With AI agents becoming more complex and widespread, there is growing interest in providing them with the ability to explain themselves. Explanations, however, are more than a matter of integrating techniques to approximate machine learning models with more straightforward techniques; explanations are interactive communication acts which must be tailored to the interests of the person seeking explanations. To better represent the communication goals behind an explanation, we propose the “Explanation Interchange Format” ontology, which has several aims. First, it formally describes the communication act of Explanation and its structure based on existing theoretical work on scientific explanations. Second, the ontology enables reasoning to construct explanations, e.g. selecting explanatory structures appropriate to a questioner’s interests. We illustrate reasoning on our ontology with some examples.
A Survey on Privacy Preserving Synthetic Data Generation and a Discussion on a Privacy-Utility Trade-off Problem

Sharing microdata is a very important part of the present day world, but when they contain sensitive information, privacy to individuals needs to be guaranteed before release of data. One idea is to study the distributional properties of a data-set and generate synthetic data which has similar properties but unlike the original data comes with a privacy guarantee. In this review paper, we describe in detail, some advanced privacy guarantees that needs to be checked before release of such information. Also, we discuss some utility metrics to measure the remaining utility of released data. Very few mechanisms have been developed to ensure utility to synthetic data, provided a very strong privacy guarantee is maintained. We discuss some existing methodologies on privacy preserving synthetic data generation and discuss a privacy utility tradeoff problem.
Fusion of Features from Mammogram and DBT Views for Detection of Breast Tumour

The early detection of breast cancer plays an important role to advance the diagnosis of patients having breast cancer disease. Detection of irregularities in breast is performed utilizing different biomedical methods. Morphological, metabolic, and functional data may be obtained using these techniques. In order to better prepare for treatment, multimodal approaches give related data. The problem, though, is that it's hard to get all of this information from one modality. For better treatment multimodal approaches provide additional information. Fusion of features from Mammogram and DBT views is used to advance precision in discovery of breast cancer. Methods are developed in the four phases like pre-processing, segmentation, feature mining and classifications. SVM and KNN algorithms are used in the classification of breast cancer.
3D Object and Hand Pose Estimation

3D object and hand pose estimation have huge potentials for Augmented Reality, to enable tangible interfaces, natural interfaces, and blurring the boundaries between the real and virtual worlds. In this chapter, we first motivate the topic and explain the challenges. After a brief review of early work and Deep Learning techniques on which recent works are based, we present the recent developments for 3D object and hand pose estimation using cameras, when considered separately and together. We examine the abilities and limitations of each technique. We conclude by discussing the possible future developments of the field.
Semantic Segmentation of Kidney and Tumors Using LinkNet Models

Automatic semantic segmentation of kidney and tumors is very essential for doctors/radiologist in diagnosis and treatment process. Kidney Cancer is more in men than woman, also tumor segmentation is challenging because they are heterogeneous, scatters in shapes and have low contrast difference with kidneys. Early detection of the tumor is essential for increasing survival chances of patients. This paper focuses on kidney and tumor segmentation using LinkNet architecture. LinkNet Architecture is applied on 2019 Kidney Tumor Segmentation (KiTS 2019) Dataset using 29 pretrained models out of which top five models are ensemble to improvise the IOU score.
Enhanced Security and Privacy for IoT Based Locker System Operated at Low Frequency Spectrum Using Blockchain

Conventional locker system faces continuous threat of breaching of data due to phishing scams, which raises high demand of encryption at server's end. Blockchain based security system provides efficient solution so far either by applying personal signature, or by adopting peer-to-peer network. In the present proposal, security is entrusted by generating new encrypted message from the already received binary information from user's end. The code is incremented at each subsequent step which helps to track the number of times the security door is opened with individual time-stamp along with image capture facility. Corresponding circuit is designed at 433 Hz spectrum where relay is connected with electromagnetic door lock through microcontroller. A novel algorithm is proposed to control cloud-based web server for accessing every HTTP request with NodeJS and a MongoDB database. Enhanced security and privacy can therefore be obtained through low-cost hardware system associated with blockchain feature.
Tracking Systems: Calibration, Hardware, and Peripherals

A single sensor that tracks the user’s pose is usually not sufficient for the creation of compelling augmented reality (AR) owing to system drift, noise, and tracking errors. Recent systems thus combine tracking information from multiple sensors, such as red green blue and infrared cameras, accelerometers, and depth sensors, to improve the illusion of an augmented world. In this chapter, we first introduce calibration methods, tracking sensors, and hardware that can track objects of interest, followed by a discussion of methods that can align the information gathered by these sensors.
Major Challenges and Threats of Blockchain Technology

Blockchain comes to the market as a distributed, decentralized open ledger or database which is linked with each other using thehash value of cryptography. Blockchain is bettersecure than other systems but several threats may break the security of the blockchain. Even if blockchain is a more secure and promising aspect of our future, some security concerns and difficulties still surround this ground-breaking method is the important topic now a day and here in this paper we concern about these. It analyzes blockchain architecture containing its immutable transactions in a decentralized manner and including their different security threats, how they can hamper the blocks assets and considerable values of the blocks and security services over it, including their authentications, privacy, and confidentiality taking part of their access control list (ACL). In this article, we tried to find out how the eclipse attack hampers the blockchain distribution and countermeasure.
Performance Evaluation of Authentication Public Blockchains

The internet of things (IoT) is an integrated, interconnected system of things without any human interaction. However, the lightweight internet of things devices have limited storage and processing capacity. Due to this limitation, a centralized authentication system was adopted on the internet of things. Unfortunately, the centralized authentication system has a lot of drawbacks, such as a single point of failure, cost, scalability, and bottleneck. Therefore, a decentralized system such as the public blockchain was introduced because it solves the drawbacks of decentralized authentication methods. This paper evaluates the performance of two authentication methods using a public blockchain. The two used blockchains for the evaluation are Rinkeby and Ropsten. The metrics used in the evaluation are time, CPU, and memory. These metrics are required in the authentication process during the association requests of members to their groups and in data message exchanges between them. The obtained results showed that Rinkeby is less time-, CPU-, and memory-consuming than Ropsten because it uses a proof of authority consensus algorithm, which depends on a lower number of participants, whereas Ropsten uses a proof of work consensus algorithm, which depends on a greater number of participants than a proof of authority.
Evaluation of the Predictability of Passwords of Computer Engineering Students

As information and communication technologies evolve every day, so does the use of technology in our daily lives. Along with our increasing dependence on digital information assets, security vulnerabilities are becoming more and more apparent. Passwords are a critical component of secure access to digital systems and applications. They not only prevent unauthorized access to these systems, but also distinguish the users of such systems. Research on password predictability often relies on surveys or leaked data. Therefore, there is a gap in the literature for studies that consider real data in this regard. This study investigates the password security awareness of 161 computer engineering students enrolled in a Linux-based undergraduate course at Ataturk University. The study is conducted in two phases, and in the first phase, 12 dictionaries containing also real student data are formed. In the second phase of the study, a dictionary-based brute-force attack is utilized by means of a serial and parallel version of a Bash script to crack the students’ passwords. In this respect, the /etc/shadow file of the Linux system is used as a basis to compare the hashed versions of the guessed passwords. As a result, the passwords of 23 students, accounting for 14% of the entire student group, were cracked. We believe that this is an unacceptably high prediction rate for such a group with high digital literacy. Therefore, due to this important finding of the study, we took immediate action and shared the results of the study with the instructor responsible for administering the information security course that is included in our curriculum and offered in one of the following semesters.
Improving Interactive Segmentation using a Novel Weighted Loss Function with an Adaptive Click Size and Two-Stream Fusion

Interactive segmentation has recently attracted at-tention for specialized segmentation tasks where expert input is required to further enhance the segmentation performance. In this work, we propose a novel interactive segmentation framework, where user clicks are dynamically adapted in size based on the current segmentation mask. The clicked regions form a weight map and are fed to a deep neural network together with the image, where the network learns to discriminate important regions through a novel weighted loss function. To evaluate our loss function, a state-of-the-art interactive V-Net (IV-Net) model which utilizes both foreground and background user clicks as the main method of interaction is employed. To further improve on the IV-Net, we propose the use of a two-stream fusion interactive If-Net (TSFIV-Net) which applies multimodal fusion to allow for the propagation of image feature information throughout the architecture. We train and validate both the models on the BCV dataset, while testing on both seen and unseen structures from the MSD dataset to determine the models generalization and segmentation abilities in comparison to the standard IV-Net. Applying adaptive user click sizes increases the overall dice score by 4.86 % and 8.59 % for seen and unseen structures respectively by utilizing only a single user interaction on the IV-Net compared to the original version, and 9.88% and 10.35% on the TSFIV-Net.
Authorship Attribution Author Style vs Content

The problem of authorship attribution has long been debated and remains a hot topic. This discipline has seen remarkable progress in the previous decade as a result of breakthroughs in digital computers. Presented study is one of it’s kind and brings a fresh perspective to investigate the relevance and significance of style-based vs content-based authorship attribution using two corpora of presidential campaign text media with varying levels of topic heterogeneity. Using the same model trained with content-based features as a baseline, it was discovered that a model with style-based features performs better on topic-homogeneous documents than topic-heterogeneous documents. It was also interesting to see that models with style-based featurization generalize more effectively than models with content-based featurization across corpora with varying degrees of topic heterogeneity. These findings show how understanding the content of the underlying texts can help when picking attributes for authorship attribution. Also highlighting some key discoveries along the way.
Word Embeddings for a Disciplinary Tutoring System

Question Answering (QA) systems have made remarkable progress in information retrieval techniques, especially in their ability to naturally access knowledge resources by querying and retrieving correct answers to various questions. In tutoring, these systems can help by reducing the requirement for interaction between learners and tutors and allowing learners to post their queries and receive answers for the same. Hence, we propose a disciplinary tutoring system based on a domain ontology ONTO-TDM (ontology for teaching domain modeling) and natural language processing (NLP) techniques to facilitate access to information and answer the learners' questions. Recently, deep learning algorithms have achieved impressive success in various natural language processing tasks. The basic concept of these techniques is to compute a distributed representation of words from continuous vectors, also known as word embedding. In this study, we use deep learning-based word embedding models for a disciplinary tutoring system. Our goal through this work is to find out whether word embedding could significantly improve the response generation task of the suggested system. Therefore, we have built word embeddings using the word2vec skip-gram model with different training parameters on a large corpus composed of question-answer pairs. Experimental results show that using the word2vec model has a significant impact on the accuracy of the proposed tool.
Exploring Critical Challenges and Factors Influencing E-Learning Systems Security During COVID-19 Pandemic

The COVID-19 pandemic swiftly forced the closure of higher education institutions across the world. As a result, all educational institutions have made a considerable move toward e-learning. E-learning systems suffer from confidentiality, integrity, and availability-related security threats. Hence, understanding this problem from various perspectives that include technology, human behavior, management, and policies, as well as implementing policies and standards will assist in finding the most influencing factors that can help in protecting e-learning information systems from potential security threats. In this research, a review has been conducted to analyze and assess 55 states of the art articles on e-learning security and to identify countermeasures to strengthen e-learning information systems. The study contributes to our understanding of how to strengthen the e-learning information systems security by answering the following questions: What are the factors that have an impact on the e-learning systems vulnerability during the COVID-19 pandemic? What are the most important technology, management, and environment-based factors that need to be considered for protecting e-learning systems from potential security attacks?. The results of this study will enable decision and policymakers at higher education institutions to better understand the essential elements of adopting an e-learning system. Knowing these factors will help the designers and developers of e-learning systems adopt secure software design and development methodologies to reduce the likelihood that such vulnerabilities and it contributes to enhancing the system's security resilience against potential attacks. In addition, university management will be more knowledgeable about how to instruct their staff and students on best practices for using e-learning web applications.
Ontology-Based Semantic Analysis of Software Requirements: A Systematic Mapping

Each stage in the software development lifecycle has its own requirements, validation criteria, and output. Requirement analysis, which is one of the initial stages of the life cycle, can be considered as the most important stage of a project. Incorrectly defined, analyzed, and incorporated, requirements can lead to errors or even project failure in the later stages of the project. Considering the existence of software products in almost every field today, we can say that different requirements have arisen in a wide variety of fields. The use of appropriate technologies can yield much more efficient results than manual analysis of these relatively large numbers of different requirements. One of them is ontology-based approaches. In this research, the use of ontologies in software requirement analysis has been investigated. Systematic mapping was chosen as the research method. At the end of the study, it was found that technologies that adopt an ontology-based approach increase efficiency and accuracy in software requirements analysis.
A Novel Probabilistic Framework for Modeling Spelling Errors by Term Length and Frequency

In this paper, we present a probabilistic framework that expresses spelling errors as a joint distribution of term length and frequency by means of formulating probability functions of typographic and orthographic errors separately. The parameters of the model are obtained by a root finding algorithm with respect to one another yielding the desired number of misspellings. When provided with term length and frequency information of true misspellings, the optimal parameter pair can be found by minimizing the relative entropy, and the individual rate of error types can be induced. We evaluated the model’s performance by a chi-square goodness of fit test on the empirical probability mass function of term lengths by spelling errors detected via a rule-based spell checker in three different datasets. It has been observed that the proposed approach perceives the empirical distribution of spelling errors more accurately in comparison to both word and character-based random error processes. We consider that it will be particularly useful in synthetic error generation and unsupervised spelling detection tasks.