Reviewerly/exHarmony huggingface dataset:
Analysis Of The Relationship Between Smart Contracts' Categories and Vulnerabilities

Smart Contracts are general-purpose programs that provide a higher level of security than traditional contracts and reduce other transaction costs associated with the bargaining practice, as they are executed in a Blockchain infrastructure. Developers use smart contracts to build their tokens and set up gambling games, crowd sales, ICO, and many others domains of application. The security of Smart Contracts is also crucial, as SCs at the very core level, move money. In recent years, researchers have provided a set of known vulnerabilities that afflict SCs. This study analyzed the relationship between the SC domain of application, domain category, and known vulnerabilities. We categorized the SC using the topic modeling on a curated dataset of SC annotated with know vulnerabilities. Indeed, we found that a certain category of SC is strongly associated with specific vulnerabilities.
On the Influential Interactive Factors on Degrees of Design Decay: A Multi-Project Study

Developers constantly perform code changes throughout the lifetime of a project. These changes may induce the introduction of design problems (design decay) over time, which may be reduced or accelerated by interacting with different factors (e.g., refactorings) that underlie each change. However, existing studies lack evidence about how these factors interact and influence design decay. Thus, this paper reports a study aimed at investigating whether and how (associations of) process and developer factors influence design decay. We studied seven software systems, containing an average of 45K commits in more than six years of project history. Design decay was characterized in terms of five internal quality attributes: cohesion, coupling, complexity, inheritance, and size. We observed and characterized 12 (sub-)factors and how they associate with design decay. To this end, we employed association rule mining. Moreover, we also differentiate between the associations found on modules with varying levels of decay. Process- and developer-related factors played a key role in discriminating these different levels of design decay. Then, we focused on analyzing the effects of potentially interacting factors regarding slightly- and largely-decayed modules. Finally, we observed diverging decay patterns in these modules. For example, individually, the developer-related sub-factor that represented first-time contributors, as well as the process-related one that represented the size of a change did not have negative effects on the changed classes. However, when analyzing specific factor interactions, we saw that changes in which both of these factors interacted tended to have a negative effect on the code, leading to decay.
Can We Automatically Fix Bugs by Learning Edit Operations?

There has been much work done in the area of automated program repair, specifically through using machine learning methods to correct buggy code. Whereas some degree of success has been attained by those efforts, there is still considerable room for growth with regard to the accuracy of results produced by such tools. In that vein, we implement Hephaestus, a novel method to improve the accuracy of automated bug repair through learning to apply edit operations. Hephaestus leverages neural machine translation and attempts to produce the edit operations needed to correct a given buggy code segment to a fixed version. We examine the effects of using various forms of edit operations in the completion of this task. Our study found that all models which learned from edit operations were not as effective at repairing bugs as models which learned from fixed code segments directly. This evidences that learning edit operations does not offer an advantage over the standard approach of translating directly from buggy code to fixed code. We conduct an analysis of this lowered efficiency and explore why the complexity of the edit operations-based models may be suboptimal. Interestingly, even though our Hephaestus model exhibited lower translation accuracy than the baseline, Hephaestus was able to perform successful bug repair. This success, albeit small, leaves the door open for other researchers to innovate unique solutions in the realm of automatic bug repair.
Fault Tolerant and Data Oriented Scientific Workflows Management and Scheduling System in Cloud Computing

Cloud computing is a virtualized, scalable, ubiquitous, and distributed computing paradigm that provides resources and services dynamically in a subscription based environment. Cloud computing provides services through Cloud Service Providers (CSPs). Cloud computing is mainly used for delivering solutions to a large number of business and scientific applications. Large-scale scientific applications are evaluated through cloud computing in the form of scientific workflows. Scientific workflows are data-intensive applications, and a single scientific workflow may be comprised of thousands of tasks. Deadline constraints, task failures, budget constraints, improper organization and management of tasks can cause inconvenience in executing scientific workflows. Therefore, we proposed a fault-tolerant and data-oriented scientific workflow management and scheduling system (FD-SWMS) in cloud computing. The proposed strategy applies a multi-criteria-based approach to schedule and manage the tasks of scientific workflows. The proposed strategy considers the special characteristics of tasks in scientific workflows, i.e., the scientific workflow tasks are executed simultaneously in parallel, in pipelined, aggregated to form a single task, and distributed to create multiple tasks. The proposed strategy schedules the tasks based on the data-intensiveness, provides a fault tolerant technique through a cluster-based approach, and makes it energy efficient through a load sharing mechanism. In order to find the effectiveness of the proposed strategy, the simulations are carried out on WorkflowSim for Montage and CyberShake workflows. The proposed FD-SWMS strategy performs better as compared with the existing state-of-the-art strategies. The proposed strategy on average reduced execution time by 25%, 17%, 22%, and 16%, minimized the execution cost by 24%, 17%, 21%, and 16%, and decreased the energy consumption by 21%, 17%, 20%, and 16%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for Montage scientific workflow. Similarly, the proposed strategy on average reduced execution time by 48%, 17%, 25%, and 42%, minimized the execution cost by 45%, 11%, 16%, and 38%, and decreased the energy consumption by 27%, 25%, 32%, and 20%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for CyberShake scientific workflow.
Secure Blockchain-Based Mental Healthcare Framework:—A Paradigm Shift from Traditional to Advanced Analytics

Modern state-of-the-art technologies, ranging from advanced statistical approaches to deep learning technologies, have fundamentally transformed the digital data world, notably in the healthcare industry. Artificial intelligence (AI), machine learning (ML), blockchain, and deep learning (DL) technologies have all evolved in the recent decade and have piqued the interest of a variety of sectors ranging from industries to environment, wellness to health, commerce to service sectors The latest technological breakthroughs are allowing for new approaches to exploit untapped opportunities in mental healthcare. Inside this mental health care sector, the value proposition for blockchain technology is to securely transmit sensitive patient data among health care institutions while also empowering patients. The healthcare sector and related organizations have begun to adopt state-of-the-art technologies for value-based healthcare diagnosis, but there is still a long way to go because the challenges these organizations face are multidimensional and necessitate the use of appropriate techniques that cannot be trumped. The purpose of this article is to learn about the technology's fundamentals, the crucial impact it can play in the mental healthcare field, and draw attention towards the conceptual framework for secure mental health analytics to be practically worked upon soon. It is crucial to note that determining the entire benefits of blockchain at this time is difficult. We won't be able to arrive at a realistic conclusion on the technology's usefulness until and until it is widely adopted and employed.
Residual 3D U-Net with Localization for Brain Tumor Segmentation

Gliomas are brain tumors originating from the neuronal support tissue called glia, which can be benign or malignant. They are considered rare tumors, whose prognosis, which is highly fluctuating, is primarily related to several factors, including localization, size, degree of extension and certain immune factors. We propose an approach using a Residual 3D U-Net to segment these tumors with localization, a technique for centering and reducing the size of input images to make more accurate and faster predictions. We incorporated different training and post-processing techniques such as cross-validation and minimum pixel threshold.
Survey of Users’ Willingness to Adopt and Pay for Cybersecurity Training

The importance of user behaviour in the cybersecurity domain is widely acknowledged. Users face cyberthreats such as phishing and fraud daily, both at work and in their private use of technology. Using training interventions to improve users' knowledge, awareness, and behaviour is a widely accepted approach to improving the security posture of users. Research into cybersecurity training has traditionally assumed that users are provided such training as members of an organization. However, users in their private capacity are expected to cater for their own security. This research addresses this gap with a survey where 1437 Swedish adults participated. Willingness to adopt and pay for different cybersecurity training types was measured. The included types were; training delivered to users in a context where the training is of direct relevance, eLearning and game-based training. The participants were most willing to adopt and pay for contextual training, while eLearning was the second most favoured training type. We also measured if willingness to pay and adopt cybersecurity training was impacted by the participant's worry about various cyber threats. Surprisingly, no meaningful correlation was found, suggesting that something else than worry mediates willingness to adopt and pay for cybersecurity training.
Statistical and Syllabification Based Model for Nepali Machine Transliteration

Machine Transliteration is one of the important modules for the development of a correct Machine Translation (MT) system. Machine Translation is the technique of converting sentences in one natural language into another using a machine, whereas Machine Transliteration is the method of converting words in one language into phonetically identical words in another. When Machine Translation is unable to translate the Out-of-Vocabulary (OOV) words, Name Entity words, technical words, abbreviation, etc. then Machine Transliteration transliterates these words phonetically. This paper presents a transliteration system for the English-Nepali language pair using the most widely used statistical method with a linguistic syllabification methodology. A model has been designed based on syllable splitting that splits 19,513 parallel entries which contains person names, place, etc. IRSTLM and GIZA++ are used to build the language model (LM) and translation model (TM) i.e. word alignment respectively over parallel entries. For English-Nepali parallel entries on Syllable based split, an accuracy of 87% has been achieved.
An Empirical Analysis on Abstractive Text Summarization

With the massive growth of blogs, news stories, and reports, extracting useful information from such a large quantity of textual documents has become a difficult task. Automatic text summarization is an excellent approach for summarising these documents. Text summarization aims to condense large documents into concise summaries while preserving essential information and meaning. A variety of fascinating summarising models have been developed to achieve state-of-the-art performance in terms of fluency, human readability, and semantically meaningful summaries. In this paper, we have investigated the OpenNMT tool for task text summarization. The OpenNMT is the encoder-decoder-based neural machine translation model which has been fine-tuned for the task of abstractive text summarization. The proposed OpenNMT based text summarization approach has been tested on freely available dataset such as CNNDM & MSMO dataset and depicts their proficiency in terms of ROUGE and BLEU score.
Blockchain Technology and International Data Spaces

Abstract The core objective of the concept of International Data Spaces (IDS) is to enable controlled exchange and sharing of data between organizations, regardless of the type of data. Sharing of data will generate services that become an asset while data providers maintain their sovereignty. IDS furnish a technology enabler for implementing data economies to exchange data and knowledge, which are according to usage policies. Thus, data turns into an economic asset. However, once data have been provided toward IDS, sovereignty of data owners is of pivotal importance, as well as the question of its use and the transfer of incentives to providers. At this point, blockchain technology enters the ballpark. It is instrumental for the implementation and operation of clearing houses as trading platform for data provision and knowledge utilization. The aim of this chapter is to examine and discuss the role of blockchain for IDS. Next to general blockchain foundations and potentials, blockchain’s specific potential for IDS is discussed and its application is demonstrated by four compelling use cases.
Brain Tumor Segmentation Using Neural Network Topology Search

We apply a method from Automated Machine Learning (AutoML), namely Neural Architecture Search (NAS), to the task of brain tumor segmentation in MRIs for the BraTS 2021 challenge. NAS methods are known to be compute-intensive, so we use a continuous and differentiable search space in order to apply a DiNTS search for optimal fully convolutional architectures. Our method obtained Dice scores of 0.9161, 0.8707 and 0.8537 for whole tumor, tumor core and enhancing tumor regions respectively on the test dataset, while requiring no manual design of the network architecture, which was found automatically from the provided training data.
Empirical Analysis of Security Enabled Quantum Computing for Cloud Environment

Although quantum cloud computing (CC) is based on theoretical notions, it still requires experimentation. CC has emerged as an on-demand accessible tool in different practical applications such as digital industry, academics, manufacturing, health sector, and others. Backup, security, processing, and localization are all issues that the cloud is dealing with. As a result, quantum computing has seen tremendous growth in several domains. In literature, the majority of those researchers working on quantum believe it will solve the cloud service issues. Quantum cloud computing (QCC) entails the deployment of quantum computation sources in a cloud environment to overcome the following challenges and concerns that the existing model of simple cloud computation faces. However, not everyone is interested in combining physics-based atomic computing with software-based CC. This study will demonstrate all of the probable benefits and drawbacks of quantum computing (QC) when used in conjunction with a cloud system. The same also cover some of the most recent updates, as well as some uncertain prospects for quantum as a cloud service. This work demonstrates the current state of the artwork in the quantum services domain. Further, a comparative analysis with similar areas is conducted.
Using Chatbots to Support Medical and Psychological Treatment Procedures Challenges, Opportunities, Technologies, Reference Architecture

The advent of chatbots (also called conversational agents) has been recognized to support various treatment procedures in the medical and psychological fields. Chatbots may be particularly useful before and after medical procedures when patients are at home. For example, while being in the preparation phase of a colonoscopy, a chatbot might answer patient questions more quickly than a doctor. It is more and more discussed whether chatbots may be the first entry point for (urgent) medical questions instead of the consultation of a medical expert, as there exist already well-established algorithms for such situations. For example, if a new medical symptom occurs, a chatbot might serve as the first expert to relieve a patient’s situation. Such situations are mainly driven by the trend that patients often have to wait very long for appointments with a proper medical expert due to capacity problems of the healthcare system. While the usage of supporting at home actions of patients with chatbot technologies is typically welcomed by medical experts, the use of this technology to replace them in their core competence, namely diagnosis and therapy, is generally seen highly critical. Apart from the domain side, it must be carefully considered what currently available chatbot technologies can do and not do. Moreover, it has also to be considered, how existing technologies can be established in such highly critical (e.g., if a chatbot gets the message of a patient that indicates to commit suicide) and interdisciplinary field, involving ethical questions as well as questions of responsibility and accountability. This work raises aspects that might be the basis for medical as well as technical experts to better collaborate for proper chatbot solutions. Concretely, an architecture is proposed that should serve as a reference for various medical and psychological scenarios. When using suitable technical solutions based on such architectures, we argue that many opportunities emerge, which can mitigate identified challenges significantly.
Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

Recent years have seen a growth in the spread of digital technologies for self-tracking and personal informatics. Smartphones' in particular, stand out as being an ideal self-tracking technology that permits both active logging (via self-reports) and passive tracking of information (via phone logs and mobile sensors). In this chapter, we present the results of a literature review of smartphone-based personal informatics studies across three different disciplinary databases (computer science, psychology, and communication). In doing so, we propose a conceptual framework for organizing the smartphone-based personal informatics literature. Our framework situates self-tracking studies based on their substantive focus across two domains: (1) the measurement domain (whether the study uses subjective or objective data) and (2) the outcome of interest domain (whether the study aims to promote insight or change in physical and/or mental characteristics). We use this framework to identify and discuss research trends and gaps in the literature. For example, most research has been concentrated on tracking of objective measurements to change either physical or mental characteristics, while less research used subjective measures to study a physical outcome of interest. We conclude by pointing to promising future directions for research on self-tracking and personal informatics and emphasize the need for a greater appreciation of individual differences in future self-tracking research.
A teaching design of ecological class based on immersive virtual reality spatial fusion

This study investigated the effectiveness of the teaching design scheme called the Immersive Virtual Reality Teaching Model (IVRTM) on a small learner population at the researcher's university. The study was based on one-term experimental teaching among the undergraduates. An ecological class was set up, extending classroom teaching to 3-layer teaching space, including a physical layer (classroom-based), an information layer (internet-based), and a virtual-reality layer (VR-based). The research aimed (1) to create an online-offline spatial ecological learning environment for higher learning efficiency with a series of learning activities, (2) to uncover the learning effectiveness of VR assistance in oral English, and (3) to acquire the students' attitudes toward the IVRTM. The study approaches involved literature review, empirical method, questionnaire, interviews, and data analysis. The findings would have a positive significance for the promotion of new technology applications in SLA teaching and provide novel and preliminary references for teachers pursuing an effective teaching design of ecological classes based on IVR spatial fusion.
Unconventional mechanism of virtual-state population through dissipation

Virtual states are a central concept in quantum mechanics. By definition, the probability of finding a quantum system in a virtual state should be vanishingly small at all times. In contrast to this notion, we report a phenomenon occurring in open quantum systems by which virtual states can acquire a sizable population in the long time limit, even if they are not directly coupled to any dissipative channel. This means that the situation where the virtual state remains unpopulated can be metastable. We describe this effect by introducing a two-step adiabiatic elimination method, that we termed hierarchical adiabatic elimination, which allows one to obtain analytical expressions of the timescale of metastability in general open quantum systems. We show how these results can be relevant for practical questions such as the generation of stable and metastable entangled states in dissipative systems of interacting qubits.
Binary cross coupled discriminant analysis for visual kinship verification

Kinship verification refers to comparing similarities between two different individuals through their facial images. In this context, feature descriptors play a crucial role, and few feature descriptors are present in literature to extract kin features from facial images. In this paper, we propose a binary cross-coupled discriminant analysis ( B C 2 D A ) based feature descriptor which is able to extract effective kin features from input facial image pairs. This method reduces the discrimination between kin pairs at the feature extraction stage itself. B C 2 D A converts original kin image pairs to encoded image pairs to reduce the discrimination between them. To make better use of tri-subject kin relations, we further propose multi cross-coupled discriminant analysis ( M C 2 D A ) . This method reduces the discrimination between child and both parents’ images at the feature extraction stage. Extensive experiments were conducted on six kinship datasets such as KinfaceW-I/II, Cornell, FIW, TSKinface UBKinface to show the efficacy of the proposed algorithm. • In this article, a novel binary cross-coupled discriminant analysis ( B C 2 D A ) based feature descriptor is proposed. It is a pixel-wise encoding method that converts the original kin pairs into decimal encoded kin image pairs. The encoding is done to increase the correlation between input pairs. This results in reducing the discrimination between kin pairs at the feature extraction stage itself. • To understand the core of a family, a new feature descriptor multi cross-coupled discriminant analysis method ( M C 2 D A ) is proposed. Tri-subject encoding leads to an increase in the correlation between child with both parents without affecting relations between child and non-family members. • In both cases various facial sections are consider for analysis of verification performance such as full-face ( F F ), right face ( F R ), left face ( F L ), lower face ( F L o ), upper face ( F U ), facial key points ( F K ), and facial smooth region ( F S ).