Reviewerly/exHarmony huggingface dataset:
Towards extending the goal-oriented requirements language with emotion-oriented goals to support socio-technical systems

Requirements engineers focus mostly on gathering and modeling functional and quality (i.e., non-functional) requirements, with a marginal consideration of emotion-oriented requirements, which describe how stakeholders already feel, or would like to feel, when using a software system. Neglecting or oversighting such goals while designing and modeling a system may lead to unacceptable and hard to use systems for some end users. This is a challenging issue, which becomes more serious when building socio-technical systems, where there is a need to understand not only the technical, but also the social requirements of users. This paper proposes a framework for integrating and incorporating Emotion-Oriented Goals (EMOG) into the goal modeling process, with the latter being one of the most important early activities in requirements engineering. Our particular focus in this paper will be on the Goal-Oriented Requirements language (GRL). We aim to characterize the different aspects of stakeholders' emotions and to provide a framework for the systematic integration of user-centric emotions with GRL goal models. This in turn will yield an emotions-aware modeling of systems, which will positively impact the design, modeling, and development of socio-technical systems. We thus propose a long-term research agenda and urge community contributions in this research direction to achieve enhanced emotion-oriented goal modelling.
Tool support for the teaching of state-based behavior modeling

Modeling tools are commonly adopted in classrooms. However, complex state-based behavioral models still pose a challenge for students to understand and validate, mostly because of the intricate semantics of these models. We investigated this challenge and developed dedicated tool support in the form of a validation framework based on the YAKINDU Statechart Tools. Our validation framework simulates environments that interact with the code generated from statecharts as a means to animate various open-ended scenarios and predefined test cases that challenge the students' models. This enables short and user-friendly feedback cycles, which lowers the barrier for students to learn state-based behavioral models. We designed the validation framework to be extensible and made it available as an open source project together with two example environments and complete teaching materials. We report on our experiences in two undergraduate modeling courses (approx. 100 students each). Our results are promising in the sense that we detected positive effects of tool adoption and a surprising lack thereof, which we discuss w.r.t. lessons learned and future work.
Background Verification of Employees using Blockchain

With the ever changing job market, finding a position in a corporation is exceedingly difficult. In order to get a job, people often tend to alter information on their curriculum vitae (CV). As a result, organisations use screening techniques that are incredibly expensive, time-consuming, and ineffective. We present a working prototype of a remedy that resolves this problem by saving and exchanging a job applicant's original documents across a consortium blockchain (Hyperledger). Using blockchain enhances the security, immutability and authenticity of data simultaneously reducing any scope of data alteration close to zero. We have explored two methodologies for storing data on blockchain namely, hashing the document, and saving the document in JSON format in the form of private data. Hashing guarantees privacy of user data and prevents any data exposure in case of security breaches. The latter method involves storing the document in private data and additionally allow users to restrict access rights for others, thereby ensuring the privacy of user data.
Model for Efficient Data Storage on Public Cloud

The increasing use of online services has a great impact on the organizations to adopt cloud services in order to scale and keep pace with upcoming new technologies that require making use of these services more efficiently and effectively with very low downtime. As cloud computing grows, companies are adapting and changing their ways of storing data in the traditional cloud. Images have tended to become a large emphasis when communicating and showing the relevant things when users search for it is a big win. This tends to propose a lot of questions when storing data in the cloud. The biggest concern here is the amount of storage it takes and when scaled is it going to cut the budget gap that the corporation/organization has to spend. This can be contained and control cloud services the organization has consuming through a system of compression and decompression. We could secure the content that is stored in the cloud using encryption and decryption. We can put more data into the images themselves for a faster approach to Retrieve them and store them in the cloud in a very need-to-have manner
Optimism or Pessimism: Is Blockchain Technology Helping the Healthcare Industry?

The healthcare industry is a multifarious system of interconnected entities. Every healthcare sector, whether it is a client or a worker, knows that the only way to stay out of competition is to provide its members with the best-in-class healthcare delivery experience while keeping prices reasonable. There is a challenge in the healthcare industry, that leads to increased costs or inferior health outcomes. Multiple stakeholders are involved in the blockchain technology, and their jobs are to share data and update/verify data. For effective decision making, blockchain technology is dependent on information flow and time bound transactions. By putting the patient at the centre of the healthcare ecosystem and enhancing the security, privacy, and interoperability of health data, blockchain has the potential to alter the way that healthcare is provided. By improving the effectiveness, disintermediation, and security of electronic medical records, this technology may offer a new paradigm for health information exchanges (HIE). Scalability is one of the blockchain's major flaws, even though it is not impenetrable. It is a difficult task to make a query on the blockchain platform and have the result be used for research, statistical analysis, and practical diagnostics. Security of patient clinical data is the main challenge in today's environment. A robust method is needed to handle the private/public key. This article discusses the blockchain's potential and potential drawbacks for the healthcare sector.
Deepfake Video Detection using Neural Networks

Over the past few years, free mobile application tools on Artificial Intelligence and deep learning have made it easy to create reliable face exchanges in a video called “DeepFake” (DF) video that leaves a little hint of traces to check if its fake. Creating a computerized edited video has been demonstrated for quite a long time by actually taking advantage of enhanced visual effects. Recently, Artificial Intelligence has led to a rise in fake content and the ability to access free tools to create it. These purported AI-engineered media are normally called DeepFake(DF). Making a DF with a computerized AI tool is a simple job. However, with regards to identifying this DF, it's a major challenging dispute. Preparing the calculations and training the model to distinguish DF is difficult. The challenge to train an algorithm model to spot the DeepFake (DF) is not simple. We have tried recognizing DF with the use of CNN and RNN. The framework utilizes a CNN for feature extraction at the frame level. The model uses features extracted from the frame level to train the RNN, which then learns to classify videos according to their temporal inconsistencies. Anticipated results, when compared with a large number of hoax videos, were gathered from standard datasets. Using a simple architecture, we will show you how this errand can make your framework accurate.
Performance Evaluation of Proof of Scope Consensus Mechanisms on Hyperledger

The distributed ledger technology first introduced as a baseline technology for Bitcoin is disrupting many fields with its features of immutability and traceability. Applying blockchain in different fields is not one size fits for all solution. The various domains came up with different drawbacks of blockchain. To resolve these problems changes in block structures and consensus mechanisms of blockchain are proposed and used for different use cases or domains. We have proposed a consensus mechanism to reduce latency and increase the throughput of transactions processed by the blockchain network. The evaluation of the newly proposed consensus mechanism is done using queuing theory, mathematical models or simulations. Beyond this, we have used the Hyperledger platform to implement our proposed consensus mechanism Proof of Scope. The performance of implementations of Raft and PoW-Ethash consensus mechanisms from Hyperledger are evaluated with the proposed consensus mechanism. The varying load was applied to the network to measure transaction throughput. It is found that the latency is lower and throughput is better of the proposed consensus mechanism than that of Raft and PoW-Ethash for the same network conditions.
A structured approach for functional analysis of context-aware systems

With the popularity of IoT (Internet of Things) technology, more and more engineering systems are integrated with context-aware functionalities. It is self-evident that functional analysis is a critical process for successful development of such context-aware systems (CASs). Since existing functional analysis approaches are primarily for developing traditional engineering systems, this paper aims at developing a structured approach for assisting engineers in carrying out functional analysis of CASs. Based on the concept of context from an interactive perspective, this research analyzes what constitutes the context of a CAS and its basic features. Then a structured approach for functional analysis is proposed, which systematically elaborates how to define and analyze the context of a CAS, how to identify functionalities from the context, and how to develop the functional structure of a CAS. A case study of the functional analysis of a self-service checkout system is employed to demonstrate the proposed approach, which shows that the proposed approach can help designers carry out functional analysis of CASs in an effective manner.
Poster

Contemporary home control systems determine real-time heating/cooling demands utilizing smart sensor devices, giving rise to demand control heating, ventilation, and cooling (DCHVAC) systems, thus improving the home's energy efficiency. The adoption of activity monitoring in the smart home control system further augments the controller efficiency and improves occupants' comfort and productivity, elderly monitoring, and so forth. Additionally, the learned occupants' activity patterns help embed machine learning (ML)-based abnormality detection capability to track inconsistencies among the zone sensor measurements. Hence, the incorporation of an activity monitoring system assists anomaly detection models (ADMs) in detecting false data injection (FDI) attacks that are being glowingly researched due to their massive damage capability. However, in this work, we propose a novel attack strategy that identified that the knowledge of occupants' activities along with indoor air quality (IAQ) and occupancy sensor measurements allows the attackers to launch even more hazardous attack (i.e., significant increment in energy cost/ worsening health conditions for the occupants). Hence, it is crucial to analyze the security of the activity monitoring-based smart home DCHVAC system. Accordingly, we propose a novel formal threat analyzer that analyzes the threat space of the smart home DCHVAC control system, which is modeled by rule-based control policies and ML-based ADMs. The rules from the ADM are extracted through an efficient algorithm. The constraints associated with the rules are solved through a satisfiability module theorem (SMT)-based solver. %We performed our initial evaluation of the proposed threat analyzer's effectiveness on the Center of Advanced Studies in Adaptive Systems (CASAS) dataset using some metrics. We will further experiment with other metrics along experimenting with our collaborator's dataset (KTH live-in lab) and open-source Örebro datasets for assessing the framework with realistic occupants' activity. Moreover, we also created our prototype testbed for evaluating the feasibility of the proposed attack and threat analyzer.
Addressing non-functional requirements of adaptive IoT systems

Non-functional requirements (NFR) of IoT systems increase the complexity of system development. The success of such systems also largely depends on dealing with NFRs correctly. However, inter-dependencies among NFRs often introduce conflicts. These conflicts impede implementing the system with all specified NFRs. Furthermore, the heterogeneous nature of IoT systems makes it critical to incorporate NFRs in the early stages of software development. This PhD thesis proposes a model-driven requirements engineering procedure to address different NFRs of adaptive IoT systems. This approach will incorporate non-functional requirements at different levels of abstraction with model-driven techniques to minimize conflicts among elicited NFRs. We are extending use case models, soft goal models, and behavioural models to elicit, analyze, and specify interoperability, scalability, availability, and context-awareness of IoT systems. As interoperability and context-awareness are two NFRs that affect the adaptiveness of IoT systems most, we addressed these two NFRs first. Availability and scalability NFRs will be incorporated as this thesis progresses.
Automated, traceable, and interactive domain modelling

In domain modelling, practitioners manually extract analyzable and more concise domain models from problem descriptions which express requirements in natural language. With automated domain modelling support using existing approaches, some challenges remain unaddressed - inadequate accuracy of extracted models, no support for traceability of modelling decisions, and no facility for system-modeller interactions. To address these challenges and better support practitioners, we present our bot-assisted solution. Furthermore, we evaluate the effectiveness of our solution and find promising results which warrant further research in this direction.
Effectiveness Verification of Various English Word Learning Methods: Focused on Simple Memorization, Keyword Method, and Visual Storytelling

We conducted a study to investigate a correlation between psychological factors such as stress level, memory efficacy, and learning confidence in learning methods on simple memorization(SM), keyword methods(KW), and visual storytelling(VS). Based on the data obtained from a survey conducted on 120 undergraduate and graduate students, we proceeded much analysis over each and every factor between the methods. As a result, VS showed superiority over SM. The three methods showed significant differences in learning stress, memory efficacy, and learning confidence in memory methods. What has been revealed about the relationship between them is that VS showed relatively low-stress levels and high memory efficacy and learning confidence. However, SM expressed a higher stress level than the other two learning methods as the memory efficacy and learning confidence were lower in SM. Through this study, we concluded that image and storytelling contents could relieve much stress on students and improve memory efficiency and learning confidence in learning methods on various new vocabulary.
A Study on the Influence of Korean Cultural Perception on Korean Language Learning Activities and Visit Intention in Korea

In this study, a survey was conducted to test the relationship between Korean culture perception, learning activity, and visit intention, and reliability, validity, hypothesis verification, and mediating effect were confirmed using the Smart-PLS analysis method. The verification results obtained based on the above research results are as follows. First, it was tested that Korean cultural awareness had a positive (+) effect on learning activism and visiting intention to Korea. Second, it was tested that learning activism had a positive (+) effect on Korea's visit intention. Third, it was tested that learning activism has a mediating effect between Korean cultural awareness and Korean visit intention. Based on these results, implications were presented.
Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration

This study is an attempt to propose a multiple representational metaphor education model that combines linguistic representation and visual representation using online telestration. The advent of the media and online era has incorporated not only the understanding of linguistic representation l but also the understanding of visual representation into an important phase of cognitive behavior and requires the implementation of online learning. In such an era's needs, it can be said that teaching-learning makes metaphors be used as a tool for thinking and cognition in an online environment, learning leads learners to a new horizon of perception by combining linguistic representation and visual representation. The multiple representational metaphor education model using online telestration will have a two-way dynamic interaction in an online environment, and it will be possible to improve learning capabilities by expressing various representations. Multiple representational metaphor education using online telestration will allow us to consider new perspectives and various possibilities of expression to interpret the world by converging and rephrasing verbal and visual representations using media in an online environment.
NL2Viz: natural language to visualization via constrained syntax-guided synthesis

Recent development in NL2CODE (Natural Language to Code) research allows end-users, especially novice programmers to create a concrete implementation of their ideas such as data visualization by providing natural language (NL) instructions. An NL2CODE system often fails to achieve its goal due to three major challenges: the user's words have contextual semantics, the user may not include all details needed for code generation, and the system results are imperfect and require further refinement. To address the aforementioned three challenges for NL to Visualization, we propose a new approach and its supporting tool named NL2VIZ with three salient features: (1) leveraging not only the user's NL input but also the data and program context that the NL query is upon, (2) using hard/soft constraints to reflect different confidence levels in the constraints retrieved from the user input and data/program context, and (3) providing support for result refinement and reuse.
Do bugs lead to unnaturalness of source code?

Texts in natural languages are highly repetitive and predictable because of the naturalness of natural languages. Recent research validated that source code in programming languages is also repetitive and predictable, and naturalness is an inherent property of source code. It was also reported that buggy code is significantly less natural than bug-free one, and bug fixing substantially improves the naturalness of the involved source code. In this paper, we revisit the naturalness of buggy code and investigate the effect of bug-fixing on the naturalness of source code. Different from the existing investigation, we leverage two large-scale and high-quality bug repositories where bug-irrelevant changes in bug-fixing commits have been explicitly excluded. Our evaluation results confirm that buggy lines are often less natural than bug-free ones. However, fixing bugs could not significantly improve the naturalness of involved code lines. Fixed lines on average are as unnatural as buggy ones. Consequently, bugs are not the root cause of the unnaturalness of source code, and it could be inaccurate to identify buggy code lines solely by the naturalness of source code. Our evaluation results suggest that the naturalness-based buggy line detection results in extremely low precision (less than one percentage).
Poster

Today, some applications encrypt our data, while many others do not. Users must accept the level of protection the application provides. Our research aims to support client-to-client encryption at the system level so that users can enable encryption for data in any application (e.g., email, Slack), even if the application does not support it. Two users can exchange sensitive data without it being accessible to any applications or systems along the path. We will describe the challenges to designing the system, the techniques we will use to build the system, and the advantages of encryption at the system level.
Sentiment in software engineering: detection and application

In software engineering the role of human aspects is an important one, especially as developers indicate that they experience a wide range of emotions while developing software. Within software engineering researchers have sought to understand the role emotions and sentiment play in the development of software by studying issues, pull-requests and commit messages. To detect sentiment, automated tools are used, and in this doctoral thesis we plan to study the use of these sentiment analysis tools, their applications, best practices for their usage and the effect of non-natural language on their performance. In addition to studying the application of sentiment analysis tools, we also aim to study self-admitted technical debt and bots in software engineering, to understand why developers express sentiment and what they signal when they express sentiment. Through studying both the application of sentiment analysis tools and the role of sentiment in software engineering, we hope to provide practical recommendations for both researchers and developers.
Second-Order Low-Randomness d + 1 Hardware Sharing of the AES

In this paper, we introduce a second-order masking of the AES using the minimal number of shares and a total of 1268 bits of randomness including the sharing of the plaintext and key. The masking of the S-box is based on the tower field decomposition of the inversion over bytes where the changing of the guards technique is used in order to re-mask the middle branch of the decomposition. The sharing of the S-box is carefully crafted such that it achieves first-order probing security without the use of randomness and such that the sharing of its output is uniform. Multi-round security is achieved by re-masking the state where we use a theoretical analysis based on the propagation of probed information to reduce the demand for fresh randomness per round. The result is a second-order masked AES which competes with the state-of-the-art in terms of latency and area, but reduces the randomness complexity over eight times over the previous known works. In addition to the corresponding theoretical analysis and proofs for the security of our masked design, it has been implemented on FPGA and evaluated via lab analysis.