Reviewerly/exHarmony huggingface dataset:
Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Abstract Throughout the years neural networks have been based on the perceptron model of the artificial neuron. Attempts to stray from it are few to none. The perceptron simply works and that has discouraged research around other neuron models. New discoveries highlight the importance of dendrites in the neuron, but the perceptron model does not include them. This brings us to the goal of the paper which is to present and test different models of artificial neurons that utilize dendrites to create an artificial neuron that better represents the biological neuron. The authors propose two models. One is made with the purpose of testing the idea of the dendritic neuron. The distinguishing feature of the second model is that it implements activation functions after its dendrites. Results from the second model suggest that it performs as well as or even better than the perceptron model.
Making the Most Out of Video Conference Learning

With rising Covid-19 infections and deaths, education authorities in Zimbabwe unceremoniously introduced online learning where Whatsapp, electronic mail, Facebook, video conferencing, and radio lessons featured most prominently. At university level, video conference learning was introduced amid much pomp and speculation with varying degrees of success. This study examines the experiences of first-year students at one state university in Zimbabwe. The study revealed that whilst first year university students benefitted from the rather new and emerging technology, some students were greatly affected by the digital divide which threatened their right to education. This mixed methods research used the exploratory sequential research design where students' qualitative responses were juxtaposed with lecturers' quantitative responses. The study proposed the Enhanced Tripartite Online Learning Support framework where universities, students, and parents collaboratively work together to make video conferencing more effective, more affordable, and more enjoyable.
Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre

Abstract In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of virtualized resources in a data center that can be configured according to users’ needs. In other words, cloud computing faces the problem of a huge number of users requesting unlimited jobs for execution on a limited number of resources, which increases energy consumption and the network cost of the system. This study provides a complete analysis of classic scheduling techniques specifically for handling data-intensive workloads to see the effectiveness of the energy and network costs of the system. The workload is selected from a real-world data center. Moreover, this study offers the pros and cons of several classical heuristics-based job scheduling techniques that take into account the time and cost of transferring data from multiple sources. This study is useful for selecting appropriate scheduling techniques for appropriate environments.
On a self-recovery digital watermarking scheme robust against spatial and temporal attacks on compressed video

In recent decades, the use of digital media has increased. With it, digital media exposure to unwanted manipulations that, although unintentional, can affect third parties. These manipulations in digital watermarking are known as attacks, consisting of intentional or unintentional editing of multimedia content such as images, videos, or audio streams. For video sequences, attacks can be performed in spatial and temporal domains. The former includes changes to individual frames, while the latter involves altering sub-sequences, such as inserting or deleting frames. This work aims to achieve approximate recovery from these attacks using digital watermarking. Unlike most existing works, this scheme uses the compression domain since most video applications use some compression standard to transmit and store these sequences. By adapting popular image block-based popular recovery schemes for large video sequences and using a novel scrambled watermark mapping, satisfactory recovery and visual quality results are achieved.
FastReach: A system for privacy-preserving reachability queries over location data

Reachability, which indicates whether a person is reachable from another person through a sequence of location-based contacts, is of importance in many domains, such as public health. Nowadays, large amounts of location data are collected by various Location-Based Services (LBS), which helps to improve the accuracy of reachability queries. Meanwhile, as many LBS systems are deployed on cloud platforms, users have huge concerns about the leakage of their location data. In this paper, we consider how to implement privacy-preserving reachability queries over private location data. We first propose SecReach, a system which can process secure reachability queries by applying Bloom filters and Homomorphic Encryption. Based on the main idea of SecReach, we propose a more efficient system, FastReach, that significantly reduces the computation costs by using Single Instruction Multiple Data operations and Z-order index. We prove the security of our systems rigorously, and demonstrate their efficiency comprehensively.
WFP-Collector: Automated dataset collection framework for website fingerprinting evaluations on Tor Browser

Website Fingerprinting (WFP) is a technique that analyses browsing network traffic to infer a webpage that a user browsed in Tor Browser. A sufficiently large and clean dataset is essential for quality and accurate WFP experiments. Thus, there is a corresponding need to automate the dataset collection, filtering, and validation processes. This work introduces a new paradigm, WFP-Collector, an automatic dataset collection framework for WFP experiments. WFP-Collector enables researchers to automatically (1) create a visit database for webpage crawling, (2) collect various data and log for in-depth analysis, (3) webpage visits in tablet and mobile browsing modes, (4) throttle network bandwidth and latency performance, (5) validate and filter the collected data, (6) compress and upload the collected data to cloud storage, and (7) completion notification using Telegram and email. We developed a proof-of-concept of the WFP framework for simulation and comparison. We found that the WFP-Collector framework collects nine data items and produces over 55% larger collected data size than existing approaches. The captured packet size in tablet and mobile browsing modes is up to 57.5% smaller than in desktop mode. Moreover, the file compression in WFP-Collector can reduce up to 39.9% of the storage space required for data collection.
One more set: Mitigating conflict-based cache side-channel attacks by extending cache set

Caches are vulnerable to side-channel attacks as a type of shared hardware resource. Most existing defense methods can be categorized into two categories: partition and randomization. However, cache partition will bring a large performance overhead and randomization has been proven to be not safe enough. In this paper, we propose a design called ExtendCache. Different from other defense schemes applied to the entire cache, ExtendCache focuses on cache sets that may cause information leakage, which can greatly reduce the impact on performance. Based on the fact that a conflict-based attack requires a large number of accesses to the target set, ExtendCache locates the cache set that may contain sensitive information and be stolen by the attacker according to the number of accesses. After locating a suspicious set, it extends the set by borrowing cache lines from another set. This prevents attackers from controlling the state of the target set and observing effective victim behavior. We implemented ExtendCache on gem5 and took the modular square algorithm as an example to prove the effectiveness of our design. When evaluated using the SPEC2017 benchmarks, ExtendCache had minimal impact on performance, with only a 0.48% average performance loss observed.
Explanations on Demand - a Technique for Eliciting the Actual Need for Explanations

Explainability is an emerging quality aspect that is just beginning to be explored in the context of requirements engineering. Since there are many different types of explanations, it is not trivial to determine where users need which type of explanations. As the need for explanations depends on the user and the context, the user should participate in the elicitation process. However, previous research has shown that simply asking users if they want explanations does not lead to meaningful results, since users tend to answer this question in the affirmative, regardless of whether they need the explanation or not. To address this problem, we developed a technique that allows users to realistically put themselves in the context of the system and thus determine where explanations are really required. This technique requires a framework as requirements that are being raised with this approach relate to the different types of explanations. These types of explanations need to be identified and classified. To evaluate our technology, we conducted a user study with 21 participants. With the help of our technique supported by the framework, requirements for explainability can be elicited more realistically.
Requirements Engineering for Explainable AI

Artificial intelligence (AI) has a growing influence on every aspect of life. These systems need to be transparent, accountable, and explainable to be reliable and safe. Explain-ability helps to reduce the opacity of such systems and aids in gaining end-user trust in the system. Therefore, explainability can be seen as an emerging requirement for AI-based systems. A number of studies emphasize transparency and explainability of AI-based systems. However, studies on identifying stakeholders to these requirements and how to elicit and specify explainability requirements are still rare and at an early stage. This Ph.D. research aims to establish a comprehensive reference process model that can serve as a guide for practitioners to address explainability requirements concerning AI-based systems.
The Benefits of Agent-Oriented Goal Model in Developing Data Protection Application for Lecturers

Ensuring that all teaching staff in the university follow data protection regulations is a great challenge. In developing a sociotechnical system that would make data protection topics easily accessible and help to avoid breaches, it is essential that the target groups are involved. One promising method to involve target groups who do not have an IT background is using agent-oriented goal models (AOM). The aim of the current study was to test the AOM usability in different stages of the requirements engineering when developing a data protection application for university lecturers. More specifically, we focused on how AOM supports the requirement elicitation with target groups, communication with funders, and the work of analysts during the requirement specification phase. Based on document analysis and pilot interviews, we created an initial goal model, which was validated with focus group interviews. The updated goal model was used to pitch the idea to funders. The feedback from focus group participants, funders and analyst concerning the AOM was analyzed. The results showed that AOM facilitated the interviews and activated the overall discussion. The presentation of AOM during the interviews helped to identify twice as many requirements than without using AOM. From a funder's perspective, the AOM made it easier to understand the necessity of the application. From an analyst perspective, AOM helped to keep focus during prioritization and creation of user stories. Overall, the AOM is a method that can be effectively implemented throughout the whole requirements engineering process.
Evaluating Privacy Questions from Stack Overflow: Can ChatGPT Compete?

Stack Overflow and other similar forums are commonly used by developers to seek answers for their software development as well as privacy-related concerns. Recently, ChatGPT has been used as an alternative to generate code or produce responses to developers' questions. In this paper, we aim to understand developers' privacy challenges by evaluating the types of privacy-related questions asked on Stack Overflow. We then conduct a comparative analysis between the accepted responses given by Stack Overflow users and the responses produced by ChatGPT for those extracted questions to identify if ChatGPT could serve as a viable alternative. Our results show that most privacy-related questions are related to choice/consent, aggregation, and identification. Furthermore, our findings illustrate that ChatGPT generates similarly correct responses for about 56% of questions while for the rest of the responses, the answers from Stack Overflow are slightly more accurate than ChatGPT.
Partitioning the Problem Space in Healthcare-Oriented Requirements Engineering

The problem space of wellbeing and healthcare domains in reviewed., with implications for RE
Automated Identification of Deontic Modalities in Software Engineering Contracts: A Domain Adaptation-Based Generative Approach

Contracts are legally binding agreements between parties that establish rights, obligations, and terms for their business relationship. They articulate the deontic modalities (Obligations, Permissions, Prohibitions, and Exclusions) that apply to those involved in the contractual agreement. Deontic modalities can be leveraged to effectively elicit Software Engineering (SE) requirements. In this paper, we propose a novel approach for identifying deontic modalities from contracts, employing a combination of text generation and domain adaptation techniques. Among the SOTA approaches we experimented with, the T5-large model yielded the best results, with an average precision and recall of 0.96 and 0.95, respectively. Comparing our approach with previous methods, the results show that our approach handles significant class imbalances in the training data and demonstrates good generalization ability on new datasets.
Zero-shot Learning for Named Entity Recognition in Software Specification Documents

Named entity recognition (NER) is a natural language processing task that has been used in Requirements Engineering for the identification of entities such as actors, actions, operators, resources, events, GUI elements, hardware, APIs, and others. NER might be particularly useful for extracting key information from Software Requirements Specification documents, which provide a blueprint for software development. However, a common challenge in this domain is the lack of annotated data. In this article, we propose and analyze two zero-shot approaches for NER in the requirements engineering domain. These are found to be particularly effective in situations where labeled data is scarce or non-existent. The first approach is a template-based zero-shot learning mechanism that uses the prompt engineering approach and achieves 93% accuracy according to our experimental results. The second solution takes an orthogonal approach by transforming the entity recognition problem into a question-answering task which results in 98% accuracy. Both zero-shot NER approaches introduced in this work perform better than the existing state-of-the-art solutions in the requirements engineering domain.
Taming Cross-Tool Traceability in the Wild

Along the process of engineering a safety-critical system, software engineers produce various artifacts, ranging from requirements and change requests to source code and test cases. In order to aid the development of the system and to adhere to the complex safety regulations and standards in place, engineers are often required to maintain bidirectional and consistent traceability between the produced artifacts. However, such artifacts are rarely maintained in one single tool. Because of that, the cross-tool bidirectional traces have to frequently be manually maintained, which can easily become a very time-consuming or infeasible task. Through interviews and observations at our industry partners in regulated domains, we observed that a number of different strategies are used to deal with this challenge. The use of naming conventions, querying, or URL links is observed in the industry. However, they have their shortcomings and hinder engineers from realizing the full potential that traceability can offer. Knowing the challenges in the industry, we explored existing literature. A range of approaches in the literature aims at dealing with traceability, but often they are context-specific and not easily transferable into practice. Given this gap between the state-of-the-art and industry needs, we performed interviews with our industry partners and analyzed tertiary studies from the literature to obtain a better understanding of what traceability properties are needed to unleash the potential of traces. We identified properties that represent the shared challenges between the related work and the industry requirements: discoverability, type checks, flexibility, navigability, and extensibility. While each property is addressed by a subset of the available solutions, we propose a novel traceability approach to support all of them in a single tool.
Second International Workshop on Requirements Engineering Frameworks: REFraming Elicitation (REFrame 2/2023)

The REFrame workshop “REFraming Elicitation” aims at bringing together a broad spectrum of experts interested in requirements elicitation, concerning practical experiences and scientific findings alike. It provides a space for reflecting on and discussing current research, challenges, and experiences. The ultimate goal of the community organizing the workshop, and to which the workshop shall contribute, is to collaboratively build and publish a compendium of elicitation frameworks (methods, techniques, best practices etc.) that shall help address and solve elicitation challenges. The workshop includes peer-reviewed paper submissions, invited talks, and interactive discussion sessions.
Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Creativity focuses on the generation of novel and useful ideas. In this paper, we propose an approach to automatically generating creative requirements candidates via the adversarial examples resulted from applying small changes (perturbations) to the original requirements descriptions. We present an architecture where the perturbator and the classifier positively influence each other. Meanwhile, we ensure that each adversarial example is uniquely traceable to an existing feature of the software, instrumenting explainability. Our experimental evaluation of six datasets shows that around 20% adversarial shift rate is achievable. In addition, a human subject study demonstrates our results are more clear, novel, and useful than the requirements candidates outputted from a state-of-the-art machine learning method. To connect the creative requirements closer with software development, we collaborate with a software development team and show how our results can support behavior-driven development for a web app built by the team.
Harnessing Cognitive and Psychological Theories for RE: Navigating Emotions in the Ocean of Requirements Elicitation

The rapid diffusion of software systems into all aspects of our lives has raised new challenges for requirements engineers. These challenges call for an in-depth understanding of system usage scenarios and user capabilities, attitudes, emotions, barriers, and more. Current methods and tools, such as the Design Thinking methodology and the Empathy Map tool, have been proposed for supporting the multifaceted requirements elicitation process. However, using these methods and tools effectively is not simple, requiring not only competency but also motivation, high self-efficacy, and positive attitudes. These emotional attributes are strongly influenced by the work environment in which the requirements engineers operate.
Create, Explore and Analyse Traceability Knowledge Graphs

“Glad you introduced a traceability process in your company. Now you are able to relate the various types of artifacts in order to build up a large traceability graph” - what are the next steps? How do you deal with the enormous amount of information? We provide use cases to create, analyse and explore traceability graphs, based on our experiences from itemis ANALYZE, our professional traceability management system, that connect your whole development toolchain, including modelling tools and code. Furthermore, we fine-grain the ‘requirements’ in traceability by emerging Machine learning (ML) techniques that aid in analysing their similarities thereby proposing a tailored solution which forms the basis for requirements-specific knowledge graph. Finally, we use graph-based algorithms to analyse different use-case of created knowledge graphs.
Mining Reddit Data to Elicit Students' Requirements During COVID-19 Pandemic

Data-driven requirements engineering leverages the abundance of openly accessible and crowdsourced information on the web. By incorporating user feedback provided about a software product, such as reviews in mobile app stores, these approaches facilitate the identification of issues, bug fixes, and implementation of change requests. However, relying solely on user feedback about a software product limits the possibility of eliciting all requirements, as users may not always have a clear understanding of their exact needs from the software, despite their wealth of experience with the problem, event, or challenges they encounter and use the software to assist them. In this study, we propose a shift in requirements elicitation, focusing on gathering feedback related to the problem itself rather than relying solely on feedback about the software product. We conducted a case study on student requirements during the COVID-19 pandemic in a higher education institution. We gathered their communications from Reddit during the pandemic and employed multiple machine-learning and natural language processing techniques to identify requirement sentences. We achieved the F-score of 0.79 using Naive Bayes with TF-IDF when benchmarking multiple techniques. The results lead us to believe that mining requirements from communication about a problem are feasible. While we present the preliminary results, we envision a future where these requirements complement conventionally elicited requirements and help to close the requirements gap.
Automating Extraction of Problem Diagrams from Natural Language Requirement Documents

Embedded systems are known for their high complexity and the time cost of manually analyzing and modeling their requirement documents is significantly high. To shorten the time for requirement modeling and reduce the workload of requirements engineers, this paper proposes an automated approach to extract the problem diagram from natural language documents of embedded systems. Specifically, we design neural network models to extract modeling elements from requirements documents and then assembled them into problem diagrams. We conduct experiments on four new datasets collected by this work, using three widely used metrics for evaluation. The experimental results indicate that (1) the approach can extract more correct entity elements, improving 12.99% relative performance compared to the baseline model. (2) The approach is effective to extract the relation elements and the F1 score reached 92.86%. (3) The approach successfully extracts the problem diagram on a real embedded system. Therefore, the approach proposed in this paper can assist in extracting the modeling elements and generating the problem diagram to improve the efficiency of embedding system requirements modeling.