Reviewerly/exHarmony huggingface dataset:
LTL cross entropy optimisation for quadcopter task orchestration

This paper presents a task orchestration framework for multi-agent systems utilising linear temporal logic (LTL) and cross entropy optimisation, a stochastic optimisation technique used for rare-event sampling. We define task orchestration as a combination of task decomposition, allocation and planning for a quadcopter or team of quadcopters given a high-level specification. Specifically, we consider tasks that are complex and consist of environment constraints, system constraints, or both, that must be satisfied. We first approach motion planning for the single agent case where transition systems for the environment allow tasks to be developed as linear temporal logic (LTL) specifications. Trajectories are then generated via motion primitives for a single quadcopter and optimised via cross entropy to ensure optimal satisfaction of a cost function. We extend this work to the multi-agent case where a team of homogeneous quadcopters are considered to satisfy an LTL specification. In order to provide faster computations and initial cost-agnostic sampling, we formulate the online version of multi-agent task allocation via cross entropy for tasks specified in LTL specifications. The results of this framework are verified in simulation and experimentally with a team of quadcopters.
M3triCity: Visualizing Evolving Software &amp; Data Cities

The city metaphor for visualizing software systems in 3D has been widely explored and has led to many diverse implementations and approaches. Common among all approaches is a focus on the software artifacts, while the aspects pertaining to the data and information (stored both in databases and files) used by a system are seldom taken into account.We present M3triCity, an interactive web application whose goal is to visualize object-oriented software systems, their evolution, and the way they access data and information. We illustrate how it can be used for program comprehension and evolution analysis of data-intensive software systems.Demo video URL: https://youtu.be/uBMvZFIlWtk
Research on software system development and practical application based on SpringBoot + Vue

This system completes the design and development of student internship and training system through several steps: development environment construction, database design, system design and development. The system adopts SpringBoot + Vue which is a front and rear end separation architecture, and the database system adopts MySQL 5 7. The security framework adopts Shiro framework for system development. For the system development, this paper expounds the process of system development and how to realize the system function through the code, gives the implementation code of some port functions, and expounds the front-end page design, program design architecture and the real and modern code of data security verification in the process of system development. The front and rear end separation mode not only shortens the system development time, but also enables the front and rear end personnel to give full play to their own advantages and develop beautiful high-performance systems.
Towards A Theory of Shared Understanding of Non-Functional Requirements in Continuous Software Engineering

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. Research has shown NFRs are, in practice, poorly defined and difficult to verify, especially in agile environments. Continuous software engineering (CSE) practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to NFRs. However, the level of shared understanding achieved across an organization is not well-understood. This dissertation builds the foundations towards a theory of the complex and intricate relationship between shared understanding of NFRs and CSE.
Dhivehi OCR Engine

Maldives is a small country located at the Arabian Sea of the Indian Ocean with a small population speaking and writing in their own language - Dhivehi. Although Dhivehi content has increased significantly over the years, it is still quite lacking in the digital literature tools domain. Therefore, most of these contents are often stored as physical copies, or digital scanned images. To ensure that these documents are not lost, it is important to digitize them. In order to eliminate the need of manual data entry as well as serve as a first step to building a storage repository for Dhivehi documents, a Dhivehi OCR engine using image processing techniques, a unique character level segmentation approach, and a character level CNN model is proposed.
Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping

A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs, Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for scikit-learn, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without affecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; • Computing methodologies → Machine learning.
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning

The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.
TauLiM: Test Data Augmentation of LiDAR Point Cloud by Metamorphic Relation

With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversifted annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. Therefore, it is important to generate diversifted point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. The result shows that TauLiM can effectively augment diversified test data and test the object detection model. The video of TauLiM is available at https://www.youtube.com/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.
Access Control Framework in the Cloud based on Multi-Blockchain with Light Privacy Protection

The applications of the blockchain technology are still being discovered. When a new potential disruptive technology emerges, there is a tendency to try to solve every problem with that technology. With the expansion and intensity of cloud computing, cloud security has become a critical concern in the area. One of the most important security measures for securing sensitive data hosted in the cloud is access control. To overcome this problem, we offer a multi-blockchain access control architecture with lightweight privacy protection in cloud called Autherization-Privacy. We begin with account address of the node in blockchain as the unique id and at the same time, define the admission permission of data for cloud, which is cypher and kept in blockchain.
Construction and Validation of Early Software Size Estimation Models Based on ADAF-Adjusted ACD Metrics

Abstract Software size estimation is a vital activity of software project planning and management. Early software size estimation is a challenging task due to the limited information available during the early phases of software development. The goal of this paper is to construct and validate early software size estimation models based on four analysis-to-design adjustment factor (ADAF)-adjusted analysis class diagram metrics (i.e. ADAF-adjusted number of classes, ADAF-adjusted number of attributes, ADAF-adjusted number of methods and ADAF-adjusted number of relationships) using stepwise multiple linear regression and leave-one-out cross-validation. Furthermore, the prediction accuracy of the best-performing proposed model is also compared with the model based on objective class points. The results of this comparison reveal that our proposed method reduces errors significantly (i.e. on average, 16% reduction in mean absolute residual and 24% reduction in mean squared error).
Towards Mining OSS Skills from GitHub Activity

Open source software (OSS) development relies on diverse skill sets. However, to our knowledge, there are no tools which detect OSSrelated skills. In this paper, we present a novel method to detect OSS skills and prototype it in a tool called DisKo. Our approach relies on identifying relevant signals, which are measurable activities or cues associated with a skill. Our tool detects how contributors 1) teach others to be involved in OSS projects, 2) show commitment towards an OSS project, 3) have knowledge in specific programming languages, and 4) are familiar with OSS practices. We then evaluate the tool by administering a survey to 455 OSS contributors. We demonstrate that DisKo yields promising results: it detects the presence of these skills with precision scores between 77% to 97%. We also find that over 54% of participants would display their high-proficiency skills. Our approach can be used to transform existing OSS experiences, such as identifying collaborators, matching mentors to mentees, and assigning project roles. Given the positive results and potential impact of our approach, we outline future research opportunities in interpreting and sharing OSS skills. CCS CONCEPTS • Software and its engineering → Open source model.
Augmented reality-based application for interactive shopping experience

Advancements in new technologies such as augmented reality (AR) and virtual reality (VA), it has provided us with a new world to explore and make most applications out of it. These technologies are still developing, and many researchers are trying to develop new applications every day. Encouraged by the recent advances in augmented reality, we propose a real-world application of augmented reality which provides an interactive in-store shopping experience. The mobile application will let users see all the essential product details in his/her real-world environment in a very interactive and effective way using graphs, pie charts, 3D models. Our idea is to utilise an already present efficient convolution neural network such as AlexNet for recognising the product from the image taken and then getting all the essential product details that are stored in the database, and then project these details out in user’s real-world environment.
Respond to Change or Die: An Educational Scrum Simulation for Distributed Teams

Scrum is best learned by doing, e.g. through simulation, as it is simple to understand but difficult to master. During the Covid-19 pandemic, we could not use the traditional face-to-face Scrum Lego simulation game, but had to utilize something workable in an online environment. In this paper we present an online Scrum simulation for distributed teams that was created in a multiplayer game "Don’t Starve Together" (DST) through iterative reflective work and analysis. We ran the simulation with 25 Scrum teams on four different courses with participants from eight universities located in three countries. We collected feedback from 244 participants by analysing 196 student learning diaries and 84 student evaluation surveys, and by running a retrospective with 19 industry participants. The participants’ feedback was highly positive. The main reported learning outcomes were communication, estimation, Scrum in practice, communication and collaboration with industrial Product Owners, Scrum events, work organisation, teamwork and prioritisation.
Terminals All the Way Down

The terminal is a remarkably resilient interface for many programming activities. From its humble beginnings as a teletypewriter more than half a century ago, through video terminals like the VT100, to present-day windowed terminal emulators, it has remained a relevant and productive, albeit very gaunt, interface. This is in stark contrast with feature-rich integrated development environments (IDEs), which on top of their innate complexity allow for the creation of custom extensions. Indeed, researchers have been prolific in proposing innumerable, but often ignored, plug-ins. We propose using inter-connected windowed terminal emulators as the foundation for a new type of distributed and language-agnostic development environment. By delegating the handling of a system’s source code to a set of dedicated windowed terminal emulators we aim at complementing existing visual tools and leveraging the large body of existing command-line and terminal-based development tools. We present the architecture of the terminal-based development environment that we envision, outline our future implementation plans, and discuss how such an environment can be evaluated both in terms of its usefulness and usability.
Footprinting and Reconnaissance: Impact and Risks

Footprinting and Reconnaissance is a vital part of every process that has existed existing on earth. The report introduces footprinting and reconnaissance, the types of footprinting and reconnaissance methods, their impacts, and ways to prevent the risks to raise awareness for possible threats from footprinting and reconnaissance. Comparison has been made between the different types of footprinting and reconnaissance and discussions on scenarios that should be used is being made as well. Examples of different types of footprinting and reconnaissance methods and tools have been listed for better understandings of the difference in types. Real-life scenarios and examples are being provided to show the impacts of footprinting and reconnaissance. The report contains demonstrations of two simple passive reconnaissance tools, theHarvester and Wayback Machine. Discussions and analysis of how the tools could be used to gain precious information from their targets and the possible impacts from information gained through the tools are being made. Possible solutions to protect the users from footprinting and reconnaissance have been provided and discussed. Critical analysis of the report and the topic by the author is being made right by the end of the conclusion. Conclusion of how the author has thought about footprinting and reconnaissance and information through researching about the topic had been mentioned in the end.
Motion Capture and Dance Image Discrimination Based on Silent Positioning Algorithm

The silent location algorithm of the sensor network is a time synchronization location algorithm that can serve multiple users. Based on the detailed analysis of the development status of human motion measurement technology, this paper proposes a design of human motion capture device based on sensor network and realizes data conversion and filtering through experimental design. In this experiment, the sensor data processing is used to capture the human body positioning information in real time. The established node does not need to make a sound during the whole process and, at the same time, has strong concealment and easy expansion. In the silent positioning algorithm for sensor network nodes, combined with the sound ray tracking technology, the correction parameters are controlled according to the change of the objective function, and the effectiveness of the method is verified by simulation analysis. In terms of motion capture, the system can monitor, detect, and record human motion in real time. In order to obtain more high-resolution dance images with high-definition details and correspondingly process the dance images to improve the resolution, this paper uses the silent positioning algorithm technology to study the image resolution, and this is of great help to improve the resolution.
Academic and Industry Training for Data Modelling: Ideas for Mutual Benefit

Finding a common ground between academic and industry training is a challenging task. However, despite all the differences between the two worlds, there is one important common aspect for the master students and industry trainees in the field of conceptual data modelling: the mistakes they make when creating data models. This idea paper describes the existing differences and similarities between two academic and industrial training approaches for teaching conceptual data modelling. We propose ideas for improvement of training quality on both sides by analysing and tackling the ground errors in conceptual data modelling made both by novices and professionals. Additionally, we propose the ideas for methodology exchanges between the two training types.
A longitudinal case study on the effects of an evidence-based software engineering training

Context: Evidence-based software engineering (EBSE) can be an effective resource to bridge the gap between academia and industry by balancing research of practical relevance and academic rigor. To achieve this, it seems necessary to investigate EBSE training and its benefits for the practice. Objective: We sought both to develop an EBSE training course for university students and to investigate what effects it has on the attitudes and behaviors of the trainees. Method: We conducted a longitudinal case study to study our EBSE course and its effects. For this, we collect data at the end of each EBSE course (2017, 2018, and 2019), and in two follow-up surveys (one after 7 months of finishing the last course, and a second after 21 months). Results: Our EBSE courses seem to have taught stu-dents adequately and consistently. Half of the respondents to the surveys report making use of the new skills from the course. The most-reported effects in both surveys indicated that EBSE concepts increase awareness of the value of research and evidence and EBSE methods improve information gathering skills. Conclusions: As suggested by research in other areas, training appears to play a key role in the adoption of evidence-based practice. Our results indicate that our training method provides an introduction to EBSE suitable for undergraduates. However, we believe it is necessary to continue investigating EBSE training and its impact on software engineering practice.
Towards Property-Based Tests in Natural Language

We consider a new approach to generate tests from natural language. Rather than relying on machine learning or templated extraction from structured comments, we propose to apply classic ideas from linguistics to translate natural-language sentences into executable tests. This paper explores the application of combinatory categorial grammars (CCGs) to generating property-based tests. Our prototype is able to generate tests from English descriptions for each example in a textbook chapter on property-based testing.
Poster: Comprehensive Comparisons of Embedding Approaches for Cryptographic API Completion

In this paper, we conduct a measurement study to comprehensively compare the accuracy of Cryptographic API completion tasks trained with multiple API embedding options. Embedding is the process of automatically learning to represent program elements as low-dimensional vectors. Our measurement aims to uncover the impacts of applying program analysis, token-level embedding, and sequence-level embedding on the Cryptographic API completion accuracies. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.10% accuracy improvement on average when program analysis preprocessing is applied to transfer byte code sequences into API dependence paths. The best accuracy (93.52%) is achieved on API dependence paths with embedding techniques. On the contrary, the pure data-driven approach without program analysis only achieves a low accuracy (around 57.60%), even after the powerful sequence-level embedding is applied. Although sequence-level embedding shows slight accuracy advantages (0.55% on average) over token-level embedding in our basic data split setting, it is not recommended considering its expensive training cost. A more obvious accuracy improvement (5.10%) from sequence-level embedding is observed under the cross-project learning scenario when task data is insufficient. Hence, we recommend applying sequence-level embedding for cross-project learning with limited task-specific data.
Vandalizing Camcorder Recording by IR Embedded Screen and Audio Signal Generator

In this modern, digitalized age, piracy has become a new wave that involves the reproduction or importing of the entirely or part of a copyrighted work and selling the same at a significantly lower price in the Grey market.it is against the law in almost every country because film theft has an extensive impact on the economy. Movie piracy is mainly caused by camcorder piracy in theatres; portable devices are used to record movies in theatres, in an attempt to resolve this problem and secure the cinema content, several techniques and strategies are explored. This paper lay out a new methodology or new design that could lead to new knowledge to vandalize camcorder recording in movie theater in order to prevent camcorder piracy, IR embedded screen (multitude of IR LED) to vandalize camcorder recording video by producing detectable signal to camera from screen, without interrupting audience entertainment Also, audio signal generator to vandalize camcorder recording audio by producing constructive interference signal to counter the coinciding audio signals of camcorder by placing audio signal generator at optimal viewing area, without interrupting audience entertainment.