Reviewerly/exHarmony huggingface dataset:
On Visually Lossless JPEG Image Compression

This paper presents an investigation into the visually lossless JPEG compression which provides maximum compression ratio applied before the resulting compressed image appears distorted. The research was conducted using two publicly available databases with the results of just noticeable difference tests. It has been shown that using visually lossless compression can save memory and communication resources by 80% compared to conventional JPEG compression approach with a fixed quality factor. Compared to JPEG lossless compression, this saves memory resources by about eight times. Also, it is shown that the mean gradient magnitude of the original uncompressed image can be used to predict the JPEG visually lossless compression ratio. The last part of the research provides comments on visually lossless quality prediction using a simple prediction approach based on only one feature of the original image with a comparison with the results of a deep learning approach.
Prediction of the First Just Noticeable Difference Point based on Simple Image Features

The aim of this paper is to predict the positions of the first just noticeable difference (JND) points as optimal levels of compression for images and videos based on simple features derived from the original visual signals. Three image datasets and one video dataset with subjectively defined JNDs were used in the analysis. We show that the position of the first JND point can be successfully predicted on the basis of simple image features. The highest degree of agreement with subjectively assigned JNDs was reached by features based on the gradient magnitude, where on two datasets with JPEG images their correlation is greater than 95%. On the dataset with VVC images, which has a larger number of images and a wider range of image content, the degree of agreement between gradient-based predictions and the results of subjective tests is 84%, while the correlation on the dataset with video sequences is 80%.
Demonstrating Secure Data Portability for Healthcare Domain

This paper proposes an architecture for secure, privacy aware data portability for healthcare domain. The components of the architecture are described along with an operational flow. Prototyping experience and results validation with a real smartwatch are summarised.
Combining delta-phi velocity measurement and DBSCAN clustering to localize slowly moving objects in short ranges with limited slow-time radar data

A method to localize slowly moving objects in range-azimuth space using a Frequency-modulated continuous-wave (FMCW) multi-input multi-output (MIMO) radar is proposed. Unlike traditional range- velocity calculation by 2D-FFT, this method calculates the velocity information in the phase domain, which yields a higher velocity resolution with very few slow-time data required. The proposed method was tested to localize a slowly moving retro-reflector and achieved satisfactory results.
Airborne Radar Forward-Looking Imaging Algorithm Based on Generative Adversarial Networks

Forward-looking radar imaging continues to gain significance due to a variety of convenient applications, like battlefield reconnaissance, target surveillance and precision guidance. Synthetic aperture radar (SAR) techniques are typically used to achieve a high azimuth resolution, but conventional SAR is not applicable in the forward direction because of the poor Doppler resolution and the “left-right” ambiguity problem. In recent years, there has been widespread use of generative adversarial networks (GAN), a common deep learning approach that has produced excellent results in inter-image translation. To apply GAN to radar forward-looking imaging, this paper builds an end-to-end forward-looking imaging network, whose output is a high-resolution image. The network eliminates the design and iterative processes of the observation matrix in imaging, which are common in conventional forward-looking imaging, increasing the effectiveness of real-time imaging. This study also proposes a method for generating training datasets for forward-looking imaging via existed high-resolution synthetic aperture radar images. As is verified by the simulated and real radar data, this approach has a robust recovery and better performance.
The impact of the Covid-19 pandemic on the evolution of cyber threats

The article deals with the development of cyber threats in the period just before and during the Covid-19 pandemic, i.e. from the turn of 2019/2020, 2021 and 2022. The focus is mainly on attacks on hospitals in the Czech Republic and briefly mentions large-scale cyber attacks on hospitals in France and Spain. Attention is also given to cyber attacks in the education sector over the period. The article also discusses cyber attacks in Ukraine in the run-up to the conflict with Russia. Attention is paid to the most common cyber attacks from that period, discusses the causes of cyber attacks, and summarizes research on phishing attacks.
Optical Sensor Placement Optimization for Unmanned Ground Vehicles by the Simulation

The article deals with the area of the unmanned ground vehicle (UGV) and optimization of sensor placement. It involves finding the optimal location of the sensors in a robot to achieve the desired performance objectives. This process usually involves several steps, such as defining the performance metrics, selecting the appropriate sensors, designing the sensor placement configurations, and evaluating the performance of the resulting system. The main objective of the article is to analyze and define the optimal number and type of sensors to provide the best environmental surveillance for the UGV. The suggested sensor types and solutions are analyzed with multi-criteria analysis and verified in the WEBOTS robot simulator synthetic environment.
DAppHunter: Identifying Inconsistent Behaviors of Blockchain-based Decentralized Applications

A blockchain-based decentralized application (DApp) refers to an application typically using web pages or mobile applications as the front-end and smart contracts as the back-end. The front-end of the DApp helps users generate transactions and send them to the user’s blockchain wallet. After the user signs and confirms the transaction using the blockchain wallet, the transaction will invoke the smart contract of the DApp. However, users bear the following risks when using DApps because of the potential inconsistent behaviors in DApps. First, the DApp front-end may generate incorrect transactions inconsistent with users’ intentions. Second, the smart contract may have misbehaviors when executing the transactions. Inconsistent behaviors of DApps not only lead to user confusion but also cause significant financial losses. In this paper, we proposed a novel approach to identify inconsistent behaviors of DApps on EVM-compatible blockchains by contrasting the behaviors of DApps that derived from the front-end, blockchain wallet, and smart contracts, respectively. We implemented our approach into a prototype named DAppHunter. We have applied DAppHunter on 92 real-world DApps of Ethereum and Binance Smart Chain and successfully identified 37 DApps with inconsistent behaviors. We confirmed that 35 of them are scam DApps and over 5 million blockchain addresses are at risk of becoming victims of these inconsistent DApps.
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft

Code review is an integral part of any mature software development process, and identifying the best reviewer for a code change is a well-accepted problem within the software engineering community. Selecting a reviewer who lacks expertise and understanding can slow development or result in more defects. To date, most reviewer recommendation systems rely primarily on historical file change and review information; those who changed or reviewed a file in the past are the best positioned to review in the future.We posit that while these approaches are able to identify and suggest qualified reviewers, they may be blind to reviewers who have the needed expertise and have simply never interacted with the changed files before. Fortunately, at Microsoft, we have a wealth of work artifacts across many repositories that can yield valuable information about our developers. To address the aforementioned problem, we present Coral, a novel approach to reviewer recommendation that leverages a socio-technical graph built from the rich set of entities (developers, repositories, files, pull requests (PRs), work items, etc.) and their relationships in modern source code management systems. We employ a graph convolutional neural network on this graph and train it on two and a half years of history on 332 repositories within Microsoft.We show that Coral is able to model the manual history of reviewer selection remarkably well. Further, based on an extensive user study, we demonstrate that this approach identifies relevant and qualified reviewers who traditional reviewer recommenders miss, and that these developers desire to be included in the review process. Finally, we find that "classical" reviewer recommendation systems perform better on smaller (in terms of developers) software projects while Coral excels on larger projects, suggesting that there is "no one model to rule them all."
Self-Optimizing Agents Using Mixed Initiative Behavior Trees

Fast paced industry requirements call for fast and easy robot programming, especially for Small and Medium sized Enterprises (SME) that often lack robot programming experience. Even with the advancement of graphical activity representation languages such as Behaviour Trees (BTs), it can still be time consuming to program robots for new behaviors due to the shifting product specifications and the dynamic production environments. This paper presents an extension of BTs that offers more flexibility as well as higher reactivity and robustness by introducing Mixed Initiative Planning (MIP) to BTs using Dynamic Sequence Nodes (DSNs). DSNs reduce the human effort needed to design a BT as well as the number of nodes to achieve a certain task while maintaining robustness, readability, and modularity of the tree. Additionally, it introduces run-time optimization to BTs, as opposed to tree synthesis approaches that guarantee convergence but overlook performance.
Automated Metamorphic Testing using Transitive Relations for Specializing Stance Detection Models

In machine-learning-based natural language processing, methods with high accuracy have been proposed for stance detection tasks. However, when they are applied to specific domains, they are often inaccurate due to domain-specific expressions. We propose an automated metamorphic testing method using transitive relations for creating training data that specializes stance detection in a specific domain. By specializing IBM Debater’s stance detection in currency exchange domain, we confirmed our proposed method can improve the accuracy of judging the currency exchange-related sentences.
Issue-Labeler: an ALBERT-based Jira Plugin for Issue Classification

Issue labels are key drivers in software maintenance as they dictate the prioritization, organization, and ultimately the resolution of encountered issues. Consequently, mislabeling issues result in inefficient prioritization, which compromises the resolution process of these issues. Thus, to increase the accuracy and effectiveness of issue labeling in software maintenance, this paper proposes "Issue-Labeler": an automated issue labeler plugin for Jira <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which utilizes a deep neural language model to predict an issue’s type based on its title and description. Specifically, the plugin would classify an issue into three types: BUG, IMPROVEMENT, and NEW FEATURE. The issue-labeler plugin was implemented by fine-tuning Google’s pre-trained ALBERT language model, using 35,889 labeled issue reports extracted from 77 projects. The plugin showed an average F1-score of 0.75, 0.58, and 0.67, respectively, for the BUG, IMPROVEMENT, and NEW FEATURE issues. The plugin will provide developers with a tool that recommends issue labels to, in turn, optimize the process of tagging and resolving these issues. Video of tool setup and runtime is available: https://voutu.be/mi2FwaXNrR4. Tool Webpage: https://issue-labeler.github.io/issue-labeler-site/. Replication package: https://github.com/issue-labeler/.
Scaling Web API Integrations

In ASAPP, a company that offers AI solutions to enterprise customers, internal services consume data from our customers’ web APIs. Implementing and maintaining integrations between our customers’ APIs and internal services is a major effort for the company. In this paper, we present a scalable approach for integrating web APIs in enterprise software that is lightweight and semi-automatic. It leverages a combination of Ontology-Based Data Access architectures (OBDA), a Domain Specific Language (DSL) called IBL, Natural Language Processing (NLP) models, and Automated Planning techniques. The OBDA architecture decouples our platform from our customers’ APIs via an ontology that acts as a single internal data access point. IBL is a functional and graphical DSL that enables domain experts to implement integrations, even if they don’t have software development expertise. To reduce the effort of manually writing the IBL code, an NLP model suggests correspondences from each web API to the ontology. Given the API, ontology, and selected mappings for a set of desired fields from the ontology, we define an Automated Planning problem. The resulting policy is finally fed to a code synthesizer that generates the appropriate IBL method implementing the desired integration.This approach has been in production in ASAPP for 2 years with more than 300 integrations already implemented. Results indicate a ≈ 50% reduction in effort due to implementing integrations with IBL. Preliminary results on the IBL automatic code generation show an encouraging further ≈ 25% reduction so far.
Walking Down the Road to Independent Mobility: An Adaptive Route Training System for the Cognitively Impaired

In this paper we describe the design and development of a route training system for individuals with cognitive impairments (CIs) living in residential care facilities. Learning to move autonomously in public spaces is a fundamental skill for people with CI, who face several challenges to independently and safely move around. Yet, exploring opportunities for route training support, especially in residential settings, has received very little attention. To explore these opportunities, we followed a design and development process based on inclusive design practices that considered the organisational context and aimed at involving people with CI in the software design. To ensure our solution addressed the identified needs and abilities of this heterogeneous population, we further framed the route training definition as a design process that is enacted by the system, making the trainer and user co-creators of a personalised training. In this paper we report on the needs and challenges for mobility training in residential settings, introduce the design and formative evaluation of the route training system, to conclude with reflections and considerations on our methodological approach.
Challenges in Adopting Artificial Intelligence Based User Input Verification Framework in Reporting Software Systems

Artificial intelligence is driving new industrial solutions for challenging problems once considered impossible. Many large-scale companies use AI to identify opportunities to improve business processes and products. Despite the promise and perils of AI, many traditional software systems (e.g., taxation or reporting) are implemented without AI in mind. Adopting AI-based capabilities in such software can be challenging due to a lack of resources and uncertainties in requirements. This paper documents our experience working with our industry partner on adopting AI capabilities in enterprise software. The enterprise software receives and processes thousands of user inputs with different configuration settings daily, which makes manual user input verification infeasible. To assist our industry partner, we design and integrate an AI-based input verification framework into the software. However, during the design and integration of the framework, we encounter many challenges that range from the requirement engineering process to the development, adoption, and verification process. We discuss the challenges we encountered and their corresponding solutions while working with our industrial partner to integrate the AI-based input verification framework into their non-AI software. Our experience report may provide valuable insight to practitioners and researchers on better integrating AI-based capabilities with existing software systems.
Identifying Defect Injection Risks from Analysis and Design Diagrams: An Industrial Case Study at Sony

Identifying the origins of potentially injected defects in implementation activities during requirement analysis and design activities is challenging, but leads to the prevention of defects, which are burdensome to correct. This study investigates whether such defect injection risks can be generalized and defined by risk occurrence conditions of the objects in the existing analysis and design diagrams and whether the defined defect injection risks are applicable to other analysis and design diagrams. Specifically, we identify defect categories, which are injected after analysis and design activities and subsequently detected during system testing of commercial products developed at Sony. Then, regarding the defect categories as the exposed defect injection risks, we define defect injection risks with the objects defined in the analysis and design diagrams of the products. Each defect injection risk consists of risk description, diagram type, and occurrence conditions of objects in the diagrams. Afterwards, we evaluate whether the defined defect injection risks appear in the analysis and design diagrams of three different products under development and five publicly available analysis and design diagrams. The results showed that three defect injection risks were defined and that the two risks appear in the analysis and design diagrams of the three products and the remaining one appears in the analysis and design diagrams of two products. The results also showed that one defect injection risk is present in all five publicly available analysis and design diagrams, and two risks appear in four of the diagrams. The three defect injection risks are general enough to identify risks in analysis and design diagrams from other domains. Developers can be more cautious about the risks and prevent defect injections with the defect injection risks.
An Empirical Comparison on the Results of Different Clone Detection Setups for C-based Projects

Code clones have been used in many different software maintenance and evaluation tasks in practice (e.g., change proportion and evolution, refactoring, and vulnerability management). There are many clone detection techniques (e.g., text-based, token-based, and AST-based) which can detect code clones not only at the source code-level but also at the compiled artifacts (e.g., IR or binary) level. Unfortunately, there are few studies which thoroughly compare the results of various clone detection setups (a.k.a., different clone detection techniques applied at different artifacts), especially for C-based projects. Therefore, in this paper, we conduct a systematic study to compare the effectiveness of six different code clone detection setups. Each setup, which uses the representative one of the three clone detection techniques: token-based technique (SourcererCC), AST-based (NiCad) technique, and text-based (MSFinder) technique, is applied either at the source code-level or at the LLVM-based IR-level. We conduct our experiments on five C-based open-source systems, Apache, Python, PostgreSQL, FFmpeg, and Linux kernel. Experimental results show that the AST-based technique is better than the token-based and text-based techniques, and clone detection setups performed at the IR-level generally yield higher performance than those performed at the source code-level. The setup of AST-based technique applied at the IR-level, yields the highest performance overall, with an F-score of 84%. However, there is no one setup which can detect all the clones. Through manual qualitative analysis, we have identified ten reasons why certain clones cannot be detected at the IR level or at the source code-level, and two reasons why one of the techniques fails to detect clones. Our findings highlight the usefulness of conducting clone detection under different setups. Furthermore, this study also motivates the need for more application-oriented clone comparison studies.
Control Action Types -Patterns of Applied Control for Self-adaptive Systems

A Self-adaptive System (SaS) modifies its domain functionality according to changes in its environment autonomously. Distributed control and central control represent two complementary paradigms to establish this capability. The selection of one of them leads to significant trade-offs regarding certain software qualities when designing a SaS. A promising approach to minimize these trade-offs is an integration, which combines the individual benefits to achieve the best of both paradigms. However, establishing such a multi-paradigm control requires comprehensive knowledge about control options and their interactions, which is hardly available.In this paper, we present patterns for integrating distributed and central control. We introduce them by a schema of Control Action Types (CATs). Each CAT describes a unique type of interaction between a central controller and a distributed controlled SaS to achieve a desired adaptation. Further, we identify involved tradeoffs between these CATs aiming at a systematic discussion of the range of multi-paradigm control for a SaS.
Reducing the Impact of Breaking Changes to Web Service Clients During Web API Evolution

Web services are self-contained distributed services enabled by web service application programming interfaces (APIs) using numerous protocol and middleware types. Web service API evolution encompasses changes to a web API made during the lifetime of a web service. Web clients, such as mobile applications, must continuously and manually adapt to breaking changes in the web API offered by web services. This paper presents an automated process for the adaptation based on web service API type-independent evolution patterns. These patterns enable the classification and resolution of web API changes in the context of continuously changing web API standards, protocols, and middleware types. Migration guides enable the generation of stable client libraries that replace the traditionally performed manual client-specific migrations. The process is instantiated using Apodini Migrators, generating Swift-based client libraries for web clients. We demonstrate how migration guides can be automatically generated using OpenAPI specifications or based on web services developed using the Apodini framework. We build two Migrators using a resource-based and a remote procedure call-based web API type. We validated the applicability of these Migrators to 13 web service version increments featuring a total of 3896 changes. The process correctly identified 86.1% of the changes, including 1132 breaking changes. The breaking changes included 424 unsolvable changes that required manual migration guide improvements by the web service developers at predefined extension points.