Reviewerly/exHarmony huggingface dataset:
Exploring the Barriers and Factors that Influence Debugger Usage for Students

Debugging is one of the most expensive and time-consuming processes in software development. To support programmers, researchers, and developers have introduced a wide variety of debuggers or tools to automatically find errors in code, to make this process more efficient. However, there is a gap between industry developers and students regarding the skillful use of debuggers. We aim to understand this gap by studying barriers that hinder new programmers from using debuggers. We conducted a survey involving 73 students with various extents of programming experience and performed qualitative analysis. The goal was to extract insights into why students do not develop debugger usage skills. Our results suggest the general lack of academic course focus on debuggers is one of the primary reasons for avoidance. At the same time, complex user interfaces and a lack of visualization also seem intimidating for many students, making using a debugger unappealing. Based on the results, we provide guidelines to motivate future debugger designs and education materials to improve debugger usage. Our survey results summary is publicly available at https://github.com/minhyukko/vlhcc23
Human Visual Consistency-Checking in the Real World Ontologies

Solving complex consistency checking tasks in natural languages is hard and requires sophisticated specialist expertise. The similar task of finding bugs in information systems can be large-scale and is often conducted with some visualisation of the data. Visualisation, therefore, could also be a useful tool when consistency checking in real world applications, such as in the case of ontology engineering. Previous experiments suggest that node-link visualisation, such as SOVA, are more effective than node-link-region visualisation, such as concept diagrams, in consistency checking tasks. In this study, we found that this tendency was not affected even in an alternative setting where multiple concept diagrams were used. Our findings have implications for the way in which information is presented visually: single (merged) visualisations are effective for these types of tasks.
HaTe Detector: A Tool for Detecting and Correcting Harmful Terminology in Computing Artifacts

As the field of computing has advanced over the years, how we engage and innovate as a society has as well. From computing practitioners that develop the technologies to end-users, the internet has become a central hub for our daily activities. There have been numerous efforts to ensure that the content in online social communities like Twitter are inclusive. However, despite the history of harmful terminology use in computing, little work explicitly attempts to provide support for detecting and correcting offensive language in online programming communities like GitHub. This paper introduces the Hate Detector, a web application for detecting and correcting harmful terminology in computing artifacts. The current implementation connects with GitHub and uses existing resources and corpora to support the analysis of repository markdown files. Our ongoing efforts aim to evaluate and scale the Hate Detector across and beyond GitHub projects to further promote inclusive practices in online programming communities.
Domain-Specific Probabilistic Programming with Multiverse Explorer

We present Multiverse Explorer, a domain-specific probabilistic programming language presented as a visual language integrated with a domain world model. The interactive visualisation presents a Monte Carlo simulation over a causal graph, allowing the user to gain an overview and query alternative outcomes in a counterfactual manner. Separate graphs express the policies attributed to multiple heterogeneous agents. The outcomes of actions are visualised in an interactive 3D animation of the environment; in this work, we apply the Multiverse Explorer to multi-agent driving scenarios by extending the CARLA simulator. The Multiverse Explorer has been evaluated with a sample of technical non-specialists, demonstrating the potential of this approach to be used in design, audit, policy, litigation, and other contexts where the outcome of multi-agent decision scenarios must be investigated by professionals beyond a specialist AI audience.
A Qualitative Study of REST API Design and Specification Practices

REST APIs expose web services to clients. Although experts have recommended guidelines for REST API design, there is little empirical evidence regarding the relationship between adherence to guidelines and benefits to API consumers. We interviewed ten REST API designers to understand what practices REST API designers follow, what opinions they have of existing guidelines, and what challenges they face when designing and using APIs. Some guidelines were widely followed, and some were rejected as being inconsistent with good practice. The participants reported additional challenges: authentication and authorization are too hard to implement; specifications and documentation are frequently missing, vague, or outdated; and standard error reporting methods are insufficient for end users and for debugging purposes. Overall, our observations present opportunities for tool developers to significantly improve the situation by assessing and assuring conformance with guidelines; by aligning documentation with specifications and implementations; and by standardizing guidelines for API design.
Traffic Signal Recognition System Using Deep Learning

The intelligent traffic system relies heavily on traffic sign recognition. It can decrease driver fatigue and improve traffic safety. The traffic sign recognition system (TSRS) is a key element of the intelligent transportation system (ITS). Accurately identifying traffic signs can improve driving safety. This paper proposes a traffic sign recognition technique formulated on deep learning, taking into consideration the main objective of identifying as well as categorizing circular and spherical signs. In conclusion, deep learning is utilized in classifying the identified road traffic signs. The paper concludes with a focus on the proposed system technology, that also utilizes neural networks. As a result, traffic-sign recognition stands as both demanding as-well-as appealing subject, and an important area of study within the field of traffic engineering research.
Chaotic Sparrow Search Optimizer for Structural Design Challenges

A traditional swarm intelligence method called the Sparrow Search Optimizer (SSO), which was motivated by the natural behaviours of sparrows, has demonstrated potential in optimization tasks. We suggest a unique modification that integrates the chaotic Singer map into the SSO framework to improve its functionality. This results in the Chaotic Sparrow Search Optimizer (CSSO). The goal of this study is to use chaos to create better structural designs. This study presents the implementation details for CSSO and conducts a thorough analysis of its effectiveness in comparison to the traditional SSO and other metaheuristics. The suggested approach is used to optimize complicated structures with various design restrictions and aims. Simulation tests show that in terms of solution quality and convergence speed, CSSO surpasses the traditional SSO and other metaheuristics. By including the chaotic Singer map, CSSO is better able to explore and exploit challenging problem areas and consistently provide better ideas. CSSO exhibits potential as a useful tool for structural engineering designs, providing a cutting-edge method to address engineeirng design issues.
Automation of SPARQL Query Generation for Global Semantic Search

The Global Information System as a network information search service on the Internet works with all methods of information search, providing the user with one common interface. After the search is completed, it should provide a direct connection to the server containing the necessary information. To optimize the information retrieval process, it is necessary to implement an appropriate service capable of scaling and parallel use. The paper proposes a method for processing a search phrase from natural language to SPARQL-query language. The method of increasing the efficiency of semantic search is described, which allows to significantly reduce time costs and ensure high relevance of search results.
PRO-MATE IOT-Based Cost-Effective Ground Motion Monitoring Acceleration Sensor Node: Hardware and Software Description

A low-cost micro electro mechanical systems (MEMS) accelerometer was used to design a cost-effective earthquake-monitoring ground motion sensing unit called PRO-MATE. A multiprocessing seismic data acquisition and processing software called RB data acquisition system (RB-DAS) was designed to operate PRO-MATE. RB-DAS can automatically detect seismic events, determine event-associated ground motion parameters, and continuously save seismic data in MiniSeed format. RB-DAS functions are encapsulated in predefined modules that interact by sending and receiving various messages through a message-transmission medium. Modularity makes system design more simple and flexible, and users can add or modify their own developed modules without affecting the operation of RB-DAS. A full description of RB-DAS modules is included in this letter. In order to validate the PRO-MATE performance, a full description of the hardware design and test had to be accomplished.
Comparative Analysis of Image Encryption and Decryption Algorithms

Image encryption and decryption techniques have drawn a lot of attention as the need for secure communication and data protection grows. This research paper offers an in-depth examination and side-by-side comparison of the various image encryption and decryption techniques. The study aims to assess the benefits and drawbacks of various strategies and offer insights into the state of the art in this area at the moment. The research methodology includes a thorough literature review as well as an analysis of the most important algorithms and methods for encrypting and decrypting images. The results of this study will advance knowledge of image security and provide practitioners and researchers working in the field of image encryption with an invaluable resource.
DPFTT: Distributed Particle Filter for Target Tracking in the Internet of Things

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.
DocDancer: Authoring Ultra-Responsive Documents with Layout Generation

Responsive design enhances user experience by adapting layout and content to different display factors. However, existing tools for authoring responsive design primarily adapt to screen width only, and they either rely on predefined templates or require significant manual effort. To expedite responsive design creation, we introduce an authoring tool called DocDancer. DocDancer supports creating ultra-responsive documents where both layout and content adapt to multiple factors (screen width, font properties, customer segments, etc), and provides layout suggestions based on user-provided content and popular responsive patterns. A comparative user study with 16 participants shows that authoring responsive documents in DocDancer takes significantly less time and effort than a commercial tool.
LITI: Learning with Interactive Time Series Information

Educational sensor-based visual programming environments (VPEs) tend to focus on directly connecting input and output sources. However, limited educational tools are designed to expose novice programmers to basic preprocessing techniques often needed to convert raw sensor data into interpretable commands. This paper presents our preliminary design process for LITI, a VPE designed to expose students to basic processing techniques used for time-series data analysis. We leveraged an iterative design process that began with focus group sessions involving local high school teachers and students. Based on educators' and students' feedback, LITI was designed to reinforce graphing and data processing concepts used in physiological computing. Afterward, we conducted usability studies with high school students in grades 10 - 12. Results from our initial user studies suggest that LITI may increase students' physiological computing self-efficacy.
Detangler: Helping Data Scientists Explore, Understand, and Debug Data Wrangling Pipelines

Data scientists spend significant time on data wrangling-a process involving data cleaning, shaping, and pre-processing. Data wrangling requires meticulous exploration and backtracking to assess data quality by applying and validating numerous data transformation chains, making it a tedious and error-prone process. In this paper, we present Detangler, an interactive tool within the RStudio IDE that helps data scientists identify and debug data quality issues and wrangling code. The design of Detangler is informed via formative interviews, and it presents data scientists with (i) insights into potential data quality issues, and (ii) always-on visual summaries of the effects of individual data transformations, enabling interactive exploration of data and wrangling code. Through a laboratory study with 18 data scientists, triangulated with telemetry data, we find that Detangler improves exploration and debugging of data smells and data wrangling code. We discuss design implications for future tools for data science programming.
Development of an AI Enabled Yoga Posture (Aasans) Prediction System Using Deep Neural Network Model

“Yoga does not just change the way we see things; it transforms the person who sees”.This is a statement that highlights not just suggests the greatness we have in the practice which was once seen as just a hobby - YOGA In this paper, we propose a method to detect correct yoga postures using deep learning techniques. Our approach involves using a dataset of various yoga asanas to train our model to detect any abnormalities between the desired pose and the user's real time pose. By using this approach, we hope to provide a useful tool for individuals who want to learn and practice yoga at home, without the need for expensive trainers or yoga centers. AI Trainer will provide an interactive platform for beginners to learn about the fundamentals of yoga and asanas, as well as provide a recommendation system for users based on their postures. With the help of MoveNet, the human pose estimation can be done fast and can be easily deployed considering and keeping the inference times as low as possible during that. The outcome of this is a model which can implement and can achieve accurate key points for estimating a wide variety of gestures, movements, environments, and setup of hardware. The proposed model is giving an accuracy of 0.9929% and we have prepared an application is built using react javascript in which the pose estimation model is integrated. This application will help the user to easily maneuver and interact with the pose estimation model.
IA-Noise2Noise: An Image Alignment Strategy for Echocardiography Despeckling

Although echocardiography plays a crucial role in cardiovascular disease diagnosis, the presence of speckle noise in echocardiography images can hinder their diagnostic effectiveness. Recent studies have focused on using deep learning models to reduce speckle noise in echocardiography. However, these deep learning models’ performances tend to be limited on real data as they are usually trained with synthesized images due to the unavailability of real noise-free echocardiography images. Moreover, although the Noise2Noise algorithm which is widely adopted for natural image denoising can be used for ultrasound image despeckling without using clean images as training targets, this algorithm still requires noisy image pairs which are hard to acquire for echocardiography due to the cardiac motion. To address this issue, we propose the IA-Noise2Noise algorithm, which incorporates an image alignment strategy into the Noise2Noise algorithm. Experiment results indicate that our method outperforms previous methods in terms of despeckling performance.
DIDO: Data Provenance from Restricted TLS 1.3 Websites

Public data can be authenticated by obtaining from a trustworthy website with TLS. Private data, such as user profile, are usually restricted from public access. If a user wants to authenticate his private data (e.g., address) provided by a restricted website (e.g., user profile page of a utility company website) to a verifier, he cannot simply give his username and password to the verifier. DECO (CCS 2020) provides a solution for liberating these data without introducing undesirable trust assumption, nor requiring server-side modification for TLS 1.2. In this paper, we propose an optimized solution for TLS 1.3 websites. We tackle a number of open problems, including the support of X25519 key exchange in TLS 1.3, the design of round-optimal three-party key exchange, the architecture of two-party computation of TLS 1.3 key scheduling, and circuit design optimized for two-party computation. We test our implementation with real world website and show that our optimization is necessary to avoid timeout in TLS handshake.
Supporting Exploratory Programming in Domain-Specific Applications

The act of computer programming can take many different forms. For example, a software engineer might rewrite C++ code to speed up some process; an analyst might use a spreadsheet program to gain insights into their data; or a creative coder might write a program to create an abstract visualization that is synchronized with music. While each of these tasks is a complex activity involving creative problem-solving, the first example has a well-defined goal, while the second and third examples are more open-ended. Generally, programming activities involving data analysis or artistic creative practice can be seen as exploratory programming, characterized by rapid experimentation and an evolving set of goals [1]. In the creative coding example, the coder might explore several options using different combinations of colors, shapes, textures, and animation speeds. There is no “correct” answer for what the final product should look like, and in fact, what they envision at the outset might be completely different from what they ultimately create.
Cross-Border Data Security from the Perspective of Risk Assessment

In the cross-border process of data, major issues such as national security and personal information security caused by complex processes and variable risk factors are gradually exposed. Based on the development status, this paper proposes a framework of cross-border data risk assessment model. The assessment framework not only considers the data protection capabilities of data controllers and data receivers, but also considers the impact of informed consent of data subjects on risk assessment results. The framework includes multiple evaluation modules such as data collection, data storage, etc., so that the framework can be updated and maintained at the module level in the future. This paper analyzes and extracts 18 important risk indicators in the six modules, as well as six potential risk events under cross-border data activities, to fully consider the possibility of potential risk accidents under each risk indicator. Finally, this paper analyzes the development needs of data cross-border risk assessment.
QR-SACP: Quantitative Risk-Based Situational Awareness Calculation and Projection Through Threat Information Sharing

When a threat is observed, one of the most important challenges is to choose the most appropriate and adequate timely decisions in response to the current and near future situation in order to have the least consequences and costs. Making the appropriate and sufficient decisions requires knowing what situations the threat has engendered or may engender. In this paper, we propose a quantitative risk-based method called QR-SACP to calculate and project situational awareness in a network based on threat information sharing. In this method, we investigate a threat from different aspects and evaluate the threat’s effects through dependency weight among the network’s services. We calculate the definite effect of a threat on a service and the cascading propagation of the threat’s definite effect on other dependent services to that service. In addition, we project the probability of a threat propagation or recurrence of the threat in other network services in three ways: procedurally, through network connections and similar infrastructure or services. Experimental results demonstrate that the QR-SACP method can calculate and project definite and probable threats’ effects across the entire network and reveal more details about the threat’s current and near future situations.
A Revocable Outsourced Data Accessing Control Scheme with Black-Box Traceability

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising solution to the fine-grained access control problem of encrypted data. Several CP-ABE-based cryptographic cloud storage systems have been proposed in recent years. However, existing CP-ABE schemes still have several limitations that make them not effective to be used in a practical application. Firstly, decryption privileges may be changed when the user revocation happens to prevent the leakage of encrypted data. Secondly, malicious users may delegate decryption keys to unauthorized users for profit. Thirdly, the complex operation of encryption and decryption may bring a huge computational cost and is usually considered to be a heavy burden for system users. Therefore, this paper proposes a new CP-ABE scheme ROBBT-CPABE, which can provide attribute revocation, black-box tracking, outsourcing encryption, and outsourcing decryption. By using the information distribution algorithm and the secure modular exponentiation outsourcing algorithm, the scheme can achieve attribute revocation and outsource some expensive encryption and decryption operations to the cloud server. Based on the construction of indistinguishable traceable ciphertext, the proposed scheme can support black-box tracking. Then the ROBBT-CPABE scheme is formally proved to be selective replay chosen ciphertext attack (RCCA) secure and black-box traceable secure. Performance analysis demonstrates the efficiency and practicality of ROBBT-CPABE.