Reviewerly/exHarmony huggingface dataset:
A Novel Metric for XAI Evaluation Incorporating Pixel Analysis and Distance Measurement

Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.
When to Let the Developer Guide: Trade-offs Between Open and Guided Test Amplification

Test amplification generates new tests by mutating existing, developer-written tests and keeping those tests that improve the coverage of the test suite. Current amplification tools focus on starting from a specific test and propose coverage improvements all over a software project, requiring considerable effort from the software engineer to understand and evaluate the different tests when deciding whether to include a test in the maintained test suite. In this paper, we propose a novel approach that lets the developer take charge and guide the test amplification process towards a specific branch they would like to test in a control flow graph visualization. We evaluate whether simple modifications to the automatic process that incorporate the guidance make the test amplification more effective at covering targeted branches. In a user study and semi-structured interviews we compare our user-guided test amplification approach to the state-of-the-art open test amplification approach. While our participants prefer the guided approach, we uncover several trade-offs that influence which approach is the better choice, largely depending on the use case of the developer.
The Effects of AR-Based Virtual Teacher Environment on Preschooler’s Language Learning

With the increasing application of technologies, Augmented Reality (AR) is increasingly being used in educational fields to facilitate the student’s learning experience. However, relatively few studies on AR combined with virtual teacher environments to facilitate preschool children’s language learning. Therefore, this study designed a mobile AR application that embeds a virtual teacher, which assists the children in learning English vocabulary. This study used a quasi-experimental method, using a pre-test and post-test to measure learning achievement, and scales to measure children’s presence and foreign language classroom anxiety. 40 kindergarten students from two classes were randomly divided into experimental and control groups. The experimental group used an AR app, while the control group learned in a traditional classroom. Results showed that compared with the control group, the children who used the AR application integrated with the virtual teacher had a stronger sense of presence, lower foreign language classroom anxiety, and higher learning achievement. The results provide a new type of learning practice case for AR-based language learning in preschool education.
Reproducing and Improving the BugsInPy Dataset

We assess the reproducibility of the BugsInPy dataset less than three years after its original publication. The bug dataset provides some information about the software environment in which the code should be run, but this information can be incomplete or can decay into something uninstallable over time. We rectify as many of these problems as we can and redesign the original dataset to be more easily reusable and reproducible by future research projects. Based on our experience, we offer suggestions to authors of Python artifacts to improve their reproducibility.
On Developing and Improving Tools for Architecture-Smell Tracking in Java Systems

Architecture smells indicate violations of software-design principles. So, identifying and assessing architecture smells facilitates refactorings to reduce technical debt and ensure maintainability. Detecting architecture smells in only one version at a time provides a static and limited picture, since, for example, historical trends remain obfuscated. Both, for practitioners and researchers, obtaining information on how specific architecture smells evolved over time can yield valuable insights, be it for avoiding the growth of critical smells, grasping the code’s degradation, or getting a better understanding of development processes. To support such analyses, we developed our tool AsTdEA, which tracks architecture smells throughout a system’s evolution. AsTdEA runs a modified version of the architecture-smell detection tool Arcan and allows the automated batch-processing of multiple versions of one or multiple systems. First, AsTdEA generates data on the components that are involved in each smell on a version-to-version basis and how these intra-version smells are related with one another across the entire system history, forming inter-version smells. Second, for every intra-version smell, inter-version smell, and system version, AsTdEA outputs a multitude of properties, which we have already used for multiple empirical studies. In this paper, we show the implementation and use of AsTdEA, as well as the lessons that we learned during its development and how we want to improve it in the future.
Learning Complicated Navigation Skills from Limited Experience via Augmenting Offline Datasets

Deep reinforcement learning has yielded remarkable results in the field of robot navigation. Most of the existing RL-based methods tend to train the navigation policy with (1) a simulation environment in which the agent interacts and collects experience iteratively, (2) a shaped reward function that defines how a robot should reach the goal while avoiding collisions with obstacles. However, these methods suffer from several challenges, including the difficulty of generalizing the trained model to real-world scenarios, sub-optimal risk due to reward-shaping, and the inefficiency of data utilization. In this paper, we address these challenges by introducing the State & Goal-Relabel techniques based on Hindsight Experience Replay (HER), enabling the robot not only to learn success from failure but also to learn more complicated navigation skills from simple tasks. Instead of training only with limited real experiences, our approach aims to generate pseudo-experiences by relabeling both the local observation and target pose, cleverly improving the scale and quality of dataset, and using target-driven style to train a model with solid generalization ability. With experiences collected just in simple environments, our approach outperforms or performs comparably to classical and other learning-based methods, and generalizes well in physical environments.
A Novel Label Selection Algorithm Based on Principal Component Analysis and Sparse Approximation Solution for Multi-label Classification*

In multi-label classification, an instance may be associated with multiple labels simultaneously and thus the class labels are correlated one another. As various applications emerge, besides large instance size and high feature dimensionality, the dimensionality of label space also grows quickly, which would increase computational costs and even deteriorate classification performance. To this end, dimensionality reduction strategy is applied to label space via exploiting label correlation information, which covers label embedding and label selection techniques. Recently a lot of label embedding work has been conducted, but less attention has been paid to label selection techniques due to its difficulty. In this case, it is still an open problem how to design more effective label selection techniques for multi-label classification. Column subset selection problem (CSSP) originally is a mathematical issue in matrix theory to select a small portion of columns from a large-scale matrix for more interpretable data summarization. Therefore, such a CSSP naturally becomes an attractive mathematical representation for label selection, which is NP-hard and generally is solved via greedy strategy. In this paper, we build a two-stage label selection algorithm. At first, we apply principal component analysis (PCA) to reduce the dimensionality of the label matrix to obtain a low dimensional real matrix as the right side term in linear systems. Then, we use sparse approximation (SA) solution for linear systems to choose several informative columns from the label matrix as approximations of the low dimensional real matrix, which ultimately obtains the sub-optimal label subset of original label matrix. This new label selection method based on PCA and SA is referred to as PCASA simply. Our proposed method is validated experimentally to work well on six benchmark data sets with more than 100 labels.
ToonMeet: A Real-time Portrait Toonification Framework with Frame Interpolation Fine-tuned for Online Meeting

In this paper, we propose ToonMeet, a hybrid frame-work for high-resolution and style-controllable online meeting toonification that ensures real-time operation speed. ToonMeet applies video frame interpolation to traditional portrait toonification pipelines, allowing for the synthesis of intermediate frames between adjacent toonified keyframes, significantly accelerating the overall process and saving computational resources. However, this approach brings a new problem, where prevailing flow-based video frame interpolation methods tend to cause more ghost and blur artifacts in toonified scenes compared to non-toonified scenes, especially when fast-moving objects exist. We study this previously undiscussed problem and explore its causes. To address this, we introduce a new dataset called TM3B (Toonified Multi-modal Meeting Behaviors), offering high-resolution and cross-platform multi-modal stylized meeting data of Japanese youth in various scenarios. Then, we fine-tune ToonMeet on these tailored data and the resulting model presents improved optical flow estimation ability on toonified videos. Extensive experiments demonstrate that ToonMeet can achieve great spatiotemporal performance and perform high-quality toonification of online meetings with real-time operation speed.
Evolutionary Feature Dependencies: Analyzing Feature Co-Changes in C Systems

Configurable software systems and software product lines build on features as first class entities for reasoning about commonalities and variability among system variants. While it is desirable to have modular features, this is not always achievable and research has shown that features interact frequently, which can come with negative effects like security vulnerabilities or bugs. Intensive research has been conducted regarding how and when features interact, focusing primarily on the implementation level and the variability mechanism therein. However, besides such structural, explicit feature dependencies represented in the code, there may also be more subtle, implicit feature dependencies. In this paper, we build on the idea that the co-evolution of features (i.e., co-changes between features) can reveal implicit dependencies, and thus point to poor design decisions that result in additional maintenance effort. We present a technique for analyzing feature co-changes based on repository mining and association rule mining to identify features that commonly change together and to reveal implicit dependencies. Moreover, we provide a large-scale multi-case study on five C systems (e.g., Linux kernel) to evaluate whether and how frequent such evolutionary dependencies occur. Our results reveal that a) feature co-changes occur quite frequently (25 to 70% of commits), b) a considerable amount of changes are supported by association rules (i.e, do not occur by chance), and c) several of these co-changes cannot be explained via explicit feature interactions. Overall, our technique and study complement existing research on feature dependencies and interactions by providing means for understanding implicit dependencies that are represented by feature co-evolution.
Implementation and evaluation of solar powered wireless intranet devices in rural schools in Ratanakiri Province, Cambodia

With the existing discrepancy in levels of education between urban and rural schools in developing nations, the issue of availability and quality of educational resources needs to continue being addressed [1]. A team of engineering students from Villanova University aim to address this issue in Ratanakiri Province, Cambodia with the implementation of an ICT4D initiative. This initiative involves collaborating with in-country partners to design, assemble, and deploy solar-powered wireless intranet devices for augmenting educational resources in rural schools. In May of 2022, the system was piloted at five rural schools and provided key findings on how to improve the hardware and software of the system as well as in-country implementation strategies. The successes, challenges, and additional insights from this pilot are documented in this paper along with generalized recommendations on how to improve the sustainability of future project deployments.
Investigating the Feasibility of Crowdsourcing Current Events Information in Kenya

Misinformation is a global challenge for our fast-paced society. With the upsurge in technology and social media use, there are growing channels for misinformation to spread. This results in uninformed decision making and amplifies existing societal insecurities. In Kenya, it can be clearly seen in how misinformation fuels political violence near election periods. While the widespread presence of trustworthy and verified information can stunt the spread of misinformation, these resources are often an unaffordable commodity for lower-resourced communities.As a result, misinformation impacts these communities disproportionately, while having no trustworthy infrastructure in place that is accessible to citizens. This is the exigence that prompted our investigation into developing a community crowdsourcing system to combat untrustworthy information sharing in communities such as Kenya. By amplifying existing collectivist attitudes in Kenya, we hope to empower citizens with trustworthy and relevant information that can be used to make informed decisions.
The SPeRA web Portal for Cooperation Associations and Projects in Africa

The importance of Organizations and Private Volunteer Projects for International Development Cooperation is widely recognized. However, although there are some websites that collect and describe information on welfare societies, very few are aimed at a detailed description of the projects. In particular, at the Italian level, the associations working for development cooperation in Africa are poorly described on the Internet, and this makes responses to humanitarian needs less efficient than they could be. Indeed, for very fruitful operations, collaborations between associations as well as funding for projects are very productive. Therefore, the dissemination of information and data in a scientific way is crucial in the framework of development cooperation. With this in mind, a web portal was created for the promotion and dissemination of humanitarian projects. It is the result of the joint work of a multidisciplinary team that includes university researchers, technology specialists and volunteers from non-profit associations. It currently places particular emphasis on small Italian associations operating in Africa, aimed at making their voices heard and promoting their activities. This contribution introduces this portal, showing how to insert it in the scenario of humanitarian projects, favoring the use of technologies for development cooperation.
SCD:Sampling-based Class Distribution for Imbalanced Semi-Supervised Learning

Currently, many methods in the field of Semi-Supervised Learning (SSL) assume that the datasets are balanced, but imbalanced datasets are very common in real scenarios. To solve the imbalance problem, many methods require prior knowledge of the class distribution of the entire dataset, so as to use the prior distribution to guide the model training on the unlabeled data. These methods often assume that the unlabeled data distribution is uniform or identical to the labeled data distribution, which may differ from the real scenario distribution. In this work, we provide a new perspective for setting the class distribution for imbalanced SSL. Without assuming the unlabeled data distribution in advance, we propose to record the model’s sampling frequency results for each class as the sampling distribution of the whole data, which can reflect the model’s true sampling situation. Based on this, we adopt logits adjustment method to deal with the imbalance problem of sampled data. To utilize more pseudo-labels, we further adjust the threshold according to the class distribution. The final experiments show that our method achieves excellent performance in various imbalanced semi-supervised settings.
Thin Clients: Empowering Low-Income Family Students with Educational Tools from Revitalized Discarded Computers in Humanitarian Non-Profits

This research paper delves into the potential of leveraging thin client operating systems (OS) to transform discarded computers into valuable educational tools for supporting low-income K-12 school students, with a particular focus on addressing humanitarian needs. The study presents its initial findings and insights obtained during a collaborative pilot project involving Awakening Your Child Total Potential (AYCTP), a non-profit organization based in Ohio, in conjunction with the College of Engineering and Computing (CEC) and the Farmer School of Business (FSB) at Miami University, along with the Miami University Humanitarian STEM Service Corp (HSSC) student club. The pilot initiative targeted 40 K-12 students including refugees, orphans, and low-income individuals in Cincinnati, OH, Athens, Greece, and Thailand. By repurposing and distributing donated computers from Miami University, AYCTP and HSSC aimed to provide these students with access to educational technology. Beyond the hardware provision, the project also explored the potential of offering crucial STEM classes, training, and mentorship to the recipients of these computer donations, with the aim of fostering their interest in STEM fields and higher education prospects. In addition to distributing educational computers, the research team developed custom courses to cultivate the student’s interest in STEM subjects and computer programing, utilizing both the donated computers and affordable devices like Raspberry Pi. The ultimate objective of this endeavor was to empower underprivileged students, enabling them to tap into their full potential and create brighter futures for themselves.
Domain adaptation method for semantic segmentation in marine vessel inspection

The interior of the marine vessel’s ballast tank changes drastically during its operational period in harsh marine environments. An accurate semantic understanding of the ballast tank is crucial to successfully performing automated inspections. Therefore there is a need for a solution, which will guarantee good segmentation results, no matter the condition of the ballast tank. In this work we have developed a domain adaptation method, to be employed together with a segmentation model to improve the performance across different domains, to address the visually continuous degradation of the environment. The approach was to develop a photo-realistic simulation to collect synthetic data for the training of deep learning models. For the purpose of segmentation two popular architectures - ResNet and Unet were employed in various complexity variants. Domain adaptation was performed by learning domain invariant features, using a gradient reversal layer-based approach. The developed models presented an increase of 12-15% over baseline methods. The domain adaptation method significantly improved the segmentation performance of the models on the target domain, while not decreasing the computational efficiency of the models.
An Affordable Approach for Implementing Hybrid Classes with Portable Equipment in Ecuadorian Academic Institutions

The COVID-19 pandemic threw harsh tests on humanity, including the need to continue learning in academic institutions now virtually. Many of the benefits of this alternative are maintained when a hybrid learning strategy is adopted, however, this is not a widely adopted strategy because of economic and connectivity restrictions, especially in underdeveloped countries such as Ecuador and particularly in conditions of low internet access, like those of the Galapagos Islands. This paper proposes a configuration of equipment that makes up an economic alternative for the development of hybrid classes in Ecuadorian educational institutions. The solution is evaluated in a pilot test developed in a local institution and the results are tabulated via surveys carried out among the participants. The solution can be replicated in other Ecuadorian institutions that have similar needs and restrictions.
Skeleton-Based Recognition of Traditional Greek Dance Steps using Machine Learning Algorithms

Classification of dance types and moves is a challenging application in the field of action recognition. Owing to its vast applicability and robustness regardless of the image background, skeleton-based recognition is increasingly attracting the attention of scholars in the robotics community. This paper presents a novel dataset of videos of five individuals performing five different types of traditional Greek dances, which can be utilized for that purpose. In addition, we create a benchmark for future work by performing a comparative study of different classifiers’ performance on dance steps from this dataset. For that purpose, the dataset has been divided into steps, with each step consisting of a sequence of skeleton data extracted from the videos. Each step is classified taking into account the data from every frame of the sequence. We use an assortment of classifiers based on different artificial neural network (NN) types, including convolutional and recurrent NNs as well as traditional ones such as Gaussian naive Bayes, decision trees, support vector machines, and k-nearest neighbors. For each classifier, we have obtained the classification accuracy first on steps from the same dance type and then on the entire dataset. We assess the classifiers’ performance both on randomly split test sets and when performing cross-validation. Out of the classifiers used, the best performance is achieved by the CNN-based classifier.