Reviewerly/exHarmony huggingface dataset:
Catalogue Visu: a Tool for Fast Visualization Prototyping

Visualization is an essential tool for exploring, understanding and presenting data. However, its use is limited by knowledge and tools. Novice users in visualization do not always know which visualization to choose for their data. The realization of a visualization with specific tools is often long and complex. In this article we present Catalogue Visu, an application allowing the free and fast realization of visualizations. Our main contribution is the design of the choice of the visualization type, which allows any user to choose an appropriate visualization for his needs. This method, the heart of Catalogue Visu, is iteratively validated by three user studies. Catalog Visu is accessible online and in permanent evolution, and is already used internally as a prototyping tool.
Présence et incarnation dans les jeux sérieux : Une revue de la littérature

Presence and embodiment are important dimensions of the user experience in immersive virtual environments. These concepts have been extensively studied, particularly in the field of video games, to model, measure and analyze their influences on the user experience. Serious games, although close to video games, have a different purpose (educational, informative or training) and few studies have yet focused on presence and embodiment in this specific context. In this paper, we conduct an in-depth analysis of the studies that have been conducted so far on the feeling of presence and embodiment in serious games. Our literature review addresses 3 research questions: (1) How are the concepts of presence and embodiment studied and measured? (2) By what modalities, factors or variables are the sense of presence and embodiment influenced? (3) What are the impacts of the degree of presence or embodiment on other dimensions of the user experience and on learning? We conclude by proposing avenues of research to explore some aspects that have not yet been fully explored in the field..
Authoring Interactive and Immersive Experiences Using Programming by Demonstration

Immersive technologies, like virtual and augmented reality, allow engaging the general public in interactive experiences, which are particularly promising in educational and cultural activities (e.g. museums, exhibits). For now, the design of such interactive experiences requires extensive knowledge of expert programming tools, and thus they are not accessible to non-developers. A promising method to ease the prototyping of interactive scenes for non-expert users stands on the concept of programming-by-demonstration. With such an approach, novice users can simply demonstrate an interaction or the expected behavior of a virtual object to build their prototype, without knowing the underlying coding mechanisms. In this paper, we propose an immersive authoring system that bases on that approach. We prototyped two use cases based on this system, as well as a user study. Using our observations and the results of the study, we discuss challenges associated with the design of such systems and provide guidelines for the development of future immersive programming-by-demonstration tools.
Human-Machine Co-Learning : Case Study on Motor Skill Acquisition

research-article Human-Machine Co-Learning : Case Study on Motor Skill Acquisition Authors: Vaynee Sungeelee Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France 0000-0001-7718-2269Search about this author , Antoine Loriette Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France 0000-0001-9429-1459Search about this author , Olivier Sigaud Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France 0000-0002-8544-0229Search about this author , Baptiste Caramiaux Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), France 0000-0002-4590-106XSearch about this author Authors Info & Claims IHM23: IHM '23: Proceedings of the 34th Conference on l'Interaction Humain-MachineApril 2023Article No.: 16Pages 1–13https://doi.org/10.1145/3583961.3583977Published:29 May 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Evaluating the Effectiveness of Attacks and Defenses on Machine Learning Through Adversarial Samples

Adversarial attacks can compromise the robustness of machine learning models, including neural networks. Adversarial defenses can be employed to mitigate the impact of adversarial attacks. Due to adaptive attacks, however, the adversarial defenses are vulnerable as well. This makes it onerous to employ neural networks in safety and security-critical areas. However, a perception of the effectiveness of adversarial attacks and defenses can facilitate the development of more robust neural networks that are suitable for applications in these areas.The purpose of this paper is to evaluate the effectiveness of adversarial attacks and defenses and determine the dependency of effectiveness on the chosen values of the underlying parameters. To that end, we evaluate the (adaptive) Carlini & Wagner attack and KDE defense to measure their effectiveness for a range of parameter values. This paper investigates the aforementioned attacks and defenses for the optimal values of the parameters. We also prove that by changing the value of parameters, the effectiveness of adversarial attacks and defenses can be improved and state the necessary trade-offs involved. Furthermore, to substantiate the effect of adversarial attacks and defenses on the effectiveness of adaptive attacks, this paper investigates the effectiveness of the adaptive CW attack for the corresponding optimal values of the CW attack and KDE defense parameters.
Deep Industry Use Cases on Context-Aware Adaptive Mobile Systems Experience Testing

Goals - This whitepaper describes Context-aware adaptive mobile systems (CAAMS) experience testing as a crucial aspect of ensuring the functionality and user experience of mobile apps. It involves testing how a mobile app responds and adapts to different contexts, such as the user’s location, time of day, activity, or environment. This type of testing is essential for location-based services, hyper-personalization, adaptation to the environment, and ambient information. From an industry perspective, CAAMS experience testing is becoming increasingly important as mobile apps continue to integrate more advanced features and technologies. It is essential to ensure that they can provide relevant and accurate information and services to users in different contexts. This requires rigorous testing against various use cases and conditions to ensure that the mobile app can adapt and respond effectively. Furthermore, CAAMS experience testing is also important for ensuring the security and privacy of users. The industry-standard tools and techniques that are supporting context-aware adaptive mobile systems/application experience testing are a critical aspect of mobile app development and testing platforms are essential for ensuring a positive user experience and maintaining trust in mobile apps. When it comes to CAAMS testing, it is a semi-automated cognitive AI-driven approach that captures device key vitals covering architecture, design, UI/UX, performance, security, and more. CAAMS testing use cases with results benchmarked with industry-specific standards are demonstrated to unearth architectural, performance experience issues and more.
Summary of Combinatorial Methods for Dynamic Gray-Box SQL Injection Testing

In a recent work [1], we present an extended and enhanced gray-box combinatorial security testing methodology for SQL injection vulnerabilities in web applications. It proposes novel attack grammars modelling SQLi attacks against MySQL-compatible databases, each targeting a specific context within a query; additionally, the produced attack vectors are dynamically optimized for the system under test. The newly developed methodology is implemented in a prototype security testing tool called SQLInjector+. In a case study against four verification frameworks and five real-world applications, our approach performs equally well or better compared to existing state-of-the-art SQL injection security testing tools.
Semantic Metamorphic Testing focusing on Object Rarity

Deep Learning (DL) driven image recognition systems have developed rapidly. Recent researches have applied metamorphic testing (MT) to detect image misrecognition. Most of them are based on non-semantic transformation, while semantic MTs don’t focus on rarity. Our fundamental concept is "Rare object is rarely well-recognized." In this paper, we propose a semantic rarity-based MT and its test architecture. It consists of two units: BERT-based unit and Stable-Diffusion-based unit. The former transforms a seed word into rare sentences. The latter generates corresponding rare images. Experiments are conducted and show that the semantic rarity-based MT detects more failures.
Elements for a Test(-ware) Architecture Language

The term "Test Architecture" has been discussed in previous InSTA editions to gain perspective that can lead to achieve consensus about a solid definition. To enrich this process, this paper presents a definition applying concepts from the discipline of Software Architecture in the context of Formal Testing, an approach in which computer languages are used in an integrated way in testing activities; this definition is used in the paper to present aspects that will be considered for the development of a test(-ware) architecture language.
Similarities of Testing Programmed and Learnt Software

This study examines to what extent the testing of traditional software components and machine learning (ML) models fundamentally differs or not. While some researchers argue that ML software requires new concepts and perspectives for testing, our analysis highlights that, at a fundamental level, the specification and testing of a software component are not dependent on the development process used or on implementation details. Although the software engineering/computer science (SE/CS) and Data Science/ML (DS/ML) communities have developed different expectations, unique perspectives, and varying testing methods, they share clear commonalities that can be leveraged. We argue that both areas can learn from each other, and a non-dual perspective could provide novel insights not only for testing ML but also for testing traditional software. Therefore, we call upon researchers from both communities to collaborate more closely and develop testing methods and tools that can address both traditional and ML software components. While acknowledging their differences has merits, we believe there is great potential in working on unified methods and tools that can address both types of software.
We Tried and Failed: An Experience Report on a Collaborative Workflow for GUI-based Testing

Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact’s quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did.
Combinatorial Methods for HTML Sanitizer Security Testing

HTML sanitization is an essential security feature for web services that incorporate user-generated content, removing the ability of malicious actors to perform injection attacks such as cross-site scripting. Exercising additional scrutiny when choosing such filters is prudent, as any insufficiencies in their design or implementation may lead to widespread exploitation of vulnerabilities. This work presents an approach for applying combinatorial security testing, which offers the unique capability to provide mathematically guaranteed coverage of the language defined by an attack grammar, to HTML sanitizers.
Twitter stance detection using deep learning model with FastText Embedding

The interactivity of social media platforms allows a large number of users to comment on different political or social issues to express their views, and identifying users' stances from online comment texts helps the government to monitor public opinion more effectively. The automatic recognition of stance information in comment text has become a new research hotspot in the field of natural language processing. Most of the existing text stance analysis corpus focuses on political topics in European and American countries, and high-quality stance analysis corpus research on political topics in Southeast Asian countries is relatively scarce. In order to stimulate this research direction, this paper provides a dataset about the 2022 Philippine presidential election, which annotates the stance information of the two popular presidential candidates and provides reliable data support for subsequent stance analysis model research. Next, we build a stance detection model of hybrid deep neural networks based on BiLSTM, CNN, and Attention, and we demonstrate its effectiveness on multiple datasets and obtain the best results on the SemEval-2016 dataset. In addition, we compare FastText and Word2Vec, two pre-trained word embeddings for word encoding, and discuss which word embedding is preferred in stance detection tasks. This result shows that the stance analysis model proposed in this paper can be effectively applied to Twitter text stance data.
An Adaptive Gradient Privacy-Preserving Algorithm for Federated XGBoost

Federated learning (FL) is a novel machine learning framework in which machine learning models are built jointly by multiple parties. We investigate the privacy preservation of XGBoost, a gradient boosting decision tree (GBDT) model, in the context of FL. While recent work relies on cryptographic schemes to preserve the privacy of model gradients, these methods are computationally expensive. In this paper, we propose an adaptive gradient privacy-preserving algorithm based on differential privacy (DP), which is more computationally efficient. Our algorithm perturbs individual data by computing an adaptive gradient mean per sample and adding appropriate noise during XGBoost training, while still making the perturbed gradient data available. The training accuracy and communication efficiency of the model are guaranteed under the premise of satisfying the definition of DP. We show the proposed algorithm outperforms other DP methods in terms of prediction accuracy and approaches the lossless federated XGBoost model while being more efficient.
Haze video image Clarity Processing Based on Optical Flow Threshold

In view of the problem of haze weather on the visual effect of video image, which causes the picture distortion, image quality degradation and definition blur of video image, a defogging processing method of haze video image based on optical flow threshold is proposed so as to restore the real and natural color image. Firstly, extract the image of the t frame at time t, track the characteristics of the image at time t + 1 to time t + n, extract the image of the t+n frame, then calculate the optical flow values of the t frame and the t + n frame, make a difference between the obtained optical flow values to obtain the optical flow threshold, compare the obtained optical flow threshold with the given threshold, if the value is greater than or equal to the given threshold, take the optical flow threshold intermediate frame image, and the middle frame and t+n frame images are processed by Retinex algorithm, and this operation is performed iteratively. Finally, the processed single frame video sequence is merged into a whole and output. The experiment shows that the processing speed of the algorithm is 0.07, much lower than other processing methods, which verifies the effectiveness and innovativeness of the proposed algorithm.
An Encryption Scheme Using Multi-Scroll Memristive Chaotic System

In this paper, a novel multi-scroll memristive chaotic system is designed based on Chua's system via introducing a nonlinear memristor. The dynamics of this system is analyzed based on bifurcation diagrams, Lyapunov exponents and phase diagrams. Subsequently, an image encryption scheme based on this system is then proposed. First, the proposed chaotic system is used to generate continuously robust chaotic sequences, the hash values of plaintext images are embedded in the generation and selection of chaotic sequences and involved in each step of encryption to establish the coupling relationship between plaintext and ciphertext. Second, Knuth-Durstenfeld algorithm is used to scramble the high four-bit plane of the plain image twice, and the chaotic sequence is used as the index sequence, which greatly improves the efficiency and randomness of the permutation process. Finally, chaotic sequences are involved in DNA coding rules and pixel-level diffusion. The algorithm is highly sensitive to plain images, and it can realize adaptive encryption. Through performance analysis and comparison with recent literature, the proposed algorithm can cope with various attacks and show excellent performance.
A hybrid Aquila Optimizer sine cosine Algorithm for Numerical Optimization

To address the shortcomings of the Aquila optimizer algorithm (AO), this paper proposes a novel hybrid Aquila Optimizer sine cosine Algorithm(AO-SCA). Firstly, Singer chaotic mapping is used for initialization, so that the initial solution position distribution was more homogeneous, and increased the richness of the population. Secondly, in the exploration phase of AO, the concept of sine and cosine algorithm is integrated and the nonlinear sine learning factor is introduced to balance the local and global digging ability and accelerate the convergence speed. Finally, through the numerical experiment simulation of 8 benchmark functions, the results show that the optimization ability and convergence speed of the proposed algorithm is better.
Discrimination of seismic and non-seismic signal using SCOUTER

Abstract— For areas with potential occurrence of blasting events, it is essential to distinguish them from natural earthquakes. An efficient processing method is needed to save manpower, especially under the current large amount of data records by seismic stations. We apply a SCOUTER algorithm to distinguish between the two types of events. The recognition precision of the trained model for natural earthquakes and blasts can reach 95% and 92.8%, respectively, and the recall can reach 93.4% and 94.6%, respectively. The testing results of data with different epicentral distances and SNR show that our method is stable, independent on regional waveform characteristics and insensitive to data of different SNR. The explanations for each classification at the final confidence also give us a profound enlightenment.
Research on Colorization of Qinghai Farmer Painting Image Based on Generative Adversarial Networks

At present, deep learning method is widely used in the field of gray image colorization. Qinghai farmer painting has distinct national characteristics. The farmer painting has bright colors, high saturation, chaotic color distribution and low color contrast, so it is difficult to restore the image color with high fidelity by using the general deep learning image colorization method. The Pix2Pix generation adversarial network of grayscale image colorization method uses the Leaky ReLU function as the activation function. The proposal algorithm replaces the maximum pooling layer with the convolution layer to retain more image feature information and further to improve the color simulation effect. Meanwhile, in view of the lack of relevant Qinghai farmer painting data set, the data set of Qinghai farmer paintings is constructed to meet the needs of network training. The experimental results show that the improved method further improves the color effect and can generate high quality color images of Qinghai farmer paintings with more real color distribution.
Explainable Deep Learning for Medical Image Segmentation With Learnable Class Activation Mapping

Medical image segmentation is crucial for facilitating pathology assessment, ensuring reliable diagnosis and monitoring disease progression. Deep-learning models have been extensively applied in automating medical image analysis to reduce human effort. However, the non-transparency of deep-learning models limits their clinical practicality due to the unaffordably high risk of misdiagnosis resulted from the misleading model output. In this paper, we propose a explainability metric as part of the loss function. The proposed explainability metric comes from Class Activation Map(CAM) with learnable weights such that the model can be optimized to achieve desirable balance between segmentation performance and explainability. Experiments found that the proposed model visibly heightened Dice score from to , Jaccard similarity from to and Recall from to respectively compared with U-net. In addition, results make clear that the drawn model outdistances the conventional U-net in terms of explainability performance.