Reviewerly/exHarmony huggingface dataset:
Cross-domain car detection model with integrated convolutional block attention mechanism

Car detection, especially through camera vision, has become a major focus in the field of computer vision and has gained widespread adoption. While current car detection systems are capable of achieving good detection performance, reliable detection can still be challenging due to factors such as car proximity, varying light conditions, and environmental visibility. To address these issues, we propose Cross-Domain Car Detection Model with integrated convolutional block Attention mechanism(CDCDMA) that is specifically designed for car recognition in autonomous driving and related domains. CDCDMA includes several novelties: 1)Building a complete cross-domain target detection framework. 2)Developing an unpaired target domain picture generation module with an integrated convolutional attention mechanism which specifically emphasizes the car headlights feature. 3)Adopting Generalized Intersection over Union (GIOU) as the loss function of the target detection framework. 4)Designing an object detection model integrated with two-headed Convolutional Block Attention Module(CBAM). To evaluate the model's effectiveness, we performed experiments on the SODA 10 M and BDD100K datasets by applying a reduced resolution process to the data, which served as our benchmark dataset for the task. The experimental results demonstrate that the performance of the cross-domain car target detection model improves by 40% compared to the model without our CDCDMA framework. Moreover, our improvements have a significant impact on cross-domain car recognition, surpassing the performance of most advanced cross-domain models.
Personal touch in digital customer service: a conceptual framework of relational personalization for conversational AI

Purpose Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization. Design/methodology/approach This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication. Findings The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has. Originality/value The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.
An edge inspired image watermarking approach using compass edge detector and LSB in cybersecurity

In the contemporary era, data holds a vital significance, and ensuring the security of data stands as an exceptionally challenging endeavor. Both industry and academia dedicate substantial efforts to mitigate security breaches. Given the evolving nature of modern cyber threats, there arises a compelling necessity for robust cybersecurity measures concerning digital content. We propose the merits of digital watermarking within the realm of cybersecurity for digital content. Researchers within the watermarking domain are diligently striving to offer the utmost security services. Unfortunately, achieving both robustness and security concurrently within these techniques has proven to be a formidable task. Hence, we delve into a novel approach for image watermarking, which draws inspiration from gradient-based methodologies. This scheme hinges upon compass edge detection, the least significant bit mechanism, and the utilization of chaotic encryption for watermark embedding. The effectiveness and performance are rigorously evaluated against a spectrum of geometrical and image-processing attacks.
The value of manual backward contact tracing to control COVID-19 in practice, the Netherlands, February to March 2021: a pilot study

BackgroundContact tracing has been a key component of COVID-19 outbreak control. Backward contact tracing (BCT) aims to trace the source that infected the index case and, thereafter, the cases infected by the source. Modelling studies have suggested BCT will substantially reduce SARS-CoV-2 transmission in addition to forward contact tracing.AimTo assess the feasibility and impact of adding BCT in practice.MethodsWe identified COVID-19 cases who were already registered in the electronic database between 19 February and 10 March 2021 for routine contact tracing at the Public Health Service (PHS) of Rotterdam-Rijnmond, the Netherlands (pop. 1.3 million). We investigated if, through a structured questionnaire by dedicated contact tracers, we could trace additional sources and cases infected by these sources. Potential sources identified by the index were approached to trace the source's contacts. We evaluated the number of source contacts that could be additionally quarantined.ResultsOf 7,448 COVID-19 cases interviewed in the study period, 47% (n = 3,497) indicated a source that was already registered as a case in the PHS electronic database. A potential, not yet registered source was traced in 13% (n = 979). Backward contact tracing was possible in 62 of 979 cases, from whom an additional 133 potential sources were traced, and four were eligible for tracing of source contacts. Two additional contacts traced had to stay in quarantine for 1 day. No new COVID-19 cases were confirmed.ConclusionsThe addition of manual BCT to control the COVID-19 pandemic did not provide added value in our study setting.
Domain Adaptation of MRI Scanners as an Alternative to MRI Harmonization

Combining large multi-center datasets can enhance statistical power, particularly in the field of neurology, where data can be scarce. However, applying a deep learning model trained on existing neuroimaging data often leads to inconsistent results when tested on new data due to domain shift caused by differences between the training (source domain) and testing (target domain) data. Existing literature offers several solutions based on domain adaptation (DA) techniques, which ignore complex practical scenarios where heterogeneity may exist in the source or target domain. This study proposes a new perspective in solving the domain shift issue for MRI data by identifying and addressing the dominant factor causing heterogeneity in the dataset. We design an unsupervised DA method leveraging the maximum mean discrepancy and correlation alignment loss in order to align domain-invariant features. Instead of regarding the entire dataset as a source or target domain, the dataset is processed based on the dominant factor of data variations, which is the scanner manufacturer. Afterwards, the target domain's feature space is aligned pairwise with respect to each source domain's feature map. Experimental results demonstrate significant performance gain for multiple inter- and intra-study neurodegenerative disease classification tasks. Source code available at (https://github.com/rkushol/DAMS) .
Tutoring in the metaverse. Study on student-teachers’ and tutors’ perceptions about NPC tutor

Introduction The metaverse is defined as a new frontier for anyone’s everyday life and a new challenge for the training and the professional development. The extended reality of the metaverse offers a new learning environment in which additional educational roles intervene to support the teaching and learning processes. Methods The work explores some aspects of the metaverse as a support for the initial training of teachers. It presents a study on the perceptions that student-teachers and school tutors of a teaching qualification path have, specifically, of the so-called ‘non-player characters’ (NPC) tutor and peers, in the metaverse. Quantitative and qualitative data were collected via mixed questionnaire and analyzed via descriptive statistics and QDA. Results The analysis found some differences in expectations between student-teachers and school tutors with respect to the metaverse, to the new educational roles related and, specifically, to the NPC tutor role. The triangulation of the early data is highlighting a general new look at the possibilities offered by the metaverse – in monitoring the learning program and in decision-making practices – as well as expectation about the teachers training – Artificial Intelligence relationship. Discussion The results of study regarding the perceptions of student-teachers and school tutors on the metaverse and on the role of the NPC tutor are offered as insights to be explored, through further investigations, to those responsible for teacher training courses and to the research that today investigates the learning effects of the metaverse as a potential professional training environment.
Tiny Machine Learning: Progress and Futures [Feature]

Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is challenging due to hardware constraints: the tiny memory resource makes it difficult to hold deep learning models designed for cloud and mobile platforms. There is also limited compiler and inference engine support for bare-metal devices. Therefore, we need to co-design the algorithm and system stack to enable TinyML. In this review, we will first discuss the definition, challenges, and applications of TinyML. We then survey the recent progress in TinyML and deep learning on MCUs. Next, we will introduce MCUNet, showing how we can achieve ImageNet-scale AI applications on IoT devices with system-algorithm co-design. We will further extend the solution from inference to training and introduce tiny on-device training techniques. Finally, we present future directions in this area. Today's large model might be tomorrow's tiny model. The scope of TinyML should evolve and adapt over time.
A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.
An empirical study of an MR-enhanced kinematic prototyping approach for articulated products

In the conceptual design and early embodiment design stages, an effective kinematic prototype can assist in detecting design errors, minimizing unnecessary parameter modifications, and optimizing subsequent resource deployment. Existing methods for creating prototypes, such as 2D sketches, 3D modelling, or analysis software, have their advantages in precise modelling and simulation. However, there is a lack of an efficient approach that can achieve both rapid 3D kinematic idea generation and early on-site interactive verification. To address these challenges, this research proposes a Mixed Reality (MR)-enhanced kinematic prototyping approach for articulated products to enable the rapid generation of 3D kinematic concepts, movement preview, and early interactions with users or the actual workspace. The proposed approach facilitates the quick and rough construction of geometry and kinematic structures through intuitive hand gestures and interactive movement preview within the MR environment. The usability of the proposed approach was evaluated by a comparison experiment with traditional sketch and CAD tools involving eight participants. It is verified that our proposed MR kinematic prototyping system could accelerate the kinematic prototype generation process, improve the user's kinematic understanding, and facilitate on-site interactive verification in advance.
Determining Web Application Vulnerabilities Using Machine Learning Methods

Information technology is a significant factor in the modern world. This area is developing quickly, becoming essential as new, more complex protection methods appear. Threat detection instruments have also been actively developing for recent decades. The available scanning systems allow to disclose many threats. Moreover, machine and deep learning Artificial intelligence methods have been developing significantly. With so many penetration testing scanners available, choosing the most effective scanner can be daunting as it depends on the specific testing environment. This paper focuses on detecting SQL injection threats with the use of such machine learning algorithms as Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and CatBoost. The analyzed dataset contains safe and dangerous SQL commands that various websites have been exposed to. The classification model results showed that most applied algorithms demonstrated perfect accuracy, precision, recall, and F1-score values above 0.95. These high scores proved to be efficient in the SQL injection classification tasks.
Self-Supervised Variational Graph Autoencoder for System-Level Anomaly Detection

Unsupervised anomaly detection methods, either reconstruction-based or prediction-based, determine anomalies based on residuals. Occasional mutations in a single variable can cause the residuals to exceed the limits. Indeed, such mutations are not variations in the operating mechanism of the system. Thus, system-level anomalies are challenging to characterize. Complex networks (or “graphs”) are well adapted for modeling and characterizing the laws of evolution of complex systems. However, most industrial scenarios are without graphs. Hence, a self-supervised variational graph autoencoders (SS-VGAE) method is proposed. First, the multisource sensor dynamic graph is constructed through detrended cross-correlation analysis. Second, target and self-supervised learning tasks are designed. The target task is to reconstruct the input graph structure to minimize the reconstruction loss. The self-supervised task is to learn the optimal center of the hypersphere in the latent space such that the mean features are gathered towards the center as much as possible. Multi-task joint optimization allows high and low- dimensional space features to be considered simultaneously, thereby improving the reliability of anomaly scores. Then, the distribution of anomaly scores is calculated and integrated into a system health indicator (HI). The system HI is more applicable to assist decision-making. Finally, the superiority of the proposed method, namely better detection accuracy and robustness, is demonstrated by nuclear PCTRAN simulation data and SKAB data. Last but not least, systematic anomalies are found to make the correlation between the variables stronger.
Augmenting Diffs With Runtime Information

Source code diffs are used on a daily basis as part of code review, inspection, and auditing. To facilitate understanding, they are typically accompanied by explanations that describe the essence of what is changed in the program. As manually crafting high-quality explanations is a cumbersome task, researchers have proposed automatic techniques to generate code diff explanations. Existing explanation generation methods solely focus on static analysis, i.e., they do not take advantage of runtime information to explain code changes. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> , a novel tool that augments code diffs with runtime difference information. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> compares the program states of the original (old) and patched (new) versions of a program to find unique variable values. Then, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> adds this novel runtime information to the source code diff as shown, for instance, in code reviewing systems. As an evaluation, we run <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">584</small> code diffs for Defects4J bugs and find it successfully augments the code diff for 95% (555/ <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">584</small> ) of them. We also perform a user study and ask eight participants to score the augmented code diffs generated by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> . Per this user study, we conclude that developers find the idea of adding runtime data to code diffs promising and useful. Overall, our experiments show the effectiveness and usefulness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collector-Sahab</small> in augmenting code diffs with runtime difference information. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Publicly-available repository:</b> <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ASSERT-KTH/collector-sahab</uri> .
Decentralized Lattice-Based Device-to-Device Authentication for the Edge-Enabled IoT

In this article, we present a novel and decentralized authentication and key exchange protocol for device-to-device (D2D) communications in the Internet of Things (IoT). The existing lattice-based D2D authentication protocols suffer from efficiency, security and privacy including high storage cost, lack of strong anonymity, and high key revocation overheads. The proposed protocol is designed to overcome these challenges by using lattice-based cryptography, edge computing, and blockchain technology. Edge computing is used to reduce computation overhead on IoT devices, and blockchain is used to maintain public keys to simplify key revocation and interdomain authentication. We have analyzed thoroughly the security of the proposed protocol using both the informal and formal security methods. The proposed protocol is resilient to several known classical attacks, such as the ephemeral secret leakage attack and public-key quantum attack. We have also conducted a thorough performance analysis and compared it to several existing approaches. The results show that the proposed protocol decreases the device-side computation overhead by approximately 58% as compared to other approaches. In addition, the proposed protocol shows approximately 2% decrease in the communication overhead compared to just post-quantum protocols.
The Influence of Personality Traits and Game Design Elements on Player Enjoyment: A Demo on GWAPs for Part-of-Speech Tagging

This demo paper presents three Games With A Purpose (GWAPs) created to revise and correct automatically tagged part-of-speech (PoS) of the most extensive collection of Spanish oral data, known as the Corpus Oral y Sonoro del Español Rural (COSER, [7], “Audible Corpus of Spoken Rural Spanish”). The goal is to create a morpho-syntactically annotated and parsed corpus of European Spanish dialects through crowdsourced contributions from non-experts. Players are tasked with assigning grammatical categories (e.g., verb, noun, adjective, pronoun) to words in input texts within the three GWAPs: Agentes, Tesoros, and Anotatlón. The creation of the games follows a user-centred design approach that involves professionals in Computational Linguistics, Dialectology, and Human-Computer Interaction experts and integrates players’ input. This demo paper presents the design motivations and solutions from this collaboration. Furthermore, it examines whether players’ personality traits and using particular Game Design Elements (GDEs) influence their enjoyment.
Human-Robot Collaboration: An Augmented Reality Toolkit for Bi-Directional Interaction

This work proposes an Augmented Reality (AR) application designed for HoloLens 2 which allows human operators, without particular experience or knowledge of robotics, to easily interact with collaborative robots. Building on the application presented in a previous work of the authors, the novel contributions are focused on a bi-directional interaction that manages the exchange of data from the robot to the human operator and, in the meantime, the flow of commands in the opposite direction. More in detail, the application includes the reading of the robot state, in terms of joint positions, velocities and torques, the visualization of the workspace and the generation and manipulation of the end-effector trajectory by directly moving a set of way-points displayed in the AR environment. Finally, the trajectory feasibility is verified and notified to the user by taking into account the workspace limits. A usability study of the AR platform has been conducted involving 45 participants with different ages and expertise in robot programming and Extended Reality (XR) platforms, comparing two programming methods: a classical kinesthetic teaching interface, provided by the Franka Emika Panda cobot, and the presented AR platform. Participants have reported the effectiveness of the proposed platform, experiencing less physical demand and higher intuitiveness and usability.