Reviewerly/exHarmony huggingface dataset:
Workshop on Participatory Exploration and Design of Human-Vehicle Interaction

An incremental but increasingly fast-paced revolution is taking place in our complex world: Automation capabilities of vehicles and development speed are growing, amplified with an even stronger push towards sustainability. As in these progressively more cooperative systems, humans, technical co-systems, organizations, society and environment all play their role to solve complex scenarios, a structured and integrated design is one of the keys to successful cooperation between automated vehicles and humans (as drivers, passengers and road users). This development not only carries risks, but also opens the opportunity to design systems, which are capable of dynamic cooperation instead of merely completing predefined tasks. In this workshop, we provide an introduction to the human systems integration approach to structured interaction design for cooperative vehicles by providing tools and methods needed for a successful design process that integrates and balances the needs of experts, users and other stakeholders.
Tamper Detection and Localization in Forensic Images

Malicious users may tamper forensic digital images for various reasons including among others the creation of false evidences. In this work we propose a tamper detection and localization technique for forensic digital images detecting whether a forensic image has been tampered and locating the area that has been modified. The methodology deploys the partition of the image into blocks and the calculation of the hash values of the red channel in particular areas of the image, the so-called integrity-blocks, embedding these values into the green channel of other blocks based on a permutation of the integrity-blocks. Moreover, once a tampering has been detected into a forensic image the derived system that implements the proposed method provides a visual evidence of the tampering by coloring the tampered area with red color identifying the extent and point of the tampering, making it easier to understand the issue and resolve it. We provide an experimental evaluation of the proposed technique by performing a set of attacks by both inserting and removing elements from forensic images proving the potentials of our model in detecting and precisely locating the tampered areas.
Reality and How to Forget about It: Increasing the Sense of Presence in Simulated Traffic Environments

The sense of presence is commonly defined as the recipient’s subjective sense of being there in a virtual environment. In the context of HCI research in traffic, virtual reality methods as well as simulators of all kinds, including driving simulators, bicycle simulators or pedestrian simulators, are part of the standard research repertoire today. In these simulated traffic environments, it is usually the aim to produce a realistic impression of the corresponding real-world situation, creating a high sense of presence as well as realistic driving behavior. Throughout the course of the presented workshop, we intend to collect measures and exchange views about methods which can be applied to increase the sense of presence in driving simulation, avoiding so called "breaks in presence". Different measures to increase presence shall be generated and evaluated by the workshop participants. We further seek to discuss different components of presence, and how these relate to various types of simulation, as well as simulation validity. Summarizing, the present workshop aims to establish current knowledge regarding the concept of presence, while identifying promising future measures to increase the sense of presence in simulated traffic environments.
Cross-Cultural Differences: Is a Weighting Scheme for the User Experience Questionnaire Worth Considering?

Various methods have been developed to measure user experience (UX). The majority of measures considers the multidimensionality of the construct including the User Experience Questionnaire (UEQ). However, the UEQ neglects that the individual UX-components might be of unequal importance to different cultures. Therefore, the aim of this contribution was to gain first insights on what users from different countries, i.e., China, Germany, and the United States, cherish in an automotive user interface (UI) using the six factors provided by the UEQ. The results indicated that participants from different cultural backgrounds preferred different UI-qualities. Based on the findings, we compiled some initial thoughts on how to consider cultural differences both in the design of an automotive UI as well as in the methodological approach. We raise the question whether a weighting scheme for the UEQ is worth considering in cross-cultural UX-research.
Can We Understand Other Drivers? An Investigation on Empathic Triggers through Voice Information Transmission in Driving

Arousing empathy is an emerging theme in human-vehicle interaction design, as it can help minimize misunderstandings between drivers and improve driving safety. In this study, we investigated potential empathic triggers in in-vehicle voice-based interactions using the "co-constructing stories method" with 11 participants. Our findings suggest that effective empathic triggers possess three characteristics: (1) explainable, (2) definite, and (3) either objective and irresistible or subjective and urgent. We propose a design principle that empathic triggers construct by one Basic element (Reason) and at least one Detail element (Identity, Destination, or Time). Additionally, we discuss drivers’ willingness to share information. This study serves as a foundation for developing design principles that utilize voice to arouse empathy in human-vehicle interactions.
Annoyed of Traffic Jams? Fly with FeliFly!

New research topics are appearing in the emerging field of urban air mobility (UAM). Successful introduction requires a high level of acceptance in society; this can be achieved by means of user-centered design and by involving citizens in the early stages of development. For immersive user studies, e.g., through user enactment, a physical prototype is helpful - especially since there are currently no drones that can fly on public routes and carry passengers. This project, therefore, developed a physical, true-to-scale prototype of a passenger drone and enhanced it with in-cabin services and a mobile booking app. Scales, interior design, and service concepts were derived from related work and own research. The prototype “FeliFly” can now visualize several phases of a customer journey and help to investigate acceptance factors as well as expectations and potential problems along a passenger drone flight to derive suggestions for later real implementation.
Intelligent personalized content recommendations based on neural networks

To effectively assist users in discovering content tailored to their specific interests, this research aims to create an intelligent content recommendation system. The inadequacy of conventional recommendation models, which depend uniquely on historical reading data, becomes evident in their limited capacity to meet contemporary users' diverse and ever-changing preferences within the information. The proposed architecture makes the most of the advancements in deep learning technology. It integrates the self-attention mechanism, allowing for precise calibration of the significance attributed to each feature within the news data. The proposed multilevel data classification network enables a more refined and personalized knowledge of users' preferences and the array of content information attributes while incorporating the users' unique characteristics. The proposed model achieved an accuracy rate of 85.2%, a recall rate of 83.7%, an F1 score of 84.3%, and an Area Under the Curve (AUC) of 84.5%. By developing a multilevel, intelligent, personalized content recommendation network, the research attempts to introduce a solution that effectively provides users' preferences, thereby enriching their experience in discovering relevant information within the modern digital system.
Predicting University Students' Mobile Learning Attitudes in Language Classrooms

This study aimed to examine the predictive role of digital literacy skills on mobile English learning attitudes. It was conducted with 135 university students studying in the school of foreign languages of a state university in Turkey, and the data were gathered using digital literacy scale and English mobile learning attitude scale. This study showed that university students' digital literacy skills and mobile English learning attitudes were high and their digital literacy skills have a moderate and positive relationship with their mobile English learning attitudes. Besides, it was concluded that university students' digital literacy skills significantly predicted their mobile English learning attitudes and they together explained 50% of the total variance on their mobile English learning attitudes. Also, university students' scores on the attitude subscale of the digital literacy scale presented the largest effect on their mobile English learning attitudes.
Superadditivity effects of quantum capacity decrease with the dimension for qudit depolarizing channels

Quantum channel capacity is a fundamental quantity in order to understand how well quantum information can be transmitted or corrected when subjected to noise. However, it is generally not known how to compute such quantities since the quantum channel coherent information is not additive for all channels, implying that it must be maximized over an unbounded number of channel uses. This leads to the phenomenon known as superadditivity, which refers to the fact that the regularized coherent information of $n$ channel uses exceeds one-shot coherent information. In this article, we study how the gain in quantum capacity of qudit depolarizing channels relates to the dimension of the considered systems. We make use of an argument based on the no-cloning bound in order to prove that the possible superadditive effects decrease as a function of the dimension for such family of channels. In addition, we prove that the capacity of the qudit depolarizing channel coincides with the coherent information when $d\ensuremath{\rightarrow}\ensuremath{\infty}$. We also discuss the private classical capacity and obtain similar results. We conclude that when high-dimensional qudits experiencing depolarizing noise are considered, the coherent information of the channel is not only an achievable rate, but essentially the maximum possible rate for any quantum block code.
Fractal belief Jensen–Shannon divergence-based multi-source information fusion for pattern classification

Multi-source information fusion is an effective method to handle pattern classification problems. Dempster–Shafer evidence theory (DSET) plays an important role in handling uncertainty problems in multi-source information fusion. However, highly conflicting evidence in DSET may cause counter-intuitive fusion results. Belief divergence theory is one of the solutions to conflict management, which is also beneficial for the improvement of accuracies of pattern classification. In this paper, a novel belief divergence measurement method, fractal belief Jensen–Shannon (FBJS) divergence is proposed to better measure the discrepancy between Basic probability assignments (BPAs) and address the problem of highly conflicting evidence in DSET. The proposed FBJS divergence is the first belief divergence that incorporates the belief divergence theory and the concept of fractal. In addition, it has the properties of non-negativeness and symmetry. Then, based on FBJS divergence, a novel multi-source information fusion algorithm is proposed. Ultimately, the proposed algorithm is effectively applied to solve a pattern classification problem with a higher classification accuracy.
Information Processing Using Soft Body Dynamics

In this chapter, we address how the introduction of softness into robots will enable unprecedented information-processing functionalities. In Sect. 15.1, we show how softening a robot’s body activates the control outsourcing to the body. Based on several examples, we provide an overview of the key concept of “embodiment,” under which an intelligent system is viewed as a brain–body–environment system. In Sect. 15.2, we present simple examples to introduce how machine learning techniques for soft robots can be used effectively. In Sect. 15.3, building on the contents of the previous sections, we introduce the concept of physical reservoir computing. We delve into the mathematics of the information-processing capabilities brought about by softness, based on the example of a computer with an octopus arm and a soft interface called a soft keyboard.
An Optimized Class Incremental Learning Network with Dynamic Backbone Based on Sonar Images

Class incremental learning with sonar images introduces a new dimension to underwater target recognition. Directly applying networks designed for optical images to our constructed sonar image dataset (SonarImage20) results in significant catastrophic forgetting. To address this problem, our study carefully selects the Dynamically Expandable Representation (DER)—recognized for its superior performance—as the baseline. We combine the intrinsic properties of sonar images with deep learning theories and optimize both the backbone and the class incremental training strategies of DER. The culmination of this optimization is the introduction of DER-Sonar, a class incremental learning network tailored for sonar images. Evaluations on SonarImage20 underscore the power of DER-Sonar. It outperforms competing class incremental learning networks with an impressive average recognition accuracy of 96.30%, a significant improvement of 7.43% over the baseline.
Using Virtual Reality to Support Retrieval Practice in Blended Learning: An Interdisciplinary Professional Development Collaboration between Novice and Expert Teachers

This small-scale study comprised an evaluation of a teacher professional learning experience that involved the collaborative creation of resources using immersive virtual reality (VR) as a retrieval practice tool, specifically focusing on the open access aspects of the SchooVR platform. SchooVR offers teachers and students tools to enhance teaching and learning by providing a range of virtual field trips and the ability to create customised virtual tours aligned with curriculum requirements. By leveraging the immersive 360° learning environment, learners can interact with content in meaningful ways, fostering engagement and deepening understanding. This study draws on the experiences of a group of postgraduate teacher education students and co-operating teachers in Ireland and Northern Ireland who collaborated on the creation of a number of immersive learning experiences across a range of subjects during a professional learning event. The research showcases how immersive realities, such as VR, can be integrated effectively into blended learning spaces to create resources that facilitate retrieval practice and self-paced study, thereby supporting the learning process. By embedding VR experiences into the curriculum, students are given opportunities for independent practice, review, and personalised learning tasks, all of which contribute to the consolidation of knowledge and the development of metacognitive skills. The findings suggest that SchooVR and similar immersive technologies have the potential to enhance educational experiences and promote effective learning outcomes across a variety of subject areas.
IMPROVING ASATIDZ'S TEACHING COMPETENCE THROUGH MAKING TEACHING MATERIALS USING MICROSOFT POWERPOINT AND CANVA

Today is an era of change in the use of information and communication technology in all aspects of human life. This adoption has also been accelerated by the emergence of the Covid-19 pandemic which has also had an impact on the education sector, including Islamic boarding schools that adopt technology on a more massive basis. Islamic boarding schools are becoming more involved in taking on digital and technological contests. Asatidz as a teacher are required to have skills in the field of learning technology. Creating and designing teaching materials that are interesting and in demand by students is a challenge for asatidz. The solution to this problem is to provide training for asatidz on how to make interesting teaching materials using Microsoft Powerpoint and Canva. This community service was carried out in November 2022 face-to-face with a total of 41 participants who were assistants at the Darussalam Kunir Islamic Boarding School, Subang Regency, West Java. The results of the training showed that there was a significant difference between the pre-test and post-test conducted by the participants. The conclusion is that this activity is perceived as useful by participants and can provide increased knowledge and skills in making presentations for asatidz.
Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model.
Review on Research of Blockchain's Impact on Bookkeeping, Fraud Detection and Trust of Auditing Process

Blockchain technology has been the topic of several recent studies in area of accounting and auditing. These studies explored the potential of blockchain on changing the traditional approach to accounting and providing more secure and efficient methods of bookkeeping. Results of these studies suggest that the blockchain has the potential of being a powerful tool or even something disruptive in sector of accounting, auditing, and accountability. Blockchain can significantly increase trust, transparency, and accountability since its nature of decentralized ledger system and the cryptographic algorithms under the system. These studies also suggest that blockchain has the potential to tackle fraud in accounting activities, which is always a challenging topic in accounting industry. Current focus of blockchain research in accounting, auditing and accountability is about exploring blockchains positive impact on accounting industry, especially on trust, fraud detection, and the design of accounting systems.
The Relationship of Cryptocurrencies and the Stock Market

In today's world, with the internet and finance closely integrated, the global-ization of cryptocurrency has deepened, prompting people to pay more atten-tion to the impact of cryptocurrency on the entire financial market. This pa-per provides a comprehensive review of relevant literature from five aspects: the development of cryptocurrency, price changes, the impact of cryptocur-rency on the stock market, the impact of cryptocurrency on the banking in-dustry, and the currency and commodity characteristics of cryptocurrency. The author also compares the price curves (in USD) of the S&amp;P 500, ETH, and BTC from March 21, 2022, to March 21, 2023, and hopes to gain in-sights into the relationship between stocks and cryptocurrencies. Our re-search shows that the emergence of cryptocurrency has had a significant im-pact on the financial market, particularly in the banking industry. Although the emergence of cryptocurrency has added vitality to the financial market and increased investment options, the instability of cryptocurrency has also brought risks to investors. As the cryptocurrency market continues to ex-pand, policymakers and investors need to understand its potential impact on traditional financial instruments and the broader economy.
RTV-SIFT: Harnessing Structure Information for Robust Optical and SAR Image Registration

Registration of optical and synthetic aperture radar (SAR) images is challenging because extracting located identically and unique features on both images are tricky. This paper proposes a novel optical and SAR image registration method based on relative total variation (RTV) and scale-invariant feature transform (SIFT), named RTV-SIFT, to extract feature points on the edges of structures and construct structural edge descriptors to improve the registration accuracy. First, a novel RTV-Harris feature point detection method by combining the RTV and the multiscale Harris algorithm is proposed to extract feature points on both images’ significant structures. This ensures a high repetition rate of the feature points. Second, the feature point descriptors are constructed on enhanced phase congruency edge (EPCE), which combines the Sobel operator and maximum moment of phase congruency (PC) to extract edges from structured images that enhance robustness to nonlinear intensity differences and speckle noise. Finally, after coarse registration, the position and orientation Euclidean distance (POED) between feature points is utilized to achieve fine feature point matching to improve the registration accuracy. The experimental results demonstrate the superiority of the proposed RTV-SIFT method in different scenes and image capture conditions, indicating its robustness and effectiveness in optical and SAR image registration.
Roe: A Computational-Efficient Anti-hallucination Fine-Tuning Technology for Large Language Model Inspired by Human Learning Process

Large language models (LLMs) have shown significant potential in natural language processing (NLP) tasks, but they are plagued by issues of hallucination, where generated text contains false or fabricated information. This paper introduces a novel method, called “Roe,” to combat hallucination in LLMs by incorporating a fine-tuning technique that promotes honesty in the model’s responses. Inspired by psychological studies on promoting honesty in children, the method rewards both correct answers and the behavior of admitting uncertainty by answering “I don’t know” when the model is unsure. The objective function is designed to encourage the model to switch between these two types of responses based on its confidence level. Experiments demonstrate that the Roe method achieves significantly better performance in addressing hallucination compared to the base Llama model and the original Alpaca model. The method also achieves comparable results to the state-of-the-art GPT4All model while using a smaller training dataset. The results of the HALTT4LLM benchmark further confirm the effectiveness of the Roe method in combating hallucination, particularly in handling fake questions. Sample outputs of the Roe model compared to the Alpaca model highlight the model’s adherence to factual information and its avoidance of direct references to fictional items in fabricated questions. Overall, the Roe method presents a promising approach for mitigating hallucination in large language models, enhancing their reliability and trustworthiness in generating text.