Reviewerly/exHarmony huggingface dataset:
Privacy in Connected Vehicles: Perspectives of Drivers and Car Manufacturers

The digital revolution has led to significant technological advancements in the automotive industry, enabling vehicles to process and share information with other vehicles and the cloud. However, as data sharing becomes more prevalent, privacy protection has become an essential issue. In this paper, we explore various privacy challenges regarding different perspectives of drivers and car manufacturers. We also propose general approaches to overcome these challenges with respect to their individual needs. Finally, we highlight the importance of collaboration between drivers and car manufacturers to establish trust and achieve better privacy protection.
Realization of Super-Resolution Using Bicubic Interpolation and an Efficient Subpixel Model for Preprocessing Low Spatial Resolution Microscopic Images of Sputum

Medical imaging explores methods and models for analyzing medical image data, however, the low resolution of images obtained using equipment with small lenses and a short focal length may limit the implementation of medical data recognition. A variety of models and methods of super resolution implement the preprocessing of low spatial resolution images in medical imaging. The paper investigates the problem of preprocessing microscopic images of sputum containing small-sized objects of interest using super-resolution methods of bicubic interpolation and a model of an effective sub-pixel convolutional neural network. The performance of the selected models and methods is evaluated using the PSNR criterion. The obtained results show that both approaches can be used for the problem of super resolution of microscopic images of sputum containing small objects of interest.
Language Models Explain Recommendations Based on Meta-Information

For recommender systems, the explanation of why the item was recommended to a user increases the reliability. In this work, we introduce a post-hoc method of explaining any recommender system’s output with the use of LLMs and meta information about a recommended item and user’s preferences. We try different models and introduce metrics for estimating the quality of generated explanations. The models are evaluated on three domains and then compared to analyze the ability for domain transfer.
Orchestrating Information Governance Workloads as Stateful Services Using Kubernetes Operator Framework

Regulatory compliance is forcing organizations to implement an information governance (IG) strategy, but many are struggling to evolve their IG solutions due to their legacy architecture, as they are not designed to adapt to new business models and for the growing amount of unstructured data produced by a potentially worldwide audience. One of the biggest problems faced is continuously determining data value and adaptation of measures to keep risks and operational costs under control. One way to solve this issue is to leverage cloud technology and find an affordable approach to migrate legacy solutions to a cloud environment. In most cases, this means de-composing monolithic applications, refactoring components and replacing outdated homegrown deployment technologies with cloud-native, automated deployment and orchestration services. Our goal is to show how operational costs can be reduced by running refactored versions of IG solutions in clouds with a minimum of human intervention. This paper discusses the steps to evolve a legacy multi-tier IG solutions from physical to containerized environments by encapsulating human operator knowledge in cloud topology and orchestration artifacts, with the goal of enabling automated deployment and operation in Kubernetes (K8s) managed execution environments.
Understanding Real-World AI Planning Domains: A Conceptual Framework

Planning is a pivotal ability of any intelligent system being developed for real-world applications. AI planning is concerned with researching and developing planning systems that automatically compute plans that satisfy some user objective. Identifying and understanding the relevant and realistic aspects that characterise real-world application domains are crucial to the development of AI planning systems. This provides guidance to knowledge engineers and software engineers in the process of designing, identifying, and categorising resources required for the development process. To the best of our knowledge, such support does not exist. We address this research gap by developing a conceptual framework that identifies and categorises the aspects of real-world planning domains in varying levels of granularity. Our framework provides not only a common terminology but also a comprehensive overview of a broad range of planning aspects exemplified using the domain of sustainable buildings as a prominent application domain of AI planning. The framework has the potential to impact the design, development, and applicability of AI planning systems in real-world application domains.
Pool Games in Various Information Environments

The emergence of Blockchain digital technology provides one of the most prominent transaction mechanisms in an increasing variety of digital and augmented environments. In the Blockchain habitat, interactions among autonomous agents, called miners, form mining pools that aggregate computational power in order to increase their possible gains. A Pool game models mining pools that compete against each other in order to improve their outcome by strategically committing their miners. Current studies in Pool games make the assumption that pools have complete and correct information about the situation. In this work we drop this assumption, studying Pool games under various information environments such as incomplete information and erroneous information.
Non-visual Eye-Movements Model During Performing Cognitive Tasks in Short-Term Memory

In the blank screen paradigm participants solved cognitive tasks. Current fixation duration and saccade peak velocity differ in learning and performing some kinds of tasks. Retrieving information from memory and following mental processing depends on the characteristics of input task and its difficulty. The best fixation accuracy performance was in the participants who named only two levels of task difficulty, the best saccade accuracy performance was in Random Forest Classifier for saccades.
Dialogue Graphs: Enhancing Response Selection Through Target Node Separation

This paper proposes a new method called Target Node Separation to address the problem of accurately performing the response selection task in dialogue systems. The proposed method enhances the performance of response selection tasks by refining the graph structure through improving edge ends. Authors compare the proposed method to other state-of-the-art methods on the MultiWoZ dataset and find that the proposed approach outperforms other graph-based methods and SBERTMap on recall metrics. Furthermore, the authors observed that increasing the number of clusters results in an improvement in the performance of the dialogue graph.
Modeling of Natural Needs of Autonomous Agents

In this paper, a model of autonomous agents with basic biological needs is constructed and investigated. The population of agents functions in a cellular environment. Each agent has four needs: 1) safety, 2) food, 3) reproduction, and 4) research. The intensities of needs form the agent’s genotype and are expressed as values from the interval [0, 1]. The model was analyzed by computer simulation. It is shown that agents with motivations have advantages as compared with agents without motivations. The experiments also confirmed that the needs of food and reproduction are the most important for the survival of the population.
Failing with Grace: Exploring the Role of Repair Costs in Conversational Breakdowns with in-Car Voice Assistants

AbstractTechnological advancements over the past years have led to a leap in Conversational User Interface (CUI) performance. However, interactions with CUIs are not error-free and error handling thus plays a crucial role in CUI design. So far, CUI repair strategies oftentimes neglect what is known from Human-Human Interaction (HHI). The present work borrows from human-centered error handling and applies the principle of Least Collaborative Effort to Human-Computer Interaction (HCI). A within-subjects WoZ driving simulator study (n = 48) is conducted to examine user preferences qualitatively and quantitatively for seven error handling strategies in an automotive setting. Our findings show that human-centered repair strategies comply with the principle of Least Collaborative Effort and lead to significantly lower repair costs than a classic HCI error handling strategy. These results introduce costs as a measurement tool for examining strategy preferences when repairing erroneous dialogs and offer assistance for designing user-centric error handling strategies.Keywords: Conversational user interfaceserror handlingin-car voice assistants Ethics statementIn accordance with the Declaration of Helsinki, all study participants gave informed consent and were informed about their right to withdraw from the study at any time and without consequences. The study did not survey particularly vulnerable groups and was non-interventional as well as non-invasive. The static driving simulator used in the study mitigated physical risks and none of the study participants reported incidents of motion sickness.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The study was conducted in German; English translations are provided by the author.2 Full dialog resets meaning a dialog restart with a fallback on generic error handling in form of “Okay, how may I help you?”Additional informationNotes on contributorsAnna-Maria MeckAnna-Maria Meck received her B.A. and M.A. degree in Phonetics and Speech Processing at Ludwig Maximilians University in Munich, Germany. She is currently pursuing her Ph.D. degree in collaboration with the BMW Group at the same institute, focusing her research on linguistic aspects and context-sensitivity in Human-Computer Interaction.Christoph DraxlerChristoph Draxler received his Ph.D. in computer science from TU Munich and Zurich University. He is a researcher at the Institute of Phonetics and Speech Processing and the co-manager of the Bavarian Archive for Speech Signals at LMU Munich. His current research interests include web-based transcription and spoken language processing.Thurid VogtThurid Vogt received her Diploma and Ph.D. in Computer Science with focus on language processing and emotion recognition from the University of Bielefeld, Germany. She is currently working at BMW Group AG on the in-car BMW Intelligent Personal Assistant.
The Discourse of Motivation: Judges’ Comments in The Voice Kids Indonesia

Abstract This chapter discusses the comments of judges of The Voice Kids Indonesia (TVKI), a competition program broadcast on television involving children. This study applies pragmatic approaches using Brown &amp; Levinson’s politeness theory (Politeness: some universal in language used. Cambride University Press, 1987) and Searle’s theory (1969) on illocution. The focus of this study is the judges’ motivational comments toward children’s performance as contestants. The analysis is also supported by Maslow’s theory (A theory of human motivation. Nalanda Digital Library, 1943) on motivation. The data are collected from TVKI judges’ open comments of assessment in TVKI Blind audition session to battle round session (episodes 1−4 and 9−11) which are taken from TVKI's official YouTube Channel. Analysis based on the politeness theory reveals that positive and negative politeness strategies help shape contestants’ needs for self-actualization. The judges’ motivating comments genuinely address each contestant’s skills and put the contestant at ease. Thus, this study can explain the existence of the relationship between politeness strategy and the hierarchy of fulfillment of human needs informing potentially motivational comments.
Using Adobe Connect application in an EFL context: Does it have an effect on learners’ speaking skills?

The main aim of the present study was to explore the impact of Adobe Connect on Iranian English as a foreign language (EFL) learners’ speaking skills. Furthermore, the study investigated whether the learners’ attitudes to language learning changed after using Adobe Connect. For that purpose, 100 upper-intermediate EFL students were chosen through the convenience sampling method. The selected students were randomly assigned to two classes. The experimental group applied the Adobe Connect application; however, the conventional teacher-fronted instruction was used for the control group. The results showed that the Adobe Connect class considerably outperformed in speaking skills in comparison to the control group. This demonstrated that the Adobe Connect application is effective in improving learners’ speaking skills. The findings suggest that Adobe Connect has a positive impact on enhancing learners’ speaking skills. The platform’s interactive features promote engagement and collaboration, leading to improvements in fluency, accuracy, pronunciation, and confidence. The results also revealed that there was a difference in attitude change between the participants in experimental and control classes. Adobe Connect has enhanced EFL learners’ attitudes toward language learning as well as speaking skills. Thus, it can be suggested that the Adobe Connect application is an effective online instrument that can be used by EFL teachers.
Joint Optimization of Memory Sharing and Communication Distance for Virtual Machine Instantiation in Cloudlet Networks

Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virtual machines (VMs) can be instantiated. Each VM runs a UD’s application components and provides dedicated services for that UD. The number of VMs that serve UDs with low latency is limited by a lack of sufficient memory of cloudlets. Memory deduplication technology is expected to solve this problem by sharing memory pages between VMs. However, maximizing page sharing means that more VMs that can share the same memory pages should be instantiated on the same cloudlet, which prevents the communication distance between UDs and their VMs from minimizing, as each VM cannot be instantiated in the cloudlet with the shortest communication distance from its UD. In this paper, we study the problem of VM instantiation with the joint optimization of memory sharing and communication distance in cloudlet networks. First, we formulate this problem as a bi-objective optimization model. Then, we propose an iterative heuristic algorithm based on the ε-constraint method, which decomposes original problems into several single-objective optimization subproblems and iteratively obtains the subproblems’ optimal solutions. Finally, the proposed algorithm is evaluated through a large number of experiments on the Google cluster workload tracking dataset and the Shanghai Telecom base station dataset. Experimental results show that the proposed algorithm outperforms other benchmark algorithms. Overall, the memory sharing between VMs increased by 3.6%, the average communication distance between VMs and UDs was reduced by 22.7%, and the running time decreased by approximately 29.7% compared to the weighted sum method.
Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning

Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available "bivalve and brachiopod fossil image dataset" (BBFID, containing >16,000 "image-label" data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. We achieved >80% identification accuracy at 22 genera and ∼64% accuracy at 343 species using EfficientNetV2s architecture. The intermediate output of the model was extracted and downscaled to obtain the morphological feature space of fossils using t-distributed stochastic neighbor embedding (t-SNE). We found a distinctive boundary between the morphological feature points of bivalves and brachiopods in fossil morphological feature distribution maps. This study provides a possible method for studying the morphological evolution of fossil clades using computer vision in the future.
Real time handwriting recognition system using CNN algorithms

Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition using the recent artificial intelligent algorithms, the conventional neural network (CNN).&#x0D; Different CNN models were tested and modified to produce a system has two important features high performance accuracy and less testing time. These features are the most important factors for real time applications. The experimental results were conducted on a dataset includes over 400,000 handwritten names; the best performance accuracy results were 99.8% for SqueezeNet model.
Fast contact detection for ellipsoids using optimization approaches

Abstract The paper is concerned with fast and robust contact detection methods for arbitrary ellipsoids. An iterative procedure, namely the Levenberg‐Marquardt method, based on the common normal concept for parametric ellipsoids, is employed together with an implementation of the widely used GJK algorithm for comparison. The performance and accuracy of both are analysed and compared to each other on the basis of two test sets, each containing a total of 70 000 pairs of prolates or oblates. Emphasis is placed on the specific error measure relating the iterative solution to the exact one, which was chosen to be the maximum angle between the normal vector and the distance vector between two ellipsoids. The results indicate increased performance when using the Levenberg‐Marquardt method over the GJK algorithm with no loss of accuracy.
Investigating student teachers’ responses to including sustainable development as part of their professional digital competence development

This article puts forward the case for the inclusion of sustainability as an element of teachers’ professional digital competence. Following this, the article explores student teachers’ reaction to a course on digital technologies and sustainability. An online survey was used to record the student teachers’ reactions to the course and how they felt its content would influence their future actions. The student teachers had limited prior knowledge of the topic, but almost all acknowledged its importance, particularly in raising their awareness of global inequality. The student teachers reported they would change their purchasing habits, although some acknowledged that it would be difficult. Most believed it would influence their teaching, but perceived curriculum demands and school expectations tempered their expectations of what they believed they could achieve.
Analisis Keefektifan Kalimat Bahasa Indonesia Dalam Karya Tulis Ilmiah

The purpose of this research is to analyze the effectiveness of Indonesian sentences in scientific writing. This research is a descriptive-qualitative study with primary data obtained directly from the data source in the form of scientific writing (thesis) by Politeknik Negeri Bali students and lecturer. Data was collected by observing method assisted by note-taking techniques. Furthermore, the data were analyzed using a qualitative descriptive method based on an inductive paradigm, namely from special to general paradigm. Research results are presented with formal and informal methods. This research is based on General Linguistics Theory with the basic concept of effective sentences. The results of the research show that students and lecturer who compose their scientific writing pay little attention to the effectiveness of sentences. This is proven by the number of ineffective sentences found in scientific writing. The ineffectiveness of the sentences is caused by inappropriateness, incongruity, inaccuracy, inefficiency, and illogicality. This is because the students and lecturer who write the scientific writing are not careful in choosing and using vocabulary.
Mssgan: Enforcing Multiple Generators to Learn Multiple Subspaces to Avoid the Mode Collapse

Generative Adversarial Networks are powerful generative models that are used in different areas and with multiple applications. However, this type of model has a training problem called mode collapse. This problem causes the generator to not learn the complete distribution of the data with which it is trained. To force the network to learn the entire data distribution, MSSGAN is introduced. This model has multiple generators and distributes the training data in multiple subspaces, where each generator is enforced to learn only one of the groups with the help of a classifier. We demonstrate that our model performs better on the FID and Sample Distribution metrics compared to previous models to avoid mode collapse. Experimental results show how each of the generators learns different information and, in turn, generates satisfactory quality samples.
Establishing a Digital Technology Play Space for Children

This paper captures thinking around the design of a technology-focused play space for children that is on a university campus. Drawing upon characteristics of play and the importance of the goals and values that shape contexts, the opportunity to develop a space designed for children’s digital play required careful conceptualization. Children’s museums as spaces where experiences are designed for children to interact with, creativity is fostered alongside developing knowledge, and learning processes were central understanding. As a research team of adults, we have worked to consider, hear, and embrace the potential experiences of children within the space. Along with an account of the underpinning design philosophies, shared in this paper is an initial experience using the space and emerging implications. The paper offers theoretically informed reflections about children’s learning through play that challenge and promote reflection about the ways spaces for children are established.