Reviewerly/exHarmony huggingface dataset:
Implementasi Augmented Reality Sebagai Media Promosi Pada Lesehan Kalisari Dengan Metode Based Marker Tracker

Multimedia, as an information technology product, has the advantage of attracting the imagination and interest of its users. One of the multimedia products whose development is quite widely accepted by most people is augmented reality (AR). With the support of technology, Augmented Reality can combine the real world with the visual world. This study aims to design and build an android-based application using augmented reality technology with the Based Marker Tracker method as an alternative promotional media so that various food/beverage products available at restaurants or Lesehan Kalisari can be more attractive to customers. This application uses Vuforia SDK software, Unity3D with C# programming language, and Blender 3D to create 3D shapes from food/beverage products. This research uses the waterfall development method or what is often referred to as the classic life cycle. This research produces an android-based augmented reality application that can display 3D forms of food/beverage products on the menu list. With this application, it is hoped that it can become an alternative promotional media that increases the number of customers.
Analysis on Daily Communication Differences Between Males and Females

Humans' most fundamental means of communicating and disseminating ideas is language. It reflects cultural norms and values. Linguistic disparities between sexes are an inevitable result of the gender gap as a social and cultural issue. Gender disparities in language reflect broader social phenomena and evolve over time, all of which have their roots in society. Language, sexuality, and culture are all intertwined in complex ways. There are communication barriers and misconceptions because men and women have different expression traits, regardless of the topic or style of speech. There are a number of factors, such as differences in social standing and brain structure, that have been cited to explain why men and women express themselves differently. This research analyses and provides examples of how men and women communicate differently in their daily lives. According to the research presented in the paper, males and females do not always use the same vocabulary when referring to the same concepts.
Masked face recognition based on facenet and genetic algorithm

This study proposes a face recognition method based on FaceNet for face masks. FaceNet converts faces into 128-dimensional feature vectors using a network of deep learning models. The smaller the Euclidean distance between different face images, the closer the person is to the same person; conversely, the larger the Euclidean distance, the more different the person is. Wearing a mask affects some feature vector dimensions of FaceNet conversion, resulting in reduced recognition ability. This study uses a Genetic Algorithm (GA) to select and remove the feature vectors affected by mask wearing. The experimental results show that the validation rate value of 55 features removed by the GA is increased from 0.550 to 0.650 for the original unremoved features.
Dual counterpart intuitionistic logic

Abstract We introduce dual counterpart intuitionistic logic (or DCInt): a constructive logic that is a conservative extension of intuitionistic logic, a sublogic of bi-intuitionistic logic, has the logical duality property of classical logic, and also retains the modal character of its interpretation of the connective dual to intuitionistic implication. We define its Kripke semantics along with the corresponding notion of a bisimulation, and then prove that it has both the disjunction property and (its dual) the constructible falsity property. Also, for any class $ {\mathcal{C}}$ of Kripke frames from our semantics, we identify a condition such that $ {\mathcal{C}}$ will have the disjunction property if it satisfies the condition. This provides a method for generating extensions of DCInt that retain the disjunction property.
Attribution problem of generative AI: a view from US copyright law

Journal Article Attribution problem of generative AI: a view from US copyright law Get access Zeynep Ülkü Kahveci Zeynep Ülkü Kahveci https://orcid.org/0000-0001-9717-168X Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, jpad076, https://doi.org/10.1093/jiplp/jpad076 Published: 12 September 2023 Article history Received: 03 July 2023 Revision received: 19 August 2023 Editorial decision: 25 August 2023 Accepted: 29 August 2023 Corrected and typeset: 12 September 2023 Published: 12 September 2023
Workshop on HMI Design in the Context of DMS and Automation: How Should the System Respond?

Camera-based driver monitoring systems (DMS) are a mature technology capable of reliably detecting a wide range of driver behaviours and states [3, 7]. However, fully realising the safety potential of DMS requires accurate monitoring be paired with effective human-machine interfaces (HMI) to facilitate behavioural change when risky behaviours or unsafe driver states are detected. Further, increasing levels of driving automation pose challenges for researchers and manufacturers in determining how automated components of the vehicle-system should react when such states are detected. This workshop will address the issue of DMS-HMI integration by providing a space for collaborators to discuss key emerging issues for driver monitoring and HMI response. The organisers will present an introduction to DMS concepts and industry standards, and outline the challenge of implementing DMS in both manual and assisted driving environments, followed by small-group discussions. The organisers will combine industry and academic perspectives to facilitate problem solving with participants.
In-Car Office: Can HMD-Based AR Alleviate Passenger Motion Sickness?

Performing non-driving-related tasks as car passenger reduces visual perception of surroundings, which may cause a conflict with the human vestibular system and thus lead to motion sickness. Augmented reality head-mounted displays offer a possible solution to this phenomenon by presenting digital content at head level as opposed to common displays placed on one’s lap, keeping the peripheral vision intact. However, technical limitations such as end-to-end latency of video see-through devices may counteract this advantage. Therefore, we investigated a mobile office scenario by comparing video see-through augmented reality to a traditional laptop setup with regard to motion sickness and task performance in a moving car. Our results suggest similar responses to motion sickness between conditions, with limited effects on task performance and improved ergonomics when using augmented reality.
Secure Heterogeneous Architecture based on RISC-V and root-of-trust

This paper describes the architecture and development of a secure heterogeneous RISC-V system with protection dedicated core that provides hardware mechanisms for establishing root-of-trust, monitoring and controlling the execution of the rest of the cores in the system. The focus is on the security core which serves as control unit for the rest of the computational processors.
2nd Workshop on Automotive Mixed Reality Applications: Transitional Interfaces, Immersive Driving Experiences, and Helmet-Mounted AR for Future Mobility

As the development of mixed reality (MR) technology continues to progress, the range of applications and purposes within vehicles and for road users is expanding. MR holds the potential to enhance road safety, enable drivers to engage in non-driving related tasks (NDRTs), and improve passenger experiences. Moreover, the utilization of head/helmet-mounted displays (HMDs) with MR technology for cyclists can augment their vision and contribute to the safety of both cyclists and pedestrians. Furthermore, MR can play a crucial role in facilitating the transition towards automated driving. Nevertheless, there remain several challenges associated with the application of MR in vehicles, as well as human factors issues that require resolution. Additionally, virtual reality (VR) has the capability to immerse passengers in virtual worlds, offering opportunities for enjoyable passenger experiences. Presently, most MR research primarily focuses on individual users at a particular point along the reality-virtuality continuum. In this workshop, we will examine the potential, limitations, impact, role, and suitability of MR in driving applications and simulations. This will encompass topics such as holistic MR experiments, transitional interfaces, and HMDs specifically designed for cyclists. The primary objective of this workshop is to establish a research agenda for the application of MR utilized by road users over the next 3 to 5 years and beyond.
The Influence of Display Location on Motion Sickness: An Analysis Based on 6-DoF SVC Model

With the prevalence of smart cockpits, passengers spend increasingly more time engaging with non-driving related tasks (NDRTs, e.g., in-vehicle entertainment and meetings) in vehicles. However, prolonged NDRT engagement can cause sensory conflict and result in motion sickness. Previous research found that motion sickness is highly associated with head motion, which can further be influenced by the positions of the NDRT displays. To guide the design of the smart cockpit, this paper examines the impact of four common NDRT display locations on passengers’ motion sickness. A motion transfer function was utilized to translate a real-world vehicle trajectory into head motion when passengers looked at displays at different locations. Then, a 6 Degree-of-Freedom subjective vertical conflict model was adopted to evaluate the motion sickness caused by head motion. The results of this study can inform the design of in-vehicle displays and improve passenger comfort.
Design of a Smart Teaching English Translation System Based on Big Data Machine Learning

In the context of artificial intelligence, the use of machine translation in English reading classroom teaching is a more common learning method. In traditional teaching methods, machine translation is more convenient and faster than human translation, but it often deviates from the original text in terms of grammar and sentence pattern. Based on the perspective of English reading class, this paper compares traditional and machine translation, and discusses the future development trend and influence mechanism of the current situation of using machine translation in English reading class under the effect of artificial intelligence.
Collaborative methodology for semantic modeling of learning domain knowledge

Technologies for structured representation of knowledge are very useful for learning and for organization of personalized tutoring. Concept maps (CMs) have been used in many different ways to support learning. They are good tool for regular note-taking, visualization of the structure of learning content, or assisting in discussions and problem-solving activities. On the other hand, ontologies can benefit personalization and adaption of the tutoring process to the needs of specific learners or groups. This research discusses benefits of joint usage of both concept maps and ontologies in e-learning. We propose a collaborative methodology for semantic modeling of learning domain knowledge, showing how to use both CMs and ontologies to improve learning.
Quantum Machine Learning Enhancing AI With Quantum Computing

Quantum circuits with certain statistical properties called t-designs allow investigations into the behavior of gradients in high dimensions, and therefore tell us something about the trainability of quantum models in regimes that cannot necessarily be simulated. Along with that, the chapter shows how quantum machine learning is being used in real-world settings in a variety of fields, such as drug discovery, financial modelling, image identification, and natural language processing. All fundamental subatomic particles fall into two classes, based on how identical particles of each type behave when swapped. They are either fermions, a class that includes electrons and other particles that make up matter, or bosons, which include particles of light known as photons. Data encoding is often the most crucial step of quantum machine learning with classical data. It illustrates the possible advantages and insights that quantum algorithms can offer to various fields and analyses the ongoing research projects aimed at converting conventional machine learning models for quantum platforms.
MillenniumDB: An Open-Source Graph Database System

ABSTRACT In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon which a variety of popular graph models can be supported, thus providing a flexible data management engine for diverse types of knowledge graph. The engine itself is founded on a combination of tried and tested techniques from relational data management, state-of-the-art algorithms for worst-case-optimal joins, as well as graph-specific algorithms for evaluating path queries. In this paper, we present the main design principles underlying MillenniumDB, describing the abstract graph model and query semantics supported, the concrete data model and query syntax implemented, as well as the storage, indexing, query planning and query evaluation techniques used. We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph, where we find that it outperforms other popular persistent graph database engines (including both enterprise and open source alternatives) that support similar query features.
Multi-level access to information systems by QR codes for wearable devices

The scientific project "Multi-level access of information systems by QR codes for wearable devices" was carried out within the framework of the scientific grant project of Batumi State University. The idea is practically implemented and discussed in the example of a higher educational institution (university). Various areas of business and state organizations where successful practical implementation of the obtained results is possible are also mentioned.
Game Playing (2048) Using Deep Neural Networks

A one-player game, 2048 is also known as stochastic puzzle. This fascinating and engaging game has gained widespread acclaim and drawn researchers to create gaming software. The game 2048 has evolved into an engaging and difficult platform for assessing the efficacy of machine learning techniques because of its simplicity and complexity. Convolutional neural networks were used to create some computer players, but they performed poorly. In this work, the authors create a 2048 agent based on the reinforcement learning method and neural networks. The authors want to outperform other neural-network-based competitors in terms of results. Additionally, cutting-edge software created using this methodology for 2048 achieves the best performance out of all learning-based algorithms.
Advancing Dynamic Hand Gesture Recognition in Driving Scenarios with Synthetic Data

Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by virtual 3D models. Our framework utilizes Unreal Engine to synthesize realistic hand gestures, offering customization options and reducing the risk of overfitting. Multiple variants, including gesture speed, performance, and hand shape, are generated to improve generalizability. Additionally, we simulate different camera locations and types, such as RGB, infrared, and depth cameras, without incurring additional time and cost to obtain these cameras. Experimental results demonstrate that our proposed framework, SynthoGestures1, enhances gesture recognition accuracy and can replace or augment real-hand datasets. By saving time and effort in dataset creation, our tool accelerates the development of gesture recognition systems for automotive applications.
An Enhanced Study of Quantum Computing in the View of Machine Learning

Emerging technologies, including quantum information science and artificial education systems, have the potential to have significant implications for the future of human civilization. Quantum information, on the one hand, and machine learning (ML) and artificial intelligence (AI), on the other, consume their personal unique set of queries and contests that have been studied in isolation up until now. However, a recent study is starting to examine whether these disciplines can teach one another anything useful. The discipline of quantum ML investigates how quantum computing and ML may work together to find solutions to challenges in both areas. Major advancements in the two areas of effect have been made recently. Particularly relevant in today's “big data” era is the use of quantum computing to speed up the solution of machine learning (ML) challenges. However, ML is already present in many state-of-the-art technologies and may play a crucial role in future quantum technologies.
A Demonstration of AutoVis: Enabling Mixed-Immersive Analysis of Automotive User Interface Interaction Studies

In this demonstration, we present novel interaction modalities and use cases for AutoVis, a tool for the mixed-immersive analysis of automotive user interface (AUI) interaction studies. AutoVis uniquely enables exploration of AUI studies’ multilayered spatio-temporal interplay between humans, vehicles, and their surroundings by combining a non-immersive desktop view with a virtual reality view. It facilitates the analysis of passenger behavior, physiology, spatial interactions, and events within replications of study environments, employing avatars, trajectories, and heatmaps. To extend AutoVis and streamline interactions with it, we created a novel concept for gaze and gesture-supported analysis control. In addition, we conducted an exemplary use case study in the context of traffic accident reconstructions to explore the applicability of AutoVis apart from AUIs. By demonstrating these extensions, we contribute to the underexplored area of immersive analytics for AUIs and promote a more efficient and effective understanding of human-vehicle interaction.
Quantum Metrology

Researchers have focused increasingly on hybrid quantum computing and brain-computer interfaces. Scientists are investigating brain-computer interface technology and applying it to additional fields as neural technology and artificial intelligence evolve. The subject of brain-computer interface has progressed rapidly over the past decades, yet the underlying technologies and novel ideas behind seemingly unconnected systems are rarely summarized from the point of quantum integration. This study describes hybrid quantum computing and brain-computer interface applications, discusses present issues, and suggests future research. Ecologists could employ quantum computers because the statistical approaches they use have proven routes on them. If the hardware, opportunity, and imagination of quantitative ecologists coincide, quantum computing could leapfrog our understanding of complex ecological processes.
An Approach to Introduce the Concept of Lexical Analysis through FPGA Based Finite State Machines

This paper presents an approach to introduce to the students the concept of the first phase of the compiler - the lexical analysis by developing FPGA based FSMs recognizing different types of lexemes. The aim is to be achieved some level of succession between two undergraduate courses, namely “Design Technology” and “Compilers”, and to be introduced a primary idea of a regular expression, a formal description of a finite automaton, and a state-transition graph of finite automaton. The expectation is that the implementation of such FSMs through a hardware modeling language and programmable logic will be a good starting point for further implementation of the lexical analyzer with a high-level programming language.
Becoming a Successful Pre-Service English as a Foreign Language Lecturer in the Digital Divide

The ephemeral nature of life is such that what was present in our lives yesterday may undergo a transformation or cease to exist altogether, as if it had never been. Technology has emerged as a significant factor in mitigating the challenges posed by various crises, particularly during recent global pandemics, natural calamities, and mass migrations. Undoubtedly, the incorporation of technology in the realm of education is a significant phenomenon. Currently, attention is directed towards academic institutions that offer programs for teacher education. Numerous responsibilities are assigned to the faculty members who provide instruction to individuals pursuing a career in teaching. The education of pre-service teachers regarding technology necessitates a distinct emphasis and instruction. The primary subject matter of this chapter pertains to the instruction of technology integration for pre-service English teachers, with a particular emphasis on the digital divide.
Exploring Models, Training Methods, and Quantum Supremacy in Machine Learning and Quantum Computing

In this chapter, the authors will discuss some of the many models and training methods that have been developed in the field of machine learning to address this learning challenge. Models like neural networks and stochastic gradient descent have their own “go-to” training algorithms, each with their own set of supporting terminology and communities of experts. Since the specifics of gate decomposition, compilation, and error correction all depend heavily on the physical implementation of qubits and quantum gates, it has been difficult to design quantum hardware capable of running such algorithms. Therefore, the authors can only provide asymptotic estimations of total execution times. Since developing quantum hardware is so prohibitively expensive, researchers are incentivized to use terms like “superior quantum algorithms” to justify their work. This has given rise to the contentious term “quantum supremacy” to describe experiments that definitively show a difference between classical and quantum levels of computational complexity.
Convolutional Neural Networks for Valid and Efficient Causal Inference

Convolutional neural networks (CNN) have been successful in machine learning applications.Their success relies on their ability to consider space invariant local features.We consider the use of CNN to fit nuisance models in semiparametric estimation of the average causal effect of a treatment.In this setting, nuisance models are functions of pretreatment covariates that need to be controlled for.In an application where we want to estimate the effect of early retirement on a health outcome, we propose to use CNN to control for time-structured covariates.Thus, CNN is used when fitting nuisance models explaining the treatment and the outcome.These fits are then combined into an augmented inverse probability weighting estimator yielding efficient and uniformly valid inference.Theoretically, we contribute by providing rates of convergence for CNN equipped with the rectified linear unit activation function and compare it to an existing result for feedforward neural networks.We also show when those rates guarantee uniformly valid inference.A Monte Carlo study is provided where the performance of the proposed estimator is evaluated and compared with other strategies.Finally, we give results on a study of the effect of early retirement on hospitalization using data covering the whole Swedish population.