Reviewerly/exHarmony huggingface dataset:
HomeView: Automatically Building Smart Home Digital Twins With Augmented Reality Headsets

Digital twins have demonstrated great capabilities in the industrial setting, but the cost of building them prohibits their usage in home environments. We present HomeView, a system that automatically builds and maintains a digital twin using data from Augmented Reality (AR) headsets and Internet of Things (IoT) devices. We evaluated the system in a simulator and found it performs better than the baseline algorithm. The user feedback on programming IoT devices also suggests that contextual information rendered by HomeView is preferable to text descriptions.
Over-The-Shoulder Training Between Redundant Wearable Sensors for Unified Gesture Interactions

Wearable computers are now prevalent, and it is not uncommon to see people wearing multiple wearable devices. These wearable devices are often equipped with sensors to detect the user’s interactions and context. As more devices are worn on the user’s body, there is an increasing redundancy between the sensors. For example, swiping gestures on a headphone are detected by its touch sensor, but the movement it caused can also be measured by the sensors in a smartwatch or smart rings. We present a new mechanism to train a gesture recognition model using redundant sensor data so that measurements from other sensors can be used to detect gestures performed on another device even if the device is missing. Our preliminary study with 13 participants revealed that a unified gesture recognition model for touch gestures achieved accuracy for 25 gestures (5 gestures × 5 scenarios) where gestures were trained by leveraging the available sensors.
iMarker: Instant and True-to-scale AR with Invisible Markers

Augmented Reality (AR) has been widely used in modern mobile devices for various applications. To achieve a stable and precise AR experience, mobile devices are equipped with various sensors (e.g., dual camera, LiDAR) to increase the robustness of camera tracking. Those sensors largely increased the cost of mobile devices and are usually not available on low-cost devices. We propose a novel AR system that leverage the advance of marker-based camera tracking to produce fast and true-to-scale AR rendering on any device with a single camera. Our method enables the computer monitor to be the host of AR markers, without taking up valuable screen space nor impacting the user experience. Unlike traditional marker-based methods, we utilize the difference between human vision and camera system, making AR markers to be invisible to human vision. We propose an efficient algorithm that allows the mobile device to detect those markers accurately and later recover the camera pose for AR rendering. Since the markers are invisible to human vision, we can embed them on any website and the user will not notice the existence of these markers. We also conduct extensive experiments that evaluate the efficacy of our method. The experimental results show that our method is faster and has a more accurate scale of the virtual objects compared to the state-of-the-art AR solution.
AIx speed: Playback Speed Optimization using Listening Comprehension of Speech Recognition Models

In recent years, more and more time has been spent watching videos for online seminars, lectures, and entertainment. In order to improve time efficiency, people often adjust the playback speed to a speed that suits them best. However, it is troublesome to adjust the optimal speed for each video and even more challenging to change and adjust the speed for each speaker within a single video. Therefore, we propose ”AIx speed,” a system that maximizes the playback speed within the range where the speech recognition model can recognize and flexibly adjusts the playback speed for the entire video. This system makes it possible to set a flexible playback speed that balances playback time and content comprehension, compared to fixing the playback speed for the entire video.
Tie Memories to E-souvenirs: Hybrid Tangible AR Souvenirs in the Museum

Traditional physical souvenirs in museums have three major limits: monotonous interaction, lack of personalization, and disconnection to the exhibition. To conquer these problems and to make personalized souvenirs a part of the visiting experience, we create a hybrid tangible Augmented Reality(AR) souvenir that combines a physical firework launcher and AR models. An application called AR Firework is designed for customizing the hybrid souvenir as well as interactive learning in an exhibition in the wild. Multiple interaction methods including mobile user interface, hand gestures, and voice are adopted to create a multi-sensory product. As the first research to propose tangible AR souvenirs, we find that they establish a long-lasting connection between visitors and their personal visiting experiences. This paper promotes the understanding of personalization, socialization and tangible AR.
Demonstrating Finger-Based Dexterous Phone Gestures

This hands-on demonstration enables participants to experience single-handed "dexterous gestures", a novel approach for the physical manipulation of a phone using the fine motor skills of fingers. A recognizer is developed for variations of "full" and "half" gestures that spin (yaw axis), rotate (roll axis), and flip (pitch axis), all detected using the built-in phone IMU sensor. A functional prototype demonstrates how recognized dexterous gestures can be used to interact with a variety of current smartphone applications.
On V-form firms: governance, cryptocurrency, and internalization on the blockchain

Although the V-form organization has received attention in light of the integration of blockchain with processes in supply chains, analysis of a V-form organization as a firm in terms of its identity as legal entity has not been considered. Building on the concept of internalization, I reflect upon the efficiency increases the V-form firm can offer to supply chain members. V-form firms can support robust, decentralized markets that use cryptocurrency native to the blockchain with which the supply chain is integrated. Likewise, the V-form firm can provide property within the firm and which the firm is incentivized to protect from predatory intervention. I follow by considering the impact of the V-form firm on institutions adjacent the supply chain. The V-form firm stands to increase the level of competition in the realms of governance and monetary systems, either directly providing alternatives or indirectly influencing existing systems for which it may serve as substitute.
On the origins of cryptocurrencies

The success of bitcoin as a medium of exchange poses a problem for the traditional Austrian view on the emergence of money. In particular, it casts doubt on the regression theorem of Ludwig von Mises, which implies that an item cannot gain acceptance as a medium of exchange without government support if it does not first possess some non-monetary use value. We describe the conflict that cryptocurrencies like bitcoin created for the traditional Austrian understanding of the emergence of money and the more recent steps those working in the field have taken to resolve it.
Development of the adaptive pattern synthesis method for accurate structured illumination

• The spatial distortion of the structured illumination system is corrected via exploiting the computer vision technique. • Nonlinear responses at each camera pixel are corrected via measuring an intensity lookup table. • All procedure is performed based on image processing; thus, it does not require fine optical alignment. • Any distortion-free structured illumination patterns could be synthesized via the proposed method. Structured illumination, which projects a predefined spatially varying intensity pattern, has been widely used in a variety of applications, including 3D shape estimation, optical property estimation, and super-resolution imaging. The precision of the structured illumination method is determined by the accuracy of the illumination pattern captured by the detector; however, acquiring images of the precise pattern is difficult due to distortion and nonlinear response in the imaging system. We proposed an adaptive pattern synthesis method that could overcome projecting system distortion and the system's nonlinear response. The proposed method consists of four steps: estimation of the geometric transformation matrix between the structured illumination unit and detector, estimation of the detector's intensity lookup table, and synthesis of the corrected patterns. Furthermore, by measuring the 3D printed target and tissue-mimicking phantom, the proposed method demonstrated its ability to improve the accuracy of phase shift profilometry and spatial frequency domain imaging. It does not require fine alignment or optical system details because all procedure is performed by projecting digital patterns and its measurement without calibration targets. The proposed method could be applied to any optical technique that uses structured illumination to improve accuracy and performance, making it useful in both academic and industrial applications.
Cognitive architectures for human factors in aviation and aerospace

Cognition is a key human factor to be considered when studying, developing, and improving on aviation and aerospace operations. People serve many important roles in aviation and aerospace, and their participation is necessary for successful operations. Improving understanding of the minds of the people in those sociotechnical systems is on the critical path to increasing system performance. Cognitive architectures are computational theories of the human mind, intending to help researchers and practitioners understand human cognition in general. These architectures are employed with aspirations, in some cases, to practical utility in applied and real-world contexts such as aviation and aerospace. The purpose of this chapter is to introduce cognitive architectures by providing a foundation of knowledge and resources for anyone who is interested in learning about cognitive architectures and applying these approaches to their research. The chapter is divided into four sections: The first section defines cognitive architectures, the second section identifies key resources within the cognitive architecture literature, the third section discusses work conducted at the Air Force Research Laboratory to improve cognitive architectures, and the fourth section summarizes limitations within cognitive architectures and future directions.
Connecting citizens with urban environments through an augmented reality pervasive game

The concept of Playable City situates games in public spaces to create connections between the citizens and the urban environment. To this end, Augmented Reality (AR) and pervasive technologies can provide additional information about urban objects or places and support innovative and engaging experiences to increase the user interest in the surrounding area. Understanding how these experiences affect the user interest is crucial for reaching a well-established connection between the people and the spaces around them. Our contribution is a preliminary framework to evaluate how being engaged in a playful activity improves interest and awareness in a specific urban area. The framework is based on the situated motivational affordances to establish a correlation among the users’ motivations, the situation, and the employed technological artifact. We use an AR pervasive game to evaluate a playful historical experience as a technology probe. The results suggest that while playing the game, the citizens started to show a growing interest in the historical facts around them. At the same time, they began to raise concerns about other issues like sustainability, socio-environmental, and socioeconomic development.
Detection of hidden marks on Hwatu cards used in illegal gambling

Hwatu is a popular card game widely played in both Korea and Japan. This study examined hwatu cards used in the fraudulent gambling in Korea. Fraudsters used the hwatu cards, with hidden marks printed on the back using special ink to identify the cards so that they could deceive unknowing game players. These marks are invisible to the naked eye under normal conditions and are only visible when wearing special lenses. When suspicious hwatu cards are sent to the forensic laboratory, detection of the hidden mark conducted by the microscope, video spectral comparator (VSC), and radiography, which are laborious. In this study, we developed visualization of the hidden mark on the hwatu cards by utilizing a set of the algorisms including color splitting, histogram normalization, FFT denoising, These algorithms were applied to mobile applications affording convenience and accessibility to help prevent fraud or to help law enforcement conduct an immediate investigation. The proposed method was confirmed to be simple and effective in detecting hidden marks on hwatu cards without the need for costly equipment.
FAPA: Transferable Adversarial Attacks Based on Foreground Attention

Deep learning models are vulnerable to attacks by adversarial examples. However, current studies are mainly limited to generating adversarial examples for specific models, and the migration of adversarial examples between different models is rarely studied. At the same time, in only studies, it is not considered that adding disturbance to the position of the image can improve the migration of adversarial examples better. As the main part of the picture, the model should give more weight to the foreground information in the recognition. Will adding more perturbations to the foreground information of the image result in a higher transfer attack rate? This paper focuses on the above problems, and proposes the FAPA algorithm, which first selects the foreground information of the image through the DINO framework, then uses the foreground information to generate M, and then uses PNA to generate the perturbation required for the whole picture. In order to show that our method attaches importance to the foreground information, we give a greater weight to the perturbation corresponding to the foreground information, and a smaller weight to the rest of the image. Finally, we optimize the generated perturbation through the gradient generated by the dual attack framework. In order to demonstrate the effectiveness of our method, we have conducted relevant comparative experiments. During the experiment, we used the three white-box ViTs models to attack the six black-box ViTs models and the three black-box CNNs models. In the transferable attack of ViTs models, the average attack success rate of our algorithm reaches 64.19%, which is much higher than 21.12% of the FGSM algorithm. In the transferable attack of CNN models, the average attack success rate of our algorithm reaches 48.07%, which is also higher than 18.65% of the FGSM algorithm. By integrating ViTs and CNNs models, the attack success rate of transfer of our algorithm reaches 56.13%, which is higher than 1.18% of the dual attack framework we refer to.
Pseudocode Generation from Source Code Using the BART Model

In the software development process, more than one developer may work on developing the same program and bugs in the program may be fixed by a different developer; therefore, understanding the source code is an important issue. Pseudocode plays an important role in solving this problem, as it helps the developer to understand the source code. Recently, transformer-based pre-trained models achieved remarkable results in machine translation, which is similar to pseudocode generation. In this paper, we propose a novel automatic pseudocode generation from the source code based on a pre-trained Bidirectional and Auto-Regressive Transformer (BART) model. We fine-tuned two pre-trained BART models (i.e., large and base) using a dataset containing source code and its equivalent pseudocode. In addition, two benchmark datasets (i.e., Django and SPoC) were used to evaluate the proposed model. The proposed model based on the BART large model outperforms other state-of-the-art models in terms of BLEU measurement by 15% and 27% for Django and SPoC datasets, respectively.
A Vehicle-to-Infrastructure beyond Visual Range Cooperative Perception Method Based on Heterogeneous Sensors

With the development of autopilot, the performance of intelligent vehicles is constrained by their inability to perceive blind and beyond visual range areas. Vehicle-to-infrastructure cooperative perception has become an effective method for achieving reliable and higher-level autonomous driving. A vehicle-to-infrastructure cooperative beyond visual range and non-blind area method, based on heterogeneous sensors, was proposed in this study. Firstly, a feature map receptive field enhancement module with spatial dilated convolution module (SDCM), based on spatial dilated convolution, was proposed and embedded into the YOLOv4 algorithm. The YOLOv4-SDCM algorithm with SDCM module achieved a 1.65% mAP improvement in multi-object detection performance with the BDD100K test set. Moreover, the backbone of CenterPoint was improved with the addition of self-calibrated convolutions, coordinate attention, and residual structure. The proposed Centerpoint-FE (Feature Enhancement) algorithm achieved a 3.25% improvement in mAP with the ONCE data set. In this paper, a multi-object post-fusion matching method of heterogeneous sensors was designed to realize the vehicle-to-infrastructure cooperative beyond visual range. Experiments conducted at urban intersections without traffic lights demonstrated that the proposed method effectively resolved the problem of beyond visual range perception of intelligent vehicles.
Image Clustering Algorithm Based on Predefined Evenly-Distributed Class Centroids and Composite Cosine Distance

The clustering algorithms based on deep neural network perform clustering by obtaining the optimal feature representation. However, in the face of complex natural images, the cluster accuracy of existing clustering algorithms is still relatively low. This paper presents an image clustering algorithm based on predefined evenly-distributed class centroids (PEDCC) and composite cosine distance. Compared with the current popular auto-encoder structure, we design an encoder-only network structure with normalized latent features, and two effective loss functions in latent feature space by replacing the Euclidean distance with a composite cosine distance. We find that (1) contrastive learning plays a key role in the clustering algorithm and greatly improves the quality of learning latent features; (2) compared with the Euclidean distance, the composite cosine distance can be more suitable for the normalized latent features and PEDCC-based Maximum Mean Discrepancy (MMD) loss function; and (3) for complex natural images, a self-supervised pretrained model can be used to effectively improve clustering performance. Several experiments have been carried out on six common data sets, MNIST, Fashion-MNIST, COIL20, CIFAR-10, STL-10 and ImageNet-10. Experimental results show that our method achieves the best clustering effect compared with other latest clustering algorithms.
Clustering of handwritten digits by Kohonen neural network

Clustering of handwritten digits is carried out for sixty thousand images contained in the training sample of the MNIST database. For clustering, the Kohonen neural network is used. For each handwritten digit, the optimal number of clusters (no more than 50) is determined. When determining the distance between objects (images of handwritten digits), the Euclidean norm is used. Checking the correctness of building clusters is carried out using data from the test sample of the MNIST database. The test sample contains ten thousand images. It is concluded that the images from the test sample belong to the "correct digit" cluster with a probability of more than 90%. For each digit, an F-measure is calculated to evaluate the clusters. The best F-measures are obtained for digits 0 and 1 (F-mean is 0.974). The worst values are obtained for the number 9 (F-mean is 0.903). A cluster analysis is also carried out, which allows drawing conclusions about possible errors in recognition by the Kohonen neural network. Intersections of clusters for images of handwritten digits are constructed. Examples of intersections of clusters are given, as well as examples of images that are incorrectly recognized by the neural network.
Game Balance Practices

The subject of the research refers to the development of game and interactive projects — the practice of game balance of computer games. Current trends formed in both scientific and commercial spheres are taken into account. The research methods are modeling and experimentation. The main result is the formed vision on the integration of the received features into the tool of game prototypes generation. Two tools for working with game balance are described. The presented results are the part of one big applied research aimed at developing a game tool for prototyping computer games, reducing development time and resources by automating through generation of all kinds of content based on natural language text, including, in the long run, game balance. The work is a logical development of research on creating a full-fledged game engine for game designers and screenwriters.
TEACHING DESIGN AND INDONESIAN LANGUAGE MATERIALS AT UNIVERSITIES OF THAILAND AND MANILA, PHILIPPINES, BASED ON LOCAL WISDOM IN BIPA LEARNING (DESAIN PENGAJARAN DAN MATERI BAHASA INDONESIA DI UNIVERSITAS THAILAND DAN MANILA FILIPINA, BERBASIS KEARIFAN LOKAL DALAM PEMBELAJARAN BIPA)

This research aims to present learning systems and teaching materials based on local wisdom teaching design Indonesian for foreign speakers at Thammasat University and Normal University Manila. This study uses qualitative methods and obtaining research data using participant observation (participant observation), in-depth interviews, and documentation. The results of this study show: 1 ) The prospects of Indonesian in Thailand and the Philippines show that the establishment of diplomatic relations and education and become the official language of ASEAN countries, 2 ) BIPA learning in Indonesia Based on the curriculum created by the Central Agency for Diplomacy, learners are targeted to master four skills: reading, writing, 3 ) BIPA materials in Thailand and the Philippines are tailored to the purposes of foreigners, th i s enabling BIPA students to speak Indonesian in a formal and informal and familiar with Indonesian culture. 4 ) Local wisdom in BIPA learning in Thailand and the Philippines introduced Indonesian culture . So , BIPA has become very important in Thailand and the Philippines because it is the language of choice of the ASEAN Economic Community.
STRUCTURAL CASE ASSIGNMENT, THEMATIC ROLES AND INFORMATION STRUCTURE<sup>*</sup>

Abstract A formal model is presented which deduces the structural case assignment profile of Finnish by relying on graph‐theoretical paths and intervention. More complex and controversial features such as nonlocal dependencies, adverbial case marking, case competition, DP‐internal case patterns and interaction of case with agreement, aspect and polarity are also calculated from the model. Abstract Case and case concord were eliminated. The model links case assignment with word order, thematic roles and information structure. The results suggest that case might play a role in language comprehension.