Reviewerly/exHarmony huggingface dataset:
Differentiable matrix product states for simulating variational quantum computational chemistry

Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver (VQE) is a promising heuristic quantum algorithm to solve real world quantum chemistry problems on near-term noisy quantum computers. Here we propose a highly parallelizable classical simulator for VQE based on the matrix product state representation of quantum state, which significantly extend the simulation range of the existing simulators. Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework, thus the gradients could be computed efficiently similar to the classical deep neural network, with a scaling that is independent of the number of variational parameters. As applications, we use our simulator to study commonly used small molecules such as HF, HCl, LiH and H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi /><mml:mn>2</mml:mn></mml:msub></mml:math>O, as well as larger molecules CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi /><mml:mn>2</mml:mn></mml:msub></mml:math>, BeH<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi /><mml:mn>2</mml:mn></mml:msub></mml:math> and H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi /><mml:mn>4</mml:mn></mml:msub></mml:math> with up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>40</mml:mn></mml:math> qubits. The favorable scaling of our simulator against the number of qubits and the number of parameters could make it an ideal testing ground for near-term quantum algorithms and a perfect benchmarking baseline for oncoming large scale VQE experiments on noisy quantum computers.
Chaos Bells: Instrument Size and Entangled Music Performance

There is a current trend in the instrument design industry of scaling down physical dimensions, resulting in smaller and smaller ‘desktop’ instruments, but research conducted with large interfaces reveals the role of instrument size on music performance. Chaos Bells is a very large digital musical instrument designed with both artistic and analytical goals in mind: it is a probe into the effect of instrument size on performance, while also being a vehicle for the first author’s performance practice. Drawing on entanglement theories of HCI we supplement the findings of lab-based studies with Chaos Bells with new discoveries made by the first author when touring with Chaos Bells. This research elucidates the indirect influences of stakeholders on instrument design culture, and ways that taking a large instrument design approach can benefit designers of instruments of all sizes.
不確実性ロスによるセマンティックセグメンテーションの教師無しドメイン適応

In recent years, deep learning models have achieved great success in many fields, but their training relies on large labeled data. Annotation to obtain labeled samples is challenging, time-consuming, and expensive, and has been a major challenge in deep learning. In this study, we propose a method for unsupervised domain adaptation that can reduce annotation cost in terms of accuracy and time compared to conventional methods by using the uncertainty of the model in semantic segmentation. Unsupervised domain adaptation aims to adapt models trained on synthetic data to real-world data without the need for costly annotation of real images. Since the training data are images and correct labels created by the game engine, there is no need to manually annotate them. However, if the source (synthetic) data is trained using conventional supervised learning, the performance is significantly degraded because the domain is different from the target (real image) data.This study closes the domain gap between source and target by calculating the uncertainty in the target data from the model output on a pixel-by-pixel basis and minimizing this uncertainty as loss. Adding this uncertainty loss to conventional unsupervised domain adaptation methods results in a model that is more robust to the target domain and achieves state-of-the-art results.
Instance Segmentation Model for Accurate Linear Object Shape Prediction

Recent advances in deep learning have dramatically improved the performance of instance segmentation, which is the task of predicting object area in images. However, depending on the shape of the target object, precise detection may still be difficult. For example, linear objects such as Wires still pose challenges for accurate instance segmentation due to their unique characteristics about shape. Therefore, we propose an instance segmentation method to accurately detect linear objects. The proposed method focuses on the characteristics of linear objects: continuity and irregularity of shape. We attempt to accurately detect linear objects by using Smooth Loss, which evaluates continuity, and Edge Enhance Loss, which focuses on the correctness of contours. In addition, we propose an evaluation metrics using the distance between contours to evaluate the accuracy of contour prediction. The proposed instance segmentation method improves by around 12% the average performance of contour prediction on the iShape dataset.
Balancing of competitive two-player Game Levels with Reinforcement Learning

The balancing process for game levels in a competitive two-player context involves a lot of manual work and testing, particularly in non-symmetrical game levels. In this paper, we propose an architecture for automated balancing of tile-based levels within the recently introduced PCGRL framework (procedural content generation via reinforcement learning).Our architecture is divided into three parts: (1) a level generator, (2) a balancing agent and, (3) a reward modeling simulation. By playing the level in a simulation repeatedly, the balancing agent is rewarded for modifying it towards the same win rates for all players. To this end, we introduce a novel family of swap-based representations to increase robustness towards playability. We show that this approach is capable to teach an agent how to alter a level for balancing better and faster than plain PCGRL. In addition, by analyzing the agent’s swapping behavior, we can draw conclusions about which tile types influence the balancing most. We test and show our results using the Neural MMO (NMMO) environment in a competitive two-player setting.
Deliberate Occlusion: Asymmetry in Facial Expression

Facial expression recognition (FER) has become increasingly common in research. However, region feature technologies that play an important role in this field have not been well developed. In this study, a psychology-based solution to address problems in FER of regional features was proposed. This method employs the use of deliberate occlusion for enhancement and attention module to find meaningful and important features in the regions processing features with high inter-class similarity and variation. The result of this study shows that the approach has higher accuracy than recent models and, through the use of feature heatmap visualization, the possibility of approaching FER from human facial asymmetry is raised.
Exploiting Monocular Depth Estimation for Style Harmonization in Landscape Painting

Style harmonization, also known as painterly image harmonization, is a technique to seamlessly blend objects from realistic photos into the background of paintings of different styles. While deep learning-based methods have produced satisfactory results, there are important factors to consider for successful style harmonization depending on the type of image being targeted. In particular, if a certain region of the target image takes up a large portion of the entire image, the object to be blended is largely affected by the region, failing to achieve plausible results of object conversion synthesis. In this study, focusing on landscape painting in which a significant portion of the entire image is occupied by sky regions, we proposed a novel method dubbed Dominant Region Discarding (DRD) that effectively removed the sky region with the monocular depth estimation and Segment Anything Model (SAM). The experimental results showed that the proposed method outperformed previous style harmonization methods.
ShuffleSR: Image Deepfake Detection Using Shuffle Transformer

We proposed an improved method based on the Shuffle Transformer for facial forgery detection. The method incorporated SE (Squeeze-and-Excitation) and ROI (Region of Interest) modules. By introducing the SE module, the network's channel attention capability was enhanced and enabled the model to focus more on important feature channels. Meanwhile, the ROI module was employed to extract the region of interest corresponding to the face, enabling the network to concentrate on processing facial regions. The performance of facial forgery detection was improved as a result. Evaluations were conducted on the DFDC dataset and a custom dataset containing various forgery algorithms. The results demonstrated that the enhanced Shuffle Transformer achieved significant performance improvements in facial forgery detection and exhibited better robustness and generalization capabilities while excelling across different forgery algorithms.
Towards a representation model and fog-based device orchestration for audio-centric pervasive storytelling

Internet of Things (IoT) devices, such as smart speakers and wearables, are increasingly accessible and part of people’s daily lives. This opens up great new possibilities for innovative storytelling experiences, allowing new forms of interactive and truly immersive content consumption, going beyond conventional multimedia. In this context, the need for advances in the representation of pervasive storytelling is perceptible and an audio-centric approach utilizing the Internet of Sounds (IoS) can potentially better fit into people’s routines because of the widespread audio capabilities in IoT devices. This work proposes a conceptual model entitled A-Presto (Audio-centric PeRvasivE STOrytelling) that aims to realize stories in a pervasive way, adapting to the users’ context and available IoT devices. By modeling the specific domain of audio-centric pervasive storytelling at a high abstraction level, the proposal transparently supports the typical variability of pervasive environments, such as changes in users’ location, device connectivity, power availability, and proximity between users, among others. Supported by latency experiments using a cloud-based orchestrator prototype and local IoT devices, this work proposes a fog-based runtime engine, capable of interpreting and orchestrating A-Presto storytelling instances with reduced latency.
Machine Learning-Based Low-Complexity Image Feature Descriptor for MPEG-CDVS Standard

Compact descriptors for visual search (CDVS) is a completed standard from the ISO/IEC moving pictures experts group (MPEG). CDVS has a low complexity and bitrate efficiency on image matching and retrieval. The MPEG CDVS framework detects key feature points in the image and extracts descriptors to obtain bitstreams to match images. We proposed a low-complexity image descriptor for visual searching within the MPEG CDVS framework. The proposed algorithm integrated traditional image processing methods with a machince learning (ML) network, resulting in reduced compression time and the size of the CDVS standard bitstream. To evaluate the effectiveness of the proposed algorithm, we tested the proposed algorithm with the CDVS standard software to ensure its compatibility. On the CDVS-Benchmark, the experimental results demonstrated that the proposed algorithm reduced matching time by 31.7%, bitstream size by 31.8%, and extracting time by 12.8% compared to the CDVS standard test model. Similarly, on the HPatches-Benchmark, the proposed algorithm reduced matching time by 5.3%, bitstream size by 25.2%, and extracting time by 32.0% compared to the CDVS standard test model.
Automatic Mathematical Word Problem Solver Based on Goal-Driven Tree Network Structure and Multi-Head Attention Improvement

The design of solvers for automatically solving mathematical problems has been used widely in artificial intelligence (AI) applications for education. Deep learning (DL) methods are mainly used to design solvers. However, most methods ignore the importance of data preprocessing and lack the judgment of the importance of numbers for constructing correct solutions. For this reason, we used FoolNLTK, a tool with higher word segmentation accuracy, and ELMo, a word embedding method, in data preprocessing to enhance the performance of the mathematical problem solver in data preprocessing. To capture longer-distance dependencies and obtain better semantic weight assignment results, we changed the single-head attention in the GTS model to multi-head attention. Based on the above improvements, we built a dataset Math-AP to verify the applicability of the improved model. We named the model Seq-GTS. Experiment results showed that the model improved accuracy significantly compared to the GTS model.
A Study of International Sharing Mechanisms of Chinese Learning Resource Based on Blockchain

The purpose of this study was to establish a model for sharing international Chinese learning resources. The research was carried out in two parts: model design and testing. In the model design, we used blockchain technology to create a mechanism that enabled the sharing of resources. This mechanism consisted of four layers: data storage, network consensus, intelligent contracts, and application services. In the testing phase, we evaluated the performance and analyzed the data of the shared model. The results demonstrated that this model provided secure and reliable resource acquisition services for Chinese learners worldwide. It facilitated collaborative resource construction and sharing, thereby promoting the development of international Chinese education. The innovation of this research was presented by the application of blockchain technology to international Chinese education.
Blockchain Based E-portfolio Ecosystem

There have been changes in the university admissions processes in Taiwan. Reviewers focus not only on entrance exam scores but also on student's long-term learning performance. The traditional documents have been replaced by the e-portfolio system, which enables reviewers to gain a better understanding of a student's learning process, including their school grades and extracurricular activities. However, there is a lack of trust between organizations, which has prevented students from freely sharing and using their data. As a solution to this problem, we proposed a Blockchain-based e-portfolio ecosystem. The Hyperledger was used to implement this ecosystem which consisted of two channels: the certificate and award channel and the access control channel. The certificate and award channels are secured platforms for managing educational certificates and awards using two smart contracts: the certificate manager contract and the award contract. The access control channel stores and manages the authorization status of users. In addition, a trusted education unit audits the data source to ensure the credibility of the information. Overall, this proposed ecosystem solved the issues of trust and credibility in the sharing of student data. Blockchain technology provided a secure and transparent way for students to share their data with organizations and make more informed decisions during the admissions process.
Perceptions of Distributed Ledger Technology Key Management - An Interview Study with Finance Professionals

Key management is an integral part of using distributed ledger technology (DLT). Previous work has primarily focused on key management for single-user scenarios on Bitcoin. Over the last decade, DLT has evolved to commercial and financial sectors; for example, a new German law allows the trading of a variety of financial securities via DLT. Instead of a single-user paradigm, financial institutions follow a multi-user paradigm. Combining multi-user key management with single-user key management solutions leads to unique challenges with usability and security. We extend current research through a two-stage qualitative interview study with 13 finance professionals. We investigate how the technical reality contrasts with perceptions of key management practices in corporate financial organizations. Our interdisciplinary study shows, among other things, that DLT does not meet real-world requirements in this particular domain. Moreover, it introduces additional challenges in terms of authentication and auditing. Our findings suggest that corporate financial institutions strongly support the adoption of blockchain solutions. However, to comply with regulatory and operational requirements, they face additional usability and security challenges, e.g., authentication and access control. Better mechanisms or novel design approaches are required to cover professional environments. This includes how multiple users can access the same assets and approve joint transactions.
Tamper Resistance Evaluation of Midori128 against Differential Power Analysis

The use of lightweight cryptography enhances the security of the Internet of Things (IoT) devices. Midori is one of the typical lightweight ciphers. Although the cryptographic algorithm is computationally secure, the risk of side-channel analysis (SCA) has been pointed out. For tamper resistance evaluation, it is important to evaluate various power analysis methods. Midori128 has been evaluated using correlation power analysis and deep learning-based power analysis. However, no evaluation has been reported for differential power analysis (DPA) though it is a typical power analysis method. Therefore, we proposed a DPA method of Midori128 for tamper resistance evaluation. In the experiment, Midori128 was implemented in a field programmable gate array. Then, we measured the power consumption waveforms and evaluated the tamper resistance using actual measured waveform data. As a result, the proposed method was used to successfully estimate more than 93% of secret keys by using 5,000 waveforms and showed that the tamper resistance of Midori128 can be quantitatively evaluated.
A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments

As human exploration of space continues to progress, the use of Mixed Reality (MR) for simulating microgravity environments and facilitating training in hand-object interaction holds immense practical significance. However, hand-object interaction in microgravity presents distinct challenges compared to terrestrial environments due to the absence of gravity. This results in heightened agility and inherent unpredictability of movements that traditional methods struggle to simulate accurately. To this end, we propose a novel MR-based hand-object interaction system in simulated microgravity environments, leveraging physics-based simulations to enhance the interaction between the user’s real hand and virtual objects. Specifically, we introduce a physics-based hand-object interaction model that combines impulse-based simulation with penetration contact dynamics. This accurately captures the intricacies of hand-object interaction in microgravity. By considering forces and impulses during contact, our model ensures realistic collision responses and enables effective object manipulation in the absence of gravity. The proposed system presents a cost-effective solution for users to simulate object manipulation in microgravity. It also holds promise for training space travelers, equipping them with greater immersion to better adapt to space missions. The system reliability and fidelity test verifies the superior effectiveness of our system compared to the state-of-the-art CLAP system.
Effects of Opaque, Transparent and Invisible Hand Visualization Styles on Motor Dexterity in a Virtual Reality Based Purdue Pegboard Test

The virtual hand interaction technique is one of the most common interaction techniques used in virtual reality (VR) systems. A VR application can be designed with different hand visualization styles, which might impact motor dexterity. In this paper, we aim to investigate the effects of three different hand visualization styles — transparent, opaque, and invisible — on participants’ performance through a VR-based Purdue Pegboard Test (PPT). A total of 24 participants were recruited and instructed to place pegs on the board as quickly and accurately as possible. The results indicated that using the invisible hand visualization significantly increased the number of task repetitions completed compared to the opaque hand visualization. However, no significant difference was observed in participants’ preference for the hand visualization styles. These findings suggest that an invisible hand visualization may enhance performance in the VR-based PPT, potentially indicating the advantages of a less obstructive hand visualization style. We hope our results can guide developers, researchers, and practitioners when designing novel virtual hand interaction techniques.
Evaluating the Feasibility of Predicting Information Relevance During Sensemaking with Eye Gaze Data

Eye gaze patterns vary based on reading purpose and complexity, and can provide insights into a reader’s perception of the content. We hypothesize that during a complex sensemaking task with many text-based documents, we will be able to use eye-tracking data to predict the importance of documents and words, which could be the basis for intelligent suggestions made by the system to an analyst. We introduce a novel eye-gaze metric called ‘GazeScore’ that predicts an analyst’s perception of the relevance of each document and word when they perform a sensemaking task. We conducted a user study to assess the effectiveness of this metric and found strong evidence that documents and words with high GazeScores are perceived as more relevant, while those with low GazeScores were considered less relevant. We explore potential real-time applications of this metric to facilitate immersive sensemaking tasks by offering relevant suggestions.
TENETvr: Comprehensible Temporal Teleportation in Time-Varying Virtual Environments

The iterative design process of virtual environments commonly generates a history of revisions that each represent the state of the scene at a different point in time. Browsing through these discrete time points by common temporal navigation interfaces like time sliders, however, can be inaccurate and lead to an uncomfortably high number of visual changes in a short time. In this paper, we therefore present a novel technique called TENETvr (Temporal Exploration and Navigation in virtual Environments via Teleportation) that allows for efficient teleportation-based travel to time points in which a particular object of interest changed. Unlike previous systems, we suggest that changes affecting other objects in the same time span should also be mediated before the teleport to improve predictability. We therefore propose visualizations for nine different types of additions, property changes, and deletions. In a formal user study with 20 participants, we confirmed that this addition leads to significantly more efficient change detection, lower task loads, and higher usability ratings, therefore reducing temporal disorientation.