Reviewerly/exHarmony huggingface dataset:
A large scale Digital Elevation Model super-resolution Transformer

The Digital Elevation Model (DEM) super-resolution approach aims to improve the spatial resolution or detail of an existing DEM by applying techniques such as machine learning or spatial interpolation. Convolutional Neural Networks and Generative Adversarial Networks have exhibited remarkable capabilities in generating high-resolution DEMs from corresponding low-resolution inputs, significantly outperforming conventional spatial interpolation methods. Nevertheless, these current methodologies encounter substantial challenges when tasked with processing exceedingly high-resolution DEMs (256×256,512×512, or higher), specifically pertaining to the accurate restore maximum and minimum elevation values, the terrain features, and the edges of DEMs. Aiming to solve the problems of current super-resolution techniques that struggle to effectively restore topographic details and produce high-resolution DEMs that preserve coordinate information, this paper proposes an improved DEM super-resolution Transformer(DSRT) network for large-scale DEM super-resolution and account for geographic information continuity. We design a window attention module that is used to engage more elevation points in low-resolution DEMs, which can learn more terrain features from the input high-resolution DEMs. A GeoTransform module is designed to generate coordinates and projections for the DSRT network. We conduct an evaluation of the network utilizing DEMs of various types of terrains and elevation differences at resolutions of 64×64,256×256 and 512 × 512. The network demonstrated leading performance across all assessments in terms of root mean square error (RMSE) for elevation, slope, aspect, and curvature, indicating that Transformer-based deep learning networks are superior to CNNs and GANs in learning DEM features.
GlassMessaging

Communicating with others while engaging in simple daily activities is both common and natural for people. However, due to the hands- and eyes-busy nature of existing digital messaging applications, it is challenging to message someone while performing simple daily activities. We present GlassMessaging, a messaging application on Optical See-Through Head-Mounted Displays (OHMDs), to support messaging with voice and manual inputs in hands- and eyes-busy scenarios. GlassMessaging is iteratively developed through a formative study identifying current messaging behaviors and challenges in common multitasking with messaging scenarios. We then evaluated this application against the mobile phone platform on varying texting complexities in eating and walking scenarios. Our results showed that, compared to phone-based messaging, GlassMessaging increased messaging opportunities during multitasking due to its hands-free, wearable nature, and multimodal input capabilities. The affordance of GlassMessaging also allows users easier access to voice input than the phone, which thus reduces the response time by 33.1% and increases the texting speed by 40.3%, with a cost in texting accuracy of 2.5%, particularly when the texting complexity increases. Lastly, we discuss trade-offs and insights to lay a foundation for future OHMD-based messaging applications.
Efficient scheme for implementing a hybrid Toffoli gate with two NV ensembles simultaneously controlling a single superconducting qubit

The Toffoli gate is a three-qubit controlled-controlled-NOT gate, which plays a central role in quantum information processing and quantum computing. Recently, hybrid systems and hybrid quantum gates have attracted much attention. We propose an efficient scheme to implement a hybrid Toffoli gate with two nitrogen-vacancy center ensembles (NV ensembles) as the control qubits and a single superconducting flux qubit as the target qubit. This hybrid Toffoli gate is realized by employing two NV ensembles coupled to a superconducting qutrit. The hybrid Toffoli gate is constructed by a controlled-controlled-phase (CCPHASE) gate sandwiched between two Hadamard gates applied on the target qubit. Since the CCPHASE gate is realized using only a single-step operation and no conventional gate decomposition protocol is applied, the Toffoli-gate implementation is greatly simplified. Because the higher energy level of the flux qutrit is not populated, the decoherence of this level is greatly suppressed. In addition, our hybrid Toffoli gate can be used to generate a hybrid Greenberger–Horne–Zeilinger entangled state of a flux qubit and two NV ensembles. Numerical simulations demonstrate that high-fidelity implementation of a hybrid Toffoli gate is feasible within current experimental technology. This proposal is universal and can be applied to accomplish the same task in a wide range of physical systems.
A Privacy-Preserving and Accountable Billing Protocol for Peer-to-Peer Energy Trading Markets

This paper proposes a privacy-preserving and accountable billing (PA-Bill) protocol for trading in peer-to-peer energy markets, addressing situations where there may be discrepancies between the volume of energy committed and delivered. Such discrepancies can lead to challenges in providing both privacy and accountability while maintaining accurate billing. To overcome these challenges, a universal cost splitting mechanism is proposed that prioritises privacy and accountability. It leverages a homomorphic encryption cryptosystem to provide privacy and employs blockchain technology to establish accountability. A dispute resolution mechanism is also introduced to minimise the occurrence of erroneous bill calculations while ensuring account-ability and non-repudiation throughout the billing process. Our evaluation demonstrates that PA-Bill offers an effective billing mechanism that maintains privacy and accountability in peer-to-peer energy markets utilising a semi-decentralised approach.
Optimization of Microarchitecture and Dataflow for Sparse Tensor CNN Acceleration

The inherent sparsity present in convolutional neural networks (CNNs) offers a valuable opportunity to significantly decrease the computational workload during inference. Nevertheless, leveraging unstructured sparsity typically comes with the trade-off of increased complexity or substantial hardware overheads for accelerators. To address these challenges, this research introduces an innovative <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inner join</i> aimed at effectively reducing the size and power consumption of the sparsity-handling circuit. Additionally, a novel dataflow named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Channel Stacking of Sparse Tensors (CSSpa)</i> is presented, focusing on maximizing data reuse to minimize memory accesses - an aspect that significantly contributes to overall power consumption. Through comprehensive simulations, CSSpa demonstrates a 1.6× speedup and a 5.6× reduction in SRAM accesses when executing inference on the ResNet50 model, compared to the existing Sparten architecture. Furthermore, the implementation results reveal a notable 2.32× enhancement in hardware resource efficiency and a 3.3× improvement in energy efficiency compared to Sparten.
Light Field-Based Underwater 3D Reconstruction via Angular Re-Sampling

Recovering 3D geometry of underwater scenes is challenging because of the non-linear refraction of light at the water-air interface, which is caused by the camera housing. We present a light field-based approach that leverages properties of angular samples for high-quality underwater 3D reconstruction from a single viewpoint. Specifically, we re-sample the light field image to angular patches. As underwater scenes exhibit weak view-dependent specularity, an angular patch tends to have uniform intensity when sampled at the correct depth. We thus impose this angular uniformity as a constraint for depth estimation. For efficient angular re-sampling, we design a fast approximation algorithm based on multivariate polynomial regression to approximate nonlinear refraction paths. We further develop a light field calibration algorithm that estimates the water-air interface geometry along with the camera parameters. Comprehensive experiments on synthetic and real data show our method produces state-of-the-art reconstruction of static and dynamic underwater scenes.
GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's odometry sensor to train a prediction model to estimate candidate trajectories' success probabilities based on partially reliable multi-modal sensor observations. We encode high-dimensional multi-modal sensory inputs to low-dimensional feature vectors using encoder networks and represent them as a connected graph. The graph is then used to train an attention-based Graph Neural Network (GNN) to predict trajectory success probabilities. We further analyze the number of features in the image (corners) and point cloud data (edges and planes) separately to quantify their reliability to augment the weights of the feature graph representation used in our GNN. During runtime, our model utilizes multi-sensor inputs to predict the success probabilities of the trajectories generated by a local planner to avoid potential collisions and failures. Our algorithm demonstrates robust predictions when one or more sensor modalities are unreliable or unavailable in complex outdoor environments. We evaluate our algorithm's navigation performance using a Spot robot in real-world outdoor environments. We observe an increase of 10-30% in terms of navigation success rate and up to 15% increase in AU-ROC compared to the state-of-the-art navigation methods.
FedMCSA: Personalized federated learning via model components self-attention

Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, heterogeneous data that is not independent and identically distributed (Non-IID) from different clients presents a tough challenge for FL. Existing personalized FL approaches rely heavily on the default treatment of one complete model as a basic unit and ignore the significance of different layers on Non-IID data of clients. In this work, we propose a new framework, namely federated model components self-attention (FedMCSA), to handle Non-IID data in FL, which employs model components self-attention mechanism to granularly promote cooperation between different clients. This mechanism facilitates collaboration between similar model components while reducing interference between model components with large differences. We conduct extensive experiments to demonstrate that FedMCSA outperforms the previous methods on four benchmark datasets. Furthermore, we empirically show the effectiveness of the model components self-attention mechanism, which is complementary to existing personalized FL and can significantly improve the performance of FL.
Characterizing Crosstalk of Superconducting Transmon Processors

Currently available quantum computing hardware based on superconducting transmon architectures realizes networks of hundreds of qubits with the possibility of controlled nearest-neighbor interactions. However, the inherent noise and decoherence effects of such quantum chips considerably alter basic gate operations and lead to imperfect outputs of the targeted quantum computations. In this work, we focus on the characterization of crosstalk effects, which manifest themselves in correlations between simultaneously executed quantum gates on neighboring qubits. After a short explanation of the physical origin of such correlations, we show how to efficiently and systematically characterize the magnitude of such crosstalk effects on an entire quantum chip using the randomized benchmarking protocol. We demonstrate the introduced protocol by running it on real quantum hardware provided by IBM and observe significant alterations in gate fidelities due to crosstalk. Lastly, based on the gained information, we propose more accurate means to simulate noisy quantum hardware by devising a crosstalk-aware noise model.
Classification of Severe Maternal Morbidity from Electronic Health Records Written in Spanish Using Natural Language Processing

One stepping stone for reducing the maternal mortality is to identify severe maternal morbidity (SMM) using Electronic Health Records (EHRs). We aim to develop a pipeline to represent and classify the unstructured text of maternal progress notes in eight classes according to the silver labels defined by the ICD-10 codes associated with SMM. We preprocessed the text, removing protected health information (PHI) and reducing stop words. We built different pipelines to classify the SMM by the combination of six word-embeddings schemes, three different approaches for the representation of the documents (average, clustering, and principal component analysis), and five well-known machine learning classifiers. Additionally, we implemented an algorithm for typos and misspelling adjustment based on the Levenshtein distance to the Spanish Billion Word Corpus dictionary. We analyzed 43,529 documents constructed by an average of 4.15 progress notes from 22,937 patients. The pipeline with the best performance was the one that included Word2Vec, typos and spelling adjustment, document representation by PCA, and an SVM classifier. We found that it is possible to identify conditions such as miscarriage complication or hypertensive disorders from clinical notes written in Spanish, with a true positive rate higher than 0.85. This is the first approach to classify SMM from the unstructured text contained in the maternal EHRs, which can contribute to the solution of one of the most important public health problems in the world. Future works must test other representation and classification approaches to detect the risk of SMM.
Can Blockchain Technology Help Overcome Contractual Incompleteness? Evidence from State Laws

Real-world contractual agreements between firms are often incomplete, leading to suboptimal investment and loss of value in supply chain relationships. To what extent can blockchain technology help alleviate problems arising from contractual incompleteness? We examine this issue by exploiting a quasi-natural experiment based on the staggered adoption of U.S. state laws that increased firms’ in-state ability to develop, adopt, and use blockchain technology. We find that, after exposure to a pro-blockchain law, firms with greater asset specificity exhibit more positive changes to Tobin’s Q, research and development, and blockchain-related innovation. Also, such firms appear to rely less on vertical integration, form more strategic alliances, and shift their emphasis to less geographically proximate customers. Overall, our results suggest that blockchain technology can help firms remedy constraints and inefficiencies arising from contractual incompleteness. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.04139 .
Multi-view subspace clustering via consistent and diverse deep latent representations

Multi-view subspace clustering has recently received great interest in the communities of computer vision and pattern recognition, as it can overcome the limitation of conventional single-view subspace clustering by fusing complementary information from multi-view features. Recent work emphasizes the learning of latent representations rather than the affinity graph from each view to accomplish the task. Although they try to obtain consistent latent representations, it is difficult for these methods to design the term explicitly enforcing the desirable property such as diversity on the latent representation in their models. In this situation, we do not know if these methods can collect sufficient complementary information from multi-view features. In this paper, we propose Consistent and Diverse Deep Latent Representations (CDDLR) to address this problem. Our method provides a framework for enforcing necessary properties such as consistency and diversity on the learned latent representation by relating K-means with spectral clustering. Adopting the Laplacian regularized deep neural networks and novel strategies about diversity and consistency, CDDLR can learn the consistent and diverse deep latent representation for multi-view subspace clustering. Our experiment shows that for CALTECH and Football, our proposed method improves accuracy by 11.37% and 4.01%, respectively, compared to the second-best method.
The token economy in a developing country

The token economy is a decentralized digital economy enabled by blockchain technology based on the premise of controlling asset ownership with digital tokens (Zhao et al., 2019). The increase in digital asset ownership among Thai residents over the past year (Kemp, 2022) is indicative of the expansion of the token economy. Therefore, the purpose of this study is to analyze the factors influencing the token economy in Thailand. Multiple regression analysis has been used to analyze the data acquired from Thai investors who are at least 20 years old, hold financial instruments, and frequently access the Internet. The findings show that an individual’s education level and monthly income, as well as their obtaining of investment news through mass, online, or print media or through meetings, have a statistically significant positive relationship to the token economy, while the savings rate has a negative one. The research proposes that these factors should be considered when a company prepares promotional activities, and that consumers with low savings rates should receive more attention because they are more inclined to embrace digital tokens.
Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO-ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO-ResNet101 over current methodologies.
An Investigation of Segment Anything Model (SAM) on Uterus Segmentation

Highly development of language-image models makes prompt-driven accurate segmentation become possible. Segment Anything Model (SAM) has recently made a breakthrough in zero-shot image segmentation, using an unprecedentedly large dataset to train a segmentation model with strong adaptability. In this paper, we investigate the capability of SAM for MRI medical images in uterus Segmentation. Experimental results that YOLOv8 with SAM implements end-to-end uterus segmentation and outperforms the traditional supervised learning U-net and U-net++ models.
Dynamic forward secure searchable encryption scheme with phrase search for smart healthcare

The phrase searchable encryption scheme improves search efficiency and accuracy by searching a set of consecutive keywords from ciphertext. However, most current phrase searchable encryption schemes still don't support dynamic data updates in practical application scenarios such as smart healthcare due to forward security issues. In this article, a dynamic forward secure searchable encryption scheme with phrase search for smart healthcare systems is proposed. On the one hand, we adopt a state chain structure to construct an inverted index, which contains the location of keywords so as to achieve phrase search. On the other hand, we have added a modification operation that modifies the location of keywords directly by traversing the inverted index to achieve indexes update. In this way, the efficiency of data updating can be raised greatly because the update operation of first-delete-then-add is avoided in the previous phrase search scheme. The scheme is proved to satisfy the forward security in the leaked search pattern and access pattern since the server cannot know the updated state of updated files in the state chain index structure which is randomly generated by the client. Experimental results show that the proposed scheme has higher search accuracy and efficiency which can save at least 50ms in search time compared to other related schemes.
Lightweight channel-topology based adaptive graph convolutional network for skeleton-based action recognition

With the development of graph convolutional network (GCN) over the recent years, skeleton-based action recognition has achieved satisfactory results. However, some existing GCN-based models were complex because of lots of parameters in the models. Moreover, a large proportion of the existing GCN-based extraction methods for temporal feature could not effectively extract temporal features. To address this problem, a lightweight channel-topology based adaptive graph convolutional network (LC-AGCN), is proposed in this paper. And it includes three innovative and important blocks. To be specific, firstly, the channel-topology adaptive graph convolution (CAGC) block is proposed for spatial feature extraction (SConv), and a modified multi-scale convolution block is introduced to extract temporal features (TConv). Then, in order to decrease the quantity of parameters, the bottleneck structure is introduced to lighten the model and obtain the desired result. Finally, in order to embody the principle of ”few parameters with high evaluating accuracy”, a parameter λap is creatively proposed to reflect the performance of lightweight models, which means the ratio of precision to parameter quantity. Extensive experiments demonstrate that our method greatly reduces the quantity of parameters of the model while ensuring high enough accuracy. The superiority of LC-AGCN has been proved on two large-scale public datasets named NTU-RGB+D and NTU-RGB+D 120, respectively.