Reviewerly/exHarmony huggingface dataset:
Explainable quantum clustering method to model medical data

Medical experts are often skeptical of data-driven models due to the lack of their explainability. Several experimental studies commence with wide-ranging unsupervised learning and precisely with clustering to obtain existing patterns without prior knowledge of newly acquired data. Explainable Artificial Intelligence (XAI) increases the trust between virtual assistance by Machine Learning models and medical experts. Awareness about how data is analyzed and what factors are considered during the decision-making process can be confidently answered with the help of XAI. In this paper, we introduce an improved hybrid classical-quantum clustering (improved qk-means algorithm) approach with the additional explainable method. The proposed model uses learning strategies such as the Local Interpretable Model-agnostic Explanations (LIME) method and improved quantum k-means (qk-means) algorithm to diagnose abnormal activities based on breast cancer images and Knee Magnetic Resonance Imaging (MRI) datasets to generate an explanation of the predictions. Compared with existing algorithms, the clustering accuracy of the generated clusters increases trust in the model-generated explanations. In practice, the experiment uses 600 breast cancer (BC) patient records with seven features and 510 knee MRI records with five features. The result shows that the improved hybrid approach outperforms the classical one in the BC and Knee MRI datasets.
Orientation Attention Network for semantic segmentation of remote sensing images

With the increasing resolution of remote sensing images, semantic segmentation has become a challenging task for the extremely abundant textures and edges that existed in images. In this paper, an Orientation Attention Network (OANet) is proposed to learn both orientation features and global semantic features of ground objects for accurate segmentation. Firstly, an Asymmetrical Convolution (AC) is constructed to explore the directional anisotropy of objects. Then an Orientation Attention Module (OAM) is advanced to enhance the intrinsic geometric features of objects, by defining two branches with stacked asymmetrical convolutions along the coordinate axis and adaptively selecting features which are beneficial for segmentation. Finally, the OANet, which combines OAM with a Global Feature Module (GFM), is proposed for both structural and semantic sensitive representations of images. Extensive experiments on four well-known public datasets show the effectiveness of the OANet.
Accurate non-covalent interaction energies on noisy intermediate-scale quantum computers <i>via</i> second-order symmetry-adapted perturbation theory

The calculation of non-covalent interaction energies on noisy intermediate-scale quantum (NISQ) computers appears to be challenging with straightforward application of existing quantum algorithms. For example, the use of the standard supermolecular method with the variational quantum eigensolver (VQE) would require extremely precise resolution of the total energies of the fragments to provide for accurate subtraction to the interaction energy. Here we present a symmetry-adapted perturbation theory (SAPT) method that may provide interaction energies with high quantum resource efficiency. Of particular note, we present a quantum extended random-phase approximation (ERPA) treatment of the SAPT second-order induction and dispersion terms, including exchange counterparts. Together with previous work on first-order terms (Chem. Sci., 2022, 13, 3094), this provides a recipe for complete SAPT(VQE) interaction energies up to second order, which is a well established truncation. The SAPT interaction energy terms are computed as first-level observables with no subtraction of monomer energies invoked, and the only quantum observations needed are the VQE one- and two-particle density matrices. We find empirically that SAPT(VQE) can provide accurate interaction energies even with coarsely optimized, low circuit depth wavefunctions from a quantum computer, simulated through ideal statevectors. The errors of the total interaction energy are orders of magnitude lower than the corresponding VQE total energy errors of the monomer wavefunctions. In addition, we present heme-nitrosyl model complexes as a system class for near term quantum computing simulations. They are strongly correlated, biologically relevant and difficult to simulate with classical quantum chemical methods. This is illustrated with density functional theory (DFT) as the predicted interaction energies exhibit a strong sensitivity with respect to the choice of functional. Thus, this work paves the way to obtain accurate interaction energies on a NISQ-era quantum computer with few quantum resources. It is the first step in alleviating one of the major challenges in quantum chemistry, where in-depth knowledge of both the method and system is required a priori to reliably generate accurate interaction energies.
BlockPaaS: Blockchain Platform as a Service

Blockchain Technology is preferred in various applications for its decentralization, integrity and availability features. Much research is going on for blockchain technology's efficient design and utilization. Also, the application of blockchain in the IoT environment is a new area of focus. However, researchers have problems testing various blockchain features on IoT devices. Hence, we developed the Blockchain Platform as a Service (BlockPaaS), which allows users to run various blockchain protocols on the IoT hardware instantly.
SurfMyoAiR: A Surface Electromyography-Based Framework for Airwriting Recognition

Airwriting Recognition is the task of identifying letters written in free space with finger movement.It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language.Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition.Most of the current research in gesture recognition is focused on identifying static gestures.However, dynamic gestures are natural and userfriendly for being used as alternate input methods in Human-Computer Interaction applications.Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution.Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population.There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work.The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed.Several different time-domain features to construct EMG envelope and two different timefrequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition.Several different deep learning architectures were exploited for this task.Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored.The best-achieved accuracy was 78.50% and 62.19% in userdependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier.Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.
Three-Stage Stackelberg Game Enabled Clustered Federated Learning in Heterogeneous UAV Swarms

In the past decade, the unmanned aerial vehicles (UAVs) swarm has become a disruptive force reshaping our lives and work. In particular, advances in artificial intelligence have allowed multiple UAVs to coordinate their operations and work together to accomplish various complex tasks, one of which is Federated Learning (FL). As a promising distributed learning paradigm, FL can be adopted well with the limited resources and dynamic network topology of UAV swarms. However, the current FL's training process relies on homogeneous data paradigms, which require distributed UAVs to hold the same structure data. This ideal hypothesis can not apply to the heterogeneous UAV swarms. To tackle this problem, in this paper, we design a clustered federated learning (CFL) architecture, in which we cluster UAV swarms based on the similarities between the participants' optimization directions. Then, we formulate the model trading among model owners, cluster heads, and UAV workers as a three-stage Stackelberg game to optimize the allocation of the limited resources. We design a hierarchical reinforcement learning algorithm to search for the Stackelberg equilibrium under the clustered federated learning system. The performance evaluation demonstrates the uniqueness and stability of the proposed three-stage leader-follower game under the clustered framework, as well as the convergence and effectiveness of the reinforcement learning algorithm.
A Novel Dual-Center-Based Intuitionistic Fuzzy Twin Bounded Large Margin Distribution Machines

Intuitionistic fuzzy (IF) set theory combined with twin support vector machines (TSVM) has shown highly advantageous performance in robust and fast classification. However, the existing methods have some inherent flaws: 1) The inaccurate confidence caused by the single-center-based IF strategy degrades the classification performance of the model. 2) The nonmembership degree is time-consuming and the threshold is difficult to determine, as it depends on sample's local neighborhood set. 3) Recent theoretical studies have demonstrated that optimizing the margin distribution achieve better generalization performance than maximizing the minimum margin in SVM-based models (SVMs). In view of the aforementioned shortcomings, we propose a novel dual-center-based IF (DC-IF) function, and introduce the margin distribution into IFTSVM, forming a DC-IF twin bounded large margin distribution machines (DC-IFTBLDM). Initially, the DC-IF strategy references the contribution of one sample to both categories, which is time efficient and more accurately reflects the credibility of the samples. Moreover, the margin mean and variance are used to optimize the margin distribution, and a regularization term is utilized to minimize structural risk. Therefore, DC-IFTBLDM has a good performance in noise immunity and classification capability. Comparative experiments based on synthetic datasets, UCI benchmark datasets, and datasets with different noise ratios verify the effectiveness of the proposed model. The parameter sensitivity analysis further proves the high accuracy and stability of DC-IFTBLDM.
MonoNet: enhancing interpretability in neural networks via monotonic features

Being able to interpret and explain the predictions made by a machine learning model is of fundamental importance. Unfortunately, a trade-off between accuracy and interpretability is often observed. As a result, the interest in developing more transparent yet powerful models has grown considerably over the past few years. Interpretable models are especially needed in high-stake scenarios, such as computational biology and medical informatics, where erroneous or biased models' predictions can have deleterious consequences for a patient. Furthermore, understanding the inner workings of a model can help increase the trust in the model.We introduce a novel structurally constrained neural network, MonoNet, which is more transparent, while still retaining the same learning capabilities of traditional neural models. MonoNet contains monotonically connected layers that ensure monotonic relationships between (high-level) features and outputs. We show how, by leveraging the monotonic constraint in conjunction with other post hoc strategies, we can interpret our model. To demonstrate our model's capabilities, we train MonoNet to classify cellular populations in a single-cell proteomic dataset. We also demonstrate MonoNet's performance in other benchmark datasets in different domains, including non-biological applications (in the Supplementary Material). Our experiments show how our model can achieve good performance, while providing at the same time useful biological insights about the most important biomarkers. We finally carry out an information-theoretical analysis to show how the monotonic constraint actively contributes to the learning process of the model.Code and sample data are available at https://github.com/phineasng/mononet.Supplementary data are available at Bioinformatics Advances online.
Real-time and accurate monocular 3D sensor using the reference plane calibration and an optimized SGM based on opencl acceleration

Speckle projection profilometry (SPP), as a promising structured light projection technique, can achieve global unambiguous 3D measurement by projecting a single random speckle pattern. In addition, the projected speckle pattern is usually etched into the microstructure of highly integrated Vertical-Cavity Surface-Emitting Laser (VCSEL), which makes the hardware system compact enough to be mounted on mobile devices such as robots. However, since the stereo matching algorithm used in SPP involves high computational overhead, it usually runs in real-time on specially customized hardware platforms such as ASIC/FPGA, rather than general-purpose mobile platforms. In this paper, we propose a real-time and accurate 3D measurement method using a monocular 3D sensor based on the infrared speckle projection. Similar to Kinect v1, our sensor mainly consists of an IR dot projector and one IR camera for projecting and capturing speckle images synchronously. Low-cost and high-quality speckle projection is achieved by customizing the projection pattern of VCSEL and using the beam copy function of Diffractive Optical Elements (DOE). Based on the 3D imaging principle of monocular 3D sensors, a reference plane calibration method is proposed to obtain a high-quality reference speckle image for improving the monocular matching accuracy. Then, benefited from the local memory mechanism and multiple operating synchronizations on the OpenCL environment, an optimized semi-global matching (SGM) algorithm using GPU is presented to achieve efficient and accurate depth reconstruction dynamically. Within the measurement range of 0.8m (length) × 0.5m (width) × 1m (depth), the proposed method can achieve real-time and single-shot 3D imaging with an accuracy of 1.277 mm at 75 FPS on GTX 1060 and 15 FPS on ARM Mail G52(mobile platform).
Focusing Fine-Grained Action by Self-Attention-Enhanced Graph Neural Networks With Contrastive Learning

With the aid of graph convolution neural network and transformer model, human action recognition has achieved significant performance based on skeleton data. However, the majority of existing works rarely focus on identifying fine-grained motion information (i.e., “read”, “write”, etc.). Furthermore, they tend to explore correlations between joints and bones ignoring the angular information. Consequently, the recognition accuracy for fine-grained actions with most models is still less desired. To address this issue, we first attempt to bring angular information as a complement to familiar joint and bone information, while learning the potential dependencies of the three kinds of information using graph neural networks. Based on this, we propose a self-attention-enhanced graph neural network (SAE-GNN), which consists of a kernel-unified graph convolution (KUGC) module and an enhanced attention graph convolution (EAGC) module. The KUGC module is devised to effectively extract rich features in the skeleton information. The EAGC consisting of a multi-scale enhanced graph convolution block and a multi-headed self-attention block is designed to learn the potential high-level semantic information in the features. Besides, we introduce contrastive learning in the two blocks to enhance feature representation by maximizing their mutual information. We conduct extensive experiments on four publicly available datasets, and results show that our model outperforms state-of-the-art methods in recognizing fine-grained actions.
Meta-Generalization for Domain-Invariant Speaker Verification

Automatic speaker verification (ASV) exhibits unsatisfactory performance under domain mismatch conditions owing to intrinsic and extrinsic factors, such as variations in speaking styles and recording devices encountered in real-world applications. To ensure robust performance under unseen conditions, domain generalization has been explored. However, an inherent contradiction exists between model discrimination and domain generalization, in which the discrimination ability may be reduced while learning to generalize. In this paper, to extract discriminative yet domain-invariant representations, we propose the meta-generalized speaker verification (MGSV) via meta-learning. Specifically, we propose a metric-based distribution optimization and a gradient-based meta-optimization to simultaneously supervise the spatial relationship between embeddings and improve the generalization ability of the model on unseen domains. In addition, we design multiple-single (MS) and simulated speaker verification (SSV) sampling strategies based on single-domain (SD) and single-single (SS) strategies to simulate the train/test domain mismatch more relevantly, thereby mining transferable speaker-related knowledge. SSV is chosen as the most effective method, as it substantially improves the domain generalization by ensuring that the model has learned to discriminate efficiently. Additionally, to intuitively reflect the model performance on the unseen domains, the proposed method is validated on cross-genre, cross-device, and cross-dataset tasks. The experimental results demonstrate that our proposed method achieves remarkable performance in handling domain mismatch issues in speaker verification.
A Comprehensive Framework for Migrating to Zero Trust Architecture

Migrating to Zero Trust Architecture (ZTA) is a strategic approach to strengthen the enterprise’s security postures. The shift to ZTA requires changes across the enterprise which can be challenging to achieve. Utilizing an effective framework for migrating from the old security architecture to ZTA can help ensure smooth transitioning to the Zero Trust journey. In previous research, the effective frameworks and processes for migrating to ZTA have not been achieved in an integrated and comprehensive manner. This study introduces a comprehensive framework for migrating to ZTA. The methodology of this study is to analyze and synthesize published studies relating to ZTA migration. The findings from existing knowledge construct a process-driven for the ZTA migration. In addition, the study addresses migration challenges and gaps in existing ZTA migration approaches. As a result, a comprehensive Zero Trust migration framework is developed to construct streamlined processes. The proposed framework can be used as a reference model for effective transitioning to ZTA.
Comparison of three full-field optical measurement techniques applied to vibration analysis

Digital image correlation, deflectometry and digital holography are some of the full-field optical measurement techniques that have matured in recent years. Their use in vibroacoustic applications is gaining attention and there is a need for cataloging their performance in order to provide, to a broad community of users and potential future users, quantitative and qualitative evaluations of these three approaches. This paper presents an experimental comparison of the three optical methods in the context of vibration measurements, along with classical reference measurements provided by an accelerometer and a laser Doppler vibrometer. The study is carried out on two mechanical structures exhibiting various vibration responses when submitted to an impact.
YOLOv4 with Deformable-Embedding-Transformer Feature Extractor for Exact Object Detection in Aerial Imagery

The deep learning method for natural-image object detection tasks has made tremendous progress in recent decades. However, due to multiscale targets, complex backgrounds, and high-scale small targets, methods from the field of natural images frequently fail to produce satisfactory results when applied to aerial images. To address these problems, we proposed the DET-YOLO enhancement based on YOLOv4. Initially, we employed a vision transformer to acquire highly effective global information extraction capabilities. In the transformer, we proposed deformable embedding instead of linear embedding and a full convolution feedforward network (FCFN) instead of a feedforward network in order to reduce the feature loss caused by cutting in the embedding process and improve the spatial feature extraction capability. Second, for improved multiscale feature fusion in the neck, we employed a depth direction separable deformable pyramid module (DSDP) rather than a feature pyramid network. Experiments on the DOTA, RSOD, and UCAS-AOD datasets demonstrated that our method's average accuracy (mAP) values reached 0.728, 0.952, and 0.945, respectively, which were comparable to the existing state-of-the-art methods.
High-Performance On-Chip Racetrack Resonator Based on GSST-Slot for In-Memory Computing

The data shuttling between computing and memory dominates the power consumption and time delay in electronic computing systems due to the bottleneck of the von Neumann architecture. To increase computational efficiency and reduce power consumption, photonic in-memory computing architecture based on phase change material (PCM) is attracting increasing attention. However, the extinction ratio and insertion loss of the PCM-based photonic computing unit are imperative to be improved before its application in a large-scale optical computing network. Here, we propose a 1 × 2 racetrack resonator based on Ge2Sb2Se4Te1 (GSST)-slot for in-memory computing. It demonstrates high extinction ratios of 30.22 dB and 29.64 dB at the through port and drop port, respectively. The insertion loss is as low as around 0.16 dB at the drop port in the amorphous state and about 0.93 dB at the through port in the crystalline state. A high extinction ratio means a wider range of transmittance variation, resulting in more multilevel levels. During the transition between crystalline and amorphous states, the tuning range of the resonant wavelength is as high as 7.13 nm, which plays an important role in the realization of reconfigurable photonic integrated circuits. The proposed phase-change cell demonstrates scalar multiplication operations with high accuracy and energy efficiency due to a higher extinction ratio and lower insertion loss compared with other traditional optical computing devices. The recognition accuracy on the MNIST dataset is as high as 94.6% in the photonic neuromorphic network. The computational energy efficiency can reach 28 TOPS/W, and the computational density of 600 TOPS/mm2. The superior performance is ascribed to the enhanced interaction between light and matter by filling the slot with GSST. Such a device enables an effective approach to power-efficient in-memory computing.