Reviewerly/exHarmony huggingface dataset:
Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based VQE simulator to reduce the amount of memory needed and increase the simulation efficiency; (2) a combination of the Density Matrix Embedding Theory with the MPS-based VQE simulator to further extend the simulation range; (3) A three-level parallelization scheme to scale up to 20 million cores; (4) Usage of the Julia script language as the main programming language, which both makes the programming easier and enables cutting edge performance as native C or Fortran; (5) Study of real chemistry systems based on the VQE simulator, achieving nearly linearly strong and weak scaling. Our simulation demonstrates the power of VQE for large quantum chemistry systems, thus paves the way for large-scale VQE experiments on near-term quantum computers.
USV path planning algorithm based on plant growth

Unmanned surface vehicles (USVs) have been widely used in patrol, exploration and military applications, replacing manned vessels in dangerous areas of the sea where humans cannot reach or where there is a high risk. When USVs are used for a variety of tasks in the complex and changing marine environment, autonomous navigation and obstacle avoidance are essential technologies for USVs to be equipped. In response to the current problems of slow search speed, low collision avoidance efficiency and long paths in path planning, this paper proposes a PGR (Plant Grow Route) algorithm. It is based on the principle of phototropism of plant growth, which guides USVs to avoid obstacles and reach target points through the magnitude of light intensity. Experiments show that this algorithm can successfully avoid static obstacles and allow USVs to follow the route with the strongest light intensity to reach the target point safely, and it requires short search time and high efficiency. The improved G-DWA algorithm is used to implement the PGR algorithm for local path planning, and local path planning is used as a key development direction for this experiment in the future.
A semi-supervised resampling method for class-imbalanced learning

Clustering analysis is widely used as a pre-process to discover the data distribution for resampling. Existing clustering-based resampling methods mostly run unsupervised clustering on labeled data without taking advantage of the class information to guide the clustering. When there are not enough labeled data, the clustering can hardly capture the underlying data distribution. In this paper, we propose a semi-supervised hybrid resampling (SSHR) method which runs semi-supervised clustering to capture the data distribution for both over-sampling and under-sampling. Firstly, we design a semi-supervised hierarchical clustering algorithm (SSHC) which uses labeled data to guide the clustering procedure on the whole dataset. Specifically, labeled data are used to initialize a clustering model and then guide its updating via an iterative cluster-splitting process. In this way, original classes are divided into multiple disjunct clusters, which contributes o disclosing not only the inter-class imbalance but also the intra-class imbalance. Subsequently, a hybrid resampling is performed according to the result of SSHC Labeled data of the majority class are under-sampled according to the distances to their cluster centroids and the adjacency to minority cluster centroids. Furthermore, we propose a novel over-sampling approach which selects some confident unlabeled data in minority clusters as pseudo-labeled data to enlarge the training set Compared with traditional over-sampling methods, our approach contributes to discovering more about the distribution of the minority class. In order to validate the effectiveness of SSHR, we conduct extensive experiments on 44 benchmark datasets. Our method achieves the best performances in terms of both F-measure and AUC. The Friedman test demonstrates that SSHR significantly outperforms the compared state-of-the-art resampling algorithms.
Adaptive spatial-temporal surrounding-aware correlation filter tracking via ensemble learning

With the advancement of computer vision, object trackers based on discriminative correlation filters (DCF) have demonstrated superior performance and accuracy compared to traditional trackers. However, most existing DCF-based trackers are easily affected by various factors, such as cluttered background, illumination variations, occlusions, rotations etc. Therefore, in order to accurately track the target, further investigation into the characteristics of the correlation filter is required. In this study, we propose an adaptive spatial-temporal surrounding-aware correlation filter tracker via the ensemble learning (ASTSAELT) technique. Specifically, the adaptive spatial-temporal regularized correlation filter to remove the boundary effects and temporal degradation is presented. And then, a method of extracting surrounding samples according to the size and shape of the tracking object, designed to preserve the integrity of the object, is proposed. Moreover, our tracker utilizes a multi-expert tracking framework to improve its performance by integrating both handcrafted features and deep convolutional layer features. And then, the update strategy is proposed to measure the reliability of the current tracking result and mitigate model corruption. Finally, numerous experiments on visual tracking benchmarks including OTB2013, OTB2015, TempleColor128, UAV123, UAVDT and DTB70 are implemented to verify the developed method achieves superior performance compared with several state-of-the-art methods.
An Overview of Supervised Machine Learning Algorithm

Machine learning is a subset of Artificial intelligence. Algorithms for machine learning automatically learn from experience and improve from it without being explicitly programmed. Machine learning defines Supervised, Unsupervised and Reinforcement Learning. Supervised algorithms are worked on under guidance but unsupervised algorithms are worked on without guidance. Machine learning provides good accuracy in both the algorithms. This paper is describing machine learning methods, different types of supervised learning algorithms, comparison of machine learning algorithms and application of machine learning algorithms.
Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy

The particle swarm optimization (PSO) algorithm has been one of the most effective methods for solving various engineering optimization problems. Most existing PSO variants frequently use fixed operators, the adoption of a fixed operator learning mode may restrict the intelligence level of each particle, thus reducing the performance of PSO in solving optimization issues with complicated fitness landscapes. To address single goal real-parameter numerical optimization while overcoming the above shortcoming, this paper proposes a reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy (NRLPSO). In NRLPSO, a dynamic oscillation inertial weight (DOW) strategy that provides particles with dynamic adjustment ability in different situations is designed. To resolve the operator selection conundrum of exploration and exploitation, a reinforcement learning-based velocity vector generation (VRL) strategy is developed. At each iteration, particles select the velocity update model based on reinforcement learning, and VRL helps to thoroughly search the problem space. A velocity updating mechanism based on cosine similarity (VCS) is applied to control the velocity learning mode to determine more promising solutions. Furthermore, to alleviate the problem of premature convergence, a local update strategy with neighborhood differential mutation (NDM) is employed to increase the diversity of the algorithm. To verify the efficiency of the proposed algorithm, the CEC2017 and CEC2022 test suites are implemented, and nine classic or state-of-the-art PSO variants are comprehensively tested. The experimental results show that NRLPSO outperforms the popular PSO variants in terms of convergence speed and accuracy. Since NRLPSO utilizes the DE mutations, it is compared with the representative LSHADE variant algorithm - LSHADE_SPACMA. Although LSHADE_SPACMA is better than NRLPSO concerning algorithm stability and convergence accuracy, we will refine our work in the future to enhance the performance in all aspects.
Cluster Contrast for Unsupervised Person Re-identification

Thanks to the recent research development in contrastive learning, the gap of visual representation learning between supervised and unsupervised approaches has been gradually closed in the tasks of computer vision. In this paper, we focus on the downstream task of unsupervised person re-identification (re-ID). State-of-the-art unsupervised re-ID methods train the neural networks using a dictionary-based non-parametric softmax loss. They store the pre-computed instance feature vectors inside the dictionary, assign pseudo labels to them using clustering algorithm, and compare the query instances to the cluster using a form of contrastive loss. To enforce a consistent dictionary, that is the features in the dictionary are computed by a similar or the same encoder network, we present Cluster Contrast which stores feature vectors and computes contrastive loss at the cluster level. Moreover, the momentum update is introduced to reinforce the cluster-level feature consistency in the sequential space. Despite the straightforward design, experiments on four representative re-ID benchmarks demonstrate the effective performance of our method.
Research on non-dependent aspect-level sentiment analysis

As a popular research field of sentiment analysis, aspect-based sentiment analysis focuses on the emotional expression in different aspects. However, the current research is not precise enough in dividing the aspects of sentiment analysis. The problem of semantic overlap between aspects occurs. Furthermore, in many cases, one aspect may be contained in several sub-aspects. When the study only focused on the emotion tends in one or several sub-aspects, the results of sentiment analysis may be distorted. To deal with these problems, we propose the concept of non-dependent aspects by analyzing the dependencies among aspects and a method for dividing non-dependent aspects. Through theoretical analysis, we demonstrate that our proposed sentiment analysis results based on non-dependent aspects are more accurate than the original one, and non-dependent aspects can be easily transferred to a new corpus. The experiments on real-world data are also supporting the results of theoretical analysis. The range of accuracy of non-dependent aspects is improved by 1.9%–13.4% than before.
Spectral Variability of Rocks and Soils on the Jezero Crater Floor: A Summary of Multispectral Observations From <i>Perseverance's</i> Mastcam‐Z Instrument

Abstract NASA's Mars‐2020 Perseverance rover spent its first year in Jezero crater studying the mafic lava flows of the Máaz formation and the ultramafic cumulates of the Séítah formation, both of which have undergone minor alteration and are variably covered by coatings, dust, and/or soil deposits. Documenting the rock and soil characteristics across the crater floor is critical for establishing the geologic context of Perseverance's cached samples—which will eventually be returned to Earth—and for interpreting the deposition and modification of the Máaz and Séítah formations. Mastcam‐Z, a pair of multispectral, stereoscopic zoom‐lens cameras, provides broadband red/green/blue and narrowband visible to near‐infrared images (VNIR, 440–1,020 nm). From multispectral observations from sols 0 to 380, we compiled a database of ∼2,400 representative Mastcam‐Z spectra. We analyzed principal components, spectral parameters, and laboratory spectra of pure minerals and natural rock surfaces to interpret the spectral diversity of rocks and soils. We define eight spectral classes of rocks: Dusty, Hematite‐like, Coated, Low‐Ca Pyroxene‐like, Olivine‐like, Weathered Olivine‐like, Fe‐rich Pyroxene‐like, and Dark Oxide‐like. The variability of soil spectra in the Jezero crater floor is controlled primarily by the amount of dust and indicates a largely consistent soil mineralogy across the traverse, with the exception of the area disturbed by the landing event. In comparison to rock spectra from the Curiosity rover's Mastcam instrument in Gale crater, rocks on the Jezero crater floor are generally less spectrally diverse, but the Olivine‐like rocks within the Séítah formation represent a new spectral rock class in Mars surface exploration.
Using eye tracking to examine expert-novice differences during simulated surgical training: A case study

Eye tracking data can serve as a unique metric for comparing expert-novice differences by providing insights into attentional processes, which can lead to timely intervention and better instruction. In this study, we used eye tracking technology to record and analyze the eye movements of experts and novices conducting a surgical procedure in a simulation environment. Specifically, we compared the differences in the eye movement parameters (i.e., average fixation, saccade, and blink durations), the distribution of visual attention, and the shift of visual focus between experts and novices across three task phases. We found that experts had longer fixation durations, shorter saccade durations, and more gaze points on task-relevant areas. The visual attention of novices was more diffuse than experts. Moreover, task complexity was found to moderate the size of expert-novice differences in their eye movements. Findings from this study inform the assessment of professional competency in the medicine domain. This study also helps to advance the use of eye tracking as a methodology for educational research.
Impact of Virtual Reality Cognitive and Motor Exercises on Brain Health

Innovative technologies of the 21st century have an extremely significant impact on all activities of modern humans. Among them, virtual reality (VR) offers great opportunities for scientific research and public health. The results of research to date both demonstrate the beneficial effects of using virtual worlds, and indicate undesirable effects on bodily functions. This review presents interesting recent findings related to training/exercise in virtual environments and its impact on cognitive and motor functions. It also highlights the importance of VR as an effective tool for assessing and diagnosing these functions both in research and modern medical practice. The findings point to the enormous future potential of these rapidly developing innovative technologies. Of particular importance are applications of virtual reality in basic and clinical neuroscience.
IoT Access Control Model Based on Blockchain and Trusted Execution Environment

With the application and popularization of the Internet of Things (IoT), while the IoT devices bring us intelligence and convenience, the privacy protection issue has gradually attracted people’s attention. Access control technology is one of the important methods to protect privacy. However, the existing IoT access control technologies have extensive problems such as coarse-grainedness, weak auditability, lack of access process control, and excessive privileges, which make the security and privacy of our IoT devices face great threats. Based on this, a blockchain-based and encrypted currency-based access control model CcBAC supported by Trusted Execution Environment (TEE) technology is proposed, which can provide fine-graininess, strong auditability, and access procedure control for the Internet of Things. In this study, the technical principle, characteristics, and research status of the control model are introduced, and the framework of the CcBAC model is expounded in detail and formally defined. Moreover, the functions in the model are described in detail, and a specific access control process in general scenarios is presented for the model. Finally, the practicability of this model is verified through theoretical analysis and experimental evaluation, which proves that this model not only enables resource owners to fully control the access to their resources, but also takes into account the fine-graininess and auditable access control.
A Secure Signcryption Scheme for Electronic Health Records Sharing in Blockchain

In the existing Electronic Health Records (EHRs), the medical information of patients is completely controlled by various medical institutions.As such, patients have no dominant power over their own EHRs.These personal data are not only inconvenient to access and share, but are also prone to cause privacy disclosure.The blockchain technology provides a new development direction in the medical field.Blockchain-based EHRs are characterized by decentralization, openness and non-tampering of records, which enable patients to better manage their own EHRs.In order to better protect the privacy of patients, only designated receivers can access EHRs, and receivers can authenticate the sharer to ensure that the EHRs are real and effective.In this study, we propose an identity-based signcryption scheme with multiple authorities for multiple receivers, which can resist N-1 collusion attacks among N authorities.In addition, the identity information of receivers is anonymous, so the relationship between them and the sharer is not disclosed.Under the random oracle model, it was proved that our scheme was secure and met the unforgeability and confidentiality requirements of signcryption.Moreover, we evaluated the performance of the scheme and found that it had the moderate signcryption efficiency and excellent signcryption attributes.
YOLOv3 Attention Face Detector with High Accuracy and Efficiency

In recent years, face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision.However, the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied.In this paper, using Darknet-53 as backbone, we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency.The attention mechanism is introduced to enhance much higher discrimination of the deep features, and the trick of data augmentation is used in the training procedure to achieve higher detection accuracy without significantly affecting the inference speed.The model has been trained and evaluated on the popular and challenging face detection benchmark, i.e., the WIDER FACE training and validation subsets, respectively, achieving AP of 0.942, 0.919 and 0.821 with the speed of 28FPS.This performance exceeds some existing SOTA algorithms, demonstrating acceptable accuracy and near real time detection for VGA resolution images, even in the complex scenarios.In addition, the proposed model shows good generation ability on another public dataset FDDB.The results indicate the proposed model is a promising face detector with high efficiency and accuracy in the wild.
Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm

This will serve as a useful and illuminating case study for demonstrating how molecular dynamics simulation may be implemented and how its computational execution time can be optimised. Molecular Dynamics is a teaching tool that simulates the behaviour of molecules in a reactive combination. To better understand the complexities of chemical processes, we have recently been obliged and forced to embrace the help of computational methodologies. In this work, we show how to reproduce the Leaf Frog Algorithm for determining the velocities and velocities of molecules in response to external forces. A solution to a basic equation of motion based on Newton's laws of motion. That kind of simulation is notoriously memory and CPU intensive. The idea of parallelizing simulation techniques is one baby step toward a more comprehensive workable solution. Time-to-execution has been simulated for both serial and parallel computing by taking kinetic and potential energy into account.
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-uniform Fog Removal

Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog’s attenuation and airlight (or veiling effect) significantly weaken the background scene information in the input image. To address this problem, we introduce a structure-representation network with uncertainty feedback learning. Specifically, we extract the feature representations from a pre-trained Vision Transformer (DINO-ViT) module to recover the background information. To guide our network to focus on non-uniform fog areas, and then remove the fog accordingly, we introduce the uncertainty feedback learning, which produces uncertainty maps, that have higher uncertainty in denser fog regions, and can be regarded as an attention map that represents fog’s density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying light colors that are possibly present in the input image. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively compared to the state-of-the-art methods in handling dense and non-uniform fog or smoke.
Design and geometric control of polynomial chaotic maps with any desired positive Lyapunov exponents

Digital chaotic maps are severely hampered by the finite calculation accuracy of the hardware device that is used to implement them, and their applications in cryptography and information assurance are seriously degraded. To resolve this issue, we put forward a universal iterative model to construct non-degenerate polynomial chaotic maps with any desired number of positive Lyapunov exponents. In addition, we innovatively propose the geometric control methods of polynomial chaotic maps, including amplitude control, offset boosting, plane rotation, shape control, and combined regulation. Furthermore, to assess the effectiveness and feasibility of the proposed method, a microcontroller-based platform was developed to demonstrate the hardware implementation and geometric control of the proposed polynomial chaotic map. Finally, a PRNG is constructed by interval quantization. Numerical experiments are performed to verify the desirable statistical properties of the PRNG in terms of local weak random test, discrete Fourier transform test, linear complexity and NIST SP800-22 test.