Reviewerly/exHarmony huggingface dataset:
SPIDER: A framework for processing, editing and presenting immersive high-resolution spherical indoor scenes

Today’s Extended Reality (XR) applications that call for specific Diminished Reality (DR) strategies to hide specific classes of objects are increasingly using 360° cameras, which can capture entire areas in a single picture. In this work, we present an interactive-based image processing, editing and rendering system named SPIDER, that takes a spherical 360° indoor scene as input. The system is composed of a novel integrated deep learning architecture for extracting geometric and semantic information of full and empty rooms, based on gated and dilated convolutions, followed by a super-resolution module for improving the resolution of the color and depth signals. The obtained high resolution representations allow users to perform interactive exploration and basic editing operations on the reconstructed indoor scene, namely: (i) rendering of the scene in various modalities (point cloud, polygonal, wireframe) (ii) refurnishing (transferring portions of rooms) (iii) deferred shading through the usage of precomputed normal maps. These kinds of scene editing and manipulations can be used for assessing the inference from deep learning models and enable several Mixed Reality applications in areas such as furniture retails, interior designs, and real estates. Moreover, it can also be useful in data augmentation, arts, designs, and paintings. We report on the performance improvement of the various processing components on public domain spherical image indoor datasets.
Toward human-level concept learning: Pattern benchmarking for AI algorithms

Artificial intelligence (AI) today is very successful at standard pattern-recognition tasks due to the availability of large amounts of data and advances in statistical data-driven machine learning. However, there is still a large gap between AI pattern recognition and human-level concept learning. Humans can learn amazingly well even under uncertainty from just a few examples and are capable of generalizing these concepts to solve new conceptual problems. The growing interest in explainable machine intelligence requires experimental environments and diagnostic/benchmark datasets to analyze existing approaches and drive progress in pattern analysis and machine intelligence. In this paper, we provide an overview of current AI solutions for benchmarking concept learning, reasoning, and generalization; discuss the state-of-the-art of existing diagnostic/benchmark datasets (such as CLEVR, CLEVRER, CLOSURE, CURI, Bongard-LOGO, V-PROM, RAVEN, Kandinsky Patterns, CLEVR-Humans, CLEVRER-Humans, and their extension containing human language); and provide an outlook of some future research directions in this exciting research domain.
Enhancing Time Series Forecasting With an Optimized Binary Gravitational Search Algorithm for Echo State Networks

The echo state network (ESN) is a cutting-edge reservoir computing technique designed to handle time-dependent data, making it highly effective for addressing time series prediction tasks. ESN inherits the more precise design of standard neural networks and the relatively simple learning process and has a strong computing capacity for solving nonlinear problems. It can disseminate low-dimensional information cues to high-dimensional areas enabling extracting data. However, this study has proven that not all reservoir output dimensions directly impact model generalization. This study desires to enhance the ESN model’s generalization abilities by decreasing the redundant reservoir output feature. A remarkable hybrid model is proposed that optimizes the ESN output association through feature selection. This model is called the binary improved gravitational search algorithm (BIGSA) echo state network (BIGSA-ESN). BIGSA’s feature selection approach complements the ESN output connection architecture. In this study, evaluation was performed using root mean square error (RMSE). The experimental findings on the Lorenz and Mackey–Glass benchmark time-series datasets demonstrate that the proposed technique outperforms conventional evolutionary methods. Moreover, empirical findings on predicting a significant water quality parameter from the wastewater treatment process (WWTP) dataset demonstrate that the proposed ensemble of BIGSA models performs very well in real-world scenarios.
Application of Data Sensor Fusion Using Extended Kalman Filter Algorithm for Identification and Tracking of Moving Targets from LiDAR–Radar Data

In surveillance and monitoring systems, the use of mobile vehicles or unmanned aerial vehicles (UAVs), like the drone type, provides advantages in terms of access to the environment with enhanced range, maneuverability, and safety due to the ability to move omnidirectionally to explore, identify, and perform some security tasks. These activities must be performed autonomously by capturing data from the environment; usually, the data present errors and uncertainties that impact the recognition and resolution in the detection and identification of objects. The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors by retrieving information simultaneously. This paper uses the constant turn and rate velocity (CTRV) kinematic model of a drone but includes the angular velocity not considered in previous works as a complementary alternative in Lidar and Radar data sensor fusion retrieved using UAVs and applying the extended Kalman filter (EKF) for the detection of moving targets. The performance of the EKF is evaluated by using a dataset that jointly includes position data captured from a LiDAR and a Radar sensor for an object in movement following a trajectory with sudden changes. Additive white Gaussian noise is then introduced into the data to degrade the data. Then, the root mean square error (RMSE) versus the increase in noise power is evaluated, and the results show an improvement of 0.4 for object detection over other conventional kinematic models that do not consider significant trajectory changes.
Inductively shunted transmons exhibit noise insensitive plasmon states and a fluxon decay exceeding 3 hours

Currently available quantum processors are dominated by noise, which severely limits their applicability and motivates the search for new physical qubit encodings. In this work, we introduce the inductively shunted transmon, a weakly flux-tunable superconducting qubit that offers charge offset protection for all levels and a 20-fold reduction in flux dispersion compared to the state-of-the-art resulting in a constant coherence over a full flux quantum. The parabolic confinement provided by the inductive shunt as well as the linearity of the geometric superinductor facilitates a high-power readout that resolves quantum jumps with a fidelity and QND-ness of >90% and without the need for a Josephson parametric amplifier. Moreover, the device reveals quantum tunneling physics between the two prepared fluxon ground states with a measured average decay time of up to 3.5 h. In the future, fast time-domain control of the transition matrix elements could offer a new path forward to also achieve full qubit control in the decay-protected fluxon basis.
Evaluation of new sparrow search algorithms with sequential fusion of improvement strategies

Sparrow search algorithm (SSA) is a novel swarm intelligent algorithm inspired by foraging and anti-predation behaviors of the sparrow population. However, the population diversity of the basic SSA decreases in iterations and it tends to fall into the local optimum. In this paper, we propose five improved sparrow search algorithms (ISSAs 1–5) by sequentially integrating the five strategies of improved sine mapping, elite opposition-based learning, sine cosine algorithm, Lévy flight, and Gaussian mutation to enhance SSA performance. In other words, the nth ISSA adopts the first n above improvement strategies. An experiment based on 23 classical benchmark functions (F1-F23) is conducted to test the performance of proposed ISSAs. Based on a comprehensive comparison with existing algorithms, it is found that ISSA 5 which fuses all five improvement strategies has the best performance, and the ISSA performance increases as more improvement strategies are integrated. For F1-F13, ISSA 5 outperforms all other algorithms for 69.23% (9/13), 76.92% (10/13), 61.54% (8/13), 76.92% (10/13), 69.23% (9/13), 61.54% (8/13) of the functions at 10, 30, 50, 100, 500, 1000 dimensions respectively. For fix-dimension functions F14-F23, ISSA 5 is top-ranked for 70.00% (7/10) of the functions. Also, it is found that the sine cosine algorithm and Lévy flight strategies have greater impact on the improvement of SSA among the five strategies. Moreover, further analysis by Friedman test and Nemenyi post-hoc test reveals the superior performance of the proposed ISSAs in a statistical sense against other competing algorithms. As such, the results indeed postively demonstrate the efficiency, robustness, convergence and practical applicability of the proposed ISSAs.
RDEIC-LFW-DSS: ResNet-based deep embedded image clustering using local feature weighting and dynamic sample selection mechanism

In existing deep clustering methods, as the model gets deeper, extracted representations can be deteriorated due to a vanishing gradient, leading to reduced performance. Also, existing deep clustering frameworks may distort the hidden space without true labels by learning from unreliable estimated pseudo-labels. Therefore, the model learns non-discriminative features from the early epochs, which results in worse estimated pseudo-labels. Moreover, in these models, it is assumed that all learned representations in the bottleneck layer have equal significance throughout the clustering procedure. To address the shortcomings, this paper introduces RDEIC-LFW-DSS, a ResNet-based deep embedded clustering framework. The advantage of residual connection is that it enables the user to add residual connections for increased model capacity without incurring the cost of degradation for unsupervised feature learning compared to standard autoencoders. Furthermore, an efficient local feature weighting mechanism is used to weight each cluster's representations correctly. In addition, we introduce a mechanism in which suitable samples with highly reliable estimated labels are selected to train the deep clustering framework. Extensive experiments conducted on benchmark datasets and comparisons with state-of-the-art approaches confirm the high performance of RDEIC-LFW-DSS. The implementation- source code- of RDEIC-LFW-DSS is made publicly accessible at https://github.com/Amin-Golzari-Oskouei/RDEIC-LFW-DSS.
Insulator defect detection with deep learning: A survey

Abstract With the improvement of smart grid, utilizing unmanned aerial vehicles (UAV) to detect the operation status of insulators has attracted widespread attention. The insulator defects can lead to serious power loss, damage the service life of power lines, and even result in power outages in serious cases. The small‐scale object, complex background, and limited‐number collected data make insulator defect still a challenging problem. Benefitted by the advances in deep learning, deep learning‐based insulator defects have achieved great progress in recent years. In the paper, the authors present a novel systematic survey of these advances, where further analysis about different processing stages methods is introduced: (i) insulator processing stage methods exploit the specific image pre‐processing algorithm for data augmentation and low‐level vision information extraction; (ii) defect detection stage model can locate and classify diagnosis fault with different task targets, like sequential task strategy and multi‐task strategy. In addition, the authors also review publicly available benchmark and datasets. The future research direction and open problem are discussed to promote the development of the community.
A Playable 3D Virtual Tour for an Interactive Campus Visit Experience: Showcasing School Facilities to Attract Potential Enrollees

Advances in technology have revolutionized student recruitment strategies employed by educational institutions. These innovations led to the adoption of virtual campus tours to provide prospective students with an immersive expedition into the school facilities replicated in a digital environment. However, the existing virtual tour technologies pose challenges, including cybersickness in virtual reality and limited interactivity in 360-degree videos. In this study, we fill these research gaps by developing a playable and interactive campus virtual tour where potential enrollees can visit and tour the campuses remotely. In addition to a series of beta tests with enrolled students, we recruited students specializing in game development and their associates to evaluate the application using an extended Technology Acceptance Model (TAM) framework. In this evaluation, we found that the application was well-received by prospective students and was regarded as useful in delivering an immersive campus visit experience. From the TAM perspective, it was evident that there was a significant difference in how enrolled and potential students assess the application in terms of perceived usefulness and behavioral intention. The positive acceptance of the application led to the recommendation of playable campus virtual tours as a tool for improving student recruitment strategies.
TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM &amp; CA Attention Mechanism

Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.
Quantum reservoir computing implementation on coherently coupled quantum oscillators

Abstract Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyze a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99% on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators.
Aluminum surface defect detection method based on a lightweight YOLOv4 network

Deep learning is currently being used to automate surface defect detection in aluminum. The common target detection models based on neural networks often have a large number of parameters and a slow detection speed, which is not conducive to real-time detection. Therefore, this paper proposes a lightweight aluminum surface defect detection model, M2-BL-YOLOv4, based on the YOLOv4 algorithm. First, in the YOLOv4 model, the complex CSPDarkNet53 backbone network was modified into an inverted residual structure, which greatly reduced the number of parameters in the model and increased the detection speed. Second, a new feature fusion network, BiFPN-Lite, is designed to improve the fusion ability of the network and further improve its detection accuracy. The final results show that the mean average precision of the improved lightweight YOLOv4 algorithm in the aluminum surface defect test set reaches 93.5%, the number of model parameters is reduced to 60% of the original, and the number of frames per second (FPS) detected is 52.99, which increases the detection speed by 30%. The efficient detection of aluminum surface defects is realized.
Movie Recommendation Using Content-Based and Collaborative Filtering Approach

A recommender system is one that tries to anticipate or filter preferences based on the user's selections. Films, music, journalism, publications, scientific papers and items in general all make use of recommender systems. We're building a recommendation system with Python and Pandas in this model. Utilizing an approach called content-based filtering, which is based on the information provided about the items, the system makes suggestions for movies that are comparable to those that a user has previously enjoyed. The information we know about the items and the user's previous choices are used to calculate this similarity. Combining collaborative filtering and content-based filtering is used to overcome the shortcomings of these two types of filtering generally so that a better recommendation system may be created.
A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.
Opportunities for Quantum Acceleration of Databases: Optimization of Queries and Transaction Schedules

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution (T-VSR) but ignore the reciprocal relations among them. 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMuNet) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up- and down projections, where spatial and temporal features are fully fused and distilled, helping high-quality video reconstruction. In addition, we also show interesting extensions for efficient network design (CycMuNet+), such as parameter sharing and dense connection on projection units and feedback mechanism in CycMuNet. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMuNet (+) with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms the state-of-the-art methods.
BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification. In particular, a dataset of 232 patients selected with DCE-MR images for benign and malignant cases is established. Each case consists of three types of sequences: pre-contrast, post-contrast, and subtraction sequences. To show the difficulty of breast DCE-MRI tumor image segmentation and classification tasks, benchmarks are achieved by state-of-the-art image segmentation and classification algorithms, including conventional hand-crafted based methods and recently-emerged deep learning-based methods. More importantly, a local-global cross attention fusion network (LG-CAFN) is proposed to further improve the performance of breast tumor images classification. Specifically, LG-CAFN achieved the highest accuracy (88.20%, 83.93%) and AUC value (0.9154,0.8826) in both groups of experiments. Extensive experiments are conducted to present strong baselines based on various typical image segmentation and classification algorithms. Experiment results also demonstrate the superiority of the proposed LG-CAFN to other breast tumor images classification methods. The related dataset and evaluation codes are publicly available at smallboy-code/Breast-cancer-dataset.