Reviewerly/exHarmony huggingface dataset:
Partially-Connected Neural Architecture Search for Reduced Computational Redundancy

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network. Side operation is introduced for bypassing (non-sampled) channels to guarantee the performance of searched architectures under extremely low sampling rates. In the spatial level, input features are down-sampled to eliminate spatial redundancy and enhance the efficiency of the mixed computation for operation selection. Furthermore, edge normalization is developed to maintain the consistency of edge selection based on channel sampling with the architectural parameters for edges. Theoretical analysis shows that partial channel connection and parameterized side operation are equivalent to regularizing the super-network on the weights and architectural parameters during bilevel optimization. Experimental results demonstrate that the proposed approach achieves higher search speed and training stability than DARTS. PC-DARTS obtains a top-1 error rate of 2.55 percent on CIFAR-10 with 0.07 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.1 percent on ImageNet (under the mobile setting) within 2.8 GPU-days.
Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification.
Decision-Tree-Initialized Dendritic Neuron Model for Fast and Accurate Data Classification

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do not contribute much to their network's output. Pruning those with low contribution may lead to a loss of accuracy of DNM. Our proposed method is novel because 1) it can reduce the number of dendrites in DNM while improving training efficiency without affecting accuracy and 2) it can select proper initialization weight and threshold of neurons. The Adam algorithm is used to train DNM after its initialization with our proposed DT-based method. To verify its effectiveness, we apply it to seven benchmark datasets. The results show that decision-tree-initialized DNM is significantly better than the original DNM, k-nearest neighbor, support vector machine, back-propagation neural network, and DT classification methods. It exhibits the lowest model complexity and highest training speed without losing any accuracy. The interactions among attributes can also be observed in its dendritic neurons.
MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences

Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed.Data in this domain exhibits complex multi-relational and temporal interactions.Learning from this data is a fundamentally challenging research problem.In this paper, we propose Modal-Temporal Attention Graph (MTAG).MTAG is an interpretable graph-based neural model that provides a suitable framework for analyzing multimodal sequential data.We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions across modalities and through time.Then, a novel graph fusion operation, called MTAG fusion, along with a dynamic pruning and read-out technique, is designed to efficiently process this modal-temporal graph and capture various interactions.By learning to focus only on the important interactions within the graph, MTAG achieves state-ofthe-art performance on multimodal sentiment analysis and emotion recognition benchmarks, while utilizing significantly fewer model parameters. 1
What is new in computer vision and artificial intelligence in medical image analysis applications

Abstract: Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.
Survey of Low-Resource Machine Translation

Abstract We present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.
Automatic modulation recognition based on CNN and GRU

Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.
GuidedStyle: Attribute knowledge guided style manipulation for semantic face editing

Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there is still a lack of control over the generation process in order to achieve semantic face editing. In this paper, we propose a novel learning framework, called GuidedStyle, to achieve semantic face editing on pretrained StyleGAN by guiding the image generation process with a knowledge network. Furthermore, we allow an attention mechanism in StyleGAN generator to adaptively select a single layer for style manipulation. As a result, our method is able to perform disentangled and controllable edits along various attributes, including smiling, eyeglasses, gender, mustache, hair color and attractive. Both qualitative and quantitative results demonstrate the superiority of our method over other competing methods for semantic face editing. Moreover, we show that our model can be also applied to different types of real and artistic face editing, demonstrating strong generalization ability.
Learnability of Quantum Neural Networks

The learnability of quantum neural network, which includes its convergence behavior in optimization and the ability to efficiently learn classes of computationally hard concepts is investigated, providing theoretical guidance for developing advanced protocols in the NISQ era.
Face mask detection and classification via deep transfer learning

Wearing a mask is an important way of preventing COVID-19 transmission and infection. German researchers found that wearing masks can effectively reduce the infection rate of COVID-19 by 40%. However, the detection of face mask-wearing in the real world is affected by factors such as light, occlusion, and multi-object. The detection effect is poor, and the wearing of cotton masks, sponge masks, scarves and other items greatly reduces the personal protection effect. Therefore, this paper proposes a new algorithm for mask detection and classification that fuses transfer learning and deep learning. Firstly, this paper proposes a new algorithm for face mask detection that integrates transfer learning and Efficient-Yolov3, using EfficientNet as the backbone feature extraction network, and choosing CIoU as the loss function to reduce the number of network parameters and improve the accuracy of mask detection. Secondly, this paper divides the mask into two categories of qualified masks (N95 masks, disposable medical masks) and unqualified masks (cotton masks, sponge masks, scarves, etc.), creates a mask classification data set, and proposes a new mask classification algorithm that the combines transfer learning and MobileNet, enhances the generalization of the model and solves the problem of small data size and easy overfitting. Experiments on the public face mask detection data set show that the proposed algorithm has a better performance than existing algorithms. In addition, experiments are performed on the created mask classification data set. The mask classification accuracy of the proposed algorithm is 97.84%, which is better than other algorithms.
Efficient Violence Detection in Surveillance

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results-experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.
Defect-Free Arbitrary-Geometry Assembly of Mixed-Species Atom Arrays

Optically trapped mixed-species single atom arrays with arbitrary geometries are an attractive and promising platform for various applications, because tunable quantum systems with multiple components provide extra degrees of freedom for experimental control. Here, we report the first demonstration of two-dimensional $6\times4$ dual-species atom assembly with a filling fraction of 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic synthetic is achieved via rearranging initially randomly distributed atoms using a sorting algorithm (heuristic heteronuclear algorithm) which is proposed for bottom-up atom assembly with both user-defined geometries and two-species atom number ratios. Our fully tunable hybrid-atom system of scalable advantages is a good starting point for high-fidelity quantum logic, many-body quantum simulation and forming defect-free single molecule arrays.
Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review

During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.
Multistable dynamics in a Hopfield neural network under electromagnetic radiation and dual bias currents

This paper investigates a Hopfield neural network under the simulation of external electromagnetic radiation and dual bias currents, in which the fluctuation of magnetic flux across the neuron membrane is used to emulate the influence of electromagnetic radiation. Utilizing conventional analytical methods, the basic properties of the proposed Hopfield neural network are discussed. Due to the addition of electromagnetic radiation and dual bias currents, the Hopfield neural network shows high sensitivity to system parameters and initial conditions. The proposed Hopfield neural network possesses multistability with periodic attractor, quasi-periodic attractor, chaotic attractor and transient chaotic attractor, and all of the attractors are hidden attractors because there is no equilibrium point in the system. In particular, when the neuron membrane magnetic flux is different, the system can present transient chaos with different chaotic times. More interestingly, with the change of system parameters, the proposed Hopfield neural network can exhibit parallel bifurcation behaviors. Finally, the Multisim simulation and hardware experiment results based on discrete electronic components are conducted to support the numerical ones. These results could give useful information to the study of nonlinear dynamic characteristics of the Hopfield neural network.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

PromptSource is a system for creating, sharing, and using natural language prompts.Prompts are functions that map an example from a dataset to a natural language input and target output.Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively.PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool.Over 2,000 prompts for roughly 170 datasets are already available in PromptSource.
Review and validation of photovoltaic solar simulation tools/software based on case study

Abstract Photovoltaic (PV) systems are an excellent solution to meet energy demand and protect the global environment in many cases. With the increasing utilization of the PV system worldwide, there is an increasing need for simulation tools to predict the PV system’s performance and profitability. This research includes testing and comparison of PV tools: photovoltaic geographical information system (PVGIS), PVWatts, SolarGIS, RETScreen, BlueSol, PVsyst, HelioScope, PV*SOL, Solarius PV, Solar Pro, PV F-Chart, PolySun, solar advisor model (SAM), and hybrid optimization model for electric renewables (HOMER), based on experimental data obtained from fixed on-grid 2 kW p PV system in 2019. The PV system is part of a research project related to the examination of the PV system operation in real climatic conditions in Niš. This research investigates the most appropriate PV software for PV systems design by testing the most commonly used PV tools. It was accomplished by comparing experimental data obtained by a 2 kW p PV system in Niš and estimated data obtained from different PV tools. The study shows that annually, the experimentally measured average daily solar irradiation on the inclined plane was 5,270 Wh/m 2 /day, and the lowest deviation of the simulation results compared to experimental measurements was obtained by SolarPro. Total annual electricity production from the given system was 2455.621 kW h, and the lowest deviation of the simulation results compared to experimental measurements was obtained by PVGIS. By analyzing and publishing the actual solar irradiation and PV power output data, this study could help researchers to increase the PV systems modeling accuracy.
SAT-GCN: Self-attention graph convolutional network-based 3D object detection for autonomous driving

Accurate 3D object detection from point clouds is critical for autonomous vehicles. However, point cloud data collected by LiDAR sensors are inherently sparse, especially at long distances. In addition, most existing 3D object detectors extract local features and ignore interactions between features, producing weak semantic information that significantly limits detection performance. We propose a self-attention graph convolutional network (SAT-GCN), which utilizes a GCN and self-attention to enhance semantic representations by aggregating neighborhood information and focusing on vital relationships. SAT-GCN consists of three modules: vertex feature extraction (VFE), self-attention with dimension reduction (SADR), and far distance feature suppression (FDFS). VFE extracts neighboring relationships between features using GCN after encoding a raw point cloud. SADR performs further weight augmentation for crucial neighboring relationships through self-attention. FDFS suppresses meaningless edges formed by sparse point cloud distributions in remote areas and generates corresponding global features. Extensive experiments are conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. The results demonstrate significant improvements in mainstream methods, PointPillars, SECOND, and PointRCNN, improving the mean of AP 3D by 4.88%, 5.02%, and 2.79% on KITTI test dataset. SAT-GCN can boost the detection accuracy of the point cloud, especially at medium and long distances. Furthermore, adding the SAT-GCN module has a limited impact on the real-time performance and model parameters.
Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator

The Integration of Unmanned Aerial Vehicles (UAVs) into transportation systems has numerous benefits, ranging from the ability to record real-time data to having high mobility and broad vision. Because of the increasing levels of congestion in the cities, many of the transportation system tasks, such as traffic management and data collection, have become complicated to handle. The implementation of UAVs in transportation systems is a feasible solution to overcoming these problems. Therefore, UAVs have become a point of interest for many transportation system authorities. This study aims to introduce an integrated Fuzzy Level Based Weight Assessment (LBWA) in a stratification environment (SF-LBWA) and Fuzzy Rough Hamacher Combined Compromise Solution (CoCoSo) model to resolve the prioritization of Unmanned Aerial Vehicles in transportation system problems. The decision framework for such a problem is defined by establishing the relevant alternatives and criteria according to the specialized literature. Hence, a case study illustrates the formulation and solution of the problem. This study will indicate the most suitable alternative for the problem and provide guidance as authorities look for ways to integrate UAVs and transportation systems.