Reviewerly/exHarmony huggingface dataset:
An hybrid particle swarm optimization with crow search algorithm for feature selection

The recent advancements in science, engineering, and technology have facilitated huge generation of datasets. These huge datasets contain noisy, redundant, and irrelevant features which negatively affects the performance of classification techniques in machine learning and data mining process. Feature selection is a pre-processing stage for reducing the dimensionality of datasets by selecting the most important attributes while increasing the accuracy of classification at the same time. In this paper, we present a novel hybrid binary version of enhanced chaotic crow search and particle swarm optimization algorithm (ECCSPSOA) to solve feature selection problems. In the proposed ECCSPSOA, in order to navigate the feature space, we hybridized the enhanced version of the CSA algorithm which has a better search strategy and particle swarm optimization (PSO) which is capable of converging into the best global solution in the search field. We further embed opposition-based learning technique in the local search of the hybrid algorithm. The ECCSPSOA was compared using 15 datasets from the UCI repository with four well-known optimization algorithms, such as particle swarm optimization (PSO), binary particle swarm optimization (BPSO), crow search algorithm (CSA), and chaotic crow search algorithm (CCSA). In the experiments with k-Nearest Neighbour (KNN) as a classifier, six different performance metrics were used. To tackle the over-fitting problem, each dataset is divided into training and testing data using K-fold cross-validation. The computational findings demonstrate that the proposed algorithm obtains an average accuracy rate of 89.67 % over 15 datasets, indicating that our technique exceeds state-of-the-art findings in 12 of the 15 datasets studied. Furthermore, the suggested approach outperforms state-of-the-art methods in terms of fitness value and standard deviation, obtaining the lowest value in 13 and 8 of the datasets studied respectively.
GAMI-Net: An explainable neural network based on generalized additive models with structured interactions

The lack of interpretability is an inevitable problem when using neural network models in real applications. In this paper, an explainable neural network based on generalized additive models with structured interactions (GAMI-Net) is proposed to pursue a good balance between prediction accuracy and model interpretability. GAMI-Net is a disentangled feedforward network with multiple additive subnetworks; each subnetwork consists of multiple hidden layers and is designed for capturing one main effect or one pairwise interaction. Three interpretability aspects are further considered, including a) sparsity, to select the most significant effects for parsimonious representations; b) heredity, a pairwise interaction could only be included when at least one of its parent main effects exists; and c) marginal clarity, to make main effects and pairwise interactions mutually distinguishable. An adaptive training algorithm is developed, where main effects are first trained and then pairwise interactions are fitted to the residuals. Numerical experiments on both synthetic functions and real-world datasets show that the proposed model enjoys superior interpretability and it maintains competitive prediction accuracy in comparison to the explainable boosting machine and other classic machine learning models.
GraphFPN: Graph Feature Pyramid Network for Object Detection

Feature pyramids have been proven powerful in image understanding tasks that require multi-scale features. State-of-the-art methods for multi-scale feature learning focus on performing feature interactions across space and scales using neural networks with a fixed topology. In this paper, we propose graph feature pyramid networks that are capable of adapting their topological structures to varying intrinsic image structures, and supporting simultaneous feature interactions across all scales. We first define an image specific superpixel hierarchy for each input image to represent its intrinsic image structures. The graph feature pyramid network inherits its structure from this superpixel hierarchy. Contextual and hierarchical layers are designed to achieve feature interactions within the same scale and across different scales. To make these layers more powerful, we introduce two types of local channel attention for graph neural networks by generalizing global channel attention for convolutional neural networks. The proposed graph feature pyramid network can enhance the multiscale features from a convolutional feature pyramid network.We evaluate our graph feature pyramid network in the object detection task by integrating it into the Faster R-CNN algorithm. The modified algorithm outperforms not only previous state-of-the-art feature pyramid based methods with a clear margin but also other popular detection methods on both MS-COCO 2017 validation and test datasets.
Transformation Invariant Few-Shot Object Detection

Few-shot object detection (FSOD) aims to learn detectors that can be generalized to novel classes with only a few instances. Unlike previous attempts that exploit meta-learning techniques to facilitate FSOD, this work tackles the problem from the perspective of sample expansion. To this end, we propose a simple yet effective Transformation Invariant Principle (TIP) that can be flexibly applied to various meta-learning models for boosting the detection performance on novel class objects. Specifically, by introducing consistency regularization on predictions from various transformed images, we augment vanilla FSOD models with the generalization ability to objects perturbed by various transformation, such as occlusion and noise. Importantly, our approach can extend supervised FSOD models to naturally cope with unlabeled data, thus addressing a more practical and challenging semi-supervised FSOD problem. Extensive experiments on PASCAL VOC and MSCOCO datasets demonstrate the effectiveness of our TIP under both of the two FSOD settings.
Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments.With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference.Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem.Under the framework of 'AirMatrix', the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones.In the multipath planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request.The constraints on a single flight path and two different flight paths are considered.The goal is to obtain several different short flight paths as alternatives.A clustering improved ant colony optimization (CIACO) algorithm is employed to solve the multi-path planning problem.The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations.In the task scheduling level, the conflicts between two drones are defined in two circumstances.One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones.A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators' requests are both considered.A 'cross-off' strategy is developed to calculate the fitness value, and a 'distributed-centralized' strategy is applied considering the task priorities of drones.A genetic algorithm (GA)-based task scheduling algorithm is also developed according to the characteristic of the established model.Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.
High-efficiency chaotic time series prediction based on time convolution neural network

The prediction of chaotic time series is important for both science and technology. In recent years, this type of prediction has improved significantly with the development of deep learning. Here, we propose a temporal convolutional network (TCN) model for the prediction of chaotic time series. Our TCN model offers highly stable training, high parallelism, and flexible perception field. Comparative experiments with the classic long short-term memory (LSTM) network and hybrid (CNN-LSTM) neural network show that the TCN model can reduce the training time by a factor of more than two. Furthermore, the network can focus on more important information because of the attention mechanism. By embedding the convolutional block attention module (CBAM), which combines the spatial and channel attention mechanisms, we obtain a new model, TCN-CBAM. This model is comprehensively better than the LSTM, CNN-LSTM, and TCN models in the prediction of classical systems (Chen system, Lorenz system, and sunspots). In terms of prediction accuracy, the TCN-CBAM model obtains better results for the four main evaluation indicators: root mean square error, mean absolute error, coefficient of determination, and Spearman's correlation coefficient, with a maximum increase of 41.4%. The TCN-CBAM has also the shortest training times among the previous classic four models.
Learning to Match Features with Seeded Graph Matching Network

Matching local features across images is a fundamental problem in computer vision. Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching Network, a graph neural network with sparse structure to reduce redundant connectivity and learn compact representation. The network consists of 1) Seeding Module, which initializes the matching by generating a small set of reliable matches as seeds. 2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs. Three novel operations are proposed as basic elements for message passing: 1) Attentional Pooling, which aggregates keypoint features within the image to seed matches. 2) Seed Filtering, which enhances seed features and exchanges messages across images. 3) Attentional Unpooling, which propagates seed features back to original keypoints. Experiments show that our method reduces computational and memory complexity significantly compared with typical attention-based networks while competitive or higher performance is achieved.
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition

This paper presents a pure transformer-based approach, dubbed the Multi-Modal Video Transformer (MM-ViT), for video action recognition. Different from other schemes which solely utilize the decoded RGB frames, MM-ViT operates exclusively in the compressed video domain and exploits all readily available modalities, i.e., I-frames, motion vectors, residuals and audio waveform. In order to handle the large number of spatiotemporal tokens extracted from multiple modalities, we develop several scalable model variants which factorize self-attention across the space, time and modality dimensions. In addition, to further explore the rich inter-modal interactions and their effects, we develop and compare three distinct cross-modal attention mechanisms that can be seamlessly integrated into the transformer building block. Extensive experiments on three public action recognition benchmarks (UCF-101, Something-Something-v2, Kinetics-600) demonstrate that MM-ViT outperforms the state-of-the-art video transformers in both efficiency and accuracy, and performs better or equally well to the state-of-the-art CNN counterparts with computationally-heavy optical flow.
Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.
Gaining-Sharing Knowledge Based Algorithm with Adaptive Parameters Hybrid with IMODE Algorithm for Solving CEC 2021 Benchmark Problems

The initiative to introduce new benchmark problems has drawn attention to the development of new optimization algorithms. Recently, a set of constrained benchmark problems has been developed as a addition to CEC benchmark series. This paper proposed a hybrid variant of gaining sharing knowledge based algorithm with adaptive parameters and improved multi-operator differential evolution (IMODE) algorithm, called APGSK-IMODE. It enhanced the performance of recently developed adaptive gaining sharing knowledge based algorithm. The performance of APGSK-IMODE has been tested on CEC2021 benchmark problems which contains 10 test functions with dimensions 10 and 20. The results obtained from the proposed algorithm have been compared with those obtained from the rival algorithms. The results elaborate the superiority of APGSK-IMODE. APGSK-IMODE outperforms the competing algorithms with regard to quality of solution, robustness and convergence.
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of large-scale DAM on difficult tasks are missing. In this work, we aim to make DAM more practical for interesting real-world applications (e.g., medical image classification). First, we propose a new margin-based min-max surrogate loss function for the AUC score (named as the AUC min-max-margin loss or simply AUC margin loss for short). It is more robust than the commonly used AUC square loss, while enjoying the same advantage in terms of large-scale stochastic optimization. Second, we conduct extensive empirical studies of our DAM method on four difficult medical image classification tasks, namely (i) classification of chest x-ray images for identifying many threatening diseases, (ii) classification of images of skin lesions for identifying melanoma, (iii) classification of mammogram for breast cancer screening, and (iv) classification of microscopic images for identifying tumor tissue. Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks. Specifically, our DAM method has achieved the 1st place on Stanford CheXpert competition on Aug. 31, 2020. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets. We also conduct extensive ablation studies to demonstrate the advantages of the new AUC margin loss over the AUC square loss on benchmark datasets. The proposed method is implemented in our open-sourced library LibAUC (www.libauc.org) whose github address is https://github.com/Optimization-AI/LibAUC.
Integration of blockchains and smart contracts into construction information flows: Proof-of-concept

The process of constructing structural systems produces a huge amount of documentation that traces human activities on a construction site. While the building information modelling approach introduces common data environments (CDEs) to support document management, communication between them is limited, and mainly involves the use of email and activities susceptible to human error. This paper proposes a proof-of-concept for the integration of blockchains and smart contracts into information flows used in various CDEs. The focus of the proposal is on reducing human error and increasing the reliability and transparency of decision-making processes on construction sites pertaining to the structural system. To this end, the proof-of-concept introduces smart contracts that have different levels of complexity, with the advanced version comparing information exchanged with data gathered by IoT sensors on site. A first implementation of the proposal is also presented.
Design of integrated energy market cloud service platform based on blockchain smart contract

Traditional integrated energy market has problems such as single transaction type, opaque dispatch subsidies, and lack of institutions that can provide professional and fair services in the integrated energy market. Therefore, based on the smart contract of blockchain, this paper designs the cloud service platform for integrated energy market. The participants in the integrated energy market are divided into integrated energy suppliers and integrated energy users. The two can use the Digital Currency Electronic Payment (DCEP) to trade energy (carbon) in the blockchain smart contract provided by cloud service platform, as well as implement decentralized intelligent dispatch by using blockchain Internet of things technology. The smart contract designed by Antchain is used to simulate the energy trading and dispatch process of cloud service platform, and the verifiable simulation results are given. It is proved that the integrated energy market cloud service platform based on the blockchain smart contract designed in this paper can effectively realize not only the security and convenience of energy trading, but also the efficiency and intelligence of energy dispatch.
Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision free path in mountainous environment, and the planned path is smooth and short. Compared with Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.
A Privacy-Preserving Storage Scheme for Logistics Data With Assistance of Blockchain

In recent years, traditional logistics systems are developing toward intelligence based on the Internet of Things (IoT). Sensing devices throughout the logistics network provide strong support for smart logistics. However, due to the insufficient local computing and storage resources of IoT devices, logistics records with sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this study, we propose a blockchain-assisted secure storage scheme for logistics data. To be specific, this scheme can be briefly divided into two parts. The first part involves data generation and aggregation, session establishing, records encryption and storage, wherein a blockchain network is used to assist the cloud server with data storage, and smart contracts are deployed to provide reliable storage interfaces. In the second part, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process. Also, the stored records can be securely audited by leveraging the deployed blockchain network. Finally, we analyze the security and privacy properties of this scheme and evaluate its performance in terms of computation and communication overhead by developing an experimental platform. The experimental results indicate that the performance of our scheme is acceptable.
Proof-of-work based blockchain technology and Anthropocene: An undermined situation?

In the increasing literature dealing with the potential applications of blockchain technology in the energy sector, one key aspect is under-estimated: the use of renewal energies to fuel the energy consumed by the blockchain technology. The vast majority of blockchain-based projects use the Proof-of-Work (POW) consensus algorithm, which paradoxically is well-known to consume a high level of electricity - how can a new solution promote green energy with transactions that are validated through a non-green process (POW protocol)? This perspective discusses this apparent contradiction by debating the extent to which the cryptographic validation of the POW-based blockchain technology (illustrated by Bitcoin here) can really fuelled with green electricity. I explain that the vast majority of the literature dealing with the ecological influence of Bitcoin mainly focus on the energetic dimension under-estimating the environmental impact. Integrating research about Life Cycle Assessments, I suggest here a conceptual framework rejecting the implicit assumption that renewable energy fuelled mining industry would make POW based operations greener. In the light of the recent ban of mining operations in China and Iran, the issue discussed in this article is timely for policymakers who need to promote the development of standards for mining operations related equipment in order to avoid potential negative environmental side effects. • Analyse the link POW-based Blockchain technology and renewables energies. • Advocate for a decrease in crypto-mining activities. • Promote research for new crypto-mining algorithms. • Promote the use of a non Proof-of-Work based Blockchain technology.