Reviewerly/exHarmony huggingface dataset:
Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification

Recent years have witnessed numerous successful applications of incorporating attention module into feed-forward convolutional neural networks. Along this line of research, we design a novel lightweight general-purpose attention module by simultaneously taking channel attention and spatial attention into consideration. Specifically, inspired by the characteristics of channel attention and spatial attention, a nonlinear hybrid method is proposed to combine such two types of attention feature maps, which is highly beneficial to better network fine-tuning. Further, the parameters of each attention branch can be adjustable for the purpose of making the attention module more flexible and adaptable. From another point of view, we found that the currently popular SE, and CBAM modules are actually two particular cases of our proposed attention module. We also explore the latest attention module ADCM. To validate the module, we conduct experiments on CIFAR10, CIFAR100, Fashion MINIST datasets. Results show that, after integrating with our attention module, existing networks tend to be more efficient in training process and have better performance as compared with state-of-the-art competitors. Also, it is worthy to stress the following two points: (1) our attention module can be used in existing state-of-the-art deep architectures and get better performance at a small computational cost; (2) the module can be added to existing deep architectures in a simple way through stacking the integration of networks block and our module.
Assessment of selected metrological properties of laser triangulation sensors

In the paper experimental results of the static characteristics of laser triangulation sensors are presented. In order to determine the uncertainty of length measurement using a measurement system equipped with a laser triangulation sensor, the procedure of calibration and estimation of static characteristics coefficients was applied. The obtained results were subjected to statistical analysis, and consequently the range of the correct values of the measured quantity was determined. In addition, a comparison using Bartlett’s test of homogeneity of multiple variances, investigating the distribution of measurements in k points of the measuring range was performed. In addition, a mathematical model was presented and verified, enabling prediction of the head features, exclusively based on the information in the data sheets of manufacturers. On the basis of studies conducted, conclusions have been made as regards the usefulness of a laser triangulation heads for measurements in the domain of length metrology.
Prospects of quantum computing for molecular sciences

Abstract Molecular science is governed by the dynamics of electrons and atomic nuclei, and by their interactions with electromagnetic fields. A faithful physicochemical understanding of these processes is crucial for the design and synthesis of chemicals and materials of value for our society and economy. Although some problems in this field can be adequately addressed by classical mechanics, many demand an explicit quantum mechanical description. Such quantum problems require a representation of wave functions that grows exponentially with system size and therefore should naturally benefit from quantum computation on a number of logical qubits that scales only linearly with system size. In this perspective, we elaborate on the potential benefits of quantum computing in the molecular sciences, i.e., in molecular physics, chemistry, biochemistry, and materials science.
Blockchain-Powered Policy Enforcement for Ensuring Flight Compliance in Drone-Based Service Systems

Drones, or unmanned aerial vehicles, can be used for commercial services such as short-distance delivery. In order to ensure quality services, multiple drone-based delivery service providers can be employed in delivery service systems. In this article, we address two very important and unexplored challenges of employing drones in delivery services. First, involving multiple drones from different service providers elevates the chance of collision during flights. Second, drones may hamper the privacy of citizens by unauthorized access to private and restricted areas. In order to solve the aforementioned issues, we propose a blockchain-based policy enforcement mechanism in the drone-based delivery service systems. At first, the mechanism will set policies to establish pre-allocated flight paths for different drones at different times to avoid collisions, and ensure the privacy of citizens by restricting their access to unauthorized areas. Later, the blockchain will enforce the policies to monitor compliance of the drone flights and identify non-compliant drone services to penalize corresponding service providers. We simulate a virtual drone-based delivery system with the Ethereum blockchain platform and examine the performance and feasibility of our proposed mechanism.
An Effective Chaos-Based Image Encryption Scheme Using Imitating Jigsaw Method

In this paper, an efficient chaos-based image encryption scheme is proposed, which uses the imitating jigsaw method containing revolving and shifting operations. In this scheme, there are three processes in encryption: preprocessing, encryption process, and postprocessing. In the preprocessing, the original image is partitioned into 64 × 64 pixel image blocks and then randomly revolved and shifted under control sequences which are generated by the hyperchaotic Lorenz system whose initial conditions are calculated by original image and keys. Therefore, the preprocessing is sensitive to plain image against differential attacks. In the encryption process, the after-preprocessing image is partitioned into 32 × 32 pixel image blocks; next they are randomly revolved and encrypted by control sequence and key blocks which are generated by the skew tent map. In postprocessing, the after-encryption image is partitioned into 16 × 16 pixels’ image blocks, and they are randomly revolved and shifted again under control sequences which are related with encrypted image and keys. The postprocessing further increases the diffusion characteristics. Moreover, the test experiment and security analyses are given; the results show that our proposed cryptosystem has both security and speed performance.
Adaptive cuckoo algorithm with multiple search strategies

Metaheuristic algorithms are important methods to solve optimization problems and maintaining a balance between the global exploration and local exploitation is crucial to the performance of such algorithms. We propose a self-adaptive multi strategy cuckoo search algorithm (MSACS) based on the cuckoo search algorithm (CS). First, five different search strategies were proposed to calculate the use probability and control parameters by using adaptive strategies to ensure that the algorithm can autonomously adjust according to the change in the functions and iteration times. Second, the performance of the MSACS was tested on 28 common benchmark functions and compared with the performance of several CS algorithms, particle swarm optimization (PSO) algorithms and difference evolution algorithms (DE). MSACS achieved the best results on 17 of these functions and performed well on the remaining 11 functions. Finally, the improved algorithm was applied to the optimization of a ball screw driving system model. By adjusting the dimensionless input velocity function, the peak acceleration of screw is reduced and the peak acceleration of crank angle is reasonable.
Scalable distributed gate-model quantum computers

A scalable model for a distributed quantum computation is a challenging problem due to the complexity of the problem space provided by the diversity of possible quantum systems, from small-scale quantum devices to large-scale quantum computers. Here, we define a model of scalable distributed gate-model quantum computation in near-term quantum systems of the NISQ (noisy intermediate scale quantum) technology era. We prove that the proposed architecture can maximize an objective function of a computational problem in a distributed manner. We study the impacts of decoherence on distributed objective function evaluation.
Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures

We present a multi-UAV Coverage Path Planning (CPP) framework for the inspection of large-scale, complex 3D structures. In the proposed sampling-based coverage path planning method, we formulate the multi-UAV inspection applications as a multi-agent coverage path planning problem. By combining two NP-hard problems: Set Covering Problem (SCP) and Vehicle Routing Problem (VRP), a Set-Covering Vehicle Routing Problem (SC-VRP) is formulated and subsequently solved by a modified Biased Random Key Genetic Algorithm (BRKGA) with novel, efficient encoding strategies and local improvement heuristics. We test our proposed method for several complex 3D structures with the 3D model extracted from OpenStreetMap. The proposed method outperforms previous methods, by reducing the length of the planned inspection path by up to 48%.
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman filter (UKF) to simultaneously estimate the three-dimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The model-based approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learning-based strategy uses a Long Short-Term Memory (LSTM) to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.
An in-depth investigation of the performance characteristics of Hyperledger Fabric

Private permissioned blockchains are deployed in ever greater numbers to facilitate cross-organizational processes in various industries, particularly in supply chain management. One popular example of this trend is Hyperledger Fabric. Compared to public permissionless blockchains, it promises improved performance and provides certain features that address key requirements of enterprises. However, also permissioned blockchains are still not as scalable as centralized systems, and due to the scarcity of theoretical results and empirical data, their real-world performance cannot be predicted with the necessary precision. We intend to address this issue by conducting an in-depth performance analysis of Hyperledger Fabric. The paper presents a detailed compilation of various performance characteristics using an enhanced version of the Distributed Ledger Performance Scan (DLPS). Researchers and practitioners alike can use the various performance properties identified and discussed as guidelines to better configure and implement their Hyperledger Fabric network. Likewise, they are encouraged to use the DLPS framework to conduct their measurements.
Improved hybrid A* path planning method for spherical mobile robot based on pendulum

This article proposes a modification of hybrid A* method used for navigation of spherical mobile robots with the ability of limited partial lateral movement driven by pendulum. For pendulum-driven spherical robots with nonzero minimal turning radius, our modification helps to find a feasible and achievable path, which can be followed in line with the low time cost. Because of spherical shell shape, the robot is point contact with the ground, showing different kinematic model compared with common ground mobile robots such as differential robot and wheeled car-like robot. Therefore, this article analyzes the kinematic model of spherical robot and proposes a novel method to generate feasible and achievable paths conforming to kinematic constraints, which can be the initial value of future trajectory tracking control and further optimization. A concept of optimal robot’s minimum area for rotation is also proposed to improve search efficiency and ensure the ability of turning to any orientation by moving forward and backward in a finite number of times within limited areas.
Tourism destination management using sentiment analysis and geo-location information: a deep learning approach

Abstract Sentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is a vital issue, the sources of geotagged information have already proven to be promising for tourism-related geographic research. The paper introduces an approach to estimate the sentiment related to Cilento’s, a well known tourism venue in Southern Italy. A newly collected dataset of tweets related to tourism is at the base of our method. We aim at demonstrating and testing a deep learning social geodata framework to characterize spatial, temporal and demographic tourist flows across the vast of territory this rural touristic region and along its coasts. We have applied four specially trained Deep Neural Networks to identify and assess the sentiment, two word-level and two character-based, respectively. In contrast to many existing datasets, the actual sentiment carried by texts or hashtags is not automatically assessed in our approach. We manually annotated the whole set to get to a higher dataset quality in terms of accuracy, proving the effectiveness of our method. Moreover, the geographical coding labelling each information, allow for fitting the inferred sentiments with their geographical location, obtaining an even more nuanced content analysis of the semantic meaning.
Self-organizing fuzzy inference ensemble system for big streaming data classification

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.
Deep multi-view document clustering with enhanced semantic embedding

Multi-view clustering, which aims to group data with multiple views, has recently attracted intense research attention. Text documents bring additional difficulties to multi-view clustering due to the sparseness, high dimensionality, and inconsistency of document views. In this paper, we introduced a novel model on multi-view document clustering with enhanced semantic embedding, namely, MDCE, to address all of the above difficulties of clustering text documents with more than one representation view. Enhanced semantic embedders are designed to learn and improve the semantic mapping from higher-dimensional document space to lower-dimensional feature space with complementary semantic information. Specifically, three types of complementary semantic information are involved in an unsupervised manner: neighbour-wise, view-wise, and cluster-wise complementary information. A deep network is designed to optimize the enhanced semantic mapping, integrate lower-dimensional features from multiple views, and discover document clustering assignments simultaneously. We conducted extensive experiments on our proposed MDCE model by using realistic datasets compared with a number of state-of-the-art multi-view clustering approaches. Experimental results demonstrate that the MDCE-related models perform substantially better than all other models.
Girasol, a sky imaging and global solar irradiance dataset

The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. We propose to achieve this using a data acquisition systems (DAQ) that simultaneously records sky imaging and Global Solar Irradiance (GSI) measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed as the \emph{Girasol Machine} (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on an outdoor tracker. The tracker updates its pan an tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal support next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.
Ciminion: Symmetric Encryption Based on Toffoli-Gates over Large Finite Fields

Motivated by new applications such as secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero-Knowledge proofs (ZK), the need for symmetric encryption schemes that minimize the number of field multiplications in their natural algorithmic description is apparent. This development has brought forward many dedicated symmetric encryption schemes that minimize the number of multiplications in $$ \mathbb {F}_{2^n} $$ or $$ \mathbb {F}_{p} $$ , with p being prime. These novel schemes have lead to new cryptanalytic insights that have broken many of said schemes. Interestingly, to the best of our knowledge, all of the newly proposed schemes that minimize the number of multiplications use those multiplications exclusively in S-boxes based on a power mapping that is typically $$x^3$$ or $$x^{-1}$$ . Furthermore, most of those schemes rely on complex and resource-intensive linear layers to achieve a low multiplication count. In this paper, we present Ciminion, an encryption scheme minimizing the number of field multiplications in large binary or prime fields, while using a very lightweight linear layer. In contrast to other schemes that aim to minimize field multiplications in $$ \mathbb {F}_{2^n} $$ or $$ \mathbb {F}_{p} $$ , Ciminion relies on the Toffoli gate to improve the non-linear diffusion of the overall design. In addition, we have tailored the primitive for the use in a Farfalle-like construction in order to minimize the number of rounds of the used primitive, and hence, the number of field multiplications as far as possible.
Inertial Sensor Data to Image Encoding for Human Action Recognition

Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use four types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework. These four types of activity images are Signal Images (SI), Gramian Angular Field (GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP) Images. Furthermore, for creating a multimodal fusion framework and to exploit activity images, we made each type of activity images multimodal by convolving with two spatial domain filters: Prewitt filter and High-boost filter. ResNet-18, a CNN model, is used to learn deep features from multi-modalities. Learned features are extracted from the last pooling layer of each ResNet and then fused by canonical correlation based fusion (CCF) for improving the accuracy of human action recognition. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). Experimental results on three publicly available inertial datasets show the superiority of the proposed method over the current state-of-the-art.
Ensemble learning-based filter-centric hybrid feature selection framework for high-dimensional imbalanced data

In recent years, research on feature selection for high-dimensional imbalanced data has attracted a considerable amount of attention. The filter-wrapper hybrid method, which is a conventional method of feature selection for high-dimensional data, aims to reduce excessive computational time. On the other hand, ensemble learning-based feature selection, even though it has a high level of computational complexity, focuses exclusively on the discovery of robust features. From this perspective, combining these two feature selection methods is not easy. However, a combined method is essential to advancing machine learning research that addresses real-world problems. We propose an filter-centric hybrid method based on ensemble-learning that can select the best feature subset for high-dimensional imbalanced data. The basic concept of the proposed method is to design a feature evaluation scheme based on the filter method and to apply ensemble learning with reasonable computational time. To achieve this objective, our innovative method utilizes predictions produced by multiple classifiers as inputs of the feature evaluation function. As a result, it can reflect the predictive performance of the classifiers and overcome the low performance of selected features by filter methods. In addition, it can find robust features simultaneously. To demonstrate the superiority of the proposed method, we perform various experiments using 14 experimental datasets that consist of low-dimensional balanced, high-dimensional balanced, and high-dimensional imbalanced datasets. Finally, we compare the proposed method with state-of-the-art feature selection methods.