Reviewerly/exHarmony huggingface dataset:
Optimization of blockchain investment portfolio under artificial bee colony algorithm

To improve the security of asset securitization and reduce investment risk, the risk reduction methods of asset securitization are investigated. First, the risk and return of investment are measured using multiple methods, and several portfolio methods are introduced. Second, there are problems of fraud risk, underlying assets, and asymmetry risk when assets are being transformed into securitization. The blockchain technology is used to reduce the probability of these risks and improve investment security. Finally, the artificial bee colony (ABC) algorithm is applied to the optimization of the blockchain investment portfolio. Since the traditional ABC algorithm can only solve the problem of single-objective optimization, an external population is constructed to improve it. The results show that the improved ABC algorithm can simultaneously optimize multiple features in the investment portfolio, reduce the error of investors when making decisions, and improve the balance between investment returns and risks. At the same time, after the algorithm is evaluated, it is found that the accuracy and practicality have been improved to a certain extent. Also, it is proved that the ABC algorithm can solve the problem of portfolio optimization, improve the security of asset securitization, and ensure the balance of investment return and risk to a certain extent.
Fully Distributed Joint Localization and Target Tracking With Mobile Robot Networks

In this article, we study the problem of joint localization and target tracking using a mobile robot network. Here, a team of mobile robots equipped with onboard sensors simultaneously localize themselves and track multiple targets. We introduce a fully distributed algorithm that is applicable to generic robot motion, target process, and measurement models and is robust to time-varying sensing and communication topologies and changing blind robots (the robots not directly sensing the targets). Instead of treating localization and target tracking as two separate problems, we explicitly account for the influence of one to the other and exploit it to improve performance in a fully distributed context. Two novel kinds of distributed estimates are derived. By employing them, each robot can estimate the pose (position and orientation) of itself (localization) and the states of targets (tracking) using only its local information and information from its one-hop communicating neighbors while preserving consistency. Furthermore, it is proven that, in the case of linear time-varying models, the estimation errors are bounded in the mean-square sense under very mild conditions on the sensing and communication graph and system observability. The effectiveness of our approach is demonstrated extensively through Monte Carlo simulations, and experiments carried out using a real-world data set. It is also shown better performance in the pose estimates of the robots is achieved when jointly estimating the robots' poses and targets' states.
An Exploration of Prosocial Aspects of Communication Cues between Automated Vehicles and Pedestrians

Road traffic is a social situation where participants heavily interact with each other. Consequently, communication plays an important role. Typically, the communication between pedestrians and drivers is nonverbal and consists of a combination of gestures, eye contact, and body movement. However, when vehicles become automated, this will change. Previous work has investigated the design and effectiveness of additional communication cues between pedestrians and automated vehicles. It remains unclear, though, how this impacts the perceptions of the quality of communication and impressions of mindfulness and prosociality. In this paper, we report an online experiment, where we evaluated the perception of communication cues in the form of on-road light projections, across different traffic scenarios and roles. Our results indicate that, while the cues can improve communication, their effect is dependent on traffic scenarios. These results provide preliminary implications for the design of communication cues that consider their prosocial aspects.
Multiobjective feature selection for key quality characteristic identification in production processes using a nondominated-sorting-based whale optimization algorithm

Identifying key quality characteristics (QCs) in production processes is essential for product quality control and improvement. This paper proposes a multiobjective wrapper-based feature selection (FS) method for key QC (KQC) identification on unbalanced production data using a novel modified nondominated-sorting-based whale optimization algorithm (MNSWOA) and the ideal point method (IPM). In the proposed approach, the FS problem is defined as maximizing the geometric mean (GM) measure and minimizing the feature (QC) subset size. To solve the defined FS problem, MNSWOA is adopted first to find a set of candidate solutions (feature subsets), and then IPM is adopted to select the final solution. In MNSWOA, a modified fast nondominated sorting approach is proposed to adapt the single objective whale optimization algorithm to the multiobjective scenario. Moreover, a uniform reference solution selection strategy and the mutation operations are embedded in MNSWOA to improve its search performance. Experimental results on four unbalanced production datasets show that the proposed FS method performs effectively and efficiently for KQC identification. Further comparisons show that MNSWOA obtains better search performance than benchmark multiobjective optimization methods, including a modified NSGA-II, SPEA2, MOEA/D, NSPSO and CMDPSO.
Abnormal events detection based on RP and inception network using distributed optical fiber perimeter system

For establishing an accurate and reliable distributed optical fiber perimeter security system, this paper proposes a novel abnormity detection solution to security using Recurrent Plot (RP) and deep learning technology. Take advantage of the temporal correlation of intrusion signals, we encode the sensing signals into two-dimensional images through the RP algorithm. The RP algorithm can extract the motion characteristics of the signal from the complex time series, and it is robust to instrument noise. These encoded image signatures can reveal the deeper temporal correlation of the intrusion signals’ motion. After that, Inception network can adaptively extract the features of these images to complete the accurate identification of a series of noisy intrusion signals. We conducted experiments on three most frequent natural events and three representative man-made intrusion events, including heavy rain, light rain, wind blowing, treading, slapping, and impacting. The results show that the detection accuracy has reached 99.7%. This method can achieve 0.35 s real-time detection in the online detection of abnormal events while ensuring accuracy, providing a new intrusion pattern identification idea for perimeter security.
Identification of spatial patterns, geochemical associations and assessment of origin-specific health risk of potentially toxic elements in soils of Armavir region, Armenia

The study of soil potentially toxic elements (PTE) contents and establishment of the geochemical characterization of areas which have never been studied is of great concern. In 2019, soil survey of the Armavir region (Armenia) was conducted in order to investigate the spatial pattern of PTE, reveal PTE geochemical associations and assess the origin-specific health risks. The application of compositional data analysis and geospatial mapping allowed to identify two clusters of samples. The first cluster was spatially located on volcanic rocks and was represented by Fe, Co, Mn, Ti, Zn, Ba, Pb suggesting a natural origin of PTE in these areas. The second cluster was allocated on the alluvial, deluvial, and proluvial sediments and represented by As, Cu, Cr, Ni. Such combination of elements in the same group indicates the anthropogenic introduction of some quantities of PTE. The latter is confirmed by the presence of outliers and extreme values for As, Cu and Ni, as well as by the spatial colocation of Fe, Mn, Co, Pb, Zn outliers and extreme contents. The health risk assessment showed that for children the multi-elemental non-carcinogenic risk was detected, while for the adults the non-carcinogenic risk and carcinogenic risk were below the allowable level. The detailed study of the risk levels showed that in first cluster comparatively higher risk were observed for Pb, V, Ba, Zn while in the second cluster: Fe, Co, Mn, As, Cr, Cu, Ni. The results indicated the necessity of additional in-depth studies with special focus on bioavailability of PTE.
Dream-Experiment: A MR User Interface with Natural Multi-channel Interaction for Virtual Experiments

This paper studies a set of MR technologies for middle school experimental teaching environments and develops a multi-channel MR user interface called Dream-Experiment. The goal of Dream-Experiment is to improve the traditional MR user interface, so that users can get a real, natural 3D interactive experience like real experiments, but without danger and pollution. In terms of visual presentation, we design multi-camera collaborative registration to realize robust 6-DoF MR interactive space, and also define a complete rendering pipeline to provide improved processing of virtual-real objects' occlusion including translucent devices. In the virtual-real interaction, we provide six interaction modes that support visual interaction, tangible interaction, virtual-real gestures with touching, voice, thermal feeling, and olfactory feeling. After users' testing, we find that Dream-Experiment has better interactive efficiency and user experience than traditional MR environments.
GLNet: Global Local Network for Weakly Supervised Action Localization

In this paper, we address the challenging problem of weakly supervised spatio-temporal action localization for which only video-level action labels are available during training. To solve this problem, we propose an end-to-end Global Local Network (GLNet) to predict the probability distribution simultaneously in both spatial and temporal space. The proposed GLNet model includes two key components: a local spatial module and a global temporal module. The local spatial module aims to predict the frame-level spatial distribution by encoding short-term temporal information. In particular, we propose a Region Actionness Network (RAN) to select the target region boxes from the precomputed exhaustive proposals. The global temporal module can predict temporal distribution by a long-term temporal structure modelling. Specifically, we design a temporal fusion-and-excitation architecture on the top of several clips, and trained by a sparse loss function. Therefore, the proposed GLNet model can perform spatio-temporal action localization in an end-to-end manner. We evaluate the performance of GLNet on the J-HMDB and UCF101-24 datasets. The experimental results demonstrate GLNet achieves a significant margin against other state-of-the-art weakly supervised methods and even some fully supervised methods in terms of frame mean Average Precision (mAP) and the video mAP (called frame-mAP and video-mAP, respectively).
Secure State Estimation Using Hybrid Homomorphic Encryption Scheme

Recently, the security of state estimation has attracted significant research attention due to the need for trustworthy situation awareness in emerging cyber-physical systems. In this article, we design an encryption-based state estimation (ESE) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network but also critical system information required by the estimator. We adopt a hybrid encryption scheme by jointly using the multiplicatively and additively homomorphic encryption methods. Armed with encryption, ESE is able to conceal comprehensive information (i.e., model parameters, measurements, and estimates) aggregated at the estimator while retaining the correctness of the normal state estimation. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he/she will not be able to obtain sufficient knowledge of the system state to guide the attack. Furthermore, due to the encryption-induced quantization error, we give a sufficient stability condition for ESE. Finally, we implement ESE with real-world hardware to illustrate its effectiveness and efficiency.
A comparative study of social group optimization with a few recent optimization algorithms

Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.
Rocky road to digital diagnostics: implementation issues and exhilarating experiences

Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in a full digital diagnostic workflow where we are currently integrating artificial intelligence algorithms. In this paper, we highlight the different steps in this process towards digital diagnostics, which was at times a rocky road with definitely issues in implementation, but eventually an exciting new way to practice pathology in a more modern and efficient way where patient safety has clearly gone up.
On the detection of community smells using genetic programming-based ensemble classifier chain

Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as suboptimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.
MadMax

Ethereum is a distributed blockchain platform, serving as an ecosystem for smart contracts: full-fledged intercommunicating programs that capture the transaction logic of an account. A gas limit caps the execution of an Ethereum smart contract: instructions, when executed, consume gas, and the execution proceeds as long as gas is available. Gas-focused vulnerabilities permit an attacker to force key contract functionality to run out of gas---effectively performing a permanent denial-of-service attack on the contract. Such vulnerabilities are among the hardest for programmers to protect against, as out-of-gas behavior may be uncommon in nonattack scenarios and reasoning about these vulnerabilities is nontrivial. In this paper, we identify gas-focused vulnerabilities and present MadMax: a static program analysis technique that automatically detects gas-focused vulnerabilities with very high confidence. MadMax combines a smart contract decompiler and semantic queries in Datalog. Our approach captures high-level program modeling concepts (such as "dynamic data structure storage" and "safely resumable loops") and delivers high precision and scalability. MadMax analyzes the entirety of smart contracts in the Ethereum blockchain in just 10 hours and flags vulnerabilities in contracts with a monetary value in billions of dollars. Manual inspection of a sample of flagged contracts shows that 81% of the sampled warnings do indeed lead to vulnerabilities.
Opposition-Based Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the construction and utilization of the opposite solution is the key issue. Ant colony optimization (ACO) generally used to solve combinatorial optimization problems is a kind of classical SI optimization algorithm. Opposition-based ACO which is combined in OBL is proposed to solve the symmetric traveling salesman problem (TSP) in this paper. Two strategies for constructing opposite path by OBL based on solution characteristics of TSP are also proposed. Then, in order to use information of opposite path to improve the performance of ACO, three different strategies, direction, indirection, and random methods, mentioned for pheromone update rules are discussed individually. According to the construction of the inverse solution and the way of using it in pheromone updating, three kinds of improved ant colony algorithms are proposed. To verify the feasibility and effectiveness of strategies, two kinds of ACO algorithms are employed to solve TSP instances. The results demonstrate that the performance of opposition-based ACO is better than that of ACO without OBL.
An Efficient Retrieval System for Biomedical Images Based on Radial Associated Laguerre Moments

The ability of any retrieval system to extract features by using its feature descriptor is the primary criterion to measure its efficiency.In this paper a novel technique for feature extraction of biomedical images is presented.The mooted system uses the Radial Associated Laguerre Moments (RALMs) as a feature descriptor to obtain features from two types of medical images: computer tomography (CT) and magnetic resonance images (MRI).RALMs represent one sort of discrete orthogonal moments.RALMs extract the features from images using orthogonal moments to retrieve images from a database.Our approach is extensively assessed with noise-free and noisy images from three different benchmark databases: Emphysema-CT, NEMA CT, and NEMA MRI.The first two databases are used for CT image retrieval, while the third is for MR image retrieval.The proposed approach was tested against the state-of-the art local feature descriptors: Local Binary Pattern (LBP), and local diagonal extrema pattern (LDEP).It was also evaluated against orthogonal Fourier-Mellin moments (OFMMs) as a global descriptor.The comparison shows a significant improvement in favor of the proposed approach in terms of three different performance metrics: ARP, ARR, and F_score.The proposed approach was also compared against the convolutional neural network (CNN) as a deep learning based method over the NEMA-MRI dataset.The RALMs based approach showed a significant improvement when compared against two state-of-the-art medical image retrieval approaches: Histogram of Compressed Scattering Coefficients (HCSCs) and a local bit-plane decodingbased AlexNet descriptor (LBpDAD), the study has done over the TCIA-CT dataset.The proposed approach was also tested with big well-known dataset from the international skin imaging collaboration (ISIC) 2018.
High-Capacity Image Steganography Based on Improved FC-DenseNet

Aiming at the problem that the traditional steganography based on carrier modification has the low steganographic capacity, a steganographic scheme based on Fully Convolutional Dense Connection Network (FC-DenseNet) is proposed. Since FC-DenseNet can effectively overcome the problems of gradient dissipation and gradient explosion, and a large number of features are multiplexed, the cascaded secret image and carrier image can reconstruct good image quality after entering the network. Effectively improve steganographic capacity. First, we reset the number of input channels of the first convolution block of FC-DenseNet and the number of output channels of the last convolution block and deleted the LogSoftmax() function. On the sender side, after the concatenated secret image and carrier image pass through the hidden network FC-DenseNet, the secret image is embedded in the carrier image to obtain a stego-image. At the receiving side, the extraction network reconstructs the secret image from the stego-image. Experimental results show that our proposed steganography scheme not only has a high Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) but also can realize large-capacity image steganography, with an average image payload capacity of 23.96 bit per pixel.