Reviewerly/exHarmony huggingface dataset:
Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.
Modulation Format Identification and OSNR Monitoring Based on Multi-Feature Fusion Network

In this paper, we propose a multi-feature fusion network (MFF-Net) for a modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring scheme. The constellation map data used in this work comes from five modulation formats, namely 56 Gbit/s 4/8 phase shift keying (PSK) and 16/32/64 quadrature amplitude modulation (QAM). The constellation maps are input to one branch network of the MFF-Net, and then the constellation maps are processed by horizontal projection and used as input to another branch network as a way to fuse the two image features. The results show that the scheme achieves 100% MFI accuracy and 98.82% OSNR monitoring accuracy for the five modulation formats. In addition, the performance of MFF-Net and binarized convolutional neural network (B-CNN), visual geometry group network (VGG-Net), and traditional weighted multi-task learning (EW-MTL) are compared to present the superiority of the method. The effect of model structure on MFF-Net is also discussed. The robustness of the model is also evaluated for different transmission distances and bit rates.
Resistance to information security due to users’ information safety behaviors: Empirical research on the emerging markets

Practitioners and academics ask if and how punishment and moral norms affect the resistance to information systems security in emerging markets. To address this question, this empirical study examines the relationship between punishment and moral norms on resistance to information security in the context of a Brazilian IT company. The data were collected from 174 employees by applying structural equation modeling (SEM). The results suggest that the severity of punishment and certainty of detection influence descriptive norms and impact moral norms, which, in sequence, enhance resistance to information security. The findings demonstrate that the national culture of values in emerging markets contributes to the behavior of employees to increase resistance to information security.
Compositional optimization of quantum circuits for quantum kernels of support vector machines

While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate a Bayesian algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data. The algorithm increases the complexity of quantum circuits incrementally by appending quantum gates selected with Bayesian information criterion as circuit selection metric and Bayesian optimization of the parameters of the locally optimal quantum circuits identified. The performance of the resulting quantum models for the classification problems considered here significantly exceeds that of optimized classical models with conventional kernels.
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot

Abstract This paper investigates the implementation of a novel evolvable hardware controller used in evolutionary robotics. The evolvable hardware consists of a Cartesian based array of logic blocks comprised of multiplexers and logic elements. The logic blocks are configured by a bit stream which is evolved using a genetic algorithm. A comparison is performed between an evolvable hardware and an artificial neural network controller evolved using the same genetic algorithm to produce the gait of a hexapod robot. To compare the two controllers, differences in their evolutionary efficiency and robot performance are investigated. The evolutionary efficiency is measured by the required number of generations to achieve an optimal fitness. An optimal hexapod controller allows the robot to walk forward in a straight line maintaining a constant heading and body attitude. It was found that the evolutionary efficiency and performance of the evolvable hardware and artificial neural network were similar, however the EHW was the most evolutionary efficient requiring less generations on average to evolve. Both evolved controllers were evaluated in simulation, and on a physical robot using a softcore processor and custom hardware implemented on a FPGA. The implementation showed that the controllers performed equally well when deployed, allowing the hexapod to meet the optimal gait criteria. These findings have shown that the evolvable hardware controller is a valid option for robotic control of a multilegged robot such as a hexapod as its evolutionary efficiency and deployed performance on a real robot is comparable to that of an artificial neural network. One future application of these evolvable controllers is in fault tolerance where the robot can dynamically adapt to a fault by evolving the controller to adjust to the fault conditions.
A modified weighted chimp optimization algorithm for training feed-forward neural network

Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.
Fusing Context Features and Spatial Attention to Improve Object Detection

Context features are mostly used to determine the boundary of a target, which allows one to better locate an object. In this paper, we propose the fusion of the spatial attention mechanism and contextual features to simulate the recognition of objects based on the human eye, thereby improving the detection accuracy of detectors. We chose the PASCAL VOC2007+2012 general dataset to test the generality of our method and examined the improved accuracy of our proposed detector on various targets. Our method showed improved accuracy for small targets and partially overlapping targets. Our proposed model improved the detector’s accuracy by 3.34%.
QuDiet: A classical simulation platform for qubit‐qudit hybrid quantum systems

Abstract In recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state‐of‐the‐art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher‐dimensional quantum computing systems. Through recent developments in higher‐dimensional quantum computing systems, it is realised that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, QuDiet , a state‐of‐the‐art user‐friendly python‐based higher‐dimensional quantum computing simulator is introduced. QuDiet offers multi‐valued logic operations by utilising generalised quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. Various benchmark quantum circuits is simulated in QuDiet and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, QuDiet provides a full qubit‐qudit hybrid quantum simulator package with quantum circuit templates of well‐known quantum algorithms for fast prototyping and simulation. Comprehensive simulation up to 20 qutrits circuit on depth 80 on QuDiet was successfully achieved. The complete code and packages of QuDiet is available at https://github.com/LegacYFTw/QuDiet .
A Personal Perspective on Evolutionary Computation: A 35-Year Journey

This paper presents a personal account of the author's 35 years "adventure" with Evolutionary Computation-from the first encounter in 1988 and many years of academic research through to working full-time in business-successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.
The quantum realization of image linear gray enhancement

Linear gray enhancement is a spatial domain image enhancement technique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. In order to realize these three linear gray enhancement techniques in the quantum computers, this paper proposes three types of linear gray transformation schemes for quantum images based on the generalized model of novel enhanced quantum image representation (GNEQR), and the quantum circuits that realize these three transformation methods are constructed according to the schemes. The proposed circuits take advantage of efficient quantum arithmetic operations and parallel Controlled-NOT modules to factor classical transformations into basic unitary operators such as the Controlled-NOT gates and the Toffoli gates. The feasibility of the schemes is verified on IBM platform by using a small size quantum image. The complexity analysis shows the linear gray enhancement algorithm for quantum images is better than the classical algorithm in both spatial complexity and time complexity. Furthermore, the effectiveness of the proposed scheme is also demonstrated by a clarity evaluation method.
Towards design science methodology for developing mobile learning applications

Mobile learning applications are being developed to support the teaching and learning process. The lack of an accepted methodology for developing these applications motivated the establishment of a design science research process for developing mobile learning applications. Design science methodology from the information systems domain was inherited and modified by consolidating common practices found in the development of mobile learning applications. The proposed methodology consolidates a set of logically related processes to support the development of mobile learning applications with ease and ensure quality results. Expert review of the proposed methodology was conducted, and the results indicate it can positively contribute to the development of mobile learning applications.
Head-Mounted Miniature Motorized Camera and Laser Pointer Driven by Eye Movements

Recording a video scene as seen by an observer, materializing where is focused his visual attention and allowing an external person to point at a given object in this scene, could be beneficial for various applications such as medical education or remote training. Such a versatile device, although tested at the experimental laboratory demonstrator stage, has never been integrated in a compact and portable way in a real environment. In this context, we built a low-cost, light-weight, head-mounted device integrating a miniature camera and a laser pointer that can be remotely controlled or servo-controlled by an eye tracker. Two motorizations were implemented and tested (pan/tilt and Rilsey-prisms-based). The video was both recorded locally and transmitted wirelessly. Risley prisms allowed finer remote control of camera or laser pointer orientation (0.1° vs. 0.35°), but data processing and Wi-Fi transmission incur significant latency (~0.5 s) limiting the servo-controlling by eye movements. The laser beam was spatially shaped by a Diffractive Optical Element to facilitate object illumination or recognition. With this first proof-of-concept prototype, the data stream needs to be optimized to make full use of the eye tracker, but this versatile device can find various applications in education, healthcare or research.
Meta attention for Off-Policy Actor-Critic

Off-Policy Actor-Critic methods can effectively exploit past experiences and thus they have achieved great success in various reinforcement learning tasks. In many image-based and multi-agent tasks, attention mechanism has been employed in Actor-Critic methods to improve their sampling efficiency. In this paper, we propose a meta attention method for state-based reinforcement learning tasks, which combines attention mechanism and meta-learning based on the Off-Policy Actor-Critic framework. Unlike previous attention-based work, our meta attention method introduces attention in the Actor and the Critic of the typical Actor-Critic framework, rather than in multiple pixels of an image or multiple information sources in specific image-based control tasks or multi-agent systems. In contrast to existing meta-learning methods, the proposed meta-attention approach is able to function in both the gradient-based training phase and the agent's decision-making process. The experimental results demonstrate the superiority of our meta-attention method in various continuous control tasks, which are based on the Off-Policy Actor-Critic methods including DDPG and TD3.
To give or not to give: Examining the prosocial effects of a 360° video endorsing a clean water charity

This study examines the prosocial effects of virtual reality (VR) technology on donation behavior, using a 360° video documentary supporting the nongovernmental organization Charity: Water. Previous research has shown how VR can be used as a way to evoke empathy towards other people. There is, however, limited research on how this technology can be used to elicit more donations for charitable organizations. This paper aims to investigate whether the use of an emotion-inducing 360° video in VR leads to higher donation rates. We conducted a between-participants experiment and collected data from 150 young adults (age 18–25). Results reveal how VR – and especially empathy – significantly impacts donation intentions. Although donation intentions were found to be higher after watching the VR video, many participants were skeptical about making an actual donation. Implications and future research regarding the capabilities of VR technology in eliciting donations and resolving donation uncertainty are discussed.
Class-homophilic-based data augmentation for improving graph neural networks

Data augmentation has been shown to improve graph neural networks (GNNs). Existing graph data augmentation is achieved by adding or removing edges or changing the input node features due to graph data’s complexity and non-Euclidean nature. However, the graphs generated by the above augmentation operations have similar or identical structures as the original graph, which leads to similar or identical structural information learned by GNNs from the original and augmented graphs. Two problems arise from this: restricted information and low applicability. To solve these problems, we propose Class-hOmophilic-based Data Augmentation (CODA), which improves existing GNNs by helping them learn adequate and extra structural class information, which is lacking in the original graph, and promotes GNNs’ application to graphs with a large number of interclass edges. We first pretrain the GNNs and then design a new augmentation method that generates an approximate class-homophilic graph according to the pretrained GNNs. In the end, we design learnable node-level self-attention mechanisms with telescopic coefficients, which result in GNNs integrating the structural information of the two graphs in a more principled way to break the constraint of pretrained GNNs. Extensive experiments on various datasets show that augmentation via CODA improves performance and applicability across GNN architectures. The source code of CODA is publicly available at https://github.com/graphNN/CODA.
DuCape: Dual Quaternion and Capsule Network–Based Temporal Knowledge Graph Embedding

Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a “deep” architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named DuCape , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a k -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.
Domain-Specific Word Embeddings with Structure Prediction

Abstract Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, for example, across time or domain. Current methods do not offer a way to use or predict information on structure between sub-corpora, time or domain and dynamic embeddings can only be compared after post-alignment. We propose novel word embedding methods that provide general word representations for the whole corpus, domain- specific representations for each sub-corpus, sub-corpus structure, and embedding alignment simultaneously. We present an empirical evaluation on New York Times articles and two English Wikipedia datasets with articles on science and philosophy. Our method, called Word2Vec with Structure Prediction (W2VPred), provides better performance than baselines in terms of the general analogy tests, domain-specific analogy tests, and multiple specific word embedding evaluations as well as structure prediction performance when no structure is given a priori. As a use case in the field of Digital Humanities we demonstrate how to raise novel research questions for high literature from the German Text Archive.
Max-SAT-based synthesis of optimal and Nash equilibrium strategies for multi-agent systems

We present techniques for verifying strategic abilities of multi-agent systems via SAT-based and Max-SAT-based bounded model checking. In our approach we focus on systems of agents that pursue goals with regard to the allocation of shared resources. One of the problems to be solved is to determine whether a coalition of agents has a joint strategy that guarantees the achievement of all resource goals, irrespective of how the opposing agents in the system act. Our approach does not only decide whether such a winning strategy exists, but also synthesises the strategy. Winning strategies are particularly useful in the presence of an opposition because they guarantee that each agent of the coalition will achieve its individual goal, no matter how the opposition behaves. However, for the grand coalition consisting of all agents in the system, following a winning strategy may involve an inefficient use of resources. A winning strategy will only ensure that each agent will reach its goal at some time. But in practical resource allocation problems it may be of additional importance that once-off resource goals will be achieved as early as possible or that repetitive goals will be achieved as frequent as possible. We present an extended technique that synthesises strategies that are collectively optimal with regard to such quantitative performance criteria. A collectively optimal strategy allows to optimise the overall system performance but it may favour certain agents over others. In competitive scenarios a Nash equilibrium strategy may be a more adequate solution. It guarantees that no agent can improve its individual performance by unilaterally deviating from the strategy. We developed an algorithm that initially generates a collectively optimal strategy and then iteratively alternates this strategy until the strategy becomes a Nash equilibrium or a cycle of non-equilibrium strategies is detected. Our approach is based on a propositional logic encoding of strategy synthesis problems. We reduce the synthesis of winning strategies to the Boolean satisfiability problem and the synthesis of optimal and Nash equilibrium strategies to the maximum satisfiability problem. Hence, efficient SAT- and Max-SAT solvers can be employed to solve the encoded strategy synthesis problems.
Digitizing and Annotating Ancient Egyptian Coffins: The Book of the Dead in 3D

3D visualizations of heritage objects such as ancient Egyptian coffins can be better used for general and specialistic studies if they also provide annotations. This paper presents the system of annotations developed for the “Book of the Dead in 3D Project,” which applies photogrammetry and digital annotations to coffins and sarcophagi produced in the 1st millennium BCE. The annotated models of the project include the transcription, translation and transliteration of the magical texts inscribed on the coffins, which can be interactively read by the user while navigating the 3D model.