Reviewerly/exHarmony huggingface dataset:
Variance minimisation on a quantum computer of the Lipkin-Meshkov-Glick model with three particles

Quantum computing opens up new possibilities for the simulation of many-body nuclear systems. As the number of particles in a many-body system increases, the size of the space if the associated Hamiltonian increases exponentially. This presents a challenge when performing calculations on large systems when using classical computing methods. By using a quantum computer, one may be able to overcome this difficulty thanks to the exponential way the Hilbert space of a quantum computer grows with the number of quantum bits (qubits). Our aim is to develop quantum computing algorithms which can reproduce and predict nuclear structure such as level schemes and level densities. As a sample Hamiltonian, we use the Lipkin-Meshkov-Glick model. We use an efficient encoding of the Hamiltonian onto many-qubit systems, and have developed an algorithm allowing the full excitation spectrum of a nucleus to be determined with a variational algorithm capable of implementation on today’s quantum computers with a limited number of qubits. Our algorithm uses the variance of the Hamiltonian,⟨ H ⟩ 2 − ⟨ H ⟩ 2 , as a cost function for the widely-used variational quantum eigensolver (VQE). In this work we present a variance based method of finding the excited state spectrum of a small nuclear system using a quantum computer, using a reduced-qubit encoding method.
A New Method for Multi-UAV Cooperative Mission Planning Under Fault

Aiming at the many-to-one mission planning problem in the case of UAV fault, a self-organizing solving method is designed. This method organically combines the situation assessment of single UAV with the collaborative optimization of multiple UAV. On the one hand, the situation assessment of single UAV is carried out based on Bayesian network, and the striking probability of each UAV is obtained. On the other hand, in order to solve the problem of multiple UAV cooperation, the improved discrete particle swarm optimization based mixed strategy (MSDPSO) is proposed. The algorithm has been improved in the following four aspects. Firstly, Sobol sequence is used to initialize the population to improve the coverage of solution space. Then, a nonlinear time-varying strategy is proposed to accelerate the convergence of the algorithm. Cauchy operator is also introduced to enhance the search space of discrete particle swarm optimization. At the same time, an adaptive cross learning strategy is proposed to enrich the diversity of the population, thereby improving the global optimization ability of the algorithm. In addition, the cubic spline interpolation is used to plan trajectory of UAV. Finally, improved discrete particle swarm optimization is used in three-dimensional space for simulation and comparison with both healthy and faulty UAV involved. Results show that the designed algorithm has significant improvement on solution optimality and convergence rate, which provides a theoretical basis for the application of multiple UAV collaborative task planning.
Portrait Reification with Generative Diffusion Models

An application of Generative Diffusion Techniques for the reification of human portraits in artistic paintings is presented. By reification we intend the transformation of the painter’s figurative abstraction into a real human face. The application exploits a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), inverting the generative process and mapping the visible image into its latent representation. In this way, we can first embed the portrait into the latent space, and then use the reverse diffusion model, trained to generate real human faces, to produce the most likely real approximation of the portrait. The actual deployment of the application involves several additional techniques, mostly aimed to automatically identify, align, and crop the relevant portion of the face, and to postprocess the generated reification in order to enhance its quality and to allow a smooth reinsertion in the original painting.
An End-to-End Deep Learning Framework for Wideband Signal Recognition

Successful management of the radio spectrum requires, as a first step, detailed information about spectrum occupancy. In this work, we present an end-to-end deep learning (DL) based framework to obtain information from wide spectrum bands through signal detection, localization, and modulation classification. By visually representing the radio signals in spectrograms, we formulate the wideband detection problem as an object detection task from the computer vision field. To this end, the proposed framework consists of two cascaded modules: an object detection network repurposed to detect and classify distinctive signals in wideband spectrograms, and a convolutional neural network (CNN) designed to extend the classification capabilities to support a wide range of analog and digital modulation schemes. To evaluate our framework, we use a public wideband recognition dataset, which we carefully analyze and curate through a series of preprocessing techniques. To tackle the challenges of insufficient training data and class imbalance observed in the dataset, we suggest a training strategy that includes data mixing and transfer learning. Our experimental results on a general test set demonstrate that the proposed approach can detect and classify a variety of narrowband signals with simultaneously high precision (77.1%), recall (81.8%), and localization accuracy, as indicated by an average Intersection over Union (IoU) of 86%.
Kettle logic in abstract argumentation

Abstract Kettle logic is a colloquial term that describes an agent’s advancement of inconsistent arguments in order to defeat a particular claim. Intuitively, a consistent subset of the advanced arguments should exist that is at least as successful at refuting the claim as the advancement of the set of inconsistent arguments. In this paper, we formalize this intuition and provide a formal analysis of kettle logic in abstract argumentation, a fundamental approach to computational argumentation, showing that all of the analysed abstract argumentation semantics (inference functions)—with the exception of naive semantics, which is considered a mere simplistic helper for the construction of other semantics—suffer from kettle logic. We also provide an approach to mitigating kettle logic under some circumstances. The key findings presented in this paper highlight that agents that apply the inference functions of abstract argumentation, are—similarly to humans—receptive to persuasion by agents who deliberately advance inconsistent and intuitively ‘illogical’ claims. As abstract argumentation can be considered one of the most basic models of computational argumentation, this raises the question to what extent and under what circumstances kettle logic-free argumentation can and should be enforced by computational means.
Robots in Tourism and Hospitality: The Perception of Future Professionals

This research is aimed at analyzing the perception of future tourism professionals about the presence of robots in the hospitality industry. It was based on data obtained from undergraduate and master students in tourism and hospitality, through a questionnaire that sought to assess their perceptions regarding hospitality, novelty, culture, robot performance management in the tourism industry, and their willingness to work in a partially robotized environment in the future. The analysis of the perceptions of future tourism and hospitality professionals was performed based on a structural equation model and revealed that perceptions of novelty and performance management have a significantly positive impact on the willingness of participants to work in a partially robot environment in the future. When the intention to use robots in the future is included in the model, this positive effect decreases. The current existing research on the application of artificial intelligence in hospitality and tourism has, mainly, focused on the customer or the hotelier, making it important to understand how generations of future employees in hospitality and tourism view the use of this type of technology, anticipating any future less favorable situations.
Learning to Propose and Refine for Accurate and Robust Tracking via an Alignment Convolution

Precise and robust feature extraction plays a key role in high-performance tracking to analyse the videos from drones, surveillance and automatic driving, etc. However, most existing Siamese network-based trackers mainly focus on constructing complicated network models and refinement strategies, while using comparatively simple and heuristic conventional or deformable convolutions to extract features from the sampling positions that may be far away from a target region. Consequently, the coarsely extracted features may introduce background noise and degrade the tracking performance. To address this issue, we present a propose-and-refine tracker (PRTracker) that combines anchor-free style proposals at the coarse level, and alignment convolution-driven refinement at the fine level. Specifically, at the coarse level, we design an anchor-free model to effectively generate proposals that provide more reliable interested regions for further verifying. At the fine level, an alignment convolution-based refinement strategy is adopted to improve the convolutional sampling positions of the proposals, thus making the classification and regression of them more accurate. Through using alignment convolution, the convolution sampling positions of the proposals can be efficiently and effectively re-localized, thus improving the accuracy of the extracted features. Finally, a simple yet robust target mask is designed to make full use of the initial state of a target to further improve the tracking performance. The proposed PRTracker achieves a competitive performance against six tracking benchmarks (i.e., UAV123, VOT2018, VOT2019, OTB100, NfS and LaSOT) at 75 FPS.
Incrementally Mining Column Constant Biclusters with FVSFP Tree

Bicluster mining has been frequently studied in the data mining field. Because column constant biclusters (CCB) can be transformed to be discriminative rules, they have been widely applied in various fields. However, no research on incrementally mining CCB has been reported in the literature. In real situations, due to the limitation of computation resources (such as memory), it is impossible to mine biclusters from very large datasets. Therefore, in this study, we propose an incremental mining CCB method. CCB can be deemed as a special case of frequent pattern (FP). Currently the most frequently used method for incrementally mining frequent patterns is FP tree based method. In this study, we innovatively propose an incremental mining CCB method with modified FP tree data structure. The technical contributions lie in two aspects. The first aspect is that we propose a modified FP tree data structure, namely Feature Value Sorting Frequent Pattern (FVSFP) tree that can be easily maintained. The second aspect is that we innovatively design a method for mining CCB from FVSFP tree. To verify the performance of the proposed method, it is tested on several datasets. Experimental results demonstrated that the proposed method has good performance for incrementally handling a newly added dataset.
Feature-based Systematic Analysis of Advanced Persistent Threats

Advanced Persistent Threats (APT) and Targeted Attacks (TA) targeting high-value organizations continue to become more common. These slow (sometimes carried on over the years), fragmented, distributed, seemingly unrelated, very sophisticated, highly adaptable, and, above all, stealthy attacks have existed since the large-scale popularization of computing in the 1990s and have intensified during the 2000s. The aim of attackers has expanded from espionage to attaining financial gain, creating disruption, and hacktivism. These activities have a negative impact on the targets, many times costing significant amounts of money and destabilizing organizations and governments. The resounding goal of this research is to analyze previous academic and industrial research of 72 major APT attacks between 2008 and 2018, using 12 features, and propose a categorization based on the targeted platform, the time elapsed to discovery, targets, type, purpose, propagation methods, and derivative attacks. This categorization provides a view of the effort of the attackers. It aims to help focus the design of intelligent detection systems on increasing the percentage of discovered and stopped attacks.
NFSP-PLT: Solving Games with a Weighted NFSP-PER-Based Method

Nash equilibrium strategy is a typical goal when solving two-player imperfect-information games (IIGs). Neural fictitious self-play (NFSP) is a popular method to find the Nash equilibrium in IIGs, which is the first end-to-end method used to compute the Nash equilibrium strategy. However, the training of NFSP requires a large number of sample data and the interactive cost of obtaining such data is often very high. Realizing the efficient training of network under limited samples is an urgent problem. In this paper, we first proposed a new NFSP-based method, NFSP with prioritized experience replay (NFSP-PER), to improve the sample training efficiency. Then, a weighted NFSP-PER with learning time (NFSP-PLT) was proposed to control the utilization degree of priority-weighted samples. Furthermore, based on the NFSP-PLT, an adaptive upper-confidence-bound applied to tree (UCT) is used to solve the optimal response strategy, which makes the solving strategy more accurate. Extensive experimental results show that the proposed NFSP-PLT effectively improves the sample learning efficiency compared with the existing works.
Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.
Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory

Abstract Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben’s Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.
Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm

Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).
Surrogate explanations for role discovery on graphs

Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capable of recognising complex graph structures when reducing nodes to dense vector representations. However, when working with large, real-world networks, it is difficult to interpret or validate a set of roles identified according to these methods. In this work, motivated by advancements in the field of explainable artificial intelligence, we propose surrogate explanation for role discovery, a new framework for interpreting role assignments on large graphs using small subgraph structures known as graphlets. We demonstrate our framework on a small synthetic graph with prescribed structure, before applying them to a larger real-world network. In the second case, a large, multidisciplinary citation network, we successfully identify a number of important citation patterns or structures which reflect interdisciplinary research.
An Eye Movement Based Patient Assistance System

Eye tracking is a developing topic of study that has made significant contributions to the medical world. Main aim of this work is to make use of eye tracking and contribute to the advancement of society. Eye tracking is a way of watching where eyeballs look on the screen using cameras. IR-based tracking is widely used, however it is neither cost effective or accessible to everyone. As a result, a webcam-based patient aid system that's open source and can operate on any web browser was developed. The primary goal of this research is to aid bedridden patients and others in their time of need when the caregiver is not with the patient, when they are taking rest. This application allows them to be mindful of what the patient wants at times like this. The caregiver or the person who installs the system must first calibrate the device before it can follow the gaze on screen with more precision. This system provides four fundamentals necessities as an option: water, food, restrooms, and emergency/help. The patient's eye will be tracked using either an inbuilt or external webcam attached to the laptop; based on where the patient is looking, the system will make detection and alert regarding the same after analysing the user's/patient's gaze to inform the caretaker or individuals around the patients.
Application of the Program-Targets Method in the Formation of the Advanced Development Strategy of the Modern Library

The coming era of digital transformation, from the point of view of the consumer of information services, has a dual character. On the one hand, there is an excess of information, and on the other hand, its absence in the form ready for adequate perception by the vast majority of citizens. The ongoing «information overload» causes the effect of reducing the motivation to use information, which entails the closure of libraries and the reduction of the library stock. Integrating modern technologies into library activities, providing the relationship between the physical and virtual worlds, will bring the library to a completely new level of relationships with readers and significantly increase the efficiency of information use. The article deals with the issues of innovative activities of the library, aimed at developing and implementing the provisions of the digital development strategy, which provides for digital transformation, which implies a significant increase in efficiency through the introduction of new user service technologies. It is substantiated that the methodology of Academician V. L.Kvint, according to which strategy is defined as a system of search, formulation and development of doctrine, which ensures long-term success with its consistent and complete implementation; in accordance with the stated principles of strategizing, the scheme for developing a strategy for the advanced development of a modern library is formalized and described. It is shown that the methodology of program-target management makes it possible to synchronize the process of strategizing with the processes of «substantiating goals in terms of their achievability in time and consistency with resource capabilities», such synchronization is provided by the logical scheme of the method: «goals — ways to achieve — means», in accordance with which the goals set for the digital development of the object of strategizing are implemented by a set of organizational and technical measures provided with the resources required for this.