Reviewerly/exHarmony huggingface dataset:
Underwater optical image object detection based on YOLOv7 algorithm

Underwater optical imaging is a highly challenging task owing to the intricate underwater environment. This task is often plagued by issues such as image blur, color distortion, and low contrast, which pose significant obstacles to target detection tasks. Traditional target detection methods depend on manually designed features, which may not accurately characterize underwater targets and can also be impacted by factors such as target occlusion and sediment burial. This paper presents a novel baseline for underwater object detection based on the YOLOv7 algorithm, an end-to-end detection algorithm with excellent performance in terms of detection speed and accuracy. The algorithm was trained and tested on the URPC dataset, and compared with the YOLOv5 series of algorithms. The experimental results demonstrate that YOLOv7 performs better in terms of accuracy, and effectively mitigates the effects of occlusion, image blurring, and color distortion. These findings have implications for target detection tasks of underwater unmanned systems in the future.
Augmented Genetic Algorithm v2 with Reinforcement Learning for PDN Decap Optimization

Genetic algorithms (GAs) use many hyperparameters, and tuning these parameters can determine the optimization performance. A GA with an augmented initial population was proposed for decap optimization but it had convergence issues by getting stuck in the local minimum. This work uses a reinforcement learning (RL) approach to adaptively tune the hyperparameters of GA during its operation. With this approach, the agent tries to change the parameters so that the GA does not get stuck in the local minimum. The proposed method combining the RL agent and Augmented GA showed better performance in terms of solution quality and time cost. Overall, in all the cases tested, the proposed method showed better performance than the Augmented GA without RL.
Complete Coverage and Path Planning for Emergency Response by UAVs in Disaster Areas

Determining paths for a team of Unmanned Aerial Vehicles (UAVs) that pass over a disaster area for reconnaissance and communication delivery for ground users is a subject of our research. It is assumed that the location of disaster victims is unknown because there is no contact with them. However, we have some statistical information about population density levels in subsequent regions of the area. Thus, to maximize the number of localized victims in the first minutes and hours of the rescue operation, we use information about these regions in the UAVs’ path planning and optimization process. We present a heuristic optimization algorithm working with a new model of the disaster area that takes into account population density. We also show the results of path planning simulations for selected regions in Poland.
A Systematic Methodology to Compute the Quantum Vulnerability Factors for Quantum Circuits

Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.
Robust Cross-Domain Pseudo-Labeling and Contrastive Learning for Unsupervised Domain Adaptation NIR-VIS Face Recognition

Near-infrared and visible face recognition (NIR-VIS) is attracting increasing attention because of the need to achieve face recognition in low-light conditions to enable 24-hour secure retrieval. However, annotating identity labels for a large number of heterogeneous face images is time-consuming and expensive, which limits the application of the NIR-VIS face recognition system to larger scale real-world scenarios. In this paper, we attempt to achieve NIR-VIS face recognition in an unsupervised domain adaptation manner. To get rid of the reliance on manual annotations, we propose a novel Robust cross-domain Pseudo-labeling and Contrastive learning (RPC) network which consists of three key components, i.e., NIR cluster-based Pseudo labels Sharing (NPS), Domain-specific cluster Contrastive Learning (DCL) and Inter-domain cluster Contrastive Learning (ICL). Firstly, NPS is presented to generate pseudo labels by exploring robust NIR clusters and sharing reliable label knowledge with VIS domain. Secondly, DCL is designed to learn intra-domain compact yet discriminative representations. Finally, ICL dynamically combines and refines intrinsic identity relationships to guide the instance-level features to learn robust and domain-independent representations. Extensive experiments are conducted to verify an accuracy of over 99% in pseudo label assignment and the advanced performance of RPC network on four mainstream NIR-VIS datasets.
Robust, revocable, forward and backward adaptively secure attribute-based encryption with outsourced decryption1

Attribute based encryption (ABE) is a cryptographic technique allowing fine-grained access control by enabling one-to-many encryption. Existing ABE constructions suffer from at least one of the following limitations. First, single point of failure on security meaning that, once an authority is compromised, an adversary can either easily break the confidentiality of the encrypted data or effortlessly prevent legitimate users from accessing data; second, the lack of user and/or attribute revocation mechanism achieving forward and backward secrecy; third, a heavy computation workload is placed on data user; last but not least, the lack of adaptive security in standard models. In this paper, we propose the first single-point-of-failure free multi-authority ciphertext-policy ABE that simultaneously (1) ensures robustness for both decryption key issuing and access revocation while achieving both backward and forward secrecy; (2) enables outsourced decryption to reduce the decryption overhead for data users that have limited computational resources; and (3) achieves adaptive (full) security in standard models. The provided theoretical complexity comparison as well as the conducted experiments show that our construction introduces linear storage and computation overheads that occurs only once during its setup phase, which we believe to be a reasonable price to pay to achieve all previous features.
Bare-Bones Based Salp Swarm Algorithm for Text Document Clustering

Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from the text clustering domain and six scientific papers datasets extracted from the top eight UAE universities. The experimental results demonstrate that the BBSSA algorithm outperforms traditional SSA and nine other optimization algorithms. Furthermore, the BBSSA algorithm achieves better results than the five traditional clustering techniques.
Smoothed Dirichlet Distribution

Abstract When the cells are ordinal in the multinomial distribution, i.e., when cells have a natural ordering, guaranteeing that the borrowing information among neighboring cells makes sense conceptually. In this paper, we introduce a novel probability distribution for borrowing information among neighboring cells in order to provide reliable estimates for cell probabilities. The proposed smoothed Dirichlet distribution forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet distribution. Basic properties of the proposed distribution, including normalizing constant, moments, and marginal distributions, are developed. Sample generation of smoothed Dirichlet distribution is discussed using the acceptance-rejection algorithm. We demonstrate the performance of the proposed smoothed Dirichlet distribution using 2018 Major League Baseball (MLB) batters data.
Knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

ABSTRACT Machine reading comprehension has been a research focus in natural language processing and intelligence engineering. However, there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain. Moreover, current research lacks the ability to embed accurate background knowledge and provide precise answers. To address these two problems, this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner. Then, it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text. To eliminate knowledge noise that could lead to semantic deviation, this paper uses a mixed mutual attention mechanism among questions, passages, and knowledge triples to select the most relevant triples before embedding their semantics into the sentences. Experiment results indicate that the proposed approach can achieve a 70.70% EM value and an 87.91% F1 score, with a 4.23% and 3.35% improvement over existing methods, respectively.
Electric Field Control of Chiral Magnonic Resonators for Spin-Wave Manipulation

By combining the phenomena of voltage-controlled magnetic anisotropy (VCMA) and chiral resonant scattering of spin waves, we demonstrate through micromagnetic simulations that it is possible, by applying a dc voltage, to achieve tunable attenuation of spin waves propagating in an yttrium iron garnet (YIG) film. The resultant device, dubbed as an electrical chiral magnonic resonator (ECMR), provides a solution for implementing the synapse function in a spin-wave-based neuromorphic computational system, which demands that the weight factor be continuously changeable. The voltage-based mechanism ensures the advantages of minimal energy consumption and physical compactness when compared with other tuning strategies, such as a local Oersted field generated by passing current through electrical leads. On the other hand, by applying a rf voltage, it is possible to utilize the same ECMR device design to achieve parametric amplification of spin waves in the same YIG film. This provides a viable solution to apply the concept of voltage-based parametric amplification to YIG materials. An analytical model is developed to describe the scattering characteristics of the ECMR amplifier, which enable fast computation in the case of magnonic circuitry design involving a large number of amplifier devices.
Quantum evolution speed induced by Hamiltonian

In the issue of quantum evolution, quantum evolution speed is usually quantified by the time rate of change of state distance between the initial sate and its time evolution. In this paper, the path distance of quantum evolution is introduced to study the evolution of a quantum system, through the approach combined with basic theory of quantum evolution and the linear algebra. In a quantum unitary system, the quantum evolution operator contains the path information of the quantum evolution, where the path distance is determined by the principal argument of the eigenvalues of the unitary operator. Accordingly, the instantaneous quantum evolution speed is proportional to the distance between the maximum and minimum eigenvalues of the Hamiltonian. As one of the applications, the path distance and the instantaneous quantum evolution speed could be used to form a new lower bound of the real evolution time, which depends on the evolution operator and Hamiltonian, and is independent of the initial state. It is found that the lower bound presented here is exactly equal to the real evolution time in the range &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \left[ {0, {\pi }/({{2{\omega _{\rm{H}}}}}}) \right]$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231009_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231009_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. The tool of path distance and instantaneous quantum evolution speed introduced here provides new method for the related researches.
Emoji, Speech Acts, and Perceived Communicative Success

Unlike prior research examining how emoji communicate emotions and modify intended meanings, the present research examined whether emoji can perform specific speech acts (remind, etc.), and how well users are able to accurately assess their ability to do so. In four experiments senders were asked to assume that they would send a specific emoji to perform a certain speech act, or to choose which emoji they would use to perform that speech act. Senders and receivers indicated their judgments of communicative success (i.e., that the receiver would recognize the speech act being performed). In two studies, receivers also made judgments regarding the intended meaning of the emoji. Participants judged receivers to be likely to recognize the intended meaning conveyed with an emoji, and there was some evidence of communicative success. However, participants significantly overestimated communicative success, and in all studies, receivers were more optimistic about communicative success than were senders.
Toward Scalable and Controllable AR Experimentation

To understand how well a proposed augmented reality (AR) solution works, existing papers often conducted tailored and isolated evaluations for specific AR tasks, e.g., depth or lighting estimation, and compared them to easy-to-setup baselines, either using datasets or resorting to time-consuming data capturing. Conceptually simple, it can be extremely difficult to evaluate an AR system fairly and in scale to understand its real-world performance. The difficulties arise for three key reasons: lack of control of the physical environment, the time-consuming data capturing, and the difficulties to reproduce baseline results.
Toward Next-generation Volumetric Video Streaming with Neural-based Content Representations

Striking a balance between minimizing bandwidth consumption and maintaining high visual quality stands as the paramount objective in volumetric content delivery. However, achieving this ambitious target is a substantial challenge, especially for mobile devices with constrained computational resources, given the voluminous amount of 3D data to be streamed, strict latency requirements, and high computational load. Inspired by the advantages offered by neural radiance fields (NeRF), we propose, for the first time, to deliver volumetric videos by utilizing neural-based content representations. We delve deep into potential challenges and explore viable solutions for both video-on-demand (VOD) and live video streaming services, in terms of the end-to-end pipeline, real-time and high-quality streaming, rate adaptation, and viewport adaptation. Our preliminary results lend credence to the feasibility of our research proposition, offering a promising starting point for further investigation.
Deep Leaning Based Frequency-Aware Single Image Deraining by Extracting Knowledge from Rain and Background

Due to the requirement of video surveillance, machine learning-based single image deraining has become a research hotspot in recent years. In order to efficiently obtain rain removal images that contain more detailed information, this paper proposed a novel frequency-aware single image deraining network via the separation of rain and background. For the rainy images, most of the background key information belongs to the low-frequency components, while the high-frequency components are mixed by background image details and rain streaks. This paper attempted to decouple background image details from high frequency components under the guidance of the restored low frequency components. Compared with existing approaches, the proposed network has three major contributions. (1) A residual dense network based on Discrete Wavelet Transform (DWT) was proposed to study the rainy image background information. (2) The frequency channel attention module was introduced into the adaptive decoupling of high-frequency image detail signals. (3) A fusion module was introduced that contains the attention mechanism to make full use of the multi receptive fields information using a two-branch structure, using the context information in a large area. The proposed approach was evaluated using several representative datasets. Experimental results shows this proposed approach outperforms other state-of-the-art deraining algorithms.
Bayesian phase difference estimation algorithm for direct calculation of fine structure splitting: accelerated simulation of relativistic and quantum many-body effects

Abstract Despite rapid progress in the development of quantum algorithms in quantum computing as well as numerical simulation methods in classical computing for atomic and molecular applications, no systematic and comprehensive electronic structure study of atomic systems that covers almost all of the elements in the periodic table using a single quantum algorithm has been reported. In this work, we address this gap by implementing the recently-proposed quantum algorithm, the Bayesian phase difference estimation (BPDE) approach, to determine fine structure splittings of a wide range of boron-like atomic systems. Since accurate estimate of fine structure splittings strongly depend on the relativistic as well as quantum many-body effects, our study can test the potential of the BPDE approach to produce results close to the experimental values. Our numerical simulations reveal that the BPDE algorithm, in the Dirac–Coulomb–Breit framework, can predict fine structure splittings of ground states of the considered systems quite precisely. We performed our simulations of relativistic and electron correlation effects on Graphics Processing Unit by utilizing NVIDIA’s cuQuantum, and observe a ×42.7 speedup as compared to the Central Processing Unit-only simulations in an 18-qubit active space.
GAN-Based Data Augmentation and Pseudo-label Refinement for Unsupervised Domain Adaptation Person Re-identification

Person re-identification (re-ID) by using an unsupervised domain adaptation (UDA) approach has drawn considerable attention in contemporary security research. Thus, UDA person re-ID usually employs a model learned from a labeled source domain, adjusted by pseudo-labels, for an unlabeled target domain. However, this method still needs to overcome two main challenges: a significant gap between the source and target domains and the accuracy of pseudo-labels generated by a clustering algorithm. To address these problems, we propose a novel method to improve UDA person re-ID performance by combining GAN-based Data Augmentation and Unsupervised Pseudo-Label Editation methods for training on Target Domain, named DAUET. In particular, we first use a generative adversarial network (GAN) method to bridge the distribution of the source and target domains. Then we propose a supervised learning approach to maximize the benefits of the virtual dataset. Finally, we utilize a pseudo-label refinement to enhance the unsupervised learning process. Extensive experiments on two popular datasets, Market-1501 and DukeMTMC-reID, indicate that our DAUET method can substantially outperform the state-of-the-art performance of the UDA person re-ID.
Infrastructure for remote experimentation in the Trondheim fjord

NTNU’s Applied Underwater Robotics Lab (AU-RLab) has been developing a full-scale subsea testing and validation facility in the Trondheim fjord since 2017. These activities are the result of NTNU’s long-term collaboration with Equinor that is related to the development of a test and validation site for underwater technology, including the autonomous/resident vehicles. Infrastructure has been further developed in the scope of Subsea node of the OceanLab project. This paper describes OceanLab infrastructure, docking station, instrumentation and subsea assets, and modes of remote access to the infrastructure. Different use-cases such as: docking, remote experimentation, subsea residency and testing and validation of Underwater Intervention Drones are briefly elaborated. The paper also presents work that has been done in the OceanLab within the various EU Horizon projects. The aim of developing OceanLab infrastructure is to contribute to the development of new technology for observing the ocean, both for academia and industry; and to be part of the overall measurement system in the Trondheim fjord, allowing better understanding of the interplay in the sea.
Learning of Conversational Systems Based on Linguistic Data Summarization Applications in BIM Environments

In this work, the authors identified opportunities for improvements in conversational systems. In order to solve the conversational systems learning problems, this investigation proposes a new architectural model for the conversational system “BRasa,” consisting of two subsystems. The first, “BRasa_Assistant,” is oriented to direct communication with users, and the second “BRasa_LDS” is oriented to conversational system learning inspired by Linguistic Data Summarization techniques. BRasa_LDS generates summaries in natural language, which incorporate new knowledge into the conversational system database. In addition, is proposed a system of indicators for the self-assessment of the human–computer interaction of the conversational system. In the analysis results section, three sets of tests were designed to measure the quality of conversational system responses. The proposal is validated based on the criteria applicability and adequacy of the conversational system responses. It is shown that the application of linguistic data summarization techniques for learning conversational systems improves the behavior of these systems significantly.