Reviewerly/exHarmony huggingface dataset:
Optimized anchor-free network for dense rotating object detection in remote sensing images

Extracting dense rotating objects accurately from remote sensing images is an emerging task in object detection. To increase the applicability of existing algorithms in the above tasks, an optimized anchor-free network optimized by a dual attention mechanism (DAM) and gate multiscale feature fusion (GMFF) is designed. The DAM module is composed of two attention mechanisms with different functions. This part can enhance the backbone network’s ability to extract and model information at different levels and reduce the accuracy loss caused by object density changes in the image. The GMFF module uses the gating structure to realize adaptive transmission and integration of multiscale information. Through this module, the useless information in features will be filtered, and the key information will be retained. Several experiments are designed to verify the feasibility of the algorithm. Compared with the baseline model, adding DAM and GMFF to the dense rotating object extraction task in remote sensing images improves the model accuracy by 3.5% and 2.1%, respectively, while adding two modules simultaneously, and the accuracy increases from 79.1% to 84.3%. In conventional object extraction tasks, such as dataset for object detection in aerial images and HRSC2016, our method has the highest accuracy compared to other similar algorithms, with 76.5% and 90.3%, respectively.
Optimal smooth polynomial lane change generation for autonomous racing

Online path re-planning is a crucial task for the control of autonomous vehicles, especially when driving at the handling limits in a dynamic environment. In this article, we propose a fast path planning algorithm meant for trajectory change scenarios, that is tailored to generate smooth reference paths. The proposed solution is based on a convex combination of the current and target paths, and guarantees heading and curvature continuity. Additionally, we complete the algorithm with a real-time compliant nonlinear program to generate nearly-time-optimal maneuvers. The proposed approach is tested on a driving simulator and compared against state-of-the-art solutions, which typically rely on numerical differentiation of path coordinates, often introducing inaccuracy and yielding inefficient or imprecise solutions.
Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition

This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors < 1µrad in a timely manner.
Using &lt;em&gt;OpenBIS&lt;/em&gt;&amp;nbsp;as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the &lt;em&gt;CRC 1411 Design of Particulate Products&lt;/em&gt;

The digital transformation and consequent use of new digital technologies not only have a substantial impact on society and companies, but also on science. Analog documentation as we have known it for centuries will eventually be replaced by intelligent and FAIR (Findable, Accessible, Interoperable, and Reusable) systems. In addition to the actual research data and results, metadata now plays an important role not only for individual, independently existing projects, but for future scientific use and interdisciplinary research groups and disciplines as well. The solution presented here, consisting of an electronic laboratory notebook and laboratory information management system (ELN-LIMS) based on the openBIS (open Biology Information System) environment, offers interesting features and advantages, especially for interdisciplinary work. The Collaborative Research Centre (CRC) 1411 ‘Design of Particulate Products’ of the German Research Foundation is characterized by the cooperation of different working groups of synthesis, characterization, and simulation, and therefore serves as a model environment to present the implementation of openBIS. OpenBIS, as an open source ELN-LIMS solution following FAIR principles, provides a common set of general entries with the possibility of sharing and linking (meta-)data to improve the scientific exchange between all users.
Experiences and perceptions of cybercrime victims in Ghana: The Perspective of digital consumers of agricultural produce

This qualitative study explores the perspectives and experiences of digital consumers who have been victims of agricultural produce-related digital crimes. This study aims to comprehend how victims experience and deal with the impact of cybercrime, as well as their perceptions of the effectiveness of the legal and criminal justice system in addressing cybercrime, through in-depth interviews with 26 digital consumers of agricultural produce associated with cybercrime from Labone, East Legon, Dzorwulu, Roman Ridge and Cantoments in the Greater Accra Region, Ghana. Thematic analysis was used to analyze the data. Data were processed in NVivo. Based on the study findings, it was concluded that cybercrime victims endured a variety of emotional, psychological, and financial repercussions. Cybercrime has a profound emotional impact on victims with regard to feeling angry, frustrated, or violated. Victims also suffer significant financial losses, which puts them in anguish and financial difficulty. Furthermore, the study concluded that victims of cybercrime have conflicting views on how well civil and criminal justice systems work to combat it. Although some victims think that the judicial system works well for them, others believe it is slow and ineffective, which makes them feel frustrated and helpless. The study recommends the need for more effective support and assistance for victims of cybercrime with respect to digital consumers of agricultural produce. This study underscores the need for greater awareness of cybercrime, as well as more effective and efficient mechanisms for reporting, investigating, and prosecuting cybercrime.
Coherent Quantum LQG Controllers with Luenberger Dynamics

This paper is concerned with the coherent quantum linear-quadratic-Gaussian control problem of minimising an infinite-horizon mean square cost for a measurement-free field-mediated interconnection of a quantum plant with a stabilising quantum controller. The plant and the controller are multimode open quantum harmonic oscillators, governed by linear quantum stochastic differential equations and coupled to each other and the external multichannel bosonic fields in the vacuum state. We discuss an interplay between the quantum physical realizability conditions and the Luenberger structure associated with the classical separation principle. This leads to a quadratic constraint on the controller gain matrices, which is formulated in the framework of a swapping transformation for the conjugate positions and momenta in the canonical representation of the controller variables. For the class of coherent quantum controllers with the Luenberger dynamics, we obtain first-order necessary conditions of optimality in the form of algebraic equations, involving a matrix-valued Lagrange multiplier.
Analysis of non-Markovian passive quantum linear systems’ response to single-photon input fields*

In this paper, we analyze the response of passive quantum linear systems in a non-Markovian environment to single photon input fields. Based on an augmented modelling method for non-Markovian quantum systems, analytic forms of output intensity and output covariance function in steady-state are derived. These results allow us to quantitatively analyze the influence of non-Markovian environment on system response under a framework of linear system theory.
Development of a novel visual servoing probe test method for fault diagnosis of printed circuit boards

Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measurement tasks using measuring instruments (e.g., oscilloscopes, multimeters) in order to uncover the condition of a particular product. However, these tasks are often repetitive and meticulous, and additionally, the results need to be collected and carefully documented so that the gathered experience regarding the product can be re-used when the next product of the same type arrives into the shop. Nevertheless, the diagnosis of used PCBs is less researched and current flexible automation possibilities are limited. In this paper, a novel visual servoing probe test method and measurement tool are proposed to provide a flexible solution for PCB diagnosis with a higher level of automation. The aim of the approach is to reduce the burden on the operators by carrying out the repetitive measurement tasks and automatically storing the results while leaving the responsibility of measurement profile setup to the human expert. The proposed visual servo system uses manually teached-in measurement points, where template patterns are recorded using cameras, and it is capable of compensating positioning errors in the range of a couple of millimeters. The proof of concept of the proposed method is presented through motherboard measuring experiments, with a 99.7% success rate.
Artificial Intelligence and the World Wide Web: Brain and friend?

This paper looks at artificial intelligence (AI) and society and posits the idea of AI on the World Wide Web as a brain comparing it to the human brain. It frames the WWW as 'friend' to all and follows this disturbing train of thought. It speaks to tacit knowledge and tools and compares the work, skill knowledge of the librarians/curators to that of the artisan crafts person. It positions the work of the INSYTE-Cooley Research lab at the heart of this situation. Furthermore, it speaks of an 'altered reality' where a dangerous disconnection is occurring, where people are becoming observers of reality because of the abstraction that the screen provides. This spectator view is desensitizing people to what is happening in front of them and will be further fueled by the creation of the 'metaverse' in the future. It speaks of the danger of AI going from judgement to calculation without employing human reason and of the danger of humans becoming a passive audience. In this techno centric world it speaks of the amplified importance of tacit knowledge and the development of Human Centered Systems (HCS). It echoes Michael Cooleys thoughts on technology and the place of humanity within a techno-centric world. It speaks of the ethical and moral obligations to society and the danger of disregarding them.
Finitely Repeated Adversarial Quantum Hypothesis Testing

Quantum technologies have been considered as a method to enhance cyber-security due to the unique properties of quantum systems. However, quantum systems themselves could also suffer from strategic attacks. In this paper, we formulate the relationship between the strategic attacker and a passive quantum detector into a Stackelberg game. By deriving the optimal detection rule at equilibrium, we study the detection performance of the detector under the setting of finite sample size of corrupted observations. Under mild assumptions, we show that the miss. We illustrate the general decaying results of the miss rate numerically, depicting that the passive detector manages to achieve a miss rate and a false alarm rate both exponentially decaying to zero given infinitely many quantum states, although at a much slower rate than a quantum non-adversarial counterpart. Finally, we adopt our formulations upon a case study of detection with quantum radars.
Multi-Dimensional Fusion Deep Learning for Side Channel Analysis

The rapid advancement of deep learning has significantly heightened the threats posed by Side-Channel Attacks (SCAs) to information security, transforming their effectiveness to a degree several orders of magnitude superior to conventional signal processing techniques. However, the majority of existing Deep-Learning Side-Channel Attacks (DLSCAs) primarily focus on the classification accuracy of the trained model at the attack stage, often assuming that adversaries have unlimited computational and time resources during the profiling stage. This might result in an inflated assessment of the trained model’s fitting capability in a real attack scenario. In this paper, we present a novel DLSCA model, called a Multi-Dimensional Fusion Convolutional Residual Dendrite (MD_CResDD) network, to enhance and speed up the feature extraction process by incorporating a multi-scale feature fusion mechanism. By testing the proposed model on two software implementations of AES-128, we show that it is feasible to improve the profiling speed by at least 34% compared to other existing deep-learning models for DLSCAs and meanwhile achieved a certain level of improvement (8.4% and 0.8% for two implementations) in the attack accuracy. Furthermore, we also investigate how different fusion approaches, fusion times, and residual blocks can affect the attack efficiency on the same two datasets.
Probabilistic Bounds with Quadratic-Exponential Moments for Quantum Stochastic Systems

This paper is concerned with quadratic-exponential moments (QEMs) for dynamic variables of quantum stochastic systems with position-momentum type canonical commutation relations. The QEMs play an important role for statistical “localisation” of the quantum dynamics in the form of upper bounds on the tail probability distribution for a positive definite quadratic function of the system variables. We employ a randomised representation of the QEMs in terms of the moment-generating function (MGF) of the system variables, which is averaged over its parameters using an auxiliary classical Gaussian random vector. This representation is combined with a family of weighted L2 -norms of the MGF, leading to upper bounds for the QEMs of the system variables. These bounds are demonstrated for open quantum harmonic oscillators with vacuum input fields and non-Gaussian initial states.
Multi-arm motion-generating method based on each robot movement vector in time slice

This paper proposes a multi-arm-motion-generation method. Arm robots are expected to substitute workers, especially since multi-arm flexibly is adaptable to diverse product models. However, due to the complexity of the multi-arm motion, many hours of manual motion teaching are required. Therefore, we developed a multi-arm motion-generating method for automatically generating collision-free multi-arm motions with an equal or faster time than manually teaching motion. Our proposed method is a collision-circumvent and stop-avoidance method that takes into account each robot movement vector in time slices. Experiments were conducted to evaluate the proposed method, and the results indicate that the method can generate a short circumvent avoidance trajectory compared with a conventional collision-avoidance method for avoiding all passing areas of robots.
Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.
Simple energy-efficient path planning based on graph algorithms for articulated robotic-manipulators

The number of robots in the world has been steadily growing for the last decades. With Industry 4.0 approaching, robots are likely to replace many people in factories and plants. Billions of robotic systems require huge amount of energy. Under these conditions, efficient energy consumption is an economic and environmental necessity. In this article, we propose a simple approach to reduce energy consumption for articulated robotic manipulators most commonly used in the industry. The article analyzes the existing approaches to optimize the energy costs of robot motion planning and proposes a new optimization method for graph-based path planning algorithms.
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts

Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles. The main challenge associated with self-learned agents in the form of deep neural networks, is their black-box nature: it is impossible for humans to interpret deep neural networks. Therefore, humans cannot directly interpret the actions of deep neural network based agents, or foresee their robustness in different scenarios. In this work, we demonstrate a method for probing which concepts self-learning agents internalise in the course of their training. For demonstration, we use a chess playing agent in a fast and light environment developed specifically to be suitable for research groups without access to enormous computational resources or machine learning models. We provide code for the environment and for self-play learning chess agents, as well as for the state-of-the-art explainable AI (XAI) method of concept detection. We present results for different chess board sizes, discussing the concepts learned by our chess agents in light of human domain knowledge.
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.
MSCAReg‐Net: Multi‐scale complexity‐aware convolutional neural network for deformable image registration

Abstract Deep learning‐based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi‐scale complexity‐aware registration network (MSCAReg‐Net) was proposed by devising a complexity‐aware technique to facilitate DLIR under a single‐resolution framework. Specifically, the complexity‐aware technique devised a multi‐scale complexity‐aware module (MSCA‐Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC‐Module) and a feature aggregation module (FA‐Module) to facilitate the MSCA‐Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg‐Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non‐positive Jacobian determinant (), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi‐atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, , and the downstream application on MAHS demonstrated the advances of this method.
Identification of Plant Disease Based on Multi-Task Continual Learning

Deep learning excels in the identification of specific plant diseases. However, dealing with multi-domain datasets, which encompass a variety of categories, presents challenges due to limited data availability. (1) Background: In real-world scenarios, data distribution is uneven, the scale continues to grow, new categories emerge, and a phenomenon known as ‘catastrophic forgetting’ occurs. Models rely on a substantial amount of data for labeling and training. (2) Methods: We introduce a two-stage approach. The first stage is the scalable feature learning phase, where the previous feature representation is fixed. Through a new feature extractor, incoming and stored data are trained to expand features. In the second stage, by introducing an auxiliary loss to determine whether key parameters are retained, we reduce the instability of weight parameters. This maintains the separability of old features and encourages the model to learn new concepts, diversity, and discriminative features. (3) Results: Our findings indicate that when the data landscape shifts, recognition accuracy in multi-task continual learning, leveraging the simultaneous availability of datasets, significantly outperforms single convolutional networks and multi-task learning models. (4) Conclusions: Our method advances continual learning towards practical applications. It is particularly effective in mitigating catastrophic forgetting in multi-domain datasets and enhancing the robustness of deep-learning models.
Fair patient model: Mitigating bias in the patient representation learned from the electronic health records

To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models.We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks.FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines.FPM is a novel method to pre-train fair and unbiased patient representations from the EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where fairness is important.