Reviewerly/exHarmony huggingface dataset:
GCGE-YOLO: Improved YOLOv5s Algorithm for Object Detection in UAV Images

Current general-purpose object detection algorithms cannot fully extract the features of small objects in UAV aerial images, and they usually suffer from problems such as large size, complex structure and large computation, making it difficult to deploy real-time object detection algorithms with high precision in UAV platforms. In response to the above problems, this paper intends to optimize the YOLOv5s algorithm and proposes a GCGE-YOLO algorithm that is lightweight and offers improvements in precision over the baseline. Firstly, the GhostNet is introduced to compress the Backbone of YOLOv5s to reduce the complexity and computation of the network model; secondly, the coordinate attention (CA) is introduced in the Backbone to make the model pay more attention to the main information to improve the precision; thirdly, GSConv combined with GhostNet is used as the Neck to fuse the detail information in the shallow feature map and enhance the feature extraction ability for small objects; finally, EIoU is used as the loss function of the algorithm to improve the object border localization precision while enhancing the speed of bounding box regression. In this paper, experimental validation is conducted on the public dataset VisDrone2021 and the algorithms are deployed on the embedded platform NVIDIA Jetson Xavier NX. The experimental results show that compared with the YOLOv5s algorithm, the GCGE-YOLO algorithm improves the FPS by 41.4%, compresses the model parameters by 39.4%, reduces the model size by 37.5%, reduces the computation by 31.7%, and improves the mAP by 1.5%. In all, the GCGE-YOLO algorithm achieves good improvements in all aspects and can meet the requirements of real-time object detection for UAV platforms.
Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking

AbstractThis paper proposes a new boosting machine based on forward stagewise additive modeling with cost-complexity pruned trees. In the Tweedie case, it deals directly with observed responses, not gradients of the loss function. Trees included in the score progressively reduce to the root-node one, in an adaptive way. The proposed Adaptive Boosting Tree (ABT) machine thus automatically stops at that time, avoiding to resort to the time-consuming cross validation approach. Case studies performed on motor third-party liability insurance claim data demonstrate the performances of the proposed ABT machine for ratemaking, in comparison with regular gradient boosting trees.Keywords: Risk classificationboostinggradient boostingregression treescost-complexity pruning Disclosure statementNo potential conflict of interest was reported by the author(s).
Proposal for a Distraction Technique Using Two-Screen Projection for Stress Relief in Children With Medical Complexity

The aim of this paper is to investigate the potential of virtual reality images as a means of reducing pain and fear in children, through the use of projection technology. While body-worn immersive displays have been previously suggested for this purpose, head-mounted displays have been found to be unsuitable due to their lack of interactivity. To address this issue, we propose a simple and cost-effective two-screen projection system that can be installed in a hospital room without interfering with communication and without compromising the immersive experience. By projecting onto screens installed on the wall and floor, an immersive digital space can be created visually. Our experiments, in which we measured α-amylase activity in saliva, showed that the proposed two-screen projection significantly reduced stress levels in healthy adults when compared to one-screen projection. Additionally, we conducted an experiment using the KOKORO scale as a subjective measure, which revealed that children with medical complexity experienced a significant reduction in stress levels after viewing the two-screen projection compared to before viewing. These findings suggest that the proposed system could serve as an ideal distraction in pediatric nursing, thereby providing much-needed nursing support to children and their families.
Pairs-Trading System Using Quantum-Inspired Combinatorial Optimization Accelerator for Optimal Path Search in Market Graphs

Pairs-trading is a trading strategy that involves matching a long position with a short position in two stocks aiming at market-neutral profits.While a typical pairstrading system monitors the prices of two statistically correlated stocks for detecting a temporary divergence, monitoring and analyzing the prices of more stocks would potentially lead to finding more trading opportunities.Here we report a stock pairs-trading system that finds trading opportunities for any two stocks in an Nstock universe using a combinatorial optimization accelerator based on a quantum-inspired algorithm called simulated bifurcation.The trading opportunities are detected through solving an optimal path search problem in an N -node directed graph with edge weights corresponding to the products of instantaneous price differences and statistical correlation factors between two stocks.The accelerator is one of Ising machines and operates consecutively to find multiple opportunities in a market situation with avoiding duplicate detections by a tabu search technique.It has been demonstrated in the Tokyo Stock Exchange that the FPGA (field-programmable gate array)-based trading system has a sufficiently low latency (33 µs for N =15 or 210 pairs) to execute the pairs-trading strategy based on optimal path search in market graphs.
Document-Level Relation Extraction With Context Guided Mention Integration and Inter-Pair Reasoning

Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond the sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation. Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between the entity pairs. In this paper, we propose two novel techniques, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontext <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> uided <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ention <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntegration and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nter-pair <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> easoning (CGM2IR), to improve DRE. Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner. Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations. We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous strong baselines and achieves remarkable effectiveness.
Research on Defect Detection Method of Railway Transmission Line Insulators Based on GC-YOLO

The insulator defect targets in UAV (Unmanned Aerial Vehicle) images are often small and set against complex backgrounds. Consequently, traditional object detection algorithms commonly struggle to identify these minor defects. To enhance precision and recall in detecting insulator defects, a novel model, GC-YOLO (ghost convolution and centralized feature pyramid -You Only Look Once), based on YOLOv5s, has been introduced. GC-YOLO incorporates the Ghost convolution module in the backbone network, reducing feature redundancy and improving the inference speed of the feature extraction network. Moreover, an attention mechanism based on Coordinate Attention (CA) is integrated at the terminal of the backbone network, aimed at emphasizing the extraction of crucial information from target features. The Explicit Visual Center Block (EVCBlock) module from Centralized Feature Pyramid Network (CFPNet) is introduced in the neck layer to effectively fuse multi-scale features and enhance the feature map’s characterization capability. Furthermore, in order to enhance the precision in detecting small-sized defects, a small object detection head is also added to the detection layer based on CFPNet. Experimental results demonstrate that GC-YOLO achieves a recall of 89.7% and mAP@0.5 of 94.2%, surpassing YOLOv5s by 7% and 6.5%, respectively. The proposed algorithm exhibits superior detection precision in complex scenes, providing a theoretical basis for intelligent and mechanized railway monitoring systems.
Boosting Short Text Classification by Solving the OOV Problem

In the field of natural language processing, text classification has received a lot of attention. Compared with long texts, short texts have fewer words and lack contextual semantic information. Existing approaches enrich short text information by linking the external knowledge graph, but they ignore the out-of-vocabulary (OOV) problem during entity linking, especially when dealing with domain-oriented data, which has some rare words or domain-specific nouns. In this paper, to alleviate the OOV problem caused by linking the external knowledge graph(KG), we propose a domain knowledge graph and entity complementation strategy to improve the performance of short text classification. Specifically, the external knowledge graph is used to enrich the information of short texts. The self-build domain knowledge graph is used to solve the problem of entities failing to link to the external knowledge graph. Finally, we conduct experiments on various datasets: 1. a labeled Chinese electronic domain dataset; 2. an open-source dataset to test the performance of our algorithm in different data distribution scenarios. The results demonstrate our dual knowledge graph model outperforms the state-of-the-art short text classification methods, especially when the OOV problem is severe.
Efficient Personal-Health-Records Sharing in Internet of Medical Things Using Searchable Symmetric Encryption, Blockchain, and IPFS

Secure storage and sharing of Personal Health Records (PHRs) in Internet of Medical Things (IoMT) is one of the significant challenges in the healthcare ecosystem.Due to the high value of personal health information, PHRs are one of the favourite targets of cyber attackers worldwide.Over the years, many solutions have been proposed; however, most solutions are inefficient for practical applications.For instance, several existing schemes rely on the bilinear pairings, which incur high computational costs.To mitigate these issues, we propose a novel PHR-sharing scheme that is dynamic, efficient, and practical.Specifically, we combine searchable symmetric encryption, blockchain technology and a decentralized storage system, known as Inter-Planetary File System (IPFS) to guarantee confidentiality of PHRs, verifiability of search results, and forward security.Moreover, we provide formal security proofs for the proposed scheme.Finally, we have conducted extensive test-bed experiments and the results demonstrate that the proposed scheme can be used in practical scenarios related to IoMT environment.
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference

Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> patial- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> emporal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ontrasting for Fine-Grained Urban <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial-temporally similar</i> flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.
Modality Exploration, Retrieval and Adaptation for Trajectory Prediction

Predicting future trajectories of dynamic agents is inherently riddled with uncertainty. Given a certain historical observation, there are multiple plausible future movements people can perform. Notably, these possible movements are usually centralized around a few representative motion patterns, e.g. acceleration, deceleration, turning, etc. In this paper, we propose a novel prediction scheme which explores human behavior modality representations from real-world trajectory data to discover such motion patterns and further uses them to aid in trajectory prediction. To explore potential behavior modalities, we introduce a deep feature clustering process on trajectory features and each cluster can represent a type of modality. Intuitively, each modality is naturally a class, and a classification network can be adopted to retrieve highly probable modalities about to happen in the future according to historical observations. On account of a wide variety of cues existing in the observation (e.g. agents’ motion states, semantics of the scene, etc.), we further design a gated aggregation module to fuse different types of cues into a unified feature. Finally, an adaptation process is proposed to adapt a certain modality to specific historical observations and generate fine-grained prediction results. Extensive experiments on four widely-used benchmarks show the superiority of our proposed approach.
CLARIFICATION OF THE CONCEPT OF INNOVATICS OF HIGHER EDUCATION

The article is devoted to clarifying the concept of “higher education innovation.” Examples of the use of the term “innovatics” in other areas and cases of application are analyzed. The formation of the modern theory of innovatics, pedagogical innovatics and innovatics as a new toolbox of skills for innovative production managers are considered.
Cooperative-theoretic optimal adaptive robust control for fuzzy multiple manipulator systems

This article presents the study of the trajectory tracking issue of multiple manipulators from the viewpoint of servo constraint-following. First, on the framework of Udwadia-Kalaba (U-K) theory, the tasks of (holonomic or nonholonomic) trajectory tracking control are transformed into a series of servo constraints, and a nominal control is presented to realize the servo constraint-following for the system without uncertainty. Second, consider that there exists the time-varying uncertainty in the system, which is bounded and characterized by the fuzzy set theory. An adaptive robust control (ARC) is presented to achieve tracking errors uniform boundedness (UB) and uniform ultimate boundedness (UUB). Third, cooperative game theory is employed to obtain the dual optimal control parameters of the presented controller, and the mathematical derivation is performed to verify that there exists the Pareto optimal solution. Finally, simulation results show the effectiveness of the proposed control for the trajectory tracking control of multiple manipulators.
Transferable adversarial distribution learning: Query-efficient adversarial attack against large language models

It is a challenging task to fool a text classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among the existing black-box attacks, decision-based methods have a large query cost due to exponential perturbation space and greedy search strategy. Transfer-based methods, on the other hand, tend to overfit the surrogate model and thus fail when applied to unknown target models. In this paper, we propose a straightforward yet highly effective adversarial attack framework for black-box transformer-based models, thereby exposing vulnerabilities within large language models. Specifically, we leverage a fine-tuned large language model as a white-box surrogate model and optimize a distribution of adversarial text. This distribution is parameterized by a continuous-valued matrix based upon the surrogate model. To avoid overfitting of the distribution and improve its adversarial transferability, we incorporate an additional causal language model into our framework as a constraint model. Based on this constraint model, we add language model perplexity and semantic consistency as regularization terms during the distribution training process. To further reduce the number of queries to the target model, i.e., improve the threat level of examples drawn from our distribution, we employ a geometric loss strategy to ensure that the distribution training process learns the optimal perturbation. Extensive experimental studies have been carried out on benchmark datasets and the results demonstrate significant improvement on the performance and query efficiency under black-box setting in comparison with well-established approaches. Our approach achieves an 80.98% reduction in BERT model accuracy while consuming only 21.86% of the query times required by prior attacks.
Complete positivity, positivity, and long-time asymptotic behavior in a two-level open quantum system

We study the concepts of complete positivity and positivity in a two-level open quantum system whose dynamics are governed by a time-local quantum master equation. We establish necessary and sufficient conditions on the time-dependent relaxation rates to ensure complete positivity and positivity of the dynamical map. We discuss their relations with the non-Markovian behavior of the open system. We also analyze the long-time asymptotic behavior of the dynamics as a function of the rates. We show under which conditions on the rates the system tends to an equilibrium state. Different examples illustrate this general study.
On-chip multifunctional self-configurable quadrilateral MZI network

Photonic integrated circuits have garnered significant attention in recent years. To enhance the functional versatility of these devices, researchers have introduced the concept of reconfiguration into photonic integrated circuits. Inspired by field programmable gate arrays in the electrical domain, programmable photonic chips employing various topologies have been developed. However, users still encounter challenges when utilizing these devices, as they need to understand the internal structure and principles of the chip and individually adjust the tunable basic units within the topology network. In this paper, we employ the quadrilateral topological network based on the on-chip Mach–Zehnder interferometer as a black box to realize a highly self-reconfigurable optical signal processor. By leveraging this approach, we achieve positive real-valued matrix computation, optical routing, and low-loss optical energy splitting. Our demonstration effectively showcases the immense potential of on-chip programmable photonic waveguide meshes.
Learning to Detect Memory-related Vulnerabilities

Memory-related vulnerabilities can result in performance degradation or even program crashes, constituting severe threats to the security of modern software. Despite the promising results of deep learning (DL)-based vulnerability detectors, there exist three main limitations: (1) rich contextual program semantics related to vulnerabilities have not yet been fully modeled; (2) multi-granularity vulnerability features in hierarchical code structure are still hard to be captured; and (3) heterogeneous flow information is not well utilized. To address these limitations, in this article, we propose a novel DL-based approach, called MVD+ , to detect memory-related vulnerabilities at the statement-level. Specifically, it conducts both intraprocedural and interprocedural analysis to model vulnerability features, and adopts a hierarchical representation learning strategy, which performs syntax-aware neural embedding within statements and captures structured context information across statements based on a novel Flow-Sensitive Graph Neural Networks, to learn both syntactic and semantic features of vulnerable code. To demonstrate the performance, we conducted extensive experiments against eight state-of-the-art DL-based approaches as well as five well-known static analyzers on our constructed dataset with 6,879 vulnerabilities in 12 popular C/C++ applications. The experimental results confirmed that MVD+ can significantly outperform current state-of-the-art baselines and make a great trade-off between effectiveness and efficiency.
Drone Based RGBT Tracking with Dual-Feature Aggregation Network

In the field of drone-based object tracking, utilization of the infrared modality can improve the robustness of the tracker in scenes with severe illumination change and occlusions and expand the applicable scene of the drone object tracking task. Inspired by the great achievements of Transformer structure in the field of RGB object tracking, we design a dual-modality object tracking network based on Transformer. To better address the problem of visible-infrared information fusion, we propose a Dual-Feature Aggregation Network that utilizes attention mechanisms in both spatial and channel dimensions to aggregate heterogeneous modality feature information. The proposed algorithm has achieved better performance by comparing with the mainstream algorithms in the drone-based dual-modality object tracking dataset VTUAV. Additionally, the algorithm is lightweight and can be easily deployed and executed on a drone edge computing platform. In summary, the proposed algorithm is mainly applicable to the field of drone dual-modality object tracking and the algorithm is optimized so that it can be deployed on the drone edge computing platform. The effectiveness of the algorithm is proved by experiments and the scope of drone object tracking is extended effectively.