Reviewerly/exHarmony huggingface dataset:
A dual-task algorithm for ship target detection and semantic segmentation based on improved YOLOv5

Marine target detection technology plays an important role in sea surface monitoring, sea rescue and other fields. Aiming at the problem of low detection accuracy rate of ship targets, this paper proposed a dual-task network model which integrates target detection and semantic segmentation for ship objects. The semantic segmentation branch composed of Encoder and Decoder is added on the basis of YOLOv5 network to obtain multi-scale context information. Encoder and Decoder are connected through ASPP module, finally the low-level features and high-level features are further fused to generate semantic segmentation results. CE Loss is used as the semantic segmentation loss function to perform gradient optimization. Finally, it is superimposed with the target detection results to generate an image with both the anchor frame information of the target position and the contour information of the target boundary as the final result. The two tasks share the backbone extraction network and feature fusion network of YOLOv5. Different tasks have different positions of local minima in the shared layer. The interaction of unrelated parts between multiple tasks helps to escape from local minima. The detection accuracy is improved through the features of information sharing and mutual complement of multi-task models, and the semantic segmentation function is implemented. The experimental results show that, on the self-built dataset, the mAP of target detection is improved by 4.3 percentage points compared with YOLOv5, and the MIoU of semantic segmentation can reach 93.6%.
Towards Reliable Distributed Quantum Computing on Quantum Interconnects

Quantum interconnects (QuICs), which connect multiple quantum devices to build distributed quantum systems, are essential for large-scale quantum information processing. While various software tools have been developed to analyze quantum programs running on QuICs, there are very few frameworks for formal reasoning about them on actual quantum devices. The lack of such tools may make distributed systems unreliable because of the complicated behavior of concurrent quantum processes and the physical constraints of quantum devices. This paper discusses the need for an infrastructure, including a distributed quantum programming language, to realize reliable distributed quantum computing.
Programmer Stories, Stories for Programmers: Exploring Storytelling in Software Development

Developers need to comprehend new code on a regular basis; a demanding task that gets complicated as projects grow in complexity and size. To help developers, documentation is typically added in the form of code comments, design documents, system documents, to name a few examples. Despite all these different kinds of documentation code base comprehension is still challenging and there is room for further exploration of assistance in this space.
Immersive Spaces for Creativity: Smart Working Environments

This paper presents an innovative approach to designing an immersive space that dynamically supports users (inter-)action based on users’ behavior, voice, and mood, providing a personalized experience. The objective of this research is to explore how a space can communicate with users in a seamless, engaging, and interactive environment. Therefore, it integrates natural language processing (NLP), generative artificial intelligence applications and human computer interaction that utilizes a combination of sensors, microphones, and cameras to collect real-time data on users’ behavior, voice, and mood. This data is then processed and analyzed by an intelligent system that employs machine learning algorithms to identify patterns and adapt the environment accordingly. The adaptive features include changes in lighting, sound, and visual elements to facilitate creativity, focus, relaxation, or socialization, depending on the user’s topics and emotional state. The paper discusses the technical aspects of implementing such a system. Additionally, it highlights the potential applications of this technology in various domains such as education, entertainment, and workplace settings. In conclusion, the immersive creative space represents a paradigm shift in human-environment interaction, offering a dynamic and personalized space that caters to the diverse needs of users. The research findings suggest that this innovative approach holds great promise for enhancing user experiences, fostering creativity, and promoting overall well-being.
Deep Semi-Supervised Learning with Fine-Grained Dynamic Weights and Pseudo-Label Constraints

Semi-supervised learning is comonly trained using a large amount of unlabeled data and a small amount of labeled data. Existing works have achieved excellent results through consistent regularization methods. However, when designing the loss function, they set a loss function for labeled and unlabeled data separately, ignoring the connection between labeled and unlabeled data. Furthermore, unlabeled data all share the same weight, but the weights of different unlabeled data should be different. Based on these observations, a pair loss is proposed in this paper to strengthen the connection between labeled and unlabeled data; in addition, a dynamic weight is designed for each unlabeled data based on EMA (Exponential Moving Average) when calculating the loss function. Finally, a deep semi-supervised learning method called DWPC is proposed by combining dynamic weighting and pair loss. Experimental results on several datasets show that the proposed method improves performance in many scenarios.
Capturing Qubit Decoherence through Paraconsistent Transition Systems

This position paper builds on the authors’ previous work on paraconsistent transition systems to propose a modelling framework for quantum circuits with explicit representation of decoherence.
Towards a Cognitive Architecture for Marine Robots

In this paper, we introduce a proven cognitive architecture to the underwater domain, where such architectures are becoming increasingly needed. Components of the proposed framework include a mission planner and executioner, knowledge base and reasoner, real-time photorealistic simulator, web-based service for replaying and explaining past missions, and ensemble-of-experts perception system. Similar isolated cognitive components have already proven useful in marine robotics. What sets the proposed framework apart is that it offers the potential for a complete, consistent, end-to-end solution—from sensor perception and actuator control to task selection and plan adaptation to post-mission analysis and explainability. The same components proposed for this framework have already found success in settings ranging from both personal and retail service robotics to collaborative human-robot search-and-rescue missions. We present the most significant additions and adaptations to these components in order for them to prove equally apt for underwater robots. To guide the discussion, we consider a motivational scenario involving a pipeline inspection performed by an autonomous underwater vehicle.
Single Step Multivariate Solar Power Forecasting using Adaptive Learning Rate LSTM Model with Optimized Window Size

Accurate photovoltaic (PV) power forecasting is crucial for the successful integration of residential PV systems into the electrical grid. It enables grid operators to optimize grid operations, ensure stability, facilitate market operations and trading, and plan for future system expansion. In this study, we propose a new model that combines an adaptive learning rate Long Short-Term Memory (LSTM) with an optimized window size for improved PV power forecasting. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The model’s performance is compared against other commonly used forecasting models, including LSTM, Bi-LSTM, LSTM-Transformer, and CNN-LSTM, for single-step size forecasting, specifically predicting PV power for the next hour. The results demonstrate that the proposed model outperforms all other models in terms of accuracy for the single-step forecasting task. The adaptive learning rate LSTM with optimized window size demonstrates superior performance, indicating its effectiveness in capturing the temporal patterns and dependencies in PV power generation.
Water Surface Cleaning using a Multi-Sensor Surface Vehicle

Autonomous robots have seen dramatic advances in recent years; however, it still lacks application in water surface cleaning, which mainly relies on manual operation. This paper presents a fully autonomous robot, SMURF, for water surface cleaning including mechanical, hardware design, and the core part-autonomy system. Through investigating the needs of water surface waste cleaning in different water scenarios, we proposed a multi-sensor fusion scheme, which has been applied to the SMURF to achieve better water surface cleaning effects. In addition, we conducted experiments in two different water scenarios to verify the accurate self-localization and independent path planning ability of the SMURF. The experimental results indicate the SMURF has the above capabilities, which can guarantee the smooth implementation of the water surface cleaning tasks. This can to some extent prevent waste from being transported to the ocean.
Smart Contract Symbol Execution Vulnerability Detection Method Based on CFG Path Pruning

In recent years, with the continuous promotion of blockchain technology, the application of smart contracts has shown an explosive growth trend, and smart contract vulnerabilities seriously threaten the ecological security of blockchain. Aiming at the inefficiency of existing smart contract Symbolic Execution vulnerability detection technology, this paper proposes an effective smart contract vulnerability detection method at the source code level. Firstly, we define the critical path. As attackers typically aim to steal or freeze funds, we define the path related to fund transfer as the critical path, and its related instructions are the critical instructions. Then, we constructed a smart contract control flowchart based on Ethereum bytecode and used a constraint solver to solve path constraints and corresponding vulnerability constraints. Detect common smart contract vulnerabilities such as reentrancy, access control, arithmetic vulnerabilities, unchecked low calls, and denial of service. The experimental results show that the proposed scheme has good detection performance, and vulnerability detection was performed on 55 smart contracts containing vulnerabilities in the dataset. Compared with the pre optimized scheme, the precision rate of this scheme has been improved by 7.52%, and the total execution time has been reduced by 34.92%.
Fruits Detections Using Single Shot MultiBox Detector

Object detection, specifically, deals with the computer vision task of detecting instances of an object such as humans, animals, or cars in the form of digital images. Single Shot multiBox Detector (SSD) MobileNet model provides confidence values for a set of items. We consider Open Images V6, a supervised dataset consisting of 1.7 million images with 600 categories and images from Fruits 360 from Kaggle for this investigation. Based on the datasets used in this research, the model was able to identify selected fruits. With sufficient lighting conditions, the model performed with a confidence value between 75% and 99%. While this experiment produces favorable results under ideal lighting conditions, under suboptimal lighting conditions the model's performance drops. This creates complications for future implementations when creating a model for detecting pedestrians in self-driving cars due to a variety of lighting conditions experienced outdoors.
Towards establishing an automated selection framework for underwater image enhancement methods

The majority of computer vision architectures are developed based on the assumption of the availability of good quality data. However, this is a particularly hard requirement to achieve in underwater conditions. To address this limitation, plenty of underwater image enhancement methods have received considerable attention during the last decades, but due to the lack of a commonly accepted framework to systematically evaluate them and to determine the likely optimal one for a given image, their adoption in practice is hindered, since it is not clear which one can achieve the best results. In this paper, we propose a standardized selection framework to evaluate the quality of an underwater image and to estimate the most suitable image enhancement technique based on its impact on the image classification performance.
CLGP: Multi-Feature Embedding based Cross-Attention for Chinese NER

The previous works fused lexicon information while ignoring two important Chinese language characteristics: glyph and pinyin, which carry significant syntax and semantics information for sequence tagging tasks. This paper proposes CLGP, which utilizes four specific extractors to obtain the embeddings of character, glyph, pinyin, and lexicon, and further uses a network based on cross-attention to perform the multi-embedding fusion. Specifically, we introduce the embedding scheme to preserve the lexicon-matching results and design two specific CNN to extract glyph and pinyin embeddings. Moreover, we fuse the four embeddings by the cross-attention-based network to enhance the Chinese NER. The experimental results on four famous datasets show that CLGP achieves state-of-the-art (SOTA) performance.
Definite descriptions and hybrid tense logic

Abstract We provide a version of first-order hybrid tense logic with predicate abstracts and definite descriptions as the only non-rigid terms. It is formalised by means of a tableau calculus working on sat-formulas. A particular theory of DD exploited here is essentially based on the approach of Russell, but with descriptions treated as genuine terms. However, the reductionist aspect of the Russellian approach is retained in several ways. Moreover, a special form of tense definite descriptions is formally developed. A constructive proof of the interpolation theorem for this calculus is given, which is an extension of the result provided by Blackburn and Marx.
Multi-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution

Motion planning is a crucial aspect of robot autonomy as it involves identifying a feasible motion path to a destination while taking into consideration various constraints, such as input, safety, and performance constraints, without violating either system or environment boundaries. This becomes particularly challenging when multiple robots run without communication, which compromises their real-time efficiency, safety, and performance. In this paper, we present a learning-based potential field algorithm that incorporates deep reinforcement learning into an artificial potential field (APF). Specifically, we introduce an observation embedding mechanism that pre-processes dynamic information about the environment and develop a soft wall-following rule to improve trajectory smoothness. Our method, while belonging to reactive planning, implicitly encodes environmental properties. Additionally, our approach can scale up to any number of robots and has demonstrated superior performance compared to APF and RL through numerical simulations. Finally, experiments are conducted to highlight the effectiveness of our proposed method.
VDU: VMS Dataset Upsampling via Multi-Source Data Fusion for Trawlers

Vessel Monitoring Systems (VMS) equipped on trawlers has recorded a large amount of trajectory data. However, due to the EU standard of sampling rate being two hours, many VMS datasets face the problem of low sampling rates, which hinders the analysis of fishing effort distributions. Previous studies apply mathematical and deep learning methods as interpolation methods to upsample the VMS dataset. However, these methods always lose precision when the sampling rate is required from two hours to six minutes because most movement features will be lost in two hours. This paper proposes a novel VMS Dataset Upsampling (VDU) method via multi-source data fusion for trawlers, which incorporates additional marine environmental factors (MEF) to help the model capture the movement features. VDU is designed on deep learning, including Multi-Layer Perceptron (MLP), Residual Network (ResNet), and Long Short-Term Memory (LSTM) neural network. MLP is used to extract VMS record features, ResNet is employed for MEF data, and LSTM is applied to generate the high sampling rate records. By applying the VMS records of 1855 trawlers in the East China Sea and the MEF in the corresponding area as a case study, VDU increases the sampling rate from two hours to six minutes with a low average interpolation error of 0.77 km. We also conduct an extensive study to verify the efficacy of multi-source data fusion for interpolation.
Feature Extraction and Accurate Localization Based on Hough Transform in Complex Scenes

Complex industrial scenes, operation of large machinery or precision machinery, often need to judge, recognition and positioning of industrial scenes, to achieve intelligent systems to improve production efficiency. In view of the shortcomings of traditional manual recognition due to the fluctuation of subjective factors of operators, insufficient reliability, large variance of discnmination accuracy and low efficiency, and the need for a strong applicability system in combination with the variable relative distance between machinery operation and recognition objects, this study proposes a feature extraction and accurate positioning method based on Hough transform in complex scenes. This method reduces the noise interference and edge burr of the image by noise reduction filtering and feature extraction, and completes the feature extraction and accurate localization in complex scenes by introducing multiple relative threshold parameters. Finally, the method is put into work condition discrimination applications and real-time experiments are conducted. The experimental results prove that the Hough transform-based feature extraction and accurate positioning method in complex scenes is widely applicable, real-time, robust, low-cost and stable and reliable.
TURTLE Robotic Lander in the context of REP2022 military exercise

This paper presents the TURTLE hybrid robotic lander in the context of the field trials performed in the REP(MUS) 2022 military exercise. The TURTLE robot combines the characteristics and mobility of an autonomous underwater vehicle with the ones of a seabed lander, having been designed for extended permanence on the sea bottom and efficient ascending and dive to the deep sea. The REP(MUS) 2022 exercises organized by the Portuguese navy in collaboration with NATO organizations and other institutions demonstrated the large-scale use of unmanned marine systems in an operational scenario. The robotic system is presented as well as some of the results and experience from the field trials.
Gaze Controlled Underwater Remotely Operated Vehicle (ROV) to Improve Accessibility in Maritime Robotics

Underwater remotely operated vehicles (ROVs) are commonly operated with joystick-style controllers, but users with upper limb disabilities may find using a joystick either difficult or impossible due to partial or complete loss of function of their hands. Eye gaze control is a method that allows individuals to control a vehicle, device, or application without the use of their hands. It is often intended for those with a disability, but the method can also be used by those without a disability for hands-free operation. Past work on vehicle gaze control has focused primarily on land and aerial vehicles, and has yet to be extended into the field of maritime robotics. This paper discusses a method for gaze controlled underwater ROVs based on ArduSub and MAVLink, with transferability to other vehicle types using ArduPilot. The result of this method is a single script that provides the ability to control a tethered ROV via eye gaze in the directions of ‘up,’ ‘down,’ ‘left yaw,’ ‘right yaw,’ ‘forward,’ and ‘reverse,’ in addition to controlling the vehicle’s lights and arming/disarming the vehicle. In-water demonstrations show successful operation of a gaze controlled ROV with no perceptible time delay.
Deep WaveNet-based YOLO V5 for Underwater Object Detection

Detecting objects under the water is always a difficult task. Due to the unique characteristics of underwater settings, identifying underwater objects can be difficult. When light penetrates deep within the water, the aqueous medium exhibits diffraction and scattering. This leads to murky, obscured films and photos, which make interpretation more difficult. Underwater object detection has become one of the most important objectives in deep-sea exploration. To overcome the above issues, we propose a methodology based on the YOLO model in which the images will be passed through for detection. The images are trained using the CNN-based deep wave net algorithm which enhances the faded or distorted image further making the YOLO algorithm infer better results. The YOLO models are compared with other detection models and different activation layers are also compared.
Intelligent System for Monitoring the Availability of Bus Passenger Seats using the YOLO Method

The existence of public transportation is very important to support the increasing mobility of the community because it has a large carrying capacity, one of which is the bus. However, people’s interest in using public transport began to decline along with the increase in private vehicles. One of them, due to inadequate facilities, especially in terms of information on the availability of passenger seats on the bus. In this paper, a system for monitoring the availability of passenger seats on buses is proposed to support a program from the Indonesian Ministry of Transportation, namely the National Movement Back to Public Transport to improve mass transportation performance. This system detects and calculates objects using the You Only Look Once version 5 (YOLOv5) method. The dataset used in this study has only one class, namely “Person”. The results of the model training test obtained the best model with a scenario of batch size 16 and epoch 200, which produced a precision value of 0.883, a recall of 0.815, a mAP of 0.905, and an F1 score of 0.85. While the model accuracy value when integrated with NVIDIA Jetson Nano is 81.56%. The system is integrated with WebGIS to display the calculation results for the number of passengers, available seats, and bus locations.
Transfer Learning of Image Classification Networks in Application to Dolphin Whistle Detection

Dolphin whistle detection is an important and multipurpose but time-consuming task. The ability to automate and streamline this process can be invaluable for future research in marine studies and other fields that aim to utilise these signals. When dealing with underwater acoustics, a large obstacle to overcome is the abundance of noise and interfering sounds, natural and anthropogenic alike. In this paper, we apply successful image classification networks to two separate datasets containing dolphin whistles with the goal of determining an effective method to conduct automated detection with minimal interference from a manual operator regardless of environment. We further investigate the impacts of shrinking the dataset size and performing parameter freezing on the networks at hand. Networks are assessed by their detection accuracy and achieve performances comparable to those in existing works, the best being 96.7%, thus proving the effectiveness of these pre-trained image classification models.