Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of underwater towing cable tension based on LSTM neural network

Aiming at the problems that the tension of underwater towing cable is not easy to monitor and the calculation time of the time domain coupling analysis process of towing cable is long, a tension early warning system of underwater towing cable based on long short-term memory (LSTM) neural network is designed. This paper considers the influence of single time variable and multiple variables (six degrees of freedom of tugboat) respectively. The tension time series of the key nodes of the towing cable are predicted and analyzed, and the predicted results are compared with the tension data obtained based on OrcaFlex numerical simulation to verify the accuracy of the prediction. Meanwhile, the predicted results are compared with the back propagation (BP) model and the kernel extreme learning machine (KEML) model. The experimental results show that the prediction accuracy based on LSTM neural network has obvious advantages, the maximum determination coefficient R2 is 0.8787, and the minimum root mean square error is 4.9082, which can achieve the effect of tension prediction, and has certain significance for the early warning of underwater towing cable tension.
Classification of IoT intrusion detection data based on WGAN-gp and E-GraphSAGE

In recent years, the frequency and complexity of IoT network attacks have significantly increased. NIDS, strategically located in IoT network nodes, is an essential tool for monitoring traffic and detecting and mitigating network-based attacks. However, with the significant increase in computer network attacks, many datasets used for training suffer from imbalanced data problems. Therefore, to address the traffic characteristics of IoT networks and the issue of imbalanced data, this paper proposes an intrusion detection method that combines graph neural networks(E-GraphSAGE) and generative adversarial networks. Based on experiments using datasets NF-BoT-IoT, we found that training ML classifiers on datasets balanced with synthetic samples generated by WGAN-gp increased their prediction accuracy to 93.7% .
Design of international railway logistics management system based on MDA and smart contract

The international railway logistics management system with a centralized system has problems such as information security, difficulty tracking logistics information, complex operating procedures, and information sharing and exchange. However, the decentralized, tamper-proof, and traceable features of blockchain technology can effectively enhance the data security and credibility of the system. Nonetheless, the development of smart contracts based on blockchain lacks a disciplined, standardized, and mature development process. On the other hand, adopting a Model-Driven Architecture (MDA) development approach can reduce human coding errors and improve development efficiency. This study proposed a modeling method based on UML class diagrams and state machine diagrams to analyze the static structure and dynamic behavior of the smart contract in the international railway logistics management system. By developing a conversion algorithm and combining the source and target metamodels, the platform-independent model (PIM) was converted into a platform-specific model (PSM), which was eventually transformed into code using a model-to-code transformation algorithm. The feasibility and correctness of this method were validated through experiments.
Personalized brand recommendation model based on knowledge graph

New retail is now a new retail model based on the new technology of the Internet era, online and offline and logistics integration. With the change of traditional retail model and user consumption habits and thus the core of the consumer, the new retail to provide quality services to consumers deeply into all walks of life. Due to the special nature of tobacco products, the traditional retail brand promotion channels are relatively limited, and the method of on-site promotion by brand managers with the help of retailer stores no longer meets the requirements. Under the new retail model, tobacco offline retail terminals can use big data analysis and recommendation technology to intelligently recommend cigarettes and personalized cigarette brands with similar styles and tastes to users based on the characteristics of new retailing, to guide customer demand and improve user experience; thus targeting marketing, reducing marketing costs and improving revenue. We propose the HLKAR model, an end-to-end recommendation model that integrates knowledge graph and attention mechanism with hybrid link prediction, to realize personalized cigarette recommendation for users in offline tobacco retail terminals.
Channel state information-based fall detection using IoT devices

The demand for fall detection technology in elderly individuals is increasing due to the growing global aging population. Wi-Fi's Channel State Information (CSI) based fall detection is gaining popularity as it does not require the installation of expensive cameras or sensors, is not affected by lighting and obstructions, and protects individual privacy. However, previous studies using commercial Wi-Fi and mobile hosts to acquire CSI and recognize falls are expensive. To address this issue, this paper proposes a low-cost fall detection solution based on ESP32-S3 and LSTM model. The proposed solution collects data from 52 subcarriers in the L-LTF, applies Butterworth low-pass filter, and PCA to reduce data dimensionality. The LSTM model achieves a testing accuracy of 96.03%, with 89% classification accuracy for falls. This solution can significantly reduce the cost of fall detection technology and has potential deployment on ESP32-S3. Future research could focus on improving accuracy or exploring different application scenarios.
Improved the lightweight part identification method of YOLOv5

Aiming at the defects of the existing part recognition methods in the industrial production process when identifying small and dense parts, and the problem that the number of model parameters and large volume are difficult to deploy on the mobile platform, an improved YOLOv5 lightweight part target recognition model is proposed. On the basis of the YOLOv5 model, the Ghost module is first used to replace the backbone network to reduce the number of parameters of the model. Secondly, a SPP_F feature pyramid structure is constructed to speed up the training speed of the model and strengthen the ability of the model to extract information. Remove the detection layer for large target scales, thereby speeding up model training and detection; Finally, the GIOU function in the YOLOv5 model is replaced with the Alpha-SIOU function to improve the positioning accuracy of the bounding box. Experimental results show that the average accuracy (mAP) of the improved model reaches 99.5%, and the volume only accounts for 3.2 MB, which is 35% faster than the original YOLOv5 model, reducing hardware costs and meeting the needs of deployment in low-computing equipment.
Forming a new and unexpected relationship through digital technologies: Lessons for child and family social work

This article is not a research paper in the conventional sense. Instead, it is a reflexive account of the building of a relationship which would not have happened without the existence of digital technologies and might not have happened but for the impact of the Covid-19 pandemic. It was through reading a digitised resource (an online PhD thesis) in the first UK ‘lockdown’ that Bob, now a semi-retired academic in the field of adult and organisational learning, began to reappraise his upbringing in a children’s home in Scotland. Meanwhile, it was in getting to know Bob (through emails and Zoom meetings) and by accessing Bob’s retrospectively digitised case record that Viv, the author of the PhD thesis and now a semi-retired social work academic, was stimulated to revisit her early research from a fresh perspective. We argue that our real-life example, serendipitous and unique as it undoubtedly is, demonstrates the democratising potential of the digital transformation that is currently being played out in child and family social work. Furthermore, it seems likely that digital technologies will continue to bring new and unexpected opportunities for forming such relationships in the future. Plain Language Summary In this article, two older academics (one from social work and the other from management learning) introduce their meeting online during the Covid-19 pandemic and the relationship they have been building since then, from the very different perspectives of a social work researcher and a former resident of a children’s home in Edinburgh in the 1940s and 50s. Bob and Viv describe coming together as an opportunity for shared learning, and they suggest that child and family social workers should be ready for, and welcome, such opportunities.
FeetThrough: Electrotactile Foot Interface that Preserves Real-World Sensations

Haptic interfaces have been extended to the feet to enhance foot-based activities, such as guidance while walking or stepping on virtual textures. Most feet haptics use mechanical actuators, namely vibration motors. However, we argue that vibration motors are not the ideal actuators for all feet haptics. Instead, we demonstrate that electrotactile stimulation provides qualities that make it a powerful feet-haptic interface: (1) Users wearing electrotactile can not only feel the stimulation but can also better feel the terrain under their feet—this is critical as our feet are also responsible for the balance on uneven terrains and stairs—electrotactile achieves this improved “feel-through” effect because it is thinner than vibrotactile actuators, at 0.1 mm in our prototype; (2) While a single vibrotactile actuator will also vibrate surrounding skin areas, we found improved two-point discrimination thresholds for electrotactile; (3) Electrotactile can be applied directly to soles, insoles or socks, enabling new applications such as barefoot interactive experiences or without requiring users to have custom-shoes with built-in vibration motors. Finally, we demonstrate applications in which electrotactile feet interfaces allow users to feel not only virtual information but also the real terrain under their shoes, such as a VR experience where users walk on ground props and a tactile navigation system that augments the ground with virtual tactile paving to assist pedestrians in low-vision situations.
Visual exploration of color usage in Vincent van Gogh's Paintings

This paper aims to explore the use of color and color harmony in Vincent van Gogh’s paintings and present the results in the form of a web-based digital museum to provide an intuitive and immersive experience to the public audience. To achieve this goal, we tested four different color extraction algorithms and determined the extcolors library to be the most effective method for extracting theme colors after interviewing ten random viewers. This study then conducted a visual analysis of the extracted colors, revealing that the predominant colors of van Gogh’s paintings were yellow and green, followed by blue and red. We also identified the main types of color harmony used in van Gogh’s paintings as monochromatic, analogous, and complementary harmony. The ultimate outcome of this research is a digital gallery that showcases the different periods of van Gogh’s work via a timeline representation, highlighting the distinctive color schemes that were employed during each period. This presentation gives the exhibits a clear context and provides the viewer with a clear and vivid experience. Overall, the study’s findings reveal insightful information about how color is employed in van Gogh’s artwork and suggest a useful application which is in the form of a timeline representation that can be used to improve the audience’s exposure to the art and cultural heritage.
A method for extracting water objects from visible image

Visible image, compared with SAR image and infrared image, has the advantage of high resolution, clear details, etc. So it can be selected for object extraction. Water objects play an important role in locating bridges, dams and other typical buildings. This paper presents a segmentation method for visible image based on gradient of the original image, and combined with the features of the water targets. According to the feature of water targets, gray uniform, smaller entropy, and smaller local variance, water objects can be extracted automatically and effectively by using clustering method from image segmentation result.
BRep-BERT: Pre-training Boundary Representation BERT with Sub-graph Node Contrastive Learning

Obtaining effective entity feature representations is crucial in the field of Boundary Representation (B-Rep), a key parametric representation method in Computer-Aided Design (CAD). However, the lack of labeled large-scale database and the scarcity of task-specific label sets pose significant challenges. To address these problems, we propose an innovative unsupervised neural network approach called BRep-BERT, which extends the concept of BERT to the B-Rep domain. Specifically, we utilize Graph Neural Network (GNN) Tokenizer to generate discrete entity labels with geometric and structural semantic information. We construct new entity representation sequences based on the structural relationships and pre-train the model through the Masked Entity Modeling (MEM) task. To address the attention sparsity issue in large-scale geometric models, we incorporate graph structure information and learnable relative position encoding into the attention module to optimize feature updates. Additionally, we employ geometric sub-graphs and multi-level contrastive learning techniques to enhance the model's ability to learn regional features. Comparisons with previous methods demonstrate that BRep-BERT achieves the state-of-the-art performance on both full-data training and few-shot learning tasks across multiple B-Rep datasets. Particularly, BRep-BERT outperforms previous methods significantly in the few-shot learning scenarios. Comprehensive experiments demonstrate the substantial advantages and potential of BRep-BERT in handling B-Rep data representation. Code will be released at https://github.com/louyz1026/Brep_Bert.
Time-series Shapelets with Learnable Lengths

Shapelets are subsequences that are effective for classifying time-series instances. Learning shapelets by a continuous optimization has recently been studied to improve computational efficiency and classification performance. However, existing methods have employed predefined and fixed shapelet lengths during the continuous optimization, despite the fact that shapelets and their lengths are inherently interdependent and thus should be jointly optimized. To efficiently explore shapelets of high quality in terms of interpretability and inter-class separability, this study makes the shapelet lengths continuous and learnable. The proposed formulation jointly optimizes not only a binary classifier and shapelets but also shapelet lengths. The derived SGD optimization can be theoretically interpreted as improving the quality of shapelets in terms of shapelet closeness to the time series for target / off-target classes. We demonstrate improvements in area under the curve, total training time, and shapelet interpretability on UCR binary datasets.
THGNN: An Embedding-based Model for Anomaly Detection in Dynamic Heterogeneous Social Networks

Anomaly detection, particularly the detection of anomalous behaviors in dynamic and heterogeneous social networks, is becoming more and more crucial in real life. Traditional rule-based and feature-based methods cannot well capture the structural and temporal patterns of ever-changing user behaviors. Moreover, most of the existing works based on network embedding either rely on discretized snapshots, which have ignored accurate temporal relations among user behaviors and weakened the impact of new edges, or fail to utilize dynamic and heterogeneous information simultaneously to distinguish varying effects of new edges on existing nodes. In this paper, we propose an end-to-end continuous-time model, named Temporal Heterogeneous Graph Neural Network (THGNN), to detect anomalous behaviors (edges) in dynamic heterogeneous social networks. Specifically, the model constantly updates node embeddings by propagating the information of a new edge to its source and target nodes as well as their neighbors. In this process, heterogeneous encoders are employed to handle different types of nodes and edges. What is more, a novel dual-level distributive attention mechanism is designed to allocate the influence degree of a currently interacting node to its multiple neighbors, considering the combined effect of edge type and time interval information. That can be regarded as an extension of the classical aggregative attention mechanism in the opposite direction. Extensive experiments on four real-world datasets demonstrate that THGNN outperforms all the baselines on the task of anomalous edge detection, achieving an average AUC gain of 6% across all datasets.
Neighbor-Environment Observer: An Intelligent Agent for Immersive Working Companionship

Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physical and virtual environments. While technological advances in personal digital devices, such as personal computers and virtual reality devices, can provide immersive experiences, they can also disrupt users’ awareness of their surroundings and enhance the frustration caused by disturbances. In this paper, we propose a joint observation strategy for artificial agents to support users across virtual and physical environments. We introduce a prototype system, neighbor-environment observer (NEO), that utilizes non-invasive sensors to assist users in dealing with disruptions to their immersive experience. System experiments evaluate NEO from different perspectives and demonstrate the effectiveness of the joint observation strategy. A user study is conducted to evaluate its usability. The results show that NEO could lessen users’ workload with the learned user preference. We suggest that the proposed strategy can be applied to various smart home scenarios.
Interactive Benefits from Switching Electrical to Magnetic Muscle Stimulation

Electrical muscle stimulation (EMS) became a popular method for force-feedback without mechanical-actuators. While much has been written about the advantages of EMS, not much work has investigated circumventing its key limitations: (1) as impulses traverse the skin, they cause an uncomfortable “tingling”; (2) impulses are delivered via gelled-electrodes, which not only require direct skin contact (must be worn under clothes); but, also (3) dry up after a few hours. To tackle these, we explore switching from electrical to magnetic muscle stimulation (MMS), via electromagnetic fields generated by coils. The first advantage is that MMS coils do not require direct skin contact and can actuate up to 5 cm away (Study#1)—this enables applications not possible with EMS, such as stimulation over the clothes and without ever replacing electrodes. Second, and more important, MMS results in ∼50 % less discomfort caused by tingling than EMS (Study#2). We found that reducing this tingling discomfort has two downstream effects for interactive systems: (1) participants rated MMS force-feedback as more realistic than that of EMS (Study#3); and (2) participants could more accurately perceive the pose actuated by the interactive system (Study#4). Finally, we demonstrated applications where our proposed switch from EMS to MMS improves user experience, including for VR feedback, gaming, and pose-control.
Hypothesizer: A Hypothesis-Based Debugger to Find and Test Debugging Hypotheses

When software defects occur, developers begin the debugging process by formulating hypotheses to explain the cause. These hypotheses guide the investigation process, determining which evidence developers gather to accept or reject the hypothesis, such as parts of the code and program state developers examine. However, existing debugging techniques do not offer support in finding relevant hypotheses, leading to wasted time testing hypotheses and examining code that ultimately does not lead to a fix. To address this issue, we introduce a new type of debugging tool, the hypothesis-based debugger, and an implementation of this tool in Hypothesizer. Hypothesis-based debuggers support developers from the beginning of the debugging process by finding relevant hypotheses until the defect is fixed. To debug using Hypothesizer, developers first demonstrate the defect, generating a recording of the program behavior with code execution, user interface events, network communications, and user interface changes. Based on this information and the developer’s descriptions of the symptoms, Hypothesizer finds relevant hypotheses, analyzes the code to identify relevant evidence to test the hypothesis, and generates an investigation plan through a timeline view. This summarizes all evidence items related to the hypothesis, indicates whether the hypothesis is likely to be true by showing which evidence items were confirmed in the recording, and enables the developer to quickly check evidence in the recording by viewing code snippets for each evidence item. A randomized controlled experiment with 16 professional developers found that, compared to traditional debugging tools and techniques such as breakpoint debuggers and Stack Overflow, Hypothesizer dramatically improved the success rate of fixing defects by a factor of five and decreased the time to debug by a factor of three.
SleeveIO: Modular and Reconfigurable Platform for Multimodal Wearable Haptic Feedback Interactions

SleeveIO is a modular and reconfigurable hardware platform for rapid prototyping of multimodal wearable haptic feedback interactions. SleeveIO features engineered machine-knitted sleeve and band substrates, and five categories of haptic feedback actuator modules including vibrotactors, bellows, muscles, suction/puffing cups, and quad-chamber actuators. A universal magnetic attachment mechanism unifies the different types of actuators, enabling countless multimodal haptic experiences involving combinations of different actuator types in different configurations. SleeveIO is compatible with a variety of hardware/software control platforms, such as FlowIO [42], which enables individual control of each haptic actuator and makes the system battery-powered and untethered. This paper presents the SleeveIO platform in detail along with replication resources, a novel generalized approach to making different types of haptic actuators modular and interoperable, new application possibilities enabled by SleeveIO, and a pilot assessment of the viability of the platform as a whole and each module individually.
Lorgnette: Creating Malleable Code Projections

Projections of computer languages are tools that help users interact with representations that better fit their needs than plain text. We collected 62 projections from the literature and from a design workshop and found that 60% of them can be implemented using a table, a graph or a form. However, projections are often hardcoded for specific languages and situations, and in most cases only the developers of a code editor can create or adapt projections, leaving no room for appropriation by their users. We introduce lorgnette, a new framework for letting programmers augment their code editor with projections. We demonstrate five examples that use lorgnette to create projections that can be reused in new contexts. We discuss how this approach could help democratise projections and conclude with future work.