Reviewerly/exHarmony huggingface dataset:
A Local-global Gaussian Weighted Attention-based Approach to Medical Image Segmentation

This work suggests a medical image segmentation approach based on local-global Gaussian-weighted attention as a remedy for the U-Net model’s lack of explicit modeling of long-range dependencies for medical image segmentation problems. The approach uses a Transformer module built on a local-global Gaussian weighted attention mechanism and a unified decoder module in the U-Net model to enable relevant relationship mining and centralized multi-resolution feature processing. Additionally, this paper validates the efficacy of the method put forth in this paper on two openly accessible datasets, Synapse and ACDC, by expanding the dataset with random angle rotation and mirroring operations for pre-processing and using a loss function combining HD95 and Dice for training. According to the experimental findings, the suggested method is competitive with other existing methods and outperforms TransUNet and U-Net in terms of the model’s generalization and segmentation effects.
Radio Frequency Signal Detection and Identification Algorithm Based on Time-Frequency Joint Domain Electromagnetic Spectrum Data

Signal recognition is an important part of electromagnetic spectrum detection. With the complexity of signals and the diversity of signal modulation methods in the future space electromagnetic environment, it is especially necessary to identify and analyze signals in the electromagnetic spectrum detection. Currently, the main method of electromagnetic spectrum detection is energy detection algorithm, which can blind source recognition without prior information. Based on the previously collected time-frequency electromagnetic spectrum data, two kinds of radio signal detection and recognition framework are proposed in this paper, meanwhile, clustering algorithm is introduced into the field of radio signals. Python is used for radio frequency signal detection and identification simulation. This paper first analyses the energy detection algorithm, then introduce two kinds of radio signal detection and recognition, and finally carries out the practice to identify the frequency band of the signal, so as to distinguish the signal from the background noise.
Research on Intelligent Alarm System for Falling Rocks on Railway

Along with the rapid development of railway industry in China, the railway network becomes more complex. In order to ensure the train run with higher speed, more safety and stability, the FlowNet2.0 is used in our alarm system to avoid or reduce the damage of natural disasters and dangerous objects on train safety. FlowNet2.0 is based on traditional optical flow method, but it is optimized optical flow algorithm based on deep learning. We use it to develop a new intelligent alarm system for falling rocks along the railway. This system uses advanced video processing technology and the object’s contour, moving speed and angle in optical flow field to process the digital video captured in monitoring place in real time. When accidents, like rocks falling from revetment, landslide displacement, debris flow, foreign object intrusion, etc. happens on the rail track, this system can detect those disasters in real-time accurately, and alarm automatically. This system can replace the traditional watchman with low-cost equipment, detect disasters and alarm in real time. The deployment of this system can not only improve the safety of train significantly, but also save the labor cost from Railway Bureau.
HCL-Net: A Single Image Rain Removal Algorithm Based on Channel Attention and Multi-scale Feature Fusion

The investigation of rain removal from pictures has always been a pivotal matter in the domain of computer vision. Nevertheless, the majority of existing algorithms employ encoder-decoder structures that have been trained on artificially synthesized datasets, without taking into account the harm that heavy rain can cause to actual images. Batch normalization is unsuitable for low-level computer vision duties, and skip connections in encoder-decoder architectures are not always appropriate for rain removal. This has resulted in setbacks in relevant domains. In this article, we propose a groundbreaking single-image rain removal algorithm that is founded on channel attention and multi-scale feature fusion. Initially, we build a contextualized dilated restoration block to restore the rain-affected image during the feature extraction phase. Secondly, we create a half-instance normalization residual block to conserve more scale information and to augment the modeling capacity of the model, without sacrificing performance while hastening model convergence. Finally, in response to the view that simple skip connections may not always be advantageous, we have constructed a LeakyReLU channel attention block to aggregate information and enhance the effectiveness of skip connections. Our comprehensive experiments on authentic rain datasets and images provide evidence that our model can more proficiently remove rain streaks and accumulations from pictures, in contrast to the existing methods.
Improved YOLOv5-based UAV Target Detection under Complex Background

A real-time moving target detection algorithm of UAVs based on improved YOLOv5 is proposed to improve the detection precision of moving targets under complex background. Based on YOLOv5, the algorithm uses Convolution Kernel Group (CKG) to enhance the feature extraction of UA V targets under complex background. The Squeeze-Excitation Attention Module (SE) is used to adjust the perception field adaptively to improve the feature extraction ability of targets under complex background. The adaptive spatial feature fusion (ASFF) module enhances the feature extraction ability and feature fusion efficiency of the original model in complex scenes to improve the detection precision of targets under complex background interference. The experimental results show that the proposed method can achieve 75.58% recall, 95.63% precision, and 78.92% mean average precision for moving target detection under complex background. The method can realize the detection of UA V moving targets in complex background, which is conducive to the cause of security and has important significance in anti-terrorism, public security control, and production safety.
A Survey of Few-Shot Image Classification Based on Transfer Learning

Although the application scenarios of image classification are very extensive, it is difficult to collect enough data to train the deep learning model in many scenarios. Transfer learning is the main approach for image classification with few-shot samples. Using transfer learning, the knowledge and experience learned by the model in the source domain are transferred to the target domain, so that the model can be quickly learned and generalized in the target domain, reducing the dependence on the target domain data. This paper conducts a systemic survey for few-shot image classification algorithms based on transfer learning. According to the differences in distribution, feature space and label space of source domain and target domain, the reviewed algorithms are roughly divided into three categories: the first category is that the distribution is different, but the feature space and label space are the same, which can be solved by distribution adaptation; the second type is that the distribution and feature space are the same, but the label space is different, in which case the meta-learning method is used; the last one is with different feature spaces, which uses heterogeneous data to assist image classification. This survey introduces the three categories of algorithms to help readers better understand the current research status. Finally, in order to demonstrate the performance of different transfer learning algorithms in few-shot image classification, we conducted experiments on Office-31 and Mini-ImageNet datasets.
Smarter CRM Solutions in the Emerging Technologies Age

There are trends across modern Customer Relationship Management (CRM) solutions that are centered on emerging technologies such as Artificial Intelligence (AI), internet of things (IoT), and Blockchain to improve the overall customer experience. Smart CRM solutions focus on embedding emerging technologies capabilities within marketing, sales, and service functionalities to turns unpredictable customer journeys into predictive and interactive experiences. For example, applying artificial intelligence and machine learning to customer data and Internet of Things data allows organizations to predict and fix problems pre-emptively, before the customer even realizes the issue. The focus of this paper is to discuss the impact of the current emerging technologies on smart CRM solutions. In particular, the benefits of the current applications of AI, IoT, and Blockchain technology in smart CRM solutions.
Exploration of User Experience in Mixed Reality for Product Virtual Interaction and Display

In the post-pandemic era, the strong impact affected the user experience and interaction of products. Web-based online stores also lack intuitive product designs, leading to frequent cognitive disputes. Therefore, a new user experience and interactive approach are continuously researched and discussed. Virtual Reality (VR) and Augmented Reality (AR) provide a Mixed Reality (MR) user experience, which has been influencing business models in the industry since 2020. Additionally, with the emergence of the metaverse at the end of 2021, MR has been recognized as a crucial technology with enormous potential for future demands. However, while many innovative applications of MR have achieved considerable success, they still only focus on entertainment-related experiences and have yet to fully explore the comprehensive range of applications and innovative directions for MR. In such circumstances, we investigated the requirements for user experiences and spatial displays in integrating MR into product experiences. We proposed a novel model that transitions from physical to virtual realms to effectively address the aforementioned challenges. The initial phase involved analyzing the prevailing issues and challenges encountered by users in the product and spatial experience services offered by physical stores and web-based online platforms. Subsequently, the results revealed specific needs were met with the MR technology and alleviated associated pain points. The outcomes of the proposed model provide a reference for future studies to research immersive service experiences within real-world settings with MR devices, thereby removing limitations in space and time.
An ontology-based model for evaluating cloud attack scenarios in CATS – a serious game in cloud security

In recent years, the market of cloud services has been growing rapidly. Consequently, cloud security has become a heavily discussed topic in the industry. If cloud assets are misconfigured, it can lead to severe security issues and be exposured to cybersecurity attacks. It is of great importance that industry practitioners understand the security challenges and their responsibilities to protect cloud assets. We designed a serious game: Cloud of Assets and Threats (CATS) as an enrichment to the traditional training method to empower users of the cloud infrastructure in terms of cloud security awareness. In this work, we propose a new ontology-based model to support the evaluation of the simulated attack scenarios in CATS. We share the implementation details and the algorithm, based on the Common Vulnerability Scoring System (CVSS), which is used in CATS to evaluate the simulated attack scenarios. With this innovative effort, we maximize the level to which the CATS game reflects real-world facts, and provide a reasonable level of abstraction in the serious game. Our work contributes to the body of knowledge by extending the existing ontology and applying it in the evaluator algorithm, which stands at the core of our serious game CATS. We apply CATS in training in the industry and discovered that CATS is a promising approach to help the industry practitioners to understand cloud security concepts and raise awareness about cloud security. Scholars in the academic world benefit from the work by gaining experience in instantiating the design science paradigm.
Alignment of Quality Models for Assessing Software Requirements in Large-scale Projects: A Case from Space

Large critical systems, such as those created in the space domain, are usually developed by a large number of organizations and, furthermore, they have to comply with standards. Yet, the different stakeholders often do not have a common understanding of the needed quality of requirements specifications. Achieving such a common understanding is a laborious process that is currently not sufficiently supported. Moreover, such a common understanding must be aligned with the standards. In this paper, we present an approach that can be used to align the different stakeholder perceptions regarding the quality of requirements specifications. Existing quality models for requirements specifications are analyzed for equivalences, and transferred into a common representation, the so-called Aligned Quality Map (AQM). Furthermore, a process is defined that supports the alignment of different stakeholder perspectives with regard to the quality of requirements specifications using AQM, which is validated in a case study in the context of European space projects. AQM has been created and populated with an initial set of quality models. It is designed in such way that it can be extended to include further quality models. The case study has shown that an alignment of different stakeholder perspectives and the quality model of the European Cooperation for Space Standardization using AQM is feasible. The approach allows for aligning different stakeholder perspectives for a common understanding of the quality of requirements specifications in the context of standards. Furthermore, AQM supports the assessment of requirements specifications.
Hybrid User Interface for Audience Feedback Guided Asymmetric Immersive Presentation of Financial Data

This paper presents the design of a system for giving engaging, immersive presentations based on financial data in an immersive environment. The system consists of a presenter controlling the presentation flow through a tablet interface and an audience experiencing the presentation content through an augmented reality (AR) head-worn display (HWD). The system leverages various sensors on the HWD to detect the audience’s cognitive state and provide actionable and context-aware suggestions to the presenter about how to adjust their presentation delivery. We discuss the user scenario motivating our system design, its associated design considerations, the features of our system, and how our system can be extended to other presentation contexts.
Composing Interface Connections for a Networked Touchscreen Ensemble

In this paper, we explore new ways to compose networked interactions for ensemble performance using touchscreen instruments. We introduce a web-based touchscreen instrument modelled on classic synthesiser designs and a system for linking interface elements from this instrument between multiple members of the ensemble. We discuss our composition system and syntax that allows these network links to be scripted allowing a mode of performance inspired by open-form composition. The experience of composing and performing with these systems is investigated through co-located live and studio performances. Our findings support the utility of the system for improvisations and suggest ways to improve the system to cope with larger numbers of network links to enable more exciting performances.
Musical Metaverse Playgrounds: exploring the design of shared virtual sonic experiences on web browsers

The "Musical Metaverse" (MM) promises a new dimension of musical expression, creation, and education through shared virtual environments. However, the research on the MM is in its infancy. Little work has been done to understand the MM musical capabilities and its user experience. One cause can be found in the lack of technologies capable of providing high-quality audio streaming, and complex enough musical interactions within shared virtual worlds. Two promising candidates for bridging these gaps are web technologies such as WebXR and Web Audio, whose combination can potentially allow more accessible and interoperable networked immersive musical experiences. To explore this possibility, we developed two prototypes of MM playgrounds. We leveraged WebXR through Networked-AFrame, and Web Audio with Tone.js and Essentia.js to create and test immersive sonic experiences that conveniently run on the web browsers integrated into commercially available standalone Head-Mounted Displays. The first playground focuses on facilitating musical creation in a multi-user immersive application through real-time sound synthesis and binaural rendering. The second explores real-time audio analysis and music information retrieval for creating audio-reactive virtual shared environments. A preliminary evaluation of these two playgrounds is also presented, which revealed some usability issues such as: accessing the immersive experiences through URLs on a headset, the ambiguity in ownership of shared musical tools, and the impact of real-time audio analysis algorithms on perceived audio-visual latency. Finally, the paper outlines future work and discusses possible developments and applications of web-based MM environments.
AmI-L: An Ambient Intelligent LiDAR-Based Framework for Smart Home Care

Over the next decade, home care aides are on track to grow in employment by around 5 times more than the average job in the United States. However, a single home care aid is unaffordable for the average family. This will create a big economic health disparity in the U.S. Consequently, this indicates the need for less expensive, high-tech supplements to at-home care. At-home care systems have the potential to fill this need. This paper proposes the Ambient Intelligent LiDAR (AmI-L) framework, a novel framework that incorporates light detection and ranging (LiDAR) sensors into a smart home system to enable ambient intelligence (AmI) for at-home care monitoring.
Design and Implementation of an immersive, cooperative Net Art installation using Web Csound

"Gathering Infinite Madness" is a Net Art installation project that arose due to the social restrictions caused by COVID-19, as an alternative method of musical interconnection through the web. The installation stems from of two artworks: "Stria Infinite Madness" and "The Gathering," both designed for web platforms. Using WAAW Csound programming language (first release 2018), an attempt was made to address the problem of audio data stream latency in Networked Music Performance by sending/receiving a data stream through a web page. This data was then processed locally to output audio on connected devices. In this way, the user can generate complex sounds that will be spatialized and virtualized in a 3-D space surrounding himself. At the same time, data related to website connections are used to affect in real-time the playback of an immersive ambisonics soundscape composition based on lockdown's binaural recordings. Thanks to Csound the audio has been processed with ambisonics encoding and binaural decoding, ensuring full compliance with multi-channel ambisonics output for future developments.
An Improved YOLOv8 Network for Photosensitive Element Defect Detection

The photosensitive element plays a critical role in ensuring high image quality within camera modules. Detecting Dead Pixel and stain defects during production is crucial for maintaining this quality. However, the current target detection algorithm faces several challenges in accurately detecting these defects. Firstly, the small size and limited image information of Dead Pixel defects make it difficult to extract accurate features for classification and positioning. Secondly, label production for small Dead Pixel defects and unclear stain defect features often leads to missed or mislabeled defects, resulting in low-quality samples and unsatisfactory regression results. Lastly, the varying sizes of stain defects pose difficulties in detection, leading to suboptimal performance. To address these challenges, we propose an accurate defect detection model for photosensitive elements based on an improved version of YOLOv8. Specifically, we integrate the CBAM attention mechanism into the convolutional layer to focus on small defects and suppress background noise, effectively extracting feature information. Moreover, we employ the WIoU loss function to improve regression on regular samples while reducing attention to low-quality labels. Additionally, we replace the SPPF module with the SimCSPSPPF module, expanding the convolution branch and reducing computation, thereby improving the detection of larger targets like stain defects and increasing inference speed. Experimental results demonstrate the superiority of our method over multiple representative methods. Compared to the second-best method (YOLOv8), our method achieves significant improvements in the average precision of Dead Pixel defects (86.9% to 88.0%), the average precision of stain defects (93.3% to 94.7%), and the average accuracy (90.1% to 91.3%).
Vehicle Detection Method in UAV Images Based on Improved ObjectBox

In recent years, unmanned aerial vehicle (UAV) has been widely applied in various fields. Most detection is limited to fixed-view scenarios, while UAV-based object detection has a broader view and can monitor wider areas. However, the size of vehicles in images may change significantly and become smaller due to the changing and increasing distance between the drone and the ground, thus making vehicle detection challenging. In order to address the issue, a vehicle detection method for UAV aerial images based on ObjectBox was proposed. We replaced the original neck network with FPN to adapt to multi-scale object detection. Moreover, the Shuffle Attention module was added to FPN, improving its capability to obtain channel interaction information and spatial information by channel and spatial attention mechanisms. The experiments demonstrate the effectiveness of the improved ObjectBox detector. With a stronger neck network, we achieved a mean average accuracy of 55% and a real-time inference time of 39 FPS on the VisDrone sub-dataset. The proposed network keeps a balance between accuracy and speed to meet the requirements of practical applications.
Exploring Collaboration for Data Analysis in Augmented Reality for Multiple Devices

Collaboration is a key aspect of Cross-Virtuality Analytics. When collaborating in Augmented Reality, there are different types of display technologies available. In this work we elaborate on differences between video see-through, optical see-through and handheld Augmented Reality technologies with currently available hardware. We then present the concept of a prototype for collaborative data analysis combining these different technologies with laptops and pen and paper. Finally, we report on the initial findings of a pilot study on the impact of these technologies on collaboration in data analysis.