Reviewerly/exHarmony huggingface dataset:
Safety helmet wearing detection algorithm based on lightweight FastestDet

Abstract—Aiming at the problems of long reasoning time and high hardware requirements of workers ' helmet wearing detection model in existing construction sites, a lightweight helmet wearing detection algorithm based on improved FastestDet is proposed. Firstly, the FASTDET backbone network is optimized to further reduce the number of parameters while ensuring accuracy. Secondly, the spatial pyramid pooling module is changed from SPP to SimSPPF proposed by Meituan YOLOv6, which accelerates the recognition speed. Finally, the EMA attention mechanism is introduced at different locations to enhance the perception ability of the model. The experimental results show that the recall rate and mAP of the improved FastestDet algorithm are 1.3 % and 1.7 % higher than FastestDet, respectively. And the inference speed is faster than other models.
Multi-scale feature and correlation volumes for Video Interpolation

Video frame interpolation involves synthesizing intermediate frames between two consecutive frames to enhance the smoothness of a video. Nevertheless, generating high-quality interpolated frames in videos featuring substantial motion and complex scenes remains a formidable challenge. To produce superior quality frames, this paper introduces an interpolation method based on multi-scale features and correlation volumes. The multi-scale feature connects the deep features of high-resolution frames with the shallow features of lower-resolution frames, thereby increasing the number of available pixels and feature details for motion analysis. Correlation volumes are employed to construct correlation features for all pairs of pixels, which are utilized to refine the underlying optical flow field. We propose a unified network approach that eliminates the need for additional complex optical flow network integration, simplifying the training process. The experimental results demonstrate that this method outperforms the baseline approach in both objective and subjective evaluations across various datasets. Particularly, this method exhibits advantages on datasets characterized by complex backgrounds and large motions.
Gathering Experts: How to Collect Knowledge Efficiently

This paper is about requirements engineering in enterprise level organizations for agile software development. In this context refining requirements is usually a multi-step process that consumes significant ressourses. Unfortunately the required knowledge is spread between several teams and their members. The time and energy of all participants are limited. So the goal is to bother them as efficiently as possible while not needing to wait for their feedback for too long. This paper introduces two patterns to deal with these challenges in order to get optimal results with limited effort.
Formal model-based argument patterns for security cases

Assuring that security requirements have been met and detecting flaws in the early phases of the system development is less expensive than changes after system deployment. The deployment of industrial critical systems requires a security assurance case that represents a credible argument, supported by evidence, demonstrating that the system satisfies its security requirements and objectives. Building arguments and generating evidence to support the claims of an assurance case is of utmost importance and should be done using a rigorous mathematical basis, namely formal methods. This paper proposes an approach to constructing security assurance cases using formal methods. The proposed approach involves the following three steps: (1) decomposing security requirements and deriving security threats; (2) formalizing the system model and security threats; and (3) deriving the security argument patterns supported by the results of the formal verification of the security requirements. We present the derived argument patterns using the Goal Structure Notation pattern notation. We apply the patterns to build security cases of an autonomous drone case study system.
Underwater Image Dehazing in YCbCr Color Space Using Superpixel Segmentation

The article introduces a novel technique to improve underwater image quality, addressing challenges such as light absorption, poor contrast, scattering effects, and color distortions. The algorithm uses the YCbCr color space to separate image luminance and chrominance, allowing a targeted treatment of these elements. It begins by converting the RGB underwater image to YCbCr, followed by decomposing it into luminance and chrominance components. The luminance undergoes dehazing using the underwater normalized total variation (UNTV) method and superpixel segmentation to improve clarity and reduce haze. Atmospheric light estimation through superpixel segmentation contributes to accurate scene radiance restoration. Chrominance is then refined using the dehazed luminance, enhancing color accuracy and contrast. After independent processing, the luminance and chrominance components are recombined in the YCbCr space, and the final image is converted back to RGB. The efficacy of the algorithm is demonstrated on two benchmark datasets, showing its promise and highlighting the potential of the YCbCr color space for effective underwater image enhancement.
Image classification trusted model based on horizontal federated learning and SGX

The rapid development of computer image processing has greatly improved the progress of artificial intelligence. When big data and distributed machine learning interact, the accuracy and efficiency of algorithm recognition can be greatly improved, but its security cannot be guaranteed in terms of privacy data protection. The traditional horizontal federated learning algorithm FedAvg can perform model training under the premise of protecting user data, but the security vulnerabilities in the parameter interaction process and the local data training process may be exploited by internal or external attackers. In order to balance the efficiency and security of the algorithm, this paper proposes an image classification model SGX-FedAvg. The model adds Intel SGX based on hardware encryption mechanism on the basis of horizontal federated learning FedAvg, which loses a small part of the algorithm efficiency of federated learning and adds reliable security to its model. In this paper, two data sets, Cifar-10 and Mnist, are selected for training. The model proposed in this paper is compared with Native local distributed machine learning and FedAvg horizontal federated learning. It is analyzed that SGX-FedAvg has good performance in execution efficiency and security within acceptable overhead, and its feasibility is verified. Finally, it points out which fields the model can be applied to.
A Vehicle Detection Method under Strong Infrared Radiation Interference

Strong infrared radiation interference is a common type of interference in optoelectronic countermeasures. An optical system exposed to strong light would disrupt the acquisition of image information and even damage the device. In this paper, we aim to design an infrared system for object detection under the circumstances of strong infrared radiation interference in order to enhance the countermeasure capability of the optoelectronic system. Firstly, we introduce the Wave-front Coding (WFC) technique to attenuate the damage caused by strong infrared radiation interference. Secondly, we use L₂ (Tikhonov) regularization to restore the blurred image caused by wavefront coding and obtain a clear low-light image. Thirdly, we use Recursively Separated and Weighted Histogram Equalization (RSWHE) for image enhancement to improve its contrast. Finally, we use the YOLOv8 algorithm for object detection. Experiments on real infrared images show that this scheme has outstanding performance under strong infrared radiation interference.
Anti-patterns in managing uncertain Non-Functional Requirements

Managing non-functional requirements (NFRs) is complex and has been challenging over the years. These requirements are typically used to make important architectural decisions early in the project, which can be a problem if they are uncertain or volatile. Identifying and demonstrating the existence of anti-patterns associated with the negligence and volatility of NFRs is a topic that deserves attention. This study identified five anti-patterns associated with managing NFRs and the volatility of these requirements. Based on a survey applied to 40 professionals with more than 10 years of experience, data were obtained from 144 NFRs of real projects, and important dysfunctions were identified. The mapped anti-patterns describe dysfunctions associated with: incorrect perception of needs (FALSE CERTAINTY); delayed elicitation of NFRs (LATE IDENTIFICATION); immutable NFRs (FROZEN NFR); projects that include all NFRs without validation of feasibility and impacts (FULL PACKAGE); and equal management for all NFRs (INVARIANT MANAGEMENT).
Dramatizing Software Patterns: Focus Group Report

The focus group on drama patterns we organized at EuroPLoP 2023 attracted 16 participants (apart from the organizers), who together built a short drama play (created by Aleksandra Vranić) based on the motifs from the stories of Alice in Wonderland. We discussed how particular drama patterns metaphorically correspond to some software patterns and elaborated on two examples of such correspondence: the Thoughts Reflecting Environment drama pattern and its correspondence to the Observer design pattern and the Loosely Coupled Situations drama pattern and its correspondence to the Visitor design pattern. Both drama patterns were first played by the participants and then related to the corresponding design pattern. We also played Observer in a technical, nonmetaphoric way. We also made a performance at the banquet, which was accepted with delight. We achieved this with no rehearsals and with the actor for the main role appointed just before the performance, which confirmed once more how powerful patterns are.
Infrared Image Enhancement Algorithm Based on Multiscale Guided Filtering

Infrared thermal imaging is capable of operating under various lighting conditions, unaffected by external factors, but it often suffers from issues like low contrast and blurry details. This article introduces an infrared image enhancement algorithm based on multi-scale guided filtering. The algorithm employs guided filtering to split the captured infrared image into a base layer and a detail layer. The base layer undergoes non-linear compression via segmented histogram equalization to enhance image contrast, while multi-scale guided filtering extracts diverse detail information from the image, which is then merged into a new detail layer to amplify fine details. Finally, the base layer and detail layer are combined to complete the enhancement of the infrared image. Experimental results demonstrate that this algorithm surpasses suboptimal approaches by 15.2% in terms of Enhancement Measure Evaluation(EME) and enhances the peak signal-to-noise ratio (PSNR) by 7.9%.
Leading a Software Architecture Revolution - Part 2a: Strategic Prioritization

Software architecture revolution is the extensive and profound transformation of a software system's fundamental structures. It aims to enhance attributes such as scalability, availability, and maintainability or to enable the implementation of new requirements incompatible with the system's current capabilities. Undertaking an architectural revolution requires significant organizational effort and commitment, and effective leadership is crucial for its success. Despite the extensive research available on the technical aspects of architectural transformations, few studies focus on the leadership perspective. Leaders play a crucial role in championing the revolution initiative, prioritizing activities, negotiating resource allocation, evaluating outcomes, implementing corrective measures, and reporting accomplishments. This article builds up on our previous work and presents practical insights to describe effective strategic prioritization patterns for enhancing the effectiveness of architectural revolution initiatives.
Introduction to work with GenAI

This paper provides an in-depth exploration of the practical applications and capabilities of generative AI technologies, specifically focusing on OpenAI's GPT-3, DALL-E and ChatGPT. It targets individuals who are already acquainted with these technologies but may not fully grasp their extensive potential beyond common use cases. The paper emphasizes the relevance of these technologies across various interdisciplinary fields. Key topics include the strategies for effective prompt construction in text and image generation, the utility of ChatGPT in generating human-like conversational responses for diverse applications and the use of personas to better understand and apply AI in corporate and decision-making contexts. Furthermore, the paper discusses the latest advancements in generative AI, including GPT-4, ChatGPT plugins, enhanced data analysis features and the introduction of DALL-E 3, highlighting their impact on the overall utility and application of these technologies.
Bidirectional Temporal Fusion Video Denoising Based on W-Net

The paper provided a brief analysis of video denoising characteristics, discussed and analyzed various existing video denoising methods, and proposed a new video denoising algorithm based on bidirectional time fusion and the W-Net architecture, designed to meet the requirements of real-time video denoising. This algorithm effectively combines past and future information, increases the temporal receptive field, and reduces memory usage. Additionally, by selecting a deeper W-Net backbone network, the algorithm achieves high-fidelity real-time video denoising. Comparative analysis with other video denoising models demonstrated that this approach outperforms others in terms of fidelity.
Underwater Image Enhancement with Color Correction using Convolutional Neural Networks

In the underwater environment, the color distortion and low contrast of the image are caused by environmental problems such as light absorption and scattering, which leads to the degradation of image quality. In order to improve the visual effect of the image, this paper proposed a color correction underwater image enhancement algorithm based on convolutional neural network. Firstly, a new underwater imaging model was used to correct the color cast problem of underwater images. Then, the convolutional neural network is used to extract the channel features of the corrected image, and the channel features are re-weighted by the multi-scale attention module to enhance the consistency of different feature maps, and the feature fusion is performed with the color corrected image. Finally, the image enhancement effect was improved by the fusion of features through the reconstruction calculation module. Experimental results show that the proposed algorithm can better correct the color distortion of the image and improve the image contrast. The main advantage is that the running speed of the proposed algorithm is two times faster than other advanced underwater image enhancement methods.
Information Hiding Algorithm Based on Three-dimensional Model Fusion Spherical Analysis

In response to the limited number of modifiable vertices or patches on a single carrier, which cannot meet the increasingly stringent requirements for capacity and robustness in the field of information hiding, this paper proposes an information hiding algorithm based on three-dimensional model fusion state spherical analysis to break through the limitations of single carrier information hiding. First, the 3D model is normalized, and the global features obtained from the shape distribution function are used to measure the similarity of the 3D model to complete the classification of similar models, and then the carrier fusion state is processed by the way of receiving balls from similar models; Secondly, robust points are labeled based on the curvature characteristics of each point in the fusion state point cloud of similar carriers, and the matching relationship between the secret information and the carrier is optimized through spherical segmentation. The secret information is segmented according to the number of fusion state models and then embedded into layers, effectively ensuring the robustness of the algorithm; Finally, the secret information is embedded by changing the least significant bit of the carrier vertex.
Human behavior recognition algorithm based on improved ST-GCN

Human behavior recognition algorithms are widely used in various fields, but the traditional human behavior recognition algorithm ST-GCN (spatial temporal graph convolutional networks) only considers the connection of visible nodes, ignoring the problem of motion features. To solve these problems, an improved algorithm based on ST-GCN is proposed in this paper. Firstly, the direct relation of each node is determined in the spatial domain, and the spatial characteristics of the human skeleton are obtained. Then, the motion change vector of the same node between adjacent frames is calculated in time to obtain the temporal characteristics of the human skeleton. Finally, the complete space-time information of the human skeleton is constructed by combining the spatial and temporal characteristics, and integrated into the graph convolutional network. On the two benchmark CS and CV of NTURGB+D dataset, the accuracy of the proposed algorithm is improved by 5.51% and 5.35%, respectively, compared with the original method, which verifies the effectiveness of the proposed method.
IDMUNet : An effective network for liver tumor segmentation

Most existing approaches for medical image segmentation has been proposed, but they can't get desirable result due to the network. The classical network lose much information during downsampling process. And the medica images segmentation is easy to be contaminated by heavy noise, poor boundaries and so on. To address these issues, we propose an effective network named IDMUNet, for liver tumor segmentation in CT images, This network introduces an information supplement(IS) module to guide the network to efficiently fuse global and local information. After that, the features obtained from each encoding layer of the encoder are fed into a dense attention-based atrous spatial pyramid pooling(DAASPP) module to further extract features on channel and spatial domains, thus it can not only obtain effective features in channel and spatial dimensions, but also suppress irrelevant weight information. What's more, to obtain better edge information, a multi-task learning(MTL) module is introduced to enable the network to achieve better performance. Experiments on the public liver tumor dataset, LiTS2017, demonstrate that the effectiveness of the proposed IDMUNet and the superiority of our method against the SOTA methods.
Face makeup transfer based on Generative Adversarial Network

The goal of the face makeup transfer algorithm is to transfer the face makeup from the specified reference image to the non-makeup face in the target image while maintaining the face structure of the target image during the transfer process. Aiming at the problem that the currently proposed facial makeup transfer network is prone to deformation and the strength of makeup is uncontrollable, we propose an algorithm to realize facial makeup transfer by using the generation of confrontation network, adding the mask area corresponding to the fusion of attention and makeup. Firstly, the corresponding masks of lip makeup, eye makeup and base makeup are generated based on the detection of face key points, and then the masks are embedded with the features extracted by the encoder to generate the corresponding feature matrix to ensure the consistency of the non-makeup area before and after the transfer. Secondly, the global style features are generated by integrating the feature results through the attention module, and the high-quality makeup image is synthesized while maintaining the integrity of the face structure. Our method can not only keep the face structure unchanged during the transfer process, but also form a more natural face makeup transfer effect. The experimental results show that the method has better training results on the data set, and the generated facial makeup transfer image is better than PSGAN and BeautyGAN in terms of visual experience, and the strength of the transferred makeup can be adjusted by parameters.
Visual Quality Assessment of HDR Omnidirectional Image System Based on Viewport Feature Learning

High dynamic range (HDR) omnidirectional image system can provide users with an immersive visual experience, but its coding, transmission and visualization processes will cause corresponding coding distortion, tone mapping distortion and mixed distortion, thereby reducing the quality of HDR omnidirectional image (HOI), and affecting the quality of user experience. Different from ordinary 2D images, omnidirectional images are usually viewed using head mounted display (HMD). Therefore, this paper proposes an HOI quality assessment method based on viewport feature learning, which includes a preprocessing module, feature extraction module and quality regression module. Firstly, in order to be consistent with what is observed on the HMD, viewport images are generated from HOIs as input images by the viewport sampling. Afterwards, they are input in parallel to feature extraction modules. Considering the distortion aliasing of images in channels and spaces, a triplet attention mechanism is used to capture joint features on spaces and channels. Finally, considering the interaction between different viewports, the quality regression module aggregates the features of different viewports to obtain the final quality score. Experimental results show that the proposed method achieves excellent performance on an HDR omnidirectional image database.
Machine Learning Model for Primary Solar Resource Assessment in Colombia

This work introduces a Machine Learning (ML) model designed to predict solar radiation in diverse cities representing Colombia's climatic variability. It is crucial to assert that the amount of solar energy received in a specific region is directly related to solar radiation and its availability, which is influenced by each area's particular climatic and geographic conditions. Due to the high variability and resulting uncertainty, various approaches have been explored, including the use of numerical models to estimate solar radiation. The primary objective of this study was to develop and validate an ML model that accurately predicts solar radiation in cities. The methodology employed was specific to data treatment and ML model development. It was structured into three fundamental stages: clustering, estimation, and response, considering that the model is based on historical data. The obtained results were assessed using appropriate statistical definitions, not only determining the model's efficiency in terms of prediction but also considering interactions between data for the approximation and prediction of solar radiation. In this context, it is crucial to emphasize that the research contributes to understanding solar radiation in Colombia. This study underscores the importance of developing ML models to predict solar radiation, emphasizing the need to consider the country's climatic diversity. The results obtained, following the model's application, provide valuable information for comprehending and anticipating the availability of this primary resource.