Reviewerly/exHarmony huggingface dataset:
Real-world rogue wave probabilities

&amp;lt;p&amp;gt;Despite several strong hypotheses on how rogue waves can be generated in idealized conditions, the actual real-world causes of these waves are still largely unknown. We credit this to insufficient amounts of observational data and a missing robust probabilistic framework to analyze the available data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We adress these issues by processing over 1 billion waves measured in the North Pacific and organizing them into a comprehensive catalogue. Through a robust, machine-learning driven analysis, we then identify several characteristic sea conditions that lead to significantly higher risks to encounter a rogue wave. This, in turn, yields quantitative evidence on the relative importance of the underlying physical mechanisms.&amp;lt;/p&amp;gt;
Bin Fitting

Imagine packing your car to go on a holiday. Probably there’s one person in your family that has the 3-D intuition needed to work out how to fit the suitcases just so, so that there’s a nook free here to fit a pair of boots and a cranny free there to fit the odd-shaped gift you’re bringing along to Aunt Mathilda. That one person always does the packing; the rest of you stay out of the way until the car is packed.
Asynchronous Programming

Asynchronous programming is tough and challenging but interesting. It is also known as asynchrony. The overall concept did not evolve in one day; it took time. The async and await keywords first appeared in C# 5.0 to make it easier. Prior to that, different programmers implemented the concept using various techniques. Each technique has its own pros and cons. The goal of this chapter is to introduce you to asynchronous programming and to go through some common implementation methods.
Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

Recently, reversible data hiding in encrypted images (RDHEI) based on pixel prediction has been a hot topic. However, existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction, and the pixel labeling scheme is inflexible. To solve these problems, this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling. First, we design an adaptive gradient prediction (AGP), which uses eight adjacent pixels and combines four scanning methods (i.e., horizontal, vertical, diagonal, and diagonal) for prediction. AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel, which improves the prediction ability of the predictor for complex images. At the same time, we adopt an adaptive huffman coding labeling scheme, which can adaptively generate huffman codes for labeling according to different images, effectively improving the scheme’s embedding performance on the dataset. The experimental results show that the algorithm has a higher embedding rate. The embedding rate on the test image Jetplane is 4.2102 bpp, and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.
Research on High-speed Object Tracking Based on Circle Migration Estimation Neighborhood

In modern industrial applications, application of object tracking for high-speed moving objects is becoming more and more extensive. However, during rapid movement of the target object, the target image will be blurred and the target moving area will be large, or even disappear for a short time. In view of the above problems, this work gives a framework of estimating offset of the target object. The neighborhood of offset estimation is calculated with Circle algorithm, and the one short learning based on SiamRPN++ is used to realize online tracking for high-speed object. With the effect of uncontrolled motion blur and short-term disappearance, this method can solve the problem of failed tracking due to large moving area of the high-speed moving object and realize online tracking for high-speed moving object under a common camera.
Fifth International Workshop on Variability and Evolution of Software-Intensive Systems (VariVolution 2022)

Software versions resulting from evolution in time (revisions) and space (variants) are still separately managed instead of being treated uniformly. Recently, several research activities have focused on the integrated management of evolution and variability. Existing approaches stem from multiple origins, most notably from the fields of software configuration management and software product line engineering. For instance, variation control systems adopt a holistic view on software evolution in time and space with the ultimate goal of systematically managing software revisions and variants. VariVolution (the 5th International Workshop on Variability and Evolution of Software-Intensive Systems) aims at bringing together active researchers studying software evolution and variability from different angles as well as practitioners who encounter these phenomena in real-world applications and systems. The workshop offers a platform for exchanging new ideas and fostering future research collaborations and synergies.
Discussion on New Engineering Talent Training System of Petrochemical Emergency Based on Multi-Integration

A deep understanding of the existing problems in the traditional engineering specialty plays a curial role in building the new engineering curriculum. The achievement orientation, curriculum crossover, and content connection induce the lack of practical, innovative, and forward-looking in traditional engineering, which weakens the high-quality development of engineering students. Here, we propose a new engineering curriculum based on a multi-disciplinary and cross-integration system, meeting the development requirement of the engineering ability and scientific-technological innovation. We adopt the multi-integration training mode, collaborating with multi-resources of government-user-industry-university-research and cross-collaborating with multi-disciplinary of science-engineering-literature-management-law-medicine. The new curriculum system promotes students' comprehensive ability on the petrochemical emergency foundation, engineering practice, engineering innovation, and multi-innovation, fulfilling the theoretical, practical, engineering, and innovation cultivation requirements, respectively. We apply this curriculum system to cultivate under-graduates of petrochemical emergency engineering disciplines, improving their comprehensive ability significantly. The results may reference the new engineering education reform and associated curriculum system construction.
MultiCity: A Personalized Multi-itinerary City Recommendation Engine

Tourism plays a vital role for every nation today, as it can generate a large number of jobs and is the major source of revenue. Tourists face a lot of difficulties in choosing their itineraries in terms of their interest, popularity and a number of constraints. In general, it is also hard to suggest a certain city to a traveler. This work is useful for tourists who desire to visit various cities across the world. The proposed approach MultiCity recommends multiple cities keeping in mind interest, popularity and travel costs. The proposed MultiCity approach results reveals that it outperforms benchmark approaches with respect to real-life values.
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning

Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new classes. Existing exemplar-free class-incremental methods focus either on successive fine tuning of the model, thus favoring plasticity, or on using a feature extractor fixed after the initial incremental state, thus favoring stability. We introduce a method which combines a fixed feature extractor and a pseudo-features generator to improve the stability-plasticity balance. The generator uses a simple yet effective geometric translation of new class features to create representations of past classes, made of pseudo-features. The translation of features only requires the storage of the centroid representations of past classes to produce their pseudo-features. Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes. The incremental process is much faster with the proposed method compared to mainstream ones which update the entire deep model. Experiments are performed with three challenging datasets, and different incremental settings. A comparison with ten existing methods shows that our method outperforms the others in most cases.
Examining vehicle detection and taxonomy for traffic analysis and reporting

Programmed vehicle distinguishing proof is an indispensable stage in clever rush hour gridlock frameworks. These days’ vehicles structure a significant piece of transportation. Furthermore, the use of vehicles has been extending considering populace development and the expanding human needs as of late. Along these lines, control of vehicles is transforming into a significant issue and fundamentally harder to determine. Programmed vehicle recognizable proof frameworks are used for the viable control of vehicles utilizing on streets. Programmed vehicle acknowledgment (AVR) is a type of programmed vehicle recognizable proof innovation. It is an innovation utilized in picture handling which is utilized to distinguish and identify vehicles just by their tags. Continuous AVR assumes a crucial job in checking the traffic rules and takes incredible consideration in keeping up the law authorization on open boulevards. Since each vehicle conveys a solitary tag, no outer cards, names or transmitters should be perceived, just tag. In the current layout, for the exploration of a vehicle in multipath system the mind-boggling foundation based on the accompanying hues technique was acknowledged. The new model has been developed to chip the lopsided light with the goal of finding numerous cars on the specific automobiles that are stuck on the similar spot. The present model aims to coordinate and process the vehicle thickness over the vehicle text, which is divided by a variety of science & morphological tasks in order to distinguish vehicle from the image. The proposed model is intended for the usage of the implementation of the neural network for organizing the cars on the edges of the video information files. The suggested model would address the question of precision in the vehicle object identification and the classification of vehicle objects. In the proposed model, the video observation details received for the exhibition assessment was evaluated in different ways. On any parameter evaluated, the proposed model results were found to be adequately efficient and superior to the current models by almost 10 to 20%.
老挝表层沉积物69种元素地球化学背景值

摘要: 老挝地质条件优越，资源潜力巨大. 为响应国家“一带一路”倡议，在老挝全境开展1∶100万国家尺度地球化学填图工作. 采集了大量地球化学数据，为研究区元素分散富集、矿产开发、环境保护、农业生产提供了高质量的基础地球化学数据.共采集地球化学样品2 079件，采用高精度分析技术及严格的质量控制，分析了69种元素. 采用X±3S一次性剔除异点后数据集的中位值作为估值，首次给出了老挝全国69种元素地球化学背景值，填补了老挝国家尺度地球化学填图工作的空白. 初步讨论了老挝全境、7个三级大地构造单元，6个三级成矿带69种元素的背景值特征. 研究表明不同构造单元中受地质背景、构造及岩浆活动的影响，元素分布具有各自特征，同时不同成矿带受成矿作用及构造岩浆活动的影响，元素在各个成矿带分布特征不同，元素的富集对矿床有很好的指示意义. 这些背景值的获得为下一步深入研究老挝地球化学填图数据提供了基础对比数据. 关键词: 69种元素背景值 / 表层沉积物 / 国家尺度填图 / 地球化学 / 老挝
Design of Robotic Arm Control Model for Rescue Applications

The paper designs the device to support rescue and rescue in disasters such as earthquakes. When disasters occur, human resources for relief are not available. Therefore, we present the research and development on the design of a robotic arm that assists in picking up objects and moving them to a safe place in this paper. We divided it into parts including the robot arm to identify and approach objects, the infrared part, and sensor to determine where to place objects, and the body part to control the system with this device The device with the goal of picking up an object at any position and lining up objects with the desired length and height. The paper consists of the block diagram design, the operating principle of the device, the results, and the development direction. Experimental results with 1:25 scale simulation model give with a deviation of no more than 1 cm for three cases of horizontal, vertical and diagonal objects. The results show that the system is suitable for applying in real environment.
Reasoning in the Description Logic ALC under Category Semantics

We present in this paper a reformulation of the usual set-theoretical semantics of the description logic $\mathcal{ALC}$ with general TBoxes by using categorical language. In this setting, $\mathcal{ALC}$ concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories. Such a category-based semantics provides a more modular representation of the semantics of $\mathcal{ALC}$. This feature allows us to define a sublogic of $\mathcal{ALC}$ by dropping the interaction between existential and universal restrictions, which would be responsible for an exponential complexity in space. Such a sublogic is undefinable in the usual set-theoretical semantics, We show that this sublogic is {\sc{PSPACE}} by proposing a deterministic algorithm for checking concept satisfiability which runs in polynomial space.
Elderly Nursing Service System Based on Improved Particle Swarm Optimization Algorithm for Camera Calibration

It is the obligation of future generations to ensure that the elderly can enjoy their old age happily and comfortably. It is also a manifestation of social civilization and progress, and it is the inheritance and development of the traditional virtues of the Chinese nation. The purpose of this paper is to study the construction and optimization of elderly care service system based on particle swarm algorithm. The basic principles of the construction of the elderly care service system, the specific content of the optimization of the service, and the hardware selection and implementation of the elderly care system are expounded, and the selection and calculation of each hardware of the system are carried out calibration and improve its calibration accuracy. The camera calibration based on improved particle swarm algorithm is applied to the system's physical sign detection module, and the reliability of data communication is tested. The average error rate of data communication is 0.33%, which ensures the safe and stable operation of the entire motion control system, making it a Simple and easy to operate.
PEMILIHAN JENIS BIBIT UNGGUL TANAMAN NANAS DENGAN METODE ORESTE

Indonesia is a country that has a tropical climate that is rich in fruits. One of the tropical fruit commodities in Indonesia is pineapple. Pineapple is most interest by public because price of the fruit is cheap, easy to obtain and has a distinctive aroma, taste, and color. Pineapple can be used as a medicinal plant. In the cultivation of pineapple plants, require the availability of good land and availability of seedlings that can be obtained in a vegetative and generative methods. Pineapple seeds obtained by generative means are fruit crowns, tiller shoots, root shoots, fruit shoots, stem cuttings. Meanwhile, pineapple seeds obtained by generative means are pineapple seeds. In pineapple cultivation carried out by pineapple farmers in Panribuan Village, Simalungun Regency, it is still based on farmer’s instincts and feelings, so that pineapple harvests often occur that are less than optimal, are not resistant to pests, yields are less than optimal, harvest age becomes longer. With the alternatives that exist in the decision support system with the ORESTE method to select superior seeds of pineapple plants. A decision support system is one of the ways to organizing information intended to be used in decisions making. ORESTE is a method that was built for conditions where a set of alternatives will be sorted by criteria according to the levels of importance. From the calculation results obtained the best alternative is sapling shoots with the preference value of 1,547
Effects of the Icon Brightness, App Folder Opacity, and Complexity of Mobile Wallpaper on the Search of Thumbnail Icons

In the past ten years, smartphones have gone far beyond communication tools. The average number of apps per capita reached more than eighty, which brings difficulties to the search and recognition of icons. As the number of apps increased, app folders became widely used, which also brought up new problems, i.e., how to search and recognize the apps by their thumbnails icons. Furthermore, those icons are displayed under all kinds of personalized wallpapers. The present study focused on the recognition of thumbnail icons in the folder on wallpapers of different complexity. According to the literature, the contrast between icon and background, and complexity of the wallpaper are two key factors for icon identification. The contrast is determined by the opacity of folder and the brightness of icons. Thus, we studied the effect of the brightness of icons, the opacity of the folder, and the complexity of the wallpapers on icon selecting. Twenty-three users participated in the experiment. The results show interaction effect of wallpaper complexity and folder opacity on search performance and subjective rating. On the complex wallpaper, with the increase of the opacity of the folder background, the search time and the subjective difficulty decreased significantly, the aesthetic and clarity increased significantly. So we recommend high folder opacity for complex wallpaper. On the simple wallpaper, the search time and subjective difficulty does not change with the opacity of the folder background. The aesthetic increased between low level to medium level but decreased at a high level, which imply the folder opacity for simple wallpaper shouldn’t be too high.
Intent Classification Based on Single Model

Intent classification is an important preprocessing step in natural language processing tasks, used to categorize input text into specific intents, in order to assign them to corresponding subsystems or processing flows. In dialogue systems, intent classification is used to identify the intentions of users, so as to respond accordingly to their needs. In previous research, various types of neural network architectures have been proposed for intent classification tasks, including feedforward neural networks and recurrent neural networks (RNNs). This chapter provides a comprehensive introduction and comparison of two common RNN architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). LSTM and GRU are two common RNN architectures used for intent classification tasks and intent recognition in dialogue systems. They perform well in handling long text sequences and large dialogue datasets, and have their own advantages and applicable scenarios. The choice between these models depends on specific task requirements, dataset size, computational resources, and other factors.
Semantic Segmentation of Images Based on Multi-Feature Fusion and Convolutional Neural Networks

Image semantic segmentation technology is one of the core research contents in the field of computer vision. With the improvement of computer performance and the continuous development of deep learning technology, researchers have more and more enthusiasm to study the actual effect and performance of image semantic segmentation. The results of deep semantic segmentation allow computers to have a more detailed and accurate understanding of images, and have a wide range of application needs in the fields of autonomous driving, intelligent security, medical imaging, remote sensing images, etc. However, the existing image semantic segmentation algorithms have the disadvantages of easy discontinuous results and insufficient prediction accuracy. In this paper, we take deep learning-based image semantic segmentation technology as the research object to explore the improvement of the image semantic segmentation algorithm and its application in road scenarios. First, this paper proposes MCU-Net method based on residual fusion and multi-scale contextual information. MCU-Net uses residual fusion module to deepen the network structure and improve the ability of U-Net to acquire deeper features. Then a top-down and bottom-up path is constructed for feature information between different levels, and the spatial and semantic information contained in shallow and deep features in the network is fully utilized by fusing features from different levels. In addition, an enhanced void space pyramid pooling module is added for feature information between the same levels, which enables the output features to have a larger range of semantic information. Second, this paper proposes the DAMCU-Net method based on attention mechanism and edge detection based on MCU-Net. DAMCU-Net extracts global contextual information by the attention mechanism optimization module, while fusing features using dense jump connections to facilitate the network to recover more spatial detail information during upsampling, and uses the FReLU activation function to improve the segmentation capability of the network for complex targets. For the edge information lost in the feature extraction process, the edge detection branch is added to supplement the feature information of the main path by feature fusion to achieve the optimization of the edge information.
Local Optimized Attention with Graph Neural Network for Session-Based Recommendation

Session recommendation based on graph neural networks has become a research focus and hotspot in the field of recommender systems in recent years. It aims to predict the user's next-clicked item through an anonymous and concise interaction sequence. Most existing recommendation systems only utilize the neighbor matrix of nodes for feature extraction in the session graph, resulting in limited access to relevant information. The consideration of user's short-term interests in extracting global preferences is not precise enough, introducing multiple sources of noise that affect the accuracy of recommendations. To address these issues, this paper proposes a Local Optimization Attention-based Graph Neural Network model (LOAGNN) for session recommendation. The model introduces attention mechanisms during session feature extraction, taking into account the out-degree, in-degree, self-connection, and bidirectional connection of the current node, thereby capturing more useful information about the current session. When assigning weights to user's short-term interest preferences through a soft attention mechanism, a GRU module is employed to eliminate noise and further refine the user's current interest preferences, ensuring the accuracy of the extracted data. Finally, a linear fusion of global preferences and local preferences is used to predict user behavior. Experimental results demonstrate a significant improvement in the recommendation performance of LOAGNN.