Reviewerly/exHarmony huggingface dataset:
Cross-guided Cross-modal Feature Fusion Network for RGB-D Salient Object Detection

RGB-D salient object detection has been one of the hottest research topics in the field of computer vision in recent years. The research in this field aims to achieve automatic detection and segmentation of salient targets in scenes by combining information from RGB images and depth images. The existing RGB-D salient object detection methods generally operate directly on different modules without considering the complementarity between different modes. And for multi-modal fusion, the differences between different modalities have not been fully explored. In order to better solve the above two problems, we propose a cross-guided cross-modal feature fusion network(CCFFNet). It is composed of a cross-guided feature enhancement (CFE) module and a multi-modal feature fusion (MFF) module. Specifically, in the cross-guided feature enhancement module, the representation of cross modal features is enhanced through guided learning of the mutual feature weights between RGB and depth, fully exploring the complementarity between RGB and depth. In addition, we also utilize multiple different levels of modal features to participate in fusion, enhancing the fusion features through attention, making the model significance prediction of RGB-D salient object detection more accurate. Finally, extensive experiments on five benchmark datasets have shown that our model outperforms the other seven state-of-the-art methods, while also demonstrating its superiority.
A Traffic Sign Recognition Method with Bi-Level Routing Attention

Traffic sign recognition has long been an essential part of automatic driving. Accurate traffic sign recognition algorithms are conducive to the development of automatic driving. However, traffic signs in natural scenes are often small targets, which contain less feature information and are difficult to detect and recognize. Aiming at the problem of low recognition accuracy of small targets in traffic sign recognition, a traffic sign recognition algorithm based on a Bi-Level Routing Attention is proposed. It utilizes a single-stage detector YOLOv5 as the basic network, with a double-layer routing attention mechanism introduced, realizing more flexible computing allocation and content awareness through double-layer routing, which could capture long-range data without incurring an increasing computational overhead and memory usage. Furthermore, our approach achieves noteworthy recognition accuracy by extracting fine-grained information of small targets. In particular, the experiment shows the appealing results on the TT100K dataset, which detection accuracy map of reaches 92.9%, and the frame processing rate is 119.8FPS.
Simulation Design of Meta-Universe App for Urban Communities in the Context of Digital Twins

In this paper, we present an attempt to apply the metaverse in a 3D multiplayer social app using Unity3D engine and Microsoft SQL Server, centering on the concept of the metaverse, and benefiting from advanced geographic information technology, using various geographic data, such as global digital elevation, OSM geographic files, and POI data, to create a digital twin of the real world in the virtual world. . The operation principle of each part is also introduced. A connection channel between virtual space and the real world is explored.
Double-encrypted Watermark Image in Dual Domain for Authentication and Recovery

The authenticity and retrievability of multimedia content must be protected at all costs in the age of digital information exchange and security issues. The use of a double-encrypted watermarking technique in dual domains is a novel method presented in this paper for improving image authentication and recovery. In order to strengthen the security of an image, we present a strong watermarking approach that uses a dual-domain modification process to embed authentication and recovery information into the image. By adding two levels of encryption, the suggested solution increases the integrity of multimedia content's resistance to alteration and illegal access. Through comprehensive testing and analysis, we show how well our strategy works for both verifying the image's source and assisting with its recovery in the event of attacks or distortions.
Human Novel View Synthesis from Monocular RGB Video

The novel view synthesis of human bodies is an important step forward for virtual reality. In particular, it is able to predict the invisible domain of moving human bodies, achieving robust and stable reconstruction performance. We propose a novel view synthesis method for human body based on monocular RGB video, while rendering a 360° novel view, avoiding expensive sensor equipment. We integrated the observation space and the canonical space. On one hand, the model could be better optimized and controlled by decomposing geometric human posture and appearance; on the other hand, the rigid and non-rigid motion could be further decoupled to facilitate better optimization approximation. At the same time, we proposed a compact feature description, which could extract the representation to the maximum to achieve better results. We also present qualitative and quantitative results on the performance of the ZJU-MoCap dataset.
Progressive Multi-scale mutual Feedback network for salient object detection

The aggregation of multi-level features extracted from convolutional neural networks has been a pivotal factor contributing to the remarkable progress achieved by most existing salient object detection models. However, the discrepancy between features of different levels has not been comprehensively studied. How to design more effective fusion strategies to reduce this discrepancy has become an important problem in SOD. In this paper, we propose a progressive multi-scale mutual feedback network (PMFNet) to solve the above problems, which is composed of the Context-aware Residual Pyramid Module (CRPM), the Cross Attention Module (CAM), and the Progressive Interweaved Feedback Decoder (PIFD). CPRM integrates various feature information using different receptive fields, enabling the model to acquire both multi-scale information and preserve fine local details of the features. CAM can distinguish between high-level and low-level features and incorporate feature crossing to mitigate the adverse impact of redundant features. Besides, to capture finer details, PIFD can iteratively refine multi-level features using feedback mechanisms. The experimental results on 5 benchmark datasets demonstrate that the proposed method outperforms 12 state-of-the-art methods, showing its superior performance.
Machine Learning for Predicting Photovoltaic Power Generation: an Application on a University Campus

This paper describes the application of machine learning in an energy management system set up to monitor the use and generation of energy at a university campus. We explain how we built precise forecasting models for producing photovoltaic energy produced by solar panels at the University of Applied Sciences Landshut, Germany. We describe the practical challenges of dealing with data dropout and erroneous data when working with data from an actual in-production setting outside a simple lab scenario. Applying several data cleaning methods based on statistical methods, we obtained models that allow us to predict electrical energy produced by the solar panels with a precision of 0.97 as measured by the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> metric. More importantly, the process described to arrive at this result, more than the result per se, serves as a real-world example of applying data science and machine learning methodology in an in-production setting.
Software Visualization using the City Metaphor: Students' Perceptions and Experiences

Software visualization is a program comprehension technique that can support activities such as the analysis of the quality during the evolution of development. The empirical evaluation of software visualizations is necessary to provide evidence of perceptions and experiences of use, and the benefits provided during software analysis. Empirical data can help evidence the advantages and limitations of software visualizations, improving their acceptance in academia and industry. This study presents an analysis of the perceptions of 16 participants using a 3D city metaphor visualization to understand the structure of a web application and identify aspects related to development and quality best practices in a software engineering university course. For this, we studied the user experience through the UE Questionnaire (UEQ) and the ease of use, the perceived usefulness, and the intention of future use through the Technology Acceptance Model (TAM). Finally, benefits and opportunities were identified based on the perceptions of the participants and the results of the activity. The results indicate that the visualization of the city can support comprehension activities and help identify improvements in development and quality practices. Furthermore, this tool was positively perceived in the context of software engineering courses. Most of the acceptance and experience constructs were evaluated positively, with some opportunities identified regarding ease of use and visualization comprehension. In general, the visualization helped identify possible opportunities required for software applications under review.
Echo state network-based metamodel for the problem of stagnant petroleum cooling in pipe-in-pipe flowlines

The low temperatures found in subsea water combined with the transport of hydrocarbons in flowlines that are immersed in this environment brings the problem of solid deposition inside the flowlines, mainly of paraffin and hydrates, which consists of considerable problems to the petroleum industry. To overcome this issue, computational simulation might be an ally. However, such simulations sometimes can be computationally demanded, making their use unpractical. In these cases, metamodels, which are computationally cheaper models, can be an alternative. The present paper proposes using an Echo State Network as a surrogate model for the problem of stagnant production fluid cooling in a subsea pipe-in-pipe system with active electric heating. The implemented metamodel shows to be twenty-four times faster than the Finite Volume Method, although it showed consistent correlations for all lags when a residual autocorrelation analysis was performed.
Blockchain and cryptocurrency

This chapter examines blockchain, cryptocurrency, and other associated technologies from a legal and regulatory perspective. It provides an overview of this nascent technological field and discusses several areas of law and regulation that are of particular relevance, including securities law and intellectual property. In each of these fields, major questions remain as to how the legal environment will accommodate various features of these technologies as they continue to evolve. While blockchain and cryptocurrency have generated significant interest, they have yet to establish widely accepted, practical use cases beyond mere financial speculation. Without a more robust direction provided by the legal environment, much of the technological development in this area appears to have gone to waste. Whether blockchain and cryptocurrency will ever find compelling uses depends, in large part, on whether the incentive structures established by law and policy can redirect this technological domain toward more productive purposes in the future.
Proposing new path-planning metrics for operating rovers on Mars

Abstract The on-ground operation of Mars rovers is a complex task that requires comprehensive planning in which path planning plays a fundamental role. The selection of paths has to be carefully chosen considering the scientific objectives, terrain, energy, and safety. In this regard, operators are assisted by path-planning algorithms that generate candidate paths based on cost functions. Distance traveled has always been considered one of the primary criteria when comparing paths. Other metrics such as the run-time to generate the solution or the number of expanded nodes are common measures considered in the literature. However, we want to analyze if those metrics provide useful information in challenging and partially known terrain. In this paper, we will review those metrics using two-path planning algorithms on real Mars maps. Based on our experience operating Mars rovers, we propose new metrics for assessing paths in real-world applications.
Legal responses to techlaw uncertainties

By creating new items, empowering new actors, and enabling new activities or rendering them newly easy, technological development upends legal assumptions and raises a host of questions. How do legal systems resolve them? This chapter reviews the two main approaches to resolving techlaw uncertainties. The first is looking back and using analogy to stretch existing law to new situations; the second is looking forward and crafting new laws or reassessing the regulatory regime.
Development Of An E-Learning Model Using CMS Moodle During Covid 19

Every aspect of life, particularly the field of education, has seen a change in orientation as a result of the COVID-19 pandemic. Education is changing, and distance learning can help. In-person interactions are no longer the main focus. The purpose of this study is to use the CMS paradigm to create an e-learning model. In addition, the paradigm of teacher-oriented learning has given way to student-oriented learning. Students are now positioned as learning subjects rather than as learning objects in this student-oriented learning environment. These days, there are a lot of learning resources available, and e-learning involves the use of technology (the internet). Through the use of the Internet, intranet, or online media, educational resources can be made available to students through e-learning. further networks of computers. Given that computer technology is used by educators, students, and educational institutions to carry out teaching and learning activities, e-learning has become essential for the academic community. Software for online and website-based learning activities is known as Moodle-based e-learning media. This medium takes the form of an online course presentation tool where instructors can post lesson plans, instructional videos, PowerPoint presentations, discussion boards, and tests pertaining to certain journal articles.
Continuous Integration and Continuous Deployment (CI/CD) Pipeline for the SaaS Documentation Delivery

User documentation is crucial in any product development lifecycle, Software or hardware. Without it, stakeholders struggle to comprehend the more complex functionalities—usually the most valuable critical components of any software. Providing comprehensive and easily accessible product documentation always helps avoid this worst‑case scenario. It enhances the user experience and takes full advantage of the Software. Many organizations prefer the unstructured local authoring license that is easy to use for the stakeholders involved in developing the product documentation. However, setting up an automated pipeline for this is a complex approach. Based on our several years of industry experience, we have presented an automated documentation deployment workflow in this article. This workflow helps stakeholders involved in decision-making to lay out a plan for documentation scope for the SaaS delivery. Following a doc-as-a-code approach, our proposed method will help the organization to save time and focus more on managing the end-user technical documentation related to SaaS customization and the application release delivery.
Many-Body Singlet Prepared by a Central-Spin Qubit

Controllable quantum many-body systems are platforms for fundamental investigations into the nature of entanglement and promise to deliver computational speed-up for a broad class of algorithms and simulations. In particular, engineering entanglement within a dense spin ensemble can turn it into a robust quantum memory or a computational platform. Recent experimental progress in dense central-spin systems motivates the design of algorithms that use a central-spin qubit as a convenient proxy for the ensemble. Here we propose a protocol that uses a central spin to initialize two dense spin ensembles into a pure antipolarized state and from there creates a many-body entangled state—a singlet—from the combined ensemble. We quantify the protocol performance for multiple material platforms and show that it can be implemented even in the presence of realistic levels of decoherence and diffusion. Our protocol introduces an algorithmic approach to the preparation of a known many-body state and to entanglement engineering in a dense spin ensemble, which can be extended towards a broad class of collective quantum states.6 MoreReceived 24 January 2023Accepted 20 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040343Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMagnetization dynamicsNuclear & electron resonanceOpen quantum systems & decoherenceQuantum circuitsQuantum controlQuantum information with solid state qubitsQuantum memoriesQuantum state engineeringQuditsSpin dynamicsTechniquesDissipative particle dynamicsElectron spin resonanceMaster equationQuantum Information, Science & TechnologyCondensed Matter, Materials & Applied Physics
Innovations in the Criminal Procedure of the Republic of Azerbaijan: Prospects for the Implementation of Modern Methods and Technologies in the Work of Specialists

The importance of the research lies in the fact that digital technologies are increasingly permeating every aspect of human existence, including the judicial system. Information technologies are being integrated into specific aspects of criminal proceedings, such as the seizure of electronic media during investigations, the utilisation of video conferencing in court hearings at different levels, and the submission of motions, applications, and complaints in electronic format. The purpose of the study was to identify the general theoretical provisions of the criminal procedure form of information technologies used to organise the activities of officials and bodies conducting criminal proceedings, collect, verify, and evaluate evidence, control the activities of officials and bodies conducting criminal proceedings, and ensure the security of information technologies used in criminal procedure of the Republic of Azerbaijan, taking into account their current state. Methods of research included dialectical, comparative-legal, systemic-analytical, and logical approaches. The results of the study state the argumentation of the position that at the present stage, it is necessary to create a unified digital environment of criminal justice, including the transition to recording the course of procedural, as well as investigative, actions by means of technical means and saving the results in electronic form. It is important improving the use of electronic document flow between state bodies and officials within the framework of criminal procedure relations and to expand the use of remote forms of procedural actions at any stage of criminal proceedings, including the participation of all interested parties in court hearings. It is equally important to provide the victim with the opportunity to track the progress of a criminal case online via the Internet, from filing an electronic application to the sentencing. It is considered effective for the future to introduce a convenient and reliable technology of certification of a procedural document by any participant in the criminal process instead of its usual signature.
INTEGRATING MOTION PRIORS FOR END-TO-END ATTENTION-BASED MULTI-OBJECT TRACKING

Abstract. Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method.
Comparative Study of K-NN, Naive Bayes and SVM for Face Expression Classification Techniques

Abstract Classification is a data mining technique used to predict group membership for data instances within a given dataset. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. This comparative study focuses on the application of K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machines (SVM) classification techniques in the context of face expression recognition. The study aims to evaluate and compare the performance of these techniques in accurately classifying facial expressions, their advantages and disadvantages. Facial expression recognition is an important area in computer vision and emotion analysis. It involves identifying and classifying different facial expressions such as happiness, sadness, anger, fear, surprise and disgust. Accurate classification of facial expressions is crucial for various applications, including human-computer interaction, affective computing and psychological research. K-NN is a non-parametric algorithm that classifies data based on the majority class of its K-nearest neighbors. Naive Bayes is a probabilistic algorithm that makes classification decisions based on the Bayes theorem and assumes independence between features. SVM is a machine learning algorithm that constructs a hyperplane to separate different classes of data. Dataset such as Affectnet is used to conduct the study including labeled facial expression images. Three algorithms are tested on this dataset to evaluate their performance in correctly classifying facial expressions. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the classification performance of each technique. Software used for conducting the experiments is Python.
360: A DEEP LEARNING MODEL USING CHEST X-RAYS TO PREDICT REINTUBATION RISK IN CRITICAL CARE

Nguyen, Kim-Anh-Nhi1; Tandon, Pranai2; Edalati, Masoud1; Kia, Arash3 Author Information
Managing and Working with Existing Virtual Machines

In this chapter we are going to look at how to manage your existing virtual machines.
Configuring Virtual Machine Options

In this chapter, now that we have built some example virtual machines to work with, we are going to look at how to configure additional options for these virtual machines. These are options that could only be configured once the virtual machine has been built and is running.
Configuring Storage Resources

In this chapter, we are going to turn our attention to virtual storage and how this works when virtualized and particularly for use in VMware Workstation Pro.