Reviewerly/exHarmony huggingface dataset:
Toward a Resilient Automotive Service-Oriented Architecture by using Dynamic Orchestration

Modern software development in vehicles is focusing on a service-oriented approach. Structuring software systems into self-sufficient software components that provide specific capabilities to the overall system allow software engineers to make changes to vehicle functions more granularly. The decentralized SOA approach offers advantages, as it enables loose coupling between components instead of statically implementing their relationships. But with the increasing degree of autonomy and dynamism of the vehicle's software, the system's safety and security requirements are also growing. Preventive measures will no longer suffice here; instead, resilient systems are required that provide a minimum level of safety even in the event of an unexpected problem. Today, a SOA's services are assigned to a hardware platform during development and executed there, which lacks being able to react to problems or changing requirements. One possibility for being more flexible at runtime, is the use of an orchestrator, which dynamically allocates resources to services while retaining the advantages of a loosely coupled architecture. This paper proposes a methodology for implementing a resilient vehicular electronic architecture based on orchestrating containerized software. To avoid a single point of failure, a distributed approach for a dynamic orchestrator that deploys the software to appropriate execution platforms is proposed. The orchestrator makes its deployment decisions based on specifiable parameters (e.g., required RAM, GPU) and dependencies between services. The decision process adapts to changes in these factors dynamically, making the system able to react to external influences. The concept differentiates itself from other approaches by tracking dynamic changes to specified parameters and easily extensible interfaces for new parameters or requirements. In addition, the concept introduces a priority metric to describe the impact of services in the system and models how this metric is inherited through dependencies. The concept is evaluated qualitatively by three exemplary use cases, demonstrating the effect of dynamic orchestration on the resilience of the vehicle.
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.
Power grid based renewable energy analysis by photovoltaic cell machine learning architecture in wind energy hybridization

Use of solar energy systems and related green energy technology has spread around the world. When compared to conventional energy sources, solar energy is still not a frequently used energy source due to the comparatively high installation prices, low conversion rates, and battery capacity concerns. Despite the difficulties, there are numerous creative studies of new substances and new techniques for enhancing the efficiency of solar energy transformation to increase competitiveness of solar energy in market. This research proposes novel method in renewable energy analysis based on photovoltaic cell and machine learning technique for wind energy hybridization. The renewable analysis has been analysed using photovoltaic (PV) cell. Wind energy hybridization is carried out using convolutional kernel support regression vector machine. Experimental analysis has been carried out in terms of scalability, QoS (quality of service), power consumption, network efficiency, training accuracy. Financial advantages of using new cooling methods for photovoltaic panels are also assessed through a cost analysis.
An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.
Noninteractive xor Quantum Oblivious Transfer: Optimal Protocols and Their Experimental Implementations

Oblivious transfer (OT) is an important cryptographic primitive. Any multi-party computation can be realised with OT as building block. XOR oblivious transfer (XOT) is a variant where the sender Alice has two bits, and a receiver Bob obtains either the first bit, the second bit, or their XOR. Bob should not learn anything more than this, and Alice should not learn what Bob has learnt. Perfect quantum OT with information-theoretic security is known to be impossible. We determine the smallest possible cheating probabilities for unrestricted dishonest parties in non-interactive quantum XOT protocols using symmetric pure states, and present an optimal protocol, which outperforms classical protocols. We also "reverse" this protocol, so that Bob becomes sender of a quantum state and Alice the receiver who measures it, while still implementing oblivious transfer from Alice to Bob. Cheating probabilities for both parties stay the same as for the unreversed protocol. We optically implemented both the unreversed and the reversed protocols, and cheating strategies, noting that the reversed protocol is easier to implement.
A Unifying Theory of Distance from Calibration

We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understood, there is no consensus on how to quantify the distance from perfect calibration. Numerous calibration measures have been proposed in the literature, but it is unclear how they compare to each other, and many popular measures such as Expected Calibration Error (ECE) fail to satisfy basic properties like continuity.
Analysis of the azimuth angles of a medium-scale PV system in non-ideal positions for roof application

The installation of photovoltaic (PV) panels on building roofs has seen a significant increase in recent years due to the rising cost of conventional energy sources. This shift towards renewable energy sources has been driven by the urgent need to mitigate the effects of climate change. PV applications is one of the most sustainable and cleanest sources of renewable energy, producing no greenhouse gas emissions during operation. By reducing reliance on fossil fuels, the use of PV panels can help to reduce carbon emissions and lower the overall carbon footprint of buildings. In addition to the environmental benefits, the installation of PV panels can also provide economic benefits, such as reduced energy costs and increased property value. In the past, installations were mostly made in the direction of the south, but now the roofs of the buildings facing west, east, and even north are also considered for PV panel installations. In this study, a grid-connected PV system with an installed power of 148 kWp at the Konya Technical University (KTUN) campus is modeled by PVsyst software. The PV systems' performance on building roofs oriented in different geographical directions (north, south, east, and west) with a 30° fixed tilt angle was investigated. In the modeling, the solar irradiation coming to the surfaces of the PV panels, electricity production values, performance ratios, and their economic feasibility were calculated. The highest effective irradiation value on the panel surface was obtained from the system facing south, found as 1964.4 kWh/m². It is 20.77%, 22.87%, and 73.48% higher than the solar irradiation obtained at -90°, +90°, and 180° azimuth angles, respectively. It is concluded that the electricity generation amounts of PV systems highly depend on the azimuth angle. Similarly, the highest annual electricity production was obtained from the system installed in the 0° azimuth angle found as 254.77 MWh. The annual total electricity generation is 19.66%, 22.55%, and 69.41% higher in systems modeled toward the east, west, and north, respectively. Performance ratio, defined as the ratio of radiation coming to the panel surface and the electricity produced, has relative values between 0.843 and 0.862 for four different azimuth angles. Furthermore, as an economic analysis, the Basic Payback Period (BPP) of the projects was found as 6.92 years, 4.08 years, 4.88 years, and 5.00 years for the systems modeled in the north, south, east, and west directions, respectively. It can be concluded that the most suitable orientation is south, and the other two directions, east, and west, can also be considered feasible.
NeRFFaceLighting: Implicit and Disentangled Face Lighting Representation Leveraging Generative Prior in Neural Radiance Fields

3D-aware portrait lighting control is an emerging and promising domain, thanks to the recent advance of generative adversarial networks and neural radiance fields. Existing solutions typically try to decouple the lighting from the geometry and appearance for disentangled control with an explicit lighting representation (e.g., Lambertian or Phong). However, they either are limited to a constrained lighting condition (e.g., directional light) or demand a tricky-to-fetch dataset as supervision for the intrinsic compositions (e.g., the albedo). We propose NeRFFaceLighting to explore an implicit representation for portrait lighting based on the pretrained tri-plane representation to address the above limitations. We approach this disentangled lighting-control problem by distilling the shading from the original fused representation of both appearance and lighting (i.e., one tri-plane) to their disentangled representations (i.e., two tri-planes) with the conditional discriminator to supervise the lighting effects. We further carefully design the regularization to reduce the ambiguity of such decomposition and enhance the ability of generalization to unseen lighting conditions. Moreover, our method can be extended to enable 3D-aware real portrait relighting. Through extensive quantitative and qualitative evaluations, we demonstrate the superior 3D-aware lighting control ability of our model compared to alternative and existing solutions.
Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence

Disinformation and fake news are used by multiple actors to manipulate and influence the public with the purpose of gaining a series of advantages. This paper describes a promising solution to the increased spread of disinformation on the Internet. Our approach leverages blockchain technology combined with both crowd intelligence and federated artificial intelligence to develop efficient capabilities that address the disinformation phenomenon. The blockchain-based architecture of the platform creates a decentralized ecosystem that ensures transparency and trust, enabling the users to make correctly informed decisions in the face of disinformation. The key differentiating factor of the platform is the incorporation of both crowd and artificial intelligence in a system that can identify and respond to disinformation quickly and efficiently. The presented architecture can be used to build reactive and proactive platforms to effectively challenge disinformation.
Static Video Summarization Using Video Coding Features with Frame-Level Temporal Subsampling and Deep Learning

There is an abundance of digital video content due to the cloud’s phenomenal growth and security footage; it is therefore essential to summarize these videos in data centers. This paper offers innovative approaches to the problem of key frame extraction for the purpose of video summarization. Our approach includes the extraction of feature variables from the bit streams of coded videos, followed by optional stepwise regression for dimensionality reduction. Once the features are extracted and their dimensionality is reduced, we apply innovative frame-level temporal subsampling techniques, followed by training and testing using deep learning architectures. The frame-level temporal subsampling techniques are based on cosine similarity and the PCA projections of feature vectors. We create three different learning architectures by utilizing LSTM networks, 1D-CNN networks, and random forests. The four most popular video summarization datasets, namely, TVSum, SumMe, OVP, and VSUMM, are used to evaluate the accuracy of the proposed solutions. This includes the precision, recall, F-score measures, and computational time. It is shown that the proposed solutions, when trained and tested on all subjective user summaries, achieved F-scores of 0.79, 0.74, 0.88, and 0.81, respectively, for the aforementioned datasets, showing clear improvements over prior studies.
Promoting Hate Speech by Dehumanizing Metaphors of Immigration

ABSTRACTABSTRACTDehumanizing metaphors of immigration prevail in public discourse in different contexts. Their use can arouse negative emotions, such as hatred and fear, contributing to hate speech, hate crimes, and increasingly restrictive public policies toward immigrants and refugees. Although previous studies have investigated metaphor use, their research designs are often based on a single country and focused on immigrants. This study adds to the literature by examining newspapers from two countries and centering on immigrants and host countries, as well as discursive oppositions between the two. It conceptualizes the immigration metaphors in Brazilian and British newspapers by drawing on Critical Metaphor Analysis (CMA). The findings show that metaphors used for immigrants are primarily dehumanizing, while nations are represented as endangered containers or houses. This study expands the existing literature by systematically examining the use of metaphors in Brazil and the UK, suggesting a new perspective for studying metaphors of immigration beyond the Global North.KEYWORDS: Immigrationmediadiscoursemetaphorscritical metaphor analysisdehumanization Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis study was supported by Deutscher Akademischer Austauschdienst - Funding Program 57507871.
Deep adaptive collaborative graph neural network for social recommendation

Most graph convolutional network (GCN)-based social recommendation frameworks fuse social links with user-item interactions to enrich user representations, which alleviate the cold-start problem and data sparsity problem. However, GCN-based recommender systems still suffer from two limitations. First, Excessive reliance on social graphs to extract user interests for rating predictions is unreliable due to social inconsistency. Second, GCN-based models suffer from over-smoothing problems, node embeddings become more similar when going deeper to enable larger receptive fields. To address the two aforementioned problems simultaneously, we propose a Deep Adaptive Collaborative Graph Neural Network for Social Recommendation (DUI-SoRec). First, the graph generation module decomposes the user-item interaction to generate two subgraphs: an u2u graph and an i2i graph. Secondly, the graph learning module utilizes a deep adaptive graph neural network to learn user and item embeddings on the two subgraphs and the existing social graph, while solving the over-smoothing problem. Finally, we designed a refined fusion module to aggregate the social graph and u2u graph to address the social inconsistency. We conducted extensive experiments on four real-world datasets and the results demonstrate the model’s effectiveness.
MVP-Human Dataset for 3-D Clothed Human Avatar Reconstruction From Multiple Frames

In this paper, we consider a novel problem of reconstructing a 3D clothed human avatar from multiple frames, independent of assumptions on camera calibration, capture space, and constrained actions. We contribute a large-scale dataset, Multi-View and multi-Pose 3D human (MVP-Human in short) to help address this problem. The dataset contains 400 subjects, each of which has 15 scans in different poses and 8-view images for each pose, providing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6,000 3\text{D}$ </tex-math></inline-formula> scans and 48,000 images in total. In addition, a baseline method that takes multiple images as inputs, and generates a shape-with-skinning avatar in the canonical space, finished in one feed-forward pass is proposed. It first reconstructs the implicit skinning fields in a multi-level manner, and then the image features from multiple images are aligned and integrated to estimate a pixel-aligned implicit function that represents the clothed shape. With the newly collected dataset and the baseline method, it shows promising performance on 3D clothed avatar reconstruction. We release the MVP-Human dataset and the baseline method in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TingtingLiao/MVPHuman</uri> , hoping to promote research and development in this field.
Adjacent Complementary Network for Salient Object Detection in Optical Remote Sensing Images

Salient object detection in optical remote sensing images (RSI-SOD) is a newly developing task and has attracted extensive attention. Although some satisfactory performances have been achieved, there are still some poor performance situations, such as complex backgrounds and objects with various scales. Because the existing methods have the issue of insufficient adjacent feature representation and inappropriate constraint supervision. To alleviate the issues, we attempt to utilize the adjacent complementary and introduce the global constraints. Accordingly, we propose the adjacent complementary network (ACNet) with global constraints for RSI-SOD. Specifically, in the encoder, we mainly design the adjacent complementary module (ACM) to complement the features among the current layer and adjacent layers. Therefore, it can effectively aggregate saliency features. Besides, we have equipped a global average pooling (GAP) to enhance global information. In the decoder, we design a fusion enhancement module (FEM) to enhance the feature conversion between the coding layer and decoding layer. Finally, we employ a novel loss, which introduces global constraints and can supervise the objects with various scales. Extensive studies on two benchmark datasets demonstrate that ACNet is competitive and outperforms the 15 state-of-the-art (SOTA) approaches.
Progressive Task-Based Universal Network for Raw Infrared Remote Sensing Imagery Ship Detection

Infrared remote sensing images are becoming increasingly popular due to their superior penetration and resistance to light interference. However, challenges still remain when applying them in real-world applications: 1) raw infrared images suffer from severe stripes interference, and the preprocessing techniques used to obtain standard image products for subsequent detection tasks tend to be time-consuming, which fails to meet the application requirements; 2) current destriping techniques may inevitably weaken the local contrast between some objects and the local background since they need to consider the gray consistency of the overall image; 3) in low-resolution images, dim and small infrared targets are challenging to discriminate, resulting in high false alarms. To address these challenges, we proposed a progressive task-based universal network for raw infrared image ship detection while simultaneously removing stripes. First, we built an integrated network consisting of two components: the stripe denoising component (SDC) and the object detection component (ODC). We also designed a feedback loss adjustment mechanism to enhance the focus of the SDC on the target area. Second, a directed two-branch network was constructed for efficient stripe noise removal, including an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> -direction branch for feature enhancement and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> -direction branch for grayscale smoothing. Finally, a parallel network with two labels was designed to extract the inherent features of the target and the background, as well as their relationship features, to achieve refined ship detection. We conducted experiments on a self-assembled dataset from the GaoFen-1 satellite to validate our approach. The experimental results demonstrated that the proposed method outperformed other state-of-the-art methods in infrared image ship detection.
Supervised Contrastive Learning Based on Fusion of Global and Local Features for Remote Sensing Image Retrieval

With the rapid development of remote sensing sensor technology, the number of remote sensing images (RSIs) has exploded. How to effectively retrieve and manage these massive data has become an urgent problem. At present, content-based image retrieval (CBIR) methods have become a mainstream method due to their excellent performance. However, most of the existing retrieval methods only consider the global features of images, which lacks the ability to discriminate images with the same semantic information but different visual representations. To alleviate this issue, supervised contrastive learning based on the fusion of global and local features method is proposed in this paper, named SCFR. Firstly, a fusion module is designed to combine global and local features to enhance the ability of image expression. Secondly, supervised contrastive learning is introduced into the retrieval task to effectively improve the feature distribution, so that the positive sample pairs are close to each other, and the negative sample pairs are far away from each other in the feature space. Furthermore, to make the distribution of features of the same class more compact, the center contrastive loss is added to the constraints, and combines the class centers that change iteratively with the network. Experimental results on three RSI datasets show that our proposed method has more effective retrieval performance than the state-of-the-art methods. The code and models are available at https://github.com/xdplay17/SCFR.