Reviewerly/exHarmony huggingface dataset:
Application principle and practice of scene moving shot in virtual live broadcast based on UE4

UE4 (Unreal Engine 4), a new digital information technology software, involves film and television production, game production, chat platform, and live broadcast applications. Model engineering files with maps in MAYA, 3dMax, and ZBrush can be imported into UE4, where the materials and special effects of this software can be distinguished. In addition, plug-ins can be imported and designed by blueprints, level editing, node editing, and C++ programming language editing, leading to the increasing use of UE4 in various industries. It is not only animation design or real-time demonstration and experience of game operation, but also simple operation and improved efficiency play an important role in promoting the whole industry. In this paper, we use principal component analysis (PCA), analytic hierarchy process (AHP) and neural network model to study image display through machine learning and other algorithms. By preprocessing the data to make it meet the conditions of neural network operation in machine learning, we will use neural network architecture for analysis and make a horizontal comparison with the live broadcast feedback of other E-sports platforms. The neural network model focuses on the image effect, production cost and playback feedback of the more intuitive sequencer scene in the Unreal Engine4 virtual live broadcast and the traditional film and television live broadcast, and obtains the specific advantages and disadvantages through algorithm comparison.
RDM in a Decentralised University Ecosystem—A Case Study of the University of Cologne

The University of Cologne (UoC) has historically grown in highly decentralised structures. This is reflected by a two-layered library structure as well as by a number of decentralised research data management (RDM) activities established on the faculty and research consortium level. With the aim to foster networking, cooperation, and synergies between existing activities, a university-wide RDM will be established. A one-year feasibility study was commissioned by the Rectorate in 2016 and carried out by the department research management, library and computing centre. One study outcome was the adoption of a university-wide research data guideline. Based on a comprehensive RDM service portfolio, measures were developed to put a central RDM into practice. The challenges have been to find the right level of integration and adaptation of existing and established decentralised structures and to develop additional new structures and services. We will report on first steps to map out central RDM practices at the UoC and to develop a structure of cooperation between loosely coupled information infrastructure actors. Central elements of this structure are a competence center, an RDM expert network, a forum for exchange about RDM and associated topics as well as the faculties with their decentralized, domain-specific RDM services. The Cologne Competence Center for Research Data Management (C<sup>3</sup>RDM) was founded at the end of 2018 and is still in its development phase. It provides a one-stop entry point for all questions regarding RDM. The center itself provides basic and generic RDM services, such as training, consulting, and data publication support, and acts as a hub to the decentral experts, information infrastructure actors, and resources.
Assessment of Deep Sequence Models for Characterization and Prediction of Cloud Workloads

Forecasting cloud workloads is necessary for allocating resources when an application request is received at a data center. The workload of a data center can be considered as a time sequence. Workload forecasting of a data center can be done using sequential models like Long Short Term Memory (LSTM) or Gated Recurrent Networks (GRN). The time sequence of workload across VMs is typically tracked at the granularity of groups of VM. In this paper, we use LSTM, GRU, and Encoder-decoder network for forecasting resource requirements and assess the performance of each model. We compare these models in grouped workload settings. We use the Azure workload dataset with approximately 2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> VMs and the Bitbrains dataset with 1250 VMs for validating the models.
Object Detection of Pedestrian Crossing Accident Using Deep Convolutional Neural Networks

Pedestrian crossing accident recently, people injured in traffic while crossing the crosswalk. As an accident happened other caution warning systems may not be in time to safe life. Various deep learning techniques are based on a deep convolutional neural network (D-CNN) these methods are capable to fulfill object detection applications. This paper proposes the pedestrian crossing accident dataset for the detection of pedestrian crossing accidents using a video camera of front cars or dash-camera and a few CCTV videos. It presents the performance comparison between the two state-of-the-art CNN algorithms approach Faster R-CNN and YOLOv3, in the context capability to correctly classify accident, fall-down, and out-of-frame classes. This paper demonstrates that Faster RCNN outperforms YOLOv3 with a better detection in accuracy. However, the conclusion of the paper is that YOLOv3 outperforms speed to detection and has good accuracy same time able to use in real-time detection.
Long-Range Real-Time Gesture Recognition for Millimeter Wave Radar

In recent years, millimeter wave radar gesture recognition has attracted more and more attention due to its advantages of low cost, not being affected by lighting conditions and protecting user privacy. This paper introduced a long-range millimeter wave radar gesture recognition method, which can be deployed on radar for real-time gesture recognition. Firstly, the moving target indication (MTI) is used to eliminate the influence of static clutters. Then the biaxial projection method is used to find the foreground images. The proposed method use the histogram of oriented gradients (HOG) algorithm to extract features. Finally, support vector machine (SVM) is used to learn and classify features. The results show that the proposed method can validly recognize 5 gestures and the average accuracy of offline test remains above 95%. Through practical test, we found that the average recognition accuracy can more than 90% in the range of 1m to 5m.
Artificial Intelligence in Content Moderation – Legal Challenges and EU Legal Framework

The article is dedicated to the concerns and challenges that the use of AI in content moderation poses to law. In the first part the legal issues and challenges discussed in the literature, as well as the proposed solutions, are summarized. In the second part a brief analysis of the current legal framework of content moderation at EU level and in different Member States is offered. The proposal for new EU legislation is also presented focusing on the provisions concerning automated content moderation.
Information visualization of scientific publications for obtaining overall pattern and trend

Information visualization is an important part of bibliometrics to describe the structure of scientific system. It can gain insight into the trends of various contributing factors to the advancement of scientific publications on a certain field of research. As an example, the global scientific production related to water saving management was evaluated by the information visualization method. A total of 2302 articles published were obtained from Web of Science core collection database. From the high- frequency keywords, the literature paid high attention to water-saving management. Much of the research has focused on field of agriculture and land use. “Climate change”, “Model”, “Footprint”, “Virtual Water”, “Reuse”, “Trade” and “Water-Use Efficiency” were hot issues. This research could provide reference value and research guidance for future research on water saving management.
TAO-Net: Task-Adaptive Operation Network for Image Restoration and Enhancement

Recently, deep learning based models have been extensively applied to image restoration and enhancement tasks. However, existing approaches neglect the potential correlation among these tasks, and do not fully consider the different intensity factors of degradation which affect image quality. To this end, we propose Task-Adaptive Operation Network (TAO-Net) that stacks a series of operation blocks, guided by the supervised attention mechanism to adaptively enable the model to assign corresponding intensity weights for different degradation factors depending on the input signals. To better recover and preserve the accurate details during the repeated operations, we make full use of the image prior to intruduce structure refinement block as auxiliary information for reconstructing high-quality results. Furthermore, feature aggregation block is also adopted to avoid feature interference caused by the direct concatenation between the backbone operation block and the auxiliary refinement block together. Extensive experiments on multiple benchmark datasets demonstrate that our proposed method outperforms previous baselines in image deraining and low-light image enhancement.
A Semantic Segmentation Algorithm Based on Contrastive Learning Using Aligned Feature Samples

With increasingly robust pixel-wise classifiers, semantic segmentation has made much progress. But these semantic segmentation methods classify each pixel only based on its feature vector and didn’t consider the relationship between them. The revival of contrastive learning in the unsupervised domain has caught our attention. It learns standard features between homogeneous instances, distinguishes differences between non-homogeneous cases, and improves the classification of the network by optimizing the distribution of image-level samples in the embedding space. We believe that contrastive learning can also optimize pixel-level samples’ feature space and enable more accurate segmentation predictions. We should also carefully consider how to reasonably migrate from the image level to the pixel level. Unlike existing approaches that enforce semantic labels on individual pixels and match tags between neighbouring pixels, we propose generating pixel-level samples and matching the semantic relations between adjacent pixels in the embedding space. The transformer’s ability to capture extended contexts makes pixel samples more comparable than CNN. We decided to add a pixel-wise contrastive loss between pixel samples extracted by the transformer encoder, which could shorten the distance between features of the same class and disperse different clusters. To ensure the consistency of comparison samples and labels, we also add elements to optimize its module. The final experiments show that optimizing the sample space can improve semantic segmentation results.
Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

Abstract The emergence of the COVID-19 pandemic has led to an unprecedented change in the lifestyle routines of millions of people. Beyond the multiple repercussions of the pandemic, we are also facing significant challenges in the population’s mental health and health programs. Typical techniques to measure the population’s mental health are semiautomatic. Social media allow us to know habits and daily life, making this data a rich silo for understanding emotional and mental well-being. This study aims to build a resilient and flexible system that allows us to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic. We built an extensive data system utilizing modern cloud-based serverless architectures to analyze 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. We provide metrics, advantages, and challenges of developing serverless cloud-based architectures for a natural language processing project of a large magnitude.
FitSleeve: Designing Wearable Display and Feedback to Improve the Fitness Experience and Motivation

In this article, we focus on fitness workout scenarios. To examine the current practices, we first conducted a substantial user research, through the content analysis procedure we summarized insights regarding fitness purpose, essential data, psychological state, emotional changes and social habits. Subsequently, we generalized related design opportunities of improving the fitness experience and motivation to enhance the fitness performance and communication. Feedback and data representation have great potential to address the challenges, therefore we first proposed a design process for the feedback mode design of fitness workout, including the contextual information, feedback strategy, and realization ways. The feedback designs are implemented on a wearable augmented feedback system ‘FitSleeve’, focusing on individual and group scenarios separately. In group sessions, FitSleeve can demonstrate participants’ experience level, real-time heart rate zone and feedback regarding the correct movement execution. In individual sessions, FitSleeve can display the training progress and provide continuous encouragement. Finally, we evaluated FitSleeve by adopting the System Usability Scale, User Experience Questionnaire, Intrinsic Motivation Inventory and user subjective interviews. The findings indicate the potential of FitSleeve to improve the user experience and motivation of fitness participants.
Caged Monkey Dataset: A New Benchmark for Caged Monkey Pose Estimation

Automatic monkey pose estimation is of great potential for quantitative behavior analysis of monkeys, which provides indispensable information for the studies of drug safety assessments and medical trials. With the development of deep learning, the performance of human pose estimation has been greatly improved, however, the study of monkey pose estimation is rare and the robustness of performance is unsatisfactory due to the lack of data and the variations of monkey poses. In this work, we propose a complete solution to address these problems in terms of data and methodology. For data, we collect a comprehensive Caged Monkey Dataset with 6021 samples, with labeled poses. For methodology, we propose a Mask Guided Attention Network (MGAN) to focus on the foreground target automatically so as to locate the occluded keypoints precisely and deal with the complex monkey postures. The proposed method is evaluated on the collected monkey dataset, achieving 79.2 Average Precision (AP), which is 6.1 improvement over the baseline method and is comparable with the state-of-the-art performance of human pose estimation. We hope our attempt on caged monkey pose estimation will serve as a regular configuration in drug safety assessments and medical trials in the future.
Correcting Ambiguous Japanese Sentences Based on Japanese Dependency Analysis

The purpose of this study is to correct ambiguous Japanese sentences into clear and understandable ones. Ambiguous sentences are ones that can be interpreted differently by different readers. To achieve this purpose, existing studies have corrected ambiguous sentences by replacing word order and inserting commas using word occurrence probabilistic models. However, such probabilistic models often produce sentences that do not reflect the writer’s intention because they prioritize frequently used sentence patterns over sentence meanings. As a result, the corrected sentences can be clearly written but missing points. To solve the problem, this study proposes a new method which replaces the word order in a sentence focusing on the semantic connection between words and inserts commas, resulting in producing clear sentences without losing the writer’s intention. In this paper, the methodology of the proposed method is stated and its effectiveness is verified using an evaluation experiment
Automatic Indonesian Image Captioning using CNN and Transformer-Based Model Approach

Image captioning involves a phrase text generation or more for the visual content descriptions from images. Caption generation for an image is considered important to aid human activities in comprehending visual material, such as captions on medical images, human contact with robots, and helping visually impaired people explain visuals. Our study aims to create an Indonesian image description and assess how successful the caption's approach is. Translated Flickr8k datasets are provided for this investigation. This study employs two methods for captions generation from a photo: CNN with ResNet as the encoder, and Transformer, a self-attention-based mechanism as the decoder. Using distinct Indonesian datasets, named Flickr8k Bahasa, we used a Transformer-based approach to create an Indonesian captions generation model from photos. We demonstrated that our Indonesian Transformer-based strategy outperformed the old one, where the best results were obtained with BLEU-1 to 4, METEOR, ROUGE_L, CIDEr of 56.00, 41.17, 29.42, 20.57, 19.50, 44.16, 57.26, respectively. Besides comparing the model's performance using different CNN's pre-trained models, a larger CNN model did not guarantee any accuracy improvement through fifty epochs of training processes.
Agreement and Persuasion

This Chapter explores the notion of agreement from the point of view of rhetoric, adopting a mainly historical approach. It starts from the assumption that it is possible to single out two moments in the process of persuasion when agreement on the premises plays a fundamental role: a preliminary stage, in which a common ground between arguer and audience is established, on the one hand; the exploitation of premises in actual argumentative sequences on the other. The former is examined with reference to the Aristotelian concept of ethos, which is the prerequisite for acceptance of any argumentative stance. For its importance and discursive character, current approaches to argumentation and rhetoric have given prominence to ethos, analysing it also in relation to the notions of face and politeness. Although centred on the speaker, ethos is fundamental for establishing a relation with the addressees, and indeed contributes to the construction of the audience (and agreement with it) as intended in the New Rhetoric. The Chapter then focuses on actual argumentative sequences, analysing the role of agreement with reference to the concepts of topos and endoxon, examining both their original meaning and subsequent interpretations.
Explicit Tool Support for Implicit Layer Activation

Implicit Layer Activation (ILA) is a declarative mechanism to scope behavior adaptations in Context-oriented Programming (COP). ILA binds the activation status of a layer to a Boolean condition. The layer is active as long as the given condition evaluates to true. This mechanism to scope layer activations is very powerful, but without dedicated tool support, it may be hard to debug due to its implicitness. A solution that can mitigate this is proper tool support, which is expensive to build and can be highly domain-specific. We have previously shown that by building the language extension not from scratch but by relying on a common more powerful shared concept, Active Expressions, the implementation becomes simpler and more elegant since it does not require deep integration into the ContextJS implementation. In this paper we show how providing tool support for ILA makes implicit dependencies to state changes more explicit. We show how such tool support can be implemented by leveraging the existing Active Expression tool suite. We illustrate the usage based on a catalog of COP questions from literature.
Disentangled Neural Architecture Search

Neural architecture search (NAS) has emerged as a hot topic recently which makes artificial intelligence techniques easier to apply and reduces the demand for experts knowledge by generating deep neural network architectures automatically. However, most existing methods for neural architecture search (NAS) heavily rely on underlying black-box controllers to generate potential candidates of network architectures, and suffer from serious problems of lacking interpretability and search efficiency. In this paper, we propose Disentangled Neural Architecture Search (DNAS), which addresses the two issues by adopting disentangled NAS controller and efficient dense-sampling strategy. Specifically, DNAS learns disentangled factors of network architecture by explicitly encouraging the latent factors to be independent. This approach could not only achieve semantic interpretability but also allow us to conveniently identify the promising regions of representations corresponding to high-performance architectures. We further propose a dense-sampling strategy that conducts targeted architecture search within the promising regions to accelerate the searching process. Our DNAS owns several attractive features: 1) it can successfully learn semantic representations of architectures, including operation selection, skip connections, and layer order; 2) it can speed up the process for neural architecture search more than 13× by using dense-sampling and disentangled factors; 3) it can achieve higher accuracy under less computational cost—DNAS achieves state-of-the-art performance of 94.16% on NASBench-101, and 22.7% top-1 test error on ImageNet with 1.6 GPU-days.
Cloud Task Scheduling Algorithms using Teaching-Learning-Based Optimization and Jaya Algorithm

Over the last few years, cloud computing has accelerated in the economic and scientific communities due to breakthroughs in virtualization technology. It is an emerging computing technology in which many users submit their requirements (i.e., compute, storage, network, etc.) in the form of tasks to process them through widely dispersed resources (i.e., virtual machines (VMs)) on a pay-as-you-go basis. However, it is pretty challenging to manage the submitted tasks and process them on the VMs, such that overall completion time (i.e., makespan) is minimized. Many researchers have proposed meta-heuristic algorithms to solve the above-discussed task scheduling problem. However, these algorithms are based on algorithm-specific parameters. This paper uses the concepts of well-known teaching-learning-based optimization (TLBO) and the Jaya algorithm, and model them to solve task scheduling problem individually. The rationality behind using these algorithms is that they are algorithm-specific parameter-less algorithms. We call the modeled algorithm as cloud-TLBO and cloud-Jaya algorithm. We model the candidate solutions as tasks and design variables as VMs, and consider the makespan as the objective function. We simulate both the cloud-TLBO and cloud-Jaya algorithm using five synthetic datasets and monitor their results over 50 iterations. Finally, we compare the results with the online benchmark algorithm, called minimum completion time (MCT), to show that the results of the proposed algorithms are near-optimal.
RunPHI: Enabling Mixed-criticality Containers via Partitioning Hypervisors in Industry 4.0

Orchestration systems are becoming a key component to automatically manage distributed computing resources in many fields with criticality requirements like Industry 4.0 (14.0). However, they are mainly linked to OS-level virtualization, which is known to suffer from reduced isolation. In this paper, we propose RunPHI with the aim of integrating partitioning hypervisors, as a solution for assuring strong isolation, with OS-level orchestration systems. The purpose is to enable container orchestration in mixed-criticality systems with isolation requirements through partitioned containers.