Reviewerly/exHarmony huggingface dataset:
Inter-Organisational Coordination Work in Digital Curation: the Case of Eurobarometer

&#x0D; Open research is predicated upon seamless access to curated research data. Major national and European funding schemes, such as Horizon Europe, strongly encourage or require publicly funded data to be FAIR - that is, Findable, Accessible, Interoperable, Reusable (Wilkinson, 2016). What underpins such initiatives are the many data organizations and repositories working with their stakeholders and each other to establish policies and practices, implement them, and do the curatorial work to increase the available, discoverability, and accessibility of high quality research data. However, such work has often been invisible and underfunded, necessitating creative and collaborative solutions.&#x0D; In this paper, we briefly describe how one such case from social science data: the processing of the Eurobarometer data set. Using content analysis of administrative documents and interviews, we detail how European data archives managed the tensions of curatorial work across borders and jurisdictions from the 1970s to the mid-2000s, the challenges that they faced in distributing work, and the solutions they found. In particular, we look at the interactions of the Council of European Social Science Data Archives (CESSDA) and social science data organizations (DO) like UKDA, ICPSR, and GESIS and the institutional and organizational collaborations that made Eurobarometer â€œtoo big to failâ€. We describe some of the invisible work that they underwent in the past in making data in Europe findable, accessible, interoperable, and conclude with implications for â€œfrictionlessâ€ data access and reuse today.&#x0D; &#x0D;
Digitalization in Healthcare and Telecommunication Support Systems in Medicine

Digital transformation and information integration provide operational management of information and communication processes in many industries.The use of digital and telemedicine technologies in healthcare is particularly important.The article presents the directions of development of telecommunication projects in medicine and considers the main tools and applications for creating an automated medical history in a digital environment.The methods of intellectual support of complex test solutions for the electronic medical history in the field of teleneurology are described.The methods of knowledge representation in telemedicine systems using rule-templates are considered.It is noted that the rule base can accelerate the comparison of patient data and the development of diagnostic hypotheses, providing data search and their distribution into separate groups in accordance with the context of a particular medical problem.The authors note that the stage of conceptual analysis or structuring of knowledge is traditionally (along with the extraction stage) a "bottleneck" in the life cycle of developing intelligent decision support systems.The development of intellectual support systems for integrated test solutions in the field of neurology belongs to this class of complex systems, since they have the main signs of complexity (hierarchy of concepts, intra-element and inter-element communication, etc.).The complexity of designing such systems is determined mainly by the complexity of subject areas, as well as the complexity of describing the behavior of individual subsystems.
Development and Implementation of Health Information Management Platform in Medical Institutions

With the advancement of the medical reform process, the scale of investment in medical and health services has been increasing, and the scale of medical institutions has become larger and larger. The medical and health undertakings are developing rapidly. However, in the original information platform of medical institutions, there are still some problems such as low level of information and backward management mode, which are not conducive to the management of medical institutions. In view of these problems, this paper develops a health information management platform to improve the health service level of medical institutions and better manage the health information of medical institutions by using the technical means of the Internet and according to the actual needs of medical institutions. The health information management platform is designed and developed according to the real work needs of medical institutions. The business operation process of the platform meets the actual requirements. The interactive interface is beautiful and easy to use. A survey of users of the health information management platform shows that 87.6% of users are satisfied with the health information management platform developed, which shows that the platform plays an important role in the management of medical institutions.
Penerapan Aplikasi Up-Event pada Tata Kelola Kegiatan Multi Vendor

An event organizer can be defined as organizing an activity that is managed professionally, systematically, efficiently and effectively. Its activities include concepts (planning), implementation and supervision. Currently the event information dissemination system is carried out by promoting events through the website, social media and also many event organizers who sell event tickets offline by opening stands at other events and in certain areas. Distribution of tickets in this way raises several problems, namely the occurrence of long queues that will spend a lot of time and the additional costs of establishing the stand. Opportunities for the dissemination of event information and online ticket sales began to be glimpsed by the event organizer as a promising land because it provides accurate information and saves time and costs while promoting. From these problems, we designed an application "Up-Event" system that is useful to assist event organizers in selling ticket events. This system besides being based on website is also based on Android. In this system, the attendance feature of the event participant uses QR Code technology. In addition to reducing the use of too much paper and long queues during attendance, QR Code also guarantees the security of the event because the authenticity of tickets is used as a condition for entry into the event.
Port Throughput Forecasting Based on Broad Learning System with Considering Influencing Factors

With the development of the shipping industry, port throughput has increased significantly in recent years. Chinese ports have become increasingly important. The fluctuation of container throughput in ports is affected by many factors. The effective and accurate forecasting of port throughput provides a scientific reference for the development of the port. Based on the port throughput data of Lianyun Port in China from the first quarter of 2005 to the fourth quarter of 2016, this paper uses univariate linear regression, multiple linear regression, and broad learning system to forecast the container throughput of the port in each quarter of 2017 and 2018. Comparing the forecasting results with the actual throughput. The experimental results show that the broad learning system can consider two economic influencing factors about the throughput at the same time and forecast port throughput accurately and effectively. Therefore, broad learning system is more suitable for the forecasting of port throughput than other methods.
Real-time Motion Planning and Trajectory Tracking in Complex Environments based on Bézier Curves and Nonlinear MPC Controller

In this paper, we propose a real-time motion planning system and a precise trajectory tracking controller for unmanned aerial vehicles autonomous navigation in complex environments. Based on the simple path planned by the path searching method, we generate the convex flight corridors for quadrotor safe flying by cube inflation. We generate the trajectories composed with piecewise Bézier curves in these convex flight corridors by formulating thé problem as a quadratic program. The time of the trajectories is re-allocated to satisfy the dynamic feasibility. We apply a nonlinear model predictive controller (NMPC) considering the full system model to track the generates trajectories. The performance of our proposed method is tested in a complex simulation system.
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty

Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems. Existing studies have employed strategies based on either loss or uncertainty to address noisy labels, and ironically some strategies contradict each other: emphasizing or discarding uncertain samples or concentrating on high or low loss samples. To elucidate how opposing strategies can enhance model performance and offer insights into training with noisy labels, we present analytical results on how loss and uncertainty values of samples change throughout the training process. From the in-depth analysis, we design a new robust training method that emphasizes clean and informative samples, while minimizing the influence of noise using both loss and uncertainty. We demonstrate the effectiveness of our method with extensive experiments on synthetic and real-world datasets for various deep learning models. The results show that our method significantly outperforms other state-of-the-art methods and can be used generally regardless of neural network architectures.
Political hate speech and the new framework of digital networks

Chapter 5 illustrates the birth and evolution of extremist groups on social networks, starting from the first realities in the United States and then reaching the current framework in Europe. The chapter analyses a report on the state of ethnonationalist and white supremacist parties and groups in Europe, with a brief overview of the main political forces that are contributing to the spread of hatred in the main countries. In the central part of the chapter the delicate aspects of the public technological sphere and the removal of content (and those who decide upon that removal) are dealt with, as are the differences between online hatred and offline hatred. The final part is devoted to the methods of hate control on platforms, including automated systems and the use of algorithms.
Use of Machine Learning and a Natural Language Processing Approach for Detecting Phishing Attacks

Phishing attacks are today a major threat to the security of the systems. And also, there is no foolproof protective system against these attacks. Phishing is one of the different cybercrimes. In this, the person interested to attack behaves like another existing individual/organization and he uses e-mails or similar types of techniques. It also can be described as appearing as a person or organization that one can trust and then to acquire certain private and significant data without the knowledge of the person concerned, such as sign-in credentials and bank-related card details, for fraudulent reasons. Even from chatting to banking, a huge community uses the online services through online transactions. Phishing attacks are happened by the execution of certain actions such as mouse clicking and hovering on malicious URLs. The attacker may also use phishing, by providing links which are malicious in nature through emails that can be used to capture login credentials, victim account information, etc. Therefore, we have to enhance the security mechanism. In this book chapter paper, types of phishing attacks are explained. It also focuses on the anti-phishing URL tool which is used to prevent phishing attacks. The main objective of this book chapter is to explain initially the characteristics of phishing attacks. There are some uniqueness and patterns associated with the websites which are used for phishing. 226There properties can be used to detect phishing. Then these attacks are detected by a hybrid machine learning model. The system has been implemented by examining the URLs used in phishing attacks with some extracted features before opening them. Some natural language processing (NLP) techniques are used in the proposed machine learning system. These techniques are used for analyzing the text semantically to detect malicious intentions which indicate phishing attacks. In order to identify the websites for their legitimacy, some machine learning algorithms (LAs) are also discussed in this book chapter. It also focuses on the Naive Bayes (NB) classifier, K-Means clustering to calculate the possibility of the website as valid phish or invalid phish.
Ontology Design for Pharmaceutical Research Outcomes

The network of scholarly publishing involves generating and exchanging ideas, certifying research, publishing in order to disseminate findings, and preserving outputs. Despite enormous efforts in providing support for each of those steps in scholarly communication, identifying knowledge fragments is still a big challenge. This is due to the heterogeneous nature of the scholarly data and the current paradigm of distribution by publishing (mostly document-based) over journal articles, numerous repositories, and libraries. Therefore, transforming this paradigm to knowledge-based representation is expected to reform the knowledge sharing in the scholarly world. Although many movements have been initiated in recent years, non-technical scientific communities suffer from transforming document-based publishing to knowledge-based publishing. In this paper, we present a model (PharmSci) for scholarly publishing in the pharmaceutical research domain with the goal of facilitating knowledge discovery through effective ontology-based data integration. PharmSci provides machine-interpretable information to the knowledge discovery process. The principles and guidelines of the ontological engineering have been followed. Reasoning-based techniques are also presented in the design of the ontology to improve the quality of targeted tasks for data integration. The developed ontology is evaluated with a validation process and also a quality verification method.
How to Look Next? A Data-Driven Approach for Scanpath Prediction

By and large, current visual attention models mostly rely, when considering static stimuli, on the following procedure. Given an image, a saliency map is computed, which, in turn, might serve the purpose of predicting a sequence of gaze shifts, namely a scanpath instantiating the dynamics of visual attention deployment. The temporal pattern of attention unfolding is thus confined to the scanpath generation stage, whilst salience is conceived as a static map, at best conflating a number of factors (bottom-up information, top-down, spatial biases, etc.). In this note we propose a novel sequential scheme that consists of a three-stage processing relying on a center-bias model, a context/layout model, and an object-based model, respectively. Each stage contributes, at different times, to the sequential sampling of the final scanpath. We compare the method against classic scanpath generation that exploits state-of-the-art static saliency model. Results show that accounting for the structure of the temporal unfolding leads to gaze dynamics close to human gaze behaviour.
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound

In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer. In order to learn meaningful representations, the model needs to identify such correlation and at the same time understand the underlying anatomical features. We designed a framework to model the correspondence between video and audio without any kind of human annotations. Within this framework, we introduce cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling. Experimental evaluations on multi-modal fetal ultrasound video and audio show that the proposed approach is able to learn strong representations and transfers well to downstream tasks of standard plane detection and eye-gaze prediction.
Object Detection with a Unified Label Space from Multiple Datasets

Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical benefits of such an object detector are obvious and significant application-relevant categories can be picked and merged form arbitrary existing datasets. However, naive merging of datasets is not possible in this case, due to inconsistent object annotations. Consider an object category like faces that is annotated in one dataset, but is not annotated in another dataset, although the object itself appears in the latter images. Some categories, like face here, would thus be considered foreground in one dataset, but background in another. To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case. We propose loss functions that carefully integrate partial but correct annotations with complementary but noisy pseudo labels. Evaluation in the proposed novel setting requires full annotation on the test set. We collect the required annotations and define a new challenging experimental setup for this task based one existing public datasets. We show improved performances compared to competitive baselines and appropriate adaptations of existing work.
Conditional Adversarial Camera Model Anonymization

The model of camera that was used to capture a particular photographic image (model attribution) is typically inferred from high-frequency model-specific artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as the process of transforming both high and low spatial frequency information. We augment the objective with the loss from a pre-trained dual-stream model attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size

Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years. While the results from these models are impressive, applying them can be extremely computationally expensive, as is pretraining new models with the latest architectures. We present a novel method for applying pretrained transformer language models which lowers their memory requirement both at training and inference time. An additional benefit is that our method removes the fixed context size constraint that most transformer models have, allowing for more flexible use. When applied to the GPT-2 language model, we find that our method attains better perplexity than an unmodified GPT-2 model on the PG-19 and WikiText-103 corpora, for a given amount of computation or memory.
Intelligence plays dice: Stochasticity is essential for machine learning

Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy. In this perspective article, we argue that stochasticity plays a fundamentally different role in machine learning (ML) and is likely a critical ingredient of intelligent systems. As we review the ML literature, we notice that stochasticity features in many ML methods, affording them robustness, generalizability, and calibration. We also note that randomness seems to be prominent in biological intelligence, from the spiking patterns of individual neurons to the complex behavior of animals. We conclude with a discussion of how we believe stochasticity might shape the future of ML.
Peningkatan Keterampilan Remaja Dengan Pelatihan Perbaikan PC Untuk Siswa SMKN2 Banjarmasin

Computers have been widely used in the community, because computers can ease human work. To operate it, you need special knowledge about computers to be able to run it properly. Although computers make human work easier, computers can also be damaged or error. Only some people can repair computers like computer technicians. Therefore, on this occasion we had the opportunity to provide training on how to assemble computers and repair them. With expertise in assembling and repairing computers, the community can also open up jobs that would certainly have a positive impact on the surrounding community.&#x0D; Computer users / Personal Computers (PC) have been found in various circles, one of them is SMKN2 students. But many computer uses (PC) do not know how to repair a computer in case of damage, not to mention a few technicians or computer experts who can be easily found. Because of this, damage to the computer will be a difficult problem to deal with. Although there are very few technicians that we can find, repairing or servicing computers requires quite expensive costs. On this occasion, we intend to hold community service in the form of PC technician training (Personal Computer) for SMA/SMK students who aim to provide understanding and skills in making repairs, assembling, and upgrading PCs with community service programs in the form of this training. Besides this training is not limited to the provision of theory, but combined with practice so that not only knowledge is given, but the skills and creativity that are honed in this training. Outcomes of this training are skills training services and publications in national service journals.
Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.