Reviewerly/exHarmony huggingface dataset:
Participatory Modelling for NBS Co-design and Implementation

Abstract This chapter summarizes the lessons learned in NAIAD concerning the role of risk perception and stakeholders’ engagement in NBS co-design and implementation. Specifically, this work describes the efforts done for overcoming two barriers hampering NBS implementation, i.e. lack of stakeholders’ engagement and lack of collaboration among different decision-makers. To this aim, scientific and stakeholders’ knowledge were integrated through participatory modelling exercises concerning risk assessment and NBS effectiveness assessment, described in this work. The impacts on stakeholders’ risk perception and NBS acceptance due to the participatory modelling process are also assessed and discussed. The chapter provides technical information on the methods and tools mainly for a scientific audience and for practitioners (particularly Sects. 5.1 and 5.2), although general principles and conclusions are suitable for a wider audience, including public authorities and decision-/policy-makers (Sect. 5.3). The work is structured around three main topics that arose during NAIAD implementation, namely: (i) the complexity of the cause-effects chains affecting NBS effectiveness and the role of socio-institutional measures; (ii) the need to account for the existence of potential trade-offs among different stakeholders in benefiting from NBS implementation and co-benefits production; (iii) the role of communication and information exchange in facilitating NBS co-design and implementation
Urban River Restoration, a Scenario for Copenhagen

Abstract Urban flooding is an increasing hazard and expected to aggravate with climate change. The opening of a piped urban river in Copenhagen is explored to mitigate urban flooding as a result of a rising groundwater table. In the Copenhagen case study, a hydrological model has been used to calculate the effect of reopening a currently piped river to its natural environment as a nature-based solution (NBS). From the results of modelling scenarios, we have set up a simple damage function to calculate the avoided costs for the river basin by comparing difference in groundwater levels with and without NBS. Stakeholders were engaged from the first stages of the case study. The integration of stakeholder’s knowledge in the co-design and implementation process of NBS to support complex decision-making processes was carried out by (1) participatory modelling activities to elicit and structure stakeholder’s risk perception, (2) mapping the interaction among decision-makers and stakeholders, and by (3) deriving Fuzzy Cognitive Maps (FCM) from Group Model Building. The results show that by reopening the river, an economic benefit is obtained because the river now functions as a drainage channel which prevents flooding by groundwater of subterranean structures, notably housing cellars, and receives stormwater from cloudburst rainfall events. The FCM simulation showed that NBS implementation requires effective cooperation among different decision-makers to define potential interventions and to reduce the level of conflicts and to facilitate collaborative decision-making.
An Effective Deep Learning Model for Content-Based Gastric Image Retrieval

In this paper, we propose a feature combination, also known as feature fusion, for improving performance in content-based gastric image retrieval (CBGIR). This study provides a CBGIR system that retrieves images by combining ResNet-18 and ResNet-50 information and finally, the Euclidean distance metric is evaluated for similarity measurement. The proposed approach is also compared to different deep learning techniques such as AlexNet, VGGs (VGG-16 & VGG-19), GoogleNet, SqueezeNet, DarkNet-19 models. The proposed method was examined on the KVASIR database with 4000 images and S different classes. We get the optimum results as average precision of 95.44% and average recall of 19.09 on a scale of 20 using the proposed deep learning model and Euclidean distance metric.
Breast Cancer Histopathology Image Classification using ADAptive Moment (ADAM) optimization Estimation

Breast cancer’s diagnosis have numerous imaging techniques like ultrasound, X-rays, MRI and many more out of those biopsy imaging is the most certain way to determine whether a breast tumor is cancerous or not, but the main problem faced by oncologist during by medical imaging is whether the person has tumor or not or the whether the tumor is cancerous as cancerous can be dangerous if not determined on times and can increase the risk of spreading to other cells and tissues. Nowadays deep learning architectures are most preferably used by the classification procedure in medical field for the diagnosis of the disease. This works aims to classify the breast cancer’s image into cancerous and non-cancerous for differentiation of benign cases from malignant cases by using a 3 convolutional module for the process of feature extraction, and two fully connected dense layers with an output layer for classification by tuning the hyperparameters on scaled images. The hypermeter we tuned to get the better result is dropout rate and the classification is performed and confusion matrix is plotted for the problem. Applied procedure is able to find benign cancer cases from malignant cancer cases of breast cancer and achieving the accuracy with training data 99.25%. On the other hand accuracy with testing data is 93% with precision percentage of 94 and fi score value of 93%. In the last this mechanism is obtaining ROC AUC value 9S.5% which is significant improvement over previous work.
Multi-directional broad learning system for the unsupervised stereo matching method

The supervised stereo matching methods usually rely on ground truth disparity maps as the training labels, this limits its practical application in many situations. In this study, we propose a novel unsupervised stereo matching method based on a multi-directional broad learning system. A multi-directional broad learning system was constructed to generate multiple candidate disparity maps. During the generation of each candidate disparity map, an update criterion is proposed for the disparity value based on the maximum similarity of the inverse mapping region to remove the abnormal disparity values of the training samples. Subsequently, multi-direction consistency verification is performed to further eliminate abnormal disparity values, which are based on the uniqueness principle of disparity truth values at the same location. Finally, an invalid depth redefinition based on a local gravity weight method is introduced to select the appropriate disparity value to fill the invalid pixel positions from their neighborhood, which is calculated based on the local region of the color, matching cost, and geometric spaces in the stereo images. We provide the results of experiments on both indoor and outdoor scenarios to demonstrate the effectiveness and flexibility of our approach, including comparisons with state-of-the-art methods.
Remote Teaching of Scrum in Practice: an analysis of participants' perception

ABSTRACTThe use of agile methods in companies is becoming more common,such as the Scrum method. So, students reported the need to acquirepractical knowledge of agile methods. Due to the COVID-19pandemic, remote teaching has become the most practical andefficient way of imparting knowledge through courses andworkshops. Based on the needs of the project's target audience, thisstudy presents a proposal for teaching a practical approach usingthe Scrum method to college students. This paper presents themethodology used, describing the students' perceptions of thestrategies, didactics, and the resources used. Also, we describe thereal examples of hands-on activities and Kahoot, and the results ofa questionnaire applied to collect feedback. The data obtained wereanalyzed qualitatively and quantitatively. According to the resultsthe satisfaction level with the course was high, given thecontribution of students to the practice of Scrum roles. Regardingthe methodology, 100% of the students considered it easy and 85%were satisfied with the course, in general. In addition, most of theparticipants stated that they enjoyed the hands-on activities and feltquite competent after completing them. Moreover, students statethat they enjoyed the course due to (a) the teaching methodology,(b) the use of real examples, and (c) the content approach. Thus, therelevance of good results obtained leads us to discussions aboutfuture actions.
DISGUISED FACIAL RECOGNITION USING NEURAL NETWORKS - A SURVEY

Face recognition software is being used more often in a wide range of applications, but worries about privacy and exploitation have sparked interest in creating methods for "disguised" face identification. The use of neural networks for disguised face identification has recently attracted the attention of academics, who trained their algorithms on pictures of people with masks, hats, and other facial coverings. Yet, there are a number of difficulties in creating efficient disguised facial recognition systems, such as a lack of training data and the requirement to take different lighting and position changes into consideration. The use of adversarial networks, infrared imaging, and deep learning techniques are among the latest advancements in the field of masked facial recognition using neural networks that are examined in this review. The study also covers prospective uses for concealed face recognition technology as well as the moral issues surrounding its implementation. The survey's findings show that neural networks may be used to mask face recognition in order to satisfy privacy concerns while still enabling accurate identification of people in a range of scenarios. Keywords — Disguised Facial Recognition, Neural Networks, Machine Learning, Deep Learning, Adversarial Networks.
A Hacker's Delight &gt; You'll Either Love or Hate the Flipper Zero

Readers of this Hands On are likely to fall into one of two camps: those who'll view the Flipper Zero with fascination, and those who'll view it with loathing. Among the former are security re-searchers and hardware developers trying to debug a wireless setup. Among the latter are IT folks charged with de-fending their realm from physical or network attacks. But whatever camp you fall into, the Flipper is something you'll need to know about.
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition

Named Entity Recognition (NER) is a well and widely studied task in natural language processing. Recently, the nested NER has attracted more attention since its practicality and difficulty. Existing works for nested NER ignore the recognition order and boundary position relation of nested entities. To address these issues, we propose a novel seq2seq model named GPRL, which formulates the nested NER task as an entity triplet sequence generation process. GPRL adopts the reinforcement learning method to generate entity triplets de-coupling the entity order in gold labels and expects to learn a reasonable recognition order of entities via trial and error. Based on statistics of boundary distance for nested entities, GPRL designs a Gaussian prior to represent the boundary distance distribution between nested entities and adjust the out-put probability distribution of nested boundary tokens. Experiments on three nested NER datasets demonstrate that GPRL outperforms previous nested NER models.
Poster Abstract: Long-Term Inventory Flow Management System Tracking Using Doorway Monitoring

The standard way to keep track of inventory is through human counting or with a hand-held device and inputting the data into the warehouse management system (WMS). WMS can be quite expensive for small warehouses and human counting is susceptible to errors. Alternatively, we propose an automated way to keep track of inventory by leveraging the surveillance cameras readily available in the warehouses. In practice, there are challenges such as incorrect stock numbers, overfitting issues, and a mundane labeling process. To identify and mitigate those challenges, we present real-world deployment to various warehouses for a period of 4 months with up to 97 percent counting accuracy.
Quadratic symmetric polynomials and an analogue of the Davenport constant

We define the constant $$ D(\varphi_F, p) $$ , an analogue for the Davenport constant, for sequences on the finite field $$ \mathbb{F}_p $$ , defined via quadratic symmetric polynomials. Next, we state a series of results presenting either the exact value of $$ D(\varphi_F, p) $$ , or lower and upper bounds for this constant.
PKM Pemutakhiran Data Penduduk di Desa Kukuh Kerambitan Tabanan

Kukuh Village is a village located in Kerambitan District, Tabanan Regency, Bali. Regional government has been promoting routine population data collection but most people do not yet have awareness of the importance of such data collection. Village population data stored in the Kukuh Village Information System is currently inaccurate, because the population of Kukuh Village is increasing every year, causing population data stored in the Kukuh Village Information System to be less accurate. Because of this, a population data update was carried out in Kukuh Village, Kerambitan, Tabanan. This program is realized in order to make population data on the official website of the Kukuh Village Information System accurate, up-to-date, integrated, of good quality so as to create accurate population data on the official website of the Kukuh Village Information System.
Trading Security Designs Based on Blockchain Techniques

Contemporarily, digital art has swiftly integrated into human beings’ daily life. While digital art is becoming increasingly popular and significantly influences conventional art, currently it is needed to be more online communities where art enthusiasts and artists may interact and offer a self-sustaining means of payment. Independent art programs based on ideas like reciprocity are what E-Gallery Art aspires to. Its goal is to encourage teamwork and co-creation in the visual arts. This research discusses the way to create decentralized Dapps based on blockchain that are mutually advantageous for platforms, artists, and users. The Dapp follows a process where users reward artists by donating, and the best-case scenario is that the artist is motivated and contributes more to the community. Users can create galleries and display their work for free, which is an important starting point for E-Gallery Art Dapp to start a self-sustainable cycle. These results shed light on guiding further exploration of trading security design based on blockchain techniques.
Avaliação de Desempenho de Estratégias de Virtualização Alternative Title: Performance Evaluation of Virtualization Strategies

RESUMOCloud computing is a computational model in which providers offeron-demand services to clients in a transparent way. In this context,the efficient provisioning of resources is extremely important, sinceit is directly related to Quality of Service (QoS), with the Service LevelAgreement (SLA) compliance and the cost of the service. In thisway, virtualization becomes a crucial factor in resource provisioning.In this paper, performance evaluations were performed consideringenvironments configured with different virtualization strategies: byvirtual machines and by containers. In the first set of experiments,we evaluated the boot time of the system with the KVM, Xen andDocker tools and with variations in the number of instances andvirtual cores. In the second, the same mechanisms were analyzedduring the execution of different benchmarks (Apache and Smallpt)and with variations in the number of virtual resources provisioned.In the results, Docker proved to be more efficient when comparedto other tools.
RANCANG BANGUN MONITORING SERVER SYSTEM BERBASIS TERMINAL LINUX DAN WHATSAPP GATEWAY STUDI KASUS: PT. XYZ

This research aims to design a server monitoring system based on a Linux terminal and Whatsapp Gateway for PT. XYZ. The purpose of this system is to monitor server conditions in real-time and provide notification to the admin if there is a problem with the server. The method used in this research is by integrating the Linux terminal application with the Whatsapp Gateway using PHP programming language. The research results show that the server monitoring system&#x0D; based on Linux terminal and Whatsapp Gateway can provide real-time notification to the admin if there is a problem with the server, which can help in identifying and solving problems quickly. Thus, this system can improve efficiency in server monitoring and reduce downtime
Investigating the Role of UTUAT in YouTube-based Educational Content Acceptance during the Post-Pandemic Era

This research also focused on the factors behind wider YouTube usage in Pakistan during the post-pandemic era. The researchers adopted primary constructs from the Unified Theory of Acceptance and employed Structural Equation Modelling for the data analysis. Data gathered from n= 374 individuals indicated that Performance Expectancy has a significant effect on Behavioral Intentions, showing that online platform performance positively shapes the students' behavior. However, the effect of Effort Expectancy was insignificant, indicating expecting less effort to search and find relevant content does not affect the students' Behavioral Intention. The effect of Performance Expectancy on Effort Expectancy also remained significant, indicating that performance expectations are linked with the effort expectations of the respondents. Further, Behavioral Intention significantly leads the study respondents to accept YouTube educational content for learning purposes. Finally, Perceived Usefulness significantly mediates the behavioral intention to accept YouTube educational content for learning purposes. Thus, this research concluded that virtual social networking, including YouTube, supports the young generation's learning and education. Consequently, the importance of these platforms is increasing, demanding that institutions expand their learning opportunities further, ensuring enhanced learning experiences for everyone.
Secure health Care Systems Using Blockchain Technology

With the technological innovation that is spreading around the globe, smart healthcare and biomedical advancements have always been a big issue to be improved in every way: The Internet of Things (IoT) has made incredible strides in the sharing of healthcare data and triggering the related necessary actions in the modern world, with ever-evolving internet models automating and digitalizing numerous industrial and household implementations. With the technological innovation that is sweeping the globe, smart healthcare and biomedical advancement have always been a vital issue to be improved in every way. The Internet of Things (IoT) has made incredible strides in sharing healthcare data and triggering the essential activities connected with it in the modern world, with ever-evolving internet models automating and digitalizing numerous industrial and household applications. With the technological innovation that is sweeping the globe, smart healthcare and biomedical advancement have always been a vital issue to be improved in every way.The Blockchain Technology has experienced numerous changes since its debut. There has been research that has shown a variety of ways to organize the conventional system's access control mechanism. In recent years, blockchain technology has also demonstrated exceptional dependability in a variety of industries, including smart homes, healthcare, banking, information storage management, security, and others.
Design of a Mini Bank Service System for Regional Public Service Agencies (BLUD) in Vocational High Schools

This study is aims to help Vocational high schools, which are partners in this activity, are also focusing on the rapid development of BLUD so that they can become financially independent and develop their educational institutions through their own efforts. With the development of this mini-bank, it is necessary to develop an information system that can assist the partner in keeping track of all the management and transaction flows that take place in this her BLUD. Here, this non-profit activity is taking place specifically for partners to realize the system that supports her BLUD Minibank. This non-profit activity has resulted in optimized efficiency of BLUD Minibank transactions and enhanced partner skills in the use of information technology
A Black-Box Attack on Optical Character Recognition Systems

Adversarial machine learning is an emerging area showing the vulnerability of deep learning models. Exploring attack methods to challenge state-of-the-art artificial intelligence (AI) models is an area of critical concern. The reliability and robustness of such AI models are one of the major concerns with an increasing number of effective adversarial attack methods. Classification tasks are a major vulnerable area for adversarial attacks. The majority of attack strategies are developed for colored or gray-scaled images. Consequently, adversarial attacks on binary image recognition systems have not been sufficiently studied. Binary images are simple—two possible pixel-valued signals with a single channel. The simplicity of binary images has a significant advantage compared to colored and gray-scaled images, namely computation efficiency. Moreover, most optical character recognition systems (OCRs), such as handwritten character recognition, plate number identification, and bank check recognition systems, use binary images or binarization in their processing steps. In this paper, we propose a simple yet efficient attack method, efficient combinatorial black-box adversarial attack (ECoBA), on binary image classifiers. We validate the efficiency of the attack technique on two different data sets and three classification networks, demonstrating its performance. Furthermore, we compare our proposed method with state-of-the-art methods regarding advantages and disadvantages as well as applicability.
Unsupervised Virtual Drift Detection Method in Streaming Environment

Real-time applications generate an enormous amount of data that can potentially change data distribution. The underline change in data distribution concerning time causes concept drift. The learning model of the data stream encounters concept drift problems while predicting the patterns. It leads to deterioration in the learning model’s performance. Additional challenges of high-dimensional data create memory and time requirements. The proposed work develops an unsupervised concept drift detection method to detect virtual drift in non-stationary data. The K-means clustering algorithm is applied to the relevant features to find the stream’s virtual drift. The proposed work reduces the complexity by detecting the drifts using the k highest score features suitable with high-dimensional data. Here, we analyze the data stream’s virtual drift by considering the changes in data distribution of recent and current window data instances.