Reviewerly/exHarmony huggingface dataset:
Pla(y)cemaking With Care

There is growing academic interest in how people use ubiquitous computing devices—particularly smartphones—for cultivating liveable, enjoyable places, replete with layers of living meaning, memory, heritage and social connections. This is a trajectory embraced by two related paradigms of urban studies: slow cities and playable cities. In this paper we investigate how locative mobile games—alternately known as location-based games and pervasive games—can be vehicles of placemaking through play, enhancing the careful processes that form places out of layered networks of affection, habit, and social bonds in the playable city. We highlight carefulness as a latent theme not previously given a close treatment in locative game scholarship, applying it as a lens in our study of locative mobile games. Drawing on autoethnography, we investigate three locative mobile games—Niantic's Ingress and Pikmin Bloom, and Meyran Games' Plant the World—conducting a qualitative analysis of the findings using Dena's elements-behaviour-experiences (EBE) framework. We distil our findings into four design implications for developing locative mobile games that support pla(y)cemaking with care: physical anchorage, slow mechanics, ownership, and co-construction.
A scalable, secure, and semantically interoperable client for cloud-enabled Demand Response

Demand Response (DR) is becoming a cornerstone element in the current energy sector, particularly for the EU energy markets. For this reason, considerable effort has been spent on standardising demand response data models. As a result, there is an ever-growing number of demand response proposals based on these standards. However, these proposals are usually centralised, and those that rely on cloud solutions use the cloud as a centralised data store assuming that the data is already homogenised when stored, i.e. all the data has the same format and model. Nevertheless, in practise, DR proposals rely on several components that provide data in heterogeneous formats and models. Furthermore, the different DR standards define models for different data formats that hinder data exchange between different DR systems. In this article, a generic tool called CIM is presented, which allows existing DR systems to distribute their components in the cloud, providing a solid security and privacy framework for data exchange. In addition, the CIM implements a semantic interoperability layer that is capable of translating data into a normalised form when exchanged so that it can be transparently consumed by DR components. Experiments advocate the CIM as a solution for DR systems to decentralise their architectures and exchange heterogeneous data even with other DR systems that follow different DR standards.
SoC FPGA Acceleration for Semantic Segmentation of Clouds in Satellite Images

During the past decades the reports produced by the satellites regarding the clouds became important for the weather prediction, the climate and lately for the generation and the distribution of the solar power. Image segmentation techniques produce these reports and they exploit convolutional neural networks (CNNs) to improve the accuracy results at the expense though of the requirements for execution time and hardware resources. Aiming at improving this trade-off the current work studies the design of a CNN model targeting the placement and the execution on a SoC FPGA while keeping the high accuracy. Moreover, it studies how to optimize the performance by distributing the execution of the CNN’s functions among the SoC’s IP cores and the SoC’s logic. The results are validated by using the Vitis-AI framework to implement the proposed CNN on a Xilinx Zynq UltraScale+ MPSoC ZCU104 board.
CNN based Prediction Analysis for Web Phishing Prevention

Phishing has grown into one of the major and supreme operative in cyber threats, triggering millions of data breaches and security failures every year. This paper proposes a CNN based prediction analysis using Optimistic Multi centric feature extraction for phishing attack detection technique that uses only URL functions to expedite and accurately locate phishing websites and explore structured databases. Using anti-phishing technology requires experts to extract the characteristics of phishing URL sites and use behavioral principle which is identified through URL Behavioral Rectifier (U-BR). Then the feature dependability is supported to create URL probability phishing index (U-PFI) to identify the relative weight for detection of phishing sites. By attaining the feature weight, the features are observed using Optimized Multi Centric Feature Selection (OMCFS) issued to reduce the dimension Log variation, and then the selected features get trained through Conventional Neural Network (CNN). This method predicts the legitimacy of URLs without the access to web content to find the phishing and depends on domain related service. The proposed technique converts URLs into standard size scales using writing embedding techniques, separates features at different levels using the CNN model, and classifies the features as risk by category.
What do we mean by “data”? A proposed classification of data types in the arts and humanities

Purpose: This article describes the interviews we conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word "data" in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term. Methodology: We invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into 5 main research areas: philology and literary criticism, language and linguistics, history of art, computer science, archival studies. The interviews were transcribed and analysed using a grounded theory approach. Findings: A list of 13 research data types has been compiled thanks to the information collected from participants. The term "data" does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported. Originality: Our findings confirm that "data" within the FAIR framework should include all types of input and outputs humanities research work with, including publications. Also, the participants to this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.
An Intelligent Approach for Fair Assessment of Online Laboratory Examinations in Laboratory Learning Systems Based on Student’s Mouse Interaction Behavior

The COVID-19 pandemic has made the world focus on providing effective and fair online learning systems. As a consequence, this paper proposed a new intelligent, fair assessment of online examinations for virtual and remotely controlled laboratory experiments running through Laboratory Learning Systems. The main idea is to provide students with an environment similar to being physically present in a Laboratory while conducting practical experiments and exams and detecting cheating with high accuracy at a minimal cost. Therefore, an intelligent assessment module is designed to detect cheating students by analyzing their mouse dynamics using Artificial Intelligence. The mouse interaction behavior method was chosen because it does not require any additional resources, such as a camera and eye tribe tracker, to detect cheating. Various AI algorithms, such as KNN, SVC, Random Forest, Logistic Regression, XGBoost, and LightGBM have been used to classify student mouse behavior to detect cheating, and many metrics are used to evaluate their performance. Moreover, experiments have been conducted on students answering online laboratory experimentations while cheating and when answering the exams honestly. Experimental results indicate that the LightGBM AI algorithm achieves the best cheat detection results up to an accuracy of 90%, precision of 88%, and degree of separation of 95%.
A Post-Processing Method Based on Radial Basis Functions for the Fast Retrieval of the Strain Field in Digital Image Correlation Methods

Digital image correlation methods allow the determination of the displacement (and thus the strain) field of a target by picture comparisons, without the application of strain gauges or other invasive devices. Homologous sites are mapped from the undeformed to the deformed configuration, and displacements retrieved at a cloud of points in a scattered fashion. Radial basis functions (RBF) offer a rapid and reliable tool to post-process on-the-fly data from image correlation, in order to compute deformations directly without the need for generating a numerical grid over the measurement points. Displacements and associated strains can be computed only where desired, tracking automatically only the most reliable features for each image. In this work, a post-processing strain evaluation method for large displacement problems, based on RBF and the Green-Lagrange tensor, is presented and demonstrated for several test cases. At first, the proposed method is adopted on a set of artificially generated pictures, demonstrating a faster convergence with respect to FEM even when few points are used. Finally, the approach is applied to cases for which experimental results are available in the literature, exhibiting a good agreement.
Transformational leaders’ approach to overcapacity: A study in correctional institutions

This research attempts to examine the effect of transformational leadership (TL) on job satisfaction (JS) and job performance (JP) mediated by leader-member exchange (LMX) at all correctional officers in West Java. The quantitative method is adopted by conducting a questionnaire survey on all officers in the West Java area, with a total of 215 respondents. The questionnaire was done through the Google Forms platform and distributed in a time-lagged method. The sample of respondents who were interviewed was obtained through a purposive sampling technique, namely taking samples with certain considerations. The data in this study was then analyzed using Structural Equation Modeling (SEM), which was assisted by the AMOS program. The findings revealed that TL has a large impact on JS, JP, and LMX, whereas LMX has a significant impact on JS and JP. This study contributes to the literature by linking TL, LMX, JS, and JP. It examines and analyzes how and why TL and LMX can affect JS and JP.
ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence

Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.
Mission-Based Cybersecurity Test and Evaluation of Weapon Systems in Association with Risk Management Framework

With the advancement of information technology (IT), the importance of cyber security is increasing because of the expansion of software utilization in the development of weapon systems. Civilian embedded systems and military weapon systems have cybersecurity-related symmetry that can increase vulnerabilities in the process of advanced information technology. Many countries, including the United States, are exploring ways to improve cybersecurity throughout the lifecycle of a weapon system. The South Korean military is applying the U.S. standard risk management framework (RMF) to some weapon systems to improve cybersecurity, but the need for a model that is more suitable for the South Korean military has been emphasized. This paper presents the results of a mission-based cybersecurity test, along with an evaluation model that can be applied to South Korean military weapon systems in parallel with the RMF. This study first examined the related international research trends, and proposed a test and evaluation method that could be utilized with the RMF throughout the entire life cycle of a weapon system. The weapon system was divided into asset, function, operational task, and mission layers based on the mission, and a mutually complementary model was proposed by linking the RMF and cybersecurity test and evaluation according to the domestic situation. In order to verify the proposed cybersecurity test and evaluation model, a simulation was developed and performed targeting the Close Air Support (CAS) mission support system, which is a virtual weapon system. In this simulation, the nodes performances by layer before and after a cyberattack were calculated, and the vulnerabilities and protection measures identified in the cyber security test and evaluation were quantified. This simulation made it possible to evaluate and derive protection measures in consideration of mission performance. It is believed that the proposed model could be used with some modifications, depending on the circumstances of each country developing weapon systems in the future.
Jenkins Pipelines: A Novel Approach to Machine Learning Operations (MLOps)

Machine Learning is a widely popular field that is being used in an increasingly large number of projects worldwide. This necessitates the use of certain practices to create a structured framework for such projects. These practices, processes and pipelines are termed as Machine Learning Operations (MLOps). The software development lifecycle for a machine learning is indeed very complex and sequential nature of it results in several repetitive tasks for developers. Automation in this aspect would greatly reduce time and manual effort required. Jenkins is an open-source continuous integration tool that can be used to build pipelines to define and automate workflows in MLOps domain. This paper proposes the design and implementation of pipelines for various stages of MLOps that are data analysis, data preparation, training, testing and deployment on a single platform.
Shape robustness in style enhanced cross domain semantic segmentation

This paper focuses on domain adaptation method based on style transfer. Previous methods based on style transfer pay attention to the transformation of texture features between domains and maintain semantic consistency to the greatest extent. However, these methods have different effects on domain gaps in different types of categories. The categories with large texture difference and small structure difference can be improved better. For the categories with small texture difference and large structure difference, it causes negative transfer. In this paper, a shape robustness enhanced domain adaptive segmentation algorithm is proposed. Firstly, we adopt adjustable style transfer methods to enhance the style diversity of source domain images. Next, we differentiated different types of image features to weaken the negative transfer in the process of adversarial training. The results of this paper on general data sets GTA5 and SYNTHIA are better than other style transfer methods. Further experiments show that we improve the shape robustness of style enhancement method in domain adaptive segmentation task.
Dealing with textual noise for robust and effective BERT re-ranking

The pre-trained language models (PLMs), such as BERT, have been successfully employed in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have reported that BERT model is vulnerable to imperceptible textual perturbations on quite a few natural language processing (NLP) tasks. As for IR tasks, current established BERT re-ranker is mainly trained on large-scale and relatively clean dataset, such as MS MARCO, but actually noisy text is more common in real-world scenarios, such as web search. In addition, the impact of within-document textual noises (perturbations) on retrieval effectiveness remains to be investigated, especially on the ranking quality of BERT re-ranker, considering its contextualized nature. To mitigate this gap, we carry out exploratory experiments on the MS MARCO dataset in this work to examine whether BERT re-ranker can still perform well when ranking text with noise. Unfortunately, we observe non-negligible effectiveness degradation of BERT re-ranker over a total of ten different types of synthetic within-document textual noise. Furthermore, to address the effectiveness losses over textual noise, we propose a novel noise-tolerant model, De-Ranker, which is learned by minimizing the distance between the noisy text and its original clean version. Our evaluation on the MS MARCO and TREC 2019–2020 DL datasets demonstrates that De-Ranker can deal with synthetic textual noise more effectively, with 3%–4% performance improvement over vanilla BERT re-ranker. Meanwhile, extensive zero-shot transfer experiments on a total of 18 widely-used IR datasets show that De-Ranker can not only tackle natural noise in real-world text, but also achieve 1.32% improvement on average in terms of cross-domain generalization ability on the BEIR benchmark. • The first investigation into the effects of within-document textual noise on BERT re-ranker. • Effectiveness of BERT re-ranker declines when coming across textual noises. • Synthetic noise injected into MS MARCO can be useful to enhance BERT re-ranker. • De-Ranker can effectively deal with textual noise by learning a noise-invariant relevance estimation.
Augmented reality assisted assembly: An action regulation theory perspective on performance and user experience

The growing demand for personalized products is changing the manufacturing industry and requires flexible assembly instructions. In this context, augmented reality (AR)-based digital instructions represent a potential solution. However, empirical evidence of their effectiveness in terms of performance, user experience, and work experience remains fragmented and mixed. Moreover, theoretical arguments related to their effects are completely lacking. In this paper, we present the results of two studies conducted in a pilot factory that investigated the differences between a screen-based, a static AR-based, and an animated AR-based application. We found that task completion time was best in the animated AR condition and hedonic user experience was higher in the AR conditions than in the screen-based one. In addition, we discovered a loss of agency in the animated AR condition, represented through hesitant behavior regarding voice input. Based on the interview data, it became clear that users saw themselves in a passive role blindly executing. They transferred the authority originally assigned to them to the technology. As a result, users remained passive even though their action was required. Relevance to industry (25–50 words): When developing AR assistance for industrial work, the inherent design challenge is to develop the technology in a way that supports mental effort sufficiently to enhance performance while keeping the user in an active state. We formulate design implications as well as a theoretical frame to meet this challenge. • Designed augmented reality (AR) instructions to support industrial assembly. • action-regulation theory as theoretical underpinning. • Two studies comparing a screen-based with a static and augmented AR instruction. • Completion time is best in animated, hedonic user experience in both AR conditions. • Loss of agency in the animated AR condition.
Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

Abstract Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.