Reviewerly/exHarmony huggingface dataset:
A New Adaptive Fault Tolerant Framework in the Cloud

The failure resilience in cloud computing is becoming mainstream due to unprecedented growth in the cloud. In order to enhance cloud service reliability, various fault tolerance techniques have been suggested in the literature. Many cloud service reliability and availability issues are arising due to the failure of virtual machines (VMs). Therefore, this study contributes a new Threshold-Based Adaptive Fault Tolerance (TBAFT) approach in the cloud by amalgamating proactive and reactive approaches. Furthermore, in this paper, VM migration and replication are being utilized in the proactive approach, whereas retry is used as a reactive approach. Additionally, experimental validation is performed by comparing three benchmark fault tolerance approaches by incorporating the first fit and least full host selection policies. The obtained experimental results of the proposed approach are presented in terms of different metrics such as failure rate, throughput, migration, and execution time using CloudSim simulator.
Performance analysis of the dynamic trust model algorithm using the fuzzy inference system for access control

Accessing company resources such as personal data, financial data, and company networks in the dynamic business environment is a vital task. This paper proposes a Dynamic Trust Model Algorithm (DTMA) using fuzzy inference rules for access control. The novelty is finding the unsteady behaviour of an employee in a varying period using trust mathematical computation. Based on four parameters such as Performance (P), Direct Observation (DO), Expected Trust (ET) and Feedback (F), the trust value is calculated. To manage the deliberate altering behaviour of the hostile employees, a dynamic Trust Value (TV) has been calculated and restrict their harmful actions. Also, the performance and accuracy of the DTMA have been assessed and compared to other models such as DLATrust, DyTrust and SecTrust. The proposed DTMA gives better results in terms of accuracy, precision, recall, F-Score and Receiver Operating Characteristics (ROC).
Cryptocurrencies Investigation: A Methodology for the Preservation of Cryptowallets

The steady growth of cryptowallets users and the widespread of cryptocurrencies adoption has inadvertently risen the numbers of cybercrime. The decentralized and pseudo-anonymous nature of cryptocurrencies impose a unique challenge to the investigators. Unlike investigation on fiat currency where banks can be contacted to freeze account, cryptocurrencies do not have a centralized entity that can be contacted. On top of that, studies have shown that using a generic digital forensics methodology to collect and preserve cryptowallets, which often involves imaging and seizing, are ineffective for cryptocurrencies investigation. This is because criminals can recover his seized cryptowallets into other devices and thus continue to make transactions for illegal activities. This defeats the purpose of halting the criminals from conducting further crimes. Hence in this study we propose a methodology for preserving cryptowallets at crime scene. We then conducted evaluation on the methodology by using real case and simulation exercise on different types of cryptowallets. The result shows that our methodology can be used to properly preserve cryptocurrencies evidence. This study aims to identify gaps in cryptocurrencies investigation and address them by proposing a proper methodology.
Vector field path following of a full-wing solar-powered Unmanned Aerial Vehicle (UAV) landing based on Dubins path: a lesson from multiple landing failures

Abstract The full-wing solar-powered UAV has a large aspect ratio, special configuration, and excellent aerodynamic performance. This UAV converts solar energy into electrical energy for level flight and storage to improve endurance performance. The UAV only uses a differential throttle for lateral control, and the insufficient control capability during crosswind landing results in a large lateral distance bias and leads to multiple landing failures. This paper analyzes 11 landing failures and finds that a large lateral distance bias at the beginning of the approach and the coupling of base and differential throttle control is the main reason for multiple landing failures. To improve the landing performance, a heading angle-based vector field (VF) method is applied to the straight-line and orbit paths following and two novel 3D Dubins landing paths are proposed to reduce the initial lateral control bias. The results show that the straight-line path simulation exhibits similar phenomenon with the practical failure; the single helical path has the highest lateral control accuracy; the left-arc to left-arc (L-L) path avoids the saturation of the differential throttle; and both paths effectively improve the probability of successful landing.
Implementing an accessible conversational user interface

In the ADMINS (Assistants for the Disclosure and Management of Information about Needs and Support) project, we have implemented a virtual assistant which is designed to enable students to disclose disabilities and to provide guidance and suggestions about appropriate accessible support. ADMINS explores the potential of conversational user interfaces (CUIs) to reduce administrative burden and improve outcomes, by replacing static forms with written and spoken dialogue. For the beta version of the assistant, we have carried out a trial to evaluate its accessibility and user experience (UX). Following the project's participatory-design approach, the trial sample included university students with accessibility needs and disability support advisors for its evaluation. The results included both qualitative and quantitative feedback from the participants (students and advisors) which helped to identify accessibility and UX barriers for improving the assistant's design in the next stages of the project.
A Machine Vision Approach for Bioreactor Foam Sensing

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.
Digital Image Tampering Detection Using Multilevel Local Binary Pattern Texture Descriptor

Digital images can be manipulated using the latest tools and techniques without leaving any visible traces. Image tampering detection is required to authenticate image validation. It is concluded from previous research that image tampering modifies the texture micropattern in a digital image. Therefore, texture descriptors can be applied to highlight these changes. A texture descriptor–based technique is proposed for detecting both copy-move and splicing forgery. In the proposed method, an RGB image is converted into a YCbCr image and Cb and Cr image components are extracted, as these components are more sensitive to tampering artifacts. Further, a standard deviation (STD) filter and higher-order texture descriptors are applied on Cb and Cr components. The STD filter is used to highlight important details of objects in the image. A support vector machine classifier is used to classify forged and tampered images. Support vector machine (SVM) classifier gives good results on both large- and small-image data sets. The performance is appraised in three online-available, widely used data sets: CASIA v1.0, CASIA v2.0, and Columbia. The proposed method outperforms most of the state-of-the-art methods.
Impact of image compression on deep learning-based mammogram classification

Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms as "malignant"-cases that lead to a cancer diagnosis and treatment-or "normal" and "benign," non-malignant cases that do not require immediate medical intervention. In this retrospective study, 9111 unique mammograms-5672 normal, 1686 benign, and 1754 malignant cases were collected from the National Cancer Center in the Republic of Korea. Image compression was applied to mammograms with compression ratios (CRs) ranging from 15 to 11 K. Convolutional neural networks (CNNs) with three convolutional layers and three fully-connected layers were trained using these images to classify a mammogram as malignant or not malignant across a range of CRs using five-fold cross-validation. Models trained on images with maximum CRs of 5 K had an average area under the receiver operating characteristic curve (AUROC) of 0.87 and area under the precision-recall curve (AUPRC) of 0.75 across the five folds and compression ratios. For images compressed with CRs of 10 K and 11 K, model performance decreased (average 0.79 in AUROC and 0.49 in AUPRC). Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR < = 5 K) images had maps encapsulating a radiologist's label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. This paper finds that while training models on images with increased the robustness of the models when tested on compressed data, moderate image compression did not substantially impact the classification performance of DL-based models.
Decoupled Representation Learning for Character Glyph Synthesis

Character glyph synthesis is still an open challenging problem, which involves two related aspects, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , font style transfer and content consistency. In this paper, we propose a novel model named FontGAN, which integrates the character structure stylization, de-stylization and texture transfer into a unified framework. Specifically, we decouple character images into style representation and content representation, which offers fine-grained control of these two types of variables, thus improving the quality of the generated results. To effectively capture the style information, a style consistency module (SCM) is introduced. Technically, SCM exploits category-guided Kullback-Leibler divergence to explicitly model the style representation into different prior distributions. In this way, our model is capable of implementing transformations between multiple domains in one framework. In addition, we propose content prior module (CPM) to provide content prior for the model to guide the content encoding process and alleviates the problem of stroke deficiency during structure de-stylization. Benefiting from the idea of decoupling and regrouping, our FontGAN suffices to achieve many-to-many translation tasks for glyph structure. Experimental results demonstrate that the proposed FontGAN achieves the state-of-the-art performance in character glyph synthesis.
Raman-XPS spectroscopy, REE chemistry, and surface morphology of Fe-Ti oxide heavy mineral sands: a case study from Varkala-Kovalam coast, south-west India

The beach sands of the Varkala-Kovalam coast, south-west India, are enriched with heavy minerals with a high concentration of ilmenite followed by sillimanite, monazite, rutile, zircon, and garnet. The Fe–Ti oxide minerals such as ilmenite, its altered product leucoxene, and rutile were successfully recovered, and their structure, chemistry, and surface morphology were analysed using advanced characterisation techniques like Raman spectroscopy, Energy Dispersive X-ray Fluorescence (ED-XRF), High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS), Thermogravimetric Analysis (TGA), UV-Visible-NIR (UV-Vis-NIR) spectroscopy, X-ray Photoelectron Spectroscopy (XPS), and Scanning Electron Microscopy (SEM-EDS). The chemical composition, surface chemistry, the oxidation state of surface elements, anisotropic crystal behaviour of the minerals due to physical or chemical processes, discrimination of polymorphs, and finally the morphological changes due to mechanical impacts were also analysed. The study provides solid information to the scientific community and policymakers for determining the grade and potential applications of these strategic minerals.
Dual Unbiased Recommender Learning for Implicit Feedback

Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback, which heavily affects their sub-optimal performance gains. This paper proposes a dual recommender learning framework that simultaneously eliminates the bias of clicked and unclicked data. Specifically, the proposed loss function adopts two propensity weighting to effectively estimate the true positive and negative preferences from clicked and unclicked data. We also prove that the proposed loss function converges to the ideal loss function for both clicked and unclicked data. Because of the model-agnostic property, it can be applied to any existing unbiased learning models. Experimental results show that the proposed method outperforms state-of-the-art unbiased models up to 5.54-24.56% for [email protected] on three datasets.
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition

Current state-of-the-art visual recognition systems usually rely on the following pipeline: 1) pretraining a neural network on a large-scale data set (e.g., ImageNet) and 2) finetuning the network weights on a smaller, task-specific data set. Such a pipeline assumes that the sole weight adaptation is able to transfer the network capability from one domain to another domain based on a strong assumption that a fixed architecture is appropriate for all domains. However, each domain with a distinct recognition target may need different levels/paths of feature hierarchy, where some neurons may become redundant, and some others are reactivated to form new network structures. In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness, compared to the existing image recognition pipeline that only tunes the weights regardless of the architecture. Our method can be easily generalized to an unsupervised paradigm by replacing supernet training with self-supervised learning in the source domain tasks and performing linear evaluation in the downstream tasks. This further improves the search efficiency of our method. Moreover, we also provide principled and empirical analysis to explain why our approach works by investigating the ineffectiveness of existing neural architecture search. We find that preserving the joint distribution of the network architecture and weights is of importance. This analysis not only benefits image recognition but also provides insights for crafting neural networks. Experiments on five representative image recognition tasks, such as person re-identification, age estimation, gender recognition, image classification, and unsupervised domain adaptation, demonstrate the effectiveness of our method.
DiffIR: Exploring Differences in Ranking Models' Behavior

Understanding and comparing the behavior of retrieval models is a fundamental challenge that requires going beyond examining average effectiveness and per-query metrics, because these do not reveal key differences in how ranking models' behavior impacts individual results. DiffIR is a new open-source web tool to assist with qualitative ranking analysis by visually 'diffing' system rankings at the individual result level for queries where behavior significantly diverges. Using one of several configurable similarity measures, it identifies queries for which the rankings of models compared have important differences in individual rankings and provides a visual web interface to compare the rankings side-by-side. DiffIR additionally supports a model-specific visualization approach based on custom term importance weight files. These support studying the behavior of interpretable models, such as neural retrieval methods that produce document scores based on a similarity matrix or based on a single document passage. Observations from this tool can complement neural probing approaches like ABNIRML to generate quantitative tests. We provide an illustrative use case of DiffIR by studying the qualitative differences between recently developed neural ranking models on a standard TREC benchmark dataset.
Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis

Background Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. Objective The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits. Methods Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018. Results Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months. Conclusions For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039
Content Based Image Retrieval using Frequency Domain Features: Zigzag Scanning of DCT Coefficients

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR the features in spatial domain, such as color, texture, shape and point features are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. This paper proposes two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image. DCT_256_Zigzag and DCT_256_2×2. These methods take into considerations the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For an query image, its feature vector is compared with feature vectors of every other image in the database using dl-distance and the images with least distance is considered most similar image to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR) and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets: Core1-1000, Corel -5000, Core1-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.
Do You Trust Me? Value and Governance in Data Sharing Business Models

Collaborations to share data can enable private companies to create new business models and improve products and services. For public authorities, it can lead the way toward a data-driven policy. Yet, sharing data poses different challenges, such as risk to comply with legalities, commercial risk, quality of data, privacy and ethical risk, data ownership etc. Managing data within the borders of a firm has been explored thoroughly in the literature of data governance. Yet, data sharing poses new challenges, as it extends to collaboration within an ecosystem. The business model literature is not investigating yet the concept of data sharing. Thus, this paper investigates the different data-sharing business model factors. The research looks into the limitations of current research, which it aims to overcome by combining the insights from the data governance, data sharing literature and business model literature. The factors influencing data-sharing collaborations are (1) value creation and capturing, (2) data governance, (3) ecosystem trust and (4) data trust.
Predicting and attending to damaging collisions for placing everyday objects in photo-realistic simulations

Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. This problem is particularly challenging because it is necessary to predict what happens if an object is placed in a cluttered designated area. We show that a rule-based approach that uses plane detection, to detect free areas, performs poorly. To address this, we develop PonNet, which has multimodal attention branches and a self-attention mechanism to predict damaging collisions, based on RGBD images. Our method can visualize the risk of damaging collisions, which is convenient because it enables the user to understand the risk. For this purpose, we build and publish an original dataset that contains 12,000 photo-realistic images of specific placing areas, with daily life objects, in home environments. The experimental results show that our approach improves accuracy compared with the baseline methods.