Reviewerly/exHarmony huggingface dataset:
Co-residence based data theft game in cloud system with virtual machine replication and cancellation

In cloud computing, the task replication with cancellation (TRC) approach aims to reduce the expected task completion time while mitigating additional load and user expenses. However, its effectiveness may be hindered by co-residence attacks, where attackers may access users’ data through co-residing their virtual machines (VMs) with users’ VMs on the same server. Particularly, creating more task replicas has two-fold effects: a task has larger chances to complete in a shorter time but becomes more vulnerable to co-residence attacks (incurring higher data theft probability). The objective of this paper is to formulate and solve an optimization problem (a minmax game problem) that finds the optimal number of task replicas balancing the two conflicting effects. Resulting solutions to the proposed optimization problem can maximize the user's utility while considering the strategic attack behavior of maximizing the attacker's utility. The solution methodology encompasses a new probabilistic model of evaluating the user's and attacker's utilities. As demonstrated through examples, there exist complicated interactions of different model parameters impacting the utilities of users and attackers and thus the optimization solutions, justifying necessity and significance of the suggested model and methodology.
Full-Reference Image Quality Assessment Based on Grünwald–Letnikov Derivative, Image Gradients, and Visual Saliency

The purpose of image quality assessment is to estimate digital images’ perceptual quality coherent with human judgement. Over the years, many structural features have been utilized or proposed to quantify the degradation of an image in the presence of various noise types. Image gradient is an obvious and very popular tool in the literature to quantify these changes in the images. However, gradient is able to characterize images locally. On the other hand, results from previous studies indicate that global contents of a scene are analyzed before the local features by the human visual system. Relying on these features of the human visual system, we propose a full-reference image quality assessment metric that characterizes the global changes of an image by the Grünwald–Letnikov derivatives and the local changes by image gradients. Moreover, visual saliency is also utilized for weighting the changes in the images and emphasizing those areas of the image which are salient to the human visual system. To prove the efficiency of the proposed method, massive experiments were carried out on publicly available benchmark image quality assessment databases.
What Makes for Effective Few-shot Point Cloud Classification?

Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and retraining when dealing with novel classes never seen before. On the other hand, we humans can quickly recognize new classes by looking at a few samples, which motivates the recent popularity of few-shot learning (FSL) in machine learning communities. Most current FSL approaches work on 2D image domain, however, its implication in 3D perception is relatively under-explored. Not only needs to recognize the unseen examples as in 2D domain, 3D few-shot learning is more challenging with unordered structures, high intra-class variances and subtle inter-class differences. Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D methods when migrating to the 3D domain.In this work, for the first time, we perform systematic and extensive studies of recent 2D FSL and 3D backbone networks for benchmarking few-shot point cloud classification, and we suggest a strong baseline and learning architectures for 3D FSL. Then, we propose a novel plug-and-play component called Cross-Instance Adaptation (CIA) module, to address the high intra-class variances and subtle inter-class differences issues, which can be easily inserted into current baselines with significant performance improvement. Extensive experiments on two newly introduced benchmark datasets, ModelNet40-FS and ShapeNet70-FS, demonstrate the superiority of our proposed network for 3D FSL.
The four dimensions of Variability and their impact on MBPLE

The development of complex systems such as aircraft product lines requires the support of advanced modeling methodologies such as Model-Based Systems Engineering (MBSE) and Product-Line Engineering (PLE). To enable the development of such complex product lines, Airbus is adopting the combination of MBSE and PLE, a method previously applied in other industries and known as Model-Based Product Line Engineering (MBPLE). In adopting MBPLE it is necessary to understand the type of constraints that influences the definition of variability within a development program or project. This paper proposes four different dimensions of variability which facilitate and guide the application of MBPLE at Airbus but that can also be extended to other industries and organizations. Those four dimensions, namely Co-Variability in product, manufacturing and services, Variability in development lifecycle, Variability in layers of abstraction, and Variability in system hierarchy levels, are first described. Afterward, their impact on the MBPLE method steps, namely “Define Product Line Feature Models”, “Define Product Line Assets”, “Select Member Product Feature Configuration” and “Derive Member Product Assets”, is analyzed with the support of an Airbus example.
Multi-source and multivariate ozone prediction based on fuzzy cognitive maps and evidential reasoning theory

Ozone prediction, a key role for ozone pollution control, is facing the following challenges, i.e., the complex evolution trend of ozone, the cross-interference phenomena between ozone and other pollutants, and the low-quality monitoring data. To overcome the above challenges, we propose a multi-source and multivariate ozone prediction model based on fuzzy cognitive maps (FCMs) and evidential reasoning theory from the perspective of spatio-temporal fusion, termed as ERC-FCM. In this framework, an FCM-based prediction model is introduced to solve the ozone forecasting problem. Inspired by the multivariate time series forecasting, a multivariate ozone prediction problem is modeled as an FCM learned by the real-coded genetic algorithm, in which each node denotes a variable (pollutant). Thus, both the complex evolution trend of ozone and the cross-interference phenomena can be reflected by the FCM. Further, we propose an ensemble theoretical framework based on evidence reasoning theory and the matrix 2 norm. This theoretical framework relieves the negative factors from the low-quality monitoring data and improves the prediction accuracy when facing multi-source and multivariate time series. The performance of ERC-FCM is validated on two real-world datasets. The experimental results demonstrate that our method yields the best prediction performance by comparison with the other classical FCM-based methods on mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). In addition, the Friedman test and Nemenyi test show that ERC-FCM gets relatively better prediction accuracy than other models.
A comparative assessment of deep object detection models for blood smear analysis

A blood smear is a common type of blood test where blood sample is taken from a patient, smear is made from the sample followed by observation of red blood cells, white blood cells and platelets. A pathologist carefully observes the sample and manually counts the number of RBC, WBC and platelets. This entire process from creating a smear to manually counting each element is tedious and susceptible to human errors. That is why, with the advancement of deep learning, various object detection techniques have become useful for automating the process and mitigating human errors in blood smear analysis. This work presents a comparative assessment of three different object detection models namely Faster R-CNN, EfficientDet D3 and CenterNet Hourglass, and presents their respective inference results. The three models have been compared using the COCO evaluation metrics to identify the best model performance for the given task. It is observed that out of the three models, the Faster R-CNN model performs the best in detecting WBCs and platelets in microscopic blood smear images with an average precision of 99.4%. Critical tasks like medical image processing require accurate predictions to prevent unintended ramifications. Therefore, while slower in terms of inference time, Faster R-CNN is the go-to model where accuracy is the priority. The work is also compared with the existing work in this domain to prove its efficiency.
Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification

Stain variation between images is a main issue in the analysis of histological images. These color variations, produced by different staining protocols and scanners in each laboratory, hamper the performance of computer-aided diagnosis (CAD) systems that are usually unable to generalize to unseen color distributions. Blind color deconvolution techniques separate multi-stained images into single stained bands that can then be used to reduce the generalization error of CAD systems through stain color normalization and/or stain color augmentation. In this work, we present a Bayesian modeling and inference blind color deconvolution framework based on the K-Singular Value Decomposition algorithm. Two possible inference procedures, variational and empirical Bayes are presented. Both provide the automatic estimation of the stain color matrix, stain concentrations and all model parameters. The proposed framework is tested on stain separation, image normalization, stain color augmentation, and classification problems.
Where are we with Human Pose Estimation in Real-World Surveillance?

The rapidly increasing number of surveillance cameras offers a variety of opportunities for intelligent video analytics to improve public safety. Among many others, the automatic recognition of suspicious and violent behavior poses a key task. To preserve personal privacy, prevent ethnic bias, and reduce complexity, most approaches first extract the pose or skeleton of persons and subsequently perform activity recognition. However, current literature mainly focuses on research datasets and does not consider real-world challenges and requirements of human pose estimation. We close this gap by analyzing these challenges, such as inadequate data and the need for real-time processing, and proposing a framework for human pose estimation in uncontrolled crowded surveillance scenarios. Our system integrates mitigation measures as well as a tracking component to incorporate temporal information. Finally, we provide a detailed quantitative and qualitative analysis on both a scientific and a real-world dataset to highlight improvements and remaining obstacles towards robust real-world human pose estimation in uncooperative scenarios.
Towards Unbiased and Robust Causal Ranking for Recommender Systems

We study the problem of optimizing ranking metrics with unbiased and robust causal estimation for recommender systems. A user may click/purchase an item regardless of whether the item is recommended or not. Thus, it is important to estimate the causal effect of recommendation and rank items higher with a larger causal effect. However, most existing works focused on improving the accuracy of recommendations, which usually have large bias and variance. Therefore, in this paper, we provide a general and theoretically rigorous framework for causal recommender systems, which enables unbiased evaluation and learning for the ranking metrics with confounding bias. We first propose a robust estimator for unbiased ranking evaluation and theoretically show that this estimator has a smaller bias and variance. We then propose a deep variational information bottleneck (IB) approach to exploit the sufficiency of the propensity score for estimation adjustment and better generalization. We also provide the learning bound and develop an unbiased learning algorithm to optimize the causal metric. Results on semi-synthetic and real-world datasets show that our evaluation and learning algorithms significantly outperform existing methods.
Explainability as a User Requirement for Artificial Intelligence Systems

As the capabilities of artificial intelligence (AI) systems constantly grow, so too does their complexity. The explainability toward their users is gaining attention, becoming a requirement that these systems should satisfy. We articulate user requirements for explainable AI systems.
Train Accident Prevention using Fuzzy Logic Based MEMS System

Train brake system uses vacuum to create brakes in case the human or animal object is found on the track by the driver. The train has inherent characteristics to stop after the application of brake at a distance of around one mile due to the minimal friction between iron wheel and iron track. The track and train simulation model was investigated on mat lab with fuzzy logic accident prevention scheme, which gave satisfactory result to prevent injury to the human object found on track. The fuzzy logic inputs are given to the brake system through the installed Visual sensor at the Driver’s Engine cabin to apply the brake by the driver. The train after the application of brake still moves and collides with the human object. The collision impact is taken over by a Micro Electro Mechanical System, which inflates the robotic airbag to grab the human object. The simulation result was found satisfactory and proves that the fuzzy rules application is a viable solution to protect the human beings found on the tract. Also, this technique accrues benefits to the railways, since it avoids the delay in the running of the train. This novel introduced idea of Micro Electro Mechanical Systems (MEMS) technology is to save the humans from the devastating train injuries. The Airbag Robot inflates after the impact on human beings without causing harm and also prevents damage to the train tracks. The simulation on Matlab with the input of the train location, its speed sensed and the coordination of Robotic MEMS airbag device using fuzzy logic has given satisfactory results. The inflated Airbag helps in developing an Air drag to create additional braking force to stop the train.
On-Device Saliency Prediction Based on Pseudoknowledge Distillation

Saliency prediction models aim to mimic the human visual system’s attention process, and the research has made significant progress due to recent advancements in deep convolution neural networks. However, the high memory requirements and intensive computational demands make these approaches less suitable for Internet-of-Things (IoT) devices, and there is a need for an improved computational efficiency and reduced memory footprint to facilitate distributed IoT intelligence. This article proposes a pseudoknowledge distillation (PKD) training method for creating a compact real-time saliency prediction model. The proposed method can effectively transfer knowledge from computationally expensive once-for-all (OFA-595) as a single teacher model and a combination of OFA-595 and EfficientNet-B7 as a multiteacher model to an early exit evolutionary algorithm network student model by utilizing knowledge distillation and pseudolabeling. Five saliency benchmark datasets are used to demonstrate PKD’s improved prediction performance and its reduced inference time without modifying the original student model.
Mutation and dynamic objective-based farmland fertility algorithm for workflow scheduling in the cloud

Nowadays, many scientific applications are deployed in the cloud to execute at a lower cost. However, the growing scale of workflows makes scheduling problems challenging. To minimize the workflow execution cost under deadline constraints, this article proposes a Mutation and Dynamic Objective-based Farmland Fertility (MDO-FF) algorithm for obtaining a near-optimal solution within a relatively shorter time. A Dynamic Objective Strategy (DOS) is introduced to accelerate the convergence speed, while a multi-swarm evolutionary approach and mutation strategies are incorporated to enhance the search diversity and help to escape from local optima. By seeking new potential solutions and searching in its corresponding neighborhoods, our proposed MDO-FF can make a good trade-off between exploration and exploitation. Extensive experiments are conducted on well-known scientific workflows with different types and sizes. The experimental results demonstrate that in most cases, our MDO-FF outperforms the existing algorithms in terms of constraint satisfiability and solution quality.
Integrating Heterogeneous Ontologies in Asian Languages Through Compact Genetic Algorithm with Annealing Re-sample Inheritance Mechanism

An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries’ intelligent applications. However, due to the different knowledge backgrounds of ontology developers, the entities in the ontologies could be defined in different ways, which hamper the communications among the intelligent applications built on them. How to find the semantic relationships among the entities that are lexicalized in different languages is called the Cross-lingual Ontology Matching problem (COM), which is a challenge problem in the ontology matching domain. To face this challenge, being inspired by the success of the Genetic Algorithm (GA) in the ontology matching domain, this work proposes a Compact GA with Annealing Re-sample Inheritance mechanism (CGA-ARI) to efficiently address the COM problem. In particular, a Cross-lingual Similarity Metric (CSM) is presented to distinguish two cross-lingual entities, a discrete optimal model is built to define the COM problem, and the compact encoding mechanism and the Annealing Re-sample Inheritance mechanism (ARI) are introduced to improve CGA’s searching performance. The experiment uses Multifarm track to test CGA-ARI’s performance, which includes 45 ontology pairs in different languages. The experimental results show that CGA-ARI is able to significantly improve the performance of GA and CGA and determine better alignments than state-of-the-art ontology matching systems.
A novel application of Decision Tree classifier in solar irradiance prediction

With an increase in integration of Solar energy in grid due to their environmental benefit, the need for correct prediction of solar irradiance becomes important. This paper aims towards improving short term accuracy of solar irradiation. Various visualization techniques have been used to show the relationship between various variables. The dataset is being pre-processed and fused with machine learning techniques to get the maximum accuracy of the model. We have used three models namely Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT), in which Decision tree has shown maximum accuracy.
Students’ Acceptance of Distance Learning as a Result of COVID-19 Impact on Higher Education in Jordan

This study aimed to investigate determinants for student’s acceptance of distance learning in Jordan at the time of COVID-19. This paper is dedicated to the higher education institutions shifting towards distance learning processes due to the global pandemic situation caused by COVID-19 in 2020. A conceptual framework was developed using a validated conceptual framework (UTAUT) that has proved its robustness in prior studies. The study made amendments for this framework as it excluded the actual use variable and was only concerned with the intention determinants, as online distance learning was imposed on students in higher education in Jordan, and there were no other available alternatives; therefore, the study investigates the drivers for intentions. This study also attempted to extend the theory, which does not underestimate the robustness and validity of the proposed framework. It was seen vital to cope with the consequences of the COVID-19 pandemic, which has become a strong influencer when considering the subject of this study. Two additional factors were suggested by the study, namely: perceived value and expected cost. The quantitative deductive exploratory methods, structural equation modelling, smart partial least squares (v.3.3), and path analysis was applied, which yielded interesting results.
Efficient Function Queryable and Privacy Preserving Data Aggregation Scheme in Smart Grid

The collection of users’ near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.
Why are some social-media contents more popular than others? Opinion and association rules mining applied to virality patterns discovery

Discovering the main features of virality patterns in Twitter is the focus of this research. Five trending topics related to the COVID-19 pandemic were selected for the study, with Spanish as the target language. To carry out the discovery of virality patterns, we applied opinion mining techniques that enable us to structure the information based on the polarity of the messages and the emotions they contain. After transforming the information from an unstructured textual representation to a structured one, data mining techniques were applied, specifically association rules mining. Message patterns with the highest virality (high shares and high likes), and at the same time the most relevant characteristics of the patterns with less impact were extracted. After an exhaustive analysis of the most relevant non-redundant rules, it can be concluded that messages with a high-negative polarity and a very high emotional charge, especially emotions that have intensified with the COVID-19 pandemic, such as fear, sadness, anger and surprise are more likely to go viral in social media. By contrast, messages with little news coverage in the media, few authors, and the absence of surprise are relevant features when it comes to seeing messages with very low dissemination in social media.