Reviewerly/exHarmony huggingface dataset:
An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification

The selection of a meta-learner determines the success of a stacked ensemble as the meta-learner is responsible for the final predictions of the stacked ensemble. Unfortunately, in imbalanced classification, selecting an appropriate and well-performing meta-learner of stacked ensemble is not straightforward as different meta-learners are advocated by different researchers. To investigate and identify a well-performing type of meta-learner in stacked ensemble for imbalanced classification, an experiment consisting of 19 meta-learners was conducted, detailed in this paper. Among the 19 meta-learners of stacked ensembles, a new weighted combination-based meta-learner that maximizes the H-measure during the training of stacked ensemble was first introduced and implemented in the empirical evaluation of this paper. The classification performances of stacked ensembles with 19 different meta-learners were recorded using both the area under the receiver operating characteristic curve (AUC) and H-measure (a metric that overcomes the deficiencies of the AUC). The weighted combination-based meta-learners of stacked ensembles have better classification performances on imbalanced datasets when compared to bagging-based, boosting-based, Decision Trees, Support Vector Machines, Naive Bayes, and Feedforward Neural Network meta-learners. Thus, the adoption of weighted combination-based meta-learners in stacked ensembles is recommended for their better performance on imbalanced datasets. Also, based on the empirical results, we identified better-performing meta-learners (such as the AUC maximizing meta-learner and the H-measure maximizing meta-learner) than the widely adopted meta-learner - Logistic Regression - in imbalanced classification.
Doing the Reading: The Decline of Long Long-Form Reading in Higher Education

Abstract Long-form reading of literary and non-literary texts is historically an essential component of education. However, in many schooling contexts, the amount of long-form reading is diminishing. Are digital technologies augmenting this trend? And are these technologies affecting assignments and student reading patterns in other ways? This article begins by arguing for the relevance of long-form reading and then reviews prior research on how much assigned reading students in higher education report completing. With these findings as background, university faculty in the United States and Norway were surveyed to gauge contemporary reading assignments and student reading practices in humanities and social sciences disciplines, which traditionally are reading intensive. Several of the questions focused on the potential impact of technology on reading assignments, including their length and complexity. This exploratory research suggests that digital technologies are contributing to reduced long-form reading in higher education.
A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures

Ensemble learning have emerged as a useful machine learning technique, which is based on the idea that combining the output of multiple models instead of using a single model. This practice, known as “diversity”, and it usually enhances the performance. On other hand, ensemble feature selection method is based on the same idea, where multiple feature subsets are combined to select an optimal subset of features. Learning methods have difficulties with the dimensionality curse that impact the performance and increase the time exponentially. To overcome this issue, we propose a parallel heterogeneous ensemble feature selection based on three well-regarded algorithms: genetic algorithm, particle swarm optimizer, and grey wolf optimizer. The proposed approach is based on four phases; namely, distribution phase, parallel ensemble feature selection phase, combining and aggregation phase, and testing phase. Three implementations of the proposed approach are presented: a sequential approach running on the central processing unit (CPU), a parallel approach running on multi-core CPU, and a parallel approach running on multi-core CPU with graphics processing units (GPU). To assess the performance of the proposed approach twenty-one large datasets were used. The results show that the proposed parallel approach improved the performance in terms of the prediction results and running time.
Quantum Software Development Lifecycle

This chapter discusses that the development of quantum applications typically incorporates the development of quantum programs, classical programs, and workflows to orchestrate them. Thus, the lifecycles of these software artifacts with their included phases, related concepts, and tools are described. Finally, the points of connection between the various lifecycles are identified, and they are integrated into the overall quantum software development lifecycle.
Region based Skull Eviction Techniques: An Experimental Review

Eviction process is one of the enhancement ways of MRI brain images, especially used in case of neoplasm. It involves the expulsion of skull from the soft tissues of brain so as to get a clear image of it to differentiate the tumor. This paper compares different skull evicting approaches that are based on the region. Also, a comparative analysis is presented based on some performance metrics such as Sorenson index, Hausdorff distance and Jaccard coefficient metrics of the image output.
PointGuard: Provably Robust 3D Point Cloud Classification

3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, adding, and/or deleting a small number of its points. Randomized smoothing is state-of-the-art technique to build certifiably robust 2D image classifiers. However, when applied to 3D point cloud classification, randomized smoothing can only certify robustness against adversarially modified points.In this work, we propose PointGuard, the first defense that has provable robustness guarantees against adversarially modified, added, and/or deleted points. Specifically, given a 3D point cloud and an arbitrary point cloud classifier, our PointGuard first creates multiple subsampled point clouds, each of which contains a random subset of the points in the original point cloud; then our PointGuard predicts the label of the original point cloud as the majority vote among the labels of the subsampled point clouds predicted by the point cloud classifier. Our first major theoretical contribution is that we show PointGuard provably predicts the same label for a 3D point cloud when the number of adversarially modified, added, and/or deleted points is bounded. Our second major theoretical contribution is that we prove the tightness of our derived bound when no assumptions on the point cloud classifier are made. Moreover, we design an efficient algorithm to compute our certified robustness guarantees. We also empirically evaluate PointGuard on ModelNet40 and ScanNet benchmark datasets.
Major 2 Satisfiability Logic in Discrete Hopfield Neural Network

Existing satisfiability (SAT) is composed of a systematic logical structure with definite literals in a set of clauses. The key problem of the existing SAT is the lack of interpretability of a logical structure that leads to low variability of the retrieved neuron states. Thus, a new non-systematic SAT with higher interpretability is needed to reduce the repetition of the patterns of the final neuron states. This paper presents Major 2 Satisfiability (MAJ2SAT) by emphasizing a ratio of 2 Satisfiability (2SAT) clauses present in non-systematic SAT. Hence, different compositions of MAJ2SAT are implemented in a Discrete Hopfield Neural Network (DHNN) by adopting an Exhaustive Search as a training algorithm. Various performance metrics are utilized to measure the compatibility and behaviour of MAJ2SAT in DHNN. Overall, the formulation of MAJ2SAT offers an alternative logical structure in the field of data mining that involves a more dynamic composition of literals and clauses.
Monocular 3D Pose Estimation via Pose Grammar and Data Augmentation

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.
Designing Fine-Grained Access Control for Software-Defined Networks Using Private Blockchain

Emerging next-generation Internet yields proper administration of a wide-ranging dynamic network to assist rapid ubiquitous resource accessibility, whilst providing higher channel bandwidth. Since its inception, the traditional static network infrastructure-based solutions involve manual configuration and proprietary controls of networked devices. It then leads to improper utilization of the overall resources, and hence experiences various security threats. Although transport layer security (TLS)-based solution is presently advocated in the said framework, it is vulnerable to many security threats like man-in-the-middle, replay, spoofing, privileged insider, impersonation, and denial-of-service attacks. Moreover, the current settings of the said tool do not facilitate any secure and reliable mechanisms for data forwarding, application flow routing, new configuration deployment, and network event management. Also, it suffers from the single point of controller failure issue. In this article, we propose a new private blockchain-enabled fine-grained access control mechanism for the SDN environment. In this regard, attribute-based encryption (ABE) and certificate-based access control protocol are incorporated. This proposed solution can resist several well-known security threats, and alleviate different system-level inconveniences. The formal and informal security inspections and performancewise comparative study of the proposed scheme endorse better qualifying scores as compared to the other existing competing state-of-the-art schemes. Besides, the experimental testbed implementation and blockchain simulation show the implementation feasibility of the proposed mechanism.
Assessing the Visual Esthetics of User Interfaces: A Ten-Year Systematic Mapping

Visual esthetics is recognized as an essential factor of perceived usability, credibility, and overall quality of software systems user interfaces. Thus, it is crucial to assess this factor as part of the development process to improve graphical user interface (GUI) quality. And, although there exist diverse approaches to assess visual esthetics today, they significantly differ in their definition and techniques for measuring it. Therefore, we performed a systematic mapping to provide an overview comparing the existing approaches from the last ten years (2010–2020). We encountered 27 articles reporting different approach types, including assessments measuring user perception of visual esthetics via human responses, metrics based on GUI elements, extracting handcrafted features from GUI screenshots, and deep-learning-based approaches. Most of them implement objective approaches, either using metrics or feature extraction. The majority of the approaches are aimed at web GUIs, with only three focusing on mobile GUIs, considering their smaller screen size, less use of color, and a more vertical element alignment. Few approaches have been comprehensively evaluated regarding reliability and validity. These results indicate the need for further research considering the importance of visual esthetics as part of software quality. The results of this study present an overview and comparison of these approaches from the last decade providing a systematic basis for researchers to better understand the challenges for GUI visual esthetics assessments and the enhancement of these approaches.
Bias Field Poses a Threat to DNN-Based X-Ray Recognition

Chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. Many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, posing a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the adversarial attack and propose a brand new attack, i.e., adversarial bias field attack where the bias field instead of the additive noise works as the adversarial perturbations for fooling DNNs. This novel attack poses a key problem: how to locally tune the bias field to realize high attack success rate while maintaining its spatial smoothness to guarantee high realisticity. These two goals contradict each other and thus has made the attack significantly challenging. To overcome this challenge, we propose the adversarial-smooth bias field attack that can locally tune the bias field with joint smooth & adversarial constraints. As a result, the adversarial X-ray images can not only fool the DNNs effectively but also retain very high level of realisticity. We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity and attack transferability. Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
Ed-Tech Within Limits: Anticipating educational technology in times of environmental crisis

Despite climate heating and rising ecological instability, environmental issues feature rarely in discussions of educational technology. Most commentators presume the continued unfettered use of digital education resources bolstered by occasional claims that emerging technologies might support the ‘greening’ of school and university provision. In contrast to such business-as-usual complacency, this article anticipates ongoing environmental degradation of the planet as radically upending the continued expansion of digital technologies in education. On the one hand, depletion of natural resources and energy curtailments might put paid to established ‘abundant’ forms of digital technology use. On the other hand, more frequent climate-related disasters might necessitate emergency forms of education for displaced and unsettled populations. As such, the article argues for a new paradigm of educational technology that is both wholly sustainable and targeted towards displaced and disadvantaged populations. The article considers a number of ways that such an ‘Ed-Tech Within Limits’ might be pursued – outlining fundamental shifts in thinking necessary to reorient educational technology along environmentally concerned lines.
Metallogenic characteristics and resource potential of antimony in China

China has the largest antimony (Sb) resources, products and consumption in the world, which plays an important role in the global antimony trade. Chinese Sb deposits are characterized by large resources, concentrated distributions, primary deposits dominating, and associating with numerous valuable elements and minerals. In China, most of Sb deposits are located at the edges of various geotectonic units and are constrained by plate suture zones or large deep fault belts. There are 5 Sb metallogenic belts were distinguished in this paper including the Tianshan-Xingmeng Sb ore belt, Qin-Qi-Kun Sb ore belt, Qiangtang-Sanjiang Sb ore belt, South China Sb ore belt and South Tibet Sb ore belt. Sb deposits are hosted in Mesoproterozoic to Quaternary formations, but most of them were occurred in Precambrian, Devonian and Triassic strata. The ages of mineralization of Sb deposits in China are mainly concentrated in the Indosinian, Yanshanian and Himalayan, while the large-scale mineralization is restricted within the Jurassic to Cretaceous of the Yanshanian. Antimony deposits have multiple sources of ore-forming metals and fluids, of which Precambrian basement strata may be one of the most important. The magmatic activity caused by asthenosphere upwelling may be the main evocator for driving the circulation of groundwater to extract mineralized elements. According to the results of China National Mineral Resources Potential Assessment Project (NMRP), China delineated 94 Sb prospect areas, in which, 15.43 million tons of unproven Sb metal were predicted within 2 km below the surface. Among the 5 Sb ore belts, the South China Sb Ore Belt has the largest number of deposits, identified resources and resources potential, showing strongest mineralization intensity. The Qiangtang-Sanjiang Sb Ore Belt followed, where may become the future antimony resource-base of China.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search

Retrieval is a crucial stage in web search that identifies a small set of query-relevant candidates from a billion-scale corpus. Discovering more semantically-related candidates in the retrieval stage is very promising to expose more high-quality results to the end users. However, it still remains non-trivial challenges of building and deploying effective retrieval models for semantic matching in real search engine. In this paper, we describe the retrieval system that we developed and deployed in Baidu Search. The system exploits the recent state-of-the-art Chinese pretrained language model, namely Enhanced Representation through kNowledge IntEgration (ERNIE), which facilitates the system with expressive semantic matching. In particular, we developed an ERNIE-based retrieval model, which is equipped with 1) expressive Transformer-based semantic encoders, and 2) a comprehensive multi-stage training paradigm. More importantly, we present a practical system workflow for deploying the model in web-scale retrieval. Eventually, the system is fully deployed into production, where rigorous offline and online experiments were conducted. The results show that the system can perform high-quality candidate retrieval, especially for those tail queries with uncommon demands. Overall, the new retrieval system facilitated by pretrained language model (i.e., ERNIE) can largely improve the usability and applicability of our search engine.
ExpressGesture: Expressive gesture generation from speech through database matching

Abstract Co‐speech gestures are a vital ingredient in making virtual agents more human‐like and engaging. Automatically generated gestures based on speech‐input often lack realistic and defined gesture form. We present a database‐driven approach guaranteeing defined gesture form. We built a large corpus of over 23,000 motion‐captured co‐speech gestures and select individual gestures based on expressive gesture characteristics that can be estimated from speech audio. The expressive parameters are gesture velocity and acceleration, gesture size, arm swivel, and finger extension. Individual, parameter‐matched gestures are then combined into animated sequences. We evaluate our gesture generation system in two perceptual studies. The first study compares our method to the ground truth gestures as well as mismatched gestures. The second study compares our method to five current generative machine learning models. Our method outperformed mismatched gesture selection in the first study and showed competitive performance in the second.
Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection

With the growth of IoT applications, sensor data quality has become increasingly important to ensure the success of these data-driven applications. Sensor data riddled with errors are redundant and affects the accuracy of decision-making results. Moreover, several constraints are inherent in anomaly detection for IoT applications such as limited manpower, time, bandwidth, computational resources and the lack of labelled datasets. Most machine learning algorithms also require a large training set to fit a satisfactory model. In this paper, we propose a fully automated anomaly detection framework, which combines systematic time series feature engineering with unsupervised feature selection. The unsupervised feature selection approach automatically selects time series features based on their predictive power with respect to the statistics of near future measurements. The proposed framework also only needs a short calibration phase with respect to the deployment phase to detect anomalies despite not having seen any in training, which is critical for real-world applications. Its feature selection reduces the amount of data required in the deployment phase on an IoT application. The selected features are suitable for building a reliable anomaly detection model while achieving a similar or better anomaly detection performance than established methods, which are operating on the raw data. Results of the evaluation on two publicly available environmental monitoring datasets show that our proposed unsupervised feature selection approach is a crucial step for having a more accurate anomaly detection while providing complex application-specific time series features, which are safeguarding the sensor system against unseen sensor anomalies.
Single Image Reflection Removal with Absorption Effect

In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly takes the absorption effect into account. The first step estimates the absorption effect from a reflection-contaminated image, while the second step recovers the transmission image by taking a reflection-contaminated image and the estimated absorption effect as the input. Experimental results on four public datasets show that our two-step solution not only successfully removes reflection artifact, but also faithfully restores the intensity distortion caused by the absorption effect. Our ablation studies further demonstrate that our method achieves superior performance on the recovery of overall intensity and has good model generalization capacity. The code is available at https://github.com/q-zh/absorption.