Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Blockchain-Based Nonlinear SVM Classifier Training for Social Networks

With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
DEAL: Difficulty-Aware Active Learning for Semantic Segmentation

Active learning aims to address the paucity of labeled data by finding the most informative samples. However, when applying to semantic segmentation, existing methods ignore the segmentation difficulty of different semantic areas, which leads to poor performance on those hard semantic areas such as tiny or slender objects. To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch. For the latter branch, with the supervision of segmentation error between the segmentation result and GT, a pixel-wise probability attention module is introduced to learn the semantic difficulty scores for different semantic areas. Finally, two acquisition functions are devised to select the most valuable samples with semantic difficulty. Competitive results on semantic segmentation benchmarks demonstrate that DEAL achieves state-of-the-art active learning performance and improves the performance of the hard semantic areas in particular.
Two-stage ship detection in synthetic aperture radar images based on attention mechanism and extended pooling

Object detection in synthetic aperture radar (SAR) images remains a challenging problem due to the particular imaging mechanism of SAR systems. The sizes of targets are relatively small and the scenes are large, indicating that the intersection over union value between the targets and anchors is probably small. In addition, SAR images are severely polluted by speckles under normal conditions. The edges of objects in SAR images are blurred. In this study, we proposed an improved object-detection framework based on a two-stage faster region-based convolutional neural network. A feature-enhancement module based on self-attention mechanism is designed to learn a target-oriented feature map, where the spatial attention and channel attention work simultaneously, such that targets can be highlighted and the speckles can be suppressed to a certain extent. Two separate feature maps serve as the input of the region proposal network to isolate the classification and regression tasks. Because only one category exists in the ship-detection task, correctly distinguishing targets from the background results in the correct detection of ships. In the final classification stage, an extended region-of-interest pooling operation is performed on the potential proposals and contexture information. The usage of extra information can improve target fine-tuning in the final network. To avoid ignoring small targets, we carefully set the anchors’ parameters based on the analysis of ground truth and select the appropriate shapes of feature maps. With the help of these modifications, the proposed method can detect small, weak, and dense targets in SAR images. Ablation experiments over networks with different configurations prove that the proposed modules are working. Experiments on real SAR Gaofen-3 and Sentinel-1 images demonstrate the efficiency of the proposed object-detection framework.
Frugal Optimization for Cost-related Hyperparameters

The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incapable to properly control cost during the optimization process. To address this problem, we develop a new cost-frugal HPO solution. The core of our solution is a simple but new randomized direct-search method, for which we provide theoretical guarantees on the convergence rate and the total cost incurred to achieve convergence. We provide strong empirical results in comparison with state-of-the-art HPO methods on large AutoML benchmarks.
Statistical Performance Evaluation of Various Metaheuristic Scheduling Techniques for Cloud Environment

Cloud computing has become the need of the hour as almost all businesses have started using the pay per use model proposed by cloud architecture instead of buying their own resources. Scheduling tasks to these sharable resources is a critical aspect of cloud computing and an area which is attracting many researchers. Scheduling workflows on a cloud architecture becomes even more critical as it contains a set of dependant tasks, and is considered an NP-hard problem. In this paper, various traditional meta-heuristic scheduling techniques have been implemented and their performance has been evaluated based on two parameters, Flowtime and Makespan. The various algorithms like PSO, DE, ETC, ABC, GA and FFOA are implemented using CloudSim and their performance is statistically evaluated in order to obtain minimized Flowtime and Makespan.
Hybrid Fuzzy C-Means CPD-Based Segmentation for Improving Sensor-Based Multiresident Activity Recognition

Multiresident activity recognition (AR), which has become a popular research field in smart environments, aims to recognize the activities of multiple residents based on data collected from various types of sensors, and sensor events segmentation is an important technique for enhancing the performance of AR. While quite some segmentation methods have been proposed for the single person setting, few studies have been done for the multiresident setting. In this article, we first evaluate the baseline and the state-of-the-art segmentation methods using the popular multiresident data set CASAS, to confirm that the performance of multiresident AR can be improved by applying segmentation techniques; we then propose a novel Hybrid fuzzy c-means (FCM) change point detection (CPD)-based segmentation method that can further enhance the performance of multiresident AR. We combine a FCM method with a CPD-based method for sensor event segmentation. The FCM method is used to classify the sensor events in terms of sensor locations, and then the CPD technique is used to probe the transition actions to determine the segmentation sequence. Our experimental results show that the proposed method significantly improves the performance of multiresident AR in comparison with the baseline and state-of-the-art classification methods.
Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity.We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score.In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%.This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.
A Graph-based Relevance Matching Model for Ad-hoc Retrieval

To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task as a term-level matching problem, which seeks exact or similar query patterns in the document. However, we argue that they are inherently based on local interactions and do not generalise to ubiquitous, non-consecutive contextual relationships. In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. In addition to the local interactions, we explicitly incorporate all contexts of a term through the graph-of-word text format. Matching patterns can be revealed accordingly to provide a more accurate relevance score. Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation

Enhancing visual quality for underexposed images is an extensively concerning task that plays an important role in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to address underexposed image correction. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. In addition, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What is more, we apply our method to solve single-image haze removal whose experimental results further demonstrate our superiorities.
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks

Adversarial poisoning attacks distort training data in order to corrupt the test-time behavior of a classifier. A provable defense provides a certificate for each test sample, which is a lower bound on the magnitude of any adversarial distortion of the training set that can corrupt the test sample's classification. We propose two novel provable defenses against poisoning attacks: (i) Deep Partition Aggregation (DPA), a certified defense against a general poisoning threat model, defined as the insertion or deletion of a bounded number of samples to the training set --- by implication, this threat model also includes arbitrary distortions to a bounded number of images and/or labels; and (ii) Semi-Supervised DPA (SS-DPA), a certified defense against label-flipping poisoning attacks. DPA is an ensemble method where base models are trained on partitions of the training set determined by a hash function. DPA is related to both subset aggregation, a well-studied ensemble method in classical machine learning, as well as to randomized smoothing, a popular provable defense against evasion (inference) attacks. Our defense against label-flipping poison attacks, SS-DPA, uses a semi-supervised learning algorithm as its base classifier model: each base classifier is trained using the entire unlabeled training set in addition to the labels for a partition. SS-DPA significantly outperforms the existing certified defense for label-flipping attacks (Rosenfeld et al., 2020) on both MNIST and CIFAR-10: provably tolerating, for at least half of test images, over 600 label flips (vs. < 200 label flips) on MNIST and over 300 label flips (vs. 175 label flips) on CIFAR-10. Against general poisoning attacks where no prior certified defenses exists, DPA can certify ≥ 50% of test images against over 500 poison image insertions on MNIST, and nine insertions on CIFAR-10. These results establish new state-of-the-art provable defenses against general and label-flipping poison attacks.
Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence

This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.
Benchmarking and Performance Evaluations on Various Configurations of Virtual Machine and Containers for Cloud-Based Scientific Workloads

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.
COVID-19 Surveillance Data: A Primer for Epidemiology and Data Science

Our primary objective is to improve COVID-19 metrics to enhance the quality of COVID-19 surveillance-an urgent need raised by several authors in professional and general media.1-5 We offer specific suggestions on how pandemic surveillance metrics can be better reported to improve the quality of analytical epidemiology. Health care providers and public health practitioners may find these criteria useful when conducting analyses, and students may be able to self-correct mistakes in written work. Data scientists may treat this as a primer for selecting and reporting metrics for dashboards. (Am J Public Health. Published online ahead of print January 21, 2021: e1-e6. https://doi.org/10.2105/AJPH.2020.306088).
The Effect of Screen Size on Students’ Cognitive Load in Mobile Learning

&lt;p&gt;Mobile learning is becoming a crucial tool in this era of face-to-face shutdown of education, and however the whole process currently faces a significant deficiency due to the kind of cognitive load that does exist and its relation with mobile device screen display size. It is well-established that certain screen sizes are more effective than others. This study aims to investigate the effect of screen size on students’ cognitive load in mobile learning. Specifically, it investigates whether screen size has a role in cognitive role and draws a comparison to reflect on the most effective size to be used in the context of mobile learning. Other factors that might interfere in the process were also investigated which include course content, gender, age, and students' GPA to see whether they play any additional role in burdening the cognitive load when using different screen sizes. To test the effect of screen size on cognitive load, an online survey was distributed to 1,570 students of the University of Jeddah who are studying at the foundation year for the academic year of 2018-2018, particularly for eight online courses. The sample was chosen randomly, where all members of the population, 6,500 students, had equal opportunities to participate in the study. Participants were invited via e-mail by sending an invitation to participate along with the questionnaire link on the "Qualrrics" platform. This research data analysis technique used ANOVA and curve estimation. The research findings revealed that small screen display size produces the lowest cognitive load as compared with larger display screens. This study also supports the use mobile learning process and gives recommendations to the instructional designers in order to make learning experiences more effective. The results of this study suggest a proper use of screen size can improve learning from smartphones, making them equal to learning from laptops and reducing the overloaded cognitive load that may affect students' understanding and hinder retention. Hence, implications were discussed, and further research recommendations were then provided.&lt;/p&gt;
RETRACTED: Construction of College English Teaching Resource Database under the Background of Big Data

Abstract At present, the construction of college English network resource database in domestic universities is still in its initial stage in practice, and it is almost a blank in theoretical research. The construction of college English teaching resource database is of great strategic significance for promoting the construction of college English subject resources and improving the talent training mode. In this regard, the purpose of this paper is to study the construction of college English teaching resource database under the background of big data. This paper first discusses the current situation of college English teaching resources construction and resources in China. This paper summarizes the main problems existing in contemporary Chinese college English teaching resources database. According to the previous investigation and research results, and combining with big data technology, this paper constructs a new college English teaching resource library. The new college English teaching resource library puts the feelings of college students at the main position, and its teaching resources are more inclined to the subjective initiative and communication of college students. The resource library also uses the storage architecture of data warehouse, combined with big data and other related technologies, to realize the storage and update of various English teaching resources, and meet the needs of effective management of teaching resources in colleges and universities. Finally, this paper compares the old and new college English teaching resources. The experimental results show that the college English teaching resource database constructed in this paper is more recognized and liked by students, and the degree of students’ liking for English has increased by about 55%, which plays a significant role in improving students’ English and provides an important reference for the construction of college English teaching resource database.
Improving the preschool education system from the perspective of the age values of children

The article reveals promising directions for improving the preschool education system from the perspective of the age values of children. The possibility of organizing preschool education with a focus on the natural emotionality of children is being considered.