Reviewerly/exHarmony huggingface dataset:
Online feature selection for rapid, low-overhead learning in networked systems

Data-driven functions for operation and management often require measurements collected through monitoring for model training and prediction. The number of data sources can be very large, which requires a significant communication and computing overhead to continuously extract and collect this data, as well as to train and update the machine-learning models. We present an online algorithm, called OSFS, that selects a small feature set from a large number of available data sources, which allows for rapid, low-overhead, and effective learning and prediction. OSFS is instantiated with a feature ranking algorithm and applies the concept of a stable feature set, which we introduce in the paper. We perform extensive, experimental evaluation of our method on data from an in-house testbed. We find that OSFS requires several hundreds measurements to reduce the number of data sources by two orders of magnitude, from which models are trained with acceptable prediction accuracy. While our method is heuristic and can be improved in many ways, the results clearly suggests that many learning tasks do not require a lengthy monitoring phase and expensive offline training.
SymmetryNet

We study the problem of symmetry detection of 3D shapes from single-view RGB-D images, where severely missing data renders geometric detection approach infeasible. We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects present in the input RGB-D image. Directly training a deep model for symmetry prediction, however, can quickly run into the issue of overfitting. We adopt a multi-task learning approach. Aside from symmetry axis prediction, our network is also trained to predict symmetry correspondences. In particular, given the 3D points present in the RGB-D image, our network outputs for each 3D point its symmetric counterpart corresponding to a specific predicted symmetry. In addition, our network is able to detect for a given shape multiple symmetries of different types. We also contribute a benchmark of 3D symmetry detection based on single-view RGB-D images. Extensive evaluation on the benchmark demonstrates the strong generalization ability of our method, in terms of high accuracy of both symmetry axis prediction and counterpart estimation. In particular, our method is robust in handling unseen object instances with large variation in shape, multi-symmetry composition, as well as novel object categories.
Semisupervised Learning via Axiomatic Fuzzy Set Theory and SVM

In this article, we present a semantic semisupervised learning (Semantic SSL) approach targeted at unifying two machine-learning paradigms in a mutually beneficial way, where the classical support vector machine (SVM) learns to reveal primitive logic facts from data, while axiomatic fuzzy set (AFS) theory is utilized to exploit semantic knowledge and correct the wrongly perceived facts for improving the machine-learning model. This novel semisupervised method can easily produce interpretable semantic descriptions to outline different categories by forming a fuzzy set with semantic explanations realized on the basis of the AFS theory. Besides, it is known that disagreement-based semisupervised learning (SSL) can be viewed as an excellent schema so that a co-training approach with SVM and the AFS theory can be utilized to improve the resulting learning performance. Furthermore, an evaluation index is used to prune descriptions to deliver promising performance. Compared with other semisupervised approaches, the proposed approach can build a structure to reflect data-distributed information with unlabeled data and labeled data, so that the hidden information embedded in both labeled and unlabeled data can be sufficiently utilized and can potentially be applied to achieve good descriptions of each category. Experimental results demonstrate that this approach can offer a concise, comprehensible, and precise SSL frame, which strikes a balance between the interpretability and the accuracy.
Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.
Online Harassment of Female Journalists in Bangladesh

The online harassment of female journalists is a rising concern around the world and also in South Asia. Bangladesh, a South Asian country, recently, has experienced an increasing number of harassments against female journalists online. Various studies explored the online harassment, mostly from the Western perspectives. Scholars have argued that the online harassments may negatively affect the freedom of expression. But little is known about Bangladesh. Drawing upon feminist theory, this study investigated the experiences of online journalists in Bangladesh. The objectives were to explore the nature and forms of online harassment and to find how this experiences of harassments affect the freedom of expression of the victims. Data were collected through content analysis, semiotic analysis of the uncivil comments available in the online news feedback and in-depth interviews. Results of the study indicate that online harassment is a frequent phenomenon where the victim journalists feel vulnerable in the ‘unsafe' online ‘patriarchal' environment.
Bidirectional Non-local Networks for Object Detection

The convolutional neural networks have reached great achievements in solving the challenging problems of computer vision tasks, such as image classification, object detection, semantic segmentation. The core element of CNNs is the convolution operation, which gathers important features by constructing pixel relationships in a local region. Even though CNNs are universally exploited in visual feature understanding, they still have drawbacks due to that the receptive field is restrained inside local neighborhoods by the physical construction of the convolution layer. The Non-local Network introduces a novel method for modeling long-range dependencies to remedy the local neighborhood problem, which computing the correlations between the query position and all positions to capture global context features and then performing a weighted sum of the features at all positions. As a complementary part of the Non-Local Network, the proposed method called Bidirectional Non-local operation designs the bidirectional relationship, which the informative feature at a specific-query position is gathered and distributed to all positions. Notably, this work relaxes the Bidirectional Non-local complexity by simplifying the network based on the same attention maps for different query positions. To evaluate the effectiveness of the proposed method, the Bidirectional Non-local block is embedded into the backbone network of the detector Mask R-CNN. Without bells and whistles, the integrated network achieves 0.9 points higher Average Precision than Global Context Network on the major baseline.
Multiple Object Tracking Using Convolutional Neural Network on Aerial Imagery Sequences

In this paper, an application of the neural network algorithm YOLO v2 for detecting and tracking a group of moving objects of complex shape on aerial imagery sequences is presented. The Kuhn – Munkres algorithm was used to set up a one-to-one correspondence between moving objects from frame to frame. The matrix elements in the algorithm are the Euclidean distances between the bounding boxes on two consecutive frames. It is proposed to use a comparison of color histograms of images of objects to handle situations with the disappearance and appearance of objects in the field of view of the camera. To compare the color histograms the Bhattacharya distance metric was selected. The proposed approach showed high results compared to the IOU Tracker algorithm.
Whole Heart Auto Segmentation of Cardiac CT Images Using U-Net Based GAN

The whole heart segmentation of medical CT images is of great significance for assisting doctors in the diagnosis of cardiovascular diseases and guiding doctors' surgery. Due to the complexity and particularity of medical images, automatic whole heart segmentation still remains challenges. Lack of annotated medical images is also a big problem. The U-Net successfully solved the problem of inadequate annotated medical images. In this study, we proposed a U-Net based GAN which uses U-Net as the generative network and FCN as the discriminative network. The experiments were performed on the dataset of the MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge (MM-WHS 2017). The proposed method achieved high segmentation accuracy with an average 86.32% and highest 93.64% Dice similarity coefficient (DSC) for the whole heart segmentation.
Event Management and Monitoring Framework for HPC Environments using ServiceNow and Prometheus

The challenge of monitoring and event response management of a high performance computing facility grows significantly as the facilities employs and orchestrates more complex and heterogeneous systems and infrastructure. As the computational components encompassing the HPC facility system increases, the computational staff experiences rise in alert fatigue due to the false alarms and noise related to the similar events generated by monitoring tools. The National Energy Research Scientific Computing Center (NERSC) at the Lawrence Berkeley National Laboratory (LBNL) has begun to address the issues of duplication of alerts and alert remediation. However, more automation and integration is needed for collecting, aggregating, correlating, analyzing, managing and visualizing the scale of events that will be generated by the emergent hybrid computing infrastructures. In this paper, we present an event management and monitoring framework that addresses the operational needs of the future pre-exascale systems at the Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC). The framework integrates the Operations Monitoring and Notification Infrastructure (OMNI) at NERSC with the Prometheus, Grafana and ServiceNow platforms to help identify, diagnose, and resolve incidents in real-time, as well as conduct more thorough post-incident reviews enabled by the intuitive dashboards that provides a single pane of glass console for an efficient operations management and real-time proactive monitoring.
An evaluation of methods to port legacy code to SGX enclaves

The Intel Security Guard Extensions (SGX) architecture enables the abstraction of enclaved execution, using which an application can protect its code and data from powerful adversaries, including system software that executes with the highest processor privilege. While the Intel SGX architecture exports an ISA with low-level instructions that enable applications to create enclaves, the task of writing applications using this ISA has been left to the software community.
&lt;p&gt;Biomedical Data Sharing Among Researchers: A Study from Jordan&lt;/p&gt;

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent's attitudes toward sharing of biomedical data.Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included "lack of regulations" (33.5%), "access to research data should be limited to the research team" (29.5%), "no place to deposit the data" (6.5%), and "lack of funding for data deposition" (6.0%). Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.
Deep Learning with network of Wearable sensors for preventing the Risk of Falls for Older People

Abstract Activity recognition (AR) systems for older adults are common in residential health care including hospitals or nursing homes; therefore, numerous solutions and studies presented to improve the performance of the AR systems. Yet, delivering sufficiently robust AR systems from sensor data recorded is a challenging task. AR in a smart environment utilizes large amounts of sensor data to derive effective features from the data to track the activity daily living. This paper maximizes the performance of AR system from using the convolutional neural network (CNN). Here, it analyzes signals from the network sensors distributed in different places in two clinical rooms at the Elizabeth hospital, such as W 2 ISP and RFID sensors. The proposed approach recognized the daily activities that consider a key to falling cases for older adults at a hospital or a nursing health house. A deep activity CNNets is used to train the effective features of daily activities sensors data then used for recognizing the highest falling risk activities in testing data. This approach used existing data of fourteen healthy older volunteers (ten females and four males) and then compared to other proposed approaches that used the same dataset. The experimental results show that this approach is superior to others. It achieved (96.37±3.63%) in the first clinic room and (98.37±1.63%) in the second clinic room. As the result, this experiment concludes that deep learning methodology is effectively assessing fall risk based on wearable sensors.
Constant-Overhead Unconditionally Secure Multiparty Computation Over Binary Fields

We study the communication complexity of unconditionally secure multiparty computation (MPC) protocols in the honest majority setting. Despite tremendous efforts in achieving efficient protocols for binary fields under computational assumptions, there are no efficient unconditional MPC protocols in this setting. In particular, there are no n-party protocols with constant overhead admitting communication complexity of O(n) bits per gate. Cascudo, Cramer, Xing and Yuan (CRYPTO 2018) were the first ones to achieve such an overhead in the amortized setting by evaluating $$O(\log n)$$ copies of the same circuit in the binary field in parallel. In this work, we construct the first unconditional MPC protocol secure against a malicious adversary in the honest majority setting evaluating just a single boolean circuit with amortized communication complexity of O(n) bits per gate.
Remote Sensing Images Dehazing Algorithm based on Cascade Generative Adversarial Networks

The existing remote sensing image dehazing methods based on deep learning networks usually use pairs of clear images and corresponding haze images to train the model. However, pairs of clear images and their haze counterparts are extremely lacking, and synthetically haze images could not accurately simulate the real haze generation process in real-world scenarios. To address this problem, a cascade method combining two GANs (generative adversarial networks) is proposed. It contains a learning-to-haze GAN (UGAN) and learning-to-dehaze GAN (PAGAN). UGAN learns how to haze remote sensing images with unpaired clear and haze images sets, and then guides the PAGAN to learn how to correctly dehaze such images. To reduce the discrepancy between real haze and synthetic haze images, we added self-attention mechanism to PAGAN. The details can be generated using cues from all feature locations. Moreover, the discriminator could check that highly detailed features in distant portions of the images that are consistent with each other. Compared with other dehazing methods, this algorithm does not require numerous pairs of images to train the network repeatedly. And the results show that the cascaded generative adversarial networks has visual and quantitative effectiveness for the removal of haze, thin clouds.
How to Implement a Non-uniform or Non-closed Shuffle

Card-based protocols allow to perform secure multiparty computations using a deck of physical cards, and rely on shuffle actions such as the (normal) shuffle, the random cut, and the random bisection cut. A shuffle action is mathematically defined by a pair of a permutation set (which is a subset of the symmetric group) and a probability distribution on it; while one can theoretically consider any shuffle action in mind, he or she may be unable to determine whether it can be easily implemented by human hands. As one of the most general results, Koch and Walzer showed that any uniform closed shuffle (meaning that its permutation set is a subgroup and its distribution is uniform) can be implemented by human hands with the help of additional cards. However, there are several existing protocols which use non-uniform and/or non-closed shuffles. To implement these specific shuffles, Nishimura et al. proposed an idea of using (special) physical cases that can store piles of cards as well as Koch and Walzer proposed an implementation of a specific non-closed shuffle with additional cards. Because their implementations handle a limited class of non-uniform and/or non-closed shuffles, it is still open to find a general method for implementing any shuffle. In this paper, we solve the above problem; we implement “any” shuffle with only additional cards, provided that every probability of its distribution is a rational number. Therefore, our implementation works for any non-closed or non-uniform shuffle (if the distribution is rational as above).
Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm

This paper presents a machine learning model able to distinguish between ionospheric scintillation and multipath in GNSS-based scintillation monitoring data. The inputs to the model are the average signal intensity, the variance in the signal intensity, and the covariance between the in-phase and the quadrature-phase outputs of the tracking loop of a GNSS receiver. The model labels the data as either scintillated, multipath affected, or clean GNSS signal. The overall accuracy of the model is 96% with 2% miss-detection rate and a negligible false alarm rate for the scintillation class in particular. The gain in the amount of scintillation data is up to 17.5% that would have been discarded if an elevation mask of 30° was implemented.
Deep random walk of unitary invariance for large-scale data representation

Data representation aims at learning an efficient low-dimensional representation, which is always a challenging task in machine learning and computer vision. It can largely improve the performance of specific learning tasks. Unsupervised methods are extensively applied to data representation, which considers the internal connection among data. Most of existing unsupervised models usually use a specific norm to favor certain distributions of the input data, leading to an unsustainable encouraging performance for given learning tasks. In this paper, we propose an efficient data representation method to address large-scale feature representation problems, where the deep random walk of unitary invariance is exploited for learning discriminative features. First, the data representation is formulated as deep random walk problems, where unitarily invariant norms are employed to capture diverse beneficial perspectives hidden in the data. It is embedded into a state transition matrix model, where an arbitrary number of transition steps is available for an accurate affinity evaluation. Second, data representation problems are then transformed as high-order matrix factorization tasks with unitary invariance. Third, a closed-form solution is proved for the formulated data representation problem, which may provide a new perspective for solving high-order matrix factorization problems. Finally, extensive comparative experiments are conducted in publicly available real-world data sets. In addition, experimental results demonstrate that the proposed method achieves better performance than other compared state-of-the-art approaches in terms of data clustering.
AdvJND: Generating Adversarial Examples with Just Noticeable Difference

Compared with traditional machine learning models, deep neural networks perform better, especially in image classification tasks. However, they are vulnerable to adversarial examples. Adding small perturbations on examples causes a good-performance model to misclassify the crafted examples, without category differences in the human eyes, and fools deep models successfully. There are two requirements for generating adversarial examples: the attack success rate and image fidelity metrics. Generally, the magnitudes of perturbation are increased to ensure the adversarial examples' high attack success rate; however, the adversarial examples obtained have poor concealment. To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference, in the constraint of a distortion function when generating adversarial examples. In fact, the visual subjective feeling of the human eyes is added as a priori information, which decides the distribution of perturbations, to improve the image quality of adversarial examples. We tested our method on the FashionMNIST, CIFAR10, and MiniImageNet datasets. Our adversarial examples keep high image quality under slightly decreasing attack success rate. Since our AdvJND algorithm yield gradient distributions that are similar to those of the original inputs, the crafted noise can be hidden in the original inputs, improving the attack concealment significantly.