Reviewerly/exHarmony huggingface dataset:
Analyzing the Separability of SAR Classification Dataset in Open Set Conditions

In synthetic aperture radar (SAR) image classification applications, there are three categories of data, including training and benchmark data with fixed classes, as well as actual data in practical applications. A real problem comes that there exist unknown classes not included in training and benchmark data, which is defined as the open set condition. However, little work on recognizing unknown classes and analyzing the separability of SAR datasets has been developed. Motivated by this observation, this article demonstrates the difficulty of practical classification and analyzes SAR dataset separability in open set conditions. In this article, the SAR separability analyzer (SAR-SA) is proposed to model each known class as a multivariate Gaussian distribution. SAR-SA can classify the known classes and recognize the samples locating in each known distribution with low probabilities as unknown. Besides, SAR datasetwise separability index (DSI) and classwise separability index (CSI) are defined to quantify the separability in open set conditions at the dataset level and class level. DSI and CSI are effective indicators of the difficulty of SAR classification datasets. Extensive experimental results demonstrate that the DSI in open set conditions is nearly half of that in supervised conditions. Dataset with low DSI is hard to realize accurate classification in open set conditions. At the class level, even though the SAR image classes are semantically different from each other, there exists more or less overlap between the distributions of supervised known classes and unknown classes. Classes with low CSI are harder to be correctly classified and recognized.
Pubic Cloud Computing: Big Three Vendors

There has been a significant increase in the adoption of cloud computing, and Azure, AWS and Google Cloud are striving to become the best cloud providers. Various extraordinary changes have occurred in 2020 that have played a significant role in bringing about a shift towards the cloud. Enterprises and SMBs are increasing the scope of their cloud strategies in the midst of the global pandemic, economic crisis and all-remote operations so as to ensure business continuity. Choosing an appropriate cloud provider is the issue of concern here. A taxonomy and survey of cloud services that are provided by three major cloud infrastructure vendors, with respect to their revenue, is carried out in this paper to enable you to make an educated decision. An extensive cloud comparison summary is then developed for AWS vs. Azure vs. Google cloud. The cloud ecosystem is first examined, after which an extensive discussion of cloud delivery models and services is carried out. Cloud computing at Google, Amazon and Microsoft is then discussed. The paper concludes with a comparison of the three major cloud service providers.
Aspect-Based Sentiment Analysis of User Reviews in 5G Networks

Aspect-based sentiment analysis can help consumers provide clear and objective sentiment recommendations through massive amounts of data and is conducive to overcoming ambiguous human weaknesses in subjective judgments. However, the robustness and accuracy of existing sentiment analysis methods must still be improved. In this article, deep learning and machine learning techniques are combined to construct a sentiment analysis model based on ensemble learning ideas. Furthermore, the proposed model is applied to a sentiment classification for user reviews about restaurants, which are the representative location-based and user-oriented applications in 5G networks. Specifically, a multi-aspect-labeling model is established, and an ensemble aspect-based model is proposed based on the concept of ensemble learning to predict the consumer's true consumption feelings and willingness to consume again, and to improve machine learning based on the developed model. The predictive performance of the algorithm lies within a single domain.
Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Short-term Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.
Design Flow for Real-Time Face Mask Detection Using PYNQ System-on-Chip Platform

Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most US states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection algorithm uses Haar cascade classifier (HCC) to find facial features from the camera feed and then utilizes it to detect the mask-wearing status. The detection system runs on a PYNQ-Z2 all-programmable SoC platform, where it will pipeline the camera feed through the FPGA unit and carry out the face mask detection algorithm in the ARM core. Potential delays are analyzed, and efforts are made to reduce them to achieve real-time detection. The experiment result shows that the presented system achieves a real-time 45fps 720p Video output, with a face mask detection response of 0.13s.
FLeX: Trading Edge Computing Resources for Federated Learning via Blockchain

Federated learning (FL) algorithms provide privileges in personal data protection and information islands elimination for distributed machine learning. As an increasing number of edge devices connected in networks, we still see a lot of computing resources and data remaining underutilized and there is no platform for users to trade FL tasks. In this demonstration, we propose a blockchain-based federated learning application trading platform called FLeX, on which users can buy and sell computing resources for training machine learning models with no sacrifice of data privacy. We design FLeX in a highly distributed and scalable manner. We separate the data plane and control plane in the platform. In FLeX, trading mechanisms and FL algorithms are deployed in smart contracts of the blockchain. Control messages and trading information are well protected in the blockchain. With FLeX, we realize a distributed trading platform for executing FL tasks.
Training Deep Code Comment Generation Models via Data Augmentation

With the development of deep neural networks (DNNs) and the publicly available source code repositories, deep code comment generation models have demonstrated reasonable performance on test datasets. However, it has been confirmed in computer vision (CV) and natural language processing (NLP) that DNNs are vulnerable to adversarial examples. In this paper, we investigate how to maintain the performance of the models against these perturbed samples. We propose a simple, but effective, method to improve the robustness by training the model via data augmentation. We conduct experiments to evaluate our approach on two mainstream sequence-sequence (seq2seq) architectures which are based on the LSTM and the Transformer with a large-scale publicly available dataset. The experimental results demonstrate that our method can efficiently improve the capability of different models to defend the perturbed samples.
What and Where: Learn to Plug Adapters via NAS for Multidomain Learning

As an important and challenging problem, multidomain learning (MDL) typically seeks a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with neural architecture search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose an NAS-adapter module for adapter structure design in an NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.
Real-Time Characterization of Finite Rupture and Its Implication for Earthquake Early Warning: Application of FinDer to Existing and Planned Stations in Southwest China

Earthquake early warning (EEW) not only improves resilience against the risk of earthquake disasters, but also provides new insights into seismological processes. The Fin ite-Fault Rupture De tecto r (FinDer) is an efficient algorithm to retrieve line-source models of an ongoing earthquake from seismic real-time data. In this study, we test the performance of FinDer in the Sichuan-Yunnan region (98.5 o E–106.0 o E, 22.0 o N–34.0 o N) of China for two datasets: the first consists of seismic broadband and strong-motion records of 58 earthquakes with 5.0 ≤ M S ≤ 8.0; the second comprises additional waveform simulations at sites where new stations will be deployed in the near future. We utilize observed waveforms to optimize the simulation approach to generate ground-motion time series. For both datasets the resulting FinDer line-source models agree well with the reported epicenters, focal mechanisms, and finite-source models, while they are computed faster compared to what traditional methods can achieve. Based on these outputs, we determine a theoretical relation that can predict for which magnitudes and station densities FinDer is expected to trigger, assuming that at least three neighboring stations must have recorded accelerations of 4.6 cm/s 2 or more. We find that FinDer likely triggers and sends out a report, if the average distance between the epicenter and the three closest stations, D epi , is equal or smaller than log 10 ( M a + b ) + c, where a = 1.91, b = 5.93, and c = 2.34 for M = M W ≥ 4.8, and c = 2.49 for M = M S ≥ 5.0, respectively. If the data used in this study had been available in real-time, 40–70% of sites experiencing seismic intensities of V-VIII (on both Chinese and MMI scales) and 20% experiencing IX-X could have been issued a warning 5–10 s before the S- wave arrives. Our offline tests provide a useful reference for the planned installation of FinDer in the nationwide EEW system of Chinese mainland.
Some Size and Structure Metrics for Quantum Software

Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it is drawing increasing attention recently. As research in quantum programming reaches maturity with a number of active research and practical products, software metric researchers need to focus on this new paradigm to evaluate it rigorously and quantitatively. As the first step, this paper proposes some basic metrics for quantum software, which mainly focus on measuring the size and structure of quantum software. These metrics are defined at different abstraction levels to represent various size and structure attributes in quantum software explicitly. The proposed metrics can be used to evaluate quantum software from various viewpoints.
Decentralized MPC-Based Trajectory Generation for Multiple Quadrotors in Cluttered Environments

Challenges in motion planning for multiple quadrotors in complex environments lie in overall flight efficiency and the avoidance of obstacles, deadlock, and collisions among themselves. In this paper, we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption. A model predictive control (MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning. First, the motion primitives of each quadrotor are formulated as the boundary state constrained primitives (BSCPs) which are constructed with jerk limited trajectory (JLT) generation method, a boundary value problem (BVP) solver, to obtain time-optimal trajectories. They are then approximated with a neural network (NN), pre-trained using this solver to reduce the computational burden. The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee flight safety without deadlock. Finally, the reference trajectories are generated using the same BVP solver. Our simulation and experimental results demonstrate the superior performance of the proposed method.
HashGraph—Scalable Hash Tables Using a Sparse Graph Data Structure

In this article, we introduce HashGraph, a new scalable approach for building hash tables that uses concepts taken from sparse graph representations—hence, the name HashGraph. HashGraph introduces a new way to deal with hash-collisions that does not use “open-addressing” or “separate-chaining,” yet it has the benefits of both these approaches. HashGraph currently works for static inputs. Recent progress with dynamic graph data structures suggests that HashGraph might be extendable to dynamic inputs as well. We show that HashGraph can deal with a large number of hash values per entry without loss of performance. Last, we show a new querying algorithm for value lookups. We experimentally compare HashGraph to several state-of-the-art implementations and find that it outperforms them on average 2× when the inputs are unique and by as much as 40× when the input contains duplicates. The implementation of HashGraph in this article is for NVIDIA GPUs. HashGraph can build a hash table at a rate of 2.5 billion keys per second on a NVIDIA GV100 GPU and can query at nearly the same rate.
Key Device and Materials Specifications for a Repeater Enabled Quantum Internet

Entangled photons can be used to create a truly secure communication link between two parties. However, the distance over which this can be achieved is limited by the transmission losses associated with optical fibers. One potential solution is using quantum repeaters (QRs) where initial entanglement is created over short distances and then extended via entanglement swapping. The system-level performance metrics (data rate, fidelity of entanglement, etc.) impose demands upon the hardware components (of a QR), which can be used to guide applied materials and device design toward these objectives. This has become increasingly important with an expanding list of candidates for quantum technologies, as the physical realization of each qubit or detector technology brings its own set of advantages and disadvantages. In this article, we present a framework that uses a modular QR and highlights the tradeoffs that exist between technological component modules. Using reported values, we take a near-term perspective and show the achievable range of rates as a function of distance and the corresponding requirements on matter qubit properties.
Design of a Sign Language Transformer to Enable the Participation of Persons with Disabilities in Remote Healthcare Systems for Ensuring Universal Healthcare Coverage

Poverty, Rurality and Disability are the three major burdens in achieving Universal Healthcare Coverage (UHC). The advent of Information and Communication Technologies (ICT) and remote healthcare systems play a significant role to reach the unreached communities and are addressing rurality and poverty issues. However, the persons with disability (PWDs), especially the speech and hearing impaired people find it difficult to participate in remote healthcare systems as they cannot communicate with a remote doctor. A design of a “Sign Language Transformer (SLT)” has been introduced in this paper for the patients who know sign languages to establish a communication with a remote doctor who cannot interpret such signs. The primary function of this SLT is to recognize the signs/gestures from video images and translate them into both text and speech (SLTT), and to translate doctor's speech into sign language (STSL). Sign representation of words and sentences requires hand gesture, movement, and orientation. Several technologies such as the two-stream CNN, the two-stream 3D CNN, the LSTM, the 3DCNN+ ConvLSTM, the 3D CNN and the 3D CNN + LSTM are commonly used techniques to recognize human gestures. The proposed SLT model will evaluate the performances of these technologies to transform the Bangla Sign Language and to recommend the suitable technology for designing a sign language transformer.
Language-independent extractive automatic text summarization based on automatic keyword extraction

This study proposes a language and domain independent approach for automatic extractive text summarization (EATS) tasks, which is based on a clustering scheme supported by a genetic algorithm (GA), to find an optimal grouping of sentences. Furthermore, our approach includes a topic modeling algorithm to find the key sentences in clusters based on automatically generated keywords. Our experimental results show that our system outperforms previous methods through the application of two general steps: clustering, which helps to increase coverage, and the addition of semantic information to the model, which facilitates the detection of the key sentences in the clusters and improves precision.
Research on remote sensing prospecting technology based on multi-source data fusion in deep-cutting areas

Tectonically deep-cut areas are difficult to access due to the treacherous terrain, making it difficult to carry out fieldwork in these areas for geological prospecting. Liwu Copper-Zinc Mine is a medium-sized rich polymetallic deposit belonging to deep-cutting areas in tectonic structures. Complicated structures influence the ore development and formation in the region. With the introduction of remote sensing technology, geologic features and mineralisation abnormality information can be recognised effectively in such regions. This can provide scientific references for dividing potential metallogenic regions. Remote sensing images with sub-meter spatial resolutions were collected using the Worldview-2(WV-2) satellite to interpret mineralisation-related geologic bodies and analyse the distribution relationships of mineralisation-controlling structures. Multispectral near-infrared spectral information was acquired through the Landsat-8. Meanwhile, the alteration information of two hydrothermal solutions in the study area related to hydroxide radicals and iron staining was extracted via principal component analysis (PCA) based on Crosta technology. The alteration-related standard spectral information and the measured spectral information of known copper outcrops in the region were collected as guiding spectra. A comparative analysis between guiding spectra and GF-5 graphic information was conducted. Possible mineralisation information distributions in the study area were collected via a spectral angle mapper (SAM) and support vector machine (SVM). The results showed that the WV-2 images were able to accurately identify the mineralised geological bodies. The overall classification precisions of SAM- and SVM-based erratic rock mapping were 80.18% and 83.51%, respectively. The information on the hydroxyl iron staining anomaly had been well fitted to the actual alteration zone by field investigation. An analogy analysis between the multivariate remote sensing prospecting information collected by the above means and the existing typical mining areas in Liwu Region was conducted; the dense areas of mineral control structures and remote sensing anomalies were well integrated. The remote sensing metallogenic prediction model of the region was constructed on this basis. Meanwhile, the potential mineralisation was delineated at target zone 1. In conclusion, the presented technology is based on remote sensing data and the fusion of multi-resolutions. It can provide guidance and references for exploring potential metallogenic regions in deep-cutting areas, especially in fold-development regions.
A : Extending area under the ROC curve for probabilistic labels

In ranking applications, AUCROC is widely used in measuring the performance of a discriminative model. But this is possible only if the labels are binary, like in {0,1}, as AUCROC is undefined for non-binary labels. In modelling applications where the labels are not from {0,1} but are probabilities of membership (p,1−p) to the binary classes, we generally use other metrics. In this paper, we propose a metric ARatio which can be used on binary as well as probabilistic labels. We prove that it is exactly equal to AUCROC for binary labels. We also prove that it extends the same semantics as AUCROC for probabilistic labels. We extend the confusion matrix for probabilistic labels and redefine metrics like precision, recall and F1-score. We define AccRatioand show that it is equivalent to area under the precision–recall curve for non-binary probabilistic labels.
F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.