Reviewerly/exHarmony huggingface dataset:
Content-Based Remote Sensing Image Retrieval Based on Fuzzy Rules and a Fuzzy Distance

The methods in remote sensing image retrieval (RSIR) usually search the whole retrieval data set in the retrieval process, which takes much time and is unnecessary. To reduce the overall search time, this letter proposes a new retrieval scheme based on fuzzy rules. The proposed method calculates the fuzzy class membership of images using two ways. The first way predicts the fuzzy class membership by convolutional neural network (CNN). The other uses the image-to-class distance that is a distance between an image and each class on the training data set. The two fuzzy class memberships are used to measure the classification confidence, and a query image is classified into three fuzzy sets, namely, “low classification confidence,” “medium classification confidence,” and “high classification confidence,” based on the classification confidence. The fuzzy rules are built according to fuzzy classification to choose the search space for each fuzzy set. The final search space is determined by the two search spaces obtained by fuzzy rules. Moreover, the fuzzy distance between a query image and a retrieved image is used to improve the retrieval performance, which is calculated according to their fuzzy class memberships and the Euclidean distance between the two images. The experimental results on University of California, Merced data set (UCMD) and PatternNet databases show that our proposed method can not only enhance the retrieval performance but also reduce the search time in comparison to other state-of-the-art techniques.
A grain-size correction for metal pollution indexes in river sediments

According to the List of Hazardous Substances of the Agency of the Toxic Substances and Disease Registry (ATSDR 2017), some metallic elements such as Pb or Cd are still considered as the most polluting elements in the world. These elements accumulate in sediments, and there are various methods available that differentiate lithogenic sources from anthropogenic sources. For that, the natural geochemical background is required, however, its definition is far from unanimous: it can be a global or local value, single or statistically estimated value, depending on the studies. Our study is focused on the Subaé River in the state of Bahia, Northeastern Brazil, which was historically contaminated by lead metallurgy. The river sediments were sampled at 21 locations: one at the discharge point of the plant's rainwater basin, ten upstream, and ten downstream. The total contents of Fe, Ti, Mn, Pb, Zn, Cu, Cr, particle-size distribution, and organic matter were analyzed. The conventional geochemical indexes, i.e., the pollution degree (mCd), pollution load index, and enrichment factor indicated contamination by Zn, Pb, Cr, and Cu. Then, a new enrichment factor is introduced, assuming that the sediments come from erosion of soils in the watershed. After obtaining the natural concentrations of metals in the clay, silt, and sand fractions of uncontaminated reference soils, we corrected the pollution indexes by calculating a new individual background in each sediment sample, according to its grain size distribution. This new approach provided more precise indexes in the assessment of sediment pollution, by highlighting higher contamination of Zn and Pb (around 50%) and at the same time the absence of Cr and Cu contamination, two metals not involved with the metallurgical activity.
Screen-Cam Robust Image Watermarking with Feature-Based Synchronization

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.
The Impact of Explanations on AI Competency Prediction in VQA

Explainability is one of the key elements for building trust in AI systems. Among numerous attempts to make AI explainable, quantifying the effect of explanations remains a challenge in conducting human-AI collaborative tasks. Aside from the ability to predict the overall behavior of AI, in many applications, users need to understand an AI agent's competency in different aspects of the task domain. In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA). We quantify users' understanding of competency, based on the correlation between the actual system performance and user rankings. We introduce an explainable VQA system that uses spatial and object features and is powered by the BERT language model. Each group of users sees only one kind of explanation to rank the competencies of the VQA model. The proposed model is evaluated through between-subject experiments to probe explanations' impact on the user's perception of competency. The comparison between two VQA models shows BERT based explanations and the use of object features improve the user's prediction of the model's competencies.
Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars

Abstract We present xspells , a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.
Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019

Abstract Purpose This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research. Design/methodology/approach We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis. Findings The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups. Research limitations First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration. Practical implications The findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research. Originality/value This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.
Insights Into Nonmerged Pull Requests in GitHub: Is There Evidence of Bias Based on Perceptible Race?

This article is a qualitative study that analyzes whether there is evidence of bias based on perceptible race in the written comments of nonmerged pull requests in GitHub.
An Evaluation of Entropy Measures for Microphone Identification

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.
Linear Discriminant Analysis via Pseudo Labels: A Unified Framework for Visual Domain Adaptation

This paper deals with the problem of visual domain adaptation in which source domain labeled data is available for training, but the target domain unlabeled data is available for testing. Many recent domain adaptation methods merely concentrate on extracting domain-invariant features via minimizing the distributional and geometrical divergence between domains simultaneously while ignoring within-class and between-class structure properties, especially for the target domain due to the unavailability of labeled data. We propose Linear Discriminant Analysis via Pseudo Labels (LDAPL), a unified framework for visual domain adaptation that can tackle these two issues together. LDAPL is to learn a domain-invariant features across both domains with preserving important properties such as minimizing the shift between domains both statistically and geometrically, retaining the original similarity of data samples, maximizing the target domain variance, and minimizing the within-class and maximizing the between-class properties of both domains. Specifically, LDAPL preserves the target domain discriminative information (or within-class and between-class properties) using pseudo labels, and these pseudo labels are refined until convergence. Extensive experiment on several visual cross-domain benchmarks, including Office+Caltech10 with all three types of features (such as Speeded Up Robust Features (SURF), Deep Convolutional Activation Feature (DeCAF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ), and Visual Geometry Group-Fully Connected layer ( VGG-FC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ) features), COIL20 (Columbia Object Image Library), digit, and PIE (Pose, Illumination, and Expression), LDAPL achieved average accuracies of 79.11%, 99.72%, 79.0%, and 84.50%, respectively. Comparative results on several visual cross-domain classification tasks verify that LDAPL can significantly outperform the state-of-the-art primitive and domain adaptation methods. Specifically, LDAPL gains over baseline Joint Geometrical and Statistical Alignment (JGSA) method with 6.6%, 5.3%, 6.3%, and 44.93% average accuracies on Office+Caltech10 (SURF, DeCAF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , and VGG-FC6 ), COIL20, digit, and PIE, respectively.
Quantum Radar Cost and Practical Issues

We compute the minimum cost for an optimal quantum radar, and we compare it with the cost of actual real world classical radars as a function of range. Our calculations show that the minimum cost quantum radar at X-band is many orders of magnitude more expensive than the corresponding classical radar, even assuming the most optimistic wideband-phased array radar architecture. We also assume that the quantum radar is optimal; that is, the effective signal-to-noise ratio is 6 dB better than for a classical radar with the same transmit power and bandwidth at low photon flux per mode. Finally, we discuss many practical issues and potential solutions.
MorphoSys

Distributed database systems are widely used to meet the demands of storing and managing computation-heavy workloads. To boost performance and minimize resource and data contention, these systems require selecting a distributed physical design that determines where to place data, and which data items to replicate and partition. Deciding on a physical design is difficult as each choice poses a trade-off in the design space, and a poor choice can significantly degrade performance. Current design decisions are typically static and cannot adapt to workload changes or are unable to combine multiple design choices such as data replication and data partitioning integrally. This paper presents MorphoSys , a distributed database system that dynamically chooses, and alters, its physical design based on the workload. MorphoSys makes integrated design decisions for all of the data partitioning, replication and placement decisions on-the-fly using a learned cost model. MorphoSys provides efficient transaction execution in the face of design changes via a novel concurrency control and update propagation scheme. Our experimental evaluation, using several benchmark workloads and state-of-the-art comparison systems, shows that MorphoSys delivers excellent system performance through effective and efficient physical designs.
Characterizing Co-Located Workloads in Alibaba Cloud Datacenters

Workload characteristics are vital for both data center operation and job scheduling in co-located data centers, where online services and batch jobs are deployed on the same production cluster. In this article, a comprehensive analysis is conducted on Alibaba's cluster-trace-v2018 of a production cluster of 4034 machines. The findings and insights are the following: (1) The workload on the production cluster poses a daily cyclical fluctuation, in terms of CPU and disk I/O utilization, and the memory system has become the performance bottleneck of a co-located cluster. (2) Batch jobs including their tasks and derived instances can be approximated as Zipf distribution. However, for all batch jobs with directed acyclic graph dependency, they suffer from co-location with online services since the online services are highly prioritized. (3) The resource usages of containers have similar cyclical fluctuation consistent with the whole cluster, while their memory usages remain approximately constant. (4) The number of batch jobs co-located with online services is dependent on the mispredictions per kilo instructions of online services. In order to guarantee the QoS of online services, when the MPKI of online services rises, the number of batch jobs to be co-located on the same machine should decrease.
Generating adversarial examples with elastic-net regularized boundary equilibrium generative adversarial network

To improve the attack success rate and image perceptual quality of adversarial examples against deep neural networks(DNNs), we propose a new Generative Adversarial Network (GAN) based attacker, named Elastic-net Regularized Boundary Equilibrium Generative Adversarial Network(ERBEGAN). Recent studies have shown that DNNs are easy to attack by adversarial examples(AEs) where benign images with small-magnitude perturbations mislead DNNs to incorrect results. A number of methods are proposed to generate AEs, but how to generate them with high attack success rate and perceptual quality needs more effort. Most attackers generate AEs by restricting L2-norm and L∞-norm of adversarial perturbations. However, very few works have been developed on L1 distortion matrix which encourages sparsity in the perturbation. In this paper, we penalize both L2-norm and L1-norm of perturbation as Elastic-Net regularization to improve the diversity and robustness of AEs. We further improve GAN by minimizing the additional pixel-wise loss derived from the Wasserstein distance between benign and adversarial auto-encoder loss distributions. Extensive experiments and visualizations on several datasets show that the proposed ERBEGAN can yield higher attack success rates than the state-of-the-art GAN-based attacker AdvGAN under the semi-whitebox and black-box attack settings. Besides, our method efficiently generates diverse adversarial examples that are more perceptually realistic.
Performance Measurements on a Cloud VR Gaming Platform

As cloud gaming and Virtual Reality (VR) games become popular in the game industry, game developers engage in these fields to boost their sales. Because cloud gaming possesses the merit of lifting computation loads from client devices to servers, it solves the high resource consumption issue of VR games on regular clients. However, it is important to know where is the bottleneck of the cloud VR gaming platform and how can it be improved in the future. In this paper, we conduct extensive experiments on the state-of-the-art cloud VR gaming platform--Air Light VR (ALVR). In particular, we analyze the performance of ALVR using both Quality-of-Service and Quality-of-Experience metrics. Our experiments reveal that latency (up to 90 ms RTT) has less influence on user experience compared to bandwidth limitation (as small as 35 Mbps) and packet loss rate (as high as 8%) . Moreover, we find that VR gamers can hardly notice the difference between the gaming experience with different latency values (between 0 and 90 ms RTT). Such findings shed some lights on how to further improve the cloud VR gaming platform, e.g., a budget of up to 90 ms RTT may be used to absorb network dynamics when bandwidth is insufficient.
Optical Image Encryption Algorithm Based on Chaotic Tinker Bell Map with Random Phase Masks in Fourier Domain

Double random phase encoding is a well-received optical encryption scheme for images by the scientific community. However, with an increase in computational power, double random phase encoding has been proven to be vulnerable by an intruder to some basic attacks like known plaintext, chosen plaintext and chosen ciphertext attacks. Some researcher applied a chaotic map with double random phase mask to endure these basic attacks. In this paper, a novel image encryption scheme in which Tinker bell map is used along with a double random phase mask in the frequency domain. Tinker bell map is a chaotic map which is relatively less explored. It has four parameters and two initial values which are highly sensitive to their values. Simulations are carried on Matlab on grayscale images. Statistical attacks based on histogram, entropy, 3-D plot and Correlation distribution analysis are performed on the scheme. The scheme is also evaluated for its efficacy against the noise and occlusion attack. The results show that the proposed scheme is highly secure and resist basic attacks.
Large-Scale Video Classification with Convolutional Neural Networks

Convolutional neural networks have been established as an unbelievable class of models for picture confirmation issues. Enabled by these results, we give CNN’s extensive trial evaluation a large degree of video-action syllabus using another dataset of 8M YouTube accounts. To get the Chronicles and its effects, we’ve used a YouTube video specification framework, which gives the names of the accounts they focus on. While the names are machine-generated, they are high-precision and are derived from a group of human-based icons, including metadata and question click signals. We have filtered the video names (Knowledge Graph Components) using both modern and manual curation strategies, including curiosity regarding whether the print is clearly indisputable. After that, we decode each video at one-layout per-second and use the deep CNN adjusted to ImageNet to remove the cover depicted immediately before the course of the action layer. Finally, we’ve stuffed the packaging features and made available both features and video level names for download. We train unique (ambiguous) game plan models on the dataset, survey them using significant evaluation estimates, and report them as baseline. Regardless of the size of the dataset, a portion of our models train the connection in less than a day on a singular machine using VGG. CNN our course release code for setting up model deals and generating predictions.