Reviewerly/exHarmony huggingface dataset:
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings

Query processing over big data is ubiquitous in modern clouds, where the system takes care of picking both the physical query execution plans and the resources needed to run those plans, using a cost-based query optimizer. A good cost model, therefore, is akin to better resource efficiency and lower operational costs. Unfortunately, the production workloads at Microsoft show that costs are very complex to model for big data systems. In this work, we investigate two key questions: (i) can we learn accurate cost models for big data systems, and (ii) can we integrate the learned models within the query optimizer. To answer these, we make three core contributions. First, we exploit workload patterns to learn a large number of individual cost models and combine them to achieve high accuracy and coverage over a long period. Second, we propose extensions to Cascades framework to pick optimal resources, i.e, number of containers, during query planning. And third, we integrate the learned cost models within the Cascade-style query optimizer of SCOPE at Microsoft. We evaluate the resulting system, Cleo, in a production environment using both production and TPC-H workloads. Our results show that the learned cost models are 2 to 3 orders of magnitude more accurate, and 20X more correlated with the actual runtimes, with a large majority (70%) of the plan changes leading to substantial improvements in latency as well as resource usage.
SR360

360-degree videos have gained increasing popularity due to its capability to provide users with immersive viewing experience. Given the limited network bandwidth, it is a common approach to only stream video tiles in the user's Field-of-View (FoV) with high quality. However, it is difficult to perform accurate FoV prediction due to diverse user behaviors and time-varying network conditions. In this paper, we re-design the 360-degree video streaming systems by leveraging the technique of super-resolution (SR). The basic idea of our proposed SR360 framework is to utilize abundant computation resources on the user devices to trade off a reduction of network bandwidth. In the SR360 framework, a video tile with low resolution can be boosted to a video tile with high resolution using SR techniques at the client side. We adopt the theory of deep reinforcement learning (DRL) to make a set of decisions jointly, including user FoV prediction, bitrate allocation and SR enhancement. By conducting extensive trace-driven evaluations, we compare the performance of our proposed SR360 with other state-of-the-art methods and the results show that SR360 significantly outperforms other methods by at least 30% on average under different QoE metrics.
A Comparative Study of Static Code Analysis tools for Vulnerability Detection in C/C++ and JAVA Source Code

Software security has become an essential component of software development process. It is necessary for an organisation to maintain software security in order to ensure integrity, authenticity and availability of the software product. To ensure software security, one of the major task is to identify vulnerabilities present in the source code before the software is being deployed. Detecting vulnerabilities in early phases of software development cycle, makes the process of fixing those vulnerabilities much easier for software developers. The vulnerability detection can be done either at the production phase, this means when the software is still being developed by statically auditing the source code, or dynamically at run time. In this study, vulnerability detection was done through Static code analysis process. Static code analysis can be done either manually or through automated tools. This paper focuses on using automated source code scanning tools for vulnerabilities detection in a software. Automated static Code Analysis tools audits the entire source code for its quality and identify any potential security vulnerability, if present. Unlike dynamic source code analysis that evaluates the source code behaviour during code execution, which is done quite late in the software development life cycle, Static Code Analysis leads to detection of security vulnerabilities in a source code in early stages of software development process, when the software is still in production phase because it does not require code to be in execution state. This paper firstly explains the importance of incorporating static code analysis in software development life cycle process so as to facilitate early detection of vulnerabilities in software product, and then present a comparative study of various static code analysis tools available for vulnerability detection in C/C++ and JAVA source code. The comparative study of three C/C++ static code analysis tools (flawfinder, RATS and CPPCheck) and two JAVA static code analysis tools (spotbugs and PMD) is done using Juliet (version1.3) test suite and APACHE tomcat dataset respectively, on the basis of category of vulnerability detected by each of the selected tool and the likelihood of false positive reported by each tool. Results showed that Flawfinder detected maximum categories of vulnerabilities and RATS and CPPCheck were almost similar in types of vulnerabilities detected. Also, it was observed that CPPCheck reported maximum number of false positives as compared to other two tools. Java static code analysis tools Spot bugs was able to detect more number of vulnerabilities than PMD.
Hybrid Neural Network for Sina Weibo Sentiment Analysis

Sina Weibo sentiment analysis technology provides the methods to survey public emotion about the related events or products in China. Most of the current works in sentiment analysis are to apply neural networks, such as convolution neural network (CNN), long short-term memory (LSTM), or C-LSTM. In this article, a novel structure of a hybrid neural network model is proposed to deal with the polysemy phenomena of words and topic confusion with Sina Weibo. First, the embeddings from language models (ELMo) and some statistical methods based on the corpus and sentiment lexicon are employed to extract the features. This method uses latent semantic relationships in different linguistic contexts and cooccurrence statistical features between words in Weibo. Second, for the classification model, unlike traditional C-LSTM which feeds CNN's output into LSTM, we employ several filters with variable window sizes to extract a sequence of high-level word representation in different granularity distributions of text data in multichannel CNN. At the same time, obtain the sentence representation in Bi-LSTM. Then, concatenate the outputs of multichannel CNN and Bi-LSTM. In conclusion, the results indicate that the proposed model performs better on the precision, recall, and F1-score for Weibo sentiment analysis.
Speech2Action: Cross-Modal Supervision for Action Recognition

Is it possible to guess human action from dialogue alone? In this work we investigate the link between spoken words and actions in movies. We note that movie screenplays describe actions, as well as contain the speech of characters and hence can be used to learn this correlation with no additional supervision. We train a BERT-based Speech2Action classifier on over a thousand movie screenplays, to predict action labels from transcribed speech segments. We then apply this model to the speech segments of a large unlabelled movie corpus (188M speech segments from 288K movies). Using the predictions of this model, we obtain weak action labels for over 800K video clips. By training on these video clips, we demonstrate superior action recognition performance on standard action recognition benchmarks, without using a single manually labelled action example.
Deep Fair Clustering for Visual Learning

Fair clustering aims to hide sensitive attributes during data partition by balancing the distribution of protected subgroups in each cluster. Existing work attempts to address this problem by reducing it to a classical balanced clustering with a constraint on the proportion of protected subgroups of the input space. However, the input space may limit the clustering performance, and so far only low-dimensional datasets have been considered. In light of these limitations, in this paper, we propose Deep Fair Clustering (DFC) to learn fair and clustering-favorable representations for clustering simultaneously. Our approach could effectively filter out sensitive attributes from representations, and also lead to representations that are amenable for the following cluster analysis. Theoretically, we show that our fairness constraint in DFC will not incur much loss in terms of several clustering metrics. Empirically, we provide extensive experimental demonstrations on four visual datasets to corroborate the superior performance of the proposed approach over existing fair clustering and deep clustering methods on both cluster validity and fairness criterion.
Superpixel-Based Seamless Image Stitching for UAV Images

Image stitching aims to generate a natural seamless high-resolution panoramic image free of distortions or artifacts as fast as possible. In this article, we propose a new seam cutting strategy based on superpixels for unmanned aerial vehicle (UAV) image stitching. Explicitly, we decompose the issue into three steps: image registration, seam cutting, and image blending. First, we employ adaptive as-natural-as-possible (AANAP) warps for registration, obtaining two aligned images in the same coordinate system. Then, we propose a novel superpixel-based energy function that integrates color difference, gradient difference, and texture complexity information to search a perceptually optimal seam located in continuous areas with high similarity. We apply the graph cut algorithm to solve the problem and thereby conceal artifacts in the overlapping area. Finally, we utilize a superpixel-based color blending approach to eliminate visible seams and achieve natural color transitions. Experimental results demonstrate that our method can effectively and efficiently realize seamless stitching, and is superior to several state-of-the-art methods in UAV image stitching.
Multi-Domain Learning for Accurate and Few-Shot Color Constancy

Color constancy is an important process in camera pipeline to remove the color bias of captured image caused by scene illumination. Recently, significant improvements in color constancy accuracy have been achieved by using deep neural networks (DNNs). However, existing DNNbased color constancy methods learn distinct mappings for different cameras, which require a costly data acquisition process for each camera device. In this paper, we start a pioneer work to introduce multi-domain learning to color constancy area. For different camera devices, we train a branch of networks which share the same feature extractor and illuminant estimator, and only employ a camera-specific channel re-weighting module to adapt to the camera-specific characteristics. Such a multi-domain learning strategy enables us to take benefit from crossdevice training data. The proposed multi-domain learning color constancy method achieved state-of-the-art performance on three commonly used benchmark datasets. Furthermore, we also validate the proposed method in a fewshot color constancy setting. Given a new unseen device with limited number of training samples, our method is capable of delivering accurate color constancy by merely learning the camera-specific parameters from the few-shot dataset. Our project page is publicly available at https://github.com/msxiaojin/MDLCC.
Tracking the debate on COVID-19 surveillance tools

Contact-tracing apps could help keep countries open before a vaccine is available. But do we have a sufficient understanding of their efficacy, and can we balance protecting public health with safeguarding civil rights? We interviewed five experts, with backgrounds in digital health ethics, internet law and social sciences.
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing

Neural Architecture Search (NAS) is a promising and rapidly evolving research area. Training a large number of neural networks requires an exceptional amount of computational power, which makes NAS unreachable for those researchers who have limited or no access to high-performance clusters and supercomputers. A few benchmarks with precomputed neural architectures performances have been recently introduced to overcome this problem and ensure reproducible experiments. However, these benchmarks are only for the computer vision domain and, thus, are built from the image datasets and convolution-derived architectures. In this work, we step outside the computer vision domain by leveraging the language modeling task, which is the core of natural language processing (NLP). Our main contribution is as follows: we have provided search space of recurrent neural networks on the text datasets and trained 14k architectures within it; we have conducted both intrinsic and extrinsic evaluation of the trained models using datasets for semantic relatedness and language understanding evaluation; finally, we have tested several NAS algorithms to demonstrate how the precomputed results can be utilized. We consider that the benchmark will provide more reliable empirical findings in the community and stimulate progress in developing new NAS methods well suited for recurrent architectures.
APPLD: Adaptive Planner Parameter Learning From Demonstration

Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this letter, we introduce appld, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human-teleoperated demonstration of desirable navigation. appld is verified on two robots running different navigation systems in different environments. Experimental results show that appld can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.
Convenience analysis of sustainable E-agriculture based on blockchain technology

Blockchain is a distributed database system with multiple independent nodes. It’s problematic to modify, forge, and track. Blockchain can record all kinds of information that has already happened. The implementation of blockchain technology can solve these problems and realize the sustainable development of agriculture. The present study aims to investigate the convenience analysis of sustainable electronic agriculture based on blockchain technology, and analyze the application prospect and challenges of blockchain in the agricultural field. In this experiment, five villages with similar development rates in China were selected as the objects.Firstly, blockchain technology was applied to sustainable e-agriculture by using data statistics to explore its convenience. Furthermore, the big data analysis method is used to fit and analyze the data to see which groups of people in each age group are the real benefits of blockchain technology. Using the single variable method to compare the convenience of traditional e-agriculture and sustainable e-agriculture based on block chain. Variance analysis and comparison of data were performed using SPSS19.0 analysis software one-way ANOVA program. Data results were expressed in the form of mean value. According to the experimental data, the e-commerce applications of fresh commodities, whole grains and aquatic products have reached a high degree of convenience, while the convenience of valuable commodities and fragile commodities is lower. However, compared with the traditional e-agriculture, the convenience has increased by more than 15%. In the middle and young age, blockchain projects all get outstanding convenient data; only in the middle and old age, the data is lower. Experimental data show that sustainable electronic agriculture based on blockchain technology brings great convenience to farmers’ sales, increasing by 25% on average compared with traditional electronic agriculture, which will bring vigorous vitality to the sustainable development of agricultural products.
Cognitive Similarity-Based Collaborative Filtering Recommendation System

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.
Dependency locality as an explanatory principle for word order

This work focuses on explaining both grammatical universals of word order and quantitative word-order preferences in usage by means of a simple efficiency principle: dependency locality. In its simplest form, dependency locality holds that words linked in a syntactic dependency (any head–dependent relationship) should be close in linear order. We give large-scale corpus evidence that dependency locality predicts word order in both grammar and usage, beyond what would be expected from independently motivated principles, and demonstrate a means for dissociating grammar and usage in corpus studies. Finally, we discuss previously undocumented variation in dependency length and how it correlates with other linguistic features such as head direction, providing a rich set of explananda for future linguistic theories.
SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing

In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need large amounts of annotated data for supervised learning, but these are often not available in the case of medical image data. One way to overcome this limitation is to generate synthetic training data, e.g., by performing simulations to artificially augment the dataset. However, simulations require domain knowledge and are limited by the complexity of the underlying physical model. Another method to perform data augmentation is the generation of images by means of neural networks.We developed a new algorithm for generation of synthetic medical images exhibiting speckle noise via generative adversarial networks (GANs). Key ingredient is a speckle layer, which can be incorporated into a neural network in order to add realistic and domain-dependent speckle. We call the resulting GAN architecture SpeckleGAN.We compared our new approach to an equivalent GAN without speckle layer. SpeckleGAN was able to generate ultrasound images with very crisp speckle patterns in contrast to the baseline GAN, even for small datasets of 50 images. SpeckleGAN outperformed the baseline GAN by up to 165 % with respect to the Fréchet Inception distance. For artery layer and lumen segmentation, a performance improvement of up to 4 % was obtained for small datasets, when these were augmented with images by SpeckleGAN.SpeckleGAN facilitates the generation of realistic synthetic ultrasound images to augment small training sets for deep learning based image processing. Its application is not restricted to ultrasound images but could be used for every imaging methodology that produces images with speckle such as optical coherence tomography or radar.
Fast Free-Text Authentication via Instance-Based Keystroke Dynamics

Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
Multi-Task Learning at the Mobile Edge: An Effective Way to Combine Traffic Classification and Prediction

Mobile traffic classification and prediction are key tasks for network optimization. Most of the works in this area present two main drawbacks. First, they treat the two tasks separately, thus requiring high computational capabilities. Second, they perform data mining on the information collected from the data plane, which is unsuitable for the mobile edge. To bridge this gap, this paper properly tailors a Multi-Task Learning model running directly at the edge of the network to anticipate information on the type of traffic to be served and the resource allocation pattern requested by each service during its execution. Our study exploits data mining from the control channel of an operative mobile network to also reduce storage and monitoring processing. Different configurations of neural networks, which adopt autoencoders (i.e. Undercomplete Autoencoder or Sequence to Sequence Autoencoder) as key building blocks of the proposed Multi-Task Learning methodology for common feature representations, are investigated to evaluate the impact of the observation window of traffic profiles on the classification accuracy, prediction loss, complexity, and convergence. The comparison with respect to conventional single-task learning approaches, that do not use autoencoders and tackle classification and prediction tasks separately, clearly demonstrates the effectiveness of the proposed Multi-Task Learning approach under different system configurations.