Reviewerly/exHarmony huggingface dataset:
Learning Class-Conditional GANs with Active Sampling

Class-conditional variants of Generative adversarial networks (GANs) have recently achieved a great success due to its ability of selectively generating samples for given classes, as well as improving generation quality. However, its training requires a large set of class-labeled data, which is often expensive and difficult to collect in practice. In this paper, we propose an active sampling method to reduce the labeling cost for effectively training the class-conditional GANs. On one hand, the most useful examples are selected for external human labeling to jointly reduce the difficulty of model learning and alleviate the missing of adversarial training; on the other hand, fake examples are actively sampled for internal model retraining to enhance the adversarial training between the discriminator and generator. By incorporating the two strategies into a unified framework, we provide a cost-effective approach to train class-conditional GANs, which achieves higher generation quality with less training examples. Experiments on multiple datasets, diverse GAN configurations and various metrics demonstrate the effectiveness of our approaches.
A new architecture for improving focused crawling using deep neural network

Cloud computing offers elastic features to alleviate the challenges of web crawling. Building crawlers in a scalable fashion has become highly needed. This paper proposes a new Focused Crawler (FC) architecture that can be introduced as a service over the cloud computing. The proposed FC has a serv ice called a Topic Filter Service (TFS), which is responsible for filtering retrieved pages before indexing and extracting links to add them in the crawling queue. TFS relies on the Deep Neural Network (DNN) classifier. TFS is trained by a dataset. This dataset is processed by an outlier rejection using support vector machine classifier. Moreover, this proposed FC has a further service called Concept Weight Handler (CWH). It is responsible for handling the keywords such as concepts based on meanings and it calculates the weight of each concept. Experimental results show that cloud computing services provide a better environment for running and improving the speed of crawling. The proposed classifier has been tested in comparison with other classification techniques and has proved highly accurate. The overall accuracy offered by the employed architecture confirms that the effectiveness and performance of the proposed FC is high.
Optimizing deep neural networks hyperparameter positions and values

Hyperparameter optimization is a crucial step in the implementation of any machine learning model. This optimization process includes regularly modifying the hyperparameter values of the model in order to minimize the testing error. A deep neural learning model hyperparameter optimization process i ncludes optimizing both the model parameters and architecture. Optimizing a model’s parameters involves deciding the values of parameters, such as learning rate and batch size. Optimizing architectural hyperparameters includes deciding the shape of the deep neural learning model, i.e., the number of layers of individual types and the number of neurons in a certain layer. The state-of-the-art hyperparameter optimization methods don’t optimize the position of the hyperparameter within the model architecture. In this work, we study the effect of changing a hyperparameter within the deep learning model architecture. Thus, we propose an architectural position optimization (ArchPosOpt) method for model architectural hyperparameter optimization. ArchPosOpt extends three different hyperparameter optimization techniques, namely grid search, random search, and Tree-structured Parzen Estimator (TPE), to include a new dimension of hyperparameter optimization problem – the hyperparameter position. We show through a set of experiments that the position of the hyperparameters does matter for model performance as well as the hyperparameter values.
SNAPS: Towards building snapshot based provenance system for virtual machines in the cloud environment

Recent threats in cloud show the necessity to perform forensics in the cloud environment. But performing forensic investigation in the cloud is different from traditional digital forensics. Cloud characteristics like multi-tenancy, rapid elasticity, diversity, and complexity raise additional challenges for cloud forensics. For each cloud Virtual Machine (VM), there will be several evidences like vdisk, vRAM, Snapshots, Volumes, Service logs, and VM logs. The forensic challenges differ from one evidence to the other. In this paper, we look at the challenges of snapshot acquisition and analysis. In reality, snapshots for a VM may not always exist. To increase their availability, we suggest the use of Cloud Forensic Readiness (CFR) models in which the snapshots are collected before the actual incident. The captured snapshots have to be transferred to the investigators environment via network. Since the cloud VM snapshots will be generally of huge size, transferring them elsewhere for processing may lead to the problem of data gravity. We resolve this problem by designing a framework named SNAPS (Snapshots based Provenance Aware System) which is derived from the existing spatio-temporal models and then customized to suit cloud forensic investigation. The motivation behind proposing SNAPS is to generate provenance for each object in the target virtual machine using its multiple snapshots. Moreover, SNAPS can be used to address various forensic challenges starting from simple to complex ones. Few of those were illustrated with the VM snapshots acquired from the Openstack Cloud Environment.
A calibration method for spatial pose of a laser beam

An articulated laser sensor, which is a new kind of trans-scale, non-contact metrological instrument, is principally made up of two articulated laser sensing modules. Each module is composed of two rotary tables and one collimated laser. Calibrating the spatial pose of a laser beam is a key aspect of the measurement system. In this paper we propose a one-dimensional linear displacement optical calibration device for high stability and easily performed calibration. Through analysis of space vectors and image processing, a calibration method for spatial pose of the laser beam is introduced in detail. The calibration method is divided into two parts: calibration of the direction vector of the laser beam and a fixed point on the laser beam. Firstly, the direction vector of the laser beam is calibrated using the proposed calibration device. Then a set of points on the laser beam is obtained and the linear fitting is performed. By comparing the direction vector of the fitted line and the direction vector of the above laser beam, the optimal fixed point on the fitted line is obtained. The simulation and experimental results show that the proposed calibration method is accurate and effective.
Using Reddit Data for Multi-Label Text Classification of Twitter Users Interests

The automation process for inferring users' interest groups is a challenge task in social networks research and it has applications in marketing and recommendation systems. Manually labeling of documents is a difficult and an expensive task, but it is essential for training an automatic text classifier. Actually, there are several approaches where the problem is treated as a multi-label prediction task. In this work, a methodology is proposed to automatically categorize data by considering Reddit and Twitter data. First, a dataset of 42.100 publications belongs to popular forums site Reddit is collected to train a model with labeled data. Then, a dataset of tweets, an average of 100 tweets per user, from 1573 profiles is collected to predict users' topics of interest with the trained model. Finally, we were able to automatically categorize data with an average precision of 75.62%.
On-Site Personal Sport Skill Improvement Support Using Only a Smartwatch

In recent years, with the widespread use of wearable devices, in the sports field and the like, research and techniques for analyzing the movement are being developed by attaching sensors to a body or a gear and analyzing the movement from the acquired data. However, most of them require dedicated sensors and post-processing by computers. Also, there is nothing to do on the spot to provide feedback for form and skill improvement. In this research, we developed a sports skill improvement support system using smartwatch sensors and feedback screen. We verified whether it can be utilized in everyday life. In order to demonstrate the versatility of sports skill improvement support, we implemented application software targeting two movements, baseball pitching action and tennis' serve action. Designed and implemented so that analysis and feedback can be presented solely by smartwatch worn on the wrist of the dominant arm without having a paired smartphone so that the user can perform the usual operation. As a result of the verification experiments, both average and maximum ball speeds increased for all the subjects with respect to the pitching motion. Concerning serve motion, a significant improvement of the pronation movement capability was observed. These results demonstrate the effectiveness of smartwatches as an individual skill improvement support system in sports where arm movement is greatly related to skills.
Survey of Challenges in Labeled Random Finite Set Distributed Multi-Sensor Multi-Object Tracking

In recent years, increasing interest in distributed sensing networks has led to a demand for robust multi-sensor multi-object tracking (MOT) methods that can take advantage of large quantities of gathered data. However, distributed sensing has unique challenges stemming from limited computational resources, limited bandwidth, and complex network topology that must be considered within a given tracking method. Several recently developed methods that are based upon the random finite set (RFS) have shown promise as statistically rigorous approaches to the distributed MOT problem. Among the most desirable qualities of RFS-based approaches is that they are derived from a common mathematical framework, finite set statistics, which provides a basis for principled fusion of full multi-object probability distributions. Yet, distributed labeled RFS tracking is a still-maturing field of research, and many practical considerations must be addressed before large-scale, real-time systems can be implemented. For example, methods that use label-based fusion require perfect label consistency of objects across sensors, which is impossible to guarantee in scalable distributed systems. This paper provides a survey of the challenges inherent in distributed tracking using labeled RFS methods. An overview of labeled RFS filtering is presented, the distributed MOT problem is characterized, and recent approaches to distributed labeled RFS filtering are examined. The problems that currently prevent implementation of distributed labeled RFS trackers in scalable real-time systems are identified and demonstrated within the scope of several exemplar scenarios.
Stabilizing Arrays of Photonic Cat States via Spontaneous Symmetry Breaking

The controlled generation and the protection of entanglement is key to quantum simulation and quantum computation. At the single-mode level, protocols based on photonic cat states hold strong promise as they present unprecedentedly long-lived coherence and may be combined with powerful error correction schemes. Here, we demonstrate that robust ensembles of "many-body photonic cat states" can be generated in a Bose-Hubbard model with pair hopping via a spontaneous U(1) symmetry-breaking mechanism. We identify a parameter region where the ground state is a massively degenerate manifold consisting of local cat states which are factorized throughout the lattice and whose conserved individual parities can be used to make a register of qubits. This phenomenology occurs for arbitrary system sizes or geometries, as soon as long-range order is established, and it extends to driven-dissipative conditions. In the thermodynamic limit, it is related to a Mott insulator to pair-superfluid phase transition.
Towards Privacy-preserving Mobile Applications with Federated Learning

Recommender systems have gained prominence in bringing users tailor-made content from the web aiding their decision making process. However, this personalization comes at a cost of privacy of sharing personal information with the recommendation provider. Moreover, with growing sizes of datasets and models, centralized processing of such data has become challenging. To this end, we propose a federated matrix factorization algorithm to enable personal data to be stored and used on-device for training while sending updates to train a centralized model. We illustrate preliminary results from our algorithm applied to recommendation of articles to users of a mobile application based on their reading history. We compare our performance with centralized matrix factorization applied on the same dataset.
Local versus Global Models for Just-In-Time Software Defect Prediction

Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k -medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP.
Data dependency reduction for high-performance FPGA implementation of DEFLATE compression algorithm

The rapid development of modern information technology has resulted in a sharp increase in the rate of data growth. This results in a lack of storage space and network bandwidth. Compression technology is typically implemented to mitigate the increasing demand for storage and the transmission cost of data. However, data compression may impose a significant computational burden on the CPU, which results in a degradation of system performance. To solve this problem, a hardware offloading technique can be used. Hardware offloading not only reduces the computational load imposed on the CPU but also improves the performance of the compression algorithm by exploiting hardware parallelism. However, data-hazards associated with the compression algorithm hinders achieving the achievement of a high degree of parallelism. DEFLATE is a widely used lossless compression scheme. Many studies have attempted to eliminate the data dependencies associated with compression algorithms. Unfortunately, existing studies do not address data dependency elimination in Huffman encoding. Our work aims to parallelize Huffman encoding by solving the data-hazard problem in the algorithm. To address the data dependency that exists in the Huffman encoding algorithm, a new data representation for the intermediate data generated during data compression is proposed. The effectiveness of the proposed scheme was evaluated via the implementation of an architecture which applied the approach in the field-programmable gate array (FPGA) platform. Experimental results show that the proposed scheme can increase the throughput of the compressor by up to 14.4%.
New Complementary Points of Cyber Security Schemes for Critical Digital Assets at Nuclear Power Plants

As the importance of cyber security for nuclear power plants has recently increased, related organizations such as manufactures, operators, regulators, and research institutes have taken into consideration the many concerns for effective application of cyber security to critical digital assets at nuclear power plants.It is essential to analyze and research how to effectively apply nuclear cyber security requirements and what complementary factors are present in the overall process for a nuclear security technology system.The complementary points of nuclear cyber security technology could be analyzed through diverse cyber security methods such as cyber security assurance, assessments, and software development life cycle.It is necessary to apply nuclear cyber security technology effectively in accordance with the operation, assurance, and system development process of a nuclear facility.These analysis results could be applied to technical points for the cyber security of critical digital assets at nuclear power plants in the future and to technology that needs to be at the appropriate level of cyber security to ensure technical assurance.This paper is a new approach to compare and analyze various methods used in the cyber security field to discover complementary points for the application of cyber security to critical systems in nuclear power plants.Additionally, this paper also presents effective methods for developing and applying, evaluating, and regulating cyber security in nuclear digital critical systems using the complementary points of various cyber security methods identified in the analysis.
A real-time webcam-based method for assessing upper-body postures

This paper presents a new vision-based method for real-time assessment of upper-body postures of a subject who is sitting in front of a desk studying or operating a computer. Unlike most existing vision-based methods that perform offline assessment from human skeletons extracted from RGB video or depth maps, the proposed method analyses directly single images captured by a webcam in front of the subject without the prone-to-error process of extracting the skeleton data from the images or depth maps. To this end, this paper proposes to assess postures by classifying them into predefined classes, without explicitly measuring the variables required for calculating risk scores. Each class of postures is associated with a configuration of the upper body, and an ergonomics risk score is assigned by following one of the scoring methods, e.g. Rapid Upper Limb Assessment (RULA). A data set of upper-body postures that cover the various scenarios when a subject is sitting in front of a desk as well as some extreme cases when the subject turns away from the desk is collected for evaluating the proposed method quantitatively. The proposed method achieved an on-average accuracy of 99.5% for binary classification (low- vs. high-risk postures), 88.2% for classification of 19 risk levels and 81.5% for classification of 30 risk levels on the data set, and the demo developed based on the method runs in real time on a regular computer.
Spartan Networks: Self-feature-squeezing neural networks for increased robustness in adversarial settings

Deep learning models are vulnerable to adversarial examples which are input samples modified in order to maximize the error on the system. We introduce Spartan Networks, resistant deep neural networks that do not require input preprocessing nor adversarial training. These networks have an adversarial layer designed to discard some information of the network, thus forcing the system to focus on relevant input. This is done using a new activation function to discard data. The added layer trains the neural network to filter-out usually-irrelevant parts of its input. Our performance evaluation shows that Spartan Networks have a slightly lower precision but report a higher robustness under attack when compared to unprotected models. Results of this study of Adversarial AI as a new attack vector are based on tests conducted on the MNIST dataset.
An FPGA-Based BWT Accelerator for Bzip2 Data Compression

The Burrows-Wheeler Transform (BWT) has played an important role in lossless data compression algorithms. To achieve a good compression ratio, the BWT block size needs to be several hundreds of kilobytes, which requires a large amount of on-chip memory resources and limits effective hardware implementations. In this paper, we analyze the bottleneck of the BWT acceleration and present a novel design to map the anti-sequential suffix sorting algorithm to FPGAs. Our design can perform BWT with a block size of up to 500KB (i.e., bzip2 level 5 compression) on the Xilinx Virtex UltraScale+ VCU1525 board, while the state-of-art FPGA implementation can only support 4KB block size. Experiments show our FPGA design can achieve ~2x speedup compared to the best CPU implementation using standard large Corpus benchmarks.
KARL: Fast Kernel Aggregation Queries

Kernel functions support a broad range of applications that require tasks like density estimation, classification, or outlier detection. In these tasks, a common online operation is to compute the weighted aggregation of kernel function values with respect to a set of points. Scalable aggregation methods are still unknown for typical kernel functions (e.g., Gaussian kernel, polynomial kernel, and sigmoid kernel) and weighting schemes. In this paper, we propose a novel and effective bounding technique to speedup the computation of kernel aggregation. We further boost its efficiency by leveraging index structures and exploiting index tuning opportunities. In addition, our technique is extensible to different types of kernel functions and weightings. Experimental studies on many real datasets reveal that our proposed method achieves speedups of 2.5-738 over the state-of-the-art.
Multi-Document Extractive Text Summarization via Deep Learning Approach

Today, given the huge amount of information, summarization has become one of the most applicable topics in data mining that can help users gain access to useful data over a short period of time. In this study, two multi-document extractive text Summarization systems are introduced. The major objective of this research is to use autoencoder neural network and deep belief network separately for scoring sentences in a document to compare their performances. Deep neural networks can improve the results by generating new features. The abovementioned systems were tested on DUC 2007 dataset and evaluated using ROUGE-1 and ROUGE-2 criteria. The results show a better performance of autoencoder network versus deep belief network. It is also possible to compare these values with results of other systems to realize the effectiveness of the proposed methods.