Reviewerly/exHarmony huggingface dataset:
On-The-Fly Parallel Data Shuffling for Graph Processing on OpenCL-Based FPGAs

Graph processing has attracted much attention recently due to its popularity in many big data analytic applications. With high performance and energy efficiency, FPGAs can be an attractive architecture for graph processing. A number of techniques such as caching using block RAMs (BRAMs) to reduce random accesses of global memory and multiple processing element (PE) instances for high throughput have been explored. OpenCL-based FPGAs natively support a high-level programming paradigm, providing good programmability to developers. However, challenges remain because the run-time dependency introduced by multiple PEs usually cannot be handled efficiently by OpenCL's high-level control granularity. In this paper, we propose a novel on-the-fly parallel data shuffling technique that can be implemented in OpenCL to solve this problem. We have integrated our shuffling technique to an edge-centric graph processing framework which achieves a throughput of more than 1,000 million traversed edges per second (MTEPS) on PageRank, SpMV, BFS and SSSP applications and is even better than existing RTL-based designs.
Online Deep Fuzzy Learning for Control of Nonlinear Systems Using Expert Knowledge

This article presents an online learning method for improved control of nonlinear systems by combining deep learning and fuzzy logic. Given the ability of deep learning to generalize knowledge from training samples, the proposed method requires minimum amount of information about the system to be controlled. However, in robotics, particularly in aerial robotics where the operating conditions may vary, online learning is required. In this article, fuzzy logic is preferred to provide supervising feedback to the deep model for adapting to variations in the system dynamics as well as new operational conditions. The learning method is divided into two phases: offline pretraining and online posttraining. In the former, the system is controlled by a conventional controller and a deep fuzzy neural network (DFNN) is pretrained based on the recorded input-output dataset, in order to approximate the inverse dynamical model of the system. In the latter, only the pretrained DFNN is used to control the system. In this phase, the fuzzy logic, which encodes the expert knowledge, is utilized to observe the behavior of the system and to correct the action of DFNN instantaneously. The experimental results show that the proposed online learning-based approach improves the trajectory tracking performance of the unmanned aerial vehicle.
An automated new approach in fast text classification (fastText)

Any Text Classification (TC) problem need pre-processing steps which may affect the classification accuracy. Especially pre-processing steps need substantial effort particularly in agglutinative languages such as Turkish. In this context, a traditional text categorization problem requires pre-processing steps such as tokenization, stop-word removal, lower-case conversion, stemming and feature dimension reduction. Before classification, one or more of these steps are applied to text and then a classifier is trained to evaluate the corresponding precision. Deep neural network classifiers combined with word embedding is one of the solutions to eliminate the pre-processing prerequisites. Another novel approach is fastText word embedding based classifier which was developed by Facebook. In this study, we evaluate a fastText classifier on TTC-3600 Turkish dataset without using any pre-processing steps and present the performance of the algorithm.
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Index Terms-Fuzzy cognitive maps (FCMs), maximum entropy, noisy data, rapid and robust learning.
Design, simulation and testing of a cloud platform for sharing digital fabrication resources for education

Cloud and IoT technologies have the potential to support applica- tions that are not strictly limited to technical fields. This paper shows how digital fabrication laboratories (Fab Labs) can leverage cloud technologies to enable resource sharing and provide remote access to distributed expensive fabrication resources over the internet. We call this new concept Fabrication as a Service (FaaS), since each resource is exposed to the internet as a web ser- vice through REST APIs. The cloud platform presented in this paper is part of the NEWTON Horizon 2020 technology-enhanced learning project. The NEWTON Fab Labs architecture is described in detail, from system concep- tion and simulation to system cloud deployment and testing in NEWTON project small and large-scale pilots for teaching and learning STEM subjects.
Disentangled Spectrum Variations Networks for NIR–VIS Face Recognition

Surveillance cameras often capture near infrared images since it provides a low-cost and effective solution to acquire high-quality images under low-light environments. However, visual versus near infrared (VIS-NIR) heterogeneous face recognition (HFR) is still a challenging issue in computer vision community due to the gap between sensing patterns of different spectrums as well as the lack of sufficient training samples. To solve the above problem, in this paper, we present an effective Disentangled Spectrum Variations Networks (DSVNs) for VISNIR HFR. Two key strategies are introduced to the DSVNs for disentangling spectrum variations between two domains: Spectrum-adversarial Discriminative Feature Learning (SaDFL) and Step-wise Spectrum Orthogonal Decomposition (SSOD). The SaDFL consists of Identity-Discriminative subnetwork (IDNet) and Auxiliary Spectrum Adversarial subnetwork (ASANet). On the one hand, the IDNet is composed of a generator G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sub> and a discriminator D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</sub> for extracting identity-discriminative feature. On the other hand, the ASANet is built by a generator G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sub> and a discriminator D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> for eliminating modality-variant spectrum information under the guidance of the discriminator D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> . The identity-label and modality-label HFR datasets are used to train the DSVNs with triplet loss. Both IDNet and ASANet can jointly enhance the domain-invariant feature representations via an adversarial learning. Furthermore, to disentangle spectrum variations effectively as well as making identity information and modality information unrelated to each other, we present a new topology of connection block called Disentangled Spectrum Variations (DSV). An orthogonality constraint is imposed to DSV at the convolution level for channel-wise orthogonal decomposition between the modality-invariant identity information and modalityvariant spectrum information. In particular, the SSOD is built by stacking multiple modularized mirco-block DSV, and thereby enjoys the benefits of disentangling spectrum variation step by step. Moreover, we investigate the similarity calculation method to further improve the HFR performance. To sum up, the designed DSVNs leads to a purification of identity information as well as an elimination of modality information. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIRVIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Software Bug Prediction Using Supervised Machine Learning Algorithms

Machine Learning algorithms sprawl their application in various fields relentlessly. Software Engineering is not exempted from that. Software bug prediction at the initial stages of software development improves the important aspects such as software quality, reliability, and efficiency and minimizes the development cost. In majority of software projects which are becoming increasingly large and complex programs, bugs are serious challenge for system consistency and efficiency. In our approach, three supervised machine learning algorithms are considered to build the model and predict the occurrence of the software bugs based on historical data by deploying the classifiers Logistic regression, Naive Bayes, and Decision Tree. Historical data has been used to predict the future software faults by deploying the classifier algorithms and make the models a better choice for predictions using random forest ensemble classifiers and validating the models with K-Fold cross validation technique which results in the model effectively working for all the scenarios.
Generative Adversarial Network Based Service Recommendation in Heterogeneous Information Networks

Service recommendation is widely used to locate developers' desired services. Previous methods mainly focus on employing collaborative filtering (CF) techniques to recommend services to developers. However, these methods have some problems, such as being sensitive to sparse data and having limited predictive ability to new developers. Generative adversarial network (GAN) can solve the above mentioned problems, since it can learn the data distribution from a limited amount of data and generate a new developer's preference score for a service, even if he/she has not invoked the service. In this paper, we propose a novel GAN based service recommendation method. It first constructs a heterogeneous information network (HIN) by utilizing mashup information, service information and their respective attribute information. Then, it samples meta-paths of different semantic relationships and constructs similarity matrices between mashups and services through meta-paths based similarity measurement. Finally, by leveraging the adversarial training between the discriminator and the generator, the discriminator can effectively guide the generator to generate a preference vector for the developer, thus recommending a list of services for him/her according to his/her given mashup attribute information. Comprehensive experimental results on a real-world dataset demonstrate the superiority of the proposed method.
Evolutionary Algorithms For k-Anonymity In Social Networks Based On Clustering Approach

Abstract The usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is a lot of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve the users’ privacy through the anonymization of social network graphs. In this regard, the utilization of the k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique, the nodes are clustered together to form the super-nodes of size at least k. Our main idea in this paper is, initially, to optimize the clustering process in the k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to minimize the normalized structural information loss (NSIL), which is equal to maximizing 1-NSIL. Although the proposed PSO-based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) method, it did not provide a lower NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from the PSO algorithm, we present hybrid solutions based on the GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships. The simulation results demonstrate the efficiency of the proposed model to balance the maximized 1-NSIL and the algorithm’s convergence rate.
E-WACGAN: Enhanced Generative Model of Signaling Data Based on WGAN-GP and ACGAN

In recent years, the generative adversarial network (GAN) has achieved outstanding performance in the image field and the derivatives of GAN, namely auxiliary classifier GAN (ACGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) have also been widely used, but the GAN applications of nonimage domain are not wide. At the time when the telecommunication fraud is rampant, the signaling data of telephone contain a lot of useful information, which is helpful for distinguishing fraudulent and nonfraudulent calls. In this article, aiming at the problem of limited amount of data and information leakage in the research of telephone signaling data, we adopt WGAN-GP and ACGAN to generate analog data, which confirms distribution of true data. In order to solve the problem of category accuracy of analog data and to enhance the stability and speed of training, we proposed a new network structure for discriminator of GAN based on WGAN-GP and ACGAN. The experiments on telecommunication fraud dataset found that our method obtains better performance on signaling data than base model.
CSSAP: Software Aging Prediction for Cloud Services Based on ARIMA-LSTM Hybrid Model

Cloud services typically compose of multiple distributed software components that communicate with each other through web service interfaces in the cloud environments. During their long time running, the accumulation of cloud software internal errors or large consumption of computing resources will very likely lead to software aging problems. In order to solve this problem, software rejuvenation technology is proposed to prevent them from causing more serious failures by restarting the services running. In the research field of software aging and rejuvenation for cloud services, how to accurately predict the cloud resource consumption in the aging software system for determining suitable time to perform rejuvenation is a significant and indispensable issue. In this paper, a novel hybrid aging prediction model named CSSAP is proposed, which well integrates the Autoregressive Integrated Moving Average (ARIMA) model and Long Short Term Memory (LSTM) model for better fitting the linear pattern and mining the nonlinear relationship in the time series of computing resource usage data for cloud services. The experiments results show that through such hybrid and unified time series analysis, our CASSP prediction method has 4% to 71% improvements in MAE evaluation criteria and 6% to 66% improvements in RMSE evaluation criteria under different time series scenarios compared with single model used, that is, the more accurate and more comprehensive aging prediction results achieved by CSSAP is definitely conducive to perform more effective and more efficient software aging and rejuvenation for cloud services.
Efficient Sentence Embedding using Discrete Cosine Transform

Vector averaging remains one of the most popular sentence embedding methods in spite of its obvious disregard for syntactic structure.While more complex sequential or convolutional networks potentially yield superior classification performance, the improvements in classification accuracy are typically mediocre compared to the simple vector averaging.As an efficient alternative, we propose the use of discrete cosine transform (DCT) to compress word sequences in an order-preserving manner.The lower order DCT coefficients represent the overall feature patterns in sentences, which results in suitable embeddings for tasks that could benefit from syntactic features.Our results in semantic probing tasks demonstrate that DCT embeddings indeed preserve more syntactic information compared with vector averaging.With practically equivalent complexity, the model yields better overall performance in downstream classification tasks that correlate with syntactic features, which illustrates the capacity of DCT to preserve word order information.
Adversarial Face De-Identification

Recently, much research has been done on how to secure personal data, notably facial images. Face de-identification is one example of privacy protection that protects person identity by fooling intelligent face recognition systems, while typically allowing face recognition by human observers. While many face de-identification methods exist, the generated de-identified facial images do not resemble the original ones. This paper proposes the usage of adversarial examples for face de-identification that introduces minimal facial image distortion, while fooling automatic face recognition systems. Specifically, it introduces P-FGVM, a novel adversarial attack method, which operates on the image spatial domain and generates adversarial de-identified facial images that resemble the original ones. A comparison between P-FGVM and other adversarial attack methods shows that P-FGVM both protects privacy and preserves visual facial image quality more efficiently.
Novel Genetic Algorithm with Dual Chromosome Representation for Resource Allocation in Container-Based Clouds

Containerization does not only support fast development and deployment of web applications but also provides the potential to improve the energy efficiency in cloud data centers. In container-based clouds, containers are allocated to virtual machines (VMs) and VMs are allocated to physical machines (PMs). This new architecture requires consolidation algorithms to select heterogeneous VMs to host containers and consolidate VMs to PMs simultaneously. Existing server consolidation techniques in VM-based clouds can hardly be applied because of the two-level architecture of the container-based clouds. This paper proposes a novel genetic algorithm (GA) with dual chromosome representation to solve the problem. The experiments show that the proposed GA achieves significantly higher energy efficiency than the compared state-of-the-art algorithms on a wide range of test problems.
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots

Arshit Gupta, Peng Zhang, Garima Lalwani, Mona Diab. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Eating and drinking gesture spotting and recognition using a novel adaptive segmentation technique and a gesture discrepancy measure

Despite the increasing developments on human activity recognition using wearable technology, there are still many open challenges in spotting and recognising sporadic gestures. As opposed to activities, which exhibit continuous behaviour, the difficulty of spotting gestures lies in their rather sparse nature. This paper proposes a novel solution to spot and recognise a set of similar eating and drinking gestures from continuous inertial data streams. First, potential segments containing an eating or a drinking gesture are found using a Crossings-based Adaptive Segmentation Technique (CAST). Second, further to the long-established range of features employed in previous human activities recognition research work, a gesture discrepancy measure is proposed to improve the classification performance of the system. At the final step, a range of state-of-the-art classification models is employed for evaluation. Various conclusions can be drawn from the results obtained. First, given the 100% recall achieved at the segmentation step, the CAST can be considered a reliable segmentation technique for spotting drinking and eating gestures which may be employed in future gesture spotting work. Second, the addition of gesture discrepancy as a feature descriptor consistently improves the classification performance of the system. Third, the reliability of the food and drink intake monitoring approach proposed in this work finds support on the out-performance of previous similar work.
People Detection and Tracking Using LIDAR Sensors

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.