Reviewerly/exHarmony huggingface dataset:
Pattern Matching: A Quantum Oriented Approach

This article influences the quantum based solutions for traditional pattern matching problem, as the quantum machines can accelerate the computation speed by inherently supporting the parallel executions. The problem identifies solution by suggesting effective algorithms, and as per the classical machines several algorithms are qualified and become criterion for other, importantly noted as Knuth Morris Pratt and Boyer Moore which solves such problem in O(M + N) time. A quantum identical pattern matching algorithms are based upon the principle of high speed Grover's quantum search method which searches an element over unstructured database of "N" elements in O(√N) time. The standard quantum algorithm was proposed by Ramesh and Vinay that could obtain computational speed and provides solution in O(√M + √N) time. We review two already existing quantum based exact and approximate pattern matching algorithms and then by combining the logic of both, a new exact pattern matching algorithm is being proposed which overcomes the algorithmic constraints and substantially proves to be equivalently better than the existing classical and quantum benchmark algorithms. So, this article includes existing and proposed quantum pattern matching algorithms, their flowcharts and examples, mathematical justification over complexity analysis. At last we discuss suitable application domains and further possible variations over the proposed work.
Bagging BERT Models for Robust Aggression Identification

Modern transformer-based models with hundreds of millions of parameters, such as BERT, achieve impressive results at text classification tasks. This also holds for aggression identification and offensive language detection, where deep learning approaches consistently outperform less complex models, such as decision trees. While the complex models fit training data well (low bias), they also come with an unwanted high variance. Especially when fine-tuning them on small datasets, the classification performance varies significantly for slightly different training data. To overcome the high variance and provide more robust predictions, we propose an ensemble of multiple fine-tuned BERT models based on bootstrap aggregating (bagging). In this paper, we describe such an ensemble system and present our submission to the shared tasks on aggression identification 2020 (team name: Julian). Our submission is the best-performing system for five out of six subtasks. For example, we achieve a weighted F1-score of 80.3% for task A on the test dataset of English social media posts. In our experiments, we compare different model configurations and vary the number of models used in the ensemble. We find that the F1-score drastically increases when ensembling up to 15 models, but the returns diminish for more models.
Sponge Examples: Energy-Latency Attacks on Neural Networks

The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully crafted $\boldsymbol{sponge}~\boldsymbol{examples}$, which are inputs designed to maximise energy consumption and latency. We mount two variants of this attack on established vision and language models, increasing energy consumption by a factor of 10 to 200. Our attacks can also be used to delay decisions where a network has critical real-time performance, such as in perception for autonomous vehicles. We demonstrate the portability of our malicious inputs across CPUs and a variety of hardware accelerator chips including GPUs, and an ASIC simulator. We conclude by proposing a defense strategy which mitigates our attack by shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective.
Auto-Tuning Structured Light by Optical Stochastic Gradient Descent

We consider the problem of optimizing the performance of an active imaging system by automatically discovering the illuminations it should use, and the way to decode them. Our approach tackles two seemingly incompatible goals: (1) ''tuning'' the illuminations and decoding algorithm precisely to the devices at hand---to their optical transfer functions, non-linearities, spectral responses, image processing pipelines---and (2) doing so without modeling or calibrating the system; without modeling the scenes of interest; and without prior training data. The key idea is to formulate a stochastic gradient descent (SGD) optimization procedure that puts the actual system in the loop: projecting patterns, capturing images, and calculating the gradient of expected reconstruction error. We apply this idea to structured-light triangulation to ''auto-tune'' several devices---from smartphones and laser projectors to advanced computational cameras. Our experiments show that despite being model-free and automatic, optical SGD can boost system 3D accuracy substantially over state-of-the-art coding schemes.
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a scheduler. The protocol and scheduler jointly determine which agent is communicating what message and to whom. Under the limited bandwidth constraint, a communication protocol is required to generate informative messages. Meanwhile, an unnecessary communication connection should not be established because it occupies limited resources in vain. In this paper, we develop an Informative Multi-Agent Communication (IMAC) method to learn efficient communication protocols as well as scheduling. First, from the perspective of communication theory, we prove that the limited bandwidth constraint requires low-entropy messages throughout the transmission. Then inspired by the information bottleneck principle, we learn a valuable and compact communication protocol and a weight-based scheduler. To demonstrate the efficiency of our method, we conduct extensive experiments in various cooperative and competitive multi-agent tasks with different numbers of agents and different bandwidths. We show that IMAC converges faster and leads to efficient communication among agents under the limited bandwidth as compared to many baseline methods.
Gender Classification and Bias Mitigation in Facial Images

Gender classification algorithms have important applications in many domains today such as demographic research, law enforcement, as well as human-computer interaction. Recent research showed that algorithms trained on biased benchmark databases could result in algorithmic bias. However, to date, little research has been carried out on gender classification algorithms' bias towards gender minorities subgroups, such as the LGBTQ and the non-binary population, who have distinct characteristics in gender expression. In this paper, we began by conducting surveys on existing benchmark databases for facial recognition and gender classification tasks. We discovered that the current benchmark databases lack representation of gender minority subgroups. We worked on extending the current binary gender classifier to include a non-binary gender class. We did that by assembling two new facial image databases: 1) a racially balanced inclusive database with a subset of LGBTQ population 2) an inclusive-gender database that consists of people with non-binary gender. We worked to increase classification accuracy and mitigate algorithmic biases on our baseline model trained on the augmented benchmark database. Our ensemble model has achieved an overall accuracy score of 90.39%, which is a 38.72% increase from the baseline binary gender classifier trained on Adience. While this is an initial attempt towards mitigating bias in gender classification, more work is needed in modeling gender as a continuum by assembling more inclusive databases.
Mobile sensor based human activity recognition: distinguishing of challenging activities by applying long short-term memory deep learning modified by residual network concept

Automated recognition of daily human tasks is a novel method for continuous monitoring of the health of elderly people. Nowadays mobile devices (i.e. smartphone and smartwatch) are equipped with a variety of sensors, therefore activity classification algorithms have become as useful, low-cost, and non-invasive diagnostic modality to implement as mobile software. The aim of this article is to introduce a new deep learning structure for recognizing challenging (i.e. similar) human activities based on signals which have been recorded by sensors mounted on mobile devices. In the proposed structure, the residual network concept is engaged as a new substructure inside the main proposed structure. This part is responsible to address the problem of accuracy saturation in convolutional neural networks, thanks to its ability in jump over some layers which leads to reducing vanishing gradients effect. Therefore the accuracy of the classification of several activities is increased by using the proposed structure. Performance of the proposed method is evaluated on real life recorded signals and is compared with existing techniques in two different scenarios. The proposed structure is applied on two well-known human activity datasets that have been prepared in university of Fordham. The first dataset contains the recorded signals which arise from six different activities including walking, jogging, upstairs, downstairs, sitting, and standing. The second dataset also contains walking, jogging, stairs, sitting, standing, eating soup, eating sandwich, and eating chips. In the first scenario, the performance of the proposed structures is compared with deep learning schemes. The obtained results show that the proposed method may improve the recognition rate at least 5% for the first dataset against its own family alternatives in distinguishing challenging activities (i.e. downstairs and upstairs). For the second data set similar improvements is obtained for some challenging activities (i.e. eating sandwich and eating chips). These superiorities even reach to at least 28% when the capability of the proposed method in recognizing downstairs and upstairs is compared to its non-family methods for the first dataset. Increasing the recognition rate of the proposed method for challenging activities (i.e. downstairs and upstairs, eating sandwich and eating chips) in parallel with its acceptable performance for other non-challenging activities shows its effectiveness in mobile sensor-based health monitoring systems.
Automatic segmentation of left ventricle using parallel end–end deep convolutional neural networks framework

Under the background of high incidence and mortality of cardiovascular diseases, the accurate and automatic left ventricle (LV) segmentation method is of essential importance for the diagnosis of the cardiovascular system. However, fully automatic LV segmentation remains challenging due to the complex structure of cardiac magnetic resonance image (MRI) and the morphological changes of LV caused by various cardiovascular diseases. In this paper, we propose a novel parallel end-to-end convolutional neural network (CNN) for LV segmentation. Our network consists of two interactive subnetworks which utilize essentially identical but formally different labels in the hope that they can learn segmentation from different perspectives. The two subnetworks take the same cardiac MRI as input and output a pair of segmentation maps in different forms. After averaging the two segmentation maps obtained from the two subnetworks, we get the final contours of the endocardium (endo) and epicardium (epi) simultaneously. The proposed method is evaluated on the dataset provided by the Left Ventricle Full Quantification Challenge of MICCAI 2019. The average Dice scores on epi, endo, and myocardium (myo) reach 0.961, 0.949, and 0.867 respectively which outperform the other methods. The experimental results show that our method has the potential for clinical application.
A Template for Data-Driven Personas: Analyzing 31 Quantitatively Oriented Persona Profiles

Following the proliferation of personified big data and data science algorithms, data-driven user personas (DDPs) are becoming more common in persona design. However, the DDP templates are seemingly diverse and fragmented, prompting a need for a synthesis of the information included in these personas. Analyzing 31 templates for DDPs, we find that DDPs vary greatly by their information richness, as the most informative layout has more than 300% more information categories than the least informative layout. We also find that graphical complexity and information richness do not necessarily correlate. Furthermore, the chosen persona development method may carry over to the information presentation, with quantitative data typically presented as scores, metrics, or tables and qualitative data as text-rich narratives. We did not find one "general template" for DDPs and defining this is difficult due to the variety of the outputs of different methods as well as different information needs of the persona users.
Performance Analysis of Distributed Computing Frameworks for Big Data Analytics: Hadoop Vs Spark

In the last one decade, the tremendous growth in data emphasizes big data storage and management issues with the highest priorities. For providing better support to software developers for dealing with big data problems, new programming platforms are continuously developing and Hadoop MapReduce is a big game-changer followed by Spark which sets the world of big data on fire with its processing speed and comfortable APIs. Hadoop framework emerged as a leading tool based on the MapReduce programming model with a distributed file system. Spark is on the other hand, recently developed big data analysis and management framework used to explore unlimited underlying features of Big Data. In this research work, a comparative analysis of Hadoop MapReduce and Spark has been presented on the basis of working principle, performance, cost, ease of use, compatibility, data processing, failure tolerance, and security. Experimental analysis has been performed to observe the performance of Hadoop MapReduce and Spark for establishing their suitability under different constraints of the distributed computing environment.
The effectiveness of data augmentation in code readability classification

Context: Training deep learning models for code readability classification requires large datasets of quality pre-labeled data. However, it is almost always time-consuming and expensive to acquire readability data with manual labels.Objective: We thus propose to introduce data augmentation approaches to artificially increase the size of training set, this is to reduce the risk of overfitting caused by the lack of readability data and further improve the classification accuracy as the ultimate goal.Method: We create transformed versions of code snippets by manipulating original data from aspects such as comments, indentations, and names of classes/methods/variables based on domain-specific knowledge. In addition to basic transformations, we also explore the use of Auxiliary Classifier GANs to produce synthetic data.Results: To evaluate the proposed approach, we conduct a set of experiments. The results show that the classification performance of deep neural networks can be significantly improved when they are trained on the augmented corpus, achieving a state-of-the-art accuracy of 87.38%.Conclusion:We consider the findings of this study as primary evidence of the effectiveness of data augmentation in the field of code readability classification.
CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus

Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets. Nevertheless, current datasets cover a limited number of languages. With the aim to foster research in massive multilingual speech translation and speech translation for low resource language pairs, we release CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. Data sanity checks provide evidence about the quality of the data, which is released under CC0 license. We also provide extensive speech recognition, bilingual and multilingual machine translation and speech translation baselines.
A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service. Nevertheless, significant concerns on privacy are raised as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secured face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, a low-complexity encrypting algorithm is developed based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. Furthermore, it is proved that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity and avoid big data transmission between edge and cloud, a distributed learning framework is established, which extracts deeper features in an intermediate space, expanded according to the dictionaries from each device. Classification is performed in this new feature space to combat the noise and modeling error. Finally, the efficiency and effectiveness of the proposed framework is demonstrated through simulation results.
An unsupervised orthogonal rotation invariant moment based fuzzy C-means approach for the segmentation of brain magnetic resonance images

Brain magnetic resonance images (MRI) suffer from many artifacts such as noise and intensity inhomogeneity. Moreover, they contain an abundant amount of fine image structures, edges, and corners in various areas of the image. These anomalies and structural complexities affect the segmentation process of the brain MRI which is required by physicians for the diagnosis purpose. Recently, we have proposed a local Zernike moment (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach that has dealt with the noise artifact in the moment domain using the LZM approach. The method provides high segmentation results for the MR images corrupted with Rician noise. However, the method does not deal with the intensity inhomogeneity artifact effectively. Moreover, the method uses a regularization parameter that needs to be adjusted to obtain effective segmentation results. This paper presents an unsupervised local Zernike moment and unbiased nonlocal means-based bias corrected fuzzy C-means (LZM-UNLM-BCFCM) approach that deals with both noise and intensity inhomogeneity artifacts. The main concept behind the proposed method is to use the attractive properties of the LZMs to effectively filter the image by determining a large number of similar regions in an MR image which is mostly corrupted by Rician noise and intensity inhomogeneity. The ability of the LZMs to determine such regions in MR images consisting of fine tissue structures in any orientation is well utilized for dealing with the high levels of noise. The intensity inhomogeneity is removed by estimating the bias field pixel-by-pixel during the segmentation process using the filtered image without the use of regularization parameter. The bias field is estimated as a linear combination of the orthogonal polynomials in which the weights are obtained by minimizing the fuzzy objective function. Experimental results on both simulated and real MR images show the superiority of the proposed method as compared to other unsupervised state-of-the-art approaches.
A convolutional neural network with sparse representation

This paper proposes a sparse representation layer in the feature extraction stage of a convolutional neural network (CNN). Our goal is to add sparse transforms to a target network to improve its performance without introducing an extra calculation burden. First, the proposed method was achieved by inserting the sparse representation layers into a target network’s shallow layers, and the network was trained end-to-end using a supervised learning algorithm. Second, In the forward pass the network captured the features through the convolutional layers and sparse representation layers accomplished with wavelet and shearlet transforms. Thirdly, in the backward pass the weights of the learned kernels of the network were updated through a back-propagated error, while the sparse representation layers were fixed and did not require updating. The proposed method was verified on five datasets with the task of image classification: FOOD-101, CIFAR10/100, DTD, Brodatz and ImageNet. The experimental results show that the proposed method leads to higher recognition accuracy in image classification, and the additional computational cost is relatively small compared to the baseline CNN model.
A Scalable Blockchain-based Approach for Authentication and Access Control in Software Defined Vehicular Networks

Software Defined Vehicular Networking (SDVN) could be the future of the vehicular networks, enabling interoperability between heterogeneous networks and mobility management. Thus, the deployment of large SDVN is considered. However, SDVN is facing major security issues, in particular, authentication and access control issues. Indeed, an unauthorized SDN controller could modify the behavior of switches (packet redirection, packet drops) and an unauthorized switch could disrupt the operation of the network (reconnaissance attack, malicious feedback). Due to the SDVN features (decentralization, mobility) and the SDVN requirements (flexibility, scalability), the Blockchain technology appears to be an efficient way to solve these authentication and access control issues. Therefore, many Blockchain-based approaches have already been proposed. However, two key challenges have not been addressed: authentication and access control for SDN controllers and high scalability for the underlying Blockchain network. That is why in this paper we propose an innovative and scalable architecture, based on a set of interconnected Blockchain sub-networks. Moreover, an efficient access control mechanism and a cross-sub-networks authentication/revocation mechanism are proposed for all SDVN devices (vehicles, roadside equipment, SDN controllers). To demonstrate the benefits of our approach, its performances are compared with existing solutions in terms of throughput, latency, CPU usage and read/write access to the Blockchain ledger. In addition, we determine an optimal number of Blockchain sub-networks according to different parameters such as the number of certificates to store and the number of requests to process.