OpenRaiser/Intern-Atlas huggingface dataset:
On Relations Between the Relative entropy and $χ^2$-Divergence, Generalizations and Applications
Venue: arXiv cs.IT 2020
Year: 2020

The relative entropy and chi-squared divergence are fundamental divergence measures in information theory and statistics. This paper is focused on a study of integral relations between the two divergences, the implications of these relations, their information-theoretic applications, and some generalizations pertaining to the rich class of $f$-divergences. Applications that are studied in this paper refer to lossless compression, the method of types and large deviations, strong~data-processing inequalities, bounds on contraction coefficients and maximal correlation, and the convergence rate to stationarity of a type of discrete-time Markov chains.
Applications of shapelet transform to time series classification of earthquake, wind and wave data
Venue: arXiv cs.LG 2020
Year: 2020

Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.
Hybrid Control from Scratch: A Design Methodology for Assured Robotic Missions
Venue: arXiv cs.RO 2020
Year: 2020

Robotic research over the last decades have lead us to different architectures to automatically synthesise discrete event controllers and implement these motion and task plans in real-world robot scenarios. However, these architectures usually build on existing robot hardware, generating as a result solutions that are influenced and/or restricted in their design by the available capabilities and sensors. In contrast to these approaches, we propose a design methodology that, given a specific domain of application, allowed us to build the first end-to-end implementation of an autonomous robot system that uses discrete event controller synthesis to generate assured mission plans. We validate this robot system in several missions of our target domain of application.
Rapidly Bootstrapping a Question Answering Dataset for COVID-19
Venue: arXiv cs.CL 2020
Year: 2020

We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is the first publicly available resource of its type, and intended as a stopgap measure for guiding research until more substantial evaluation resources become available. While this dataset, comprising 124 question-article pairs as of the present version 0.1 release, does not have sufficient examples for supervised machine learning, we believe that it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. This paper describes our methodology for constructing the dataset and presents the effectiveness of a number of baselines, including term-based techniques and various transformer-based models. The dataset is available at http://covidqa.ai/
Extending the Lora modulation to add further parallel channels and improve the LoRaWAN network performance
Venue: arXiv cs.NI 2020
Year: 2020

In this paper we present a new modulation, called DLoRa, similar in principle to the conventional LoRa modulation and compatible with it in terms of bandwidth and numerology. DLoRa departs from the conventional LoRa modulation as it is using a decreasing instantaneous frequency in the chirps instead of an increasing one as for the conventional LoRa modulation. Furthermore we describe a software environment to accurately evaluate the "isolation" of the different virtual channels created both by LoRa and DLoRa when using different Spreading Factors. Our results are in agreement with the ones present in literature for the conventional LoRa modulation and show that it is possible to double the number of channels by using simultaneously LoRa and DLora. The higher (double) number of subchannels available is the key to improve the network level performance of LoRa based networks.
Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into embedding copyright protection for neural networks has been limited. One of the main methods for achieving such protection involves relying on the susceptibility of neural networks to backdoor attacks, but the robustness of these tactics has been primarily evaluated against pruning, fine-tuning, and model inversion attacks. In this work, we propose a neural network "laundering" algorithm to remove black-box backdoor watermarks from neural networks even when the adversary has no prior knowledge of the structure of the watermark. We are able to effectively remove watermarks used for recent defense or copyright protection mechanisms while achieving test accuracies above 97% and 80% for both MNIST and CIFAR-10, respectively. For all backdoor watermarking methods addressed in this paper, we find that the robustness of the watermark is significantly weaker than the original claims. We also demonstrate the feasibility of our algorithm in more complex tasks as well as in more realistic scenarios where the adversary is able to carry out efficient laundering attacks using less than 1% of the original training set size, demonstrating that existing backdoor watermarks are not sufficient to reach their claims.
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
Venue: arXiv cs.CV 2020
Year: 2020

Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surrounding environment. But the detection in 3D point cloud still needs a strong algorithmic challenge. This paper consists of three parts.(1)Lidar-camera calib. (2)YOLO, based detection and PointCloud extraction, (3) k-means based point cloud segmentation. In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar. By comparing whether 2D coordinate transferred from the 3D point is in the object bounding box or not, and doing a k-means clustering can achieve High-speed 3D object recognition function in GPU.
Jealousy-freeness and other common properties in Fair Division of Mixed Manna
Venue: arXiv cs.GT 2020
Year: 2020

We consider a fair division setting where indivisible items are allocated to agents. Each agent in the setting has strictly negative, zero or strictly positive utility for each item. We, thus, make a distinction between items that are good for some agents and bad for other agents (i.e. mixed), good for everyone (i.e. goods) or bad for everyone (i.e. bads). For this model, we study axiomatic concepts of allocations such as jealousy-freeness up to one item, envy-freeness up to one item and Pareto-optimality. We obtain many new possibility and impossibility results in regard to combinations of these properties. We also investigate new computational tasks related to such combinations. Thus, we advance the state-of-the-art in fair division of mixed manna.
Introducing Virtual Security Functions into Latency-aware Placement for NFV Applications
Venue: arXiv cs.NI 2020
Year: 2020

The shift towards a completely virtualized networking environment is triggered by the emergence of software defined networking and network function virtualization (NFV). Network service providers have unlocked immense capabilities by these technologies, which have enabled them to dynamically adapt to user needs by deploying their network services in real-time through generating Service Function Chain (SFCs). However, NFV still faces challenges that hinder its full potentials, including availability guarantees, network security, and other performance requirements. For this reason, the deployment of NFV applications remains critical as it should meet different service level agreements while insuring the security of the virtualized functions. In this paper, we tackle the challenge of securing these SFCs by introducing virtual security functions (VSFs) into the latencyaware deployment of NFV applications. This work insures the optimal placement of the SFC components including the security functions while considering the performance constraints and the VSFs' operational rules such as, functions' alliance, proximity, and anti-affinity. This paper develops a mixed integer linear programming model to optimally place all the requested SFCs while satisfying the above constraints and minimizing the latency of every SFC and the intercommunication delay between the SFC components. The simulations are evaluated against a greedy algorithm on the virtualized Evolved Packet Core use case and have shown promising results in maintaining the security rules while achieving minimum delays.
Having our omic cake and eating it too: Evaluating User Response to using Blockchain Technology for Private & Secure Health Data Management and Sharing
Venue: arXiv cs.CR 2020
Year: 2020

This paper reports on the development and evaluation of a prototype blockchain solution for private and secure individual omics health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful user interface design of a blockchain solution.
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
Venue: arXiv cs.CR 2020
Year: 2020

Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features. In practice, most of the ML classifications only provide a binary label to mobile users and app security analysts. However, stakeholders are more interested in the reason why apps are classified as malicious in both academia and industry. This belongs to the research area of interpretable ML but in a specific research domain (i.e., mobile malware detection). Although several interpretable ML methods have been exhibited to explain the final classification results in many cutting-edge Artificial Intelligent (AI) based research fields, till now, there is no study interpreting why an app is classified as malware or unveiling the domain-specific challenges. In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile. (1) The first classification phase of XMal hinges multi-layer perceptron (MLP) and attention mechanism, and also pinpoints the key features most related to the classification result. (2) The second interpreting phase aims at automatically producing neural language descriptions to interpret the core malicious behaviors within apps. We evaluate the behavior description results by comparing with the existing interpretable ML-based methods (i.e., Drebin and LIME) to demonstrate the effectiveness of XMal. We find that XMal is able to reveal the malicious behaviors more accurately. Additionally, our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers. Our study peeks into the interpretable ML through the research of Android malware detection and analysis.
Dropout as an Implicit Gating Mechanism For Continual Learning
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, neural networks have demonstrated an outstanding ability to achieve complex learning tasks across various domains. However, they suffer from the "catastrophic forgetting" problem when they face a sequence of learning tasks, where they forget the old ones as they learn new tasks. This problem is also highly related to the "stability-plasticity dilemma". The more plastic the network, the easier it can learn new tasks, but the faster it also forgets previous ones. Conversely, a stable network cannot learn new tasks as fast as a very plastic network. However, it is more reliable to preserve the knowledge it has learned from the previous tasks. Several solutions have been proposed to overcome the forgetting problem by making the neural network parameters more stable, and some of them have mentioned the significance of dropout in continual learning. However, their relationship has not been sufficiently studied yet. In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active. Our experiments show that the stability achieved by this implicit gating plays a very critical role in leading to performance comparable to or better than other involved continual learning algorithms to overcome catastrophic forgetting.
Generative Data Augmentation for Commonsense Reasoning
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on. We investigate G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. Our approach generates synthetic examples using pretrained language models, and selects the most informative and diverse set of examples for data augmentation. In experiments with multiple commonsense reasoning benchmarks, G-DAUG^C consistently outperforms existing data augmentation methods based on back-translation, and establishes a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA. Further, in addition to improvements in in-distribution accuracy, G-DAUG^C-augmented training also enhances out-of-distribution generalization, showing greater robustness against adversarial or perturbed examples. Our analysis demonstrates that G-DAUG^C produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance. Our findings encourage future research toward generative data augmentation to enhance both in-distribution learning and out-of-distribution generalization.
High performance SIMD modular arithmetic for polynomial evaluation
Venue: arXiv cs.DC 2020
Year: 2020

Two essential problems in Computer Algebra, namely polynomial factorization and polynomial greatest common divisor computation, can be efficiently solved thanks to multiple polynomial evaluations in two variables using modular arithmetic. In this article, we focus on the efficient computation of such polynomial evaluations on one single CPU core. We first show how to leverage SIMD computing for modular arithmetic on AVX2 and AVX-512 units, using both intrinsics and OpenMP compiler directives. Then we manage to increase the operational intensity and to exploit instruction-level parallelism in order to increase the compute efficiency of these polynomial evaluations. All this results in the end to performance gains up to about 5x on AVX2 and 10x on AVX-512.
Dynamic Sampling for Deep Metric Learning
Venue: arXiv cs.CV 2020
Year: 2020

Deep metric learning maps visually similar images onto nearby locations and visually dissimilar images apart from each other in an embedding manifold. The learning process is mainly based on the supplied image negative and positive training pairs. In this paper, a dynamic sampling strategy is proposed to organize the training pairs in an easy-to-hard order to feed into the network. It allows the network to learn general boundaries between categories from the easy training pairs at its early stages and finalize the details of the model mainly relying on the hard training samples in the later. Compared to the existing training sample mining approaches, the hard samples are mined with little harm to the learned general model. This dynamic sampling strategy is formularized as two simple terms that are compatible with various loss functions. Consistent performance boost is observed when it is integrated with several popular loss functions on fashion search, fine-grained classification, and person re-identification tasks.
Enabling Big Data Analytics at Manufacturing Fields of Farplas Automotive
Venue: arXiv cs.DC 2020
Year: 2020

Digitization and data-driven manufacturing process is needed for today's industry. The term Industry 4.0 stands for today industrial digitization which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer's high-quality expectations. However, due to the increase in the number of connected devices and the variety of data, it has become difficult to store and analyze data with conventional systems. The motivation of this paper is to provide an overview of the understanding of the big data pipeline, providing a real-time on-premise data acquisition, data compression, data storage and processing with Apache Kafka and Apache Spark implementation on Apache Ha-doop cluster, and identifying the challenges and issues occurring with implementation the Farplas manufacturing company, which is one of the biggest Tier 1 automotive supplier in Turkey, to study the new trends and streams related to topics via Industry 4.0.
Deep Learning for One-dimensional Consolidation
Venue: arXiv cs.CE 2020
Year: 2020

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research. In line with such efforts, a deep learning model for one-dimensional consolidation where the governing equation is applied as a constraint in the neural network is presented here. A review of related research is first presented and discussed. The deep learning model relies on automatic differentiation for applying the governing equation as a constraint. The total loss is measured as a combination of the training loss (based on analytical and model predicted solutions) and the constraint loss (a requirement to satisfy the governing equation). Two classes of problems are considered: forward and inverse problems. The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for one-dimensional consolidation problems. Inverse problems show prediction of the coefficient of consolidation. Terzaghi's problem with varying boundary conditions are used as example and the deep learning model shows a remarkable performance in both the forward and inverse problems. While the application demonstrated here is a simple one-dimensional consolidation problem, such a deep learning model integrated with a physical law has huge implications for use in, such as, faster real-time numerical prediction for digital twins, numerical model reproducibility and constitutive model parameter optimization.
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Venue: arXiv cs.CV 2020
Year: 2020

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and middle income countries. This has inspired the development of machine learning based automation of the screening process. While recent efforts demonstrate a performance benchmark using an ensemble of deep convolutional neural networks (CNN), our systematic search over multiple standard CNN architectures identified single candidate CNN models whose classification performances were found to be at par with ensembles. Over 63 experiments spanning 400 hours, executed on a 11:3 FP32 TensorTFLOPS compute system, we found the Xception and ResNet-18 architectures to be consistent performers in identifying co-existing disease conditions with an average AUC of 0.87 across nine pathologies. We conclude on the reliability of the models by assessing their saliency maps generated using the randomized input sampling for explanation (RISE) method and qualitatively validating them against manual annotations locally sourced from an experienced Radiologist. We also draw a critical note on the limitations of the publicly available CheXpert dataset primarily on account of disparity in class distribution in training vs. testing sets, and unavailability of sufficient samples for few classes, which hampers quantitative reporting due to sample insufficiency.
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
Venue: arXiv cs.IR 2020
Year: 2020

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.