OpenRaiser/Intern-Atlas huggingface dataset:
End-to-end-Architekturen zur Datenmonetarisierung im IIoT. Konzepte und Implementierungen
Venue: arXiv cs.NI 2020
Year: 2020

The value creation potential of the Internet of Things (IoT), that is the connection of arbitrary objects to the Internet, lies in the creation of business benefits through accessing and processing the circa 80 Zettabytes (1 ZB = 10^21 Bytes) of data produced by an estimated 40 billions of IoT endpoints (prognosis for 2025). This contribution derives and presents the information technology-related fundament and basis required to be able to reap this potential. Quantity and heterogeneity of the devices and machines especially encountered in the industry at large in the so-called industrial IoT (IIoT) require the use of a typically cloud-based IoT platform for logical concentration and more efficient management of the -- unavoidable in industry -- complexity. Stringent non-functional requirements especially regarding (low) latency, (high) bandwidth, access to large processing capacities, and security and privacy-related aspects necessitate the deployment of intermediary IoT gateways endowed with different capability sets in the edge continuum between IoT endpoints and the IoT platform in the cloud. This will be illustrated in the form of two use cases from corporate projects using a component architecture view point. Finally we argue that this classical concept of IoT projects needs to be strategically widened towards application integration (key word: IT/OT integration) and API management resulting in the coupling of a suitable integration and API management platforms to the IoT platform in order to use this end-to-end understanding of IoT/IIoT to fully leverage the innovation-stimulating and transformational character of IIoT and Industry 4.0.
Dynamically Throttleable Neural Networks (TNN)
Venue: arXiv cs.LG 2020
Year: 2020

Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network. In this work, we present a runtime throttleable neural network (TNN) that can adaptively self-regulate its own performance target and computing resources. We designed TNN with several properties that enable more flexibility for dynamic execution based on runtime context. TNNs are defined as throttleable modules gated with a separately trained controller that generates a single utilization control parameter. We validate our proposal on a number of experiments, including Convolution Neural Networks (CNNs such as VGG, ResNet, ResNeXt, DenseNet) using CiFAR-10 and ImageNet dataset, for object classification and recognition tasks. We also demonstrate the effectiveness of dynamic TNN execution on a 3D Convolustion Network (C3D) for a hand gesture task. Results show that TNN can maintain peak accuracy performance compared to vanilla solutions, while providing a graceful reduction in computational requirement, down to 74% reduction in latency and 52% energy savings.
Comparing the Results of Replications in Software Engineering
Venue: arXiv cs.SE 2020
Year: 2020

Context: It has been argued that software engineering replications are useful for verifying the results of previous experiments. However, it has not yet been agreed how to check whether the results hold across replications. Besides, some authors suggest that replications that do not verify the results of previous experiments can be used to identify contextual variables causing the discrepancies. Objective: Study how to assess the (dis)similarity of the results of SE replications when they are compared to verify the results of previous experiments and understand how to identify whether contextual variables are influencing results. Method: We run simulations to learn how different ways of comparing replication results behave when verifying the results of previous experiments. We illustrate how to deal with context-induced changes. To do this, we analyze three groups of replications from our own research on test-driven development and testing techniques. Results: The direct comparison of p-values and effect sizes does not appear to be suitable for verifying the results of previous experiments and examining the variables possibly affecting the results in software engineering. Analytical methods such as meta-analysis should be used to assess the similarity of software engineering replication results and identify discrepancies in results. Conclusion: The results achieved in baseline experiments should no longer be regarded as a result that needs to be reproduced, but as a small piece of evidence within a larger picture that only emerges after assembling many small pieces to complete the puzzle.
Postulate-based proof of the P != NP hypothesis
Venue: arXiv cs.CC 2020
Year: 2020

The paper contains a proof for the P != NP hypothesis with the help of the two "natural" postulates. The postulates restrict capacity of the Turing machines and state that each independent and necessary condition of the problem should be considered by a solver (Turing machine) individually, not in groups. That is, a solver should spend at least one step to deal with the condition and, therefore, if the amount of independent conditions is exponentially growing with polynomially growing problem sizes then exponential time is needed to find a solution. With the postulates, it is enough to build a natural (not pure mathematical) proof that P != NP.
Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
Venue: arXiv cs.SI 2020
Year: 2020

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded.
Hyperrealistic Image Inpainting with Hypergraphs
Venue: arXiv cs.CV 2020
Year: 2020

Image inpainting is a non-trivial task in computer vision due to multiple possibilities for filling the missing data, which may be dependent on the global information of the image. Most of the existing approaches use the attention mechanism to learn the global context of the image. This attention mechanism produces semantically plausible but blurry results because of incapability to capture the global context. In this paper, we introduce hypergraph convolution on spatial features to learn the complex relationship among the data. We introduce a trainable mechanism to connect nodes using hyperedges for hypergraph convolution. To the best of our knowledge, hypergraph convolution have never been used on spatial features for any image-to-image tasks in computer vision. Further, we introduce gated convolution in the discriminator to enforce local consistency in the predicted image. The experiments on Places2, CelebA-HQ, Paris Street View, and Facades datasets, show that our approach achieves state-of-the-art results.
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate PRNGs quality by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state. We use a Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time steps, by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP's states. We show that modeling a PRNG with a partially observable MDP and a LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work.
MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision
Venue: arXiv cs.CL 2020
Year: 2020

The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus. Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component. Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Venue: arXiv cs.CV 2020
Year: 2020

Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Venue: arXiv cs.CV 2020
Year: 2020

Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors. While there exist a number of studies on the problem of inferring the position and shape of vehicle actors, understanding actors' orientation remains a challenge for existing state-of-the-art detectors. Orientation is an important property for downstream modules of an autonomous system, particularly relevant for motion prediction of stationary or reversing actors where current approaches struggle. We focus on this task and present a method that extends the existing models that perform joint object detection and motion prediction, allowing us to more accurately infer vehicle orientations. In addition, the approach is able to quantify prediction uncertainty, outputting the probability that the inferred orientation is flipped, which allows for improved motion prediction and safer autonomous operations. Empirical results show the benefits of the approach, obtaining state-of-the-art performance on the open-sourced nuScenes data set.
Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining
Venue: arXiv cs.DC 2020
Year: 2020

Graph pattern mining (GPM) is used in diverse application areas including social network analysis, bioinformatics, and chemical engineering. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide low-level interfaces that can express a wide variety of GPM algorithms at the cost of increased programming complexity. Moreover, existing systems lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications. We present Sandslash, an in-memory Graph Pattern Mining (GPM) framework that uses a novel programming interface to support productive, expressive, and efficient GPM on large graphs. Sandslash provides a high-level API that needs only a specification of the GPM problem, and it implements fast subgraph enumeration, provides efficient data structures, and applies high-level optimizations automatically. To achieve performance competitive with expert-optimized implementations, Sandslash also provides a low-level API that allows users to express algorithm-specific optimizations. This enables Sandslash to support both high-productivity and high-efficiency without losing expressiveness. We evaluate Sandslash on shared-memory machines using five GPM applications and a wide range of large real-world graphs. Experimental results demonstrate that applications written using Sandslash high-level or low-level API outperforms state-of-the-art GPM systems AutoMine, Pangolin, and Peregrine on average by 13.8x, 7.9x, and 5.4x, respectively. We also show that these Sandslash applications outperform expert-optimized GPM implementations by 2.3x on average with less programming effort.
Confusable Learning for Large-class Few-Shot Classification
Venue: arXiv cs.CV 2020
Year: 2020

Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., the large-class few-shot scenario. In this novel scenario, existing approaches do not perform well because they ignore confusable classes, namely similar classes that are difficult to distinguish from each other. These classes carry more information. In this paper, we propose a biased learning paradigm called Confusable Learning, which focuses more on confusable classes. Our method can be applied to mainstream meta-learning algorithms. Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset. Such a confusion matrix helps meta learners to emphasize on confusable classes. Comprehensive experiments on Omniglot, Fungi, and ImageNet demonstrate the efficacy of our method over state-of-the-art baselines.
Explainable AI meets Healthcare: A Study on Heart Disease Dataset
Venue: arXiv cs.LG 2020
Year: 2020

With the increasing availability of structured and unstructured data and the swift progress of analytical techniques, Artificial Intelligence (AI) is bringing a revolution to the healthcare industry. With the increasingly indispensable role of AI in healthcare, there are growing concerns over the lack of transparency and explainability in addition to potential bias encountered by predictions of the model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by medical practitioners as well as AI researchers, and thus, eventually, leads to an increasingly widespread deployment of AI in healthcare. In this paper, we present different interpretability techniques. The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques available which can be very advantageous in the health-care domain. Medical diagnosis model is responsible for human life and we need to be confident enough to treat a patient as instructed by a black-box model. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness while using AI systems in healthcare.
Optimal Online Algorithms for File-Bundle Caching and Generalization to Distributed Caching
Venue: arXiv cs.DS 2020
Year: 2020

We consider a generalization of the standard cache problem called file-bundle caching, where different queries (tasks), each containing $l\ge 1$ files, sequentially arrive. An online algorithm that does not know the sequence of queries ahead of time must adaptively decide on what files to keep in the cache to incur the minimum number of cache misses. Here a cache miss refers to the case where at least one file in a query is missing among the cache files. In the special case where $l=1$, this problem reduces to the standard cache problem. We first analyze the performance of the classic least recently used (LRU) algorithm in this setting and show that LRU is a near-optimal online deterministic algorithm for file-bundle caching with regard to competitive ratio. We then extend our results to a generalized $(h,k)$-paging problem in this file-bundle setting, where the performance of the online algorithm with a cache size $k$ is compared to an optimal offline benchmark of a smaller cache size $h<k$. In this latter case, we provide a randomized $O(l \ln \frac{k}{k-h})$-competitive algorithm for our generalized $(h,k)$-paging problem, which can be viewed as an extension of the classic marking algorithm. We complete this result by providing a matching lower bound for the competitive ratio, indicating that the performance of this modified marking algorithm is within a factor of two of any randomized online algorithm. Finally, we look at the distributed version of the file-bundle caching problem where there are $m\ge 1$ identical caches in the system. In this case we show that for $m=l+1$ caches, there is a deterministic distributed caching algorithm which is $(l^2+l)$-competitive and a randomized distributed caching algorithm which is $O(l\ln(2l+1))$-competitive when $l\ge 2$.
Hybrid Multicast/Unicast Design in NOMA-based Vehicular Caching System with Supplementary Material
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate a hybrid multicast/ unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart.
Communication-efficient Decentralized Local SGD over Undirected Networks
Venue: arXiv cs.LG 2020
Year: 2020

We consider the distributed learning problem where a network of $n$ agents seeks to minimize a global function $F$. Agents have access to $F$ through noisy gradients, and they can locally communicate with their neighbors a network. We study the Decentralized Local SDG method, where agents perform a number of local gradient steps and occasionally exchange information with their neighbors. Previous algorithmic analysis efforts have focused on the specific network topology (star topology) where a leader node aggregates all agents' information. We generalize that setting to an arbitrary network by analyzing the trade-off between the number of communication rounds and the computational effort of each agent. We bound the expected optimality gap in terms of the number of iterates $T$, the number of workers $n$, and the spectral gap of the underlying network. Our main results show that by using only $R=Ω(n)$ communication rounds, one can achieve an error that scales as $O({1}/{nT})$, where the number of communication rounds is independent of $T$ and only depends on the number of agents. Finally, we provide numerical evidence of our theoretical results through experiments on real and synthetic data.
Power-Aware Run-Time Scheduler for Mixed-Criticality Systems on Multi-Core Platform
Venue: arXiv cs.DC 2020
Year: 2020

In modern multi-core Mixed-Criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power and thermal management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment, that has the most impact on the system peak power and temperature. However, changing the frequency and selecting a proper task for slack assignment and a proper core for task re-mapping at runtime can be time-consuming and may cause deadline violation which is not admissible for high-criticality tasks. Therefore, we analyze and then optimize our run-time scheduler and evaluate it for various platforms. The proposed approach is experimentally validated on the ODROID-XU3 (DVFS-enabled heterogeneous multi-core platform) with various embedded real-time benchmarks. Results show that our heuristic achieves up to 5.25% reduction in system peak power and 20.33\% reduction in maximum temperature compared to an existing method while meeting deadline constraints in different criticality modes.
Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Traditional visual place recognition (VPR) methods generally use frame-based cameras, which is easy to fail due to dramatic illumination changes or fast motions. In this paper, we propose an end-to-end visual place recognition network for event cameras, which can achieve good place recognition performance in challenging environments. The key idea of the proposed algorithm is firstly to characterize the event streams with the EST voxel grid, then extract features using a convolution network, and finally aggregate features using an improved VLAD network to realize end-to-end visual place recognition using event streams. To verify the effectiveness of the proposed algorithm, we compare the proposed method with classical VPR methods on the event-based driving datasets (MVSEC, DDD17) and the synthetic datasets (Oxford RobotCar). Experimental results show that the proposed method can achieve much better performance in challenging scenarios. To our knowledge, this is the first end-to-end event-based VPR method. The accompanying source code is available at https://github.com/kongdelei/Event-VPR.