OpenRaiser/Intern-Atlas huggingface dataset:
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders
Venue: arXiv cs.NE 2021
Year: 2021

In this paper, we present a novel neuroevolutionary method to identify the architecture and hyperparameters of convolutional autoencoders. Remarkably, we used a hypervolume indicator in the context of neural architecture search for autoencoders, for the first time to our current knowledge. Results show that images were compressed by a factor of more than 10, while still retaining enough information to achieve image classification for the majority of the tasks. Thus, this new approach can be used to speed up the AutoML pipeline for image compression.
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Venue: arXiv cs.LG 2021
Year: 2021

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. In particular, our algorithm can also provide the interpretability for Res-Net to a certain extent. From a theoretical perspective, we prove that our method attains linear convergence. In practice, extensive empirical results verify the advantages of our method.
A Simple and Practical Approach to Improve Misspellings in OCR Text
Venue: arXiv cs.CL 2021
Year: 2021

The focus of our paper is the identification and correction of non-word errors in OCR text. Such errors may be the result of incorrect insertion, deletion, or substitution of a character, or the transposition of two adjacent characters within a single word. Or, it can be the result of word boundary problems that lead to run-on errors and incorrect-split errors. The traditional N-gram correction methods can handle single-word errors effectively. However, they show limitations when dealing with split and merge errors. In this paper, we develop an unsupervised method that can handle both errors. The method we develop leads to a sizable improvement in the correction rates. This tutorial paper addresses very difficult word correction problems - namely incorrect run-on and split errors - and illustrates what needs to be considered when addressing such problems. We outline a possible approach and assess its success on a limited study.
Active Exploitation of Redundancies in Reconfigurable Multi-Robot Systems
Venue: arXiv cs.RO 2021
Year: 2021

While traditional robotic systems come with a monolithic system design, reconfigurable multi-robot systems can share and shift physical resources in an on-demand fashion. Multi-robot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this paper details an organization model as basis for planning with reconfigurable multi-robot systems. The model allows to exploit redundancies when optimizing a multi-robot system's probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multi-robot missions. We use a simulated multi-robot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape.
Diversity and density of urban functions in station areas
Venue: arXiv cs.CY 2021
Year: 2021

The diversity and density of urban functions have been known to affect urban vibrancy positively, but the relation between the two has not been empirically examined; if high density is associated with low diversity in an area, its vibrancy may not increase. To obtain a better understanding of the metabolism of cities and directions for urban planning interventions, this paper offers empirical evidence on the association between the diversity and density of urban functions in the Tokyo Metropolitan Area, using a robust density index that was determined via a Monte Carlo simulation. By conducting association analyses, it was found that highly dense station areas tended to display low diversity at multiple scales. Further investigation indicated that this negative correlation was owing to different spatial characteristics of functions and complementary functioning among highly accessible station areas. This paper argues for considering both diversity and density in urban planning to make station areas vibrant and resilient.
Statistical Rendering for Visualization of Red Sea Eddy Simulation Data
Venue: arXiv cs.GR 2021
Year: 2021

Analyzing the effects of ocean eddies is important in oceanology for gaining insights into transport of energy and biogeochemical particles. We present an application of statistical visualization algorithms for the analysis of the Red Sea eddy simulation ensemble. Specifically, we demonstrate the applications of statistical volume rendering and statistical Morse complex summary maps to a velocity magnitude field for studying the eddy positions in the flow dataset. In statistical volume rendering, we model per-voxel data uncertainty using noise models, such as parametric and nonparametric, and study the propagation of uncertainty into the volume rendering pipeline. In the statistical Morse complex summary maps, we derive histograms charactering uncertainty of gradient flow destinations to understand Morse complex topological variations across the ensemble. We demonstrate the utility of our statistical visualizations for an effective analysis of the potential eddy positions and their spatial uncertainty.
Reporting Revenge Porn: a Preliminary Expert Analysis
Venue: arXiv cs.CY 2021
Year: 2021

In our research, we focus on the response to the non-consensual distribution of intimate or sexually explicit digital images of adults, also referred as revenge porn, from the point of view of the victims. In this paper, we present a preliminary expert analysis of the process for reporting revenge porn abuses in selected content sharing platforms. Among these, we included social networks, image hosting websites, video hosting platforms, forums, and pornographic sites. We looked at the way to report abuse, concerning both the non-consensual online distribution of private sexual image or video (revenge pornography), as well as the use of deepfake techniques, where the face of a person can be replaced on original visual content with the aim of portraying the victim in the context of sexual behaviours. This preliminary analysis is directed to understand the current practices and potential issues in the procedures designed by the providers for reporting these abuses.
Forced pairs in A-Stick graphs
Venue: arXiv cs.DM 2021
Year: 2021

A Stick graph G=(A\cup B, E) is the intersection graph of a set A of horizontal segments and a set B of vertical segments in the plane, whose left and respectively bottom endpoints lie on the same ground line with slope -1. These endpoints are respectively called A-origins and B-origins. When a total order is provided for the A-origins, the resulting graphs are called A-Stick graphs. In this paper, we propose a characterization of the class of A-Stick graphs using forced pairs, which are pairs of segments in B with the property that only one left-to-right order of their origins is possible on the ground line. We deduce a recognition algorithm for A-Stick graphs running in O(|A|+|B|+|E|) time, thus improving the running time of O(|A|\cdot |B|) of the best current algorithm. We also introduce the problem of finding, for a Stick graph, a representation using segments of minimum total length. The canonical order on the A- and B-origins, output by our recognition algorithm, allows us to obtain partial results on this problem.
3D human tongue reconstruction from single "in-the-wild" images
Venue: arXiv cs.CV 2021
Year: 2021

3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination. Since the introduction of the 3D Morphable Model in the late 90's, we witnessed an explosion of research aiming at particularly tackling this task. Nevertheless, despite the increasing level of detail in the 3D face reconstructions from single images mainly attributed to deep learning advances, finer and highly deformable components of the face such as the tongue are still absent from all 3D face models in the literature, although being very important for the realness of the 3D avatar representations. In this work we present the first, to the best of our knowledge, end-to-end trainable pipeline that accurately reconstructs the 3D face together with the tongue. Moreover, we make this pipeline robust in "in-the-wild" images by introducing a novel GAN method tailored for 3D tongue surface generation. Finally, we make publicly available to the community the first diverse tongue dataset, consisting of 1,800 raw scans of 700 individuals varying in gender, age, and ethnicity backgrounds. As we demonstrate in an extensive series of quantitative as well as qualitative experiments, our model proves to be robust and realistically captures the 3D tongue structure, even in adverse "in-the-wild" conditions.
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
Venue: arXiv cs.CV 2021
Year: 2021

Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been expanded by the notion of model robustness, \ie the generalization abilities of models towards previously unseen changes in the data distribution. While new benchmarks, like ImageNet-C, have been introduced to measure robustness properties, we argue that fixed testsets are only able to capture a small portion of possible data variations and are thus limited and prone to generate new overfitted solutions. To overcome these drawbacks, we suggest to estimate the robustness of a model directly from the structure of its learned feature-space. We introduce robustness indicators which are obtained via unsupervised clustering of latent representations from a trained classifier and show very high correlations to the model performance on corrupted test data.
CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot
Venue: arXiv cs.HC 2021
Year: 2021

We present CharacterChat, a concept and chatbot to support writers in creating fictional characters. Concretely, writers progressively turn the bot into their imagined character through conversation. We iteratively developed CharacterChat in a user-centred approach, starting with a survey on character creation with writers (N=30), followed by two qualitative user studies (N=7 and N=8). Our prototype combines two modes: (1) Guided prompts help writers define character attributes (e.g. User: "Your name is Jane."), including suggestions for attributes (e.g. Bot: "What is my main motivation?") and values, realised as a rule-based system with a concept network. (2) Open conversation with the chatbot helps writers explore their character and get inspiration, realised with a language model that takes into account the defined character attributes. Our user studies reveal benefits particularly for early stages of character creation, and challenges due to limited conversational capabilities. We conclude with lessons learned and ideas for future work.
Weighted and Near Weighted Potential Games with Application to Game Theoretic Control
Venue: arXiv cs.GT 2021
Year: 2021

An algorithm is proposed to verify whether a finite game is a weighted potential game (WPG) without pre-knowledge on its weights. Then the algorithm is also applied to find the closest WPG for a given finite game. The concept and criterion of near weighted potential games (NWPGs) are given, indicating the evolutionary equivalence between a finite game and its closest WPG. Based on this, a method is proposed for replacing PGs by NWPGs in game theoretic control, which improved the applicability of PG-based optimization.
Beginners' Quest to Formalize Mathematics: A Feasibility Study in Isabelle
Venue: arXiv cs.LO 2021
Year: 2021

How difficult are interactive theorem provers to use? We respond by reviewing the formalization of Hilbert's tenth problem in Isabelle/HOL carried out by an undergraduate research group at Jacobs University Bremen. We argue that, as demonstrated by our example, proof assistants are feasible for beginners to formalize mathematics. With the aim to make the field more accessible, we also survey hurdles that arise when learning an interactive theorem prover. Broadly, we advocate for an increased adoption of interactive theorem provers in mathematical research and curricula.
Real-time Neural Radiance Caching for Path Tracing
Venue: arXiv cs.GR 2021
Year: 2021

We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead -- about 2.6ms on full HD resolution -- thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias. Most applications to date have been in computer vision, although some work in healthcare has started to emerge. The use of deep learning (DL) in cardiac MR segmentation has led to impressive results in recent years, and such techniques are starting to be translated into clinical practice. However, no work has yet investigated the fairness of such models. In this work, we perform such an analysis for racial/gender groups, focusing on the problem of training data imbalance, using a nnU-Net model trained and evaluated on cine short axis cardiac MR data from the UK Biobank dataset, consisting of 5,903 subjects from 6 different racial groups. We find statistically significant differences in Dice performance between different racial groups. To reduce the racial bias, we investigated three strategies: (1) stratified batch sampling, in which batch sampling is stratified to ensure balance between racial groups; (2) fair meta-learning for segmentation, in which a DL classifier is trained to classify race and jointly optimized with the segmentation model; and (3) protected group models, in which a different segmentation model is trained for each racial group. We also compared the results to the scenario where we have a perfectly balanced database. To assess fairness we used the standard deviation (SD) and skewed error ratio (SER) of the average Dice values. Our results demonstrate that the racial bias results from the use of imbalanced training data, and that all proposed bias mitigation strategies improved fairness, with the best SD and SER resulting from the use of protected group models.
False perfection in machine prediction: Detecting and assessing circularity problems in machine learning
Venue: arXiv cs.LG 2021
Year: 2021

This paper is an excerpt of an early version of Chapter 2 of the book "Validity, Reliability, and Significance. Empirical Methods for NLP and Data Science", by Stefan Riezler and Michael Hagmann, published in December 2021 by Morgan & Claypool. Please see the book's homepage at https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1688 for a more recent and comprehensive discussion.
TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments
Venue: arXiv cs.DC 2021
Year: 2021

Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical tasks on data streams called operators and their distributed execution on a set of infrastructure. The adaptivity of DCEP to the dynamics of IoT applications is essential and very challenging in the face of changing demands concerning Quality of Service. In our previous work, we addressed this issue by enabling transitions, which allow for the adaptive use of multiple operator placement mechanisms. In this article, we extend the transition methodology by optimizing the costs of transition and analyzing the behaviour using multiple operator placement mechanisms. Furthermore, we provide an extensive evaluation on the costs of transition imposed by operator migrations and learning, as it can inflict overhead on the performance if operated uncoordinatedly.
Fuel: A Compiler Framework for Safe Memory Management
Venue: arXiv cs.PL 2021
Year: 2021

Flow-sensitive type systems offer an elegant way to ensure memory-safety in programming languages. Unfortunately, their adoption in new or existing languages is often hindered by a painful effort to implement or integrate them into compilers. This paper presents early results in our effort to alleviate this task. We introduce Fuel, a type capability-based library that can be plugged onto a compiler toolchain to check for memory-safety properties. Fuel builds upon well-established ideas in the domain of capability-based system, and adds a mechanism leveraging dynamic checks to recover capabilities where static reasoning is either too difficult or impossible. This approach allows the analysis to potentially cover situations where a typical type system might not be expressive enough to statically reason about memory safety.
Prospects for Analog Circuits in Deep Networks
Venue: arXiv cs.NE 2021
Year: 2021

Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASIC) designs that implement these algorithms using techniques such as charge sharing circuits and subthreshold transistors, achieve very high power efficiencies. With the recent advances in deep learning algorithms, focus has shifted to hardware digital accelerator designs that implement the prevalent matrix-vector multiplication operations. Power in these designs is usually dominated by the memory access power of off-chip DRAM needed for storing the network weights and activations. Emerging dense non-volatile memory technologies can help to provide on-chip memory and analog circuits can be well suited to implement the needed multiplication-vector operations coupled with in-computing memory approaches. This paper presents abrief review of analog designs that implement various machine learning algorithms. It then presents an outlook for the use ofanalog circuits in low-power deep network accelerators suitable for edge or tiny machine learning applications.
Advancing Protocol Diversity in Network Security Monitoring
Venue: arXiv cs.NI 2021
Year: 2021

With information technology entering new fields and levels of deployment, e.g., in areas of energy, mobility, and production, network security monitoring needs to be able to cope with those environments and their evolution. However, state-of-the-art Network Security Monitors (NSMs) typically lack the necessary flexibility to handle the diversity of the packet-oriented layers below the abstraction of TCP/IP connections. In this work, we advance the software architecture of a network security monitor to facilitate the flexible integration of lower-layer protocol dissectors while maintaining required performance levels. We proceed in three steps: First, we identify the challenges for modular packet-level analysis, present a refined NSM architecture to address them and specify requirements for its implementation. Second, we evaluate the performance of data structures to be used for protocol dispatching, implement the proposed design into the popular open-source NSM Zeek and assess its impact on the monitor performance. Our experiments show that hash-based data structures for dispatching introduce a significant overhead while array-based approaches qualify for practical application. Finally, we demonstrate the benefits of the proposed architecture and implementation by migrating Zeek's previously hard-coded stack of link and internet layer protocols to the new interface. Furthermore, we implement dissectors for non-IP based industrial communication protocols and leverage them to realize attack detection strategies from recent applied research. We integrate the proposed architecture into the Zeek open-source project and publish the implementation to support the scientific community as well as practitioners, promoting the transfer of research into practice.
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification
Venue: arXiv cs.CL 2021
Year: 2021

Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications. In this paper, we present our deep learning-based system, submitted to the second NADI shared task for country-level and province-level identification of Modern Standard Arabic (MSA) and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-Task Learning (MTL) model to tackle both country-level and province-level MSA/DA identification. The latter MTL model consists of a shared Bidirectional Encoder Representation Transformers (BERT) encoder, two task-specific attention layers, and two classifiers. Our key idea is to leverage both the task-discriminative and the inter-task shared features for country and province MSA/DA identification. The obtained results show that our MTL model outperforms single-task models on most subtasks.