OpenRaiser/Intern-Atlas huggingface dataset:
Stakeholder identification for a structured release planning approach in the automotive domain
Venue: arXiv cs.SE 2020
Year: 2020

Context: In regulated domains like automotive, release planning is a complex process. The agreement between traditional product development processes for hardware as well as mechanic systems and agile development approaches for software development is a major challenge. Especially the creation and synchronization of a release plan is challenging. Objective: The aim of this work is to present identified stakeholders of a release plan as an appropriate approach to create transparency in release planning in the automotive domain. Method: Action research to elaborate relevant stakeholders for release planning was conducted at Dr. Ing. h. c. F. Porsche AG. Results: We present a detailed overview of identified stakeholders due to release planning as well as their required content and added value regarding to two pilot projects. The results confirm the fact that almost every stakeholder is involved in a release plan in a certain way. Conclusions: Release planning within a complex project environment and complicated customer constellations is difficult to manage. We discuss how the presented stakeholders could meet with the given conditions in the automotive domain. With this contribution, identified stakeholders of release planning from hardware and software point of view is introduced. It helps to reach transparency and to handle the given complexity.
A Qualitative Study of Application-level Caching
Venue: arXiv cs.SE 2020
Year: 2020

Latency and cost of Internet-based services are encouraging the use of application-level caching to continue satisfying users' demands, and improve the scalability and availability of origin servers. Despite its popularity, this level of caching involves the manual implementation by developers and is typically addressed in an ad-hoc way, given that it depends on specific details of the application. As a result, application-level caching is a time-consuming and error-prone task, becoming a common source of bugs. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. In this paper, we present the results of a qualitative study of how developers handle caching logic in their web applications, which involved the investigation of ten software projects with different characteristics. The study we designed is based on comparative and interactive principles of grounded theory, and the analysis of our data allowed us to extract and understand how developers address cache-related concerns to improve performance and scalability of their web applications. Based on our analysis, we derived guidelines and patterns, which guide developers while designing, implementing and maintaining application-level caching, thus supporting developers in this challenging task that is crucial for enterprise web applications.
ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Knowledge Distillation (KD) refers to transferring knowledge from a large model to a smaller one, which is widely used to enhance model performance in machine learning. It tries to align embedding spaces generated from the teacher and the student model (i.e. to make images corresponding to the same semantics share the same embedding across different models). In this work, we focus on its application in face recognition. We observe that existing knowledge distillation models optimize the proxy tasks that force the student to mimic the teacher's behavior, instead of directly optimizing the face recognition accuracy. Consequently, the obtained student models are not guaranteed to be optimal on the target task or able to benefit from advanced constraints, such as large margin constraints (e.g. margin-based softmax). We then propose a novel method named ProxylessKD that directly optimizes face recognition accuracy by inheriting the teacher's classifier as the student's classifier to guide the student to learn discriminative embeddings in the teacher's embedding space. The proposed ProxylessKD is very easy to implement and sufficiently generic to be extended to other tasks beyond face recognition. We conduct extensive experiments on standard face recognition benchmarks, and the results demonstrate that ProxylessKD achieves superior performance over existing knowledge distillation methods.
Scene Flow from Point Clouds with or without Learning
Venue: arXiv cs.CV 2020
Year: 2020

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based approaches seek to estimate the scene flow directly from point clouds and have achieved state-of-the-art performance. However, supervised learning methods are inherently domain specific and require a large amount of labeled data. Annotation of scene flow on real-world point clouds is expensive and challenging, and the lack of such datasets has recently sparked interest in self-supervised learning methods. How to accurately and robustly learn scene flow representations without labeled real-world data is still an open problem. Here we present a simple and interpretable objective function to recover the scene flow from point clouds. We use the graph Laplacian of a point cloud to regularize the scene flow to be "as-rigid-as-possible". Our proposed objective function can be used with or without learning---as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime. Our approach outperforms related works in many datasets. We also show the immediate applications of our proposed method for two applications: motion segmentation and point cloud densification.
Real-Time Text Detection and Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Inrecentyears,ConvolutionalNeuralNet-work(CNN) is quite a popular topic, as it is a powerful andintelligent technique that can be applied in various fields.The YOLO is a technique that uses the algorithms for real-time text detection tasks. However, issues like, photometricdistortion and geometric distortion, could affect the systemYOLO accuracy and cause system failure. Therefore, thereare improvements that can make the system work better. Inthis paper, we are going to present our solution - a potentialsolution of a fast and accurate real-time text direction andrecognition system. The paper covers the topic of Real-TimeText detection and recognition in three major areas: 1. videoand image preprocess, 2. Text detection, 3. Text recognition. Asa mature technique, there are many existing methods that canpotentially improve the solution. We will go through some ofthose existing methods in the literature review session. In thisway, we are presenting an industrial strength, high-accuracy,Real-Time Text Detection and recognition tool.
Robust localization for planar moving robot in changing environment: A perspective on density of correspondence and depth
Venue: arXiv cs.RO 2020
Year: 2020

Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar moving robot is proposed. The key component is a minimal solution which computes the absolute camera pose with one 3D-2D correspondence and one 2D-2D correspondence. The advantages are obvious in two aspects. First, the robustness is enhanced as the sample set for pose estimation is maximal by utilizing all correspondences with or without depth. Second, no extra effort for dense map construction is required to exploit dense correspondences for handling textureless and repetitive texture scenes. That is meaningful as building a dense map is computational expensive especially in large scale. Moreover, a probabilistic analysis among different solutions is presented and an automatic solution selection mechanism is designed to maximize the success rate by selecting appropriate solutions in different environmental characteristics. Finally, a complete visual localization pipeline considering situations from the perspective of correspondence and depth density is summarized and validated on both simulation and public real-world indoor localization dataset. The code is released on github.
A Parallel Approach for Real-Time Face Recognition from a Large Database
Venue: arXiv cs.CV 2020
Year: 2020

We present a new facial recognition system, capable of identifying a person, provided their likeness has been previously stored in the system, in real time. The system is based on storing and comparing facial embeddings of the subject, and identifying them later within a live video feed. This system is highly accurate, and is able to tag people with their ID in real time. It is able to do so, even when using a database containing thousands of facial embeddings, by using a parallelized searching technique. This makes the system quite fast and allows it to be highly scalable.
Fast Adaptation of Manipulator Trajectories to Task Perturbation By Differentiating through the Optimal Solution
Venue: arXiv cs.RO 2020
Year: 2020

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. But most importantly, our algorithm achieves a worst-case speed-up of 160x over the latter approach.
Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization
Venue: arXiv cs.CL 2020
Year: 2020

We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level recurrence hierarchy that operates on the word and character levels separately---enabling faster training and inference than comparable traditional models. A cross-level attention module further connects the two, and opens the door for network interpretability. The task module is a softmax classifier that enumerates valid combinations of diacritics. This architecture can be extended with a recurrent decoder that optionally accepts priors from partially diacritized text, which improves results. We employ extra tricks such as sentence dropout and majority voting to further boost the final result. Our best model achieves a WER of 5.34%, outperforming the previous state-of-the-art with a 30.56% relative error reduction.
Adversarial Self-Supervised Scene Flow Estimation
Venue: arXiv cs.CV 2020
Year: 2020

This work proposes a metric learning approach for self-supervised scene flow estimation. Scene flow estimation is the task of estimating 3D flow vectors for consecutive 3D point clouds. Such flow vectors are fruitful, \eg for recognizing actions, or avoiding collisions. Training a neural network via supervised learning for scene flow is impractical, as this requires manual annotations for each 3D point at each new timestamp for each scene. To that end, we seek for a self-supervised approach, where a network learns a latent metric to distinguish between points translated by flow estimations and the target point cloud. Our adversarial metric learning includes a multi-scale triplet loss on sequences of two-point clouds as well as a cycle consistency loss. Furthermore, we outline a benchmark for self-supervised scene flow estimation: the Scene Flow Sandbox. The benchmark consists of five datasets designed to study individual aspects of flow estimation in progressive order of complexity, from a moving object to real-world scenes. Experimental evaluation on the benchmark shows that our approach obtains state-of-the-art self-supervised scene flow results, outperforming recent neighbor-based approaches. We use our proposed benchmark to expose shortcomings and draw insights on various training setups. We find that our setup captures motion coherence and preserves local geometries. Dealing with occlusions, on the other hand, is still an open challenge.
CURE: Collection for Urdu Information Retrieval Evaluation and Ranking
Venue: arXiv cs.IR 2020
Year: 2020

Urdu is a widely spoken language with 163 million speakers worldwide across the globe. Information Retrieval (IR) for Urdu entails special consideration of research community due to its rich morphological features and a large number of speakers. In general, IR evaluation task is not extensively explored for Urdu. The most important missing element is the availability of a standardized evaluation corpus specific to Urdu. In this research work, we propose and construct a standard test collection of Urdu documents for IR evaluation and named it Collection for Urdu Retrieval Evaluation (CURE). We select 1,096 unique documents against 50 diverse queries from a large collection of 0.5 million crawled documents using two IR models. The purpose of test collection is the evaluation of IR models, ranking algorithms, and different natural language processing techniques. Next, we perform binary relevance judgment on the selected documents. We also built two other language resources for lemmatization and query expansion specific to our test collection. Evaluation of test collection is carried out using four retrieval models as well using the stop-words list, lemmatization, and query expansion. Furthermore, error analysis was performed for each query with different NLP techniques. To the best of our knowledge, this work is the first attempt for preparing a standardized information retrieval evaluation test collection for the Urdu language.
A Novel Chaotic System for Text Encryption Optimized with Genetic Algorithm
Venue: arXiv cs.CR 2020
Year: 2020

With meteoric developments in communication systems and data storage technologies, the need for secure data transmission is more crucial than ever. The level of security provided by any cryptosystem relies on the sensitivity of the private key, size of the key space as well as the trapdoor function being used. In order to satisfy the aforementioned constraints, there has been a growing interest over the past few years, in studying the behavior of chaotic systems and their applications in various fields such as data encryption due to characteristics like randomness, unpredictability and sensitivity of the generated sequence to the initial value and its parameters. This paper utilizes a novel 2D chaotic function that displays a uniform bifurcation over a large range of parameters and exhibits high levels of chaotic behavior to generate a random sequence that is used to encrypt the input data. The proposed method uses a genetic algorithm to optimize the parameters of the map to enhance security for any given textual data. Various analyses demonstrate an adequately large key space and the existence of multiple global optima indicating the necessity of the proposed system and the security provided by it.
Unsupervised Metric Relocalization Using Transform Consistency Loss
Venue: arXiv cs.CV 2020
Year: 2020

Training networks to perform metric relocalization traditionally requires accurate image correspondences. In practice, these are obtained by restricting domain coverage, employing additional sensors, or capturing large multi-view datasets. We instead propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. Guided by this intuition, we derive a novel transform consistency loss. Using this loss function, we train a deep neural network to infer dense feature and saliency maps to perform robust metric relocalization in dynamic environments. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
5G New Radio Resource Allocation Optimization for Heterogeneous Services
Venue: arXiv cs.NI 2020
Year: 2020

5G new radio (NR) introduced flexible numerology to provide the necessary flexibility for multiplexing the communication of heterogeneous services on a shared channel. One of the fundamental challenges of 5G NR is to develop resource allocation schemes to efficiently exploit such flexibility to optimize resource allocation of ultra-reliable low-latency communications (URLLC) in coexistence with enhanced mobile broadband (eMBB) while ensuring their colliding performance requirements. To address this challenge, we present a new formulation of 5G NR resource allocation to accommodate eMBB and URLLC services, by considering their interplay. The objective of the formulated problem is to meet throughput quality of service (QoS) requirements probabilistically when the optimal global solution is infeasible. To this end, we express the problem as an integer linear program and consider two formulations with hard and soft URLLC throughput constraints. Furthermore, we propose a low-complexity two-step heuristic approach, inspired by an instance of bin packing optimization, to provide a trade-off between resource allocation efficiency and computational complexity. Finally, performance results are provided and demonstrate that the proposed approaches can provide low-complexity resource allocation solutions, in balance with the performance targets of the services.
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Venue: arXiv cs.CL 2020
Year: 2020

Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research. Previous work on Indonesian has been hampered by a lack of annotated datasets, a sparsity of language resources, and a lack of resource standardization. In this work, we release the IndoLEM dataset comprising seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. We additionally release IndoBERT, a new pre-trained language model for Indonesian, and evaluate it over IndoLEM, in addition to benchmarking it against existing resources. Our experiments show that IndoBERT achieves state-of-the-art performance over most of the tasks in IndoLEM.
An interactive sequential-decision benchmark from geosteering
Venue: arXiv cs.HC 2020
Year: 2020

Geosteering workflows are increasingly based on the quantification of subsurface uncertainties during real-time operations. As a consequence operational decision making is becoming both better informed and more complex. This paper presents an experimental web-based decision support system, which can be used to both aid expert decisions under uncertainty or further develop decision optimization algorithms in controlled environment. A user of the system (either human or AI) controls the decisions to steer the well or stop drilling. Whenever a user drills ahead, the system produces simulated measurements along the selected well trajectory which are used to update the uncertainty represented by model realizations using the ensemble Kalman filter. To enable informed decisions the system is equipped with functionality to evaluate the value of the selected trajectory under uncertainty with respect to the objectives of the current experiment. To illustrate the utility of the system as a benchmark, we present the initial experiment, in which we compare the decision skills of geoscientists with those of a recently published automatic decision support algorithm. The experiment and the survey after it showed that most participants were able to use the interface and complete the three test rounds. At the same time, the automated algorithm outperformed 28 out of 29 qualified human participants. Such an experiment is not sufficient to draw conclusions about practical geosteering, but is nevertheless useful for geoscience. First, this communication-by-doing made 76% of respondents more curious about and/or confident in the presented technologies. Second, the system can be further used as a benchmark for sequential decisions under uncertainty. This can accelerate development of algorithms and improve the training for decision making.
Context-based Image Segment Labeling (CBISL)
Venue: arXiv cs.CV 2020
Year: 2020

Working with images, one often faces problems with incomplete or unclear information. Image inpainting can be used to restore missing image regions but focuses, however, on low-level image features such as pixel intensity, pixel gradient orientation, and color. This paper aims to recover semantic image features (objects and positions) in images. Based on published gated PixelCNNs, we demonstrate a new approach referred to as quadro-directional PixelCNN to recover missing objects and return probable positions for objects based on the context. We call this approach context-based image segment labeling (CBISL). The results suggest that our four-directional model outperforms one-directional models (gated PixelCNN) and returns a human-comparable performance.
Data-free Knowledge Distillation for Segmentation using Data-Enriching GAN
Venue: arXiv cs.CV 2020
Year: 2020

Distilling knowledge from huge pre-trained networks to improve the performance of tiny networks has favored deep learning models to be used in many real-time and mobile applications. Several approaches that demonstrate success in this field have made use of the true training dataset to extract relevant knowledge. In absence of the True dataset, however, extracting knowledge from deep networks is still a challenge. Recent works on data-free knowledge distillation demonstrate such techniques on classification tasks. To this end, we explore the task of data-free knowledge distillation for segmentation tasks. First, we identify several challenges specific to segmentation. We make use of the DeGAN training framework to propose a novel loss function for enforcing diversity in a setting where a few classes are underrepresented. Further, we explore a new training framework for performing knowledge distillation in a data-free setting. We get an improvement of 6.93% in Mean IoU over previous approaches.