OpenRaiser/Intern-Atlas huggingface dataset:
Fully-Automated Packaging Structure Recognition in Logistics Environments
Venue: arXiv cs.CV 2020
Year: 2020

Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points. Often, huge manual effort is involved in recognizing or verifying packet identity or packaging structure, for instance to check the delivery for completeness. We propose a method for complete automation of packaging structure recognition: Based on a single image, one or multiple transport units are localized and, for each of these transport units, the characteristics, the total number and the arrangement of its packaging units is recognized. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images, as well as computer vision methods and heuristic components. We use a custom data set of realistic logistics images for training and evaluation of our method. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91%) when focusing on most common package types.
Real-Time Sign Language Detection using Human Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing. We extract optical flow features based on human pose estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%, evaluated on the DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
Identifying Implicit Vulnerabilities through Personas as Goal Models
Venue: arXiv cs.SE 2020
Year: 2020

When used in requirements processes and tools, personas have the potential to identify vulnerabilities resulting from misalignment between user expectations and system goals. Typically, however, this potential is unfulfilled as personas and system goals are captured with different mindsets, by different teams, and for different purposes. If personas are visualised as goal models, it may be easier for stakeholders to see implications of their goals being satisfied or denied, and designers to incorporate the creation and analysis of such models into the broader RE tool-chain. This paper outlines a tool-supported approach for finding implicit vulnerabilities from user and system goals by reframing personas as social goal models. We illustrate this approach with a case study where previously hidden vulnerabilities based on human behaviour were identified.
Security Analysis on Tangle-based Blockchain through Simulation
Venue: arXiv cs.CR 2020
Year: 2020

The Tangle-based structure becomes one of the most promising solutions when designing DAG-based blockchain systems. The approach improves the scalability by directly confirming multiple transactions in parallel instead of single blocks in linear. However, the performance gain may bring potential security risks. In this paper, we construct three types of attacks with comprehensive evaluations, namely parasite attack (PS), double spending attack (DS), and hybrid attack (HB). To achieve that, we deconstruct the Tangle-based projects (e.g. IOTA) and abstract the main components to rebuild a simple but flexible network for the simulation. Then, we informally define three smallest actions to build up the attack strategies layer by layer. Based on that, we provide analyses to evaluate different types of attacks. To the best of our knowledge, this is the first study to provide a comprehensive security analysis of Tangle-based blockchains.
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Venue: arXiv cs.CL 2020
Year: 2020

We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of MXNet's Gluon API, a focus on state of the art model architectures, distributed mixed precision training, and efficient CPU decoding with 8-bit quantization. These improvements result in faster training and inference, higher automatic metric scores, and a shorter path from research to production.
Metaheuristics for the operating theater planning and scheduling: A systematic review
Venue: arXiv cs.AI 2020
Year: 2020

There are found a vast number of papers studying the problem of operating theater planning and scheduling. Different variants of this problem are generally recognized to be NP-complete; thus, several solution approaches have been utilized in the literature to confront with these complicated problems. The lack of a thorough review of the main characteristics of solution approaches is tangible in the literature (reviewing them separately and with regards to the characteristics of studied problems), which can provide pragmatic guidelines for practitioners and future research projects. This paper aims to address this issue. Since different types of solution approaches usually have different characteristics, this paper focuses only on metaheuristic algorithms. Through both automatic and manual search methods, we have selected and reviewed 28 papers with respect to their main problem and solution approach features. Finally, some directions are introduced for future research.
Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization in Linear Time
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we study the non-monotone adaptive submodular maximization problem subject to a cardinality constraint. We first revisit the adaptive random greedy algorithm proposed in \citep{gotovos2015non}, where they show that this algorithm achieves a $1/e$ approximation ratio if the objective function is adaptive submodular and pointwise submodular. It is not clear whether the same guarantee holds under adaptive submodularity (without resorting to pointwise submodularity) or not. Our first contribution is to show that the adaptive random greedy algorithm achieves a $1/e$ approximation ratio under adaptive submodularity. One limitation of the adaptive random greedy algorithm is that it requires $O(n\times k)$ value oracle queries, where $n$ is the size of the ground set and $k$ is the cardinality constraint. Our second contribution is to develop the first linear-time algorithm for the non-monotone adaptive submodular maximization problem. Our algorithm achieves a $1/e-ε$ approximation ratio (this bound is improved to $1-1/e-ε$ for monotone case), using only $O(nε^{-2}\log ε^{-1})$ value oracle queries. Notably, $O(nε^{-2}\log ε^{-1})$ is independent of the cardinality constraint. For the monotone case, we propose a faster algorithm that achieves a $1-1/e-ε$ approximation ratio in expectation with $O(n \log \frac{1}ε)$ value oracle queries. We also generalize our study by considering a partition matroid constraint, and develop a linear-time algorithm for monotone and fully adaptive submodular functions.
Open Source Software Development Process: A Systematic Review
Venue: arXiv cs.SE 2020
Year: 2020

Open Source Software (OSS) has been recognized by the software development community as an effective way to deliver software. Unlike traditional software development, OSS development is driven by collaboration among developers spread geographically and motivated by common goals and interests. Besides this fact, it is recognized by OSS community the need of understand OSS development process and its activities. Our goal is to investigate the state-of-art about OSS process through conducting a systematic literature review providing an overview of how the OSS community has been investigating OSS process over past years identifying and summarizing OSS process activities and their characteristics as well as translating OSS process in a macro process through BPMN notation. As a result, we systematically analysed 33 studies presenting an overview of the state-of-art of researches regarding OSS process, a generalized OSS development macro process represented by BPMN notation with a detailed description of each OSS process activity and roles in OSS environment. We conclude that OSS process can be in practice further investigated by researchers. In addition, the presented OSS process can be used as a guide for OSS projects and being adapted according to each OSS project reality. It provides insights to managers and developers who want to improve their development process even in OSS and traditional environments. Finally, recommendations for OSS community regarding OSS process activities are provided.
An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we introduce an intelligent prediction system for mobile source localization in industrial Internet of things. The position and velocity of mobile source are jointly predicted by using Time Delay (TD) measurements in the intelligent system. To predict the position and velocity, the Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward a Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the optimization problem by introducing the penalty terms. Then an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty by adaptively choosing the penalty coefficient. We conduct experiments in both a simulation environment and a real system to demonstrate the effectiveness of the proposed method. The results have demonstrated that the proposed intelligent APF-SDP algorithm outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.
A Parallel Repetition Theorem for the GHZ Game
Venue: arXiv cs.CC 2020
Year: 2020

We prove that parallel repetition of the (3-player) GHZ game reduces the value of the game polynomially fast to 0. That is, the value of the GHZ game repeated in parallel $t$ times is at most $t^{-Ω(1)}$. Previously, only a bound of $\approx \frac{1}{α(t)}$, where $α$ is the inverse Ackermann function, was known. The GHZ game was recently identified by Dinur, Harsha, Venkat and Yuen as a multi-player game where all existing techniques for proving strong bounds on the value of the parallel repetition of the game fail. Indeed, to prove our result we use a completely new proof technique. Dinur, Harsha, Venkat and Yuen speculated that progress on bounding the value of the parallel repetition of the GHZ game may lead to further progress on the general question of parallel repetition of multi-player games. They suggested that the strong correlations present in the GHZ question distribution represent the "hardest instance" of the multi-player parallel repetition problem. Another motivation for studying the parallel repetition of the GHZ game comes from the field of quantum information. The GHZ game, first introduced by Greenberger, Horne and Zeilinger, is a central game in the study of quantum entanglement and has been studied in numerous works. For example, it is used for testing quantum entanglement and for device-independent quantum cryptography. In such applications a game is typically repeated to reduce the probability of error, and hence bounds on the value of the parallel repetition of the game may be useful.
Learning to Caricature via Semantic Shape Transform
Venue: arXiv cs.CV 2020
Year: 2020

Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professional skills, and thus it is of great interest to design a method to automatically generate such drawings. To deal with large shape changes, we propose an algorithm based on a semantic shape transform to produce diverse and plausible shape exaggerations. Specifically, we predict pixel-wise semantic correspondences and perform image warping on the input photo to achieve dense shape transformation. We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures. In addition, our model allows users to manipulate the shape via the semantic map. We demonstrate the effectiveness of our approach on a large photograph-caricature benchmark dataset with comparisons to the state-of-the-art methods.
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Venue: arXiv cs.CL 2020
Year: 2020

We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why did my model make this prediction? When does it perform poorly? What happens under a controlled change in the input? LIT integrates local explanations, aggregate analysis, and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. We include case studies for a diverse set of workflows, including exploring counterfactuals for sentiment analysis, measuring gender bias in coreference systems, and exploring local behavior in text generation. LIT supports a wide range of models--including classification, seq2seq, and structured prediction--and is highly extensible through a declarative, framework-agnostic API. LIT is under active development, with code and full documentation available at https://github.com/pair-code/lit.
Coded Elastic Computing on Machines with Heterogeneous Storage and Computation Speed
Venue: arXiv cs.IT 2020
Year: 2020

We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem for finding the optimal computation load and a "filling problem" for finding the exact computation assignment. A low-complexity "filling algorithm" is adapted and can be completed within a number of iterations equals at most the number of available machines.
Prevalence, Contents and Automatic Detection of KL-SATD
Venue: arXiv cs.SE 2020
Year: 2020

When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories with 13,588 KL-SATD comments. We find that the median percentage of KL-SATD comments among all comments is only 1,52%. We find that KL-SATD comment contents include words expressing code changes and uncertainty, such as remove, fix, maybe and probably. This makes them different compared to other comments. KL-SATD comment contents are similar to manually labeled SATD comments of prior work. Our machine learning classifier using logistic Lasso regression has good performance in detecting KL-SATD comments (AUC-ROC 0.88). Finally, we demonstrate that using machine learning we can identify comments that are currently missing but which should have a SATD keyword in them. Automating SATD identification of comments that lack SATD keywords can save time and effort by replacing manual identification of comments. Using KL-SATD offers a potential to bootstrap a complete SATD detector.
Rule-based Anomaly Detection for Railway Signalling Networks
Venue: arXiv cs.CR 2020
Year: 2020

We propose a rule-based anomaly detection system for railway signalling that mitigates attacks by a Dolev-Yao attacker who is able to inject control commands and to perform semantic attacks. The system as well mitigates the effects of a compromised signal box that an attacker uses to issue licit but mistimed control messages. We consider an attacker that could cause train derailments and collisions, if our countermeasure is not employed. We apply safety principles of railway operation to a distributed anomaly detection system that inspects incoming commands on the signals and points. The proposed anomaly detection system detects all attacks of our model without producing false positives, while it requires only a small amount of overhead in terms of network communication and latency compared to normal train operation.
The Topology of Shapes Made with Points
Venue: arXiv cs.GR 2020
Year: 2020

In architecture, city planning, visual arts, and other design areas, shapes are often made with points, or with structural representations based on point-sets. Shapes made with points can be understood more generally as finite arrangements formed with elements (i.e. points) of the algebra of shapes $U_i$, for $i = 0$. This paper examines the kind of topology that is applicable to such shapes. From a mathematical standpoint, any "shape made with points" is equivalent to a finite space, so that topology on a shape made with points is no different than topology on a finite space: the study of topological structure naturally coincides with the study of preorder relations on the points of the shape. After establishing this fact, some connections between the topology of shapes made with points and the topology of "point-free" pictorial shapes (when $i > 0$) are discussed and the main differences between the two are summarized.
Inference of a universal social scale and segregation measures using social connectivity kernels
Venue: arXiv cs.SI 2020
Year: 2020

How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive. We use the connectivity kernel of conditionally-independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals, and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies. Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from eleven ego-network datasets collected in four surveys in the United Kingdom and United States. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life.
LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
Venue: arXiv cs.CV 2020
Year: 2020

Logo detection has been gaining considerable attention because of its wide range of applications in the multimedia field, such as copyright infringement detection, brand visibility monitoring, and product brand management on social media. In this paper, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. We describe the collection and annotation process of our dataset, analyze its scale and diversity in comparison to other datasets for logo detection. We further propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the state-of-the-art YOLOv3 framework for large-scale logo detection. Logo-Yolo can solve the problems of multi-scale objects, logo sample imbalance and inconsistent bounding-box regression. It obtains about 4% improvement on the average performance compared with YOLOv3, and greater improvements compared with reported several deep detection models on LogoDet-3K. The evaluations on other three existing datasets further verify the effectiveness of our method, and demonstrate better generalization ability of LogoDet-3K on logo detection and retrieval tasks. The LogoDet-3K dataset is used to promote large-scale logo-related research and it can be found at https://github.com/Wangjing1551/LogoDet-3K-Dataset.
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
Venue: arXiv cs.CY 2020
Year: 2020

The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic Identification System) data, together with recent outstanding progresses of deep learning, make vessel monitoring using neural networks (NNs) a very promising approach. This paper analyses a novel neural network we have recently introduced -- GeoTrackNet -- regarding operational contexts. Especially, we aim to evaluate (i) the relevance of the abnormal behaviours detected by GeoTrackNet with respect to expert interpretations, (ii) the extent to which GeoTrackNet may process AIS data streams in real time. We report experiments showing the high potential to meet operational levels of the model.
Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal
Venue: arXiv cs.CV 2020
Year: 2020

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multidomain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.