OpenRaiser/Intern-Atlas huggingface dataset:
The ubiquitous digital file: A review of file management research
Venue: arXiv cs.HC 2021
Year: 2021

Computer users spend time every day interacting with digital files and folders, including downloading, moving, naming, navigating to, searching for, sharing, and deleting them. Such file management has been the focus of many studies across various fields, but has not been explicitly acknowledged nor made the focus of dedicated review. In this article we present the first dedicated review of this topic and its research, synthesizing more than 230 publications from various research domains to establish what is known and what remains to be investigated, particularly by examining the common motivations, methods, and findings evinced by the previously furcate body of work. We find three typical research motivations in the literature reviewed: understanding how and why users store, organize, retrieve, and share files and folders, understanding factors that determine their behavior, and attempting to improve the user experience through novel interfaces and information services. Relevant conceptual frameworks and approaches to designing and testing systems are described, and open research challenges and the significance for other research areas are discussed. We conclude that file management is a ubiquitous, challenging, and relatively unsupported activity that invites and has received attention from several disciplines and has broad importance for topics across information science.
BFAR-Bounded False Alarm Rate detector for improved radar odometry estimation
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localization and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR to the use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5deg/100 m to 1.55%/0.46deg/100 m; an improvement of 12.5%.
Configuring Multiple Instances with Multi-Configuration
Venue: arXiv cs.AI 2021
Year: 2021

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.
BERT Cannot Align Characters
Venue: arXiv cs.CL 2021
Year: 2021

In previous work, it has been shown that BERT can adequately align cross-lingual sentences on the word level. Here we investigate whether BERT can also operate as a char-level aligner. The languages examined are English, Fake-English, German and Greek. We show that the closer two languages are, the better BERT can align them on the character level. BERT indeed works well in English to Fake-English alignment, but this does not generalize to natural languages to the same extent. Nevertheless, the proximity of two languages does seem to be a factor. English is more related to German than to Greek and this is reflected in how well BERT aligns them; English to German is better than English to Greek. We examine multiple setups and show that the similarity matrices for natural languages show weaker relations the further apart two languages are.
Model Bias in NLP -- Application to Hate Speech Classification using transfer learning techniques
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, a BERT based neural network model is applied to the JIGSAW data set in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), in this case Twitter. Three other neural network architectures and a GPT-2 model are also applied on the provided data set in order to compare these different models. The trained BERT model is then applied on two different data sets to evaluate its generalisation power, namely on another Twitter data set and the data set HASOC 2019 which includes Twitter and also Facebook comments; we focus on the English HASOC 2019 data. In addition, it can be shown that by fine-tuning the trained BERT model on these two data sets by applying different transfer learning scenarios via retraining partial or all layers the predictive scores improve compared to simply applying the model pre-trained on the JIGSAW data set. With our results, we get precisions from 64% to around 90% while still achieving acceptable recall values of at least lower 60s%, proving that BERT is suitable for real use cases in social platforms.
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pre-trained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.
Prediction of severe thunderstorm events with ensemble deep learning and radar data
Venue: arXiv cs.LG 2021
Year: 2021

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the present paper illustrates how a deep learning method, exploiting videos of radar reflectivity frames as input, can be used to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. From a technical viewpoint, the computational core of this approach is the use of a value-weighted skill score for both transforming the probabilistic outcomes of the deep neural network into binary classification and assessing the forecasting performances. The warning machine has been validated against weather radar data recorded in the Liguria region, in Italy,
Transforming Fake News: Robust Generalisable News Classification Using Transformers
Venue: arXiv cs.CL 2021
Year: 2021

As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which transformers have achieved state-of-the-art results. Using the publicly available ISOT and Combined Corpus datasets, this study explores transformers' abilities to identify fake news, with particular attention given to investigating generalisation to unseen datasets with varying styles, topics and class distributions. Moreover, we explore the idea that opinion-based news articles cannot be classified as real or fake due to their subjective nature and often sensationalised language, and propose a novel two-step classification pipeline to remove such articles from both model training and the final deployed inference system. Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F1 scores of up to 4.9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10.1% following the implementation of our two-step classification pipeline. To the best of our knowledge, this study is the first to investigate generalisation of transformers in this context.
Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks
Venue: arXiv cs.RO 2021
Year: 2021

The problem of multi-robot target tracking asks for actively planning the joint motion of robots to track targets. In this paper, we focus on such target tracking problems in adversarial environments, where attacks or failures may deactivate robots' sensors and communications. In contrast to the previous works that consider no attacks or sensing attacks only, we formalize the first robust multi-robot tracking framework that accounts for any fixed numbers of worst-case sensing and communication attacks. To secure against such attacks, we design the first robust planning algorithm, named Robust Active Target Tracking (RATT), which approximates the communication attacks to equivalent sensing attacks and then optimizes against the approximated and original sensing attacks. We show that RATT provides provable suboptimality bounds on the tracking quality for any non-decreasing objective function. Our analysis utilizes the notations of curvature for set functions introduced in combinatorial optimization. In addition, RATT runs in polynomial time and terminates with the same running time as state-of-the-art algorithms for (non-robust) target tracking. Finally, we evaluate RATT with both qualitative and quantitative simulations across various scenarios. In the evaluations, RATT exhibits a tracking quality that is near-optimal and superior to varying non-robust heuristics. We also demonstrate RATT's superiority and robustness against varying attack models (e.g., worst-case and bounded rational attacks).
Chemical-Reaction-Aware Molecule Representation Learning
Venue: arXiv cs.LG 2021
Year: 2021

Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take SMILES strings as input that have difficulty in encoding molecule structure information, or over-emphasize the importance of GNN architectures but neglect their generalization ability. Here we propose using chemical reactions to assist learning molecule representation. The key idea of our approach is to preserve the equivalence of molecules with respect to chemical reactions in the embedding space, i.e., forcing the sum of reactant embeddings and the sum of product embeddings to be equal for each chemical equation. This constraint is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Moreover, our model can use any GNN as the molecule encoder and is thus agnostic to GNN architectures. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks, e.g., 17.4% absolute Hit@1 gain in chemical reaction prediction, 2.3% absolute AUC gain in molecule property prediction, and 18.5% relative RMSE gain in graph-edit-distance prediction, respectively, over the best baseline method. The code is available at https://github.com/hwwang55/MolR.
Meta-Model Structure Selection: Building Polynomial NARX Model for Regression and Classification
Venue: arXiv cs.LG 2021
Year: 2021

This work presents a new meta-heuristic approach to select the structure of polynomial NARX models for regression and classification problems. The method takes into account the complexity of the model and the contribution of each term to build parsimonious models by proposing a new cost function formulation. The robustness of the new algorithm is tested on several simulated and experimental system with different nonlinear characteristics. The obtained results show that the proposed algorithm is capable of identifying the correct model, for cases where the proper model structure is known, and determine parsimonious models for experimental data even for those systems for which traditional and contemporary methods habitually fails. The new algorithm is validated over classical methods such as the FROLS and recent randomized approaches.
PoRCH: A Novel Consensus Mechanism for Blockchain-Enabled Future SCADA Systems in Smart Grids and Industry 4.0
Venue: arXiv cs.CR 2021
Year: 2021

Smart Grids and Industry 4.0 (I4.0) are neither a dream nor a near-future thing anymore, rather it is happening now. The integration of more and more embedded systems and IoT devices is pushing smart grids and I4.0 forward at a breakneck speed. To cope up with this, the modification of age-old SCADA (Supervisory Control and Data Acquisition) systems in terms of decentralization, near-real-time operation, security, and privacy is necessary. In this context, blockchain technology has the potential of providing not only these essential features of the data acquisition process of future SCADA systems but also many other useful add-ons. On the other side, it is evident that various type of security breach tends to take place more during any economic turmoil. These can cause even more serious devastation to the global economy and human life. Thus, it is necessary to make our industries robust, automated, and resilient with secured and immutable data acquiring systems. This paper deals with the implementation scopes of blockchain in the data acquisition part of SCADA systems in the area of the smart grid and I4.0. There are several consensus mechanisms to support blockchain integration in the field of cryptocurrencies, vehicular networks, healthcare systems, e-commerce, etc. But little attention has been paid to developing efficient and easy-to-implement consensus mechanisms in the field of blockchain-enabled SCADA systems. From this perspective, a novel consensus mechanism, which we call PoRCH (Proof of Random Count in Hashes), with a customized mining node selection scheme has been proposed in this paper. Also, a small-scale prototype of a blockchain-enabled data acquisition system has been developed. The performance evaluation of the implemented prototype shows the benefits of blockchain technology.
Geometrically exact static isogeometric analysis of an arbitrarily curved spatial Bernoulli-Euler beam
Venue: arXiv cs.CE 2021
Year: 2021

The objective of this research is the development of a geometrically exact model for the analysis of arbitrarily curved spatial Bernoulli-Euler beams. The complete metric of the beam is utilized in order to include the effect of curviness on the nonlinear distribution of axial strain over the cross section. The exact constitutive relation between energetically conjugated pairs is employed, along with four reduced relations. The isogeometric approach, which allows smooth connections between finite elements, is used for the spatial discretization of the weak form. Two methods for updating the local basis are applied and discussed in the context of finite rotations. All the requirements of geometrically exact beam theory are satisfied, such as objectivity and path-independence. The accuracy of the formulation is verified by a thorough numerical analysis. The influence of the curviness on the structural response is scrutinized for two classic examples. If the exact response of the structure is sought, the curviness must be considered when choosing the appropriate beam model.
Single Person Pose Estimation: A Survey
Venue: arXiv cs.CV 2021
Year: 2021

Human pose estimation in unconstrained images and videos is a fundamental computer vision task. To illustrate the evolutionary path in technique, in this survey we summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. Specifically, we examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone, supervision representation, post-processing, standard datasets, evaluation metrics. To envisage the future directions, we finally discuss the key unsolved problems and potential trends for human pose estimation.
PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, using a deep convolutional neural network (CNN) as a feature encoder (or backbone) is the most commonly observed architectural pattern in several computer vision methods, and semantic segmentation is no exception. The two major drawbacks of this architectural pattern are: (i) the networks often fail to capture small classes such as wall, fence, pole, traffic light, traffic sign, and bicycle, which are crucial for autonomous vehicles to make accurate decisions. (ii) due to the arbitrarily increasing depth, the networks require massive labeled data and additional regularization techniques to converge and to prevent the risk of over-fitting, respectively. While regularization techniques come at minimal cost, the collection of labeled data is an expensive and laborious process. In this work, we address these two drawbacks by proposing a novel lightweight architecture named point-wise dense flow network (PDFNet). In PDFNet, we employ dense, residual, and multiple shortcut connections to allow a smooth gradient flow to all parts of the network. The extensive experiments on Cityscapes and CamVid benchmarks demonstrate that our method significantly outperforms baselines in capturing small classes and in few-data regimes. Moreover, our method achieves considerable performance in classifying out-of-the training distribution samples, evaluated on Cityscapes to KITTI dataset.
Pilot-Based Unsourced Random Access with a Massive MIMO Receiver: Interference Cancellation and Power Control
Venue: arXiv cs.IT 2021
Year: 2021

In this work we treat the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we consider the slowly fading scenario where the coherence block-length is large compared to the number of active users and the message can be transmitted in one coherence block. Unsourced random access refers to a form of grant-free random access where users are considered to be a-priori indistinguishable and the receiver recovers a list of transmitted messages up to permutation. In this work we show that, when the coherence block length is large enough, a conventional approach based on the transmission of non-orthogonal pilot sequences with subsequent channel estimation and Maximum-Ratio-Combining (MRC) provides a simple energy-efficient solution whose performance can be well approximated in closed form. Furthermore, we analyse the MRC step when successive interference cancellation (SIC) is done in groups, which allows to strike a balance between receiver complexity and reduced transmit powers. Finally, we investigate the impact of power control policies taking into account the unique nature of massive random access, including short message lengths, uncoordinated transmission, a very large amount of concurrent transmitters with unknown identities, channel estimation errors and decoding errors. As a byproduct we also present an extension of the MMV-AMP algorithm which allows to treat pathloss coefficients as deterministic unknowns by performing maximum likelihood estimation in each step of the MMV-AMP algorithm.
A Comprehensive Review on Summarizing Financial News Using Deep Learning
Venue: arXiv cs.CL 2021
Year: 2021

Investors make investment decisions depending on several factors such as fundamental analysis, technical analysis, and quantitative analysis. Another factor on which investors can make investment decisions is through sentiment analysis of news headlines, the sole purpose of this study. Natural Language Processing techniques are typically used to deal with such a large amount of data and get valuable information out of it. NLP algorithms convert raw text into numerical representations that machines can easily understand and interpret. This conversion can be done using various embedding techniques. In this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT, GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM. This work aims to evaluate these model's performance to choose the robust model in identifying the significant factors influencing the prediction. During this research, it was expected that Deep Leaming would be applied to get the desired results or achieve better accuracy than the state-of-the-art. The models are compared to check their outputs to know which one has performed better.
A Quality Assessment Instrument for Systematic Literature Reviews in Software Engineering
Venue: arXiv cs.SE 2021
Year: 2021

Context: Systematic literature reviews (SLRs) have become standard practise as part of software engineering research, although their quality varies. To build on the reviews, both for future research and industry practice, they need to be of high quality. Objective: To assess the quality of SLRs in software engineering, we put forward an appraisal instrument for SLRs. The instrument is intended for use by appraisers of reviews, but authors may also use it as a checklist when designing and documenting their reviews. Method: A well-established appraisal instrument from research in healthcare was used as a starting point to develop a quality assessment instrument. It is adapted to software engineering using guidelines, checklists, and experiences from software engineering. As a validation step, the first version was reviewed by four external experts on SLRs in software engineering and updated based on their feedback. Results: The outcome of the research is an appraisal instrument for the quality assessment of SLRs in software engineering. The instrument intends to support the appraiser in assessing the quality of an SLR. The instrument includes 16 items with different options to capture the quality. The item is assessed on a two or three-grade scale, depending on the item. The instrument also supports consolidating the items into groups, which are then used to assess the overall quality of a systematic literature review. Conclusion: It is concluded that the presented instrument may be helpful support for an appraiser in assessing the quality of SLRs in software engineering.