OpenRaiser/Intern-Atlas huggingface dataset:
SwarmPlay: A Swarm of Nano-Quadcopters Playing Tic-tac-toe Board Game against a Human
Venue: arXiv cs.HC 2021
Year: 2021

We present a new paradigm of games, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. The motivation behind the research is to make the games with machines tangible and interactive. Although some research on the robotic players for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with a human as it proposes a multi-agent swarm instead of a single interactive robot. The proposed system consists of a robotic swarm, a workstation, a computer vision (CV), and Game Theory-based algorithms. A novel game algorithm was developed to provide a natural game experience to the user. The preliminary user study revealed that participants were highly engaged in the game with drones (69% put a maximum score on the Likert scale) and found it less artificial compared to the regular computer-based systems (77% put maximum score). The affection of the user's game perception from its outcome was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.
Correlation of biological and computer viruses through evolutionary game theory
Venue: arXiv cs.GT 2021
Year: 2021

Computer viruses have many similarities to biological viruses, and their association may offer new perspectives and new opportunities in the effort to tackle and even eradicate them. Evolutionary game theory has been established as a useful tool for modeling viral behaviors. This work attempts to correlate a well-known virus, namely Virlock, with the bacteriophage $\phi6$. Furthermore, the paper suggests certain efficient strategies and practical ways that may reduce infection by Virlock and similar such viruses.
Agile Elicitation of Scalability Requirements for Open Systems: A Case Study
Venue: arXiv cs.SE 2021
Year: 2021

Eliciting scalability requirements during agile software development is complicated and poorly described in previous research. This article presents a lightweight artifact for eliciting scalability requirements during agile software development: the ScrumScale model. The ScrumScale model is a simple spreadsheet. The scalability concepts underlying the ScrumScale model are clarified in this design science research, which also utilizes coordination theory. This paper describes the open banking case study, where a legacy banking system becomes open. This challenges the scalability of this legacy system. The first step in understanding this challenge is to elicit the new scalability requirements. In the open banking case study, key stakeholders from TietoEVRY spent 55 hours eliciting TietoEVRY's open banking project's scalability requirements. According to TietoEVRY, the ScrumScale model provided a systematic way of producing scalability requirements. For TietoEVRY, the scalability concepts behind the ScrumScale model also offered significant advantages in dialogues with other stakeholders.
From "study with me" to study with you: how activities of Study With Me livestream on Bilibili facilitate SRL community
Venue: arXiv cs.HC 2021
Year: 2021

It has become a trend to use study with me (SWM) Livestream to create a personalized study ambiance. However, we still have little understanding of the activities of SWM livestream and the streamer's motivation to produce SWM livestream. This paper provides an overview of the activities and how streamers regulate these activities of SWM livestream on a Chinese popular User Generated Content(UGC) website, Bilibili. We observed the number and popularity of the SWM livestreams and analyzed 800 livestreams to understand the streamers' study goals. We analyzed 20 SWM livestreams in detail and interviewed 12 streamers and 10 viewers to understand the activities and the streamer's motivation. We found that streamers produced SWM livestream to seek supervision, find like-minded study partners and help and company others. Streamers don't interact or instruct with the viewers directly but use chat-bot and autonomous interaction to alleviated the interaction burden. Unique sessions like checking-in and study progress reporting promote the viewers' social presence, promoting SOC, and enhancing their engagement. Strict rules and punishment are widely used to concentrate the members on study and contribute to positive atmosphere. We also found that SWM livestream often disappears when the examination is done and the streamer faces doubts on motivation and appearance. These findings suggest that SRL community can provide cognitive and socioemotional support for lonely learners to stick to a long-term study. The activities and streamer's practice inspired how streamers can focus on contemplative efforts while controlling the interaction.
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
Venue: arXiv cs.CL 2021
Year: 2021

Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods.
Angle Estimation for Terahertz Ultra-Massive MIMO-Based Space-to-Air Communications
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates terahertz ultra-massive (UM)-MIMO-based angle estimation for space-to-air communications, which can solve the performance degradation problem caused by the dual delay-beam squint effects of terahertz UM-MIMO channels. Specifically, we first design a grouping true-time delay unit module that can significantly mitigate the impact of delay-beam squint effects to establish the space-to-air THz link. Based on the subarray selection scheme, the UM hybrid array can be equivalently considered as a low-dimensional fully-digital array, and then the fine estimates of azimuth/elevation angles at both UAVs and satellite can be separately acquired using the proposed prior-aided iterative angle estimation algorithm. The simulation results that close to Cramér-Rao lower bounds verify the effectiveness of our solution.
Improving Deep Learning for HAR with shallow LSTMs
Venue: arXiv cs.HC 2021
Year: 2021

Recent studies in Human Activity Recognition (HAR) have shown that Deep Learning methods are able to outperform classical Machine Learning algorithms. One popular Deep Learning architecture in HAR is the DeepConvLSTM. In this paper we propose to alter the DeepConvLSTM architecture to employ a 1-layered instead of a 2-layered LSTM. We validate our architecture change on 5 publicly available HAR datasets by comparing the predictive performance with and without the change employing varying hidden units within the LSTM layer(s). Results show that across all datasets, our architecture consistently improves on the original one: Recognition performance increases up to 11.7% for the F1-score, and our architecture significantly decreases the amount of learnable parameters. This improvement over DeepConvLSTM decreases training time by as much as 48%. Our results stand in contrast to the belief that one needs at least a 2-layered LSTM when dealing with sequential data. Based on our results we argue that said claim might not be applicable to sensor-based HAR.
YASMIN: a Real-time Middleware for COTS Heterogeneous Platforms
Venue: arXiv cs.DC 2021
Year: 2021

Commercial-Off-The-Shelf heterogeneous platforms provide immense computational power, but are difficult to program and to correctly use when real-time requirements come into play: A sound configuration of the operating system scheduler is needed, and a suitable mapping of tasks to computing units must be determined. Flawed designs may lead a sub-optimal system configurations and thus to wasted resources, or even to deadline misses and failures. We propose YASMIN, a middleware to schedule end-user applications with real-time requirements in user space and on behalf of the operating system. YASMIN provides an easy-to-use programming interface and portability. It treats heterogeneity on COTS heterogeneous embedded platforms as a first-class citizen: It supports multiple functionally equivalent task implementations with distinct extra-functional behaviour. This enables the system designer to quickly explore different scheduling policies and task-to-core mappings, and thus, to improve overall system performance. In this paper, we present the design and implementation of YASMIN and provide an analysis of the scheduling overhead on an Odroid-XU4 platform. Last but not least, we demonstrate the merits of YASMIN on an industrial use-case involving a Search & Rescue drone.
Analysis and Transformation of Constrained Horn Clauses for Program Verification
Venue: arXiv cs.LO 2021
Year: 2021

This paper surveys recent work on applying analysis and transformation techniques that originate in the field of constraint logic programming (CLP) to the problem of verifying software systems. We present specialisation-based techniques for translating verification problems for different programming languages, and in general software systems, into satisfiability problems for constrained Horn clauses (CHCs), a term that has become popular in the verification field to refer to CLP programs. Then, we describe static analysis techniques for CHCs that may be used for inferring relevant program properties, such as loop invariants. We also give an overview of some transformation techniques based on specialisation and fold/unfold rules, which are useful for improving the effectiveness of CHC satisfiability tools. Finally, we discuss future developments in applying these techniques.
Self-supervised Answer Retrieval on Clinical Notes
Venue: arXiv cs.IR 2021
Year: 2021

Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context. We approach this challenge specifically in a clinical scenario, where doctors retrieve cohorts of patients based on diagnoses and other latent medical aspects. We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. In addition, we contribute a novel retrieval dataset based on clinical notes to simulate this scenario on a large corpus of clinical notes. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. From our extensive evaluation on MIMIC-III and three other healthcare datasets, we report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages. This makes the model powerful especially in zero-shot scenarios where only limited training data is available.
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling
Venue: arXiv cs.CV 2021
Year: 2021

According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architectures in a real-world scenario. To model such a noise, in this paper we have proposed the homoscedastic aleatoric uncertainty estimation, and present a series of novel loss functions to address the problem of image object detection at scale. Specifically, the proposed functions are based on Bayesian inference and we have incorporated them into the common community-adopted object detection deep learning architecture RetinaNet. We have also shown that modeling of homoscedastic aleatoric uncertainty using our novel functions allows to increase the model interpretability and to improve the object detection performance being evaluated on the COCO dataset.
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Venue: arXiv cs.CL 2021
Year: 2021

Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks. Despite the success, most current pre-trained language models, such as BERT, are trained based on single-grained tokenization, usually with fine-grained characters or sub-words, making it hard for them to learn the precise meaning of coarse-grained words and phrases. In this paper, we propose a simple yet effective pre-training method named LICHEE to efficiently incorporate multi-grained information of input text. Our method can be applied to various pre-trained language models and improve their representation capability. Extensive experiments conducted on CLUE and SuperGLUE demonstrate that our method achieves comprehensive improvements on a wide variety of NLU tasks in both Chinese and English with little extra inference cost incurred, and that our best ensemble model achieves the state-of-the-art performance on CLUE benchmark competition.
Estimating the Peer Degree of Reachable Peers in the Bitcoin P2P Network
Venue: arXiv cs.NI 2021
Year: 2021

A recent spam wave of IP addresses in the Bitcoin P2P network allowed us to estimate the degree distribution of reachable peers in the network. The resulting distribution shows that about every second reachable peer runs with Bitcoin Core's default setting of a maximum of 125 concurrent connections and nearly all connection slots are taken. We validate this result and, in addition, use our observations of the spam wave to group addresses that belong to the same peer. By doing this grouping, we improve on previous measurements and show that simply counting addresses overestimates the number of reachable peers by 13 %.
Counterexample Classification
Venue: arXiv cs.SE 2021
Year: 2021

In model checking, when a given model fails to satisfy the desired specification, a typical model checker provides a counterexample that illustrates how the violation occurs. In general, there exist many diverse counterexamples that exhibit distinct violating behaviors, which the user may wish to examine before deciding how to repair the model. Unfortunately, obtaining this information is challenging in existing model checkers since (1) the number of counterexamples may be too large to enumerate one by one, and (2) many of these counterexamples are redundant, in that they describe the same type of violating behavior. In this paper, we propose a technique called counterexample classification. The goal of classification is to partition the space of all counterexamples into a finite set of counterexample classes, each of which describes a distinct type of violating behavior for the given specification. These classes are then presented as a summary of possible violating behaviors in the system, freeing the user from manually having to inspect or analyze numerous counterexamples to extract the same information. We have implemented a prototype of our technique on top of an existing formal modeling and verification tool, the Alloy Analyzer, and evaluated the effectiveness of the technique on case studies involving the well-known Needham-Schroeder protocol with promising results.
On the Privacy of National Contact Tracing COVID-19 Applications: The Coronavírus-SUS Case
Venue: arXiv cs.CR 2021
Year: 2021

The 2019 Coronavirus disease (COVID-19) pandemic, caused by a quick dissemination of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has had a deep impact worldwide, both in terms of the loss of human life and the economic and social disruption. The use of digital technologies has been seen as an important effort to combat the pandemic and one of such technologies is contact tracing applications. These applications were successfully employed to face other infectious diseases, thus they have been used during the current pandemic. However, the use of contact tracing poses several privacy concerns since it is necessary to store and process data which can lead to the user/device identification as well as location and behavior tracking. These concerns are even more relevant when considering nationwide implementations since they can lead to mass surveillance by authoritarian governments. Despite the restrictions imposed by data protection laws from several countries, there are still doubts on the preservation of the privacy of the users. In this article, we analyze the privacy features in national contact tracing COVID-19 applications considering their intrinsic characteristics. As a case study, we discuss in more depth the Brazilian COVID-19 application Coronavírus-SUS, since Brazil is one of the most impacted countries by the current pandemic. Finally, as we believe contact tracing will continue to be employed as part of the strategy for the current and potential future pandemics, we present key research challenges.
Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks
Venue: arXiv cs.NI 2021
Year: 2021

Air traffic management (ATM) of manned and unmanned aerial vehicles (AVs) relies critically on ubiquitous location tracking. While technologies exist for AVs to broadcast their location periodically and for airports to track and detect AVs, methods to verify the broadcast locations and complement the ATM coverage are urgently needed, addressing anti-spoofing and safe coexistence concerns. In this work, we propose an ATM solution by exploiting noncoherent crowdsourced wireless networks (CWNs) and correcting the inherent clock-synchronization problems present in such non-coordinated sensor networks. While CWNs can provide a great number of measurements for ubiquitous ATM, these are normally obtained from unsynchronized sensors. This article first presents an analysis of the effects of lack of clock synchronization in ATM with CWN and provides solutions based on the presence of few trustworthy sensors in a large non-coordinated network. Secondly, autoregressive-based and long short-term memory (LSTM)-based approaches are investigated to achieve the time synchronization needed for localization of the AVs. Finally, a combination of a multilateration (MLAT) method and a Kalman filter is employed to provide an anti-spoofing tracking solution for AVs. We demonstrate the performance advantages of our framework through a dataset collected by a real-world CWN. Our results show that the proposed framework achieves localization accuracy comparable to that acquired using only GPS-synchronized sensors and outperforms the localization accuracy obtained based on state-of-the-art CWN synchronization methods.
Robust Semantic Segmentation with Superpixel-Mix
Venue: arXiv cs.CV 2021
Year: 2021

Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertainty and reduced bias. To improve reliability, we introduce Superpixel-mix, a new superpixel-based data augmentation method with teacher-student consistency training. Unlike other mixing-based augmentation techniques, mixing superpixels between images is aware of object boundaries, while yielding consistent gains in segmentation accuracy. Our proposed technique achieves state-of-the-art results in semi-supervised semantic segmentation on the Cityscapes dataset. Moreover, Superpixel-mix improves the reliability of semantic segmentation by reducing network uncertainty and bias, as confirmed by competitive results under strong distributions shift (adverse weather, image corruptions) and when facing out-of-distribution data.
Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining
Venue: arXiv cs.SE 2021
Year: 2021

Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful edit operations will be the ones that compress the model differences. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We find that our approach is able to discover frequent edit operations that have actually been applied. Furthermore, Ockham is able to extract edit operations in an industrial setting that are meaningful to practitioners.
Information Batteries: Storing Opportunity Power with Speculative Execution
Venue: arXiv cs.DC 2021
Year: 2021

Coping with the intermittency of renewables is a fundamental challenge, with load shifting and grid-scale storage as key responses. We propose Information Batteries (IB), in which energy is stored in the form of information -- specifically, the results of completed computational tasks. Information Batteries thus provide storage through speculative load shifting, anticipating computation that will be performed in the future. We take a distributed systems perspective, and evaluate the extent to which an IB storage system can be made practical through augmentation of compiler toolchains, key-value stores, and other important elements in modern hyper-scale compute. In particular, we implement one specific IB prototype by augmenting the Rust compiler to enable transparent function-level precomputation and caching. We evaluate the overheads this imposes, along with macro-level job prediction and power prediction. We also evaluate the space of operation for an IB system, to identify the best case efficiency of any IB system for a given power and compute regime.
Analysis of Online Toxicity Detection Using Machine Learning Approaches
Venue: arXiv cs.CL 2021
Year: 2021

Social media and the internet have become an integral part of how people spread and consume information. Over a period of time, social media evolved dramatically, and almost half of the population is using social media to express their views and opinions. Online hate speech is one of the drawbacks of social media nowadays, which needs to be controlled. In this paper, we will understand how hate speech originated and what are the consequences of it; Trends of machine-learning algorithms to solve an online hate speech problem. This study contributes by providing a systematic approach to help researchers to identify a new research direction and elucidating the shortcomings of the studies and model, as well as providing future directions to advance the field.