OpenRaiser/Intern-Atlas huggingface dataset:
Universality Problem for Unambiguous VASS
Venue: arXiv cs.FL 2020
Year: 2020

We study languages of unambiguous VASS, that is, Vector Addition Systems with States, whose transitions read letters from a finite alphabet, and whose acceptance condition is defined by a set of final states (i.e., the coverability language). We show that the problem of universality for unambiguous VASS is ExpSpace-complete, in sheer contrast to Ackermann-completeness for arbitrary VASS, even in dimension 1. When the dimension d is fixed, the universality problem is PSpace-complete if d is at least 2, and coNP-hard for 1-dimensional VASSes (also known as One Counter Nets).
XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual models, deep transformer layers are applied to perform multi-head attentions. For the ensemble model, the utterance representations taken from those individual models are concatenated and fed into a linear decoder to make the final decisions. Our ensemble model outperforms the individual models and shows up to 8.6% improvement over the individual models on the development set. On the test set, it achieves macro-F1 of 90.9% and becomes one of the high performing systems among 85 participants in the sub-task A of this shared task. Our analysis shows that although the ensemble model significantly improves the accuracy on the development set, the improvement is not as evident on the test set.
For a "computational" study of the intellectuals satellites
Venue: arXiv cs.DL 2020
Year: 2020

The present paper wishes to extend the theoretical reflections that we're currently working on: the "satellite intellectuals". This concept points a new "repertoire" of intellectuals that critics have not studied thoroughly and that is emerging with the amplification of the archives' digitalization of archives and their digital edition. Today, it is necessary to rethink and reevaluate the criteria we used to apply to define, integrate or exclude works and authors from, or in, a "canon". At the same time, it suggests new methods to process and deal with some concepts of computer tools and science.
Curriculum Vitae Recommendation Based on Text Mining
Venue: arXiv cs.CL 2020
Year: 2020

During the last years, the development in diverse areas related to computer science and internet, allowed to generate new alternatives for decision making in the selection of personnel for state and private companies. In order to optimize this selection process, the recommendation systems are the most suitable for working with explicit information related to the likes and dislikes of employers or end users, since this information allows to generate lists of recommendations based on collaboration or similarity of content. Therefore, this research takes as a basis these characteristics contained in the database of curricula and job offers, which correspond to the Peruvian ambit, which highlights the experience, knowledge and skills of each candidate, which are described in textual terms or words. This research focuses on the problem: how we can take advantage from the growth of unstructured information about job offers and curriculum vitae on different websites for CV recommendation. So, we use the techniques from Text Mining and Natural Language Processing. Then, as a relevant technique for the present study, we emphasize the technique frequency of the Term - Inverse Frequency of the documents (TF-IDF), which allows identifying the most relevant CVs in relation to a job offer of website through the average values (TF-IDF). So, the weighted value can be used as a qualification value of the relevant curriculum vitae for the recommendation.
Challenges in the Safety-Security Co-Assurance of Collaborative Industrial Robots
Venue: arXiv cs.RO 2020
Year: 2020

The coordinated assurance of interrelated critical properties, such as system safety and cyber-security, is one of the toughest challenges in critical systems engineering. In this chapter, we summarise approaches to the coordinated assurance of safety and security. Then, we highlight the state of the art and recent challenges in human-robot collaboration in manufacturing both from a safety and security perspective. We conclude with a list of procedural and technological issues to be tackled in the coordinated assurance of collaborative industrial robots.
Nonlinear Strain-limiting Elasticity for Fracture Propagation with Phase-Field Approach
Venue: arXiv cs.CE 2020
Year: 2020

The conventional model governing the spread of fractures in elastic material is formulated by coupling linear elasticity with deformation systems. The classical linear elastic fracture mechanics (LEFM) model is derived based on the assumption of small strain values. However, since the strain values in the model are linearly proportional to the stress values, the strain value can be large if the stress value increases. Thus this results in the contradiction of the assumption to LEFM and it is one of the major disadvantages of the model. In particular, this singular behavior of the strain values is often observed especially near the crack-tip, and it may not accurately predict realistic phenomena. Thus, we investigate the framework of a new class of theoretical model, which is known as the nonlinear strain-limiting model. The advantage of the nonlinear strain-limiting models over LEFM is that the strain value remains bounded even if the stress value tends to the infinity. This is achieved by assuming the nonlinear relation between the strain and stress in the derivation of the model. Moreover, we consider the quasi-static fracture propagation by coupling with the phase-field approach to present the effectiveness of the proposed strain-limiting model. Several numerical examples to evaluate and validate the performance of the new model and algorithms are presented. Detailed comparisons of the strain values, fracture energy, and fracture propagation speed between nonlinear strain-limiting model and LEFM for the quasi-static fracture propagation are discussed.
Designing a Novel Method for Personalizing Recommendations to Decrease Plastic Pollution
Venue: arXiv cs.CY 2020
Year: 2020

Third world countries tend to have a higher share of plastic waste that is inadequately managed while the First world countries have higher plastic waste generation per person. A difference in the characteristics of plastic pollution depending on the country's standing results in varying optimal recommendations for users. Through Big Text and OSOME meme analysis, I constructed a list with optimal recommendations for First World and Third World countries. Based on the list, I designed a User Interface wit Google Apps Scripts that provide personalized recommendations based on the country's standing and user's preferred difficulty and reassessed the code based on the six qualities of code. The purpose of the User Interface is to aid people who wish to help solve plastic pollution by offering a set of personalized tasks for each user and keeping their progress accountable through a point tracking system. With a significant number of users, the application could eventually contribute to solving the problem of plastic pollution.
Internet of Things for Current COVID-19 and Future Pandemics: An Exploratory Study
Venue: arXiv cs.CY 2020
Year: 2020

In recent years, the Internet of Things (IoT) has drawn convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT enabled /linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we present our approaches for the FinSim 2020 shared task on "Learning Semantic Representations for the Financial Domain". The goal of this task is to classify financial terms into the most relevant hypernym (or top-level) concept in an external ontology. We leverage both context-dependent and context-independent word embeddings in our analysis. Our systems deploy Word2vec embeddings trained from scratch on the corpus (Financial Prospectus in English) along with pre-trained BERT embeddings. We divide the test dataset into two subsets based on a domain rule. For one subset, we use unsupervised distance measures to classify the term. For the second subset, we use simple supervised classifiers like Naive Bayes, on top of the embeddings, to arrive at a final prediction. Finally, we combine both the results. Our system ranks 1st based on both the metrics, i.e., mean rank and accuracy.
Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments
Venue: arXiv cs.CV 2020
Year: 2020

The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, anti-viruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this paper, we present Fragments-Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 22 categories of features extracted from file fragments. These features can be employed by 7 categories of machine learning algorithms for the task of classification among various file formats.
FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation
Venue: arXiv cs.GR 2020
Year: 2020

Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high-fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial-and-error processes. To address both challenges, we present a novel data-driven framework, FASTSWARM, to model complex behaviors of flying insects based on real-world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real-time performance for large swarms. The high-fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, the self-propelled behaviors such as target following and obstacle avoidance, and other characteristics such as the random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modelled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real-world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable.
Information disorders on Italian Facebook during COVID-19 infodemic
Venue: arXiv cs.SI 2020
Year: 2020

In this work we carry out an exploratory analysis of online conversations on the Italian Facebook during the recent COVID-19 pandemic. We analyze the circulation of controversial topics associated with the origin of the virus, which involve popular targets of misinformation, such as migrants and 5G technology. We collected over 1.5 M posts in Italian language and related to COVID-19, shared by nearly 80k public pages and groups for a period of four months since January 2020. Overall, we find that potentially harmful content shared by unreliable sources is substantially negligible compared to traditional news websites, and that discussions over controversial topics has a limited engagement w.r.t to the pandemic in general. Besides, we highlight a "small-worldness" effect in the URL sharing diffusion network, indicating that users navigating through a limited set of pages could reach almost the entire pool of shared content related to the pandemic, thus being easily exposed to harmful propaganda as well as to verified information on the virus.
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification
Venue: arXiv cs.CL 2020
Year: 2020

Question paraphrase identification is a key task in Community Question Answering (CQA) to determine if an incoming question has been previously asked. Many current models use word embeddings to identify duplicate questions, but the use of topic models in feature-engineered systems suggests that they can be helpful for this task, too. We therefore propose two ways of merging topics with word embeddings (early vs. late fusion) in a new neural architecture for question paraphrase identification. Our results show that our system outperforms neural baselines on multiple CQA datasets, while an ablation study highlights the importance of topics and especially early topic-embedding fusion in our architecture.
Multi-Spectral Facial Biometrics in Access Control
Venue: arXiv cs.CV 2020
Year: 2020

This study demonstrates how facial biometrics, acquired using multi-spectral sensors, such as RGB, depth, and infrared, assist the data accumulation in the process of authorizing users of automated and semi-automated access systems. This data serves the purposes of person authentication, as well as facial temperature estimation. We utilize depth data taken using an inexpensive RGB-D sensor to find the head pose of a subject. This allows the selection of video frames containing a frontal-view head pose for face recognition and face temperature reading. Usage of the frontal-view frames improves the efficiency of face recognition while the corresponding synchronized IR video frames allow for more efficient temperature estimation for facial regions of interest. In addition, this study reports emerging applications of biometrics in biomedical and health care solutions. Including surveys of recent pilot projects, involving new sensors of biometric data and new applications of human physiological and behavioral biometrics. It also shows the new and promising horizons of using biometrics in natural and contactless control interfaces for surgical control, rehabilitation and accessibility.
Watchlist Risk Assessment using Multiparametric Cost and Relative Entropy
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses the facial biometric-enabled watchlist technology in which risk detectors are mandatory mechanisms for early detection of threats, as well as for avoiding offense to innocent travelers. We propose a multiparametric cost assessment and relative entropy measures as risk detectors. We experimentally demonstrate the effects of mis-identification and impersonation under various watchlist screening scenarios and constraints. The key contributions of this paper are the novel techniques for design and analysis of the biometric-enabled watchlist and the supporting infrastructure, as well as measuring the impersonation impact on e-border performance.
When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games
Venue: arXiv cs.GT 2020
Year: 2020

The actions of intelligent agents, such as chatbots, recommender systems, and virtual assistants are typically not fully transparent to the user. Consequently, using such an agent involves the user exposing themselves to the risk that the agent may act in a way opposed to the user's goals. It is often argued that people use trust as a cognitive shortcut to reduce the complexity of such interactions. Here we formalise this by using the methods of evolutionary game theory to study the viability of trust-based strategies in repeated games. These are reciprocal strategies that cooperate as long as the other player is observed to be cooperating. Unlike classic reciprocal strategies, once mutual cooperation has been observed for a threshold number of rounds they stop checking their co-player's behaviour every round, and instead only check with some probability. By doing so, they reduce the opportunity cost of verifying whether the action of their co-player was actually cooperative. We demonstrate that these trust-based strategies can outcompete strategies that are always conditional, such as Tit-for-Tat, when the opportunity cost is non-negligible. We argue that this cost is likely to be greater when the interaction is between people and intelligent agents, because of the reduced transparency of the agent. Consequently, we expect people to use trust-based strategies more frequently in interactions with intelligent agents. Our results provide new, important insights into the design of mechanisms for facilitating interactions between humans and intelligent agents, where trust is an essential factor.
Validation Frameworks for Self-Driving Vehicles: A Survey
Venue: arXiv cs.SE 2020
Year: 2020

As a part of the digital transformation, we interact with more and more intelligent gadgets. Today, these gadgets are often mobile devices, but in the advent of smart cities, more and more infrastructure---such as traffic and buildings---in our surroundings becomes intelligent. The intelligence, however, does not emerge by itself. Instead, we need both design techniques to create intelligent systems, as well as approaches to validate their correct behavior. An example of intelligent systems that could benefit smart cities are self-driving vehicles. Self-driving vehicles are continuously becoming both commercially available and common on roads. Accidents involving self-driving vehicles, however, have raised concerns about their reliability. Due to these concerns, the safety of self-driving vehicles should be thoroughly tested before they can be released into traffic. To ensure that self-driving vehicles encounter all possible scenarios, several millions of hours of testing must be carried out; therefore, testing self-driving vehicles in the real world is impractical. There is also the issue that testing self-driving vehicles directly in the traffic poses a potential safety hazard to human drivers. To tackle this challenge, validation frameworks for testing self-driving vehicles in simulated scenarios are being developed by academia and industry. In this chapter, we briefly introduce self-driving vehicles and give an overview of validation frameworks for testing them in a simulated environment. We conclude by discussing what an ideal validation framework at the state of the art should be and what could benefit validation frameworks for self-driving vehicles in the future.
Human-Centered Unsupervised Segmentation Fusion
Venue: arXiv cs.CV 2020
Year: 2020

Segmentation is generally an ill-posed problem since it results in multiple solutions and is, therefore, hard to define ground truth data to evaluate algorithms. The problem can be naively surpassed by using only one annotator per image, but such acquisition doesn't represent the cognitive perception of an image by the majority of people. Nowadays, it is not difficult to obtain multiple segmentations with crowdsourcing, so the only problem that stays is how to get one ground truth segmentation per image. There already exist numerous algorithmic solutions, but most methods are supervised or don't consider confidence per human segmentation. In this paper, we introduce a new segmentation fusion model that is based on K-Modes clustering. Results obtained from publicly available datasets with human ground truth segmentations clearly show that our model outperforms the state-of-the-art on human segmentations.
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning
Venue: arXiv cs.SI 2020
Year: 2020

Network Embedding has been widely studied to model and manage data in a variety of real-world applications. However, most existing works focus on networks with single-typed nodes or edges, with limited consideration of unbalanced distributions of nodes and edges. In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes. To tackle these challenges, in this paper we propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions and propose a unified framework for the embedding learning. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba. The results empirically demonstrate that MSM can achieve relatively significant gains over previous state-of-arts on link prediction.
A 3/2-Approximation for the Metric Many-visits Path TSP
Venue: arXiv cs.DM 2020
Year: 2020

In the Many-visits Path TSP, we are given a set of $n$ cities along with their pairwise distances (or cost) $c(uv)$, and moreover each city $v$ comes with an associated positive integer request $r(v)$. The goal is to find a minimum-cost path, starting at city $s$ and ending at city $t$, that visits each city $v$ exactly $r(v)$ times. We present a $\frac32$-approximation algorithm for the metric Many-visits Path TSP, that runs in time polynomial in $n$ and poly-logarithmic in the requests $r(v)$. Our algorithm can be seen as a far-reaching generalization of the $\frac32$-approximation algorithm for Path TSP by Zenklusen (SODA 2019), which answered a long-standing open problem by providing an efficient algorithm which matches the approximation guarantee of Christofides' algorithm from 1976 for metric TSP. One of the key components of our approach is a polynomial-time algorithm to compute a connected, degree bounded multigraph of minimum cost. We tackle this problem by generalizing a fundamental result of Király, Lau and Singh (Combinatorica, 2012) on the Minimum Bounded Degree Matroid Basis problem, and devise such an algorithm for general polymatroids, even allowing element multiplicities. Our result directly yields a $\frac32$-approximation to the metric Many-visits TSP, as well as a $\frac32$-approximation for the problem of scheduling classes of jobs with sequence-dependent setup times on a single machine so as to minimize the makespan.