OpenRaiser/Intern-Atlas huggingface dataset:
Classifications based on response times for detecting early-stage Alzheimer's disease
Venue: arXiv cs.LG 2021
Year: 2021

Introduction- This paper mainly describes a way to detect with high accuracy patients with early-stage Alzheimer's disease (ES-AD) versus healthy control (HC) subjects, from datasets built with handwriting and drawing task records. Method- The proposed approach uses subject's response times. An optimal subset of tasks is first selected with a "Support Vector Machine" (SVM) associated with a grid search. Mixtures of Gaussian distributions defined in the space of task durations are then used to reproduce and explain the results of the SVM. Finally, a surprisingly simple and efficient ad hoc classification algorithm is deduced from the Gaussian mixtures. Results- The solution presented in this paper makes two or even four times fewer errors than the best results of the state of the art concerning the classification HC/ES-AD from handwriting and drawing tasks. Discussion- The best SVM learning model reaches a high accuracy for this classification but its learning capacity is too large to ensure a low overfitting risk regarding the small size of the dataset. The proposed ad hoc classification algorithm only requires to optimize three real-parameters. It should therefore benefit from a good generalization ability.
VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
Venue: arXiv cs.CL 2021
Year: 2021

Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv) a veracity classifier. In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system. VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components. The co-train engine helps the VAEs to tune their latent representations to be classifier-friendly. We also show that VRoC is able to classify unseen rumors with high levels of accuracy. For the PHEME dataset, VRoC consistently outperforms several state-of-the-art techniques, on both observed and unobserved rumors, by up to 26.9%, in terms of macro-F1 scores.
F-RouND: Fog-based Rogue Nodes Detection in Vehicular Ad hoc Networks
Venue: arXiv cs.NI 2021
Year: 2021

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.
Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data
Venue: arXiv cs.CV 2021
Year: 2021

The presence of people in an urban area throughout the day -- often called 'urban vitality' -- is one of the qualities world-class cities aspire to the most, yet it is one of the hardest to achieve. Back in the 1970s, Jane Jacobs theorized urban vitality and found that there are four conditions required for the promotion of life in cities: diversity of land use, small block sizes, the mix of economic activities, and concentration of people. To build proxies for those four conditions and ultimately test Jane Jacobs's theory at scale, researchers have had to collect both private and public data from a variety of sources, and that took decades. Here we propose the use of one single source of data, which happens to be publicly available: Sentinel-2 satellite imagery. In particular, since the first two conditions (diversity of land use and small block sizes) are visible to the naked eye from satellite imagery, we tested whether we could automatically extract them with a state-of-the-art deep-learning framework and whether, in the end, the extracted features could predict vitality. In six Italian cities for which we had call data records, we found that our framework is able to explain on average 55% of the variance in urban vitality extracted from those records.
Can You Accept LaTeX Files from Strangers? Ten Years Later
Venue: arXiv cs.CR 2021
Year: 2021

It is well-known that Microsoft Word/Excel compatible documents or PDF files can contain malicious content. LaTeX files are unfortunately no exception either. LaTeX users often include third-party codes through sources or packages (.sty or .cls files). But those packages can execute malicious commands on the users' system, in order to capture sensitive information or to perform denial of service attacks. Checkoway et al. [3] were the first to warn LaTeX users of these threats. Collaborative cloud-based LaTeX editors and services compiling LaTeX sources are particularly concerned. In this paper, we have created a LaTeX package that collects system data and hides them inside the PDF file produced by the target. Then, we have measured what can be recovered by hackers using malicious LaTeX file on online services, and which measures those services have enforced to thwart the threats. Services defend themselves using sandbox or commands restrictions. Commands restrictions are more difficult to setup and we found one service (PMLatex) which is too permissive.
Automatically Identifying Parameter Constraints in Complex Web APIs: A Case Study at Adyen
Venue: arXiv cs.SE 2021
Year: 2021

Web APIs may have constraints on parameters, such that not all parameters are either always required or always optional. Moreover, the presence or value of one parameter could cause another parameter to be required, or parameters could have restrictions on what kinds of values are valid. Having a clear overview of the constraints helps API consumers to integrate without the need for additional support and with fewer integration faults. We made use of existing documentation and code analysis approaches for identifying parameter constraints in complex web APIs. In this paper, we report our case study of several APIs at Adyen, a large-scale payment company that offers complex Web APIs to its customers. Our results show that the documentation- and code-based approach can identify 23% and 53% of the constraints respectively and, when combined, 68% of them. We also reflect on the current challenges that these approaches face. In particular, the absence of information that explicitly describes the constraints in the documentation (in the documentation analysis), and the engineering of a sound static code analyser that is sensitive to data-flow, maintains longer parameter references throughout the API's code, and that is able to symbolically execute the several libraries and frameworks used by the API (in the static analysis).
The Complexity of Learning Linear Temporal Formulas from Examples
Venue: arXiv cs.FL 2021
Year: 2021

In this paper we initiate the study of the computational complexity of learning linear temporal logic (LTL) formulas from examples. We construct approximation algorithms for fragments of LTL and prove hardness results; in particular we obtain tight bounds for approximation of the fragment containing only the next operator and conjunctions, and prove NP-completeness results for many fragments.
Decentralized Federated Learning Preserves Model and Data Privacy
Venue: arXiv cs.LG 2021
Year: 2021

The increasing complexity of IT systems requires solutions, that support operations in case of failure. Therefore, Artificial Intelligence for System Operations (AIOps) is a field of research that is becoming increasingly focused, both in academia and industry. One of the major issues of this area is the lack of access to adequately labeled data, which is majorly due to legal protection regulations or industrial confidentiality. Methods to mitigate this stir from the area of federated learning, whereby no direct access to training data is required. Original approaches utilize a central instance to perform the model synchronization by periodical aggregation of all model parameters. However, there are many scenarios where trained models cannot be published since its either confidential knowledge or training data could be reconstructed from them. Furthermore the central instance needs to be trusted and is a single point of failure. As a solution, we propose a fully decentralized approach, which allows to share knowledge between trained models. Neither original training data nor model parameters need to be transmitted. The concept relies on teacher and student roles that are assigned to the models, whereby students are trained on the output of their teachers via synthetically generated input data. We conduct a case study on log anomaly detection. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher. In addition, we demonstrate that our method allows the synchronization of several models trained on different distinct training data subsets.
Text-to-hashtag Generation using Seq2seq Learning
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we studied whether models based on BiLSTM and BERT can predict hashtags in Brazilian Portuguese for Ecommerce websites. Hashtags have a sizable financial impact on Ecommerce. We processed a corpus of Ecommerce reviews as inputs, and predicted hashtags as outputs. We evaluated the results using four quantitative metrics: NIST, BLEU, METEOR and a crowdsourced score. A word cloud was used as a qualitative metric. While all computer-generated metrics (NIST, BLEU and METEOR) indicated bad results, the crowdsourced results produced amazing scores. We concluded that the texts predicted by the neural networks are very promising for use as hashtags for products on Ecommerce websites. The code for this work is available at https://github.com/augustocamargo/text-to-hashtag.
Quadratic type checking for objective type theory
Venue: arXiv cs.LO 2021
Year: 2021

We introduce a modification of standard Martin-Lof type theory in which we eliminate definitional equality and replace all computation rules by propositional equalities. We show that type checking for such a system can be done in quadratic time and that it has a natural homotopy-theoretic semantics.
Counting Protests in News Articles: A Dataset and Semi-Automated Data Collection Pipeline
Venue: arXiv cs.CL 2021
Year: 2021

Between January 2017 and January 2021, thousands of local news sources in the United States reported on over 42,000 protests about topics such as civil rights, immigration, guns, and the environment. Given the vast number of local journalists that report on protests daily, extracting these events as structured data to understand temporal and geographic trends can empower civic decision-making. However, the task of extracting events from news articles presents well known challenges to the NLP community in the fields of domain detection, slot filling, and coreference resolution. To help improve the resources available for extracting structured data from news stories, our contribution is three-fold. We 1) release a manually labeled dataset of news article URLs, dates, locations, crowd size estimates, and 494 discrete descriptive tags corresponding to 42,347 reported protest events in the United States between January 2017 and January 2021; 2) describe the semi-automated data collection pipeline used to discover, sort, and review the 144,568 English articles that comprise the dataset; and 3) benchmark a long-short term memory (LSTM) low dimensional classifier that demonstrates the utility of processing news articles based on syntactic structures, such as paragraphs and sentences, to count the number of reported protest events.
On Secure Distributed Linearly Separable Computation
Venue: arXiv cs.IT 2021
Year: 2021

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a subset of input datasets to each server, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost. Then we focus on the case where the computation cost of each server is minimum and aim to minimize the size of the randomness variable introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment. We then propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.
Human-Robot Interaction: Applications
Venue: arXiv cs.RO 2021
Year: 2021

Recently, human-robot interaction (HRI) is an extensive research topic and theme which gained importance and significance. HRI aims at the complementary combination between the robot capabilities and human skills. The robots assist humans in terms of precision, speed, and force. The humans contribute in terms of the experience, knowledge of executing the task, intuition, and easy adaptation and learning, and understanding of control strategies. In this work, the applications of human-robot interaction are reviewed. These applications could be industrial, medical, agricultural, servical, and educational. HRI can be found in industrial applications in picking and placing in the production lines, welding processes, parts assembly, and painting. Assistive robotics are one from the highest profile areas in HRI. For people with the physical and the mental challenges, the robots can provide the opportunity of interaction and therapy. In addition, HRI can be widely applied in hospitals. Nowadays, HRI is very important for facing the new coronavirus (COVID-19) pandemic. In agriculture, the cooperation between human and the robot helps with many tasks including harvesting, seeding, fertilizing, spraying, weed detection, hauling, and mowing. HRI can also be found in other applications such as education, mining, and home use.
Faulty picture-hanging improved
Venue: arXiv cs.DM 2021
Year: 2021

A picture-hanging puzzle is the task of hanging a framed picture with a wire around a set of nails in such a way that it can remain hanging on certain specified sets of nails, but will fall if any more are removed. The classical brain teaser asks us to hang a picture on two nails in such a way that it falls when any one is detached. Demaine et al (2012) proved that all reasonable puzzles of this kind are solvable, and that for the $k$-out-of-$n$ problem, the size of a solution can be bounded by a polynomial in $n$. We give simplified proofs of these facts, for the latter leading to a reasonable exponent in the polynomial bound.
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
Venue: arXiv cs.LG 2021
Year: 2021

We resolve the long-standing "impossible tuning" issue for the classic expert problem and show that, it is in fact possible to achieve regret $O\left(\sqrt{(\ln d)\sum_t \ell_{t,i}^2}\right)$ simultaneously for all expert $i$ in a $T$-round $d$-expert problem where $\ell_{t,i}$ is the loss for expert $i$ in round $t$. Our algorithm is based on the Mirror Descent framework with a correction term and a weighted entropy regularizer. While natural, the algorithm has not been studied before and requires a careful analysis. We also generalize the bound to $O\left(\sqrt{(\ln d)\sum_t (\ell_{t,i}-m_{t,i})^2}\right)$ for any prediction vector $m_t$ that the learner receives, and recover or improve many existing results by choosing different $m_t$. Furthermore, we use the same framework to create a master algorithm that combines a set of base algorithms and learns the best one with little overhead. The new guarantee of our master allows us to derive many new results for both the expert problem and more generally Online Linear Optimization.
Brain Performance Analysis based on an Electroencephalogram Headset
Venue: arXiv cs.HC 2021
Year: 2021

Deficit of attention, anxiety, sleep disorders are some of the problems which affect many persons. As these issues can evolve into severe conditions, more factors should be taken into consideration. The paper proposes a conception which aims to help students to enhance their brain performance. An electrocephalogram headset is used to trigger the brainwaves, along with a web application which manages the input data which comes from the headset and from the user. Factors like current activity, mood, focus, stress, relaxation, engagement, excitement and interest are provided in numerical format through the use of the headset. The users offer information about their activities related to relaxation, listening to music, watching a movie, and studying. Based on the analysis, it was found that the users consider the application easy to use. As the users are more equilibrated emotionally, their results are improved. This allowed the persons to be more confident on themselves. In the case of students, the neurofeedback can be studied for the better sport and artistic performances, including the case of the attention deficit hyperactivity disorder. Aptitudes for a subject can be determined based on the relevant generated brainwaves. The learning environment is an important factor during the analysis of the results. Teachers, professors, students and parents can collaborate and, based on the gathered data, new teaching methods can be adopted in the classroom and at home. The proposed solution can guide the students while studying, as well as the persons who wish to be more productive while solving their tasks.
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence
Venue: arXiv cs.AI 2021
Year: 2021

Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge. ML techniques often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Rule-based systems and decision tree learning are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Here, we construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. Our approach transparently mimics the knowledge representation and clinical decision-making of practicing clinicians. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Probabilistic logic provides one form of explainable artificial intelligence that may be more acceptable for use in healthcare, if it can achieve acceptable levels of performance.
toon2real: Translating Cartoon Images to Realistic Images
Venue: arXiv cs.CV 2021
Year: 2021

In terms of Image-to-image translation, Generative Adversarial Networks (GANs) has achieved great success even when it is used in the unsupervised dataset. In this work, we aim to translate cartoon images to photo-realistic images using GAN. We apply several state-of-the-art models to perform this task; however, they fail to perform good quality translations. We observe that the shallow difference between these two domains causes this issue. Based on this idea, we propose a method based on CycleGAN model for image translation from cartoon domain to photo-realistic domain. To make our model efficient, we implemented Spectral Normalization which added stability in our model. We demonstrate our experimental results and show that our proposed model has achieved the lowest Frechet Inception Distance score and better results compared to another state-of-the-art technique, UNIT.
On optimal relay placement in directional networks
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study the problem of optimal topology design in wireless networks equipped with highly-directional transmission antennas. We use the 1-2-1 network model to characterize the optimal placement of two relays that assist the communication between a source-destination pair. We analytically show that under some conditions on the distance between the source-destination pair, the optimal topology in terms of maximizing the network throughput is to place the relays as close as possible to the source and the destination.
Generative Spoken Language Modeling from Raw Audio
Venue: arXiv cs.CL 2021
Year: 2021

We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo-text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder-dependent way, and that some combinations approach text-based systems.