OpenRaiser/Intern-Atlas huggingface dataset:
Online Monitoring of Global Attitudes Towards Wildlife
Venue: arXiv cs.CY 2020
Year: 2020

Human factors are increasingly recognised as central to conservation of biodiversity. Despite this, there are no existing systematic efforts to monitor global trends in perceptions of wildlife. With traditional news reporting now largely online, the internet presents a powerful means to monitor global attitudes towards species. In this work we develop a method using the Global Database of Events, Language, and Tone (GDELT) to scan global news media, allowing us to identify and download conservation-related articles. Applying supervised machine learning techniques, we filter irrelevant articles to create a continually updated global dataset of news coverage for seven target taxa: lion, tiger, saiga, rhinoceros, pangolins, elephants, and orchids, and observe that over two-thirds of articles matching a simple keyword search were irrelevant. We examine coverage of each taxa in different regions, and find that elephants, rhinos, tigers, and lions receive the most coverage, with daily peaks of around 200 articles. Mean sentiment was positive for all taxa, except saiga for which it was neutral. Coverage was broadly distributed, with articles from 73 countries across all continents. Elephants and tigers received coverage in the most countries overall, whilst orchids and saiga were mentioned in the smallest number of countries. We further find that sentiment towards charismatic megafauna is most positive in non-range countries, with the opposite being true for pangolins and orchids. Despite promising results, there remain substantial obstacles to achieving globally representative results. Disparities in internet access between low and high income countries and users is a major source of bias, with the need to focus on a diversity of data sources and languages, presenting sizable technical challenges...
Towards a Better Modelling of the Cell Formation Problem: An Overview of Decisions and a Critical analysis of Constraints and Objectives
Venue: arXiv cs.CE 2020
Year: 2020

Cell formation problem is among the first obstacles the designer of cellular production systems must overcome. This paper presents a critical analysis of the various criteria and constraints considered in the literature. The objective is to help any researcher who wants to model the problem by adopting a multi-criteria approach.
Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks
Venue: arXiv cs.RO 2020
Year: 2020

One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in handling the uncertainty in the location of the target hole. In order to address it, high-dimensional sensor inputs from sensor modalities such as vision, force/torque sensing, and proprioception can be combined to learn control policies that are robust to this uncertainty in the target pose. Whereas deep learning has shown success in recognizing objects and making decisions with high-dimensional inputs, the learning procedure might damage the robot when applying directly trial- and-error algorithms on the real system. At the same time, learning from Demonstration (LfD) methods have been shown to achieve compelling performance in real robotic systems by leveraging demonstration data provided by experts. In this paper, we investigate the merits of multiple sensor modalities such as vision, force/torque sensors, and proprioception when combined to learn a controller for real world assembly operation tasks using LfD techniques. The study is limited to PiH insertions; we plan to extend the study to more experiments in the future. Additionally, we propose a multi-step-ahead loss function to improve the performance of the behavioral cloning method. Experimental results on a real manipulator support our findings, and show the effectiveness of the proposed loss function.
The Multi-Source Preemptive M/PH/1/1 Queue with Packet Errors: Exact Distribution of the Age of Information and Its Peak
Venue: arXiv cs.PF 2020
Year: 2020

Age of Information (AoI) and Peak AoI (PAoI) and their analytical models have recently drawn substantial amount of attention in information theory and wireless communications disciplines, in the context of qualitative assessment of information freshness in status update systems. We take a queueing-theoretic approach and study a probabilistically preemptive bufferless $M/PH/1/1$ queueing system with arrivals stemming from $N$ separate information sources, with the aim of modeling a generic status update system. In this model, a new information packet arrival from source $m$ is allowed to preempt a packet from source $n$ in service, with a probability depending on $n$ and $m$. To make the model even more general than the existing ones, for each of the information sources, we assume a distinct PH-type service time distribution and a distinct packet error probability. Subsequently, we obtain the exact distributions of the AoI and PAoI for each of the information sources using matrix-analytical algorithms and in particular the theory of Markov fluid queues and sample path arguments. This is in contrast with existing methods that rely on Stochastic Hybrid Systems (SHS) which obtain only the average values and in less general settings. Numerical examples are provided to validate the proposed approach as well as to give engineering insight on the impact of preemption probabilities on certain AoI and PAoI performance figures.
Demystifying COVID-19 digital contact tracing: A survey on frameworks and mobile apps
Venue: arXiv cs.CR 2020
Year: 2020

The coronavirus pandemic is a new reality and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures has been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, and specifically its Low Energy (BLE) power-conserving variant has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge, full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely centralised, decentralised, or hybrid, but it equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic, but also brings new insights and angles to contemplate upon.
Exploratory Experiments on Programming Autonomous Robots in Jadescript
Venue: arXiv cs.MA 2020
Year: 2020

This paper describes exploratory experiments to validate the possibility of programming autonomous robots using an agent-oriented programming language. Proper perception of the environment, by means of various types of sensors, and timely reaction to external events, by means of effective actuators, are essential to provide robots with a sufficient level of autonomy. The agent-oriented programming paradigm is relevant with this respect because it offers language-level abstractions to process events and to command actuators. A recent agent-oriented programming language called Jadescript is presented in this paper together with its new features specifically designed to handle events. Exploratory experiments on a simple case-study application are presented to show the validity of the proposed approach and to exemplify the use of the language to program autonomous robots.
Zero-Shot Recognition through Image-Guided Semantic Classification
Venue: arXiv cs.CV 2020
Year: 2020

We present a new embedding-based framework for zero-shot learning (ZSL). Most embedding-based methods aim to learn the correspondence between an image classifier (visual representation) and its class prototype (semantic representation) for each class. Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. Given an input image, the proposed Image-Guided Semantic Classification (IGSC) method creates a label classifier, being applied to all label embeddings to determine whether a label belongs to the input image. Therefore, semantic classifiers are image-adaptive and are generated during inference. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms state-of-the-art embedding-based generalized ZSL approaches on standard benchmarks.
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams
Venue: arXiv cs.LG 2020
Year: 2020

Many learning tasks involve multi-modal data streams, where continuous data from different modes convey a comprehensive description about objects. A major challenge in this context is how to efficiently interpret multi-modal information in complex environments. This has motivated numerous studies on learning unsupervised representations from multi-modal data streams. These studies aim to understand higher-level contextual information (e.g., a Twitter message) by jointly learning embeddings for the lower-level semantic units in different modalities (e.g., text, user, and location of a Twitter message). However, these methods directly associate each low-level semantic unit with a continuous embedding vector, which results in high memory requirements. Hence, deploying and continuously learning such models in low-memory devices (e.g., mobile devices) becomes a problem. To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams. We evaluate METEOR using two types of multi-modal data streams (i.e., social media streams and shopping transaction streams) to demonstrate its ability to adapt to different domains. Our results show that METEOR preserves the quality of the representations while reducing memory usage by around 80% compared to the conventional memory-intensive embeddings.
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state of the art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of the training tuples, the sentences and their labels. The outcome is a state of the art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.
Detecting the Insider Threat with Long Short Term Memory (LSTM) Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

Information systems enable many organizational processes in every industry. The efficiencies and effectiveness in the use of information technologies create an unintended byproduct: misuse by existing users or somebody impersonating them - an insider threat. Detecting the insider threat may be possible if thorough analysis of electronic logs, capturing user behaviors, takes place. However, logs are usually very large and unstructured, posing significant challenges for organizations. In this study, we use deep learning, and most specifically Long Short Term Memory (LSTM) recurrent networks for enabling the detection. We demonstrate through a very large, anonymized dataset how LSTM uses the sequenced nature of the data for reducing the search space and making the work of a security analyst more effective.
A Privacy-Preserving Architecture for the Protection of Adolescents in Online Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Online social networks (OSN) constitute an integral part of people's every day social activity. Specifically, mainstream OSNs such as Twitter, YouTube, and Facebook are especially prominent in adolescents' lives for communicating with other people online, expressing and entertain themselves, and finding information. However, adolescents face a significant number of threats when using online platforms. Some of these threats include aggressive behavior and cyberbullying, sexual grooming, false news and fake activity, radicalization, and exposure of personal information and sensitive content. There is a pressing need for parental control tools and Internet content filtering techniques to protect the vulnerable groups that use online platforms. Existing parental control tools occasionally violate the privacy of adolescents, leading them to use other communication channels to avoid moderation. In this work, we design and implement a user-centric Family Advice Suite with Guardian Avatars aiming at preserving the privacy of the individuals towards their custodians and towards the advice tool itself. Moreover, we present a systematic process for designing and developing state of the art techniques and a system architecture to prevent minors' exposure to numerous risks and dangers while using Facebook, Twitter, and YouTube on a browser.
Model Driven Engineering for Data Protection and Privacy: Application and Experience with GDPR
Venue: arXiv cs.SE 2020
Year: 2020

In Europe and indeed worldwide, the General Data Protection Regulation (GDPR) provides protection to individuals regarding their personal data in the face of new technological developments. GDPR is widely viewed as the benchmark for data protection and privacy regulations that harmonizes data privacy laws across Europe. Although the GDPR is highly beneficial to individuals, it presents significant challenges for organizations monitoring or storing personal information. Since there is currently no automated solution with broad industrial applicability, organizations have no choice but to carry out expensive manual audits to ensure GDPR compliance. In this paper, we present a complete GDPR UML model as a first step towards designing automated methods for checking GDPR compliance. Given that the practical application of the GDPR is influenced by national laws of the EU Member States, we suggest a two-tiered description of the GDPR, generic and specialized. In this paper, we provide (1) the GDPR conceptual model we developed with complete traceability from its classes to the GDPR, (2) a glossary to help understand the model, (3) the plain-English description of 35 compliance rules derived from GDPR along with their encoding in OCL, and (4) the set of 20 variations points derived from GDPR to specialize the generic model. We further present the challenges we faced in our modeling endeavor, the lessons we learned from it, and future directions for research.
PP-YOLO: An Effective and Efficient Implementation of Object Detector
Venue: arXiv cs.CV 2020
Year: 2020

Object detection is one of the most important areas in computer vision, which plays a key role in various practical scenarios. Due to limitation of hardware, it is often necessary to sacrifice accuracy to ensure the infer speed of the detector in practice. Therefore, the balance between effectiveness and efficiency of object detector must be considered. The goal of this paper is to implement an object detector with relatively balanced effectiveness and efficiency that can be directly applied in actual application scenarios, rather than propose a novel detection model. Considering that YOLOv3 has been widely used in practice, we develop a new object detector based on YOLOv3. We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged. Since all experiments in this paper are conducted based on PaddlePaddle, we call it PP-YOLO. By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.2% mAP) and efficiency (72.9 FPS), surpassing the existing state-of-the-art detectors such as EfficientDet and YOLOv4.Source code is at https://github.com/PaddlePaddle/PaddleDetection.
The Asymmetric Travelling Salesman Problem in Sparse Digraphs
Venue: arXiv cs.DS 2020
Year: 2020

Asymmetric Travelling Salesman Problem (ATSP) and its special case Directed Hamiltonicity are among the most fundamental problems in computer science. The dynamic programming algorithm running in time $O^*(2^n)$ developed almost 60 years ago by Bellman, Held and Karp, is still the state of the art for both of these problems. In this work we focus on sparse digraphs. First, we recall known approaches for Undirected Hamiltonicity and TSP in sparse graphs and we analyse their consequences for Directed Hamiltonicity and ATSP in sparse digraphs, either by adapting the algorithm, or by using reductions. In this way, we get a number of running time upper bounds for a few classes of sparse digraphs, including $O^*(2^{n/3})$ for digraphs with both out- and indegree bounded by 2, and $O^*(3^{n/2})$ for digraphs with outdegree bounded by 3. Our main results are focused on digraphs of bounded average outdegree $d$. The baseline for ATSP here is a simple enumeration of cycle covers which can be done in time bounded by $O^*(μ(d)^n)$ for a function $μ(d)\le(\lceil{d}\rceil!)^{1/{\lceil{d}\rceil}}$. One can also observe that Directed Hamiltonicity can be solved in randomized time $O^*((2-2^{-d})^n)$ and polynomial space, by adapting a recent result of Björklund [ISAAC 2018] stated originally for Undirected Hamiltonicity in sparse bipartite graphs. We present two new deterministic algorithms for ATSP: the first running in time $O(2^{0.441(d-1)n})$ and polynomial space, and the second in exponential space with running time of $O^*(τ(d)^{n/2})$ for a function $τ(d)\le d$.
Is Multichannel Access Useful in Timely Information Update?
Venue: arXiv cs.NI 2020
Year: 2020

This paper investigates information freshness of multichannel access in information update systems. Age of information (AoI) is a fundamentally important metric to characterize information freshness, defined as the time elapsed since the generation of the last successfully received update. When multiple devices share the same wireless channel to send updates to a common receiver, an interesting question is whether dividing the whole channel into several subchannels will lead to better AoI performance. Given the same frequency band, dividing it into different numbers of subchannels lead to different transmission times and packet error rates (PER) of short update packets, thus affecting information freshness. We focus on a multichannel access system where different devices take turns to transmit with a cyclic schedule repeated over time. We first derive the average AoI by estimating the PERs of short packets. Then we examine bounded AoI, for which the instantaneous AoI is required to be below a threshold a large percentage of the time. Simulation results indicate that multichannel access can provide low average AoI and uniform bounded AoI simultaneously across different received powers. Overall, our investigations provide insights into practical designs of multichannel access systems with AoI requirements.
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
Venue: arXiv cs.CV 2020
Year: 2020

Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the mode of the documents (e.g., images) from a given training set. Such a setup assumes that the training set contains an exhaustive representation of all possible classes of queries. In reality, a retrieval model may need to be deployed on previously unseen classes, which implies a zero-shot IR setup. In this paper, we propose a novel GAN-based model for zero-shot text to image retrieval. When given a textual description as the query, our model can retrieve relevant images in a zero-shot setup. The proposed model is trained using an Expectation-Maximization framework. Experiments on multiple benchmark datasets show that our proposed model comfortably outperforms several state-of-the-art zero-shot text to image retrieval models, as well as zero-shot classification and hashing models suitably used for retrieval.
Anecdotal Survey of Variations in Path Stroking among Real-world Implementations
Venue: arXiv cs.GR 2020
Year: 2020

Stroking a path is one of the two basic rendering operations in vector graphics standards (e.g., PostScript, PDF, SVG). We survey path stroking rendering results from real-world software implementations of path stroking for anecdotal evidence that such implementations are prone to rendering variances. While our survey is limited and informal, the rendering results we gathered indicate widespread rendering variations for simple-but-problematic stroked paths first identified decades ago. We conclude that creators of vector graphics content would benefit from a mathematically grounded standardization for how a stroked path should be rasterized.
Tight Distributed Sketching Lower Bound for Connectivity
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we study the distributed sketching complexity of connectivity. In distributed graph sketching, an $n$-node graph $G$ is distributed to $n$ players such that each player sees the neighborhood of one vertex. The players then simultaneously send one message to the referee, who must compute some function of $G$ with high probability. For connectivity, the referee must output whether $G$ is connected. The goal is to minimize the message lengths. Such sketching schemes are equivalent to one-round protocols in the broadcast congested clique model. We prove that the expected average message length must be at least $Ω(\log^3 n)$ bits, if the error probability is at most $1/4$. It matches the upper bound obtained by the AGM sketch [AGM12], which even allows the referee to output a spanning forest of $G$ with probability $1-1/\mathrm{poly}\, n$. Our lower bound strengthens the previous $Ω(\log^3 n)$ lower bound for spanning forest computation [NY19]. Hence, it implies that connectivity, a decision problem, is as hard as its "search" version in this model.