OpenRaiser/Intern-Atlas huggingface dataset:
ODT Flow Explorer: Extract, Query, and Visualize Human Mobility
Venue: arXiv cs.SI 2020
Year: 2020

Understanding human mobility dynamics among places provides fundamental knowledge regarding their interactive gravity, benefiting a wide range of applications in need of prior knowledge in human spatial interactions. The ongoing COVID-19 pandemic uniquely highlights the need for monitoring and measuring fine-scale human spatial interactions. In response to the soaring needs of human mobility data under the pandemic, we developed an interactive geospatial web portal by extracting worldwide daily population flows from billions of geotagged tweets and United States (U.S.) population flows from SafeGraph mobility data. The web portal is named ODT (Origin-Destination-Time) Flow Explorer. At the core of the explorer is an ODT data cube coupled with a big data computing cluster to efficiently manage, query, and aggregate billions of OD flows at different spatial and temporal scales. Although the explorer is still in its early developing stage, the rapidly generated mobility flow data can benefit a wide range of domains that need timely access to the fine-grained human mobility records. The ODT Flow Explorer can be accessed via http://gis.cas.sc.edu/GeoAnalytics/od.html.
Whac-A-Mole: Six Years of DNS Spoofing
Venue: arXiv cs.CR 2020
Year: 2020

DNS is important in nearly all interactions on the Internet. All large DNS operators use IP anycast, announcing servers in BGP from multiple physical locations to reduce client latency and provide capacity. However, DNS is easy to spoof: third parties intercept and respond to queries for benign or malicious purposes. Spoofing is of particular risk for services using anycast, since service is already announced from multiple origins. In this paper, we describe methods to identify DNS spoofing, infer the mechanism being used, and identify organizations that spoof from historical data. Our methods detect overt spoofing and some covertly-delayed answers, although a very diligent adversarial spoofer can hide. We use these methods to study more than six years of data about root DNS servers from thousands of vantage points. We show that spoofing today is rare, occurring only in about 1.7% of observations. However, the rate of DNS spoofing has more than doubled in less than seven years, and it occurs globally. Finally, we use data from B-Root DNS to validate our methods for spoof detection, showing a true positive rate over 0.96. B-Root confirms that spoofing occurs with both DNS injection and proxies, but proxies account for nearly all spoofing we see.
FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge
Venue: arXiv cs.SD 2020
Year: 2020

Nowadays more and more applications can benefit from edge-based text-to-speech (TTS). However, most existing TTS models are too computationally expensive and are not flexible enough to be deployed on the diverse variety of edge devices with their equally diverse computational capacities. To address this, we propose FBWave, a family of efficient and scalable neural vocoders that can achieve optimal performance-efficiency trade-offs for different edge devices. FBWave is a hybrid flow-based generative model that combines the advantages of autoregressive and non-autoregressive models. It produces high quality audio and supports streaming during inference while remaining highly computationally efficient. Our experiments show that FBWave can achieve similar audio quality to WaveRNN while reducing MACs by 40x. More efficient variants of FBWave can achieve up to 109x fewer MACs while still delivering acceptable audio quality. Audio demos are available at https://bichenwu09.github.io/vocoder_demos.
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Venue: arXiv cs.LG 2020
Year: 2020

Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements were found for our adversarial models, which could be further improved by the application of dual batch normalization. Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used. To ensure transferability, we additionally validated our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability.
A Survey on Modulation Techniques in Molecular Communication via Diffusion
Venue: arXiv cs.ET 2020
Year: 2020

This survey paper focuses on modulation aspects of molecular communication, an emerging field focused on building biologically-inspired systems that embed data within chemical signals. The primary challenges in designing these systems are how to encode and modulate information onto chemical signals, and how to design a receiver that can detect and decode the information from the corrupted chemical signal observed at the destination. In this paper, we focus on modulation design for molecular communication via diffusion systems. In these systems, chemical signals are transported using diffusion, possibly assisted by flow, from the transmitter to the receiver. This tutorial presents recent advancements in modulation and demodulation schemes for molecular communication via diffusion. We compare five different modulation types: concentration-based, type-based, timing-based, spatial, and higher-order modulation techniques. The end-to-end system designs for each modulation scheme are presented. In addition, the key metrics used in the literature to evaluate the performance of these techniques are also presented. Finally, we provide a numerical bit error rate comparison of prominent modulation techniques using analytical models. We close the tutorial with a discussion of key open issues and future research directions for design of molecular communication via diffusion systems.
Patterns of retractions from 1981-2020 : Does a fraud lead to another fraud?
Venue: arXiv cs.DL 2020
Year: 2020

Misconduct accounts for the majority of retracted scientific publications and this database reveals the disturbing trend in science~\citep{fang2012misconduct, brainard2018massive}. The objective of the study is to find the association among the authors' collaboration, the number of retracted papers, the number of retracted citations, journal impact factor, and research areas. We present a detailed analysis of 12231 research papers indexed by Web of Science (WoS) as retracted publications from 1981-2020. The study demonstrates the collaboration patterns of retracted publications where 61.5% of authors have only one and 24.6% have two retracted papers; however, 2% of authors have more than 10 retracted papers. To study the impact of citing retracted papers, we investigated the retracted papers with citations. The study reveals that 55.2% of retracted papers have been cited at least once, where 25.4% of papers are such papers where at least one citation turned out to be a retraction. This shows the impact of scientific misconduct or fraud on new research. The number of retractions is independent of the journal impact factor and as compared to high impact papers, low impact papers are attracting more citations. We also investigate the citations received by retracted papers published in higher as well as lower impact factor journals. 1/4th of the papers are retracted citations that cited the retracted papers; however, there is no significant relationship exists between the higher impact or lower impact journals with retractions or citations. Finally, how the average team size and average retracted citations vary among different research areas are studied. The study provides an insight that how a fraud leads to another fraud in the scientific world. Also, the rising trend of citations of retracted papers is a serious concern.
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space
Venue: arXiv cs.RO 2020
Year: 2020

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios.
Real-time error correction and performance aid for MIDI instruments
Venue: arXiv cs.SD 2020
Year: 2020

Making a slight mistake during live music performance can easily be spotted by an astute listener, even if the performance is an improvisation or an unfamiliar piece. An example might be a highly dissonant chord played by mistake in a classical-era sonata, or a sudden off-key note in a recurring motif. The problem of identifying and correcting such errors can be approached with artificial intelligence -- if a trained human can easily do it, maybe a computer can be trained to spot the errors quickly and just as accurately. The ability to identify and auto-correct errors in real-time would be not only extremely useful to performing musicians, but also a valuable asset for producers, allowing much fewer overdubs and re-recording of takes due to small imperfections. This paper examines state-of-the-art solutions to related problems and explores novel solutions for music error detection and correction, focusing on their real-time applicability. The explored approaches consider error detection through music context and theory, as well as supervised learning models with no predefined musical information or rules, trained on appropriate datasets. Focusing purely on correcting musical errors, the presented solutions operate on a high-level representation of the audio (MIDI) instead of the raw audio domain, taking input from an electronic instrument (MIDI keyboard/piano) and altering it when needed before it is sent to the sampler. This work proposes multiple general recurrent neural network designs for real-time error correction and performance aid for MIDI instruments, discusses the results, limitations, and possible future improvements. It also emphasizes on making the research results easily accessible to the end user - music enthusiasts, producers and performers -- by using the latest artificial intelligence platforms and tools.
Nose to Glass: Looking In to Get Beyond
Venue: arXiv cs.CY 2020
Year: 2020

Brought into the public discourse through investigative work by journalists and scholars, awareness of algorithmic harms is at an all-time high. An increasing amount of research has been conducted under the banner of enhancing responsible artificial intelligence (AI), with the goal of addressing, alleviating, and eventually mitigating the harms brought on by the roll out of algorithmic systems. Nonetheless, implementation of such tools remains low. Given this gap, this paper offers a modest proposal: that the field, particularly researchers concerned with responsible research and innovation, may stand to gain from supporting and prioritising more ethnographic work. This embedded work can flesh out implementation frictions and reveal organisational and institutional norms that existing work on responsible artificial intelligence AI has not yet been able to offer. In turn, this can contribute to more insights about the anticipation of risks and mitigation of harm. This paper reviews similar empirical work typically found elsewhere, commonly in science and technology studies and safety science research, and lays out challenges of this form of inquiry.
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
Venue: arXiv cs.LG 2020
Year: 2020

Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.
Polyhedral Friction Cone Estimator for Object Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

A polyhedral friction cone is a set of reaction wrenches that an object can experience whilst in contact with its environment. This polyhedron is a powerful tool to control an object's motion and interaction with the environment. It can be derived analytically, upon knowledge of object and environment geometries, contact point locations and friction coefficients. We propose to estimate the polyhedral friction cone so that a priori knowledge of these quantities is no longer required. Additionally, we introduce a solution to transform the estimated friction cone to avoid re-estimation while the object moves. We present an analysis of the estimated polyhedral friction cone and demonstrate its application for manipulating an object in simulation and with a real robot.
Why Charles Can Pen-test: an Evolutionary Approach to Vulnerability Testing
Venue: arXiv cs.CR 2020
Year: 2020

Discovering vulnerabilities in applications of real-world complexity is a daunting task: a vulnerability may affect a single line of code, and yet it compromises the security of the entire application. Even worse, vulnerabilities may manifest only in exceptional circumstances that do not occur in the normal operation of the application. It is widely recognized that state-of-the-art penetration testing tools play a crucial role, and are routinely used, to dig up vulnerabilities. Yet penetration testing is still primarily a human-driven activity, and its effectiveness still depends on the skills and ingenuity of the security analyst driving the tool. In this paper, we propose a technique for the automatic discovery of vulnerabilities in event-based systems, such as web and mobile applications. Our approach is based on a collaborative, co-evolutionary and contract-driven search strategy that iteratively (i) executes a pool of test cases, (ii) identifies the most promising ones, and (iii) generates new test cases from them. The approach makes a synergistic combination of evolutionary algorithms where several "species" contribute to solving the problem: one species, the test species, evolves to find the target test case, i.e., the set of instruction whose execution lead to the vulnerable statement, whereas the other species, called contract species, evolve to select the parameters for the procedure calls needed to trigger the vulnerability. To assess the effectiveness of our approach, we implemented a working prototype and ran it against both a case study and a benchmark web application. The experimental results confirm that our tool automatically discovers and executes a number of injection flaw attacks that are out of reach for state-of-the-art web scanners.
Unigram-Normalized Perplexity as a Language Model Performance Measure with Different Vocabulary Sizes
Venue: arXiv cs.CL 2020
Year: 2020

Although Perplexity is a widely used performance metric for language models, the values are highly dependent upon the number of words in the corpus and is useful to compare performance of the same corpus only. In this paper, we propose a new metric that can be used to evaluate language model performance with different vocabulary sizes. The proposed unigram-normalized Perplexity actually presents the performance improvement of the language models from that of simple unigram model, and is robust on the vocabulary size. Both theoretical analysis and computational experiments are reported.
Analysing Social Media Network Data with R: Semi-Automated Screening of Users, Comments and Communication Patterns
Venue: arXiv cs.SI 2020
Year: 2020

Communication on social media platforms is not only culturally and politically relevant, it is also increasingly widespread across societies. Users not only communicate via social media platforms, but also search specifically for information, disseminate it or post information themselves. However, fake news, hate speech and even radicalizing elements are part of this modern form of communication: Sometimes with far-reaching effects on individuals and societies. A basic understanding of these mechanisms and communication patterns could help to counteract negative forms of communication, e.g. bullying among children or extreme political points of view. To this end, a method will be presented in order to break down the underlying communication patterns, to trace individual users and to inspect their comments and range on social media platforms; Or to contrast them later on via qualitative research. This approeach can identify particularly active users with an accuracy of 100 percent, if the framing social networks as well as the topics are taken into account. However, methodological as well as counteracting approaches must be even more dynamic and flexible to ensure sensitivity and specifity regarding users who spread hate speech, fake news and radicalizing elements.
ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.
Learning from Lexical Perturbations for Consistent Visual Question Answering
Venue: arXiv cs.CV 2020
Year: 2020

Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations and regularizes the visual reasoning process between them to be consistent during training. We show that our framework markedly improves consistency and generalization ability, demonstrating the value of controlled linguistic perturbations as a useful and currently underutilized training and regularization tool for VQA models. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations of VQA questions. Our benchmark uniquely draws from large-scale linguistic resources, avoiding human annotation effort while maintaining data quality compared to generative approaches. We benchmark existing VQA models using VQA P2 and provide robustness analysis on each type of linguistic variation.
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation
Venue: arXiv cs.LG 2020
Year: 2020

Trust and credibility in machine learning models is bolstered by the ability of a model to explain itsdecisions. While explainability of deep learning models is a well-known challenge, a further chal-lenge is clarity of the explanation itself, which must be interpreted by downstream users. Layer-wiseRelevance Propagation (LRP), an established explainability technique developed for deep models incomputer vision, provides intuitive human-readable heat maps of input images. We present the novelapplication of LRP for the first time with structured datasets using a deep neural network (1D-CNN),for Credit Card Fraud detection and Telecom Customer Churn prediction datasets. We show how LRPis more effective than traditional explainability concepts of Local Interpretable Model-agnostic Ex-planations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectivenessis both local to a sample level and holistic over the whole testing set. We also discuss the significantcomputational time advantage of LRP (1-2s) over LIME (22s) and SHAP (108s), and thus its poten-tial for real time application scenarios. In addition, our validation of LRP has highlighted features forenhancing model performance, thus opening up a new area of research of using XAI as an approachfor feature subset selection