OpenRaiser/Intern-Atlas huggingface dataset:
JugglingSwap: Scriptless Atomic Cross-Chain Swaps
Venue: arXiv cs.CR 2020
Year: 2020

The blockchain space is changing constantly. New chains are being implemented frequently with different use cases in mind. As more and more types of crypto assets are getting real world value there is an increasing need for blockchain interoperability. Exchange services today are still dominated by central parties which require custody of funds. This trust imposes costs and security risks as frequent breaches testify. Atomic cross-chain swaps (ACCS) allow mutual distrusting parties to securely exchange crypto assets in a peer-to-peer manner while preserving self-custody. Fundamental ACCS protocols leveraged the scripting capabilities of blockchains to conditionalize the transfer of funds between trading parties. Recent work showed that such protocols can be realized in a scriptless setting. This has many benefits to blockchains throughput, efficiency of swap protocols and also to fungibility and privacy. The proposed protocols are limited to assets transferable by either Schnorr signatures or ECDSA that are assuming the same elliptic curve parameters. In this work we present JugglingSwap, a scriptless atomic cross-chain swap protocol with a higher degree of interoperability. We weaken the assumptions about blockchains that can be included in the ACCS protocol, and only require that (1) a threshold variant exists to the underlying digital signature scheme and (2) it is based on the elliptic curve discrete logarithm problem (ECDLP). The fair exchange is achieved by a gradual release of secrets. To achieve this we use a new building block we call Juggling: a public key verifiable encryption scheme to transfer segments of secret shares between parties, which can also be of separate interest. Juggling is then tailored to a specific private key management system design with threshold signatures security.
Munchausen Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most algorithms, based on temporal differences, replace the true value of a transiting state by their current estimate of this value. Yet, another estimate could be leveraged to bootstrap RL: the current policy. Our core contribution stands in a very simple idea: adding the scaled log-policy to the immediate reward. We show that slightly modifying Deep Q-Network (DQN) in that way provides an agent that is competitive with distributional methods on Atari games, without making use of distributional RL, n-step returns or prioritized replay. To demonstrate the versatility of this idea, we also use it together with an Implicit Quantile Network (IQN). The resulting agent outperforms Rainbow on Atari, installing a new State of the Art with very little modifications to the original algorithm. To add to this empirical study, we provide strong theoretical insights on what happens under the hood -- implicit Kullback-Leibler regularization and increase of the action-gap.
A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children
Venue: arXiv cs.CV 2020
Year: 2020

Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in which children with ASD react to visual stimulation in a controlled environment have been developed to help facilitate early diagnosis. Previous studies have shown that, when exposed to a video that presents both social and abstract scenes side by side, a child with ASD will focus their attention towards the abstract images on the screen to a greater extent than a child without ASD. Such differential responses make it possible to implement an algorithm for the rapid diagnosis of ASD based on eye tracking against different visual stimuli. Here we propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons and employing a different camera than the one used during testing. In addition to this, the proposed algorithm achieves a fast response time, providing a near real-time evaluation of ASD. Thereby, by applying the proposed method, we could significantly reduce the diagnosis time and facilitate the diagnosis of ASD in low resource regions.
A user-centered approach to designing an experimental laboratory data platform
Venue: arXiv cs.CY 2020
Year: 2020

While automated experiments and high-throughput methods are becoming more mainstream in the age of data, empowering individual researchers to capture, collate, and contextualize their data faster and more reproducibly still remains a challenge in science. Despite the abundance of software products to help digitize and organize scientific information, their broader adoption in the scientific community has been hindered by the lack of a holistic understanding of the diverse needs of researchers and their experimental processes. In this work, we take a user-centered approach to understand what essential elements of design and functionality researchers (in chemical and materials science) want in an experimental data platform to address the problem of data capture in their experimental processes. We found that having the capability to contextualize rich, complex experimental datasets is the primary user requirement. We synthesize this and other key findings into design criteria for a potential solution.
Measuring prominence of scientific work in online news as a proxy for impact
Venue: arXiv cs.CL 2020
Year: 2020

The impact made by a scientific paper on the work of other academics has many established metrics, including metrics based on citation counts and social media commenting. However, determination of the impact of a scientific paper on the wider society is less well established. For example, is it important for scientific work to be newsworthy? Here we present a new corpus of newspaper articles linked to the scientific papers that they describe. We find that Impact Case studies submitted to the UK Research Excellence Framework (REF) 2014 that refer to scientific papers mentioned in newspaper articles were awarded a higher score in the REF assessment. The papers associated with these case studies also feature prominently in the newspaper articles. We hypothesise that such prominence can be a useful proxy for societal impact. We therefore provide a novel baseline approach for measuring the prominence of scientific papers mentioned within news articles. Our measurement of prominence is based on semantic similarity through a graph-based ranking algorithm. We find that scientific papers with an associated REF case study are more likely to have a stronger prominence score. This supports our hypothesis that linguistic prominence in news can be used to suggest the wider non-academic impact of scientific work.
Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
Venue: arXiv cs.CV 2020
Year: 2020

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.
$S^3$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data
Venue: arXiv cs.CV 2020
Year: 2020

Solving depth estimation with monocular cameras enables the possibility of widespread use of cameras as low-cost depth estimation sensors in applications such as autonomous driving and robotics. However, learning such a scalable depth estimation model would require a lot of labeled data which is expensive to collect. There are two popular existing approaches which do not require annotated depth maps: (i) using labeled synthetic and unlabeled real data in an adversarial framework to predict more accurate depth, and (ii) unsupervised models which exploit geometric structure across space and time in monocular video frames. Ideally, we would like to leverage features provided by both approaches as they complement each other; however, existing methods do not adequately exploit these additive benefits. We present $S^3$Net, a self-supervised framework which combines these complementary features: we use synthetic and real-world images for training while exploiting geometric, temporal, as well as semantic constraints. Our novel consolidated architecture provides a new state-of-the-art in self-supervised depth estimation using monocular videos. We present a unique way to train this self-supervised framework, and achieve (i) more than $15\%$ improvement over previous synthetic supervised approaches that use domain adaptation and (ii) more than $10\%$ improvement over previous self-supervised approaches which exploit geometric constraints from the real data.
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
Venue: arXiv cs.LG 2020
Year: 2020

Predictive models in acute care settings must be able to immediately recognize precipitous changes in a patient's status when presented with data reflecting such changes. Recurrent neural networks (RNNs) have become common for training and deploying clinical decision support models. They frequently exhibit a delayed response to acute events. New information must propagate through the RNN's cell state memory before the total impact is reflected in the model's predictions. This work presents input data perseveration as a method of training and deploying an RNN model to make its predictions more responsive to newly acquired information: input data is replicated during training and deployment. Each replication of the data input impacts the cell state and output of the RNN, but only the output at the final replication is maintained and broadcast as the prediction for evaluation and deployment purposes. When presented with data reflecting acute events, a model trained and deployed with input perseveration responds with more pronounced immediate changes in predictions and maintains globally robust performance. Such a characteristic is crucial in predictive models for an intensive care unit.
A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph
Venue: arXiv cs.CV 2020
Year: 2020

Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with aerial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment.To cope with the case of lost tracking, many visual SLAM systems employ a relocalization strategy. This involves the tracking thread continuing the online working by inspecting the connections between the subsequent new frames and the generated map before the tracking was lost. To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method.
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods
Venue: arXiv cs.LG 2020
Year: 2020

Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.
Expansion and Flooding in Dynamic Random Networks with Node Churn
Venue: arXiv cs.DC 2020
Year: 2020

We study expansion and information diffusion in dynamic networks, that is in networks in which nodes and edges are continuously created and destroyed. We consider information diffusion by {\em flooding}, the process by which, once a node is informed, it broadcasts its information to all its neighbors. We study models in which the network is {\em sparse}, meaning that it has $\mathcal{O}(n)$ edges, where $n$ is the number of nodes, and in which edges are created randomly, rather than according to a carefully designed distributed algorithm. In our models, when a node is "born", it connects to $d=\mathcal{O}(1)$ random other nodes. An edge remains alive as long as both its endpoints do. If no further edge creation takes place, we show that, although the network will have $Ω_d(n)$ isolated nodes, it is possible, with large constant probability, to inform a $1-exp(-Ω(d))$ fraction of nodes in $\mathcal{O}(\log n)$ time. Furthermore, the graph exhibits, at any given time, a "large-set expansion" property. We also consider models with {\em edge regeneration}, in which if an edge $(v,w)$ chosen by $v$ at birth goes down because of the death of $w$, the edge is replaced by a fresh random edge $(v,z)$. In models with edge regeneration, we prove that the network is, with high probability, a vertex expander at any given time, and flooding takes $\mathcal{O}(\log n)$ time. The above results hold both for a simple but artificial streaming model of node churn, in which at each time step one node is born and the oldest node dies, and in a more realistic continuous-time model in which the time between births is Poisson and the lifetime of each node follows an exponential distribution.
On the Low-latency Region of Best-effort Links for Delay-Sensitive Streaming Traffic
Venue: arXiv cs.NI 2020
Year: 2020

This letter analyzes the Low-latency Region (LLR) of a best-effort link (i.e., no traffic differentiation, and first come first serve scheduling) carrying both delay-sensitive (DS) streaming and non-delay-sensitive (NDS) background traffic. Moreover, inside the LLR, we show it exists a proportional fair arrival rate allocation for both the DS and NDS traffic streams. This optimal operating point results from maximizing a simple throughput-delay trade-off that considers the NDS traffic load, and the mean delay of the DS packets. To show how the presented trade-off could be used to allocate NDS traffic in a realistic scenario, we use Google Stadia traffic traces to generate the DS flow. Results from this use-case confirm that the throughput-delay trade-off also works regardless the distribution of the packet arrival and packet service times.
On the constructions of $n$-cycle permutations
Venue: arXiv cs.IT 2020
Year: 2020

Any permutation polynomial is an $ n $-cycle permutation. When $n$ is a specific small positive integer, one can obtain efficient permutations, such as involutions, triple-cycle permutations and quadruple-cycle permutations. These permutations have important applications in cryptography and coding theory. Inspired by the AGW Criterion, we propose criteria for $ n $-cycle permutations, which mainly are of the form $ x^rh(x^s) $. We then propose unified constructing methods including recursive ways and a cyclotomic way for $ n $-cycle permutations of such form. We demonstrate our approaches by constructing three classes of explicit triple-cycle permutations with high index and two classes of $ n $-cycle permutations with low index.
Survey on Congestion Detection and Control in Connected Vehicles
Venue: arXiv cs.NI 2020
Year: 2020

The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications.
Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning
Venue: arXiv cs.RO 2020
Year: 2020

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the data used for learning consists almost entirely of textual descriptions, which tend to be cleaner, clearer, and more grammatical than actual human interactions. In this work, we present the Grounded Language Dataset (GoLD), a multimodal dataset of common household objects described by people using either spoken or written language. We analyze the differences and present an experiment showing how the different modalities affect language learning from human in-put. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, text, and speech interact, as well as show differences in the vernacular of these modalities impact results.
Return-Oriented Programming in RISC-V
Venue: arXiv cs.CR 2020
Year: 2020

RISC-V is an open-source hardware ISA based on the RISC design principles, and has been the subject of some novel ROP mitigation technique proposals due to its open-source nature. However, very little work has actually evaluated whether such an attack is feasible assuming a typical RISC-V implementation. We show that RISC-V ROP can be used to perform Turing complete calculation and arbitrary function calls by leveraging gadgets found in a version of the GNU libc library. Using techniques such as self-modifying ROP chains and algorithmic ROP chain generation, we demonstrate the power of RISC-V ROP by creating a compiler that converts code of arbitrary complexity written in a popular Turing-complete language into RISC-V ROP chains.
dMelodies: A Music Dataset for Disentanglement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Representation learning focused on disentangling the underlying factors of variation in given data has become an important area of research in machine learning. However, most of the studies in this area have relied on datasets from the computer vision domain and thus, have not been readily extended to music. In this paper, we present a new symbolic music dataset that will help researchers working on disentanglement problems demonstrate the efficacy of their algorithms on diverse domains. This will also provide a means for evaluating algorithms specifically designed for music. To this end, we create a dataset comprising of 2-bar monophonic melodies where each melody is the result of a unique combination of nine latent factors that span ordinal, categorical, and binary types. The dataset is large enough (approx. 1.3 million data points) to train and test deep networks for disentanglement learning. In addition, we present benchmarking experiments using popular unsupervised disentanglement algorithms on this dataset and compare the results with those obtained on an image-based dataset.
Bayesian Inference by Symbolic Model Checking
Venue: arXiv cs.AI 2020
Year: 2020

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.