OpenRaiser/Intern-Atlas huggingface dataset:
Strengthened Fault Tolerance in Byzantine Fault Tolerant Replication
Venue: arXiv cs.DC 2021
Year: 2021

Byzantine fault tolerant (BFT) state machine replication (SMR) is an important building block for constructing permissioned blockchain systems. In contrast to Nakamoto Consensus where any block obtains higher assurance as buried deeper in the blockchain, in BFT SMR, any committed block is secure has a fixed resilience threshold. In this paper, we investigate strengthened fault tolerance (SFT) in BFT SMR under partial synchrony, which provides gradually increased resilience guarantees (like Nakamoto Consensus) during an optimistic period when the network is synchronous and the number of Byzantine faults is small. Moreover, the committed blocks can tolerate more than one-third (up to two-thirds) corruptions even after the optimistic period. Compared to the prior best solution Flexible BFT which requires quadratic message complexity, our solution maintains the linear message complexity of state-of-the-art BFT SMR protocols and requires only marginal bookkeeping overhead. We implement our solution over the open-source Diem project, and give experimental results that demonstrate its efficiency under real-world scenarios.
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

This paper introduces our methodology to estimate sidewalk accessibilities from wheelchair behavior via a triaxial accelerometer in a smartphone installed under a wheelchair seat. Our method recognizes sidewalk accessibilities from environmental factors, e.g. gradient, curbs, and gaps, which influence wheelchair bodies and become a burden for people with mobility difficulties. This paper developed and evaluated a prototype system that visualizes sidewalk accessibility information by extracting knowledge from wheelchair acceleration using deep neural networks. Firstly, we created a supervised convolutional neural network model to classify road surface conditions using wheelchair acceleration data. Secondly, we applied a weakly supervised method to extract representations of road surface conditions without manual annotations. Finally, we developed a self-supervised variational autoencoder to assess sidewalk barriers for wheelchair users. The results show that the proposed method estimates sidewalk accessibilities from wheelchair accelerations and extracts knowledge of accessibilities by weakly supervised and self-supervised approaches.
Investigating the Vision Transformer Model for Image Retrieval Tasks
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Transformer network while it does not require any training data to adjust parameters. In image retrieval tasks, the use of Handcrafted global and local descriptors has been very successfully replaced, over the last years, by the Convolutional Neural Networks (CNN)-based methods. However, the experimental evaluation conducted in this paper on several benchmarking datasets against 36 state-of-the-art descriptors from the literature demonstrates that a neural network that contains no convolutional layer, such as Vision Transformer, can shape a global descriptor and achieve competitive results. As fine-tuning is not required, the presented methodology's low complexity encourages adoption of the architecture as an image retrieval baseline model, replacing the traditional and well adopted CNN-based approaches and inaugurating a new era in image retrieval approaches.
Coronary Plaque Analysis for CT Angiography Clinical Research
Venue: arXiv cs.CV 2021
Year: 2021

The analysis of plaque deposits in the coronary vasculature is an important topic in current clinical research. From a technical side mostly new algorithms for different sub tasks - e.g. centerline extraction or vessel/plaque segmentation - are proposed. However, to enable clinical research with the help of these algorithms, a software solution, which enables manual correction, comprehensive visual feedback and tissue analysis capabilities, is needed. Therefore, we want to present such an integrated software solution. It is able to perform robust automatic centerline extraction and inner and outer vessel wall segmentation, while providing easy to use manual correction tools. Also, it allows for annotation of lesions along the centerlines, which can be further analyzed regarding their tissue composition. Furthermore, it enables research in upcoming technologies and research directions: it does support dual energy CT scans with dedicated plaque analysis and the quantification of the fatty tissue surrounding the vasculature, also in automated set-ups.
Object-Level Reasoning with Logics Encoded in HOL Light
Venue: arXiv cs.LO 2021
Year: 2021

We present a generic framework that facilitates object level reasoning with logics that are encoded within the Higher Order Logic theorem proving environment of HOL Light. This involves proving statements in any logic using intuitive forward and backward chaining in a sequent calculus style. It is made possible by automated machinery that take care of the necessary structural reasoning and term matching automatically. Our framework can also handle type theoretic correspondences of proofs, effectively allowing the type checking and construction of computational processes via proof. We demonstrate our implementation using a simple propositional logic and its Curry-Howard correspondence to the lambda-calculus, and argue its use with linear logic and its various correspondences to session types.
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks (DNNs) are widely used in pattern-recognition tasks for which a human comprehensible, quantitative description of the data-generating process, e.g., in the form of equations, cannot be achieved. While doing so, DNNs often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This is one of the reasons why DNNs are not extensively used in physics-signal processing: physicists generally require their analyses to yield quantitative information about the studied systems. In this article we use DNNs to disentangle components of oscillating time series, and recover meaningful information. We show that, because DNNs can find useful abstract feature representations, they can be used when prior knowledge about the signal-generating process exists, but is not complete, as it is particularly the case in "new-physics" searches. To this aim, we train our DNN on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings (LS-fit) with true latent parameters' initial guesses, in spite of the DNN needing no initial guesses at all. We then explore applications in which we believe our architecture could prove useful for time-series processing in physics, when prior knowledge is incomplete. As an example, we employ DNNs as a tool to inform LS-fits when initial guesses are unknown. We show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.
How many years can a tiny unbalanced parenthesis go unnoticed on a widely accessed Internet document, older than the World Wide Web itself ?
Venue: arXiv cs.DL 2021
Year: 2021

We conducted an investigation to find when a mistake was introduced in a widely accessed Internet document, namely the RFC index. With great surprise, we discovered that a it may go unnoticed for a very long period, namely more that twenty-six years. This raises some questions to what does it mean to have open access and the meaning of Linus' laws that "given enough eyeballs, all bugs are shallow"
Language Detection Engine for Multilingual Texting on Mobile Devices
Venue: arXiv cs.CL 2021
Year: 2021

More than 2 billion mobile users worldwide type in multiple languages in the soft keyboard. On a monolingual keyboard, 38% of falsely auto-corrected words are valid in another language. This can be easily avoided by detecting the language of typed words and then validating it in its respective language. Language detection is a well-known problem in natural language processing. In this paper, we present a fast, light-weight and accurate Language Detection Engine (LDE) for multilingual typing that dynamically adapts to user intended language in real-time. We propose a novel approach where the fusion of character N-gram model and logistic regression based selector model is used to identify the language. Additionally, we present a unique method of reducing the inference time significantly by parameter reduction technique. We also discuss various optimizations fabricated across LDE to resolve ambiguity in input text among the languages with the same character pattern. Our method demonstrates an average accuracy of 94.5% for Indian languages in Latin script and that of 98% for European languages on the code-switched data. This model outperforms fastText by 60.39% and ML-Kit by 23.67% in F1 score for European languages. LDE is faster on mobile device with an average inference time of 25.91 microseconds.
A Neural Question Answering System for Basic Questions about Subroutines
Venue: arXiv cs.SE 2021
Year: 2021

A question answering (QA) system is a type of conversational AI that generates natural language answers to questions posed by human users. QA systems often form the backbone of interactive dialogue systems, and have been studied extensively for a wide variety of tasks ranging from restaurant recommendations to medical diagnostics. Dramatic progress has been made in recent years, especially from the use of encoder-decoder neural architectures trained with big data input. In this paper, we take initial steps to bringing state-of-the-art neural QA technologies to Software Engineering applications by designing a context-based QA system for basic questions about subroutines. We curate a training dataset of 10.9 million question/context/answer tuples based on rules we extract from recent empirical studies. Then, we train a custom neural QA model with this dataset and evaluate the model in a study with professional programmers. We demonstrate the strengths and weaknesses of the system, and lay the groundwork for its use in eventual dialogue systems for software engineering.
VIDA: A simulation model of domestic VIolence in times of social DistAncing
Venue: arXiv cs.MA 2021
Year: 2021

Violence against women occurs predominantly in the family and domestic context. The COVID-19 pandemic led Brazil to recommend and, at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense coexistence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to illustrate and examine multi-causal factors that influence events that generate violence. A central part of the model is the multi-causal stress indicator, created as a probability trigger of domestic violence occurring within the family environment. Two experimental design tests were performed: (a) absence or presence of the deterrence system of domestic violence against women and (b) measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neighbourhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA suggests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formalising, to the best of our knowledge, the first model of domestic violence through agent-based modelling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban level (census sectors).
Query Lifting: Language-integrated query for heterogeneous nested collections
Venue: arXiv cs.PL 2021
Year: 2021

Language-integrated query based on comprehension syntax is a powerful technique for safe database programming, and provides a basis for advanced techniques such as query shredding or query flattening that allow efficient programming with complex nested collections. However, the foundations of these techniques are lacking: although SQL, the most widely-used database query language, supports heterogeneous queries that mix set and multiset semantics, these important capabilities are not supported by known correctness results or implementations that assume homogeneous collections. In this paper we study language-integrated query for a heterogeneous query language $NRC_λ(Set,Bag)$ that combines set and multiset constructs. We show how to normalize and translate queries to SQL, and develop a novel approach to querying heterogeneous nested collections, based on the insight that ``local'' query subexpressions that calculate nested subcollections can be ``lifted'' to the top level analogously to lambda-lifting for local function definitions.
Online rating system development using blockchain-based distributed ledger technology
Venue: arXiv cs.CR 2021
Year: 2021

In most websites, the online rating system provides the ratings of products and services to users. Lack of trust in data integrity and its manipulation has hindered fulfilling user satisfaction. Since existing online rating systems deal with a central server, all rating data is stored on the central server. Therefore, all rating data can be removed, modified, and manipulated by the system manager to change the ratings in favor of the service or product provider. In this paper, an online rating system using distributed ledger technologies has been presented as the proposed system to solve all the weaknesses of current systems. Distributed ledger technologies are completely decentralized and there is no centralization on them by any institution. Distributed ledger technologies have different variants. Among distributed ledger technologies, blockchain technology has been used in the proposed rating system because of its support for smart contracts. In the proposed online rating system, the Ethereum platform has been chosen from different blockchain platforms that have a public permission network. In this system, the raters cannot rate unless they submit a request to the system and be authorized to take part in the online product rating process. The important feature of the Ethereum platform is its support for smart contracts, which can be used to write the rating contract in the Solidity language. Also, using Proof of Authority consensus mechanisms, all rating transactions are approved by the surveyors. Since in the real Ethereum system, each rating transaction is sent to the network by the raters, some gas must be paid for each rating transaction. However, since this method is expensive, TestNet blockchain can be used in the rating system. Finally, the proposed rating system was used for rating the restaurants of a website and its features were tested.
Where you live matters: a spatial analysis of COVID-19 mortality
Venue: arXiv cs.CV 2021
Year: 2021

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning
Venue: arXiv cs.CL 2021
Year: 2021

Likelihood training and maximization-based decoding result in dull and repetitive generated texts even when using powerful language models (Holtzman et al., 2019). Adding a loss function for regularization was shown to improve text generation output by helping avoid unwanted properties, such as contradiction or repetition (Li at al., 2020). In this work, we propose fine-tuning a language model by using policy gradient reinforcement learning, directly optimizing for better generation. We apply this approach to minimizing repetition in generated text, and show that, when combined with unlikelihood training (Welleck et al., 2020), our method further reduces repetition without impacting the language model quality. We also evaluate other methods for improving generation at training and decoding time, and compare them using various metrics aimed at control for better text generation output.
Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations
Venue: arXiv cs.HC 2021
Year: 2021

More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender's ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users.
A Brief Survey of Associations Between Meta-Learning and General AI
Venue: arXiv cs.AI 2021
Year: 2021

This paper briefly reviews the history of meta-learning and describes its contribution to general AI. Meta-learning improves model generalization capacity and devises general algorithms applicable to both in-distribution and out-of-distribution tasks potentially. General AI replaces task-specific models with general algorithmic systems introducing higher level of automation in solving diverse tasks using AI. We summarize main contributions of meta-learning to the developments in general AI, including memory module, meta-learner, coevolution, curiosity, forgetting and AI-generating algorithm. We present connections between meta-learning and general AI and discuss how meta-learning can be used to formulate general AI algorithms.
On the Calibration and Uncertainty of Neural Learning to Rank Models
Venue: arXiv cs.IR 2021
Year: 2021

According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported with certainty. We know however that deep neural networks (DNNs) are often not well calibrated and have several sources of uncertainty, and thus [C1] and [C2] might not be satisfied by neural rankers. Given the success of neural Learning to Rank (L2R) approaches-and here, especially BERT-based approaches-we first analyze under which circumstances deterministic, i.e. outputs point estimates, neural rankers are calibrated. Then, motivated by our findings we use two techniques to model the uncertainty of neural rankers leading to the proposed stochastic rankers, which output a predictive distribution of relevance as opposed to point estimates. Our experimental results on the ad-hoc retrieval task of conversation response ranking reveal that (i) BERT-based rankers are not robustly calibrated and that stochastic BERT-based rankers yield better calibration; and (ii) uncertainty estimation is beneficial for both risk-aware neural ranking, i.e.taking into account the uncertainty when ranking documents, and for predicting unanswerable conversational contexts.
Symbolic Loop Compilation for Tightly Coupled Processor Arrays
Venue: arXiv cs.DC 2021
Year: 2021

Loop compilation for Tightly Coupled Processor Arrays (TCPAs), a class of massively parallel loop accelerators, entails solving NP-hard problems, yet depends on the loop bounds and number of available processing elements (PEs), parameters known only at runtime because of dynamic resource management and input sizes. Therefore, this article proposes a two-phase approach called symbolic loop compilation: At compile time, the necessary NP-complete problems are solved and the solutions compiled into a space-efficient symbolic configuration. At runtime, a concrete configuration is generated from the symbolic configuration according to the parameters values. We show that the latter phase, called instantiation, runs in polynomial time with its most complex step, program instantiation, not depending on the number of PEs. As validation, we performed symbolic loop compilation on real-world loops and measured time and space requirements. Our experiments confirm that a symbolic configuration is space-efficient and suited for systems with little memory -- often, a symbolic configuration is smaller than a single concrete configuration -- and that program instantiation scales well with the number of PEs -- for example, when instantiating a symbolic configuration of a matrix-matrix multiplication, the execution time is similar for $4\times 4$ and $32\times 32$ PEs.