OpenRaiser/Intern-Atlas huggingface dataset:
A Linguistic Study on Relevance Modeling in Information Retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does relevance modeling in these tasks really show differences in terms of natural language understanding (NLU)? We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question. 2) If there do exist differences, how can we leverage the findings to enhance the relevance modeling? We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. We believe the way we study the problem as well as our findings would be beneficial to the IR community.
Practices for Engineering Trustworthy Machine Learning Applications
Venue: arXiv cs.SE 2021
Year: 2021

Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address this issue, policy makers and other stakeholders, such as the European Commission or NIST, have proposed high-level guidelines aiming to promote trustworthy ML (i.e., lawful, ethical and robust). However, these guidelines do not specify actions to be taken by those involved in building ML systems. In this paper, we argue that guidelines related to the development of trustworthy ML can be translated to operational practices, and should become part of the ML development life cycle. Towards this goal, we ran a multi-vocal literature review, and mined operational practices from white and grey literature. Moreover, we launched a global survey to measure practice adoption and the effects of these practices. In total, we identified 14 new practices, and used them to complement an existing catalogue of ML engineering practices. Initial analysis of the survey results reveals that so far, practice adoption for trustworthy ML is relatively low. In particular, practices related to assuring security of ML components have very low adoption. Other practices enjoy slightly larger adoption, such as providing explanations to users. Our extended practice catalogue can be used by ML development teams to bridge the gap between high-level guidelines and actual development of trustworthy ML systems; it is open for review and contribution
Moment-Based Variational Inference for Stochastic Differential Equations
Venue: arXiv cs.LG 2021
Year: 2021

Existing deterministic variational inference approaches for diffusion processes use simple proposals and target the marginal density of the posterior. We construct the variational process as a controlled version of the prior process and approximate the posterior by a set of moment functions. In combination with moment closure, the smoothing problem is reduced to a deterministic optimal control problem. Exploiting the path-wise Fisher information, we propose an optimization procedure that corresponds to a natural gradient descent in the variational parameters. Our approach allows for richer variational approximations that extend to state-dependent diffusion terms. The classical Gaussian process approximation is recovered as a special case.
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method
Venue: arXiv cs.LG 2021
Year: 2021

Big data methods are becoming an important tool for tax fraud detection around the world. Unsupervised learning approach is the dominant framework due to the lack of label and ground truth in corresponding data sets although these methods suffer from low interpretability. HUNOD, a novel hybrid unsupervised outlier detection method for tax evasion risk management, is presented in this paper. In contrast to previous methods proposed in the literature, the HUNOD method combines two outlier detection approaches based on two different machine learning designs (i.e, clustering and representational learning) to detect and internally validate outliers in a given tax dataset. The HUNOD method allows its users to incorporate relevant domain knowledge into both constituent outlier detection approaches in order to detect outliers relevant for a given economic context. The interpretability of obtained outliers is achieved by training explainable-by-design surrogate models over results of unsupervised outlier detection methods. The experimental evaluation of the HUNOD method is conducted on two datasets derived from the database on individual personal income tax declarations collected by the Tax Administration of Serbia. The obtained results show that the method indicates between 90% and 98% internally validated outliers depending on the clustering configuration and employed regularization mechanisms for representational learning.
Predictive Maintenance Tool for Non-Intrusive Inspection Systems
Venue: arXiv cs.LG 2021
Year: 2021

Cross-border security is of topmost priority for societies. Economies lose billions each year due to counterfeiters and other threats. Security checkpoints equipped with X-ray Security Systems (NIIS-Non-Intrusive Inspection Systems) like airports, ports, border control and customs authorities tackle the myriad of threats by using NIIS to inspect bags, air, land, sea and rail cargo, and vehicles. The reliance on the X-ray scanning systems necessitates their continuous 24/7 functioning being provided for. Hence the need for their working condition being closely monitored and preemptive actions being taken to reduce the overall X-ray systems downtime. In this paper, we present a predictive maintenance decision support system, abbreviated as PMT4NIIS (Predictive Maintenance Tool for Non-Intrusive Inspection Systems), which is a kind of augmented analytics platforms that provides real-time AI-generated warnings for upcoming risk of system malfunctioning leading to possible downtime. The industrial platform is the basis of a 24/7 Service Desk and Monitoring center for the working condition of various X-ray Security Systems.
Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared Task
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we tackle the Nuanced Arabic Dialect Identification (NADI) shared task (Abdul-Mageed et al., 2021) and demonstrate state-of-the-art results on all of its four subtasks. Tasks are to identify the geographic origin of short Dialectal (DA) and Modern Standard Arabic (MSA) utterances at the levels of both country and province. Our final model is an ensemble of variants built on top of MARBERT that achieves an F1-score of 34.03% for DA at the country-level development set -- an improvement of 7.63% from previous work.
Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation
Venue: arXiv cs.RO 2021
Year: 2021

We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hard-contact model is assumed, and the unilateral constraints are imposed in the form of complementarity conditions. Newton's impact law is adopted for enhanced physical correctness. The optimal control problem is formulated as a multi-staged program through a multiple-shooting scheme. This problem structure is exploited within the FORCES Pro framework to retrieve optimal motion plans, contact sequences and control inputs with increased computational efficiency. We investigate our method on a variety of dynamic object manipulation tasks, performed by a six degrees of freedom robot. The dynamic feasibility of the optimal trajectories, as well as the repeatability and accuracy of the task-satisfaction are verified through simulations and real hardware experiments on one of the manipulation problems.
Measuring Inconsistency over Sequences of Business Rule Cases
Venue: arXiv cs.AI 2021
Year: 2021

In this report, we investigate (element-based) inconsistency measures for multisets of business rule bases. Currently, related works allow to assess individual rule bases, however, as companies might encounter thousands of such instances daily, studying not only individual rule bases separately, but rather also their interrelations becomes necessary, especially in regard to determining suitable re-modelling strategies. We therefore present an approach to induce multiset-measures from arbitrary (traditional) inconsistency measures, propose new rationality postulates for a multiset use-case, and investigate the complexity of various aspects regarding multi-rule base inconsistency measurement.
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles
Venue: arXiv cs.CR 2021
Year: 2021

The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing. Nevertheless, privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks. To this end, federated learning (FL) as an emerging learning paradigm, where data providers only send local model updates trained on their local raw data rather than upload any raw data, has been recently proposed to build a privacy-preserving data sharing models. Unfortunately, by analyzing on the differences of uploaded local model updates from data providers, private information can still be divulged, and performance of the system cannot be guaranteed when partial federated nodes executes malicious behavior. Additionally, traditional cloud-based FL poses challenges to the communication overhead with the rapid increase of terminal equipment in IoV system. All these issues inspire us to propose an autonomous blockchain empowered privacy-preserving FL framework in this paper, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.
DTW-Merge: A Novel Data Augmentation Technique for Time Series Classification
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, neural networks achieved much success in various applications. The main challenge in training deep neural networks is the lack of sufficient data to improve the model's generalization and avoid overfitting. One of the solutions is to generate new training samples. This paper proposes a novel data augmentation method for time series based on Dynamic Time Warping. This method is inspired by the concept that warped parts of two time series have similar temporal properties and therefore, exchanging them between the two series generates a new training sample. The proposed method selects an element of the optimal warping path randomly and then exchanges the segments that are aligned together. Exploiting the proposed approach with recently introduced ResNet reveals improved results on the 2018 UCR Time Series Classification Archive. By employing Gradient-weighted Class Activation Mapping (Grad-CAM) and Multidimensional Scaling (MDS), we manifest that our method extract more discriminant features out of time series.
Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks
Venue: arXiv cs.LG 2021
Year: 2021

The beginning of 2021 saw a surge in volatility for certain stocks such as GameStop company stock (Ticker GME under NYSE). GameStop stock increased around 10 fold from its decade-long average to its peak at \$485. In this paper, we hypothesize a buy-and-hold strategy can be outperformed in the presence of extreme volatility by predicting and trading consolidation breakouts. We investigate GME stock for its volatility and compare it to SPY as a benchmark (since it is a less volatile ETF fund) from February 2002 to February 2021. For strategy 1, we develop a Long Short-term Memory (LSTM) Neural Network to predict stock prices recurrently with a very short look ahead period in the presence of extreme volatility. For our strategy 2, we develop an LSTM autoencoder network specifically designed to trade only on consolidation breakouts after predicting anomalies in the stock price. When back-tested in our simulations, our strategy 1 executes 863 trades for SPY and 452 trades for GME. Our strategy 2 executes 931 trades for SPY and 325 trades for GME. We compare both strategies to buying and holding one single share for the period that we picked as a benchmark. In our simulations, SPY returns \$281.160 from buying and holding one single share, \$110.29 from strategy 1 with 53.5% success rate and \$4.34 from strategy 2 with 57.6% success rate. GME returns \$45.63 from buying and holding one single share, \$69.046 from strategy 1 with 47.12% success rate and \$2.10 from strategy 2 with 48% success rate. Overall, buying and holding outperforms all deep-learning assisted prediction models in our study except for when the LSTM-based prediction model (strategy 1) is applied to GME. We hope that our study sheds more light into the field of extreme volatility predictions based on LSTMs to outperform buying and holding strategy.
Automated data-driven approach for gap filling in the time series using evolutionary learning
Venue: arXiv cs.LG 2021
Year: 2021

In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the optimal structure for a composite data-driven model. It allows adapting the model for the effective gap-filling in a specific dataset without the involvement of the data scientist. As a case study, both synthetic and real datasets from different fields (environmental, economic, etc) are used. The experiments confirm that the proposed approach allows achieving the higher quality of the gap restoration and improve the effectiveness of forecasting models.
New Public-Key Crypto-System EHT
Venue: arXiv cs.CR 2021
Year: 2021

In this note, an LWE problem with a hidden trapdoor is introduced. It is used to construct an efficient public-key crypto-system EHT. The new system is significantly different from LWE based NIST candidates like FrodoKEM. The performance of EHT compares favorably with FrodoKEM.
Unsupervised Classification of Voiced Speech and Pitch Tracking Using Forward-Backward Kalman Filtering
Venue: arXiv cs.SD 2021
Year: 2021

The detection of voiced speech, the estimation of the fundamental frequency, and the tracking of pitch values over time are crucial subtasks for a variety of speech processing techniques. Many different algorithms have been developed for each of the three subtasks. We present a new algorithm that integrates the three subtasks into a single procedure. The algorithm can be applied to pre-recorded speech utterances in the presence of considerable amounts of background noise. We combine a collection of standard metrics, such as the zero-crossing rate, for example, to formulate an unsupervised voicing classifier. The estimation of pitch values is accomplished with a hybrid autocorrelation-based technique. We propose a forward-backward Kalman filter to smooth the estimated pitch contour. In experiments, we are able to show that the proposed method compares favorably with current, state-of-the-art pitch detection algorithms.
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions
Venue: arXiv cs.CL 2021
Year: 2021

Recent complementary strands of research have shown that leveraging information on the data source through encoding their properties into embeddings can lead to performance increase when training a single model on heterogeneous data sources. However, it remains unclear in which situations these dataset embeddings are most effective, because they are used in a large variety of settings, languages and tasks. Furthermore, it is usually assumed that gold information on the data source is available, and that the test data is from a distribution seen during training. In this work, we compare the effect of dataset embeddings in mono-lingual settings, multi-lingual settings, and with predicted data source label in a zero-shot setting. We evaluate on three morphosyntactic tasks: morphological tagging, lemmatization, and dependency parsing, and use 104 datasets, 66 languages, and two different dataset grouping strategies. Performance increases are highest when the datasets are of the same language, and we know from which distribution the test-instance is drawn. In contrast, for setups where the data is from an unseen distribution, performance increase vanishes.
Performance Variability in Zero-Shot Classification
Venue: arXiv cs.LG 2021
Year: 2021

Zero-shot classification (ZSC) is the task of learning predictors for classes not seen during training. Although the different methods in the literature are evaluated using the same class splits, little is known about their stability under different class partitions. In this work we show experimentally that ZSC performance exhibits strong variability under changing training setups. We propose the use ensemble learning as an attempt to mitigate this phenomena.
Automatic Generation of Challenging Road Networks for ALKS Testing based on Bezier Curves and Search
Venue: arXiv cs.SE 2021
Year: 2021

In this paper, we outline an approach for automatic generation of challenging road networks for virtual testing of an automated lane keep system. Based on a set of control points, we construct a parametric curve that represents a road network, which defines the dynamic driving task an automated lane keep system equipped vehicle has to perform. Changing control points has global influence on the resulting road geometry. Our approach uses search to find a set of control points that results in a challenging road geometry, eventually forcing the vehicle to leave the intended path. We evaluated our approach in three different search-configurations regarding test efficiency and practical applicability for automatic virtual testing of an automated lane keep system.
How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking
Venue: arXiv cs.SI 2021
Year: 2021

Rankings of people and items has been highly used in selection-making, match-making, and recommendation algorithms that have been deployed on ranging of platforms from employment websites to searching tools. The ranking position of a candidate affects the amount of opportunities received by the ranked candidate. It has been observed in several works that the ranking of candidates based on their score can be biased for candidates belonging to the minority community. In recent works, the fairness-aware representative ranking was proposed for computing fairness-aware re-ranking of results. The proposed algorithm achieves the desired distribution of top-ranked results with respect to one or more protected attributes. In this work, we highlight the bias in fairness-aware representative ranking for an individual as well as for a group if the group is sub-active on the platform. We define individual unfairness and group unfairness and propose methods to generate ideal individual and group fair representative ranking if the universal representation ratio is known or unknown. The simulation results show the quantified analysis of fairness in the proposed solutions. The paper is concluded with open challenges and further directions.
Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
Venue: arXiv cs.IT 2021
Year: 2021

Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active learning, and enable continual learning. Bayesian learning provides a principled approach to address all these limitations, at the cost of an increase in computational complexity. This paper studies distributed Bayesian learning in a wireless data center setting encompassing a central server and multiple distributed workers. Prior work on wireless distributed learning has focused exclusively on frequentist learning, and has introduced the idea of leveraging uncoded transmission to enable "over-the-air" computing. Unlike frequentist learning, Bayesian learning aims at evaluating approximations or samples from a global posterior distribution in the model parameter space. This work investigates for the first time the design of distributed one-shot, or "embarrassingly parallel", Bayesian learning protocols in wireless data centers via consensus Monte Carlo (CMC). Uncoded transmission is introduced not only as a way to implement "over-the-air" computing, but also as a mechanism to deploy channel-driven MC sampling: Rather than treating channel noise as a nuisance to be mitigated, channel-driven sampling utilizes channel noise as an integral part of the MC sampling process. A simple wireless CMC scheme is first proposed that is asymptotically optimal under Gaussian local posteriors. Then, for arbitrary local posteriors, a variational optimization strategy is introduced. Simulation results demonstrate that, if properly accounted for, channel noise can indeed contribute to MC sampling and does not necessarily decrease the accuracy level.
Stop Building Castles on a Swamp! The Crisis of Reproducing Automatic Search in Evidence-based Software Engineering
Venue: arXiv cs.SE 2021
Year: 2021

The evidence-based approach has increasingly been employed to synthesize empirical findings from the primary research in software engineering. Nevertheless, the reproducibility of evidence-based software engineering (EBSE) studies seems to be underemphasized. In our investigation into the automatic search of 311 sample studies, more than 50% of the search strings are not reusable; about 87.5% of the search activities (e.g., search field settings) are unrepeatable; and more than 95% of the whole automatic search implementations are unreproducible. Considering that searching is a cornerstone of an EBSE study, we are afraid that the reproducibility of the current secondary research could be worse than we can imagine. By analyzing and reporting the root causes of the aforementioned observations, we urge collaboration and cooperation among all the stakeholders in our community to improve the research reproducibility in EBSE.