OpenRaiser/Intern-Atlas huggingface dataset:
Machine Assistance for Credit Card Approval? Random Wheel can Recommend and Explain
Venue: arXiv cs.LG 2021
Year: 2021

Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to mitigate this challenge. However, the effectiveness of such automation may depend on the richness of the training dataset and model efficiency. We have recently developed a novel classifier named random wheel which provides a more interpretable output. In this work, we have used an enhanced version of random wheel to facilitate a trustworthy recommendation for credit card approval process. It not only produces more accurate and precise recommendation but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit card application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which in turn can enhance the efficiency of the credit card approval process.
SciNote: Collaborative Problem Solving and Argumentation Tool
Venue: arXiv cs.CY 2021
Year: 2021

As educators push for students to learn science by doing science, there is a need for computational scaffolding to assist students' evaluation of scientific evidence and argument building. In this paper, we present a pilot study of SciNote, a CSCL tool allowing educators to integrate third-party software into a flexible and collaborative workspace. We explore how SciNote enables teams to build data-driven arguments during inquiry-based learning activities.
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels
Venue: arXiv cs.IR 2021
Year: 2021

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e. widgets or swipeable carousels, each built with a specific criterion (e.g. most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. This is not considered by traditional offline evaluation protocols. Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We report experiments on publicly available datasets on the movie domain and notice that under a carousel setting the ranking of the algorithms change. In particular, when a SLIM carousel is available, matrix factorization models tend to be preferred, while item-based models are penalized. We also propose to extend ranking metrics to the two-dimensional carousel layout in order to account for a known position bias, i.e. users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen.
Ergodic Capacity of High Throughput Satellite Systems With Mixed FSO-RF Transmission
Venue: arXiv cs.IT 2021
Year: 2021

We study a high throughput satellite system, where the feeder link uses free-space optical (FSO) and the user link uses radio frequency (RF) communication. In particular, we first propose a transmit diversity using Alamouti space time block coding to mitigate the atmospheric turbulence in the feeder link. Then, based on the concept of average virtual signal-to-interference-plus-noise ratio and one-bit feedback, we propose a beamforming algorithm for the user link to maximize the ergodic capacity (EC). Moreover, by assuming that the FSO links follow the Malaga distribution whereas RF links undergo the shadowed-Rician fading, we derive a closed-form EC expression of the considered system. Finally, numerical simulations validate the accuracy of our theoretical analysis, and show that the proposed schemes can achieve higher capacity compared with the reference schemes.
Applying information theory and entropy to eliminate errors in mouse-tracking results
Venue: arXiv cs.HC 2021
Year: 2021

Mouse-tracking of computer system users represents a less expensive, but also a far more applicable alternative to eye-tracking. The main disadvantage of mouse-tracking are errors manifested as discrepancies between the actual eye-gaze position and the mouse cursor position. This paper presents a method for automated correction of errors arising in mouse-tracking. Our approach draws upon information theory and employs Shannon entropy. The method is based on calculating the entropy of a visual representation of a Web page, i.e., we quantify information potential values of various positions. Information obtained, thereby, is paired with cumulative time intervals, spent by the mouse cursor in each position. In this way, we identify cursor positions that do not match eye-gaze positions. To verify the effectiveness of our method, we compared the eye gaze and mouse cursor heat maps in the following ways: We calculated the coefficient of correlation between the two; we computed Euclidean distance between their centers of gravity; and we performed visual comparison.
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss
Venue: arXiv cs.LG 2021
Year: 2021

Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with real-life impact. Many different targeted solutions have been proposed to address separately these three problems, however a unifying perspective seems to be missing. With this work, we provide a general formalization, showing that they are different expressions of unbalance. Following this intuition, we formulate a unified loss correction to address issues related to Fairness, Biases and Imbalances (FBI-loss). The correction capabilities of the proposed approach are assessed on three real-world benchmarks, each associated to one of the issues under consideration, and on a family of synthetic data in order to better investigate the effectiveness of our loss on tasks with different complexities. The empirical results highlight that the flexible formulation of the FBI-loss leads also to competitive performances with respect to literature solutions specialised for the single problems.
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
Venue: arXiv cs.LG 2021
Year: 2021

The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by deep generative networks). In this work, we study the algorithmic aspects of such a learning-based approach from a theoretical perspective. For certain generative network architectures, we establish a simple non-convex algorithmic approach that (a) theoretically enjoys linear convergence guarantees for certain linear and nonlinear inverse problems, and (b) empirically improves upon conventional techniques such as back-propagation. We support our claims with the experimental results for solving various inverse problems. We also propose an extension of our approach that can handle model mismatch (i.e., situations where the generative network prior is not exactly applicable). Together, our contributions serve as building blocks towards a principled use of generative models in inverse problems with more complete algorithmic understanding.
Building a consistent system for faculty appraisal using Data Envelopment Analysis
Venue: arXiv cs.CY 2021
Year: 2021

Data Envelopment Analysis (DEA) appears more than just an instrument of measurement. DEA models can be seen as a mathematical structure for democratic voicing within decisional contexts. Such an important aspect of DEA is enhanced through the performance evaluation of a group of professors in a virtual Business college. We show that the outcomes of the analysis can be very useful to support decision processes at many levels. There are three categories of professors: Assistant professors, Associate professors, and Full professors. The evaluation process of these professors is investigated through two different cases. The first case handles each category of professors as a separate sample representing an independent population. The results show that the mean efficiency scores fall between 0.85 and 0.93 for all professors no matters their category. In spite of enabling more fairness, such an approach suffers from its exclusive character, which is contrary to the democratic spirit of DEA. The second case tries to cope with this deficiency through the assessment of the faculty members as a single sample drawn from the same population, i.e., Assistant professors, Associate professors, and Full professors are treated equally, only on the ground of their respective inputs and outputs, no matters their academic rank. A clear efficiency decline is reported, basically due to the very nature of DEA as a procedure that is more efficiency than output focused.
OpenFL: An open-source framework for Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.
Interval Deep Learning for Uncertainty Quantification in Safety Applications
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks (DNNs) are becoming more prevalent in important safety-critical applications, where reliability in the prediction is paramount. Despite their exceptional prediction capabilities, current DNNs do not have an implicit mechanism to quantify and propagate significant input data uncertainty -- which is common in safety-critical applications. In many cases, this uncertainty is epistemic and can arise from multiple sources, such as lack of knowledge about the data generating process, imprecision, ignorance, and poor understanding of physics phenomena. Recent approaches have focused on quantifying parameter uncertainty, but approaches to end-to-end training of DNNs with epistemic input data uncertainty are more limited and largely problem-specific. In this work, we present a DNN optimized with gradient-based methods capable to quantify input and parameter uncertainty by means of interval analysis, which we call Deep Interval Neural Network (DINN). We perform experiments on an air pollution dataset with sensor uncertainty and show that the DINN can produce accurate bounded estimates from uncertain input data.
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles
Venue: arXiv cs.CL 2021
Year: 2021

In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.
NLP is Not enough -- Contextualization of User Input in Chatbots
Venue: arXiv cs.CL 2021
Year: 2021

AI chatbots have made vast strides in technology improvement in recent years and are already operational in many industries. Advanced Natural Language Processing techniques, based on deep networks, efficiently process user requests to carry out their functions. As chatbots gain traction, their applicability in healthcare is an attractive proposition due to the reduced economic and people costs of an overburdened system. However, healthcare bots require safe and medically accurate information capture, which deep networks aren't yet capable of due to user text and speech variations. Knowledge in symbolic structures is more suited for accurate reasoning but cannot handle natural language processing directly. Thus, in this paper, we study the effects of combining knowledge and neural representations on chatbot safety, accuracy, and understanding.
Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goal of the robot is to safely collect observations of the disturbance and construct an accurate estimate of the underlying disturbance function. We use Gaussian Process (GP) to get an estimate of the disturbance from data with a high-confidence bound on the regression error. Furthermore, we use neural Contraction Metrics to derive a tracking controller and the corresponding high-confidence uncertainty tube around the nominal trajectory planned for the robot, based on the estimate of the disturbance. From the robustness of the Contraction Metric, error bound can be pre-computed and used by the motion planner such that the actual trajectory is guaranteed to be safe. As the robot collects more and more observations along its trajectory, the estimate of the disturbance becomes more and more accurate, which in turn improves the performance of the tracking controller and enlarges the free space that the robot can safely explore. We evaluate the proposed method using a carefully designed environment with a ground vehicle. Results show that with the proposed method the robot can thoroughly explore the environment safely and quickly.
The Dynamics of Gradient Descent for Overparametrized Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

We consider the dynamics of gradient descent (GD) in overparameterized single hidden layer neural networks with a squared loss function. Recently, it has been shown that, under some conditions, the parameter values obtained using GD achieve zero training error and generalize well if the initial conditions are chosen appropriately. Here, through a Lyapunov analysis, we show that the dynamics of neural network weights under GD converge to a point which is close to the minimum norm solution subject to the condition that there is no training error when using the linear approximation to the neural network. To illustrate the application of this result, we show that the GD converges to a prediction function that generalizes well, thereby providing an alternative proof of the generalization results in Arora et al. (2019).
Input-distribution-aware parallel decoding of block codes
Venue: arXiv cs.IT 2021
Year: 2021

Many channel decoders rely on parallel decoding attempts to achieve good performance with acceptable latency. However, most of the time fewer attempts than the foreseen maximum are sufficient for successful decoding. Input-distribution-aware (IDA) decoding allows to determine the parallelism of polar code list decoders by observing the distribution of channel information. In this work, IDA decoding is proven to be effective with different codes and decoding algorithms as well. Two techniques, M-IDA and MD-IDA, are proposed: they exploit the sampling of the input distribution inherent to particular decoding algorithms to perform low-cost IDA decoding. Simulation results on the decoding of BCH codes via the Chase and ORBGRAND algorithms show that they perform at least as well as the original IDA decoding, allowing to reduce run-time complexity down to 17% and 67\% with minimal error correction degradation.
Performance Analysis of Cell-Free Massive MIMO Systems with Massive Connectivity
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we empirically evaluate biased methods for alpha-divergence minimization. In particular, we focus on how the bias affects the final solutions found, and how this depends on the dimensionality of the problem. We find that (i) solutions returned by these methods appear to be strongly biased towards minimizers of the traditional "exclusive" KL-divergence, KL(q||p), and (ii) in high dimensions, an impractically large amount of computation is needed to mitigate this bias and obtain solutions that actually minimize the alpha-divergence of interest.
An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research
Venue: arXiv cs.SI 2021
Year: 2021

There is an increasing interest in ensuring machine learning (ML) frameworks behave in a socially responsible manner and are deemed trustworthy. Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative. Consequently, decisions about how to address issues of trustworthiness and future research goals are often left to the interested researcher. In this paper, we present the first quantitative approach to characterize the comprehension of TwML research. We build a co-occurrence network of words using a web-scraped corpus of more than 7,000 peer-reviewed recent ML papers -- consisting of papers both related and unrelated to TwML. We use community detection to obtain semantic clusters of words in this network that can infer relative positions of TwML topics. We propose an innovative fingerprinting algorithm to obtain probabilistic similarity scores for individual words, then combine them to give a paper-level relevance score. The outcomes of our analysis inform a number of interesting insights on advancing the field of TwML research.
Consumer, Commercial and Industrial IoT (In)Security: Attack Taxonomy and Case Studies
Venue: arXiv cs.CR 2021
Year: 2021

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications spanning from the consumer domain to commercial and industrial systems. The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life-cycles. IoT-related vulnerabilities, if successfully exploited can affect, not only the device itself, but also the application field in which the IoT device operates. Evidently, identifying and addressing every single vulnerability is an arduous, if not impossible, task. Attack taxonomies can assist in classifying attacks and their corresponding vulnerabilities. Security countermeasures and best practices can then be leveraged to mitigate threats and vulnerabilities before they emerge into catastrophic attacks and ensure overall secure IoT operation. Therefore, in this paper, we provide an attack taxonomy which takes into consideration the different layers of IoT stack, i.e., device, infrastructure, communication, and service, and each layer's designated characteristics which can be exploited by adversaries. Furthermore, using nine real-world cybersecurity incidents, that had targeted IoT devices deployed in the consumer, commercial, and industrial sectors, we describe the IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and potential mitigation mechanisms and protection strategies. These (and many other) incidents highlight the underlying security concerns of IoT systems and demonstrate the potential attack impacts of such connected ecosystems, while the proposed taxonomy provides a systematic procedure to categorize attacks based on the affected layer and corresponding impact.
Time Flies When Looking out of the Window: Timed Games with Window Parity Objectives
Venue: arXiv cs.GT 2021
Year: 2021

The window mechanism was introduced by Chatterjee et al. to reinforce mean-payoff and total-payoff objectives with time bounds in two-player turn-based games on graphs. It has since proved useful in a variety of settings, including parity objectives in games and both mean-payoff and parity objectives in Markov decision processes. We study window parity objectives in timed automata and timed games: given a bound on the window size, a path satisfies such an objective if, in all states along the path, we see a sufficiently small window in which the smallest priority is even. We show that checking that all time-divergent paths of a timed automaton satisfy such a window parity objective can be done in polynomial space, and that the corresponding timed games can be solved in exponential time. This matches the complexity class of timed parity games, while adding the ability to reason about time bounds. We also consider multi-dimensional objectives and show that the complexity class does not increase. To the best of our knowledge, this is the first study of the window mechanism in a real-time setting.
On Bisimilarities for Closure Spaces - Preliminary Version
Venue: arXiv cs.LO 2021
Year: 2021

Closure spaces are a generalisation of topological spaces obtained by removing the idempotence requirement on the closure operator. We adapt the standard notion of bisimilarity for topological models, namely Topo-bisimilarity, to closure models -- we call the resulting equivalence CM-bisimilarity -- and refine it for quasi-discrete closure models. We also define two additional notions of bisimilarity that are based on paths on space, namely Path-bisimilarity and Compatible Path-bisimilarity, CoPa-bisimilarity for short. The former expresses (unconditional) reachability, the latter refines it in a way that is reminishent of Stuttering Equivalence on transition systems. For each bisimilarity we provide a logical characterisation, using variants of the Spatial Logic for Closure Spaces (SLCS). We also address the issue of (space) minimisation via the three equivalences.