OpenRaiser/Intern-Atlas huggingface dataset:
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
Venue: arXiv cs.CY 2022
Year: 2022

Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others have focused on enhancing the performance. Models that consider both interpretability and the performance improvement have been insufficient. Moreover, models that focus on performance improvements have not shown an overwhelming performance compared with existing models. In this study, we propose MonaCoBERT, which achieves the best performance on most benchmark datasets and has significant interpretability. MonaCoBERT uses a BERT-based architecture with monotonic convolutional multihead attention, which reflects forgetting behavior of the students and increases the representation power of the model. We can also increase the performance and interpretability using a classical test-theory-based (CTT-based) embedding strategy that considers the difficulty of the question. To determine why MonaCoBERT achieved the best performance and interpret the results quantitatively, we conducted ablation studies and additional analyses using Grad-CAM, UMAP, and various visualization techniques. The analysis results demonstrate that both attention components complement one another and that CTT-based embedding represents information on both global and local difficulties. We also demonstrate that our model represents the relationship between concepts.
Selective manipulation of disentangled representations for privacy-aware facial image processing
Venue: arXiv cs.CV 2022
Year: 2022

Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as function creep and malicious usage by third-party cloud providers. To alleviate this, we propose an edge-based filtering stage that removes privacy-sensitive attributes before the sensor data are transmitted to the cloud. We use state-of-the-art image manipulation techniques that leverage disentangled representations to achieve privacy filtering. We define opt-in and opt-out filter operations and evaluate their effectiveness for filtering private attributes from face images. Additionally, we examine the effect of naturally occurring correlations and residual information on filtering. We find the results promising and believe this elicits further research on how image manipulation can be used for privacy preservation.
Smart Healthcare System Implementation Challenges: A stakeholder perspective
Venue: arXiv cs.CY 2022
Year: 2022

The smart healthcare system has gained significant attention for the improvement of the customary healthcare system. The system is comprised of several key stakeholders that make the whole ecosystem successful. However, these stakeholders offer considerable challenges that need much research to address for making the system acceptable and reliable. Furthermore, very few studies examine the key challenges from the perspective of stakeholders of the smart healthcare system. The objective of this research study is to identify the key challenges associated with each stakeholder of the smart healthcare system. We have identified 27 challenges associated with eight key stakeholders of smart healthcare reported in the state-of-the-art literature. Further, a quantitative survey was conducted and the data from 85 respondents were collected in order to assess the significance of challenges in the real-world smart healthcare system. The collected data from the respondents were further analyzed using the smart-PSL (3.0). The results indicated that all the identified challenges associated with each stakeholder negatively influence the smart healthcare system.
Targeted Influence with Community and Gender-Aware Seeding
Venue: arXiv cs.SI 2022
Year: 2022

When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focus solely on maximizing the total number of reached nodes, overlooking the issue of group fairness, in particular, gender imbalance. To tackle the challenge of maximizing information spread on certain target groups, e.g., females, we introduce the concept of the community and gender-aware potential of users. We first show that the network's community structure is closely related to the gender distribution. Then, we propose an algorithm that leverages the information about community structure and its gender potential to iteratively modify a seed set such that the information spread on the target group meets the target ratio. Finally, we validate the algorithm by performing experiments on synthetic and real-world datasets. Our results show that the proposed seeding algorithm achieves not only the target ratio but also the highest information spread, compared to the state-of-the-art gender-aware seeding algorithm.
Uncertainty Guided Depth Fusion for Spike Camera
Venue: arXiv cs.CV 2022
Year: 2022

Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity scenario since traditional cameras can only capture blurred images. To deal with this problem, the spike camera is designed to capture the pixel-wise luminance intensity at high frame rate. However, depth estimation with spike camera remains very challenging using traditional monocular or stereo depth estimation algorithms, which are based on the photometric consistency. In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera. Our framework is motivated by the fact that stereo spike depth estimation achieves better results at close range while monocular spike depth estimation obtains better results at long range. Therefore, we introduce a dual-task depth estimation architecture with a joint training strategy and estimate the distributed uncertainty to fuse the monocular and stereo results. In order to demonstrate the advantage of spike depth estimation over traditional camera depth estimation, we contribute a spike-depth dataset named CitySpike20K, which contains 20K paired samples, for spike depth estimation. UGDF achieves state-of-the-art results on CitySpike20K, surpassing all monocular or stereo spike depth estimation baselines. We conduct extensive experiments to evaluate the effectiveness and generalization of our method on CitySpike20K. To the best of our knowledge, our framework is the first dual-task fusion framework for spike camera depth estimation. Code and dataset will be released.
Automatic Assessment of the Design Quality of Student Python and Java Programs
Venue: arXiv cs.CY 2022
Year: 2022

Programs are a kind of communication to both computers and people, hence as students are trained to write programs they need to learn to write well-designed, readable code rather than code that simply functions correctly. The difficulty in teaching good design practices that promote readability is the labor intensiveness of assessing student programs. Typically assessing design quality involves a careful reading of student programs in order to give personalized feedback which naturally is time consuming for instructors. We propose a rule-based system that assesses student programs for quality of design of and provides personalized, precise feedback on how to improve their work. To study its effectiveness, we made the system available to students by deploying it online, allowing students to receive feedback and make corrections before turning in their assignments. The students benefited from the system and the rate of design quality flaws dropped 47.84\% on average over 4 different assignments, 2 in Python and 2 in Java, in comparison to the previous 2 to 3 years of student submissions.
Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning
Venue: arXiv cs.GR 2022
Year: 2022

Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thousands of detailed 3D assets are used to create a single scene. As each asset's polygon mesh can contain millions of polygons, the number of polygons that need to be drawn every frame may exceed several billions. Therefore, the computational resources often limit how many detailed objects that can be displayed in a scene. To push this limit and to optimize performance one can reduce the polygon count of the assets when possible. Basically, the idea is that an object at farther distance from the capturing camera, consequently with relatively smaller screen size, its polygon count may be reduced without affecting the perceived quality. Level of Detail (LOD) refers to the complexity level of a 3D model representation. The process of removing complexity is often called LOD reduction and can be done automatically with an algorithm or by hand by artists. However, this process may lead to deterioration of the visual quality if the different LODs differ significantly, or if LOD reduction transition is not seamless. Today the validation of these results is mainly done manually requiring an expert to visually inspect the results. However, this process is slow, mundane, and therefore prone to error. Herein we propose a method to automate this process based on the use of deep convolutional networks. We report promising results and envision that this method can be used to automate the process of LOD reduction testing and validation.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
Venue: arXiv cs.CV 2022
Year: 2022

Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification, object detection, etc. However, few-shot object detection methods suffer from an intrinsic defect that the limited training data makes the model cannot sufficiently explore semantic information. To tackle this, we introduce knowledge distillation to the few-shot object detection learning paradigm. We further run a motivating experiment, which demonstrates that in the process of knowledge distillation, the empirical error of the teacher model degenerates the prediction performance of the few-shot object detection model as the student. To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model. Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&R), to perform conditional causal intervention toward the corresponding Structural Causal Model. Empirically, the experiments on benchmarks demonstrate that D&R can yield significant performance boosts in few-shot object detection. Code is available at https://github.com/ZYN-1101/DandR.git.
A Subquadratic $n^ε$-approximation for the Continuous Fréchet Distance
Venue: arXiv cs.CG 2022
Year: 2022

The Fréchet distance is a commonly used similarity measure between curves. It is known how to compute the continuous Fréchet distance between two polylines with $m$ and $n$ vertices in $\mathbb{R}^d$ in $O(mn (\log \log n)^2)$ time; doing so in strongly subquadratic time is a longstanding open problem. Recent conditional lower bounds suggest that it is unlikely that a strongly subquadratic algorithm exists. Moreover, it is unlikely that we can approximate the Fréchet distance to within a factor $3$ in strongly subquadratic time, even if $d=1$. The best current results establish a tradeoff between approximation quality and running time. Specifically, Colombe and Fox (SoCG, 2021) give an $O(α)$-approximate algorithm that runs in $O((n^3 / α^2) \log n)$ time for any $α\in [\sqrt{n}, n]$, assuming $m = n$. In this paper, we improve this result with an $O(α)$-approximate algorithm that runs in $O((n + mn / α) \log^3 n)$ time for any $α\in [1, n]$, assuming $m \leq n$ and constant dimension $d$.
A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)
Venue: arXiv cs.AI 2022
Year: 2022

The paper investigates the relative expressiveness of two logic-based languages for reasoning over streams, namely LARS Programs -- the language of the Logic-based framework for Analytic Reasoning over Streams called LARS -- and LDSR -- the language of the recent extension of the I-DLV system for stream reasoning called I-DLV-sr. Although these two languages build over Datalog, they do differ both in syntax and semantics. To reconcile their expressive capabilities for stream reasoning, we define a comparison framework that allows us to show that, without any restrictions, the two languages are incomparable and to identify fragments of each language that can be expressed via the other one.
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings
Venue: arXiv cs.SD 2022
Year: 2022

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNU\_Mobile dataset, and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition.
Quality Diversity Evolutionary Learning of Decision Trees
Venue: arXiv cs.NE 2022
Year: 2022

Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant paradigm in the field is based on black-box models, typically in the form of (deep) neural networks, these models lack direct interpretability for human users, i.e., their outcomes (and, even more so, their inner working) are opaque and hard to understand. This is hindering the adoption of AI in safety-critical applications, where high interests are at stake. In these applications, explainable by design models, such as decision trees, may be more suitable, as they provide interpretability. Recent works have proposed the hybridization of decision trees and Reinforcement Learning, to combine the advantages of the two approaches. So far, however, these works have focused on the optimization of those hybrid models. Here, we apply MAP-Elites for diversifying hybrid models over a feature space that captures both the model complexity and its behavioral variability. We apply our method on two well-known control problems from the OpenAI Gym library, on which we discuss the "illumination" patterns projected by MAP-Elites, comparing its results against existing similar approaches.
A Latency-driven Availability Assessment for Multi-Tenant Service Chains
Venue: arXiv cs.NI 2022
Year: 2022

Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests.
LUCID: Exposing Algorithmic Bias through Inverse Design
Venue: arXiv cs.LG 2022
Year: 2022

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming
Venue: arXiv cs.AI 2022
Year: 2022

We consider learning and compositionality as the key mechanisms towards simulating human-like intelligence. While each mechanism is successfully achieved by neural networks and symbolic AIs, respectively, it is the combination of the two mechanisms that makes human-like intelligence possible. Despite the numerous attempts on building hybrid neuralsymbolic systems, we argue that our true goal should be unifying learning and compositionality, the core mechanisms, instead of neural and symbolic methods, the surface approaches to achieve them. In this work, we review and analyze the strengths and weaknesses of neural and symbolic methods by separating their forms and meanings (structures and semantics), and propose Connectionist Probabilistic Program (CPPs), a framework that connects connectionist structures (for learning) and probabilistic program semantics (for compositionality). Under the framework, we design a CPP extension for small scale sequence modeling and provide a learning algorithm based on Bayesian inference. Although challenges exist in learning complex patterns without supervision, our early results demonstrate CPP's successful extraction of concepts and relations from raw sequential data, an initial step towards compositional learning.
Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
Venue: arXiv cs.LG 2022
Year: 2022

Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks in order to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.
Incrementality Bidding and Attribution
Venue: arXiv cs.LG 2022
Year: 2022

The causal effect of showing an ad to a potential customer versus not, commonly referred to as "incrementality", is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising's causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.
Waymo's Fatigue Risk Management Framework: Prevention, Monitoring, and Mitigation of Fatigue-Induced Risks while Testing Automated Driving Systems
Venue: arXiv cs.RO 2022
Year: 2022

This report presents Waymo's proposal for a systematic fatigue risk management framework that addresses prevention, monitoring, and mitigation of fatigue-induced risks during on-road testing of ADS technology. The proposed framework remains flexible to incorporate continuous improvements, and was informed by state of the art practices, research, learnings, and experience (both internal and external to Waymo). Fatigue is a recognized contributory factor in a substantial fraction of on-road crashes involving human drivers, and mitigation of fatigue-induced risks is still an open concern researched world-wide. While the proposed framework was specifically designed in relation to on-road testing of SAE Level 4 ADS technology, it has implications and applicability to lower levels of automation as well.