pwc-archive/papers-with-abstracts huggingface dataset:
Dbias: Detecting biases and ensuring Fairness in news articles
Authors: Shaina Raza, Deepak John Reji, Chen Ding
Date: 2022-08-11 00:00:00
Tasks: Articles, Fairness

Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
Authors: Lixin Liu, Yanling Wang, Tianming Wang, Dong Guan, Jiawei Wu, Jingxu Chen, Rong Xiao, Wenxiang Zhu, Fei Fang
Date: 2022-08-11 00:00:00
Tasks: Click-Through Rate Prediction, Prediction, Recommendation Systems, Transfer Learning

As one of the largest e-commerce platforms in the world, Taobao's recommendation systems (RSs) serve the demands of shopping for hundreds of millions of customers. Click-Through Rate (CTR) prediction is a core component of the RS. One of the biggest characteristics in CTR prediction at Taobao is that there exist multiple recommendation domains where the scales of different domains vary significantly. Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue. However, existing cross-domain CTR prediction methods are proposed for static knowledge transfer, ignoring that all domains in real-world RSs are continually time-evolving. In light of this, we present a necessary but novel task named Continual Transfer Learning (CTL), which transfers knowledge from a time-evolving source domain to a time-evolving target domain. In this work, we propose a simple and effective CTL model called CTNet to solve the problem of continual cross-domain CTR prediction at Taobao, and CTNet can be trained efficiently. Particularly, CTNet considers an important characteristic in the industry that models has been continually well-trained for a very long time. So CTNet aims to fully utilize all the well-trained model parameters in both source domain and target domain to avoid losing historically acquired knowledge, and only needs incremental target domain data for training to guarantee efficiency. Extensive offline experiments and online A/B testing at Taobao demonstrate the efficiency and effectiveness of CTNet. CTNet is now deployed online in the recommender systems of Taobao, serving the main traffic of hundreds of millions of active users.
Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation
Authors: Yuxiang Shi, Yue Ding, Bo Chen, YuYang Huang, Yule Wang, Ruiming Tang, Dong Wang
Date: 2022-08-11 00:00:00
Tasks: Graph Neural Network, Meta-Learning, Recommendation Systems

The cold-start problem is a long-standing challenge in recommender systems due to the lack of user-item interactions, which significantly hurts the recommendation effect over new users and items. Recently, meta-learning based methods attempt to learn globally shared prior knowledge across all users, which can be rapidly adapted to new users and items with very few interactions. Though with significant performance improvement, the globally shared parameter may lead to local optimum. Besides, they are oblivious to the inherent information and feature interactions existing in the new users and items, which are critical in cold-start scenarios. In this paper, we propose a Task aligned Meta-learning based Augmented Graph (TMAG) to address cold-start recommendation. Specifically, a fine-grained task aligned constructor is proposed to cluster similar users and divide tasks for meta-learning, enabling consistent optimization direction. Besides, an augmented graph neural network with two graph enhanced approaches is designed to alleviate data sparsity and capture the high-order user-item interactions. We validate our approach on three real-world datasets in various cold-start scenarios, showing the superiority of TMAG over state-of-the-art methods for cold-start recommendation.
On the Value of Behavioral Representations for Dense Retrieval
Authors: Nan Jiang, Dhivya Eswaran, Choon Hui Teo, Yexiang Xue, Yesh Dattatreya, Sujay Sanghavi, Vishy Vishwanathan
Date: 2022-08-11 00:00:00
Tasks: Retrieval, Text Retrieval

We consider text retrieval within dense representational space in real-world settings such as e-commerce search where (a) document popularity and (b) diversity of queries associated with a document have a skewed distribution. Most of the contemporary dense retrieval literature presents two shortcomings in these settings. (1) They learn an almost equal number of representations per document, agnostic to the fact that a few head documents are disproportionately more critical to achieving a good retrieval performance. (ii) They learn purely semantic document representations inferred from intrinsic document characteristics which may not contain adequate information to determine the queries for which the document is relevant--especially when the document is short. We propose to overcome these limitations by augmenting semantic document representations learned by bi-encoders with behavioral document representations learned by our proposed approach MVG. To do so, MVG (1) determines how to divide the total budget for behavioral representations by drawing a connection to the Pitman-Yor process, and (2) simply clusters the queries related to a given document (based on user behavior) within the representational space learned by a base bi-encoder, and treats the cluster centers as its behavioral representations. Our central contribution is the finding such a simple intuitive light-weight approach leads to substantial gains in key first-stage retrieval metrics by incurring only a marginal memory overhead. We establish this via extensive experiments over three large public datasets comparing several single-vector and multi-vector bi-encoders, a proprietary e-commerce search dataset compared to production-quality bi-encoder, and an A/B test.
Exploiting Hierarchical Dependence Structures for Unsupervised Rank Fusion in Information Retrieval
Authors: J. Hermosillo-Valadez, E. Morales-González, F. Fernández-Reyes, M. Montes-y-Gómez, J. Fuentes-Pacheco, J. M. Rendón-Mancha
Date: 2022-08-10 00:00:00
Tasks: Information Retrieval, Retrieval

The goal of rank fusion in information retrieval (IR) is to deliver a single output list from multiple search results. Improving performance by combining the outputs of various IR systems is a challenging task. A central point is the fact that many non-obvious factors are involved in the estimation of relevance, inducing nonlinear interrelations between the data. The ability to model complex dependency relationships between random variables has become increasingly popular in the realm of information retrieval, and the need to further explore these dependencies for data fusion has been recently acknowledged. Copulas provide a framework to separate the dependence structure from the margins. Inspired by the theory of copulas, we propose a new unsupervised, dynamic, nonlinear, rank fusion method, based on a nested composition of non-algebraic function pairs. The dependence structure of the model is tailored by leveraging query-document correlations on a per-query basis. We experimented with three topic sets over CLEF corpora fusing 3 and 6 retrieval systems, comparing our method against the CombMNZ technique and other nonlinear unsupervised strategies. The experiments show that our fusion approach improves performance under explicit conditions, providing insight about the circumstances under which linear fusion techniques have comparable performance to nonlinear methods.
Malaria Parasite Detection using Efficient Neural Ensembles
Authors: Saurav Mishra
Date: 2021-10-15 00:00:00
Proceeding: Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) 2021 10
Tasks: Diagnostic, Medical Image Classification, Transfer Learning

Caused by the bite of the Anopheles mosquito infected with the parasite of genus Plasmodium, malaria has remained a major burden towards healthcare for years with an approximate 400,000 deaths reported globally every year. The traditional diagnosis process for malaria involves an examination of the blood smear slide under the microscope. This process is not only time consuming but also requires pathologists to be highly skilled in their work. Timely diagnosis and availability of robust diagnostic facilities and skilled laboratory technicians are very much vital to reduce the mortality rate. This study aims to build a robust system by applying deep learning techniques such as transfer learning and snapshot ensembling to automate the detection of the parasite in the thin blood smear images. All the models were evaluated against the following metrics - F1 score, Accuracy, Precision, Recall, Mathews Correlation Coefficient (MCC), Area Under the Receiver Operating Characteristics (AUC-ROC) and the Area under the Precision Recall curve (AUC-PR). The snapshot ensembling model created by combining the snapshots of the EfficientNet-B0 pre-trained model outperformed every other model achieving a f1 score - 99.37%, precision - 99.52% and recall - 99.23%. The results show the potential of  model ensembles which combine the predictive power of multiple weal models to create a single efficient model that is better equipped to handle the real world data. The GradCAM experiment displayed the gradient activation maps of the last convolution layer to visually explicate where and what a model sees in an image to classify them into a particular class. The models in this study correctly activate the stained parasitic region of interest in the thin blood smear images. Such visuals make the model more transparent, explainable, and trustworthy which are very much essential for deploying AI based models in the healthcare network.
Multi-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition
Authors: Yuya Chiba1, Takashi Nose1, Akinori Ito
Date: 2020-10-25 00:00:00
Proceeding: Interspeech 2020 10
Tasks: Data Augmentation, Emotional Speech Synthesis, Emotion Recognition, Speech Emotion Recognition, Speech Synthesis

This paper proposes a speech emotion recognition technique
that considers the suprasegmental characteristics and temporal
change of individual speech parameters. In recent years, speech
emotion recognition using Bidirectional LSTM (BLSTM) has
been studied actively because the model can focus on a particular temporal region that contains strong emotional characteristics. One of the model’s weaknesses is that it cannot consider
the statistics of speech features, which are known to be effective
for speech emotion recognition. Besides, this method cannot
train individual attention parameters for different descriptors
because it handles the input sequence by a single BLSTM. In
this paper, we introduce feature segmentation and multi-stream
processing into attention-based BLSTM to solve these problems. In addition, we employed data augmentation based on
emotional speech synthesis in a training step. The classification experiments between four emotions (i.e., anger, joy, neutral, and sadness) using the Japanese Twitter-based Emotional
Speech corpus (JTES) showed that the proposed method obtained a recognition accuracy of 73.4%, which is comparable to
human evaluation (75.5%).
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Authors: P. Bergmann, K. Batzner, M. Fauser, D. Sattlegger, and C. Steger
Date: 2021-04-01 00:00:00
Proceeding: International Journal of Computer Vision 2021 4
Tasks: Anomaly Detection

The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec anomaly detection dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth annotations for all anomalies. We conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pretrained convolutional neural networks, as well as classical computer vision methods. We highlight the advantages and disadvantages of multiple performance metrics as well as threshold estimation techniques. This benchmark indicates that methods that leverage descriptors of pretrained networks outperform all other approaches and deep-learning-based generative models show considerable room for improvement.
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
Authors: Zihan Liu, Yun Luo, Lirong Wu, Siyuan Li, Zicheng Liu, Stan Z. Li
Date: 2022-08-07 00:00:00
Tasks: Computational Efficiency

Graph edge perturbations are dedicated to damaging the prediction of graph neural networks by modifying the graph structure. Previous gray-box attackers employ gradients from the surrogate model to locate the vulnerable edges to perturb the graph structure. However, unreliability exists in gradients on graph structures, which is rarely studied by previous works. In this paper, we discuss and analyze the errors caused by the unreliability of the structural gradients. These errors arise from rough gradient usage due to the discreteness of the graph structure and from the unreliability in the meta-gradient on the graph structure. In order to address these problems, we propose a novel attack model with methods to reduce the errors inside the structural gradients. We propose edge discrete sampling to select the edge perturbations associated with hierarchical candidate selection to ensure computational efficiency. In addition, semantic invariance and momentum gradient ensemble are proposed to address the gradient fluctuation on semantic-augmented graphs and the instability of the surrogate model. Experiments are conducted in untargeted gray-box poisoning scenarios and demonstrate the improvement in the performance of our approach.
Incentivizing Hidden Types in Secretary Problem
Authors: Longjian Li, Alexis Akira Toda
Date: 2022-08-11 00:00:00

We study a game between $N$ job applicants who incur a cost $c$ (relative to the job value) to reveal their type during interviews and an administrator who seeks to maximize the probability of hiring the best. We define a full learning equilibrium and prove its existence, uniqueness, and optimality. In equilibrium, the administrator accepts the current best applicant $n$ with probability $c$ if $n<n^*$ and with probability 1 if $n\ge n^*$ for a threshold $n^*$ independent of $c$. In contrast to the case without cost, where the success probability converges to $1/\mathrm{e}\approx 0.37$ as $N$ tends to infinity, with cost the success probability decays like $N^{-c}$.
Barrier Certificate based Safe Control for LiDAR-based Systems under Sensor Faults and Attacks
Authors: Hongchao Zhang, Shiyu Cheng, Luyao Niu, Andrew Clark
Date: 2022-08-11 00:00:00
Tasks: State Estimation

Autonomous Cyber-Physical Systems (CPS) fuse proprioceptive sensors such as GPS and exteroceptive sensors including Light Detection and Ranging (LiDAR) and cameras for state estimation and environmental observation. It has been shown that both types of sensors can be compromised by malicious attacks, leading to unacceptable safety violations. We study the problem of safety-critical control of a LiDAR-based system under sensor faults and attacks. We propose a framework consisting of fault tolerant estimation and fault tolerant control. The former reconstructs a LiDAR scan with state estimations, and excludes the possible faulty estimations that are not aligned with LiDAR measurements. We also verify the correctness of LiDAR scans by comparing them with the reconstructed ones and removing the possibly compromised sector in the scan. Fault tolerant control computes a control signal with the remaining estimations at each time step. We prove that the synthesized control input guarantees system safety using control barrier certificates. We validate our proposed framework using a UAV delivery system in an urban environment. We show that our proposed approach guarantees safety for the UAV whereas a baseline fails.
A Geometric Procedure for Computing Differential Characteristics of Multi-phase Electrical Signals using Geometric Algebra
Authors: Ahmad H. Eid, Francisco G. Montoya
Date: 2022-08-11 00:00:00

This paper presents exploratory investigations on the concept of generalized geometrical frequency in electrical systems with an arbitrary number of phases by using Geometric Algebra and Differential Geometry. By using the concept of Darboux bivector it is possible to find a bivector that encodes the invariant geometrical properties of a spatial curve named electrical curve. It is shown how the traditional concept of instantaneous frequency in power networks can be intimately linked to the Darboux bivector. Several examples are used to illustrate the findings of this work.
Stability Analysis of a Class of Discontinuous Discrete-Time Systems
Authors: Francesco Ferrante, Giorgio Valmorbida
Date: 2022-08-11 00:00:00

The stability analysis of a class of discontinuous discrete-time systems is studied in this paper. The system under study is modeled as a feedback interconnection of a linear system and a set-valued nonlinearity. An equivalent representation, based on a constrained optimization problem, is proposed to represent the set-valued nonlinearity via a collection of linear and quadratic constraints. Relying on this description and on the use of a generalized quadratic set-valued Lyapunov functions, sufficient conditions in the form of linear matrix inequalities for global exponential stability are obtained. Numerical examples corroborate the theoretical findings.
Robust Coordinated Longitudinal Control of MAV Based on Energy State
Authors: Chenlong Zhang, Dawei Li, Haodong Li
Date: 2022-08-11 00:00:00

Fixed-wing Miniature Air Vehicle (MAV) is not only coupled with longitudinal motion, but also more susceptible to wind disturbance due to its lighter weight, which brings more challenges to its altitude and airspeed controller design. Therefore, in this paper, an improved longitudinal control strategy based on energy state, is proposed to address the above-mentioned issues. The control strategy utilizes the Linear Extended State Observer (LESO) to observe the energy states and the disturbance of the MAV, and then designs a Multiple-Input Multiple-Output (MIMO) controller based on a more coordinated Total Energy Control (TEC) strategy to control the airspeed and altitude of the MAV. The performance of this control strategy has been successfully verified in a Model-in-the-Loop (MIL) simulation with Simulink, and a comparative test with the classical TEC algorithm is carried out.
Sampled-data control design for systems with quantized actuators
Authors: Francesco Ferrante, Sophie Tarbouriech
Date: 2022-08-11 00:00:00
Tasks: Quantization

This paper deals with the problem of designing a sampled-data state feedback control law for continuous-time linear control systems subject to uniform input quantization. The sampled-data state feedback is designed to ensure the uniform global asymptotic stability (UGAS) of an attractor surrounding the origin. The closed-loop system is rewritten as a hybrid dynamical system. To do this, an auxiliary clock variable triggering the occurrence of sampling events is introduced. A numerically tractable algorithm with feasibility guarantees, based on concave-convex decomposition, is then proposed allowing to minimize the size of the attractor. Theoretical results are illustrated in a numerical example.
Exact low-dimensional description for fast neural oscillations with low firing rates
Authors: Pau Clusella, Ernest Montbrió
Date: 2022-08-10 00:00:00

Recently, low-dimensional models of neuronal activity have been exactly derived for large networks of deterministic, Quadratic Integrate-and-Fire (QIF) neurons. Such firing rate models (FRM) describe the emergence of fast collective oscillations (>30~Hz) via the frequency-locking of a subset of neurons to the global oscillation frequency. However, the suitability of such models to describe realistic neuronal states is seriously challenged by fact that during episodes of fast collective oscillations, neuronal discharges are often very irregular and have low firing rates compared to the global oscillation frequency. Here we extend the theory to derive exact FRM for QIF neurons to include noise, and show that networks of stochastic neurons displaying irregular discharges at low firing rates during episodes of fast oscillations, are governed by exactly the same evolution equations as deterministic networks. Our results reconcile two traditionally confronted views on neuronal synchronization, and upgrade the applicability of exact FRM to describe a broad range of biologically realistic neuronal states.
Assessments in Education
Authors: Hans Henrik Sievertsen
Date: 2022-08-11 00:00:00

Assessments such as standardized tests and teacher evaluations of students' classroom participation are central elements of most educational systems. Assessments inform the student, parent, teacher, and school about the student learning progress. Individuals use the information to adjust their study efforts and to make guide their course choice. Schools and teachers use the information to evaluate effectiveness and inputs. Assessments are also used to sort students into tracks, educational programmes, and on the labor market. Policymakers use assessments to reward or penalise schools and parents use assessment results to select schools. Consequently, assessments incentivize the individual, the teacher, and the school to do well. Because assessments play an important role in individuals' educational careers, either through the information or the incentive channel, they are also important for efficiency, equity, and well-being. The information channel is important for ensuring the most efficient human capital investments: students learn about the returns and costs of effort investments and about their abilities and comparative advantages. However, because students are sorted into educational programs and on the labor market based on assessment results, students optimal educational investment might not equal their optimal human capital investment because of the signaling value. Biases in assessments and heterogeneity in access to assessments are sources of inequality in education according to gender, origin, and socioeconomic background. These sources have long-running implications for equality and opportunity. Finally, because assessment results also carry important consequences for individuals' educational opportunities and on the labor market, they are a source of stress and reduced well-being.