pwc-archive/papers-with-abstracts huggingface dataset:
Source Class Selection with Label Propagation for Partial Domain Adaptation
Authors: Wang, Qian; Breckon, Toby P.
Date: 2021-09-01 00:00:00
Proceeding: ICIP 2021 9
Tasks: Domain Adaptation, Partial Domain Adaptation, Unsupervised Domain Adaptation

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes and it is not known which classes the target-domain data belong to since they are unlabeled. This problem has been formulated as Partial Domain Adaptation (PDA) in the literature and is a challenging task due to the negative transfer issue (i.e. source-domain data belonging to the irrelevant classes harm the domain adaptation). 
We address the PDA problem by detecting the outlier classes in the source domain progressively. As a result, the PDA is boiled down to an easier unsupervised domain adaptation problem which can be solved without the issue of negative transfer. Specifically, we employ the locality preserving projection to learn a latent common subspace in which a label propagation algorithm is used to label the target-domain data. The outlier classes can be detected if no target-domain data are labeled as these classes. We remove the detected outlier classes from the source domain and repeat the process for multiple iterations until convergence. Experimental results on commonly used datasets Office31 and Office-Home demonstrate our proposed method achieves state-of-the-art performance with an average accuracy of 98.1\% and 75.4\% respectively.
Gram-Elites: N-Gram Based Quality-Diversity Search
Authors: Colan F. Biemer, Alejandro Hervella, Seth Cooper
Date: 2021-10-21 00:00:00
Proceeding: Proceedings of the FDG workshop on Procedural Content Generation 2021 10
Tasks: Diversity

In the context of procedural content generation via machine learning (PCGML), quality-diversity (QD) algorithms are a powerful tool to generate diverse game content. A branch of QD uses genetic operators to generate content (e.g. MAP-Elites). Problematically, levels generated with these operators have no guarantee of matching the style of a game. This can be addressed by incorporating whether a level is generable by an n-gram into the fitness function. Unfortunately, this leads to wasted computation and poor results. In this work, we introduce n-gram genetic operators, which produce only solutions that are generable by the n-gram model; we call MAP-Elites combined with these operators Gram-Elites. We test on a tile-based side-scrolling platformer, vertical platformer, and roguelike. For all three, n-gram operators outperform standard operators and random n-gram generation, finding more usable (i.e. completable and generable) solutions at a faster rate. By integrating structure into operators, instead of fitness, these genetic operators could be beneficial to QD in PCGML.
Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems
Authors: Xin Wang, Julian Berberich, Jian Sun, Gang Wang, Frank Allgöwer, Jie Chen
Date: 2022-02-16 00:00:00
Tasks: STS

The present paper considers the model-based and data-driven control of unknown linear time-invariant discrete-time systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods.
Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images
Authors: Qiang Wang, Susan Fernandes, Gareth O. S. Williams, Neil Finlayson, Ahsan R. Akram, Kevin Dhaliwal, James R. Hopgood, Marta Vallejo
Date: 2022-02-15 00:00:00
Tasks: Image-to-Image Translation, Medical Image Registration, Unsupervised Image-To-Image Translation

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison by experienced researchers shows the superiority of our method on co-registration. The results also indicate that the approach is applicable to various image formats, like fluorescence intensity images. With the registration, stitching outcomes illustrate the distinct differences of the spectral lifetime across an unstained tissue, enabling macro-level rapid visual identification of lung cancer and cellular-level characterisation of cell variants and common types. The approach could be effortlessly extended to lifetime images beyond this range and other staining technologies.
An Intrusion Response System utilizing Deep Q-Networks and System Partitions
Authors: Valeria Cardellini, Emiliano Casalicchio, Stefano Iannucci, Matteo Lucantonio, Sudip Mittal, Damodar Panigrahi, Andrea Silvi
Date: 2022-02-16 00:00:00
Tasks: Reinforcement Learning (RL), Transfer Learning

Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper countermeasure to take in order to stop or mitigate an ongoing attack. However, most of them do not consider the fact that systems can change over time or, in other words, that systems exhibit a non-stationary behavior. Furthermore, stateful approaches, such as those based on RL, suffer the curse of dimensionality, due to a state space growing exponentially with the size of the protected system. In this paper, we introduce and develop an IRS software prototype, named irs-partition. It leverages the partitioning of the protected system and Deep Q-Networks to address the curse of dimensionality by supporting a multi-agent formulation. Furthermore, it exploits transfer learning to follow the evolution of non-stationary systems.
Ditto: Building Digital Twins of Articulated Objects from Interaction
Authors: Zhenyu Jiang, Cheng-Chun Hsu, Yuke Zhu
Date: 2022-02-16 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D geometry, Mixed Reality, Object

Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating interactive digital twins of real-world articulated objects, which can be directly imported into virtual environments. We introduce Ditto to learn articulation model estimation and 3D geometry reconstruction of an articulated object through interactive perception. Given a pair of visual observations of an articulated object before and after interaction, Ditto reconstructs part-level geometry and estimates the articulation model of the object. We employ implicit neural representations for joint geometry and articulation modeling. Our experiments show that Ditto effectively builds digital twins of articulated objects in a category-agnostic way. We also apply Ditto to real-world objects and deploy the recreated digital twins in physical simulation. Code and additional results are available at https://ut-austin-rpl.github.io/Ditto
TalkTive: A Conversational Agent Using Backchannels to Engage Older Adults in Neurocognitive Disorders Screening
Authors: Zijian Ding, Jiawen Kang, Tinky Oi Ting HO, Ka Ho Wong, Helene H. Fung, Helen Meng, Xiaojuan Ma
Date: 2022-02-16 00:00:00

Conversational agents (CAs) have the great potential in mitigating the clinicians' burden in screening for neurocognitive disorders among older adults. It is important, therefore, to develop CAs that can be engaging, to elicit conversational speech input from older adult participants for supporting assessment of cognitive abilities. As an initial step, this paper presents research in developing the backchanneling ability in CAs in the form of a verbal response to engage the speaker. We analyzed 246 conversations of cognitive assessments between older adults and human assessors, and derived the categories of reactive backchannels (e.g. "hmm") and proactive backchannels (e.g. "please keep going"). This is used in the development of TalkTive, a CA which can predict both timing and form of backchanneling during cognitive assessments. The study then invited 36 older adult participants to evaluate the backchanneling feature. Results show that proactive backchanneling is more appreciated by participants than reactive backchanneling.
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference
Authors: Burak Çakmak, Yue M. Lu, Manfred Opper
Date: 2022-02-16 00:00:00

We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student-teacher scenario. To model nontrivial dependencies between the latent variables, we assume random covariance matrices drawn from rotation invariant ensembles. Moreover, we consider a model mismatching setting, where the teacher model and the one used by the student may be different. By means of dynamical functional approach, we obtain exact dynamical mean-field equations characterizing the dynamics of the inference algorithm. We also derive a range of model parameters for which the sequential algorithm does not converge. The boundary of this parameter range coincides with the de Almeida Thouless (AT) stability condition of the replica symmetric ansatz for the static probabilistic model.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Authors: Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu
Date: 2022-02-16 00:00:00
Tasks: Fairness, Privacy Preserving

This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks, and describes available mitigation measures for the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine-learning or decisions-making tasks under a fairness lens.
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
Authors: Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali, Evren Catak, Ozgur Guler
Date: 2022-02-16 00:00:00
Tasks: Prediction

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting the beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks (DNNs) in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable against adversarial attacks, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Momentum Iterative Method (MIM), because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study also offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence (AI)-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods effectively defend the DNN models against adversarial attacks in next-generation wireless networks.
Geometry of the Minimum Volume Confidence Sets
Authors: Heguang Lin, Mengze Li, Daniel Pimentel-Alarcón, Matthew Malloy
Date: 2022-02-16 00:00:00

Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement learning algorithms. This paper studies the geometry of the minimum-volume confidence sets for the multinomial parameter. When used in place of more standard confidence sets and intervals based on bounds and asymptotic approximation, learning algorithms can exhibit improved sample complexity. Prior work showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value. While the confidence sets are optimal in that they have minimum average volume, computation of membership of a single point in the set is challenging for problems of modest size. Since the confidence sets are level-sets of discontinuous functions, little is apparent about their geometry. This paper studies the geometry of the minimum volume confidence sets by enumerating and covering the continuous regions of the exact p-value function. This addresses a fundamental question in A/B testing: given two multinomial outcomes, how can one determine if their corresponding minimum volume confidence sets are disjoint? We answer this question in a restricted setting.
Bias and unfairness in machine learning models: a systematic literature review
Authors: Tiago Palma Pagano, Rafael Bessa Loureiro, Fernanda Vitória Nascimento Lisboa, Gustavo Oliveira Ramos Cruz, Rodrigo Matos Peixoto, Guilherme Aragão de Sousa Guimarães, Lucas Lisboa dos Santos, Maira Matos Araujo, Marco Cruz, Ewerton Lopes Silva de Oliveira, Ingrid Winkler, Erick Giovani Sperandio Nascimento
Date: 2022-02-16 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Fairness, Systematic Literature Review, Unfairness Detection

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context.
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation
Authors: Md. Mohi Uddin Khan, Abdullah Bin Shams, Md. Mohsin Sarker Raihan
Date: 2022-02-16 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Human Interaction Recognition, Time Series Analysis

Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. ...
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Authors: Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Date: 2022-02-16 00:00:00
Tasks: Anomaly Detection, Video Anomaly Detection

Anomaly detection aims at identifying data points that show systematic deviations from the majority of data in an unlabeled dataset. A common assumption is that clean training data (free of anomalies) is available, which is often violated in practice. We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models. The idea is to jointly infer binary labels to each datum (normal vs. anomalous) while updating the model parameters. Inspired by outlier exposure (Hendrycks et al., 2018) that considers synthetically created, labeled anomalies, we thereby use a combination of two losses that share parameters: one for the normal and one for the anomalous data. We then iteratively proceed with block coordinate updates on the parameters and the most likely (latent) labels. Our experiments with several backbone models on three image datasets, 30 tabular data sets, and a video anomaly detection benchmark showed consistent and significant improvements over the baselines.
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models
Authors: Thierry Denoeux
Date: 2022-02-16 00:00:00

We introduce a general theory of epistemic random fuzzy sets for reasoning with fuzzy or crisp evidence. This framework generalizes both the Dempster-Shafer theory of belief functions, and possibility theory. Independent epistemic random fuzzy sets are combined by the generalized product-intersection rule, which extends both Dempster's rule for combining belief functions, and the product conjunctive combination of possibility distributions. We introduce Gaussian random fuzzy numbers and their multi-dimensional extensions, Gaussian random fuzzy vectors, as practical models for quantifying uncertainty about scalar or vector quantities. Closed-form expressions for the combination, projection and vacuous extension of Gaussian random fuzzy numbers and vectors are derived.
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing
Authors: Kenny Schlegel, Peer Neubert, Peter Protzel
Date: 2022-02-16 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Classification

Classification of time series data is an important task for many application domains. One of the best existing methods for this task, in terms of accuracy and computation time, is MiniROCKET. In this work, we extend this approach to provide better global temporal encodings using hyperdimensional computing (HDC) mechanisms. HDC (also known as Vector Symbolic Architectures, VSA) is a general method to explicitly represent and process information in high-dimensional vectors. It has previously been used successfully in combination with deep neural networks and other signal processing algorithms. We argue that the internal high-dimensional representation of MiniROCKET is well suited to be complemented by the algebra of HDC. This leads to a more general formulation, HDC-MiniROCKET, where the original algorithm is only a special case. We will discuss and demonstrate that HDC-MiniROCKET can systematically overcome catastrophic failures of MiniROCKET on simple synthetic datasets. These results are confirmed by experiments on the 128 datasets from the UCR time series classification benchmark. The extension with HDC can achieve considerably better results on datasets with high temporal dependence without increasing the computational effort for inference.
Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN
Authors: Lilach Barkat, Moti Freiman, Haim Azhari
Date: 2022-02-16 00:00:00
Tasks: Anatomy, Generative Adversarial Network, Translation

Ultrasound is the second most used modality in medical imaging. It is cost effective, hazardless, portable and implemented routinely in numerous clinical procedures. Nonetheless, image quality is characterized by granulated appearance, poor SNR and speckle noise. Specific for malignant tumors, the margins are blurred and indistinct. Thus, there is a great need for improving ultrasound image quality. We hypothesize that this can be achieved, using neural networks, by translation into a more realistic display which mimics an anatomical cut through the tissue. In order to achieve this goal, the preferable approach would be to use a set of paired images. However, this is practically impossible in our case. Therefore, Cycle Generative Adversarial Network (CycleGAN) was used, in order to learn each domain properties separately and enforce cross domain cycle consistency. The two datasets which were used for training the model were "Breast Ultrasound Images" (BUSI) and a set of optic images of poultry breast tissue samples acquired at our lab. The generated pseudo anatomical images provide improved visual discrimination of the lesions with clearer border definition and pronounced contrast. In order to evaluate the preservation of the anatomical features, the lesions in the ultrasonic images and the generated pseudo anatomical images were both automatically segmented and compared. This comparison yielded median dice score of 0.91 for the benign tumors and 0.70 for the malignant ones. The median lesion center error was 0.58% and 3.27% for the benign and malignancies respectively and the median area error index was 0.40% and 4.34% for the benign and malignancies respectively. In conclusion, these generated pseudo anatomical images, which are presented in a more intuitive way, enhance tissue anatomy and can potentially simplify the diagnosis and improve the clinical outcome.