Reviewerly/exHarmony huggingface dataset:
Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models

The integration of discrete algorithmic components in deep learning architectures has numerous applications. Recently, Implicit Maximum Likelihood Estimation, a class of gradient estimators for discrete exponential family distributions, was proposed by combining implicit differentiation through perturbation with the path-wise gradient estimator. However, due to the finite difference approximation of the gradients, it is especially sensitive to the choice of the finite difference step size, which needs to be specified by the user. In this work, we present Adaptive IMLE (AIMLE), the first adaptive gradient estimator for complex discrete distributions: it adaptively identifies the target distribution for IMLE by trading off the density of gradient information with the degree of bias in the gradient estimates. We empirically evaluate our estimator on synthetic examples, as well as on Learning to Explain, Discrete Variational Auto-Encoders, and Neural Relational Inference tasks. In our experiments, we show that our adaptive gradient estimator can produce faithful estimates while requiring orders of magnitude fewer samples than other gradient estimators.
Multi-Level Confidence Learning for Trustworthy Multimodal Classification

With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.
Towards Safe AI: Sandboxing DNNs-Based Controllers in Stochastic Games

Nowadays, AI-based techniques, such as deep neural networks (DNNs), are widely deployed in autonomous systems for complex mission requirements (e.g., motion planning in robotics). However, DNNs-based controllers are typically very complex, and it is very hard to formally verify their correctness, potentially causing severe risks for safety-critical autonomous systems. In this paper, we propose a construction scheme for a so-called Safe-visor architecture to sandbox DNNs-based controllers. Particularly, we consider the construction under a stochastic game framework to provide a system-level safety guarantee which is robust to noises and disturbances. A supervisor is built to check the control inputs provided by a DNNs-based controller and decide whether to accept them. Meanwhile, a safety advisor is running in parallel to provide fallback control inputs in case the DNN-based controller is rejected. We demonstrate the proposed approaches on a quadrotor employing an unverified DNNs-based controller.
The Many Faces of Adversarial Machine Learning

Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision, natural language processing, and network science. In addition, the problems of strategic classification, algorithmic recourse, and counterfactual explanations have essentially the same core mathematical structure as AML, despite distinct motivations. I give a simplified overview of the central problems in AML, and then discuss both the security-motivated AML domains, and the problems above unrelated to security. These together span a number of important AI subdisciplines, but can all broadly be viewed as concerned with trustworthy AI. My goal is to clarify both the technical connections among these, as well as the substantive differences, suggesting directions for future research.
Towards Fair and Selectively Privacy-Preserving Models Using Negative Multi-Task Learning (Student Abstract)

Deep learning models have shown great performances in natural language processing tasks. While much attention has been paid to improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In order to tackle these problems, we protect individuals' sensitive information and mitigate gender bias simultaneously. First, we propose a selective privacy-preserving method that only obscures individuals' sensitive information. Then we propose a negative multi-task learning framework to mitigate the gender bias which contains a main task and a gender prediction task. We analyze two existing word embeddings and evaluate them on sentiment analysis and a medical text classification task. Our experimental results show that our negative multi-task learning framework can mitigate the gender bias while keeping models’ utility.
Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)

DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.
Weighted Policy Constraints for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn policy from the passively collected offline dataset. Applying existing RL methods on the static dataset straightforwardly will raise distribution shift, causing these unconstrained RL methods to fail. To cope with the distribution shift problem, a common practice in offline RL is to constrain the policy explicitly or implicitly close to behavioral policy. However, the available dataset usually contains sub-optimal or inferior actions, constraining the policy near all these actions will make the policy inevitably learn inferior behaviors, limiting the performance of the algorithm. Based on this observation, we propose a weighted policy constraints (wPC) method that only constrains the learned policy to desirable behaviors, making room for policy improvement on other parts. Our algorithm outperforms existing state-of-the-art offline RL algorithms on the D4RL offline gym datasets. Moreover, the proposed algorithm is simple to implement with few hyper-parameters, making the proposed wPC algorithm a robust offline RL method with low computational complexity.
Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.
Lifted Inference with Linear Order Axiom

We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula φ, domain size n and a pair of weight functions, what is the weighted sum of all models of φ over a domain of size n? It was shown that computing WFOMC of any logical sentence with at most two logical variables can be done in time polynomial in n. However, it was also shown that the task is #P1-complete once we add the third variable, which inspired the search for extensions of the two-variable fragment that would still permit a running time polynomial in n. One of such extension is the two-variable fragment with counting quantifiers. In this paper, we prove that adding a linear order axiom (which forces one of the predicates in φ to introduce a linear ordering of the domain elements in each model of φ) on top of the counting quantifiers still permits a computation time polynomial in the domain size. We present a new dynamic programming-based algorithm which can compute WFOMC with linear order in time polynomial in n, thus proving our primary claim.
Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)

Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD.
Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation

Unsupervised image segmentation aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.
USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network

Unsupervised/self-supervised graph neural networks (GNN) are vulnerable to inherent randomness in the input graph data which greatly affects the performance of the model in downstream tasks. In this paper, we alleviate the interference of graph randomness and learn appropriate representations of nodes without label information. To this end, we propose USER, an unsupervised robust version of graph neural networks that is based on structural entropy. We analyze the property of intrinsic connectivity and define intrinsic connectivity graph. We also identify the rank of the adjacency matrix as a crucial factor in revealing a graph that provides the same embeddings as the intrinsic connectivity graph. We then introduce structural entropy in the objective function to capture such a graph. Extensive experiments conducted on clustering and link prediction tasks under random-noises and meta-attack over three datasets show USER outperforms benchmarks and is robust to heavier randomness.
Fighting noise with noise: a stochastic projective quantum eigensolver

In the current noisy intermediate scale quantum era of quantum computation, available hardware is severely limited by both qubit count and noise levels, precluding the application of many current hybrid quantum-classical algorithms to non-trivial quantum chemistry problems. In this paper we propose applying some of the fundamental ideas of conventional Quantum Monte Carlo algorithms -- stochastic sampling of both the wavefunction and the Hamiltonian -- to quantum algorithms in order to significantly decrease quantum resource costs. In the context of an imaginary-time propagation based projective quantum eigensolver, we present a novel approach to estimating physical observables which leads to a two order of magnitude reduction in the required sampling of the quantum state to converge the ground state energy of a system relative to current state-of-the-art eigensolvers. The method can be equally applied to excited-state calculations and, combined with stochastic approximations of the system Hamiltonian, provides a promising near-term approach to Hamiltonian simulation for general chemistry on quantum devices.
Exploration of transferable deep learning-aided radio frequency fingerprint identification systems

Radio frequency fingerprint identification (RFFI) shows great potential as a means for authenticating wireless devices. As RFFI can be addressed as a classification problem, deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance. RFFI is suitable for securing the legacy existing Internet of Things (IoT) networks since it does not require any modifications to the existing end-node hardware and communication protocols. However, most deep learning-based RFFI systems require the collection of a great number of labelled signals for training, which is time-consuming and not ideal, especially for the IoT end nodes that are already deployed and configured with long transmission intervals. Moreover, the long time required to train a neural network from scratch also limits rapid deployment on legacy IoT networks. To address the above issues, two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning. More specifically, they rely on fine-tuning and distance metric learning, respectively, and only require only a small amount of signals from the legacy IoT network. As the dataset used for transfer is small, we propose to apply augmentation in the transfer process to generate more training signals to improve performance. A LoRa-RFFI testbed consisting of 40 commercial-off-the-shelf (COTS) LoRa IoT devices and a software-defined radio (SDR) receiver is built to experimentally evaluate the proposed approaches. The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another IoT network with less than ten signals from each LoRa device. The classification accuracy is over 90%, and the augmentation technique can improve the accuracy by up to 20%.
Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Abstract One of the challenges in cloud computing is increasing efficiency, which calls for the application of task scheduling and load balancing. Numerous load-balancing methods that have been suggested for the cloud environment can increase efficiency. The Throttled policy is one of them that aids in evenly distributing user requests among virtual machines. In the first part of this article, we examined the cost of the Clonal selection algorithm with Throttled load balancing Policy and the Clonal selection algorithm with Distributed Service Broker Policy and contrasted them to the clonal selection and other immune systems mechanisms. Additionally, we suggest two additional mechanisms based on the artificial immune system. In the two proposed mechanisms, algorithms are introduced so that by benefiting from load balancing policies when receiving requests from users, in the shortest possible time and the fastest processing of data and with the lowest cost, they respond to users. In the second part, we introduce two other new algorithms that were created as a result of combining an evolutionary algorithm with the artificial immune system mechanisms. Algorithms have been evaluated and compared with other algorithms using the Cloud Analyst simulator in terms of average response time, average data center processing time, total data transfer cost, and total cost. The results attained show how much better the suggested algorithms are.
Space Theory

The author introduces a novel theoretical framework that suggests matter and energy are both converted from curved space. In Space Theory, gravitation is generated by the flow of space, instead of being transmitted by the graviton as in String Theory. This theory also suggests that Newton's gravitational constant, denoted as G, may not be truly constant but could vary over time. The equivalent equation of space is S=Ec2=mc4, and the gravitation of hollow sphere space is S&amp;mu;&amp;nu;=4&amp;pi;Gm=(4/3)&amp;pi;((r+a)3-r3).
Streaming egocentric action anticipation: An evaluation scheme and approach

Egocentric action anticipation aims to predict the future actions the camera wearer will perform from the observation of the past. While predictions about the future should be available before the predicted events take place, most approaches do not pay attention to the computational time required to make such predictions. As a result, current evaluation schemes assume that predictions are available right after the input video is observed, i.e., presuming a negligible runtime, which may lead to overly optimistic evaluations. We propose a streaming egocentric action evaluation scheme which assumes that predictions are performed online and made available only after the model has processed the current input segment, which depends on its runtime. To evaluate all models considering the same prediction horizon, we hence propose that slower models should base their predictions on temporal segments sampled ahead of time. Based on the observation that model runtime can affect performance in the considered streaming evaluation scenario, we further propose a lightweight action anticipation model based on feed-forward 3D CNNs which is optimized using knowledge distillation techniques with a novel past-to-future distillation loss. Experiments on the three popular datasets EPIC-KITCHENS-55, EPIC-KITCHENS-100 and EGTEA Gaze+ show that (i) the proposed evaluation scheme induces a different ranking on state-of-the-art methods as compared to classic evaluations, (ii) lightweight approaches tend to outmatch more computationally expensive ones, and (iii) the proposed model based on feed-forward 3D CNNs and knowledge distillation outperforms current art in the streaming egocentric action anticipation scenario.
Evaluating the Impact of Strategies on Students’ Perceptions of Digital Transformation – A Case Study of a Swedish Higher Education Institution

Digital transformation (DT) in Higher Education Institutions (HEIs) affects the learning environment with digitally-enhanced teaching methods, student assistance, and administration processes. HEIs develop strategies to exploit the chances offered by DT. Our study investigates the connection between the strategic work of HEIs on DT and how students perceive the results of this work in their daily studies. We applied a case study design on a Swedish HEI to gather our results. Results show that students are somewhat positive about the strategic work but still perceive digitalization as a barrier to collaborating with peers and lecturers. Our research contributes to knowing if the time and effort spent on an HEI’s DT impact the student stakeholder group. By bringing forth ways of improvement, we generate new knowledge about DT processes in HEIs. Thus, we inspire educators and administrators in this industry by putting forward lessons learned and improvements.