OpenRaiser/Intern-Atlas huggingface dataset:
Annotating Implicit Reasoning in Arguments with Causal Links
Venue: arXiv cs.CL 2021
Year: 2021

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge. However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise). In this work, we focus on identifying the implicit knowledge in the form of argumentation knowledge which can help in understanding the reasoning link in arguments. Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge that explicates the implicit reasoning in arguments via causality. We create a novel two-phase annotation process with simplified guidelines and show how to collect and filter high-quality implicit reasonings via crowdsourcing. We find substantial inter-annotator agreement for quality evaluation between experts, but find evidence that casts a few questions on the feasibility of collecting high-quality semi-structured implicit reasoning through our crowdsourcing process. We release our materials(i.e., crowdsourcing guidelines and collected implicit reasonings) to facilitate further research towards the structured representation of argumentation knowledge.
Encoding of Predicate Subtyping with Proof Irrelevance in the $λ$$Π$-Calculus Modulo Theory
Venue: arXiv cs.LO 2021
Year: 2021

The $λ$$Π$-calculus modulo theory is a logical framework in which various logics and type systems can be encoded, thus helping the cross-verification and interoperability of proof systems based on those logics and type systems. In this paper, we show how to encode predicate subtyping and proof irrelevance, two important features of the PVS proof assistant. We prove that this encoding is correct and that encoded proofs can be mechanically checked by Dedukti, a type checker for the $λ$$Π$-calculus modulo theory using rewriting.
TNTC: two-stream network with transformer-based complementarity for gait-based emotion recognition
Venue: arXiv cs.CV 2021
Year: 2021

Recognizing the human emotion automatically from visual characteristics plays a vital role in many intelligent applications. Recently, gait-based emotion recognition, especially gait skeletons-based characteristic, has attracted much attention, while many available methods have been proposed gradually. The popular pipeline is to first extract affective features from joint skeletons, and then aggregate the skeleton joint and affective features as the feature vector for classifying the emotion. However, the aggregation procedure of these emerged methods might be rigid, resulting in insufficiently exploiting the complementary relationship between skeleton joint and affective features. Meanwhile, the long range dependencies in both spatial and temporal domains of the gait sequence are scarcely considered. To address these issues, we propose a novel two-stream network with transformer-based complementarity, termed as TNTC. Skeleton joint and affective features are encoded into two individual images as the inputs of two streams, respectively. A new transformer-based complementarity module (TCM) is proposed to bridge the complementarity between two streams hierarchically via capturing long range dependencies. Experimental results demonstrate TNTC outperforms state-of-the-art methods on the latest dataset in terms of accuracy.
DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning
Venue: arXiv cs.CY 2021
Year: 2021

Most current affect scales and sentiment analysis on written text focus on quantifying valence (sentiment) -- the most primary dimension of emotion. However, emotions are broader and more complex than valence. Distinguishing negative emotions of similar valence could be important in contexts such as mental health. This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. First, we trained the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with their responses to the Depression Anxiety Stress Scale (DASS-21). Within the framework of cognitive network science, we model every list of recalled emotions as a walk over a networked mental representation of semantic memory, with emotions connected according to free associations in people's memory. Among several tested machine learning approaches, we find that a multilayer perceptron neural network trained on word sequences and semantic network distances can achieve state-of-art, cross-validated predictions for depression ($R = 0.7$), anxiety ($R = 0.44$) and stress ($R = 0.52$). Though limited by sample size, this first-of-its-kind approach enables quantitative explorations of key semantic dimensions behind DAS levels. We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress. We also find that semantic distance of recalls from "fear" can boost the prediction of anxiety but it becomes redundant when the "sad-happy" dyad is considered. Adopting DASentimental as a semi-supervised learning tool to estimate DAS in text, we apply it to a dataset of 142 suicide notes. We conclude by discussing key directions for future research enabled by artificial intelligence detecting stress, anxiety and depression.
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
Venue: arXiv cs.RO 2021
Year: 2021

Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they should also handle uncertainty at the input rather than only at the output of the DL components. Considering a probability distribution in the input enables the propagation of uncertainty through different components to provide a representative measure of the overall system uncertainty. In this position paper, we propose a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our early experiments show that the proposed method improves the robustness of the navigation policy in Out-of-Distribution (OoD) scenarios.
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty
Venue: arXiv cs.DB 2021
Year: 2021

Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees take into account information about the expected workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. Operating in shared infrastructure like the cloud, however, comes with a degree of workload uncertainty due to multi-tenancy and the fast-evolving nature of modern applications. Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance. To address this problem, we introduce Endure - a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policies, size-ratio, and memory allocation on the overall performance. Endure considers a robust formulation of the throughput maximization problem, and recommends a tuning that maximizes the worst-case throughput over a neighborhood of each expected workload. Additionally, an uncertainty tuning parameter controls the size of this neighborhood, thereby allowing the output tunings to be conservative or optimistic. Through both model-based and extensive experimental evaluation of Endure in the state-of-the-art LSM-based storage engine, RocksDB, we show that the robust tuning methodology consistently outperforms classical tun-ing strategies. We benchmark Endure using 15 workload templates that generate more than 10000 unique noisy workloads. The robust tunings output by Endure lead up to a 5$\times$ improvement in through-put in presence of uncertainty. On the flip side, when the observed workload exactly matches the expected one, Endure tunings have negligible performance loss.
Detecting speaking persons in video
Venue: arXiv cs.CV 2021
Year: 2021

We present a novel method for detecting speaking persons in video, by extracting facial landmarks with a neural network and analysing these landmarks statistically over time
Defensive Tensorization
Venue: arXiv cs.LG 2021
Year: 2021

We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network. The layers of a network are first expressed as factorized tensor layers. Tensor dropout is then applied in the latent subspace, therefore resulting in dense reconstructed weights, without the sparsity or perturbations typically induced by the randomization.Our approach can be readily integrated with any arbitrary neural architecture and combined with techniques like adversarial training. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio classification task and binary networks. In all cases, we demonstrate improved performance compared to prior works.
Overcoming Pedestrian Blockage in mm-Wave Bands using Ground Reflections
Venue: arXiv cs.IT 2021
Year: 2021

mm-Wave communication employs directional beams to overcome high path loss. High data rate communication is typically along line-of-sight (LoS). In outdoor environments, such communication is susceptible to temporary blockage by pedestrians interposed between the transmitter and receiver. It results in outages in which the user is lost, and has to be reacquired as a new user, severely disrupting interactive and high throughput applications. It has been presumed that the solution is to have a densely deployed set of base stations that will allow the mobile to perform a handover to a different non-blocked base station every time a current base station is blocked. This is however a very costly solution for outdoor environments. Through extensive experiments we show that it is possible to exploit a strong ground reflection with a received signal strength (RSS) about 4dB less than the LoS path in outdoor built environments with concrete or gravel surfaces, for beams that are narrow in azimuth but wide in zenith. While such reflected paths cannot support the high data rates of LoS paths, they can support control channel communication, and, importantly, sustain time synchronization between the mobile and the base station. This allows a mobile to quickly recover to the LoS path upon the cessation of the temporary blockage, which typically lasts a few hundred milliseconds. We present a simple in-band protocol that quickly discovers ground reflected radiation and uses it to recover the LoS link when the temporary blockage disappears.
Boosted CVaR Classification
Venue: arXiv cs.LG 2021
Year: 2021

Many modern machine learning tasks require models with high tail performance, i.e. high performance over the worst-off samples in the dataset. This problem has been widely studied in fields such as algorithmic fairness, class imbalance, and risk-sensitive decision making. A popular approach to maximize the model's tail performance is to minimize the CVaR (Conditional Value at Risk) loss, which computes the average risk over the tails of the loss. However, for classification tasks where models are evaluated by the zero-one loss, we show that if the classifiers are deterministic, then the minimizer of the average zero-one loss also minimizes the CVaR zero-one loss, suggesting that CVaR loss minimization is not helpful without additional assumptions. We circumvent this negative result by minimizing the CVaR loss over randomized classifiers, for which the minimizers of the average zero-one loss and the CVaR zero-one loss are no longer the same, so minimizing the latter can lead to better tail performance. To learn such randomized classifiers, we propose the Boosted CVaR Classification framework which is motivated by a direct relationship between CVaR and a classical boosting algorithm called LPBoost. Based on this framework, we design an algorithm called $α$-AdaLPBoost. We empirically evaluate our proposed algorithm on four benchmark datasets and show that it achieves higher tail performance than deterministic model training methods.
A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

The stringent requirements on reliability and processing delay in the fifth-generation ($5$G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.
Training Wasserstein GANs without gradient penalties
Venue: arXiv cs.LG 2021
Year: 2021

We propose a stable method to train Wasserstein generative adversarial networks. In order to enhance stability, we consider two objective functions using the $c$-transform based on Kantorovich duality which arises in the theory of optimal transport. We experimentally show that this algorithm can effectively enforce the Lipschitz constraint on the discriminator while other standard methods fail to do so. As a consequence, our method yields an accurate estimation for the optimal discriminator and also for the Wasserstein distance between the true distribution and the generated one. Our method requires no gradient penalties nor corresponding hyperparameter tuning and is computationally more efficient than other methods. At the same time, it yields competitive generators of synthetic images based on the MNIST, F-MNIST, and CIFAR-10 datasets.
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis
Venue: arXiv cs.AR 2021
Year: 2021

Power delivery network (PDN) design is a nontrivial, time-intensive, and iterative task. Correct PDN design must account for considerations related to power bumps, currents, blockages, and signal congestion distribution patterns. This work proposes a machine learning-based methodology that employs a set of predefined PDN templates. At the floorplan stage, coarse estimates of current, congestion, macro/blockages, and C4 bump distributions are used to synthesize a grid for early design. At the placement stage, the grid is incrementally refined based on more accurate and fine-grained distributions of current and congestion. At each stage, a convolutional neural network (CNN) selects an appropriate PDN template for each region on the chip, building a safe-by-construction PDN that meets IR drop and electromigration (EM) specifications. The CNN is initially trained using a large synthetically-created dataset, following which transfer learning is leveraged to bridge the gap between real-circuit data (with a limited dataset size) and synthetically-generated data. On average, the optimization of the PDN frees thousands of routing tracks in congestion-critical regions, when compared to a globally uniform PDN, while staying within the IR drop and EM limits.
Encoder-Decoder Networks for Analyzing Thermal and Power Delivery Networks
Venue: arXiv cs.AR 2021
Year: 2021

Power delivery network (PDN) analysis and thermal analysis are computationally expensive tasks that are essential for successful IC design. Algorithmically, both these analyses have similar computational structure and complexity as they involve the solution to a partial differential equation of the same form. This paper converts these analyses into image-to-image and sequence-to-sequence translation tasks, which allows leveraging a class of machine learning models with an encoder-decoder-based generative (EDGe) architecture to address the time-intensive nature of these tasks. For PDN analysis, we propose two networks: (i) IREDGe: a full-chip static and dynamic IR drop predictor and (ii) EMEDGe: electromigration (EM) hotspot classifier based on input power, power grid distribution, and power pad distribution patterns. For thermal analysis, we propose ThermEDGe, a full-chip static and dynamic temperature estimator based on input power distribution patterns for thermal analysis. These networks are transferable across designs synthesized within the same technology and packing solution. The networks predict on-chip IR drop, EM hotspot locations, and temperature in milliseconds with negligibly small errors against commercial tools requiring several hours.
Denoised Non-Local Neural Network for Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

The non-local network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most of the current popular non-local models tend to ignore the phenomenon that the calculated attention map appears to be very noisy, containing inter-class and intra-class inconsistencies, which lowers the accuracy and reliability of the non-local methods. In this paper, we figuratively denote these inconsistencies as attention noises and explore the solutions to denoise them. Specifically, we inventively propose a Denoised Non-Local Network (Denoised NL), which consists of two primary modules, i.e., the Global Rectifying (GR) block and the Local Retention (LR) block, to eliminate the inter-class and intra-class noises respectively. First, GR adopts the class-level predictions to capture a binary map to distinguish whether the selected two pixels belong to the same category. Second, LR captures the ignored local dependencies and further uses them to rectify the unwanted hollows in the attention map. The experimental results on two challenging semantic segmentation datasets demonstrate the superior performance of our model. Without any external training data, our proposed Denoised NL can achieve the state-of-the-art performance of 83.5\% and 46.69\% mIoU on Cityscapes and ADE20K, respectively.
FedPrune: Towards Inclusive Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades when there is (i) variability in client characteristics in terms of computational and memory resources (system heterogeneity) and (ii) non-IID data distribution across clients (statistical heterogeneity). For example, slow clients get dropped in FL schemes, such as Federated Averaging (FedAvg), which not only limits overall learning but also biases results towards fast clients. We propose FedPrune; a system that tackles this challenge by pruning the global model for slow clients based on their device characteristics. By doing so, slow clients can train a small model quickly and participate in FL which increases test accuracy as well as fairness. By using insights from Central Limit Theorem, FedPrune incorporates a new aggregation technique that achieves robust performance over non-IID data. Experimental evaluation shows that Fed- Prune provides robust convergence and better fairness compared to Federated Averaging.
SOAR: Minimizing Network Utilization with Bounded In-network Computing
Venue: arXiv cs.NI 2021
Year: 2021

In-network computing via smart networking devices is a recent trend for modern datacenter networks. State-of-the-art switches with near line rate computing and aggregation capabilities are developed to enable, e.g., acceleration and better utilization for modern applications like big data analytics, and large-scale distributed and federated machine learning. We formulate and study the problem of activating a limited number of in-network computing devices within a network, aiming at reducing the overall network utilization for a given workload. Such limitations on the number of in-network computing elements per workload arise, e.g., in incremental upgrades of network infrastructure, and are also due to requiring specialized middleboxes, or FPGAs, that should support heterogeneous workloads, and multiple tenants. We present an optimal and efficient algorithm for placing such devices in tree networks with arbitrary link rates, and further evaluate our proposed solution in various scenarios and for various tasks. Our results show that having merely a small fraction of network devices support in-network aggregation can lead to a significant reduction in network utilization. Furthermore, we show that various intuitive strategies for performing such placements exhibit significantly inferior performance compared to our solution, for varying workloads, tasks, and link rates.
Tight FPT Approximation for Constrained k-Center and k-Supplier
Venue: arXiv cs.DS 2021
Year: 2021

In this work, we study a range of constrained versions of the $k$-supplier and $k$-center problems such as: capacitated, fault-tolerant, fair, etc. These problems fall under a broad framework of constrained clustering. A unified framework for constrained clustering was proposed by Ding and Xu [SODA 2015] in context of the $k$-median and $k$-means objectives. In this work, we extend this framework to the $k$-supplier and $k$-center objectives. This unified framework allows us to obtain results simultaneously for the following constrained versions of the $k$-supplier problem: $r$-gather, $r$-capacity, balanced, chromatic, fault-tolerant, strongly private, $\ell$-diversity, and fair $k$-supplier problems, with and without outliers. We obtain the following results: We give $3$ and $2$ approximation algorithms for the constrained $k$-supplier and $k$-center problems, respectively, with $\mathsf{FPT}$ running time $k^{O(k)} \cdot n^{O(1)}$, where $n = |C \cup L|$. Moreover, these approximation guarantees are tight; that is, for any constant $ε>0$, no algorithm can achieve $(3-ε)$ and $(2-ε)$ approximation guarantees for the constrained $k$-supplier and $k$-center problems in $\mathsf{FPT}$ time, assuming $\mathsf{FPT} \neq \mathsf{W}[2]$. Furthermore, we study these constrained problems in outlier setting. Our algorithm gives $3$ and $2$ approximation guarantees for the constrained outlier $k$-supplier and $k$-center problems, respectively, with $\mathsf{FPT}$ running time $(k+m)^{O(k)} \cdot n^{O(1)}$, where $n = |C \cup L|$ and $m$ is the number of outliers.
From Complexity Measurement to Holistic Quality Evaluation for Automotive Software Development
Venue: arXiv cs.SE 2021
Year: 2021

In recent years, the role and the importance of software in the automotive domain have changed dramatically. Being able to systematically evaluate and manage software quality is becoming even more crucial. In practice, however, we still find a largely static approach for measuring software quality based on a predefined list of complexity metrics with static thresholds to fulfill. We propose using a more flexible framework instead, which systematically derives measures and evaluation rules based on the goals and context of a development project.