pwc-archive/papers-with-abstracts huggingface dataset:
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation
Authors: Vatsal Raina, Nataliia Molchanova, Mara Graziani, Andrey Malinin, Henning Muller, Meritxell Bach Cuadra, Mark Gales
Date: 2023-02-10 00:00:00
Tasks: Lesion Segmentation, Segmentation

The development of automatic segmentation techniques for medical imaging tasks requires assessment metrics to fairly judge and rank such approaches on benchmarks. The Dice Similarity Coefficient (DSC) is a popular choice for comparing the agreement between the predicted segmentation against a ground-truth mask. However, the DSC metric has been shown to be biased to the occurrence rate of the positive class in the ground-truth, and hence should be considered in combination with other metrics. This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient (nDSC) for binary segmentation tasks as an adaptation of DSC which scales the precision at a fixed recall rate to tackle this bias. White matter lesion segmentation on magnetic resonance images of multiple sclerosis patients is selected as a case study task to empirically assess the suitability of nDSC. We validate the normalised DSC using two different models across 59 subject scans with a wide range of lesion loads. It is found that the nDSC is less biased than DSC with lesion load on standard white matter lesion segmentation benchmarks measured using standard rank correlation coefficients. An implementation of nDSC is made available at: https://github.com/NataliiaMolch/nDSC .
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions
Authors: Nay San, Martijn Bartelds, Blaine Billings, Ella de Falco, Hendi Feriza, Johan Safri, Wawan Sahrozi, Ben Foley, Bradley McDonnell, Dan Jurafsky
Date: 2023-02-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recent research using pre-trained transformer models suggests that just 10 minutes of transcribed speech may be enough to fine-tune such a model for automatic speech recognition (ASR) -- at least if we can also leverage vast amounts of text data (803 million tokens). But is that much text data necessary? We study the use of different amounts of text data, both for creating a lexicon that constrains ASR decoding to possible words (e.g. *dogz vs. dogs), and for training larger language models that bias the system toward probable word sequences (e.g. too dogs vs. two dogs). We perform experiments using 10 minutes of transcribed speech from English (for replicating prior work) and two additional pairs of languages differing in the availability of supplemental text data: Gronings and Frisian (~7.5M token corpora available), and Besemah and Nasal (only small lexica available). For all languages, we found that using only a lexicon did not appreciably improve ASR performance. For Gronings and Frisian, we found that lexica and language models derived from 'novel-length' 80k token subcorpora reduced the word error rate (WER) to 39% on average. Our findings suggest that where a text corpus in the upper tens of thousands of tokens or more is available, fine-tuning a transformer model with just tens of minutes of transcribed speech holds some promise towards obtaining human-correctable transcriptions near the 30% WER rule-of-thumb.
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
Authors: Haoran Xu, Jean Maillard, Vedanuj Goswami
Date: 2023-02-10 00:00:00
Tasks: Cross-Lingual Transfer, Decoder, Denoising, Machine Translation, Self-Supervised Learning, Translation

Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework to systematically learn language-specific parameters. Self-supervised learning (SSL) approaches that leverage large quantities of monolingual data (where parallel data is unavailable) have shown promise by improving translation performance as complementary tasks to the MMT task. However, jointly optimizing SSL and MMT tasks is even more challenging. In this work, we first investigate how to utilize intra-distillation to learn more *language-specific* parameters and then show the importance of these language-specific parameters. Next, we propose a novel but simple SSL task, concurrent denoising, that co-trains with the MMT task by concurrently denoising monolingual data on both the encoder and decoder. Finally, we apply intra-distillation to this co-training approach. Combining these two approaches significantly improves MMT performance, outperforming three state-of-the-art SSL methods by a large margin, e.g., 11.3\% and 3.7\% improvement on an 8-language and a 15-language benchmark compared with MASS, respectively
ControversialQA: Exploring Controversy in Question Answering
Authors: Zhen Wang, Peide Zhu, Jie Yang
Date: 2023-02-10 00:00:00
Tasks: Question Answering

Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks.
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks
Authors: Piotr Gaiński, Klaudia Bałazy
Date: 2023-02-10 00:00:00
Tasks: Adversarial Text, Quantization

We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities. Our algorithm mitigates the gap between adversarial loss for continuous and discrete text representations by performing multi-step quantization in a quantization-compensation loop. Experiments show that our method significantly outperforms other approaches on various natural language processing (NLP) tasks.
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Authors: Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Binhua Li, Yongbin Li
Date: 2023-02-10 00:00:00
Tasks: Table-to-Text Generation, Text Generation

Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. However, they are computationally expensive due to the non-parallelizable nature of autoregressive decoding and the redundant parameters of two networks. In this paper, we propose the first totally non-autoregressive table-to-text model (Plan-then-Seam, PTS) that produces its outputs in parallel with one single network. PTS firstly writes and calibrates one plan of the content to be generated with a novel rethinking pointer predictor, and then takes the plan as the context for seaming to decode the description. These two steps share parameters and perform iteratively to capture token inter-dependency while keeping parallel decoding. Experiments on two public benchmarks show that PTS achieves 3.0~5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors.
Adversarial Transformer Language Models for Contextual Commonsense Inference
Authors: Pedro Colon-Hernandez, Henry Lieberman, Yida Xin, Claire Yin, Cynthia Breazeal, Peter Chin
Date: 2023-02-10 00:00:00
Tasks: Knowledge Graphs, Language Modeling, Language Modelling, Sentence

Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions (i.e., facts) from a given story, and a particular sentence from that story. Some problems with the task are: lack of controllability for topics of the inferred facts; lack of commonsense knowledge during training; and, possibly, hallucinated or false facts. In this work, we utilize a transformer model for this task and develop techniques to address the aforementioned problems in the task. We control the inference by introducing a new technique we call "hinting". Hinting is a kind of language model prompting, that utilizes both hard prompts (specific words) and soft prompts (virtual learnable templates). This serves as a control signal to advise the language model "what to talk about". Next, we establish a methodology for performing joint inference with multiple commonsense knowledge bases. Joint inference of commonsense requires care, because it is imprecise and the level of generality is more flexible. You want to be sure that the results "still make sense" for the context. To this end, we align the textual version of assertions from three knowledge graphs (ConceptNet, ATOMIC2020, and GLUCOSE) with a story and a target sentence. This combination allows us to train a single model to perform joint inference with multiple knowledge graphs. We show experimental results for the three knowledge graphs on joint inference. Our final contribution is exploring a GAN architecture that generates the contextualized commonsense assertions and scores them as to their plausibility through a discriminator. The result is an integrated system for contextual commonsense inference in stories, that can controllably generate plausible commonsense assertions, and takes advantage of joint inference between multiple commonsense knowledge bases.
Implementation of Hybrid Blockchain in a Pre-Owned Electric Vehicle Supply Chain
Authors: Ganesan Subramanian; Anand Sreekantan Thampy Centre for Nanotechnology Research, Vellore Institute of Technology, Vellore, India
Date: 2021-05-31 00:00:00
Proceeding: IEEE Access ( Volume: 9) 2021 5

Ecumenical carbon dioxide (CO 2 ) emission emanates from car, bus, taxi, and motorcycle is around 45%. Electric vehicle could be the major contributor to abbreviate the pollution level in a transportation sector. According to the Forbes report the ecumenical passenger electric vehicle demand expected to reach 15% in 2025, and 23% in 2030. This research provides a blockchain predicated solution for a pre-owned electric vehicle market that could engender a trust, transparency, immutable records, and an efficient way to track the entire life cycle of a futuristic electric vehicle supply chain. At present most of the pre-owned electric conveyance purchase transpires through the third-party accommodations, websites, and mobile applications. This does not provide the authentic information on electric conveyance history, charging capabilities of the battery, history of the charging records, and performance of the driver (wear and tear affect battery life). Our solution implemented utilizing hybrid blockchain technology. Each stakeholder assigned with the Ethereum blockchain address to track the vehicle records as a distributed ledger. This design considered electric vehicle manufacturer, charging station, battery manufacturer, road convey ascendancy as major stakeholders. Astute contract designed with solidity programming. Truffle platform used to deploy keenly intellective contract in private blockchain that ascertain the privacy of electric conveyance owners and other stakeholders. Meta mask a mobile application utilized kovan network for tracking vehicle in a public blockchain. This application tested with 0.1 Ether for each transaction. Blockchain Electric Vehicle Cloud of Things (BEVCoT) concept proposed to integrate the IoT-Blockchain application in a cloud environment.
Generalization of the Dark Channel Prior for Single Image Restoration
Authors: Yan-Tsung Peng, Keming Cao, and Pamela C. Cosman, Fellow, IEEE
Date: 2019-03-07 00:00:00
Proceeding: IEEE Transactions on Image Processing 2019 3
Tasks: Image Restoration, Underwater Image Restoration

Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions. Index Terms— Haze removal, sandstorm, underwater, image restoration, transmission estimation, ambient light estimation.
Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency
Authors: Felix Kuhnke, Jörn Ostermann
Date: 2023-01-19 00:00:00
Proceeding: IEEE Transactions on Biometrics, Behavior, and Identity Science 2023 1
Tasks: Domain Adaptation, Head Pose Estimation, Pose Estimation

Head pose estimation plays a vital role in biometric systems related to facial and human behavior analysis. Typically, neural networks are trained on head pose datasets. Unfortunately, manual or sensor-based annotation of head pose is impractical. A solution is synthetic training data generated from 3D face models, which can provide an infinite number of perfect labels. However, computer generated images only provide an approximation of real-world images, leading to a performance gap between training and application domain. Therefore, there is a need for strategies that allow simultaneous learning on labeled synthetic data and unlabeled real-world data to overcome the domain gap. In this work we propose relative pose consistency, a semi-supervised learning strategy for head pose estimation based on consistency regularization. Consistency regularization enforces consistent network predictions under random image augmentations, including pose-preserving and pose-altering augmentations. We propose a strategy to exploit the relative pose introduced by pose-altering augmentations between augmented image pairs, to allow the network to benefit from relative pose labels during training on unlabeled data. We evaluate our approach in a domain-adaptation scenario and in a commonly used cross-dataset scenario. Furthermore, we reproduce related works to enforce consistent evaluation protocols and show that for both scenarios we outperform SOTA.
Rethinking Zero-shot Action Recognition: Learning from Latent Atomic Actions
Authors: Yijun Qian, Lijun Yu, Wenhe Liu, and Alexander G. Hauptmann
Date: 2022-03-28 00:00:00
Proceeding: ECCV 2022 10
Tasks: Action Recognition, Zero-Shot Action Recognition

To avoid time-consuming annotating and retraining cycle in
applying supervised action recognition models, Zero-Shot Action Recognition (ZSAR) has become a thriving direction. ZSAR requires models to recognize actions that never appear in training set through bridging visual features and semantic representations. However, due to the complexity of actions, it remains challenging to transfer knowledge learned from source to target action domains. Previous ZSAR methods mainly focus on mitigating representation variance between source and target actions through integrating or applying new action-level features. However, the action-level features are coarse-grained and make the learned one-to-one bridge fragile to similar target actions. Meanwhile, integration or application of features usually requires extra computation or annotation. These methods didn’t notice that two actions with different names may still share the same atomic action components. It enables humans to quickly understand an unseen action given bunch of atomic actions learned from seen actions. Inspired by this, we propose Jigsaw Network (JigsawNet) which recognizes complex actions through unsupervisedly decomposing them into combinations of atomic actions and
bridging group to group relationships between visual features and semantic representations. To enhance the robustness of learned group-to-group bridge, we propose Group Excitation (GE) module to model intra-sample
knowledge and Consistency Loss to enforce the model learn from inter-sample knowledge. Our JigsawNet achieves state-of-the-art performance on three benchmarks and surpasses previous works with noticeable margins.
A Review on Orbital Angular Momentum With the Approach of Using in Fifth-Generation Mobile Communications
Authors: Seyed Ali Khorasani
Date: 2023-02-10 00:00:00

In this paper, different generations of mobile communication have been concisely mentioned. The need for advanced antenna systems capable of sending and receiving massive data is felt in the fifth generation of mobile communication. The beamforming method and multi-input multi-output systems (MIMO) are the proposed solutions to increase the channel capacity of the communication network. Orbital angular momentum (OAM), an inherent feature of electromagnetic waves, is a suitable solution to increase channel capacity. This feature will increase the channel capacity by producing orthogonal modes. Using antenna arrays is an effective way to produce these modes. The results of FEKO simulations show the capability of this method.
Graph-Theoretic Analyses and Model Reduction for an Open Jackson Queueing Network
Authors: Chenyan Zhu, Sandip Roy
Date: 2023-02-10 00:00:00

A graph-theoretic analysis of the steady-state behavior of an open Jackson queueing network is developed. In particular, a number of queueing-network performance metrics are shown to exhibit a spatial dependence on local drivers (e.g. increments to local exogenous arrival rates), wherein the impacts fall off across graph cutsets away from a target queue. This graph-theoretic analysis is also used to motivate a structure-preserving model reduction algorithm, and an algorithm that exactly matches performance statistics of the original model is proposed. The graph-theoretic results and model-reduction method are evaluated via simulations of an example queueing-network model.
Spoken language change detection inspired by speaker change detection
Authors: Jagabandhu Mishra, S. R. Mahadeva Prasanna
Date: 2023-02-10 00:00:00
Tasks: Change Detection

Spoken language change detection (LCD) refers to identifying the language transitions in a code-switched utterance. Similarly, identifying the speaker transitions in a multispeaker utterance is known as speaker change detection (SCD). Since tasks-wise both are similar, the architecture/framework developed for the SCD task may be suitable for the LCD task. Hence, the aim of the present work is to develop LCD systems inspired by SCD. Initially, both LCD and SCD are performed by humans. The study suggests humans require (a) a larger duration around the change point and (b) language-specific prior exposure, for performing LCD as compared to SCD. The larger duration requirement is incorporated by increasing the analysis window length of the unsupervised distance-based approach. This leads to a relative performance improvement of 29.1% and 2.4%, and a priori language knowledge provides a relative improvement of 31.63% and 14.27% on the synthetic and practical codeswitched datasets, respectively. The performance difference between the practical and synthetic datasets is mostly due to differences in the distribution of the monolingual segment duration.
A Composite T60 Regression and Classification Approach for Speech Dereverberation
Authors: Yuying Li, Yuchen Liu, Donald S. Williamson
Date: 2023-02-09 00:00:00
Tasks: regression, Speech Dereverberation

Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments.
The Impact of Network Design Interventions on the Security of Interdependent Systems
Authors: Pradeep Sharma Oruganti, Parinaz Naghizadeh, Qadeer Ahmed
Date: 2023-02-10 00:00:00
Tasks: Form

We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an orthogonal set of defense strategies in the form of network design interventions in the CPS to protect it against the attacker. We first propose an algorithm to simplify the CPS attack graph to an equivalent form which reduces the computational requirements for characterizing the defender's optimal security investments. We then evaluate four types of design interventions in the network in the form of adding nodes in the attack graph, interpreted as introducing additional safeguards, introducing structural redundancies, introducing functional redundancies, and introducing new functionalities. We identify scenarios in which interventions that strengthen internal components of the CPS may be more beneficial than traditional approaches such as perimeter defense. We showcase our proposed approach in two practical use cases: a remote attack on an industrial CPS and a remote attack on an automotive system. We highlight how our results closely match recommendations made by security organizations and discuss the implications of our findings for CPS design.
Optimized utilization of decentral flexibility for the operational management of cellular multi-modal distribution grids
Authors: Béla Wiegel, Lando Helmrich von Elgott, Davood Babazadeh, Christian Becker
Date: 2023-02-10 00:00:00
Tasks: Management

Future energy systems rely mostly on supply-dependent resources like wind and solar energy. Since most of the produced energy from distributed renewable sources arises as electricity, electrification of energy consumers and producers, called prosumers, expands. In this context, multi-modal distribution grids make use of the storage capabilities of other energy sectors like heat, and abolish the supply-dependency and uncertainty of the feed-in of decentralized generators. This provides flexibility in the energy system, which increases security of supply and at the same time ensures economical energy provision. The subject of this paper is to exploit the flexibility of such a multi-modal energy system for operation in an optimal way. The cellular approach serves here as the energy system architecture. Aim is to provide flexibility with a cell by optimally distributing a flexibility request to subordinate prosumers changing their active and reactive power. The load change is hereby to be optimally dispatched to the different components of the prosumers under consideration of technical restrictions and dynamic behavior of components, the grids and economical aspects. For dynamic modelling and simulation the open-source modelling language Modelica is used. Using a low-voltage benchmark model expanded by multi-modal technologies, the proposed methodology shows that the cells are capable of providing flexibility to the overlying grid with high accuracy at low costs.