pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Fake News Detection
Authors: Santiago Alonso-Bartolome, Isabel Segura-Bedmar
Date: 2021-12-09 00:00:00
Tasks: Fake News Detection, Misinformation

Over the last years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have in different segments of our society. Thus, the development of tools for automatic detection of fake news plays and important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories such as Manipulated content, Satire or False connection strongly benefit from the use of images. Using images also improves the results of the other categories, but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model with an accuracy of 78%. Therefore, exploiting both text and image data significantly improves the performance of fake news detection.
Towards Neural Functional Program Evaluation
Authors: Torsten Scholak, Jonathan Pilault, Joey Velez-Ginorio
Date: 2021-12-09 00:00:00
Proceeding: NeurIPS Workshop AIPLANS 2021 12

This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional programming languages. We introduce a new program generation mechanism that allows control over syntactic sugar for semantically equivalent programs. T5 experiments reveal that neural functional program evaluation performs surprisingly well, achieving high 90% exact program match scores for most in-distribution and out-of-distribution tests. Using pretrained T5 weights has significant advantages over random initialization. We present and evaluate on three datasets to study generalization abilities that are specific to functional programs based on: type, function composition, and reduction steps. Code and data are publicly available at https://github.com/ElementAI/neural-interpreters.
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
Authors: Jiaying Gong, Hoda Eldardiry
Date: 2021-12-08 00:00:00
Tasks: Classification, Question Answering, Relation, Relation Classification, Relation Extraction, RTE, Sentence, Translation, Triplet, Zero-shot Relation Classification

In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. However, these approaches miss the semantic information about connections between seen and unseen relations. In this paper, We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting. We present a new word-level analogy-based sentence translation rule and generate augmented instances with unseen relations from instances with seen relations using that new rule. We design prompts with weighted virtual label construction based on an external knowledge graph to integrate semantic knowledge information learned from seen relations. Instead of using the actual label sets in the prompt template, we construct weighted virtual label words. We learn the representations of both seen and unseen relations with augmented instances and prompts. We then calculate the distance between the generated representations using prototypical networks to predict unseen relations. Extensive experiments conducted on three public datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms other methods under zero-shot setting. Results also demonstrate the effectiveness and robustness of ZS-SKA.
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
Authors: Peng Zhou, Alexander J. Edwards, Fred B. Mancoff, Dimitri Houssameddine, Sanjeev Aggarwal, Joseph S. Friedman
Date: 2021-12-09 00:00:00
Tasks: Handwritten Digit Recognition

We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication. We also simulate a large MTJ network performing MNIST handwritten digit recognition, demonstrating that MTJ crossbars can match memristor accuracy while providing increased precision, stability, and endurance.
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach
Authors: Chi Thang Duong, Dimitri Percia David, Ljiljana Dolamic, Alain Mermoud, Vincent Lenders, Karl Aberer
Date: 2021-12-09 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and incomplete approach. In this work, we propose an end-to-end recommendation based retrieval approach to support automatic retrieval of technologies and their associated companies from raw Web data. This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies. Our proposed framework approaches the first task by leveraging DistilBERT which is a state-of-the-art language model. For the retrieval task, we introduce a recommendation-based retrieval technique to simultaneously support retrieving related companies, technologies related to a specific company and companies relevant to a technology. To evaluate these tasks, we also construct a data set that includes company documents and entities extracted from these documents together with company categories and technology labels. Experiments show that our approach is able to return 4 times more relevant companies while outperforming traditional retrieval baseline in retrieving technologies.
"What can I cook with these ingredients?" -- Understanding cooking-related information needs in conversational search
Authors: Alexander Frummet, David Elsweiler, Bernd Ludwig
Date: 2021-12-09 00:00:00
Tasks: Classification, Conversational Search, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

As conversational search becomes more pervasive, it becomes increasingly important to understand the user's underlying information needs when they converse with such systems in diverse domains. We conduct an in-situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples, which types of need are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.
Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach
Authors: Jiyun Luo, Yan Yang, Valerie Nayak, Grace Hui Yang
Date: 2021-12-09 00:00:00

Searcher struggle is important feedback to Web search engines. Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments. Their underlying assumption is that the more effort a user spends, the more struggling the user may be. However, recent studies have suggested this simple association might be incorrect. This paper proposes a new feature modulation method for struggle detection and refers to the reversal theory in psychology. The reversal theory (RT) points out that instead of having a static personality trait, people constantly switch between opposite psychological states, complicating the relationship between the efforts they spend and the level of frustration they feel. Supported by the theory, our method modulates the effort-based features based on RT's bi-modal arousal model. Evaluations on week-long Web search logs confirm that the proposed method can statistically significantly improve state-of-the-art struggle detection methods.
Densifying Sparse Representations for Passage Retrieval by Representational Slicing
Authors: Sheng-Chieh Lin, Jimmy Lin
Date: 2021-12-09 00:00:00
Tasks: Passage Retrieval, Retrieval, Text Retrieval

Learned sparse and dense representations capture different successful approaches to text retrieval and the fusion of their results has proven to be more effective and robust. Prior work combines dense and sparse retrievers by fusing their model scores. As an alternative, this paper presents a simple approach to densifying sparse representations for text retrieval that does not involve any training. Our densified sparse representations (DSRs) are interpretable and can be easily combined with dense representations for end-to-end retrieval. We demonstrate that our approach can jointly learn sparse and dense representations within a single model and then combine them for dense retrieval. Experimental results suggest that combining our DSRs and dense representations yields a balanced tradeoff between effectiveness and efficiency.
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial Domains
Authors: Daniel Steininger, Verena Widhalm, Julia Simon, Andreas Kriegler, Christoph Sulzbachner
Date: 2021-10-17 00:00:00
Proceeding: Proceedings of the IEEE/CVF International Conference on Computer Vision 2021 10
Tasks: Fine-Grained Image Classification, Instance Segmentation, Object Detection, Robust Object Detection, Semantic Segmentation, Small Object Detection

Despite their increasing demand for assistant and autonomous systems, the recent shift towards data-driven approaches has hardly reached aerial domains, partly due to a lack of specific training and test data. We introduce the Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, respectively. In addition to fine-grained annotations for multiple learning tasks, we define and apply a set of relevant meta-parameters and showcase their potential to quantify dataset variability as well as the impact of environmental conditions on model performance. Baseline experiments are conducted for detection, classification and semantic labeling on multiple dataset variants. Their evaluation clearly shows that our contribution is an essential step towards overcoming the data gap and that the proposed variability concept significantly increases the efficiency of specializing models as well as continuously and purposefully extending the dataset.
X-Vector based voice activity detection for multi-genre broadcast speech-to-text
Authors: Misa Ogura, Matt Haynes
Date: 2021-12-09 00:00:00
Proceeding: n/a 2021 9
Tasks: Action Detection, Activity Detection, Audio Classification, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

Voice Activity Detection (VAD) is a fundamental preprocessing step in automatic speech recognition. This is especially true within the broadcast industry where a wide variety of audio materials and recording conditions are encountered. Based on previous studies which indicate that xvector embeddings can be applied to a diverse set of audio classification tasks, we investigate the suitability of x-vectors in discriminating speech from noise. We find that the proposed x-vector based VAD system achieves the best reported score in detecting clean speech on AVA-Speech, whilst retaining robust VAD performance in the presence of noise and music. Furthermore, we integrate the x-vector based VAD system into an existing STT pipeline and compare its performance on multiple broadcast datasets against a baseline system with WebRTC VAD. Crucially, our proposed x-vector based VAD improves the accuracy of STT transcription on real-world broadcast audio
Refined Commonsense Knowledge from Large-Scale Web Contents
Authors: Tuan-Phong Nguyen, Simon Razniewski, Julien Romero, Gerhard Weikum
Date: 2021-11-30 00:00:00
Tasks: Open Information Extraction

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This paper presents a method called ASCENT++ to automatically build a large-scale knowledge base (KB) of CSK assertions, with refined expressiveness and both better precision and recall than prior works. ASCENT++ goes beyond SPO triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter is essential to express the temporal and spatial validity of assertions and further qualifiers. Furthermore, ASCENT++ combines open information extraction (OpenIE) with judicious cleaning and ranking by typicality and saliency scores. For high coverage, our method taps into the large-scale crawl C4 with broad web contents. The evaluation with human judgments shows the superior quality of the ASCENT++ KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of ASCENT++. A web interface, data, and code can be accessed at https://ascentpp.mpi-inf.mpg.de/.
Fast Electromagnetic Validations of Large-Scale Digital Coding Metasurfaces Accelerated by Recurrence Rebuild and Retrieval Method
Authors: Yu Zhao, Shang Xiang, Long Li
Date: 2021-12-04 00:00:00
Tasks: CPU, Retrieval

The recurrence rebuild and retrieval method (R3M) is proposed in this paper to accelerate the electromagnetic (EM) validations of large-scale digital coding metasurfaces (DCMs). R3M aims to accelerate the EM validations of DCMs with varied codebooks, which involves the analysis of a group of similar but not identical structures. The method transforms general DCMs to rigorously periodic arrays by replacing each coding unit with the macro unit, which comprises all possible coding states. The system matrix corresponding to the rigorously periodic array is globally shared for DCMs with arbitrary codebooks via implicit retrieval. The discrepancy of the interactions for edge and corner units are precluded by the basis extension of periodic boundaries. Moreover, the hierarchical pattern exploitation (HPE) algorithm is leveraged to efficiently assemble the system matrix for further acceleration. Due to the fully utilization of the rigid periodicity, the computational complexity of R3M-HPE is theoretically lower than that of $\mathcal{H}$-matrix within the same paradigm. Numerical results for two types of DCMs indicate that R3M-HPE is accurate in comparison with commercial software. Besides, R3M-HPE is also compatible with the preconditioning for efficient iterative solutions. The efficiency of R3M-HPE for DCMs outperforms the conventional fast algorithms in both the storage and CPU time cost.
On The Effect Of Coding Artifacts On Acoustic Scene Classification
Authors: Nagashree K. S. Rao, Nils Peters
Date: 2021-12-09 00:00:00
Tasks: Acoustic Scene Classification, Audio Compression, Classification, Scene Classification

Previous DCASE challenges contributed to an increase in the performance of acoustic scene classification systems. State-of-the-art classifiers demand significant processing capabilities and memory which is challenging for resource-constrained mobile or IoT edge devices. Thus, it is more likely to deploy these models on more powerful hardware and classify audio recordings previously uploaded (or streamed) from low-power edge devices. In such scenario, the edge device may apply perceptual audio coding to reduce the transmission data rate. This paper explores the effect of perceptual audio coding on the classification performance using a DCASE 2020 challenge contribution [1]. We found that classification accuracy can degrade by up to 57% compared to classifying original (uncompressed) audio. We further demonstrate how lossy audio compression techniques during model training can improve classification accuracy of compressed audio signals even for audio codecs and codec bitrates not included in the training process.
Harmonic and non-Harmonic Based Noisy Reverberant Speech Enhancement in Time Domain
Authors: G. Zucatelli, R. Coelho
Date: 2021-12-09 00:00:00
Tasks: Speech Enhancement

This paper introduces the single step time domain method named HnH-NRSE, whihc is designed for simultaneous speech intelligibility and quality improvement under noisy-reverberant conditions. In this solution, harmonic and non-harmonic elements of speech are separated by applying zero-crossing and energy criteria. An objective evaluation of the its non-stationarity degree is further used for an adaptive gain to treat masking components. No prior knowledge of speech statistics or room information is required for this technique. Additionally, two combined solutions, IRMO and IRMN, are proposed as composite methods for improvement on noisy-reverberant speech signals. The proposed and baseline methods are evaluated considering two intelligibility and three quality measures, applied for the objective prediction. The results show that the proposed scheme leads to a higher intelligibility and quality improvement when compared to competing methods in most scenarios. Additionally, a perceptual intelligibility listening test is performed, which corroborates with these results. Furthermore, the proposed HnH-NRSE solution attains SRMR quality measure with similar results when compared to the composed IRMO and IRMN techniques.
Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking
Authors: Anthony Trezza, Donald J. Bucci Jr., Pramod K. Varshney
Date: 2021-08-11 00:00:00

This paper provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density. The proposed truncation criterion is shown to have a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. This criterion is used to construct a Gibbs sampler that produces a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. A closed-form solution of the conditional sampling distribution assuming linear Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling. Multiple simulation results are provided to verify the efficacy of the truncation criterion, as well as the reduction in complexity.
An Australian DER Bill of Rights and Responsibilities
Authors: Niraj Lal, Lee Brown
Date: 2021-12-09 00:00:00

Australia's world-leading penetration of distributed solar photovoltaics (PV) is now impacting power system security and as a result how customers can use and export their own PV-generated energy. Several programs of Australian regulatory reform for distributed energy resources (DER) have emphasised the importance of placing consumers at the centre of any energy transition, but this has occurred against a haphazard backdrop of proposals for solar export taxes, updated inverter standards, and diminishing feed-in-tariffs. Absent from the discussion is a coherent espousal of reasonable consumer expectations with practical technical definitions of how these may be applied. Whilst American legislation has enshrined initial rights to connect PV, they do not consider the evolution of rights in a DER-dominated future. This paper proposes a first attempt at a 'DER Bill of Rights and Responsibilities' for both passive and active participation in energy markets, using Australia as an example jurisdiction. The intent is that clarity on rights to self-consumption and passive participation will support customer trust in the guiderails of active DER integration and control. Guiding principles are presented with practical definitions referencing existing instruments including inverter standards, network connection agreements, the reliability standard, and central ancillary service markets. We highlight how these proposed rights are already being breached regularly in Australia, before outlining a pathway to enshrine them for a DER-dominated future with broad sector endorsement. These questions are critical for Australia to address now; it is likely other countries will be required to do so in the near future.
Contraction Analysis and Control Synthesis for Discrete-time Nonlinear Processes
Authors: Lai Wei, Ryan Mccloy, Jie Bao
Date: 2021-12-09 00:00:00

Shifting away from the traditional mass production approach, the process industry is moving towards more agile, cost-effective and dynamic process operation (next-generation smart plants). This warrants the development of control systems for nonlinear chemical processes to be capable of tracking time-varying setpoints to produce products with different specifications as per market demand and deal with variations in the raw materials and utility (e.g., energy). This article presents a systematic approach to the implementation of contraction-based control for discrete-time nonlinear processes. Through the differential dynamic system framework, the contraction conditions to ensure the exponential convergence to feasible time-varying references are derived. The discrete-time differential dissipativity condition is further developed, which can be used for control designs for disturbance rejection. Computationally tractable equivalent conditions are then derived and additionally transformed into an SOS programming problem, such that a discrete-time control contraction metric and stabilising feedback controller can be jointly obtained. Synthesis and implementation details are provided and demonstrated through a numerical case study.