pwc-archive/papers-with-abstracts huggingface dataset:
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection
Authors: Tulika Bose, Irina Illina, Dominique Fohr
Date: 2022-10-17 00:00:00
Tasks: Hate Speech Detection

The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task. State-of-the-art deep learning-based approaches usually require a substantial amount of labeled resources for training. However, annotating hate speech resources is expensive, time-consuming, and often harmful to the annotators. This creates a pressing need to transfer knowledge from the existing labeled resources to low-resource hate speech corpora with the goal of improving system performance. For this, neighborhood-based frameworks have been shown to be effective. However, they have limited flexibility. In our paper, we propose a novel training strategy that allows flexible modeling of the relative proximity of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. In particular, we incorporate neighborhood information with Optimal Transport, which permits exploiting the geometry of the data embedding space. By aligning the joint embedding and label distributions of neighbors, we demonstrate substantial improvements over strong baselines, in low-resource scenarios, on different publicly available hate speech corpora.
Phenomenological Model of Superconducting Optoelectronic Loop Neurons
Authors: Jeffrey M. Shainline, Bryce A. Primavera, Saeed Khan
Date: 2022-10-18 00:00:00
Tasks: Computational Efficiency, model

Superconducting optoelectronic loop neurons are a class of circuits potentially conducive to networks for large-scale artificial cognition. These circuits employ superconducting components including single-photon detectors, Josephson junctions, and transformers to achieve neuromorphic functions. To date, all simulations of loop neurons have used first-principles circuit analysis to model the behavior of synapses, dendrites, and neurons. These circuit models are computationally inefficient and leave opaque the relationship between loop neurons and other complex systems. Here we introduce a modeling framework that captures the behavior of the relevant synaptic, dendritic, and neuronal circuits at a phenomenological level without resorting to full circuit equations. Within this compact model, each dendrite is discovered to obey a single nonlinear leaky-integrator ordinary differential equation, while a neuron is modeled as a dendrite with a thresholding element and an additional feedback mechanism for establishing a refractory period. A synapse is modeled as a single-photon detector coupled to a dendrite, where the response of the single-photon detector follows a closed-form expression. We quantify the accuracy of the phenomenological model relative to circuit simulations and find that the approach reduces computational time by a factor of ten thousand while maintaining accuracy of one part in ten thousand. We demonstrate the use of the model with several basic examples. The net increase in computational efficiency enables future simulation of large networks, while the formulation provides a connection to a large body of work in applied mathematics, computational neuroscience, and physical systems such as spin glasses.
$k$-Means Clustering for Persistent Homology
Authors: Yueqi Cao, Prudence Leung, Anthea Monod
Date: 2022-10-18 00:00:00
Tasks: Clustering, Topological Data Analysis

Persistent homology is a methodology central to topological data analysis that extracts and summarizes the topological features within a dataset as a persistence diagram; it has recently gained much popularity from its myriad successful applications to many domains. However, its algebraic construction induces a metric space of persistence diagrams with a highly complex geometry. In this paper, we prove convergence of the $k$-means clustering algorithm on persistence diagram space and establish theoretical properties of the solution to the optimization problem in the Karush--Kuhn--Tucker framework. Additionally, we perform numerical experiments on various representations of persistent homology, including embeddings of persistence diagrams as well as diagrams themselves and their generalizations as persistence measures; we find that $k$-means clustering performance directly on persistence diagrams and measures outperform their vectorized representations.
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Authors: Xiangyang Li, Bo Chen, Huifeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, JinXing Liu, Zhenhua Dong, Ruiming Tang
Date: 2022-10-18 00:00:00

Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tower} model since the ``user-item decoupling architecture'' paradigm is able to balance the \textit{efficiency} and \textit{effectiveness}. However, the cost of high efficiency is the neglect of the potential information interaction between user and item towers, hindering the prediction accuracy critically. In this paper, we show it is possible to design a two-tower model that emphasizes both information interactions and inference efficiency. The proposed model, IntTower (short for \textit{Interaction enhanced Two-Tower}), consists of Light-SE, FE-Block and CIR modules. Specifically, lightweight Light-SE module is used to identify the importance of different features and obtain refined feature representations in each tower. FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly. Experimental results on three public datasets show that IntTower outperforms the SOTA pre-ranking models significantly and even achieves comparable performance in comparison with the ranking models. Moreover, we further verify the effectiveness of IntTower on a large-scale advertisement pre-ranking system. The code of IntTower is publicly available\footnote{https://github.com/archersama/IntTower}
CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval
Authors: Wenbiao Li, Pan Tang, Zhengfan Wu, Weixue Lu, Minghua Zhang, Zhenlei Tian, Daiting Shi, Yu Sun, Simiu Gu, Dawei Yin
Date: 2022-10-18 00:00:00
Tasks: Retrieval, Sentence

Embedding-based retrieval (EBR) is a technique to use embeddings to represent query and document, and then convert the retrieval problem into a nearest neighbor search problem in the embedding space. Some previous works have mainly focused on representing the web page with a single embedding, but in real web search scenarios, it is difficult to represent all the information of a long and complex structured web page as a single embedding. To address this issue, we design a click feedback-aware web page summarization for multi-embedding-based retrieval (CPS-MEBR) framework which is able to generate multiple embeddings for web pages to match different potential queries. Specifically, we use the click data of users in search logs to train a summary model to extract those sentences in web pages that are frequently clicked by users, which are more likely to answer those potential queries. Meanwhile, we introduce sentence-level semantic interaction to design a multi-embedding-based retrieval (MEBR) model, which can generate multiple embeddings to deal with different potential queries by using frequently clicked sentences in web pages. Offline experiments show that it can perform high quality candidate retrieval compared to single-embedding-based retrieval (SEBR) model.
Addressing the Extreme Cold-Start Problem in Group Recommendation
Authors: Guo linxin, Tao yinghui, Gao Min, Yu Junliang, Zhao Liang, Li Wentao
Date: 2022-10-18 00:00:00
Tasks: Recommendation Systems

The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice. Most existing work exploits associations between groups and items to mitigate the data scarcity problem. However, existing approaches inevitably fail in extreme cold-start scenarios where associations between groups and items are lacking. For this reason, we design a group recommendation model for EXreme cold-star} in group REcommendation (EXTRE) suitable for the extreme cold start scenario. The basic idea behind EXTRE is to use the limit theory of graph convolutional neural networks to establish implicit associations between groups and items, and the derivation of these associations does not require explicit interaction data, making it suitable for cold start scenarios. The training process of EXTRE depends on the newly defined and interpretable concepts of consistency and discrepancy, other than commonly used negative sampling with pairwise ranking, which can improve the performance of the group recommendation. Extensive experiments validate the efficacy of the proposed model EXTRE.
Relationships between patenting trends and research activity for green energy technologies
Authors: Regina Tuganova, Anna Permyakova, Anna Kuznetsova, Karina Rakhmanova, Natalia Monzul, Roman Uvarov, Elizaveta Kovtun, Semen Budennyy
Date: 2022-10-18 00:00:00

Green technology is viewed as a means of creating a sustainable society and a catalyst for sustainable development by the global community. It is responsible for both the potential reduction of production waste and the reduction of carbon footprint and CO2 emissions. However, alongside with the growing popularity of green technologies, there is an emerging skepticism about their contribution to solving environmental challenges. This article focuses on three areas of eco-innovation in green technology: renewable energy, hydrogen power, and decarbonization. Our main goal is to analyze the relationship between publication activity and the number of patented research results, thus shedding light on the real-world applicability of scientific outcomes. We used several bibliometric methods for analyzing global publication and patent activity, applied to the Scopus citation database and the European Patent Office's patent database. Our results show that the advancement of research in all three areas of eco-innovation does not automatically lead to the increase in the number of patents. We offer possible reasons for such dependency based on the observations of the worldwide tendencies in green innovation sphere.
Evaluating Search System Explainability with Psychometrics and Crowdsourcing
Authors: Catherine Chen, Carsten Eickhoff
Date: 2022-10-17 00:00:00
Tasks: Fairness, Information Retrieval, Recommendation Systems, Retrieval

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite recent advances in explainable AI and IR techniques, there is no consensus on the definition of explainability. Existing approaches often treat it as a singular notion, disregarding the multidimensional definition postulated in the literature. In this paper, we use psychometrics and crowdsourcing to identify human-centered factors of explainability in Web search systems and introduce SSE (Search System Explainability), an evaluation metric for explainable IR (XIR) search systems. In a crowdsourced user study, we demonstrate SSE's ability to distinguish between explainable and non-explainable systems, showing that systems with higher scores indeed indicate greater interpretability. We hope that aside from these concrete contributions to XIR, this line of work will serve as a blueprint for similar explainability evaluation efforts in other domains of machine learning and natural language processing.
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
Authors: Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Thorsten Joachims
Date: 2022-10-15 00:00:00
Tasks: Learning-To-Rank, model, Off-policy evaluation, Position, Recommendation Systems

A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. Unfortunately, widely used off-policy evaluation methods either make strong assumptions about how users behave that can lead to excessive bias, or they make fewer assumptions and suffer from large variance. We tackle this problem by developing a new estimator that mitigates the problems of the two most popular off-policy estimators for rankings, namely the position-based model and the item-position model. In particular, the new estimator, called INTERPOL, addresses the bias of a potentially misspecified position-based model, while providing an adaptable bias-variance trade-off compared to the item-position model. We provide theoretical arguments as well as empirical results that highlight the performance of our novel estimation approach.
Client-driven Lightweight Method to Generate Artistic Media for Feature-length Sports Videos
Authors: Ghulam Mujtaba, Jaehyuk Choi, Eun-Seok Ryu
Date: 2022-07-01 00:00:00
Proceeding: Conference 2022 7
Tasks: Animated GIF Generation, Sports Analytics, Video Generation

This paper proposes a lightweight methodology to attract users and increase views of videos through personalized artistic media i.e., static thumbnails and animated Graphics Interchange Format (GIF) images. The proposed method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from feature-length sports videos. In addition, instead of processing the entire video, small video segments are used in order to generate artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data. Further, the proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data that are used during artistic media generation. After conducting experiments on the NVIDIA Jetson TX2, the computational complexity of our method was 3.78 times lower than that of the state-of-the-art method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services on resource-constrained devices.
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm
Authors: Ziqi Su, Wendong Mao, Zhongfeng Wang, Jun Lin, WenQiang Wang, Haitao Sun
Date: 2022-10-18 00:00:00
Tasks: Computational Efficiency

Three-dimensional generative adversarial networks (3D-GAN) have attracted widespread attention in three-dimension (3D) visual tasks. 3D deconvolution (DeConv), as an important computation of 3D-GAN, significantly increases computational complexity compared with 2D DeConv. 3D DeConv has become a bottleneck for the acceleration of 3D-GAN. Previous accelerators suffer from several problems, such as large memory requirements and resource underutilization. To handle the above issues, a fast algorithm for 3D DeConv (F3DC) is proposed in this paper. F3DC applies a fast algorithm to reduce the number of multiplications and achieves a significant algorithmic strength reduction. Besides, F3DC removes the extra memory requirement for overlapped partial sums and avoids computational imbalance to fully utilize resources. Moreover, we design an F3DC-based hardware architecture, which consists of four fast processing units (FPUs). Each FPU includes a pre-process module, a EWMM module and a post-process module for F3DC transformation. By implementing our design on the Xilinx VC709 platform for 3D-GAN, we achieve a throughput up to 1700 GOPS and 4$\times$ computational efficiency improvement compared with prior works.
Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
Authors: Jiawen Kang, Rong Y u, Xumin Huang, Maoqiang Wu, Sabita Maharjan, Member, Shengli Xie, and Y an Zhang, Senior Member, IEEE
Date: 2018-10-11 00:00:00
Proceeding: IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 10
Tasks: Edge-computing

The drastically increasing volume and the growing
trend on the types of data have brought in the possibility of real-
izing advanced applications such as enhanced driving safety, and
have enriched existing vehicular services through data sharing
among vehicles and data analysis. Due to limited resources with
vehicles, vehicular edge computing and networks (VECONs) i.e.,
the integration of mobile edge computing and vehicular networks,
can provide powerful computing and massive storage resources.
However, road side units that primarily presume the role of vehic-
ular edge computing servers cannot be fully trusted, which may
lead to serious security and privacy challenges for such inte-
grated platforms despite their promising potential and benefits.
We exploit consortium blockchain and smart contract technolo-
gies to achieve secure data storage and sharing in vehicular
edge networks. These technologies efficiently prevent data sharing
without authorization. In addition, we propose a reputation-based
data sharing scheme to ensure high-quality data sharing among
vehicles. A three-weight subjective logic model is utilized for
precisely managing reputation of the vehicles. Numerical results
based on a real dataset show that our schemes achieve reasonable
efficiency and high-level of security for data sharing in VECONs.
Beamforming Towards Seamless Sensing Coverage for Cellular Integrated Sensing and Communication
Authors: Ruoguang Li∗, Zhiqiang Xiao∗, and Y ong Zeng
Date: 2022-04-12 00:00:00
Proceeding: WS07 IEEE ICC 2022 the 4th Workshop on  Integrated Sensing and Communication (ISAC) 2022 4
Tasks: Integrated sensing and communication, ISAC

Abstract—The sixth generation (6G) mobile communication
networks are expected to offer a new paradigm of cellular
integrated sensing and communication (ISAC). However, due to
the intrinsic difference between sensing and communication in
terms of coverage requirement, current cellular networks that
are deliberately planned mainly for communication coverage are
difficult to achieve seamless sensing coverage. To address this
issue, this paper studies the beamforming optimization towards
seamless sensing coverage for a basic bi-static ISAC system, while
ensuring that the communication requirements of multiple users
equipment (UEs) are satisfied. Towards this end, an optimization
problem is formulated to maximize the worst-case sensing signal-
to-noise ratio (SNR) in a prescribed coverage region, subject to
the signal-to-interference-plus-noise ratio (SINR) requirement for
each UE. To gain some insights, we first investigate the special
case with one single UE and one single sensing point, for which
a closed-from expression of the optimal beamforming is obtained.
For the general case with multiple communication UEs and
contiguous regional sensing coverage, an efficient algorithm based
on successive convex approximation (SCA) is proposed to solve the
non-convex beamforming optimization problem. Numerical results
demonstrate that the proposed design is able to achieve seamless
sensing coverage in the prescribed region, while guaranteeing the
communication requirements of the UEs.
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization
Authors: Gonçal V. Garcés Díaz-Munío, Joan-Albert Silvestre-Cerdà, Javier Jorge, Adrià Giménez Pastor, Javier Iranzo-Sánchez, Pau Baquero-Arnal, Nahuel Roselló, Alejandro Pérez-González-de-Martos, Jorge Civera, Albert Sanchis, Alfons Juan
Date: 2021-08-30 00:00:00
Proceeding: Interspeech 2021 8
Tasks: Benchmarking, Data Augmentation, Speech Recognition

We introduce Europarl-ASR, a large speech and text corpus of parliamentary debates including 1 300 hours of transcribed speeches and 70 million tokens of text in English extracted from European Parliament sessions. The training set is labelled with the Parliament’s non-fully-verbatim official transcripts, time-aligned. As verbatimness is critical for acoustic model training, we also provide automatically noise-filtered and automatically verbatimized transcripts of all speeches based on speech data filtering and verbatimization techniques. Additionally, 18 hours of transcribed speeches were manually verbatimized to build reliable speaker-dependent and speaker-independent development/test sets for streaming ASR benchmarking. The availability of manual non-verbatim and verbatim transcripts for dev/test speeches makes this corpus useful for the assessment of automatic filtering and verbatimization techniques. This paper describes the corpus and its creation, and provides off-line and streaming ASR baselines for both the speaker-dependent and speaker-independent tasks using the three training transcription sets. The corpus is publicly released under an open licence.
ProtoFold Neighborhood Inspector
Authors: Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Anna Vilanova
Date: 2022-10-17 00:00:00

Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness. Whether or not a PTM is pathogenic depends on its type and the status of neighboring residues. In this paper, we present the ProtoFold Neighborhood Inspector (PFNI), a visualization system for analyzing residues neighborhoods. The main contribution is a visualization idiom, the Residue Constellation (RC), for identifying and comparing three-dimensional neighborhoods based on per-residue features and spatial characteristics. The RC leverages two-dimensional representations of the protein's three-dimensional structure to overcome problems like occlusion, easing the analysis of neighborhoods that often have complicated spatial arrangements. Using the PFNI, we explored proteins' structural PTM data, which allowed us to identify patterns in the distribution and quantity of per-neighborhood PTMs that might be related to their pathogenic status. In the following, we define the tasks that guided the development of the PFNI and describe the data sources we derived and used. Then, we introduce the PFNI and illustrate its usage through an example of an analysis workflow. We conclude by reflecting on preliminary findings obtained while using the tool on the provided data and future directions concerning the development of the PFNI.