pwc-archive/papers-with-abstracts huggingface dataset:
YZR-net : Self-supervised Hidden representations Invariant to Transformations for profanity detection
Authors: Vedant Sandeep Joshi, Sivanagaraja Tatinati, YuBo Wang
Date: 2022-11-22 00:00:00

On current {\it e-}learning platforms, live classes are an important tool that provides students with an opportunity to get more involved while learning new concepts. In such classes, the element of interaction with teachers and fellow peers helps in removing learning silos and gives each student a chance to experience some aspects relevant to offline learning in this era of virtual classes. One common way of interaction in a class is through the chats / messaging framework, where the teacher can broadcast messages as well as get instant feedback from the students in the live class. This freedom of interaction is a crucial aspect for any student's learning growth but misuse of it can have serious repercussions. Some miscreants use this framework to send profane messages which can have a negative impact on other students as well as the teacher of the class. These rare but high impact situations obviate the need for automatic detection mechanisms that prevent the posting of such chats on any platform. In this work we develop YZR-Net which is a self-supervised framework that is able to robustly detect profane words used in a chat even if the student tries to add clever modifications to fool the system. The matching mechanism on token / word level allows us to maintain a compact as well as dynamic profane vocabulary which can be updated without retraining the underlying model. Our profanity detection framework is language independent and can handle abuses in both English as well as its transliterated counterpart Hinglish (Hindi language words written in English).
Investigating Prompt Engineering in Diffusion Models
Authors: Sam Witteveen, Martin Andrews
Date: 2022-11-21 00:00:00
Tasks: Prompt Engineering

With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques for measuring the effect that specific words and phrases in prompts have, and (in the Appendix) present guidance on the selection of prompts to produce desired effects.
GLAMI-1M: A Multilingual Image-Text Fashion Dataset
Authors: Vaclav Kosar, Antonín Hoskovec, Milan Šulc, Radek Bartyzal
Date: 2022-11-17 00:00:00
Proceeding: BMVC 2022 11
Tasks: Classification, Image Generation, Image-text Classification, Multilingual Image-Text Classification, text-classification, Text Classification

We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text. The dataset, source code and model checkpoints are published at https://github.com/glami/glami-1m
A Boosting Approach to Constructing an Ensemble Stack
Authors: Zhilei Zhou, Ziyu Qiu, Brad Niblett, Andrew Johnston, Jeffrey Schwartzentruber, Nur Zincir-Heywood, Malcolm Heywood
Date: 2022-11-28 00:00:00
Tasks: Benchmarking, Ensemble Learning

An approach to evolutionary ensemble learning for classification is proposed in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.
Properties of SN P system and its Configuration Graph
Authors: Henry N. Adorna
Date: 2022-11-28 00:00:00

Several studies have been reported in the literature about SN P system and its variants. Often, the results provide universality of various variants and the classes of languages that these variants generate and recognize. The state of SN P system is its configuration. We refer to our previous result on reachability of configuration as the {\it Fundamental state equation for SN P system.} This paper provides a preliminary investigation on the behavioral and structural properties of SN P system without delay that depend primarily to this fundamental state equation. Also, we introduce the idea of configuration graph $CG_{\Pi}$ of an SN P system $\Pi$ without delay to characterize behavioral properties of $\Pi$ with respect to $CG_{\Pi}.$ The matrix $M_{\Pi}$ of an SN P system $\Pi$ without delay is used to characterize structural properties of $\Pi.$
Matrix representations of spiking neural P systems: Revisited
Authors: Henry N. Adorna
Date: 2022-11-28 00:00:00

In the 2010, matrix representation of SN P system without delay was presented while in the case of SN P systems with delay, matrix representation was suggested in the 2017. These representations brought about series of simulation of SN P systems using computer software and hardware technology. In this work, we revisit these representation and provide some observations on the behavior of the computations of SN P systems. The concept of reachability of configuration is considered in both SN P systems with and without delays. A better computation of next configuration is proposed in the case of SN P system with delay.
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
Authors: Huixin Ma, Kai Wu, Handing Wang, Jing Liu
Date: 2022-11-28 00:00:00
Tasks: Community Detection, Dynamic Community Detection, Transfer Learning

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.
Linear Classification of Neural Manifolds with Correlated Variability
Authors: Albert J. Wakhloo, Tamara J. Sussman, SueYeon Chung
Date: 2022-11-27 00:00:00
Tasks: Classification, Object

Understanding how the statistical and geometric properties of neural activity relate to performance is a key problem in theoretical neuroscience and deep learning. Here, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the axes effectively shrinks their radii, revealing a duality between correlations and geometry with respect to the problem of classification. We then apply our results to accurately estimate the capacity of deep network data.
PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks
Authors: Mengting Lan, Xiaogang Xiong, Zixuan Jiang, Yunjiang Lou
Date: 2022-11-24 00:00:00

Deemed as the third generation of neural networks, the event-driven Spiking Neural Networks(SNNs) combined with bio-plausible local learning rules make it promising to build low-power, neuromorphic hardware for SNNs. However, because of the non-linearity and discrete property of spiking neural networks, the training of SNN remains difficult and is still under discussion. Originating from gradient descent, backprop has achieved stunning success in multi-layer SNNs. Nevertheless, it is assumed to lack biological plausibility, while consuming relatively high computational resources. In this paper, we propose a novel learning algorithm inspired by predictive coding theory and show that it can perform supervised learning fully autonomously and successfully as the backprop, utilizing only local Hebbian plasticity. Furthermore, this method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs: test accuracy of 99.25% for the Caltech Face/Motorbike dataset, 84.25% for the ETH-80 dataset, 98.1% for the MNIST dataset and 98.5% for the neuromorphic dataset: N-MNIST. Furthermore, our work provides a new perspective on how supervised learning algorithms are directly implemented in spiking neural circuitry, which may give some new insights into neuromorphological calculation in neuroscience.
Whose Emotion Matters? Speaking Activity Localisation without Prior Knowledge
Authors: Hugo Carneiro, Cornelius Weber, Stefan Wermter
Date: 2022-11-23 00:00:00
Tasks: Active Speaker Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, speech-recognition, Speech Recognition

The task of emotion recognition in conversations (ERC) benefits from the availability of multiple modalities, as provided, for example, in the video-based Multimodal EmotionLines Dataset (MELD). However, only a few research approaches use both acoustic and visual information from the MELD videos. There are two reasons for this: First, label-to-video alignments in MELD are noisy, making those videos an unreliable source of emotional speech data. Second, conversations can involve several people in the same scene, which requires the localisation of the utterance source. In this paper, we introduce MELD with Fixed Audiovisual Information via Realignment (MELD-FAIR) by using recent active speaker detection and automatic speech recognition models, we are able to realign the videos of MELD and capture the facial expressions from speakers in 96.92% of the utterances provided in MELD. Experiments with a self-supervised voice recognition model indicate that the realigned MELD-FAIR videos more closely match the transcribed utterances given in the MELD dataset. Finally, we devise a model for emotion recognition in conversations trained on the realigned MELD-FAIR videos, which outperforms state-of-the-art models for ERC based on vision alone. This indicates that localising the source of speaking activities is indeed effective for extracting facial expressions from the uttering speakers and that faces provide more informative visual cues than the visual features state-of-the-art models have been using so far. The MELD-FAIR realignment data, and the code of the realignment procedure and of the emotional recognition, are available at https://github.com/knowledgetechnologyuhh/MELD-FAIR.
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
Authors: Victor Robila, Kexin Pei, Junfeng Yang
Date: 2022-11-12 00:00:00
Tasks: Neural Architecture Search

It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits. Through a comparison between human-developed machine learning model and models sampled through Neural Architecture Search (NAS) we determine an efficient approach to solve this problem with a final testing loss of 0.2937 for a human-developed model.
Online Kernel CUSUM for Change-Point Detection
Authors: Song Wei, Yao Xie
Date: 2022-11-28 00:00:00
Tasks: Change Point Detection

We present a computationally efficient online kernel Cumulative Sum (CUSUM) method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including Scan-B statistic, corresponding to a non-parametric Shewhart chart-type procedure. We provide accurate analytic approximations for two key performance metrics: the Average Run Length (ARL) and Expected Detection Delay (EDD), which enable us to establish an optimal window length to be on the order of the logarithm of ARL to ensure minimal power loss relative to an oracle procedure with infinite memory. Moreover, we introduce a recursive calculation procedure for detection statistics to ensure constant computational and memory complexity, which is essential for online implementation. Through extensive experiments on both simulated and real data, we demonstrate the competitive performance of our method and validate our theoretical results.
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Authors: Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann
Date: 2022-11-27 00:00:00
Tasks: Synthetic Data Generation

We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a greedy algorithm called GnIES to recover the equivalence class of the data-generating model without knowledge of the intervention targets. In addition, we develop a novel procedure to generate semi-synthetic data sets with known causal ground truth but distributions closely resembling those of a real data set of choice. We leverage this procedure and evaluate the performance of GnIES on an array of synthetic and semi-synthetic data sets, and real data from a biological system and a tightly controlled physical system. We provide, in the Python packages gnies and sempler, implementations of GnIES and our semi-synthetic data generation procedure.
Incentive-Aware Recommender Systems in Two-Sided Markets
Authors: Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan
Date: 2022-11-23 00:00:00
Tasks: Fairness, Recommendation Systems, Vocal Bursts Valence Prediction

Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises from myopic agents who are naturally incentivized to exploit by choosing the optimal arm based on current information, rather than exploring various alternatives to gather information that benefits the collective. We propose a novel recommender system that aligns with agents' incentives while achieving asymptotically optimal performance, as measured by regret in repeated interactions. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets, where the interactions of agents and arms are facilitated by recommender systems on online platforms. This model incorporates incentive constraints induced by agents' opportunity costs. In scenarios where opportunity costs are known to the platform, we show the existence of an incentive-compatible recommendation algorithm. This algorithm pools recommendations between a genuinely good arm and an unknown arm using a randomized and adaptive strategy. Moreover, when these opportunity costs are unknown, we introduce an algorithm that randomly pools recommendations across all arms, utilizing the cumulative loss from each arm as feedback for strategic exploration. We demonstrate that both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation. All code for using the proposed algorithms and reproducing results is made available on GitHub.
Learning Recommendations from User Actions in the Item-poor Insurance Domain
Authors: Simone Borg Bruun, Maria Maistro, Christina Lioma
Date: 2022-11-28 00:00:00
Tasks: Session-Based Recommendations

While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community.
Recent Advances in RecBole: Extensions with more Practical Considerations
Authors: Lanling Xu, Zhen Tian, Gaowei Zhang, Lei Wang, Junjie Zhang, Bowen Zheng, YiFan Li, Yupeng Hou, Xingyu Pan, Yushuo Chen, Wayne Xin Zhao, Xu Chen, Ji-Rong Wen
Date: 2022-11-28 00:00:00

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole. In general, we focus on the flexibility and efficiency of RecBole in the past few months. More specifically, we have four development targets: (1) more flexible data processing, (2) more efficient model training, (3) more reproducible configurations, and (4) more comprehensive user documentation. Readers can download the above updates at: https://github.com/RUCAIBox/RecBole.
Toward Universal Text-to-Music Retrieval
Authors: Seungheon Doh, Minz Won, Keunwoo Choi, Juhan Nam
Date: 2022-11-26 00:00:00
Tasks: Music Classification, Retrieval, Sentence, TAG

This paper introduces effective design choices for text-to-music retrieval systems. An ideal text-based retrieval system would support various input queries such as pre-defined tags, unseen tags, and sentence-level descriptions. In reality, most previous works mainly focused on a single query type (tag or sentence) which may not generalize to another input type. Hence, we review recent text-based music retrieval systems using our proposed benchmark in two main aspects: input text representation and training objectives. Our findings enable a universal text-to-music retrieval system that achieves comparable retrieval performances in both tag- and sentence-level inputs. Furthermore, the proposed multimodal representation generalizes to 9 different downstream music classification tasks. We present the code and demo online.
Graceful Forgetting II. Data as a Process
Authors: Alain de Cheveigné
Date: 2022-11-20 00:00:00
Tasks: Dimensionality Reduction

Data are rapidly growing in size and importance for society, a trend motivated by their enabling power. The accumulation of new data, sustained by progress in technology, leads to a boundless expansion of stored data, in some cases with an exponential increase in the accrual rate itself. Massive data are hard to process, transmit, store, and exploit, and it is particularly hard to keep abreast of the data store as a whole. This paper distinguishes three phases in the life of data: acquisition, curation, and exploitation. Each involves a distinct process, that may be separated from the others in time, with a different set of priorities. The function of the second phase, curation, is to maximize the future value of the data given limited storage. I argue that this requires that (a) the data take the form of summary statistics and (b) these statistics follow an endless process of rescaling. The summary may be more compact than the original data, but its data structure is more complex and it requires an on-going computational process that is much more sophisticated than mere storage. Rescaling results in dimensionality reduction that may be beneficial for learning, but that must be carefully controlled to preserve relevance. Rescaling may be tuned based on feedback from usage, with the proviso that our memory of the past serves the future, the needs of which are not fully known.