pwc-archive/papers-with-abstracts huggingface dataset:
Neural network based human reliability analysis method in production systems
Authors: Rasoul Jamshidi, Mohammad Ebrahim Sadeghi
Date: 2022-06-17 00:00:00

Purpose: In addition to playing an important role in creating economic security and investment development, insurance companies also invest. The country's insurance industry as one of the country's financial institutions has a special place in the investment process and special attention to appropriate investment policies in the field of insurance industry is essential. So that the efficiency of this industry in allocating the existing budget stimulates other economic sectors. This study seeks to model investment in the performance of dynamic networks of insurance companies. Methodology: In this paper, a new investment model is designed to examine the dynamic network performance of insurance companies in Iran. The designed model is implemented using GAMS software and the outputs of the model are analyzed based on regression method. The required information has been collected based on the statistics of insurance companies in Iran between 1393 and 1398. Findings: After evaluating these units, out of 15 companies evaluated, 6 companies had unit performance and were introduced as efficient companies. The average efficiency of insurance companies is 0.78 and the standard deviation is 0.2. The results show that the increase in the value of investments is due to the large reduction in costs and in terms of capital and net profit of companies is a large number that has a clear and strong potential for insurance companies. Originality/Value: In this paper, investment modeling is performed to examine the performance of dynamic networks of insurance companies in Iran.
Application of accelerated life testing in human reliability analysis
Authors: Rasoul Jamshidi, Mohammad Ebrahim Sadeghi
Date: 2022-06-17 00:00:00

As manufacturers and technologies become more complicated, manufacturing errors such as machine failure and human error have also been considered more over the past. Since machines and humans are not error-proof, managing the machines and human errors is a significant challenge in manufacturing systems. There are numerous methods for investigating human errors, fatigue, and reliability that categorized under Human Reliability Analysis (HRA) methods. HRA methods use some qualitative factors named Performance Shaping Factors (PSFs) to estimate Human Error Probability (HEP). Since the PSFs can be considered as the acceleration factors in Accelerated Life Test (ALT). We developed a method for Accelerated Human Fatigue Test (AHFT) to calculate human fatigue, according to fatigue rate and other effective factors. The proposed method reduces the time and cost of human fatigue calculation. AHFT first extracts the important factors affecting human fatigue using Principal Component Analysis (PCA) and then uses the accelerated test to calculate the effect of PSFs on human fatigue. The proposed method has been applied to a real case, and the provided results show that human fatigue can be calculated more effectively using the proposed method.
A core-selecting auction for portfolio's packages
Authors: Lamprirni Zarpala, Dimitris Voliotis
Date: 2022-06-23 00:00:00

We introduce the "local-global" approach for a divisible portfolio and perform an equilibrium analysis for two variants of core-selecting auctions. Our main novelty is extending the Nearest-VCG pricing rule in a dynamic two-round setup, mitigating bidders' free-riding incentives and further reducing the sellers' costs. The two-round setup admits an information-revelation mechanism that may offset the "winner's curse", and it is in accord with the existing iterative procedure of combinatorial auctions. With portfolio trading becoming an increasingly important part of investment strategies, our mechanism contributes to increasing interest in portfolio auction protocols.
Non-Obvious Manipulability of the Rank-Minimizing Mechanism
Authors: Peter Troyan
Date: 2022-06-22 00:00:00

In assignment problems, the rank distribution of assigned objects is often used to evaluate match quality. Rank-minimizing (RM) mechanisms directly optimize for average rank. While appealing, a drawback is RM mechanisms are not strategyproof. This paper investigates whether RM satisfies the weaker incentive notion of non-obvious manipulability (NOM, Troyan and Morrill, 2020). I show any RM mechanism with full support - placing positive probability on all rank-minimizing allocations - is NOM. In particular, uniform randomization satisfies this condition. Without full support, whether an RM mechanism is NOM or not depends on the details of the selection rule.
A Proposed Sub-optimal Power Allocation using Simulated Annealing in Cognitive Radio Networks
Authors: Abdelhamed Mohamed, Mona Shokair, Mohamed Elkordy, Said ElHalafawy
Date: 2022-06-23 00:00:00

Due to the rapid demand for wireless services and the increase in the wireless device count, there is a lack of available spectrum bands which constrain the further development of wireless communication .Therefore, Cognitive Radio (CR) has been adopted as a promising solution because of its ability to exploit the inefficiently used spectrum of licensed bands. Orthogonal Frequency Division Multiplexing (OFDM) become the enabling technique for CR due to its flexibility of allocating the available spectrum in dynamic environment. In this paper, a proposed distributed resource allocation framework based on Simulated Annealing (SA) algorithm for downlink OFDM-Based Cognitive Radio Network (CRN) will be applied. This algorithm gives less computational complexity for maximizing the total SUs transmission capacity. Moreover, the interference introduced from other Secondary Users (SUs) will be considered. For the sake of comparison, Lagrange dual method will be used. Simulation results showed that the proposed algorithm gives a better transmission capacity compared with Lagrange dual method. The parameters which are considered for comparison are maximum transmitted power, number of Primary Users (PUs) and number of SUs.
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks
Authors: Yanxiang Jiang, Min Zhang, Fu-Chun Zheng, Yan Chen, Mehdi Bennis, Xiaohu You
Date: 2022-06-23 00:00:00
Tasks: Deep Reinforcement Learning, Quantization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) property of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a federated reinforcement learning method with quantization (FRLQ) to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communications resources caused by model transmission, we prune and quantize the shared DRL models to reduce the number of model transfer parameters. The communications interval is increased and the communications rounds are reduced by periodical model global aggregation. We analyze the global convergence and computational complexity of our policy. Simulation results verify that our policy has better performance in reducing user request delay and improving cache hit rate compared to benchmark schemes. The proposed policy is also shown to have faster training speed and higher communications efficiency with minimal loss of model accuracy.
LoneSTAR: Analog Beamforming Codebooks for Full-Duplex Millimeter Wave Systems
Authors: Ian P. Roberts, Sriram Vishwanath, Jeffrey G. Andrews
Date: 2022-06-22 00:00:00

This work develops LoneSTAR, a novel enabler of full-duplex millimeter wave (mmWave) communication systems through the design of analog beamforming codebooks. LoneSTAR codebooks deliver high beamforming gain and broad coverage while simultaneously reducing the self-interference coupled by transmit and receive beams at a full-duplex mmWave transceiver. Our design framework accomplishes this by tolerating some variability in transmit and receive beamforming gain to strategically shape beams that reject self-interference spatially while accounting for digitally-controlled analog beamforming networks and self-interference channel estimation error. By leveraging the coherence time of the self-interference channel, a mmWave system can use the same LoneSTAR design over many time slots to serve several downlink-uplink user pairs in a full-duplex fashion without the need for additional self-interference cancellation. Compared to those using conventional codebooks, full-duplex mmWave systems employing LoneSTAR codebooks can mitigate higher levels of self-interference, tolerate more cross-link interference, and demand lower SNRs in order to outperform half-duplex operation -- all while supporting beam alignment. This makes LoneSTAR a potential standalone solution for enabling simultaneous transmission and reception in mmWave systems, from which it derives its name.
On Surface Wave Propagation Characteristics of Porosity-Based Reconfigurable Surface
Authors: Z. Chu, K. K. Wong, K. F. Tong
Date: 2022-06-22 00:00:00

Reconfigurable surfaces facilitating energy-efficient, intelligent surface wave propagation have recently emerged as a technology that finds applications in many-core systems and 6G wireless communications. In this paper, we consider the porosity-based reconfigurable surface where there are cavities that can be filled on-demand with fluid metal such as Galinstan, in order to create adaptable channels for efficient wave propagation. We aim to investigate the propagation phenomenon of signal fluctuation resulting from the diffraction of discrete porosity and study how different porosity patterns affect this phenomenon. Our results cover the frequency range between 21.7GHz and 31.6GHz when a WR-34 waveguide is used as the transducer.
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics
Authors: Marc Andrew Valdez, Alex J. Yuffa, Michael B. Wakin
Date: 2022-06-22 00:00:00
Tasks: Compressive Sensing, Denoising

We derive a compressive sampling method for acoustic field reconstruction using field measurements on a predefined spherical grid that has theoretically guaranteed relations between signal sparsity, measurement number, and reconstruction accuracy. This method can be used to reconstruct band-limited spherical harmonic or Wigner $D$-function series (spherical harmonic series are a special case) with sparse coefficients. Contrasting typical compressive sampling methods for Wigner $D$-function series that use arbitrary random measurements, the new method samples randomly on an equiangular grid, a practical and commonly used sampling pattern. Using its periodic extension, we transform the reconstruction of a Wigner $D$-function series into a multi-dimensional Fourier domain reconstruction problem. We establish that this transformation has a bounded effect on sparsity level and provide numerical studies of this effect. We also compare the reconstruction performance of the new approach to classical Nyquist sampling and existing compressive sampling methods. In our tests, the new compressive sampling approach performs comparably to other guaranteed compressive sampling approaches and needs a fraction of the measurements dictated by the Nyquist sampling theorem. Moreover, using one-third of the measurements or less, the new compressive sampling method can provide over 20 dB better denoising capability than oversampling with classical Fourier theory.
Optimal allocation in annual plants with density dependent fitness
Authors: Sergiy Koshkin, Zachary Zalles, Michael F. Tobin, Nicolas Toumbacaris, Cameron Spiess
Date: 2022-06-22 00:00:00

We study optimal two-sector (vegetative and reproductive) allocation models of annual plants in temporally variable environments, that incorporate effects of density dependent lifetime variability and juvenile mortality in a fitness function whose expected value is maximized. Only special cases of arithmetic and geometric mean maximizers have previously been considered in the literature, and we also allow a wider range of production functions with diminishing returns. The model predicts that the time of maturity is pushed to an earlier date as the correlation between individual lifetimes increases, and while optimal schedules are bang-bang at the extremes, the transition is mediated by schedules where vegetative growth is mixed with reproduction for a wide intermediate range. The mixed growth lasts longer when the production function is less concave allowing for better leveraging of plant size when generating seeds. Analytic estimates are obtained for the power means that interpolate between arithmetic and geometric mean and correspond to partially correlated lifetime distributions.
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
Authors: Armin W. Thomas, Christopher Ré, Russell A. Poldrack
Date: 2022-06-22 00:00:00
Tasks: Causal Language Modeling, Language Modeling, Language Modelling, Self-Supervised Learning

Self-supervised learning techniques are celebrating immense success in natural language processing (NLP) by enabling models to learn from broad language data at unprecedented scales. Here, we aim to leverage the success of these techniques for mental state decoding, where researchers aim to identify specific mental states (e.g., the experience of anger or joy) from brain activity. To this end, we devise a set of novel self-supervised learning frameworks for neuroimaging data inspired by prominent learning frameworks in NLP. At their core, these frameworks learn the dynamics of brain activity by modeling sequences of activity akin to how sequences of text are modeled in NLP. We evaluate the frameworks by pre-training models on a broad neuroimaging dataset spanning functional Magnetic Resonance Imaging data from 11,980 experimental runs of 1,726 individuals across 34 datasets, and subsequently adapting the pre-trained models to benchmark mental state decoding datasets. The pre-trained models transfer well, generally outperforming baseline models trained from scratch, while models trained in a learning framework based on causal language modeling clearly outperform the others.
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity
Authors: Yiming Fang, Xuejun Liu, Hui Liu
Date: 2022-05-17 00:00:00
Tasks: Contrastive Learning, Specificity

It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC). Computational prediction of T cell binding to neoantigens is an challenging and unresolved task. In this paper, we propose an attentive-mask contrastive learning model, ATMTCR, for inferring TCR-antigen binding specificity. For each input TCR sequence, we used Transformer encoder to transform it to latent representation, and then masked a proportion of residues guided by attention weights to generate its contrastive view. Pretraining on large-scale TCR CDR3 sequences, we verified that contrastive learning significantly improved the prediction performance of TCR binding to peptide-MHC complex (pMHC). Beyond the detection of important amino acids and their locations in the TCR sequence, our model can also extracted high-order semantic information underlying the TCR-antigen binding specificity. Comparison experiments were conducted on two independent datasets, our method achieved better performance than other existing algorithms. Moreover, we effectively identified important amino acids and their positional preferences through attention weights, which indicated the interpretability of our proposed model.
Chat, Shift and Perform: Bridging the Gap between Task-oriented and Non-task-oriented Dialog Systems
Authors: Teppei Yoshino, Yosuke Fukuchi, Shoya Matsumori, Michita Imai
Date: 2022-06-05 00:00:00

We propose CASPER (ChAt, Shift and PERform), a novel dialog system consisting of three types of dialog models: chatter, shifter, and performer. Shifter, which is designed for topic switching, enables a seamless flow of dialog from open-domain chat- to task-oriented dialog. In a user study, CASPER gave a better impression in terms of naturalness of response, lack of forced topic switching, and satisfaction compared with a baseline dialog system trained in an end-to-end manner. In an ablation study, we found that naturalness of response, dialog satisfaction, and task-elicitation rate improved compared with when shifter was removed from CASPER, indicating that topic shift with shifter supports the introduction of natural task-oriented dialog.
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Authors: Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy, Alexander Panchenko
Date: 2022-06-07 00:00:00
Proceeding: COLING 2020 8
Tasks: Data Augmentation, Relation Extraction, Word Sense Induction

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.
Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions
Authors: Aarish Chhabra, Nandini Bansal, Venktesh V, Mukesh Mohania, Deep Dwivedi
Date: 2022-05-25 00:00:00

Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have sufficient subjective questions that can gauge whether the learner has understood the concept by mandating a detailed answer. Hence, in this paper, we propose a novel hybrid unsupervised approach leveraging rule-based methods and pre-trained dense retrievers for the novel task of automatically converting the objective questions to subjective questions. We observe that our approach outperforms the existing data-driven approaches by 36.45% as measured by Recall@k and Precision@k.
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels
Authors: Do-Myoung Lee, Yeachan Kim, Chang-gyun Seo
Date: 2022-05-29 00:00:00
Proceeding: LREC 2022 6
Tasks: Memorization, text-classification, Text Classification

Deep neural networks (DNNs) have a high capacity to completely memorize noisy labels given sufficient training time, and its memorization, unfortunately, leads to performance degradation. Recently, virtual adversarial training (VAT) attracts attention as it could further improve the generalization of DNNs in semi-supervised learning. The driving force behind VAT is to prevent the models from overfitting data points by enforcing consistency between the inputs and the perturbed inputs. This strategy could be helpful in learning from noisy labels if it prevents neural models from learning noisy samples while encouraging the models to generalize clean samples. In this paper, we propose context-based virtual adversarial training (ConVAT) to prevent a text classifier from overfitting to noisy labels. Unlike the previous works, the proposed method performs the adversarial training at the context level rather than the inputs. It makes the classifier not only learn its label but also its contextual neighbors, which alleviates the learning from noisy labels by preserving contextual semantics on each data point. We conduct extensive experiments on four text classification datasets with two types of label noises. Comprehensive experimental results clearly show that the proposed method works quite well even with extremely noisy settings.
HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
Authors: Youngju Joung, Taeuk Kim
Date: 2022-06-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sentence, Task 2

We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions. Through extensive experiments, we demonstrate that our approach based on the inter- and inner-sentence context of a target MWE is effective in improving the performance of related models. We also share our experience in detail on the task of SemEval-2022 Tasks 2 such that future work on the same task can be benefited from this.
Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning
Authors: Sameea Naeem, Dr. Arif ur Rahman, Syed Mujtaba Haider, Abdul Basit Mughal
Date: 2022-05-29 00:00:00
Tasks: Articles, Machine Translation, text-to-speech, Text to Speech, Transliteration

Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different language other than the native language of user, there is a need for a Machine Learning based automatic system to find the similarity between two inter-language news articles. In this article, we propose a Machine Learning model with the combination of English Urdu word transliteration which will show whether the English news article is similar to the Urdu news article or not. The existing approaches to find similarities has a major drawback when the archives contain articles of low-resourced languages like Urdu along with English news article. The existing approaches to find similarities has drawback when the archives contain low-resourced languages like Urdu along with English news articles. We used lexicon to link Urdu and English news articles. As Urdu language processing applications like machine translation, text to speech, etc are unable to handle English text at the same time so this research proposed technique to find similarities in English and Urdu news articles based on transliteration.