pwc-archive/papers-with-abstracts huggingface dataset:
Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam
Authors: Alexandra Blenkinsop, Mélodie Monod, Ard van Sighem, Nikos Pantazis, Daniela Bezemer, Eline Op de Coul, Thijs van de Laar, Christophe Fraser, Maria Prins, Peter Reiss, Godelieve de Bree, Oliver Ratmann
Date: 2022-03-15 00:00:00

Amsterdam and other UNAIDS Fast-Track cities aim for zero new HIV infections. Utilising molecular and clinical data of the ATHENA observational HIV cohort, our primary aims are to estimate the proportion of undiagnosed HIV infections and the proportion of locally acquired infections in Amsterdam in 2014-2018, both in MSM and heterosexuals and Dutch-born and foreign-born individuals. We located diagnosed HIV infections in Amsterdam using postcode data at time of registration to the cohort, and estimated their date of infection using clinical HIV data. We then inferred the proportion undiagnosed from the estimated times to diagnosis. To determine sources of Amsterdam infections, we used HIV sequences of people living with HIV (PLHIV) within a background of other Dutch and international sequences to phylogenetically reconstruct transmission chains. Frequent late diagnoses indicate that more recent phylogenetically observed chains are increasingly incomplete, and we use a Bayesian model to estimate the actual growth of Amsterdam transmission chains, and the proportion of locally acquired infections. We estimate that 20% [95% CrI 18-22%] of infections acquired among MSM between 2014-2018 were undiagnosed by the start of 2019, and 44% [37-50%] among heterosexuals, with variation by place of birth. The estimated proportion of MSM infections in 2014-2018 that were locally acquired was 68% [61-74%], with no substantial differences by region of birth. In heterosexuals, this was 57% [41-71%] overall, with heterogeneity by place of birth. The data indicate substantial potential to further curb local transmission, in both MSM and heterosexual Amsterdam residents. In 2014-2018 the largest proportion of local transmissions in Amsterdam are estimated to have occurred in foreign-born MSM, who would likely benefit most from intensified interventions.
Characteristics-driven returns in equilibrium
Authors: Guillaume Coqueret
Date: 2022-03-15 00:00:00

We reverse-engineer the equilibrium construction process of asset prices in order to obtain returns which depend on firm characteristics, possibly in a linear fashion. One key requirement is that agents must have demands that rely separately on firm characteristics and on the log-price of assets. Market clearing via exogenous (non-factor driven) supply, combined with linear demands in characteristics, yields the sought form. The coefficients in the resulting linear expressions are scaled net aggregate demands for characteristics, as well as their variations, and both can be jointly estimated via panel regressions. Conditions underpinning asset pricing anomalies are derived and underline the theoretical importance of the links between characteristics. Empirically, when the number of characteristics is small, the value and momentum anomalies are mostly driven by firm-specific fixed-effects, i.e., latent demands, which highlights the shortcomings of low-dimensional models.
On the deterministic-shift extended CIR model in a negative interest rate framework
Authors: Marco Di Francesco, Kevin Kamm
Date: 2022-03-14 00:00:00

In this paper, we propose a new exogenous model to address the problem of negative interest rates that preserves the analytical tractability of the original Cox-Ingersoll-Ross (CIR) model with a perfect fit to the observed term-structure. We use the difference of two independent CIR processes and apply the deterministic-shift extension technique. To allow for a fast calibration to the market swaption surface, we apply the Gram-Charlier expansion to calculate the swaption prices in our model. We run several numerical tests to demonstrate the strengths of this model by using Monte-Carlo techniques. In particular, the model produces close Bermudan swaption prices compared to Bloomberg's Hull-White one-factor model. Moreover, it finds constant maturity swap (CMS) rates very close to Bloomberg's CMS rates.
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Authors: Anahita Mazloomi, Hani Sami, Jamal Bentahar, Hadi Otrok, Azzam Mourad
Date: 2022-02-21 00:00:00
Tasks: Cloud Computing, Combinatorial Optimization, Edge-computing, Reinforcement Learning (RL)

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
Authors: Evan Crothers, Nathalie Japkowicz, Herna Viktor, Paula Branco
Date: 2022-03-02 00:00:00
Tasks: Adversarial Robustness, Adversarial Text

The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of current state-of-the-art models, but despite a developing threat landscape, there has been minimal analysis of the robustness of detection methods to adversarial attacks. To this end, we evaluate neural and non-neural approaches on their ability to detect computer-generated text, their robustness against text adversarial attacks, and the impact that successful adversarial attacks have on human judgement of text quality. We find that while statistical features underperform neural features, statistical features provide additional adversarial robustness that can be leveraged in ensemble detection models. In the process, we find that previously effective complex phrasal features for detection of computer-generated text hold little predictive power against contemporary generative models, and identify promising statistical features to use instead. Finally, we pioneer the usage of $\Delta$MAUVE as a proxy measure for human judgement of adversarial text quality.
DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks
Authors: Zening Liu, Xiumei Yang, Yang Yang, Kunlun Wang, and Guoqiang Mao, Fellow, IEEE
Date: 2018-12-01 00:00:00
Proceeding: Conference 2018 12
Tasks: Scheduling, STS

Abstract—Fog computing has risen as a promising architecture
for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay
requirements along the cloud to things continuum. For a typical
fog network consisting of heterogeneous fog nodes (FNs) with
different computing resources and communication capabilities,
how to effectively schedule complex computation tasks to multiple
FNs in the neighborhood to achieve minimal service delay is a
fundamental challenge. To tackle this problem, a new concept
named processing efficiency (PE) is first defined to incorporate
computing resources and communication capacities. Further,
to minimize service delay in heterogeneous fog networks, a
scalable, stable and decentralized algorithm, namely dispersive
stable task scheduling (DATS), is proposed and evaluated, which
consists of two key components: (i) a PE-based progressive
computing resources competition (PCRC) and (ii) a QoE-oriented
synchronized task scheduling (STS). Theoretical proofs and
simulation results show that the proposed DATS algorithm
can achieve effective tradeoff between computing resources and
communication capabilities, thus significantly reducing service
delay in heterogeneous fog networks.
Real-time Relevant Recommendation Suggestion
Authors: Ruobing Xie, Rui Wang, Shaoliang Zhang, Zhihong Yang, Feng Xia, Leyu Lin   Authors Info & Claims
Date: 2021-03-08 00:00:00
Proceeding: WSDM 2021 3
Tasks: Mixture-of-Experts, Recommendation Systems, Semantic Similarity, Semantic Textual Similarity

Users of recommendation systems usually focus on one topic at a time. When finishing reading an item, users may want to access more relevant items related to the last read one as extended reading. However, conventional recommendation systems are hard to provide the continuous extended reading function of these relevant items, since the main recommendation results should be diversified. In this paper, we propose a new task named recommendation suggestion, which aims to (1) predict whether users want extended reading, and (2) provide appropriate relevant items as suggestions. These recommended relevant items are arranged in a relevant box and instantly inserted below the clicked item in the main feed. The challenge of recommendation suggestion on relevant items is that it should further consider semantic relevance and information gain besides CTR-related factors. Moreover, the real-time relevant box insertion may also harm the overall performance when users do not want extended reading. To address these issues, we propose a novel Real-time relevant recommendation suggestion (R3S) framework, which consists of an Item recommender and a Box trigger. We extract features from multiple aspects including feature interaction, semantic similarity and information gain as different experts, and propose a new Multi-critic multi-gate mixture-of-experts (M3oE) strategy to jointly consider different experts with multi-head critics. In experiments, we conduct both offline and online evaluations on a real-world recommendation system with detailed ablation tests. The significant improvements in item/box related metrics verify the effectiveness of R3S. Moreover, we have deployed R3S on WeChat Top Stories, which affects millions of users. The source codes are in https://github.com/modriczhang/R3S.
A DEEP LEARNING PIPELINE FOR BREAST CANCER KI-67PROLIFERATION INDEX SCORING
Authors: Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, L.H MOUSS, Noureddine Zerhouni
Date: 2022-03-16 00:00:00
Proceeding: arXiv 2022 3
Tasks: Semantic Segmentation

The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose andselect appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nucleioverlapping and complex variations in their properties. This paper proposes an integrated pipeline foraccurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted.First, semantic segmentation is performed by combining the Squeez and Excitation Resnet andUnet algorithms to extract nuclei from the background. The extracted nuclei are then divided intooverlapped and non-overlapped regions based on eight geometric and statistical features. A marker-based Watershed algorithm is subsequently proposed and applied only to the overlapped regions toseparate nuclei. Finally, deep features are extracted from each nucleus patch using Resnet18 andclassified into positive or negative by a random forest classifier. The proposed pipeline’s performanceis validated on a dataset from the Department of Pathology at Hôpital Nord Franche-Comté hospital.
A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell
Authors: Khaled Benaggoune, Meiling Yue, S. Jemei, Noureddine Zerhouni
Date: 2022-05-01 00:00:00
Proceeding: Applied Energy 2022 5

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.
Data Labeling Impact on Deep Learning Models in Digital Pathology: a Breast Cancer Case Study
Authors: Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, S. VALMARY-DEGANO, L.H MOUSS, Noureddine Zerhouni
Date: 2022-01-01 00:00:00
Proceeding: Studies in Autonomic, Data-driven and Industrial Computing 2022 1
Tasks: Deep Learning

Image data labeling is a vital step for deep learning model training. Studies on data labeling have not considered its impact on model performance and only focused on problems such as the curse of big data labeling or labeling tools. Furthermore, it seems clear that errors in labeling have a significant impact and should be fixed. However, in the medical domain, it is hard to ensure proper data labeling. In general, trained engineers are asked to annotate histology images, which causes errors in labeling. The aim of this study is to highlight the impact of data labeling on deep learning models. For that purpose, deep learning models are trained on two different annotations with different levels of expertise. Results show the importance of including expertise in deep learning model development. The impact of data labeling is shown through a case study on the proliferation of biomarker Ki-67 labeling index scoring.
Industrial IoT middleware using a multi-agent system for consistency-based diagnostic in cement factory
Authors: Ayoub Benayache, Azeddine Bilami, Khaled Benaggoune, Hayet Mouss
Date: 2021-09-01 00:00:00
Proceeding: International Journal of Autonomous and Adaptive Communications Systems 2021 9
Tasks: Diagnostic

With the evolution of the internet of things (IoT) in several domains, and due to its big need in the industry, Industrial Internet of things (IIoT) becomes the suitable naming for this accompaniment IIoT changed the general view of the industry intelligently and over the internet This overlapping of IoT and industry requires special treatment when the system deals with heterogeneous devices in distributed environment and complex tasks In this paper, we propose a middleware solution based on multi-agents-system (MAS) to handle the distributed control of complex systems autonomously in industrial environment The proposed middleware enables M2M communications among system’s components In this work, we also addressed the distributed diagnostic for real industrial system using MAS with new suitable communication strategy to support the heterogeneity and interoperability issued in IIoT and assure real-time system monitoring and control Finally, we present a qualitative evaluation of the proposed solution on real case-study Keywords: industrial internet of things; multi-agents system; middleware; distributed diagnostic; manufacturing system.
Holonic agent-based approach for system-level remaining useful life estimation with stochastic dependence
Authors: Khaled Benaggoune, Hayet Mouss, Ahmed Adnane Abdessemed, Mohamed Bensakhria
Date: 2020-07-01 00:00:00
Proceeding: International Journal of Computer Integrated Manufacturing 2020 7

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.
Post Prognostic Decision for Predictive Maintenance Planning with Remaining Useful Life Uncertainty
Authors: Khaled Benaggoune, Safa Meraghni, Jian Ma, L.H MOUSS, Noureddine Zerhouni
Date: 2020-05-01 00:00:00
Proceeding: 2020 Prognostics and Health Management Conference (PHM-Besançon) 2020 5

This paper investigates the use of the Particle Swarm Optimization (PSO) algorithm to quantify the effect of RUL uncertainty on predictive maintenance planning. The prediction of RUL is influenced by many sources of uncertainty, and it is required to quantify their combined impact by incorporating the RUL uncertainty in the optimization process to minimize the total maintenance cost. In this work, predictive maintenance of a multi-functional single machine problem is adopted to study the impact of RUL uncertainty on maintenance planning. Therefore, the PSO algorithm is integrated with a random sampling-based strategy to select a sequence that performs better for different values of RUL associated with different jobs. Through a numerical example, results show the importance of optimizing maintenance actions under the consideration of RUL randomness.
Agent-based prognostic function for multi-components system with stochastic dependence
Authors: Khaled Benaggoune, Hayet Mouss, Ahmed Adnane Abdessemed, Mohamed Bensakhria
Date: 2019-02-01 00:00:00
Proceeding: 2018 International Conference on Applied Smart Systems (ICASS) 2019 2
Tasks: Management

Prognostic and Health Management of multi-components systems is a challenging task, where different types of dependencies may exist. To tackle the complexity of such system, many distributed approaches are proposed. They deal with structural and functional dependencies and suppose that components are independent, which is not the case in reality. The transition of one component to more degraded states may affect other dependent components. In this paper we address the problem of prognosing components under stochastic dependence, we propose a prognostic based agent function in which a set of agents interact and coordinate to estimate the component remaining useful life with degradation interactions in a distributed manner. The simulation of an example taken from the literature is done on jade framework, the results indicate that the precision of the component RUL is enhanced.
Restricted Boltzmann Machines for Collaborative Filtering
Authors: Ruslan Salakhutdinov, andriy mnih, Geoffrey Hinton
Date: 2007-06-07 00:00:00
Proceeding: ICML: Proceedings of the 24th international conference on Machine learning 2007 6
Tasks: Collaborative Filtering, Recommendation Systems

Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM's can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM's slightly outperform carefully-tuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6% better than the score of Netflix's own system.
Two-Stream Boosted TCRNet for Range-Tolerant Infra-Red Target Detection
Authors: Md Jibanul Haque Jiban, Shah Hassan, Abhijit Mahalanobis
Date: 2021-08-23 00:00:00
Proceeding: IEEE International Conference on Image Processing 2021 8
Tasks: Vocal Bursts Valence Prediction

The detection of vehicular targets in infra-red imagery is a challenging task, both due to the relatively few pixels on target and the false alarms produced by the surrounding terrain clutter. It has been previously shown [1] that a relatively simple network (known as TCRNet) can outperform conventional deep CNNs for such applications by maximizing a target to clutter ratio (TCR) metric. In this paper, we introduce a new form of the network (referred to as TCRNet-2) that further improves the performance by first processing target and clutter information in two parallel channels and then combining them to optimize the TCR metric. We also show that the overall performance can be considerably improved by boosting the performance of a primary TCRNet-2 detector, with a secondary network that enhances discrimination between targets and clutter in the false alarm space of the primary network. We analyze the performance of the proposed networks using a publicly available data set of infra-red images of targets in natural terrain. It is shown that the TCRNet-2 and its boosted version yield considerably better performance than the original TCRNet over a wide range of distances, in both day and night conditions.