pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling
Authors: Vikram Krishnamurthy
Date: 2022-12-26 00:00:00
Tasks: Change Detection, Decision Making, State Estimation

This paper surveys mathematical models, structural results and algorithms in controlled sensing with social learning in social networks. Part 1, namely Bayesian Social Learning with Controlled Sensing addresses the following questions: How does risk averse behavior in social learning affect quickest change detection? How can information fusion be priced? How is the convergence rate of state estimation affected by social learning? The aim is to develop and extend structural results in stochastic control and Bayesian estimation to answer these questions. Such structural results yield fundamental bounds on the optimal performance, give insight into what parameters affect the optimal policies, and yield computationally efficient algorithms. Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1. The agents exhibit sophisticated decision making in a behavioral economics sense; namely the agents make anticipatory decisions (thus the decision strategies are time inconsistent and interpreted as subgame Bayesian Nash equilibria). Part 3, namely {\em Interactive Sensing in Large Networks}, addresses the following questions: How to track the degree distribution of an infinite random graph with dynamics (via a stochastic approximation on a Hilbert space)? How can the infected degree distribution of a Markov modulated power law network and its mean field dynamics be tracked via Bayesian filtering given incomplete information obtained by sampling the network? We also briefly discuss how the glass ceiling effect emerges in social networks. Part 4, namely \emph{Efficient Network Polling} deals with polling in large scale social networks. In such networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimates?
Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review
Authors: Satyam Bhatti, Habib Ullah Manzoor, Bruno Michel, Ruy Sebastian Bonilla, Richard Abrams, Ahmed Zoha, Sajjad Hussain, Rami Ghannam
Date: 2022-12-26 00:00:00
Tasks: Bayesian Optimization, GPR

Solar photovoltaic (PV) technology has merged as an efficient and versatile method for converting the Sun's vast energy into electricity. Innovation in developing new materials and solar cell architectures is required to ensure lightweight, portable, and flexible miniaturized electronic devices operate for long periods with reduced battery demand. Recent advances in biomedical implantable and wearable devices have coincided with a growing interest in efficient energy-harvesting solutions. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted as game changers in energy harvesting, especially in solar energy materials. In this article, we systematically review a range of ML techniques for optimizing the performance of low-cost solar cells for miniaturized electronic devices. Our systematic review reveals that these ML techniques can expedite the discovery of new solar cell materials and architectures. In particular, this review covers a broad range of ML techniques targeted at producing low-cost solar cells. Moreover, we present a new method of classifying the literature according to data synthesis, ML algorithms, optimization, and fabrication process. In addition, our review reveals that the Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) enables the design of the most promising low-solar cell architecture. Therefore, our review is a critical evaluation of existing ML techniques and is presented to guide researchers in discovering the next generation of low-cost solar cells using ML techniques.
Spectral analysis of signals by time-domain statistical characterization and neural network processing: Application to correction of spectral amplitude alterations in pulse-like waveforms
Authors: Guillermo H. Bustos, Héctor H. Segnorile
Date: 2022-12-23 00:00:00

We present a time-domain method to detect and correct spectral alterations of signals by employing statistical characterization of waveforms and a pattern-recognition procedure using simple Artificial Neural Networks. The proposed strategy implements very-fast routines with a computational cost proportional to the number of signal samples, being convenient for applications in embedded environments with limited computational capabilities or fast real-time control tasks. We use the proposed algorithms to correct spectral amplitude attenuations in a pulse-like waveform with a sinc profile as an application example.
Functional Expansions
Authors: Bruno Dupire, Valentin Tissot-Daguette
Date: 2022-12-27 00:00:00

Path dependence is omnipresent in many disciplines such as engineering, system theory and finance. It reflects the influence of the past on the future, often expressed through functionals. However, non-Markovian problems are often infinite-dimensional, thus challenging from a conceptual and computational perspective. In this work, we shed light on expansions of functionals. First, we treat static expansions made around paths of fixed length and propose a generalization of the Wiener series$-$the intrinsic value expansion (IVE). In the dynamic case, we revisit the functional Taylor expansion (FTE). The latter connects the functional It\^o calculus with the signature to quantify the effect in a functional when a "perturbation" path is concatenated with the source path. In particular, the FTE elegantly separates the functional from future trajectories. The notions of real analyticity and radius of convergence are also extended to the path space. We discuss other dynamic expansions arising from Hilbert projections and the Wiener chaos, and finally show financial applications of the FTE to the pricing and hedging of exotic contingent claims.
SVSBI: Sequence-based virtual screening of biomolecular interactions
Authors: Li Shen, Hongsong Feng, Yuchi Qiu, Guo-Wei Wei
Date: 2022-12-27 00:00:00
Tasks: Drug Design, Drug Discovery, Molecular Docking

Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes deep $K$-embedding strategies to encode biomolecular interactions without invoking 3D structure-based docking. We demonstrate the state-of-art performance of SVS for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for the protein-protein interactions in five biological species. SVS has the potential to dramatically change the current practice in drug discovery and protein engineering.
Democratising Knowledge Representation with BioCypher
Authors: Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach, Balazs Bohar, Pornpimol Charoentong, Katharina Danhauser, Tunca Doğan, Johann Dreo, Ian Dunham, Adrià Fernandez-Torras, Benjamin M. Gyori, Michael Hartung, Charles Tapley Hoyt, Christoph Klein, Tamas Korcsmaros, Andreas Maier, Matthias Mann, David Ochoa, Elena Pareja-Lorente, Ferdinand Popp, Martin Preusse, Niklas Probul, Benno Schwikowski, Bünyamin Sen, Maximilian T. Strauss, Denes Turei, Erva Ulusoy, Judith Andrea Heidrun Wodke, Julio Saez-Rodriguez
Date: 2022-12-27 00:00:00
Tasks: Federated Learning, Knowledge Graphs

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to non-specialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://biocypher.org) frees up valuable developer time; we encourage further development and usage by the community.
Cortical Xi-Alpha model for resting state electric neuronal activity
Authors: Roberto D. Pascual-Marqui, Kieko Kochi, Toshihiko Kinoshita
Date: 2022-12-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Power spectra of awake resting state EEG recordings in humans typically have an Alpha peak at around 10 Hz riding a decreasing background "Xi process". Normal and pathological variations may have more than one peak or none. The single channel Xi-Alpha model (Pascual-Marqui et al 1988, https://doi.org/10.3109/00207458808985730) separated these two processes, providing a low-dimensional parametric description of EEG spectra. Currently lacking is a generative whole cortex model for activity spectra and intracortical functional connectivity. Here we introduce the "cortical Xi-Alpha model". The cross-spectral density matrices are modeled as additive components, each one consisting of a scalar spectrum that multiplies a frequency invariant Hermitian covariance matrix. This simple "separation of variables" form offers a very rich repertoire of spatio-spectral properties, as well as diverse whole cortex functional connectivity patterns. The scalp EEG model conserves the same form, allowing simple estimation from scalp to cortex. Two independent open-access, resting state eyes open and closed EEG data sets (203 participants with 61 electrodes, and 47 participants with 26 electrodes) were used to demonstrate, test, and validate the model. Results summary: - The average dimension of cross-spectra lies between 1.7 and 2.6, indicating two processes. - Non-negative matrix factorization of population power spectra sampled at 6239 cortical voxels with only two components (identified as Xi and Alpha) explains 99% of the variance. - The median value of explained variance was 95% for the "cortical Xi-Alpha model" across all datasets and conditions. - Alpha process generators more occipital, Xi more frontal. - Xi cortical lagged connectivities are isotropic with interdistance. - Laminar recordings suggest layers 2/3 pyramidal neurons generate Xi; layers 5/6 pyramidal neurons generate Alpha.
The limitations of comonotonic additive risk measures: a literature review
Authors: Samuel Solgon Santos, Marcelo Brutti Righi, Eduardo de Oliveira Horta
Date: 2022-12-28 00:00:00

Risk measures satisfying the axiom of comonotonic additivity are extensively studied, arguably because of the plethora of results indicating interesting aspects of such risk measures. Recent research, however, has shown that this axiom is incompatible with central properties in specific contexts. In this paper, we present a literature review of these incompatibilities. In addition, we use the Choquet representation of comonotonic additive risk measures to show they cannot be surplus invariant.
Tool flank wear prediction using high-frequency machine data from industrial edge device
Authors: D. Bilgili, G. Kecibas, C. Besirova, M. R. Chehrehzad, G. Burun, T. Pehlivan, U. Uresin, E. Emekli, I. Lazoglu
Date: 2022-12-12 00:00:00
Tasks: Computational Efficiency, Feature Engineering

Tool flank wear monitoring can minimize machining downtime costs while increasing productivity and product quality. In some industrial applications, only a limited level of tool wear is allowed to attain necessary tolerances. It may become challenging to monitor a limited level of tool wear in the data collected from the machine due to the other components, such as the flexible vibrations of the machine, dominating the measurement signals. In this study, a tool wear monitoring technique to predict limited levels of tool wear from the spindle motor current and dynamometer measurements is presented. High-frequency spindle motor current data is collected with an industrial edge device while the cutting forces and torque are measured with a rotary dynamometer in drilling tests for a selected number of holes. Feature engineering is conducted to identify the statistical features of the measurement signals that are most sensitive to small changes in tool wear. A neural network based on the long short-term memory (LSTM) architecture is developed to predict tool flank wear from the measured spindle motor current and dynamometer signals. It is demonstrated that the proposed technique predicts tool flank wear with good accuracy and high computational efficiency. The proposed technique can easily be implemented in an industrial edge device as a real-time predictive maintenance application to minimize the costs due to manufacturing downtime and tool underuse or overuse.
Extreme Image Transformations Affect Humans and Machines Differently
Authors: Girik Malik, Dakarai Crowder, Ennio Mingolla
Date: 2022-11-30 00:00:00
Tasks: Object Recognition

Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on others that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking of difficulty for our transforms for human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs
Authors: Helene Orsini, Hongyan Bao, Yujun Zhou, Xiangrui Xu, Yufei Han, Longyang Yi, Wei Wang, Xin Gao, Xiangliang Zhang
Date: 2022-12-13 00:00:00
Tasks: Adversarial Robustness, Fake News Detection, Intrusion Detection

Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns. Despite effectiveness, their robustness against adversarial attacks is one of the key trust concerns for MLaaS deployment. We are thus motivated to assess the adversarial robustness of the Machine Learning models residing at the core of these security-critical applications with categorical inputs. Previous research efforts on accessing model robustness against manipulation of categorical inputs are specific to use cases and heavily depend on domain knowledge, or require white-box access to the target ML model. Such limitations prevent the robustness assessment from being as a domain-agnostic service provided to various real-world applications. We propose a provably optimal yet computationally highly efficient adversarial robustness assessment protocol for a wide band of ML-driven cybersecurity-critical applications. We demonstrate the use of the domain-agnostic robustness assessment method with substantial experimental study on fake news detection and intrusion detection problems.
Machine Learning for Detecting Malware in PE Files
Authors: Collin Connors, Dilip Sarkar
Date: 2022-12-12 00:00:00
Tasks: Malware Detection

The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection techniques. Using a large benchmark dataset, we evaluate features of PE files using the most common machine learning techniques to detect malware.
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes
Authors: Akhil Vaid, Joy Jiang, Ashwin Sawant, Stamatios Lerakis, Edgar Argulian, Yuri Ahuja, Joshua Lampert, Alexander Charney, Hayit Greenspan, Benjamin Glicksberg, Jagat Narula, Girish Nadkarni
Date: 2022-12-13 00:00:00
Tasks: Diagnostic, Transfer Learning

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes.
Edge types vs privacy in K-anonymization of shortest paths
Authors: Yu-Chuan Tsai
Date: 2015-03-18 00:00:00
Proceeding: Applied Soft Computing 2015 3

Information breaches in social networks and other published data have caused many concerns of privacy issues in recent years. Since information in networks can be modeled as graphs, various techniques
have been proposed to preserve privacy of sensitive data on directed/un-directed, weighted/un-weighted
graphs. To preserve privacy on graphs, most works for publishing anonymized social networks have
attempted to prevent unique patterns to be re-identified, such as, nodes, links, and sub-graphs. In this
work, we study the problem of anonymizing sensitive shortest paths in graphs. We examine the concept
of k-shortest path privacy, in which at least k indistinguishable shortest paths exist between specified
sensitive source and destination vertices. Due to the overlaps of shortest paths, we propose three algorithms to modify three categories of edges to achieve the k-shortest path privacy. Numerical experiments
showing the characteristics of the proposed algorithms are given. The results demonstrate that the proposed algorithms are all feasible to achieve k-shortest path privacy, with different degrees of execution
times and information losses, and can be served as design reference for shortest path anonymization.
A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system
Authors: Ahmed A. Mawgoud, Mohamed Hamed N. Taha, Amr Abu‑Talleb, Amira Kotb
Date: 2022-12-21 00:00:00
Proceeding: Journal of Cloud Computing 2022 12
Tasks: Cloud Computing

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID‑19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad‑hoc Cloud System” idea and deployment plan were set up with the help of V‑BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad‑hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data‑hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad‑hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad‑hoc cloud system environment.
QoS Provision for Controlling Energy Consumption in Ad-Hoc Wireless Sensor Networks
Authors: Ahmed A. Mawgoud, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa
Date: 2020-08-01 00:00:00
Proceeding: ICIC Express Letters 2020 8

In this paper, a new quality of service (QoS) approach is proposed to control the consumed energy in ad-hoc wireless sensor network (WSN). The main reason is to reduce the node's energy through discovering the best optimum route that meets QoS requirements; QoS technique is used to find the optimum methodology for nodes packets transmission and energy consumption. The primary goals of the research are to discover the best techniques to 1) minimize the total consumed energy in the ad-hoc wireless sensor network, and 2) maximize the ad-hoc wireless sensor network lifetime. The simulations of the problem will be formulated with the use of integer linear programming.
Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks
Authors: Ahmed A. Mawgoud, Amir Albusuny, Amr Abu-Talleb, Benbella S. Tawfik
Date: 2021-05-28 00:00:00
Proceeding: Algorithms for Intelligent Systems 2021 5
Tasks: Binary Classification, Decoder

Throughout digital media forensics, the identification of manipulated images and videos is a key topic. Many methods of detection use a binary classification to assess the likelihood of manipulation of a message. Another significant subject is the position, mainly due to three standard attacks, of the exploited regions (i.e., segmentation): elimination, copy-move and splicing. In order to simultaneously detect manipulated images and videos and locate the region for every question, a convolutional neural network is built which uses the multi-work learning approach. The information gained during the execution of one task would be exchanged and both tasks strengthened. To improve network generation, a semi-supervised learning approach is used. A decoder and a Y-shaped decoder are part of the network. For binary classification, activation of the encoded features is used. The output from one decoder branch is used to segment the areas manipulated and from the other branch to reconstruct the input to boost overall efficiency. Experiments using FaceForensics and FaceForensics++ have shown the network's ability to deal with the flaws in the preceding attacks and to counter facial reenactment attacks. In addition, the network can deal with unknown attacks by fine-tuning just a limited amount of data.