pwc-archive/papers-with-abstracts huggingface dataset:
Predictive models for wind speed using artificial intelligence and copula
Authors: Md Amimul Ehsan
Date: 2021-07-06 00:00:00

Electricity generation from burning fossil fuels is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from the power industry. These energy sources are the building blocks of green energy, which all have different characteristics. Their availabilities are also diverse, depending on geographical locations and other parameters. Low implementation cost and distributed availability all over the world uplifts their popularity exponentially. Therefore, it has unlocked opportunities for consumers to produce electricity locally and use it on-site, which reduces dependency on centralized utility companies. The research considers two main objectives: the prediction of wind speed that simplifies wind farm planning and feasibility study. Secondly, the need to understand the dependency structure of the wind speeds of multiple distant locations. To address the first objective, twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%. For dependency, a multivariate cumulative distribution function, Copula, was used to find the joint distribution of two or more distant location wind speeds, followed by a case study. We found that the appropriate copula family and the parameters vary based on the distance in between. For the case study, Joe-Frank (BB8) copula shows an efficient joint distribution fit for a wind speed pair with a standard error of 0.0094. Finally, some insights about the uncertainty aspects of wind speed dependency were addressed.
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Authors: Javad Hassannataj Joloudari, Sanaz Mojrian, Issa Nodehi, Amir Mashmool, Zeynab Kiani Zadegan, Sahar Khanjani Shirkharkolaie, Roohallah Alizadehsani, Tahereh Tamadon, Samiyeh Khosravi, Mitra Akbari Kohnehshari, Edris Hassannatajjeloudari, Danial Sharifrazi, Amir Mosavi, Hui Wen Loh, Ru-San Tan, U Rajendra Acharya
Date: 2021-07-05 00:00:00
Tasks: Electrocardiography (ECG)

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.
An Efficient Shock Advice Algorithm based on K-nearest Neighbors for Automated External Defibrillators
Authors: Dao Thanh Hai, Nguyen Minh Tuan, Nguyen Thi Thu Hang, Le Hai Chau
Date: 2021-06-30 00:00:00
Tasks: feature selection

Shockable rhythms, namely ventricular fibrillation and ventricular tachycardia, are the main cause of sudden cardiac arrests, which can be detected quickly by the automated external defibrillator (AED) devices. In this paper, a simple but effective algorithm is proposed as the shock advice algorithm applied in AED. The proposed algorithm consists of K-nearest neighbor classifier and an optimal set of 36 features, which are extracted from original ECG and shockable, non-shockable signals using modified variational mode decomposition technique. Cross-validation procedure and sequential forward feature selection are carefully applied to select an optimal set from entire feature space. The performance results show that the MVMD is the key element for SCA detection performance, and the proposed algorithm is simpler while remaining relatively high detection performance compared to previous publications.
Triple Coding Empowered FDMA-CDMA Mode High Security CAOS Camera
Authors: Nabeel A. Riza, Mohsin A. Mazhar
Date: 2021-06-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

For the first time, the hybrid triple coding empowered Frequency Division Multiple Access (FDMA) Code Division Multiple Access (CDMA) mode of the CAOS (i.e., Coded Access Optical Sensor) camera is demonstrated. Compared to the independent FDMA and CDMA modes, the FDMA-CDMA mode has a novel high security space-time-frequency triple signal encoding design for robust, faster, linear irradiance extraction at a moderately High Dynamic Range (HDR). Specifically, this hybrid mode simultaneously combines the linear HDR strength of the FDMA mode Fast Fourier Transform (FFT) Digital Signal Processing (DSP)-based spectrum analysis with the high Signal to Noise Ratio (SNR) provided by the many simultaneous CAOS pixels photodetection of the CDMA mode. In particular, the demonstrated FDMA CDMA mode with P FDMA channels provides a P times faster camera operation versus the equivalent linear HDR Frequency Modulation (FM)CDMA mode. The active FDMA CDMA mode CAOS camera operation is also demonstrated using P equal to 3 LED light sources, each with its unique optical spectral content driven by its independent FDMA frequency. This illuminated target spectral signature matched active CAOS mode allows simultaneous capture of P images without the use of P time multiplexed slots operation tunable optical filter.
Robust Blind Source Separation by Soft Decision-Directed Non-Unitary Joint Diagonalization
Authors: Wenjuan Liu, DaZheng Feng, Bingnan Pei, Mengdao Xing, Xinhong Meng, QianRu Wei
Date: 2021-06-28 00:00:00
Tasks: blind source separation

Approximate joint diagonalization of a set of matrices provides a powerful framework for numerous statistical signal processing applications. For non-unitary joint diagonalization (NUJD) based on the least-squares (LS) criterion, outliers, also referred to as anomaly or discordant observations, have a negative influence on the performance, since squaring the residuals magnifies the effects of them. To solve this problem, we propose a novel cost function that incorporates the soft decision-directed scheme into the least-squares algorithm and develops an efficient algorithm. The influence of the outliers is mitigated by applying decision-directed weights which are associated with the residual error at each iterative step. Specifically, the mixing matrix is estimated by a modified stationary point method, in which the updating direction is determined based on the linear approximation to the gradient function. Simulation results demonstrate that the proposed algorithm outperforms conventional non-unitary diagonalization algorithms in terms of both convergence performance and robustness to outliers.
Orthogonal and Non-Orthogonal Signal Representations Using New Transformation Matrices Having NPM Structure
Authors: Shaik Basheeruddin Shah, Vijay Kumar Chakka, Arikatla Satyanarayana Reddy
Date: 2021-06-20 00:00:00

In this paper, we introduce two types of real-valued sums known as Complex Conjugate Pair Sums (CCPSs) denoted as CCPS$^{(1)}$ and CCPS$^{(2)}$, and discuss a few of their properties. Using each type of CCPSs and their circular shifts, we construct two non-orthogonal Nested Periodic Matrices (NPMs). As NPMs are non-singular, this introduces two non-orthogonal transforms known as Complex Conjugate Periodic Transforms (CCPTs) denoted as CCPT$^{(1)}$ and CCPT$^{(2)}$. We propose another NPM, which uses both types of CCPSs such that its columns are mutually orthogonal, this transform is known as Orthogonal CCPT (OCCPT). After a brief study of a few OCCPT properties like periodicity, circular shift, etc., we present two different interpretations of it. Further, we propose a Decimation-In-Time (DIT) based fast computation algorithm for OCCPT (termed as FOCCPT), whenever the length of the signal is equal to $2^v,\ v{\in} \mathbb{N}$. The proposed sums and transforms are inspired by Ramanujan sums and Ramanujan Period Transform (RPT). Finally, we show that the period (both divisor and non-divisor) and frequency information of a signal can be estimated using the proposed transforms with a significant reduction in the computational complexity over Discrete Fourier Transform (DFT).
Statistics of the number of equilibria in random social dilemma evolutionary games with mutation
Authors: Manh Hong Duong, The Anh Han
Date: 2021-07-13 00:00:00
Tasks: Diversity

In this paper, we study analytically the statistics of the number of equilibria in pairwise social dilemma evolutionary games with mutation where a game's payoff entries are random variables. Using the replicator-mutator equations, we provide explicit formulas for the probability distributions of the number of equilibria as well as other statistical quantities. This analysis is highly relevant assuming that one might know the nature of a social dilemma game at hand (e.g., cooperation vs coordination vs anti-coordination), but measuring the exact values of its payoff entries is difficult. Our delicate analysis shows clearly the influence of the mutation probability on these probability distributions, providing insights into how varying this important factor impacts the overall behavioural or biological diversity of the underlying evolutionary systems.
Geometric insights into robust portfolio construction
Authors: Lara Dalmeyer, Tim Gebbie
Date: 2021-07-13 00:00:00

We investigate and extend the result that an alpha-weight angle from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix. This is known to imply that better conditioned covariance matrices produce weights from unconstrained mean-variance optimisations that are better aligned with each assets expected return. Here we relate the inequality between the alpha-weight angle and the condition number to extend the result to include portfolio optimisations with gearing constraints to provide an extended family of robust optimisations. We use this to argue that in general the equally weighted portfolio is not preferable to the mean-variance portfolio even with poor forecast ability and a badly conditioned covariance matrix. We confirm the distribution free theoretical arguments with a simple Gaussian simulation.
Wavelet Analysis of Dengue Incidence and its Correlation with Weather and Vegetation Variables in Costa Rica
Authors: Yury E. García, Luis A. Barboza, Fabio Sanchez, Paola Vásquez, Juan G. Calvo
Date: 2021-07-02 00:00:00
Tasks: Time Series Analysis

Dengue represents a serious public health problem in tropical and subtropical regions worldwide. The number of dengue cases and its geographical expansion has increased in recent decades, driven mostly after by social and environmental factors. In Costa Rica, it has been endemic since it was first introduced in 1993. In this article, wavelet analyzes (wavelet power spectrum and wavelet coherence) were performed to detect and quantify dengue periodicity and describe patterns of synchrony between dengue incidence and climatic and environmental factors: Normalized Difference Water Index, Enhanced Vegetation Index, Normalized Difference Vegetation Index, Tropical North Atlantic indices, Land Surface Temperature, and El Ni\~no Southern Oscillation indices in 32 different cantons, using dengue surveillance from 2000 to 2019. Results showed that the dengue dominant cycles are in periods of 1, 2, and 3 years. The wavelet coherence analysis showed that the vegetation indices are correlated with dengue incidence in places located in the central and Northern Pacific of the country in the period of 1 year. Climatic variables such as El Ni\~no 3, 3.4, 4, showed a strong correlation with dengue incidence in the period of 3 years and the Tropical North Atlantic is correlated with dengue incidence in the period of 1 year. Land Surface Temperature showed a strong correlation with dengue time series in the 32 cantons.
Intercross: An Android app for plant breeding and genetics cross management
Authors: Trevor W. Rife, Chaney Courtney, Guillaume Bauchet, Mitchell Neilsen, Jesse A. Poland
Date: 2021-07-13 00:00:00
Tasks: Management

Plant breeding is fundamentally comprised of three cyclic activities: 1) intermating lines to generate novel allelic combinations, 2) evaluation of new plant cultivars in distinct environments, and 3) selection of superior individuals to be used as parents in the next breeding cycle. While digital technologies and tools are commonly utilized for the latter two stages, many plant research programs still rely on manual annotation and paper tags to track the crosses that constitute the basis of a plant breeding program. This presence of analog data is a crack in the foundation of a digital breeding ecosystem and a significant occasion for errors to be introduced that will propagate through the entire breeding program. However, implementing digital cross tracking into breeding programs is difficult due to the non-standardized workflows that different breeders have adopted. Intercross, an open-source Android app, aims to provide scientists with a robust and simple solution for planning, tracking, and managing the crosses being made each season and aims to serve as the primary tool to digitize crossing data for breeding programs. The simplicity and flexibility of Intercross allows rapid and broad adoption by diverse breeding programs and will solidify the concepts of a digital breeding ecosystem.
Effects of Fine Particulate Matter on Cardiovascular Disease Morbidity: A Study on Seven Metropolitan Cities in South Korea
Authors: Eunjung Cho, Yeonggyeong Kang, Youngsang Cho
Date: 2021-07-07 00:00:00

Objectives: The primary purpose of this study is to analyze the relationship between the first occurrence of hospitalization for cardiovascular disease (CVD) and particulate matter less than 2.5 micrometre in diameter (PM2.5) exposure, considering average PM2.5 concentration and the frequency of high PM2.5 concentration simultaneously. Methods: We used large-scale cohort data from seven metropolitan cities in South Korea. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using the Cox proportional-hazards model, including annual average PM2.5 and annual hours of PM2.5 concentration exceeding 55.5 microgram/m3 (FH55). Results: We found that the risk was elevated by 11.6% (95% CI, 9.7-13.6) for all CVD per 2.9 microgram/m3 increase of average PM2.5. In addition, a 94-h increase in FH55 increased the risk of all CVD by 3.8% (95% CI, 2.8-4.7). Regarding stroke, we found that people who were older and had a history of hypertension were more vulnerable to PM2.5 exposure. Conclusion: Based on the findings, we conclude that accurate forecasting, information dissemination, and timely warning of high concentrations of PM2.5 at the national level may reduce the risk of CVD occurrence.
Multiplicative Error Models: 20 years on
Authors: Fabrizio Cipollini, Giampiero M. Gallo
Date: 2021-07-13 00:00:00
Tasks: Time Series, Time Series Analysis

Several phenomena are available representing market activity: volumes, number of trades, durations between trades or quotes, volatility - however measured - all share the feature to be represented as positive valued time series. When modeled, persistence in their behavior and reaction to new information suggested to adopt an autoregressive-type framework. The Multiplicative Error Model (MEM) is borne of an extension of the popular GARCH approach for modeling and forecasting conditional volatility of asset returns. It is obtained by multiplicatively combining the conditional expectation of a process (deterministically dependent upon an information set at a previous time period) with a random disturbance representing unpredictable news: MEMs have proved to parsimoniously achieve their task of producing good performing forecasts. In this paper we discuss various aspects of model specification and inference both for the univariate and the multivariate case. The applications are illustrative examples of how the presence of a slow moving low-frequency component can improve the properties of the estimated models.
Fragility curves for power transmission towers in Odisha, India, based on observed damage during 2019 Cyclone Fani
Authors: Surender V Raj, Manish Kumar, Udit Bhatia
Date: 2021-06-26 00:00:00

Lifeline infrastructure systems such as a power transmission network in coastal regions are vulnerable to strong winds generated during tropical cyclones. Understanding the fragility of individual towers is helpful in improving the resilience of such systems. Fragility curves have been developed in the past for some regions, but without considering relevant epistemic uncertainties. Further, risk and resilience studies are best performed using the fragility curves specific to a region. Such studies become particularly important if the region is exposed to cyclones rather frequently. This paper presents the development of fragility curves for high-voltage power transmission towers in the state of Odisha, India, based on macro-level damage data from 2019 cyclone Fani, which was obtained through concerned government offices. Two types of damages were identified, namely, collapse and partial damage. Accordingly, fragility curves for collapse and functionality disruption damage states were developed considering relevant aleatory and epistemic uncertainties. The latter class of uncertainties included that associated with wind speed estimation at a location and the finite sample uncertainty. The most significant contribution in the epistemic uncertainty was due to the wind speed estimation at a location. The median and logarithmic standard deviation for the 50th percentile fragility curve associated with collapse was close to that for the functionality disruption damage state. These curves also compared reasonably well with those reported for similar structures in other parts of the world.
aiSTROM -- A roadmap for developing a successful AI strategy
Authors: Dorien Herremans
Date: 2021-06-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.
Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training
Authors: Darshan Gera, S Balasubramanian
Date: 2021-07-08 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Memorization

Facial expression recognition (FER) in the wild is crucial for building reliable human-computer interactive systems. However, annotations of large scale datasets in FER has been a key challenge as these datasets suffer from noise due to various factors like crowd sourcing, subjectivity of annotators, poor quality of images, automatic labelling based on key word search etc. Such noisy annotations impede the performance of FER due to the memorization ability of deep networks. During early learning stage, deep networks fit on clean data. Then, eventually, they start overfitting on noisy labels due to their memorization ability, which limits FER performance. This report presents Consensual Collaborative Training (CCT) framework used in our submission to expression recognition track of the Affective Behaviour Analysis in-the-wild (ABAW) 2021 competition. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss, without making any assumption about the noise distribution. A dynamic transition mechanism is used to move from supervision loss in early learning to consistency loss for consensus of predictions among networks in the later stage. Co-training reduces overall error, and consistency loss prevents overfitting to noisy samples. The performance of the model is validated on challenging Aff-Wild2 dataset for categorical expression classification. Our code is made publicly available at https://github.com/1980x/ABAW2021DMACS.
SECBENCH: A Database of Real Security Vulnerabilities
Authors: Sofia Reis, Rui Abreu
Date: 2017-10-31 00:00:00
Proceeding: International Workshop on Secure Software Engineering in DevOps and Agile Development co-located with the European Symposium on Research in Computer Security (ESORICS) 2017 10
Tasks: software testing

Currently, to satisfy the high number of system requirements, complex software is created which turns its development cost-intensive and more susceptible to security vulnerabilities. In software security testing, empirical studies typically use artificial faulty programs because of the challenges involved in the extraction or reproduction of real security vulnerabilities. Thus, researchers tend to use hand-seeded faults or mutations to overcome these issues which might not be suitable for software testing techniques since the two approaches can create samples that inadvertently differ from the real vulnerabilities and thus might lead to misleading assessments of the capabilities of the tools. Although there are databases targeting security vulnerabilities test cases, one database contains only real vulnerabilities, the other ones are a mix of real and artificial or even only artificial samples. Secbench is a database of real security vulnerabilities mined from Github which hosts millions of open-source projects carrying a considerable number of security vulnerabilities. We mined 248 projects-accounting to almost 2M commits for 16 different vulnerability patterns, yielding a Database with 682 real security vulnerabilities.