pwc-archive/papers-with-abstracts huggingface dataset:
IoT trust and reputation: a survey and taxonomy
Authors: Muhammad Aaqib, Aftab Ali, Liming Chen, Omar Nibouche
Date: 2023-03-23 00:00:00
Tasks: Articles, Management, Survey

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.
Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders
Authors: Georgina Curto, Flavio Comim
Date: 2023-03-22 00:00:00
Tasks: Decision Making, Fairness

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.
Analyzing ChatGPT's Aptitude in an Introductory Computer Engineering Course
Authors: Sanjay Deshpande, Jakub Szefer
Date: 2023-03-13 00:00:00

ChatGPT has recently gathered attention from the general public and academia as a tool that is able to generate plausible and human-sounding text answers to various questions. One potential use, or abuse, of ChatGPT is in answering various questions or even generating whole essays and research papers in an academic or classroom setting. While recent works have explored the use of ChatGPT in the context of humanities, business school, or medical school, this work explores how ChatGPT performs in the context of an introductory computer engineering course. This work assesses ChatGPT's aptitude in answering quizzes, homework, exam, and laboratory questions in an introductory-level computer engineering course. This work finds that ChatGPT can do well on questions asking about generic concepts. However, predictably, as a text-only tool, it cannot handle questions with diagrams or figures, nor can it generate diagrams and figures. Further, also clearly, the tool cannot do hands-on lab experiments, breadboard assembly, etc., but can generate plausible answers to some laboratory manual questions. One of the key observations presented in this work is that the ChatGPT tool could not be used to pass all components of the course. Nevertheless, it does well on quizzes and short-answer questions. On the other hand, plausible, human-sounding answers could confuse students when generating incorrect but still plausible answers.
Exploring AI-Generated Text in Student Writing: How Does AI Help?
Authors: David James Woo, Hengky Susanto, Chi Ho Yeung, Kai Guo, April Ka Yeng Fung
Date: 2023-03-10 00:00:00

English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools.
Distributed Gesture Controlled Systems for Human-Machine Interface
Authors: Hans Johnson, Jafar Saniie
Date: 2023-04-12 00:00:00

This paper presents the design flow of an IoT human machine touchless interface. The device uses embedded computing in conjunction with the Leap Motion Controller to provide an accurate and intuitive touchless interface. Its main function is to augment current touchscreen devices in public spaces through a combination of computer vision technology, event driven programming, and machine learning. Especially following the COVID 19 pandemic, this technology is important for hygiene and sanitation purposes for public devices such as airports, food, and ATM kiosks where hundreds or even thousands of people may touch these devices in a single day. A prototype of the touchless interface was designed with a Leap Motion Controller housed on a Windows PC exchanging information with a Raspberry Pi microcontroller via internet connection.
Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions
Authors: Anna Vizziello, Maurizio Magarini, Pietro Savazzi, Laura Galluccio
Date: 2023-04-03 00:00:00

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.
Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface
Authors: Saadat Ullah Khan, Muhammad Majid, Syed Muhammad Anwar
Date: 2023-04-01 00:00:00
Tasks: Brain Computer Interface, Classification, EEG

An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motion that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition
Authors: Rushuang Zhou, Weishan Ye, Zhiguo Zhang, Yanyang Luo, Li Zhang, Linling Li, Gan Huang, Yining Dong, Yuan-Ting Zhang, Zhen Liang
Date: 2023-03-27 00:00:00
Tasks: Data Augmentation, Domain Adaptation, EEG, Emotion Recognition, valid

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this paper, we propose a novel semi-supervised learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an EEG-Mixup based data augmentation method is developed to generate more valid samples for model learning. Second, a semi-supervised two-step pairwise learning method is proposed to bridge prototype-wise and instance-wise pairwise learning, where the prototype-wise pairwise learning measures the global relationship between EEG data and the prototypical representation of each emotion class and the instance-wise pairwise learning captures the local intrinsic relationship among EEG data. Third, a semi-supervised multi-domain adaptation is introduced to align the data representation among multiple domains (labeled source domain, unlabeled source domain, and target domain), where the distribution mismatch is alleviated. Extensive experiments are conducted on two benchmark databases (SEED and SEED-IV) under a cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6.89% improvement on SEED and 1.44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals. The source code is available at https://github.com/KAZABANA/EEGMatch.
Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data
Authors: Eric Modesitt, Ruiqi Yang, Qi Liu
Date: 2023-03-25 00:00:00
Tasks: EEG, feature selection

Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.
Low Complexity Optimization for Line-of-Sight RIS-Aided Holographic Communications
Authors: Juan Carlos Ruiz-Sicilia, Marco Di Renzo, Merouane Debbah, H. Vincent Poor
Date: 2023-04-13 00:00:00

The synergy of metasurface-based holographic surfaces (HoloS) and reconfigurable intelligent surfaces (RIS) is considered a key aspect for future communication networks. However, the optimization of dynamic metasurfaces requires the use of numerical algorithms, for example, based on the singular value decomposition (SVD) and gradient descent methods, which are usually computationally intensive, especially when the number of elements is large. In this paper, we analyze low complexity designs for RIS-aided HoloS communication systems, in which the configurations of the HoloS transmitter and the RIS are given in a closed-form expression. We consider implementations based on diagonal and non-diagonal RISs. Over line-of-sight channels, we show that the proposed schemes provide performance that is close to that offered by complex numerical methods.
Effect of hydraulic conductivity and permeability on drug distribution, an investigation based on a part of a real tissue
Authors: Masod Sadipour, Mohammad Masoud Momeni, Majid Soltani
Date: 2023-04-13 00:00:00

In this study, a computational simulation is employed to place two essential parameters, the permeability of vessels and hydraulic conductivity, under assessment. These parameters impact the movement of drug particles through vessels, and normal and tumoral tissue to examine the concentration of nanoparticles, interstitial pressure, and velocity. To provide a geometric model detailing the capillary network under normal and tumoral tissue conditions, the geometry is extracted via real image processing. Subsequently, the real conditions were considered to solve the equations pertaining to drug transport and intravascular and interstitial flows in the tissue. The results showed that an increase in permeability and hydraulic conductivity leads to an increase in drug concentration in the tumor. Finally, Methotrexate drug has the most effect in the treatment of tumors. Overall, the computational model for anti-cancer delivery provides a powerful tool for understanding and optimizing drug delivery strategies for the treatment of cancer.
Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task
Authors: Prerna Singh, Ayush Tripathi, Lalan Kumar, Tapan Kumar Gandhi
Date: 2023-04-13 00:00:00
Tasks: EEG, Functional Connectivity

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of $89.4\%$ and $88.4\%$ are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.
Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech data
Authors: Matthew C. Kelley
Date: 2023-04-12 00:00:00
Tasks: Dynamic Time Warping, speech-recognition, Speech Recognition, Time Series

The speech signal is a consummate example of time-series data. The acoustics of the signal change over time, sometimes dramatically. Yet, the most common type of comparison we perform in phonetics is between instantaneous acoustic measurements, such as formant values. In the present paper, I discuss the concept of absement as a quantification of differences between two time-series. I then provide an experimental example of absement applied to phonetic analysis for human and/or computer speech recognition. The experiment is a template-based speech recognition task, using dynamic time warping to compare the acoustics between recordings of isolated words. A recognition accuracy of 57.9% was achieved. The results of the experiment are discussed in terms of using absement as a tool, as well as the implications of using acoustics-only models of spoken word recognition with the word as the smallest discrete linguistic unit.
Inertia-Aware Microgrid Investment Planning Using Tractable Decomposition Algorithms
Authors: Agnes Marjorie Nakiganda, Shahab Dehghan, Petros Aristidou
Date: 2023-04-13 00:00:00

The integration of the frequency dynamics into Micro-Grid (MG) investment and operational planning problems is vital in ensuring the security of the system in the post-contingency states. However, the task of including transient security constraints in MG planning problems is non-trivial. This is due to the highly non-linear and non-convex nature of the analytical closed form of the frequency metrics (e.g., frequency nadir) and power flow constraints. To handle this issue, this paper presents two algorithms for decomposing the MG investment planning problem into multiple levels to enhance computational tractability and optimality. Furthermore, the sensitivity of the decisions made at each level is captured by corresponding dual cutting planes to model feasible secure regions. This, in turn, ensures both the optimal determination and placement of inertia services and accelerates the convergence of the proposed decomposition algorithms. The efficient and effective performance of the proposed algorithms is tested and verified on an 18-bus Low Voltage (LV) network and a 30-bus Medium Voltage (MV) network under various operating scenarios.
Modeling Cell Size Distribution with Heterogeneous Flux Balance Analysis
Authors: Michiel Busschaert, Florence H. Vermeire, Steffen Waldherr
Date: 2023-04-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

For over two decades, Flux Balance Analysis (FBA) has been successfully used for predicting growth rates and intracellular reaction rates in microbiological metabolism. An aspect that is often omitted from this analysis, is segregation or heterogeneity between different cells. In this work, we propose an extended FBA method to model cell size distributions in balanced growth conditions. Hereto, a mathematical description of the concept of balanced growth in terms of cell mass distribution is presented. The cell mass distribution, quantified by the Number Density Function (NDF), is affected by cell growth and cell division. An optimization program is formulated in a general manner in which the NDF, average cell culture growth rate and reaction rates per cell mass are treated as optimization variables. As qualitative proof of concept, the methodology is illustrated on a core carbon model of Escherichia coli under aerobic growth conditions. This illustrates feasibility and applications of this method, while indicating some shortcomings intrinsic to the simplified biomass structuring and the time invariant approach.
Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration
Authors: Shilei Li, Yunjiang Lou, Dawei Shi, Lijing Li, Ling Shi
Date: 2023-04-13 00:00:00
Tasks: regression

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and reveal that the kernel bandwidth should be neither too small nor too big in the sense of the lowest upper error bound. Meanwhile, we find that the proposed MKC is related to a specific heavy-tail distribution, and the level of the heavy tail is controlled by the kernel bandwidth solely. Interestingly, this distribution becomes the Gaussian distribution when the bandwidth is set to be infinite, which allows one to tackle both Gaussian and non-Gaussian problems. We propose an expectation-maximization (EM) algorithm to estimate the parameter vectors and explore the kernel bandwidths alternatively. The results show that our algorithm is equivalent to the traditional linear regression under Gaussian noise and outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method.
Network-Reconfiguration-Aware Power Oscillation Damping Controller for Newly Commissioned Converter-Interfaced Power Plants
Authors: Njegos Jankovic, Javier Roldan-Perez, Milan Prodanovic, Jon Are Suul, Salvatore D'Arco, Luis Rouco-Rodriguez
Date: 2023-04-13 00:00:00

In recent years, transmission system operators have started requesting converter-interfaced generators (CIGs) to participate in grid services such as power oscillation damping (POD). As power systems are prone to topology changes because of connection and disconnection of generators and electrical lines, one of the most important requirements in the POD controller design is to account for these changes and to deal with them by using either adaptive or robust approaches. The robust approach is usually preferred by system operators because of the fixed structure of the controller. In this paper, a procedure to design POD controllers for CIG-based power plants that takes into consideration all possible network configurations is presented. This procedure is based on frequency-response techniques, so it is suitable for the commissioning in newly installed power plants, even in those cases when a detailed small-signal model of the system is not available. This procedure can be used to damp critical system modes by using active power, reactive power, or both power components simultaneously. The proposed procedure is applied to the design of the POD controller for a CIG-based power plant connected to the IEEE 39 Bus system. Simulations performed in Matlab and SimPowerSystems are used to validate the proposed design procedure.