OpenRaiser/Intern-Atlas huggingface dataset:
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Venue: arXiv cs.CY 2022
Year: 2022

Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior. Similarly, limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined common neurodegenerative disease that mimics the clinical symptoms of AD. At present, the risk factors implicated in LATE and those distinguishing LATE from AD are largely unknown. We leveraged an integrated feature selection-based algorithmic approach, to identify important factors differentiating subjects with LATE and/or AD from Control on significantly imbalanced data. We analyzed two datasets ROSMAP and NACC and discovered that alcohol consumption was a top lifestyle and environmental factor linked with LATE and AD and their associations were differential. In particular, we identified a specific subpopulation consisting of APOE e4 carriers. We found that, for this subpopulation, light-to-moderate alcohol intake was a protective factor against both AD and LATE, but its protective role against AD appeared stronger than LATE. The codes for our algorithms are available at https://github.com/xinxingwu-uk/PFV.
Reliable and Resilient AI and IoT-based Personalised Healthcare Services: A Survey
Venue: arXiv cs.CY 2022
Year: 2022

Recent technological and economic developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized through control and monitoring applications that are typically developed using artificial intelligence/machine learning-based algorithms, which play a significant role in highlighting the efficiency of traditional healthcare systems. Current personalized healthcare services are dedicated to a specific environment to support technological personalization. However, they are unable to consider different interrelated health conditions, leading to inappropriate diagnoses and affecting sustainability and the long-term health of patients. To this end, current Healthcare 5.0 technology has evolved that supersede previous healthcare technologies. The goal of healthcare 5.0 is to achieve an autonomous healthcare service, that takes into account the interdependent effect of different health conditions of a patient. This paper conducts a comprehensive survey on personalized healthcare services. In particular, we first present an overview of key requirements of comprehensive personalized healthcare services in modern healthcare Internet of Things (HIoT), including the definition of personalization and an example use case scenario as a representative for modern HIoT. Second, we explored a fundamental three-layer architecture for IoT-based healthcare systems using AI and non-AI-based approaches, considering key requirements for CPHS followed by their strengths and weaknesses in the frame of personalized healthcare services. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions. Finally, we propose a methodology to develop reliable, resilient, and personalized healthcare services that address the identified weaknesses of existing approaches.
The digital harms of smart home devices: A systematic literature review
Venue: arXiv cs.CY 2022
Year: 2022

The connection of home electronic devices to the internet allows remote control of physical devices and involves the collection of large volumes of data. With the increase in the uptake of Internet-of-Things home devices, it becomes critical to understand the digital harms of smart homes. We present a systematic literature review on the security and privacy harms of smart homes. PRISMA methodology is used to systematically review 63 studies published between January 2011 and October 2021; and a review of known cases is undertaken to illustrate the literature review findings with real-world scenarios. Published literature identifies that smart homes may pose threats to confidentiality (unwanted release of information), authentication (sensing information being falsified) and unauthorised access to system controls. Most existing studies focus on privacy intrusions as a prevalent form of harm against smart homes. Other types of harms that are less common in the literature include hacking, malware and DoS attacks. Digital harms, and data associated with these harms, may vary extensively across smart devices. Most studies propose technical measures to mitigate digital harms, while fewer consider social prevention mechanisms. We also identify salient gaps in research, and argue that these should be addressed in future cross-disciplinary research initiatives.
Why Are Some Online Educational Programs Successful? Student Cognition and Success
Venue: arXiv cs.CY 2022
Year: 2022

Massive Open Online Courses (MOOCs) once offered the promise of accessibility and affordability. However, MOOCs typically lack expert feedback and social interaction, and have low student engagement and retention. Thus, alternative programs for online education have emerged including an online graduate program in computer science at a major public university in USA. This program is considered a success with over 9000 students now enrolled in the program. We adopt the perspective of cognitive science to answer the question why do only some online educational courses succeed? We measure learner motivation and self-regulation in one course in the program, specifically a course on artificial intelligence (AI). Surveys of students indicate that students self-reported assessments of self-efficacy, cognitive strategy use, and intrinsic value of the course are not only fairly high, but also generally increase over the course of learning. This data suggests that the online AI course might be a success because the students have high self-efficacy and the class fosters self-regulated learning.
Modelling Business Agreements in the Multimodal Transportation Domain through Ontological Smart Contracts
Venue: arXiv cs.CY 2022
Year: 2022

The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.
Understanding the Behavior of Belief Propagation
Venue: arXiv cs.AI 2022
Year: 2022

Probabilistic graphical models are a powerful concept for modeling high-dimensional distributions. Besides modeling distributions, probabilistic graphical models also provide an elegant framework for performing statistical inference; because of the high-dimensional nature, however, one must often use approximate methods for this purpose. Belief propagation performs approximate inference, is efficient, and looks back on a long success-story. Yet, in most cases, belief propagation lacks any performance and convergence guarantees. Many realistic problems are presented by graphical models with loops, however, in which case belief propagation is neither guaranteed to provide accurate estimates nor that it converges at all. This thesis investigates how the model parameters influence the performance of belief propagation. We are particularly interested in their influence on (i) the number of fixed points, (ii) the convergence properties, and (iii) the approximation quality.
Tackling problems, harvesting benefits -- A systematic review of the regulatory debate around AI
Venue: arXiv cs.CY 2022
Year: 2022

How to integrate an emerging and all-pervasive technology such as AI into the structures and operations of our society is a question of contemporary politics, science and public debate. It has produced a considerable amount of international academic literature from different disciplines. This article analyzes the academic debate around the regulation of artificial intelligence (AI). The systematic review comprises a sample of 73 peer-reviewed journal articles published between January 1st, 2016, and December 31st, 2020. The analysis concentrates on societal risks and harms, questions of regulatory responsibility, and possible adequate policy frameworks, including risk-based and principle-based approaches. The main interests are proposed regulatory approaches and instruments. Various forms of interventions such as bans, approvals, standard-setting, and disclosure are presented. The assessments of the included papers indicate the complexity of the field, which shows its prematurity and the remaining lack of clarity. By presenting a structured analysis of the academic debate, we contribute both empirically and conceptually to a better understanding of the nexus of AI and regulation and the underlying normative decisions. A comparison of the scientific proposals with the proposed European AI regulation illustrates the specific approach of the regulation, its strengths and weaknesses.
Systems-theoretic Hazard Analysis of Digital Human-System Interface Relevant to Reactor Trip
Venue: arXiv cs.HC 2022
Year: 2022

Human-system interface is one of the key advanced design features applied to modern digital instrumentation and control systems of nuclear power plants. The conventional design is based on a compact workstation-based system within the control room. The compact workstation provides both a strategic operating environment while also a convenient display for plant status information necessary to the operator. The control environment is further enhanced through display panels, visual and auditory alarms, and procedure systems. However, just like the legacy control, the HSI should incorporate diversity to demonstrate sufficient defense-in-depth protection against common cause failures of the safety system. Furthermore, the vulnerability of the HSI is affected by a plethora of factors, such as human error, cyberattacks, software common cause failures, etc., that complicate the design and analysis. Therefore, this work aims to identify and evaluate existing system vulnerabilities to support the licensing, deployment and operation of HSI designs, especially the functions that are relevant to a reactor trip. We performed a systematic hazard analysis to investigate potential vulnerabilities within the HSI design using the novel redundancy-guided systems-theoretic hazard analysis. This method was developed and demonstrated by Idaho National Laboratory under a project initiated by the Risk-Informed Systems Analysis Pathway of the U.S. Department of Energy's Light Water Reactor Sustainability Program. The goal of the project is to develop a strong technical basis for risk assessment strategies to support effective, reliable, and licensable digital instrumentation and control technologies.
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey
Venue: arXiv cs.CR 2022
Year: 2022

With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line of defense, in detecting attacks on the internet. As intrusion tactics from intruders become more sophisticated and difficult to detect, researchers have started to apply novel Machine Learning (ML) techniques to effectively detect intruders and hence preserve internet users' information and overall trust in the entire internet network security. Over the last decade, there has been an explosion of research on intrusion detection techniques based on ML and Deep Learning (DL) architectures on various cyber security-based datasets such as the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we review contemporary literature and provide a comprehensive survey of different types of intrusion detection technique that applies Support Vector Machines (SVMs) algorithms as a classifier. We focus only on studies that have been evaluated on the two most widely used datasets in cybersecurity namely: the KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method, identifying the role of the SVMs classifier, and all other algorithms involved in the studies. Furthermore, we present a critical review of each method, in tabular form, highlighting the performance measures, strengths, and limitations of each of the methods surveyed.
Phase Dependent Loss Analysis for RIS Systems
Venue: arXiv cs.IT 2022
Year: 2022

In this paper we focus on phase dependent loss (PDL), an important aspect of reconfigurable intelligent surfaces (RIS) where the signals reflected from the RIS elements are attenuated by varying amounts depending on the phase rotation provided by the element. To evaluate the effects of PDL, we analyse the SNR of a SIMO RIS-aided wireless link. We assume that the channel between the base station (BS) and RIS is a rank-1 LOS channel while the user (UE)-BS and UE-RIS are correlated Rayleigh channels. The RIS design is optimal in the absence of PDL and maximizes the SNR in this scenario. Specifically, we derive a closed form expression for the mean SNR in the presence of PDL. The attenuation function used for PDL was developed from a detailed circuit analysis of RIS elements. Leveraging the derived results, we analytically characterise the impact of PDL on the mean SNR. Numerical results are conducted to validate the derived expressions and verify the analysis.
SENDER: SEmi-Nonlinear Deep Efficient Reconstructor for Extraction Canonical, Meta, and Sub Functional Connectivity in the Human Brain
Venue: arXiv cs.LG 2022
Year: 2022

Deep Linear and Nonlinear learning methods have already been vital machine learning methods for investigating the hierarchical features such as functional connectivity in the human brain via functional Magnetic Resonance signals; however, there are three major shortcomings: 1). For deep linear learning methods, although the identified hierarchy of functional connectivity is easily explainable, it is challenging to reveal more hierarchical functional connectivity; 2). For deep nonlinear learning methods, although non-fully connected architecture reduces the complexity of neural network structures that are easy to optimize and not vulnerable to overfitting, the functional connectivity hierarchy is difficult to explain; 3). Importantly, it is challenging for Deep Linear/Nonlinear methods to detect meta and sub-functional connectivity even in the shallow layers; 4). Like most conventional Deep Nonlinear Methods, such as Deep Neural Networks, the hyperparameters must be tuned manually, which is time-consuming. Thus, in this work, we propose a novel deep hybrid learning method named SEmi-Nonlinear Deep Efficient Reconstruction (SENDER), to overcome the aforementioned shortcomings: 1). SENDER utilizes a multiple-layer stacked structure for the linear learning methods to detect the canonical functional connectivity; 2). SENDER implements a non-fully connected architecture conducted for the nonlinear learning methods to reveal the meta-functional connectivity through shallow and deeper layers; 3). SENDER incorporates the proposed background components to extract the sub-functional connectivity; 4). SENDER adopts a novel rank reduction operator to implement the hyperparameters tuning automatically. To further validate the effectiveness, we compared SENDER with four peer methodologies using real functional Magnetic Resonance Imaging data for the human brain.
Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale
Venue: arXiv cs.CL 2022
Year: 2022

Suicide is a major public health crisis. With more than 20,000,000 suicide attempts each year, the early detection of suicidal intent has the potential to save hundreds of thousands of lives. Traditional mental health screening methods are time-consuming, costly, and often inaccessible to disadvantaged populations; online detection of suicidal intent using machine learning offers a viable alternative. Here we present Robin, the largest non-keyword generated suicidal corpus to date, consisting of over 1.1 million online forum postings. In addition to its unprecedented size, Robin is specially constructed to include various categories of suicidal text, such as suicide bereavement and flippant references, better enabling models trained on Robin to learn the subtle nuances of text expressing suicidal ideation. Experimental results achieve state-of-the-art performance for the classification of suicidal text, both with traditional methods like logistic regression (F1=0.85), as well as with large-scale pre-trained language models like BERT (F1=0.92). Finally, we release the Robin dataset publicly as a machine learning resource with the potential to drive the next generation of suicidal sentiment research.
Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm
Venue: arXiv cs.LG 2022
Year: 2022

The time needed to apply a hierarchical clustering algorithm is most often dominated by the number of computations of a pairwise dissimilarity measure. Such a constraint, for larger data sets, puts at a disadvantage the use of all the classical linkage criteria but the single linkage one. However, it is known that the single linkage clustering algorithm is very sensitive to outliers, produces highly skewed dendrograms, and therefore usually does not reflect the true underlying data structure -- unless the clusters are well-separated. To overcome its limitations, we propose a new hierarchical clustering linkage criterion called Genie. Namely, our algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini- or Bonferroni-index) of the cluster sizes does not drastically increase above a given threshold. The presented benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage's speed. The Genie algorithm is easily parallelizable and thus may be run on multiple threads to speed up its execution even further. Its memory overhead is small: there is no need to precompute the complete distance matrix to perform the computations in order to obtain a desired clustering. It can be applied on arbitrary spaces equipped with a dissimilarity measure, e.g., on real vectors, DNA or protein sequences, images, rankings, informetric data, etc. A reference implementation of the algorithm has been included in the open source 'genie' package for R. See also https://genieclust.gagolewski.com for a new implementation (genieclust) -- available for both R and Python.
Exemplar-Based Image Colorization with A Learning Framework
Venue: arXiv cs.CV 2022
Year: 2022

Image learning and colorization are hot spots in multimedia domain. Inspired by the learning capability of humans, in this paper, we propose an automatic colorization method with a learning framework. This method can be viewed as a hybrid of exemplar-based and learning-based method, and it decouples the colorization process and learning process so as to generate various color styles for the same gray image. The matching process in the exemplar-based colorization method can be regarded as a parameterized function, and we employ a large amount of color images as the training samples to fit the parameters. During the training process, the color images are the ground truths, and we learn the optimal parameters for the matching process by minimizing the errors in terms of the parameters for the matching function. To deal with images with various compositions, a global feature is introduced, which can be used to classify the images with respect to their compositions, and then learn the optimal matching parameters for each image category individually. What's more, a spatial consistency based post-processing is design to smooth the extracted color information from the reference image to remove matching errors. Extensive experiments are conducted to verify the effectiveness of the method, and it achieves comparable performance against the state-of-the-art colorization algorithms.
Genetic-based fog colony optimization hybridized with hierarchical clustering and its influence in the placement of fog services
Venue: arXiv cs.DC 2022
Year: 2022

The organization of fog devices into fog colonies has reduced the complexity management of fog domains. One of the main influencing factors on this complexity is the large number of devices, i.e. the high scale level of the infrastructure. Fog colonies are subsets of fog devices that are managed independently from the other colonies. Thus, the number of devices involved in the management of a colony is much smaller. Previous studies have evaluated the influence of the fog colony layout on system performance metrics. We propose to use a hierarchical clustering as the base definition of the fog colony layout of the fog infrastructure. The dendrogram obtained from this hierarchical clustering includes all the colony candidates. A genetic algorithm is in charge of selecting the subset of colony candidates that optimizes the two performance metrics under study: the network communication time between users and applications, and the execution time of the algorithms that manage internally the placement of the applications in each colony. We implemented the NSGA-II, a common multi-objective approach for GAs, to evaluate our proposal. The results show that a meta-heuristic such as a GA improves the performance metrics by defining the fog colony layout through the use of the dendrogram. Nine different experiment scenarios, varying the number of applications and fog devices, were studied. In the worst of the cases, 137 generations were enough to the results of the GA dominated the solutions obtained with two control algorithms. The number of genetic solutions and their homogeneous distribution in the Pareto front were also satisfactory.