mstyslavity/prisma_v3 huggingface dataset:
The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.
This paper addresses the challenge of enhancing artificial intelligence reasoning capabilities, focusing on logicality within the Abstraction and Reasoning Corpus (ARC). Humans solve such visual reasoning tasks based on their observations and hypotheses, and they can explain their solutions with a proper reason. However, many previous approaches focused only on the grid transition and it is not enough for AI to provide reasonable and human-like solutions. By considering the human process of solving visual reasoning tasks, we have concluded that the thinking process is likely the abductive reasoning process. Thus, we propose a novel framework that symbolically represents the observed data into a knowledge graph and extracts core knowledge that can be used for solution generation. This information limits the solution search space and helps provide a reasonable mid-process. Our approach holds promise for improving AI performance on ARC tasks by effectively narrowing the solution space and providing logical solutions grounded in core knowledge extraction.
We explore the drawing of an axisymmetric viscoelastic tube subject to inertial and surface tension effects. We adopt the Giesekus constitutive model and derive asymptotic long-wave equations for weakly viscoelastic effects. Intuitively, one might imagine that the elastic stresses should act to prevent hole closure during the drawing process. Surprisingly, our results show that the hole closure at the outlet is enhanced by elastic effects for most parameter values. However, the opposite is true if the tube has a very large hole size at the inlet of the device or if the axial stretching is very weak. We explain the physical mechanism underlying this phenomenon by examining how the second normal stress difference induced by elastic effects modifies the hole evolution process. We also determine how viscoelasticity affects the stability of the drawing process and show that elastic effects are always destabilizing for negligible inertia. This is in direct contrast to the case of a thread without a hole for which elastic effects are always stabilizing. On the other hand, our results show that if the inertia is non-zero, elastic effects can be either stabilizing or destabilizing depending on the parameters.
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analysing medical images from computed tomography coronary angiography (CTCA). Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction. These indirect methods are not designed in a clinician-friendly manner, and they complicate the interventional procedure. Furthermore, the current deep learning methods do not provide exact explainability and limit the usefulness of these methods to be deployed in clinical settings. In this study, we first propose a 3D Resnet-50 deep learning model to directly classify normal subjects and CAD patients on CTCA images, then we demonstrate a 2D modified U-Net model can be subsequently employed to segment the coronary arteries. Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy. The classification model with focal loss provides a better and more focused heat map, and the segmentation model provides better explainability than the classification-only model. The proposed holistic approach not only provides a simpler and clinician-friendly solution but also good classification accuracy and exact explainability for CAD diagnosis.
Kalman's fundamental notion of a controllable state space system \cite{k} has been generalised to higher order systems by Willems \cite{w}, and further to distributed systems defined by partial differential equations \cite{ps}. It turns out, that for systems defined in several important spaces of distributions, controllability is now identical to the notion of vector potential in physics, or of vanishing homology in mathematics. These notes will explain this relationship, and a few of its consequences. It will also pose an important question: does a controllable system, in any space of distributions, always admit a vector potential? In other words, is Kalman's notion of a controllable system, suitably generalised, nothing more -- nor less -- than the possibility of describing the dynamics of the system by means of a vector potential? Furthermore, it also turns out that the category of distributed systems bears many formal similarities to the category of affine algebraic sets. This raises a second important question: what is the category for which these distributed systems are `local models', just as affine algebraic sets are local models for the category of algebraic varieties? It would then be possible to extend the theory of control described in these notes to this larger category of systems.
Vascular age is traditionally measured using invasive methods or through 12-lead electrocardiogram (ECG). This paper utilizes a low-cost single-lead (lead-I) ECG module to predict the vascular age of an apparently healthy young person. In addition, we also study the impact of smoking on ECG traces of the light-but-habitual smokers. We begin by collecting (lead-I) ECG data from 42 apparently healthy subjects (smokers and non-smokers) aged 18 to 30 years, using our custom-built low-cost single-lead ECG module, and anthropometric data, e.g., body mass index, smoking status, blood pressure, etc. Under our proposed method, we first pre-process our dataset by denoising the ECG traces, followed by baseline drift removal, followed by z-score normalization. Next, we create another dataset by dividing the ECG traces into overlapping segments of five-second duration. We then feed both segmented and unsegmented datasets to a number of machine learning models, a 1D convolutional neural network, and ResNet18 model, for vascular ageing prediction. We also do transfer learning whereby we pre-train our models on a public PPG dataset, and later, fine-tune and evaluate them on our unsegmented ECG dataset. The random forest model outperforms all other models and previous works by achieving a mean squared error (MSE) of 0.07 and coefficient of determination R2 of 0.99, MSE of 3.56 and R2 of 0.26, MSE of 0.99 and R2 of 0.87, for segmented ECG dataset, for unsegmented ECG dataset, and for transfer learning scenario, respectively. Finally, we utilize the explainable AI framework to identify those ECG features that get affected due to smoking. This work is aligned with the sustainable development goals 3 and 10 of the United Nations which aim to provide low-cost but quality healthcare solutions to the unprivileged. This work also finds its applications in the broad domain of forensic science.
Non-pharmaceutical interventions (NPIs) aimed at limiting human mobility have demonstrated success in curbing the transmission of airborne diseases. However, their effectiveness in managing vector-borne diseases remains less clear. In this study, we introduce a framework that integrates mobility data with vulnerability matrices to evaluate the differential impacts of mobility-based NPIs on both airborne and vector-borne pathogens. Focusing on the city of Santiago de Cali in Colombia, our analysis illustrates how mobility-based policies previously proposed to contain airborne disease can make cities more prone to the spread of vector-borne diseases. By proposing a simplified synthetic model, we explain the limitations of the latter policies and exploit the synergies between both types of diseases to find new interventions reshaping the mobility network for their simultaneous control. Our results thus offer valuable insights into the epidemiological trade-offs of concurrent disease management, providing a foundation for the design and assessment of targeted interventions that reshape human mobility.
Today, GPS-equipped mobile devices are ubiquitous, and they generate Location-Based Service (LBS) data, which has become a critical resource for understanding human mobility. However, inherent limitations in LBS datasets, primarily characterized by discontinuity and sparsity, may introduce significant biases in representing individual movement patterns. This study develops data quality metrics for LBS data, examines their disparities among different populations, and quantifies their effects on inferred individual movement, stays in particular, in the Boston Metropolitan Area. We find that data from higher-income, more educated, and predominantly white census block groups (CBGs) show higher sampling rates but paradoxically lower data quality. This contradiction may stem from greater privacy awareness in these communities. Additionally, we propose a new framework to resample LBS data and quantitatively evaluate the inferential biases associated with data of varying quality. This versatile framework can analyze the impacts originating from different data processing workflows with LBS data. Using linear regression models with clustered standard error, we assess the impact of data quality metrics on inferring the number of stay points. The results show that better data quality, characterized by the number of observations and temporal occupancy, can significantly reduce the bias when calculating the stay points of an individual. The introduction of additional data quality metrics into the regression model can further explain the bias. Overall, this study provides insights into how data quality can influence our understanding of human mobility patterns, highlighting the importance of carefully handling LBS data in research.
Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important determinants that explain differences in the spreading of hateful vs. normal content. For example, hateful content authored by verified users is disproportionally more likely to go viral than hateful content from non-verified ones: hateful content from a verified user (as opposed to normal content) has a 3.5 times larger cascade size, a 3.2 times longer cascade lifetime, and a 1.2 times larger structural virality. Altogether, we offer novel insights into the virality of hate speech on social media.
The cybercriminal underground consists of hundreds of forum communities that function as marketplaces and information-exchange platforms for both established and wannabe cybercriminals. The ecosystem is continuously evolving, with users migrating between forums and platforms. The emergence of cybercrime communities in Telegram and Discord only highlights the rising fragmentation and adaptability of the ecosystem. In this position paper, we explore the economic incentives and trust-building mechanisms that may drive a participant (hereafter, Dmitry) of the cybercriminal underground ecosystem to migrate from one forum or platform to another. What are the market signals that matter to Dmitry's decision of joining a specific community, and what roles and purposes do these communities or platforms play within the broader ecosystem? Ultimately, we build towards our thesis that by studying these mechanisms we could explain, and therefore act upon, Dmitry's choice of joining a criminal community rather than another. To build this argument, we first discuss previous work evaluating differences in trust signals depicted in criminal forums. We then present preliminary results evaluating criminal channels on Telegram using those same lenses. Further, we analyze the different roles these channels play in the criminal ecosystem. We then discuss implications for future research.
Large Language Models (LLMs) have significantly impacted nearly every domain of human knowledge. However, the explainability of these models esp. to laypersons, which are crucial for instilling trust, have been examined through various skeptical lenses. In this paper, we introduce a novel notion of LLM explainability to laypersons, termed $\textit{ReQuesting}$, across three high-priority application domains -- law, health and finance, using multiple state-of-the-art LLMs. The proposed notion exhibits faithful generation of explainable layman-understandable algorithms on multiple tasks through high degree of reproducibility. Furthermore, we observe a notable alignment of the explainable algorithms with intrinsic reasoning of the LLMs.
The application of Virtual Reality Environments (VRE) has been gaining momentum as a relatively new tool to assist with mitigating various difficulties including abstractness of concepts, lack of user engagement, perception of disconnection from other users. A VRE may offer both synchronous and asynchronous experiences, in addition to an immersive environment which promotes users' engagement. Past research has shown that, in general, VRE do improve the experiences they try to enhance in many aspects of human activity. Terms like immersiveness and 3D representation of real life objects and environments are, as it appears, the two most obvious positive effects of Virtual Reality (VR) applications. However, despite these benefits it does not come without challenges. The main three concepts/challenges are the spatial design, the collaboration interaction between its members and the VRE, and the audio and video fidelity. Each of the three includes a number of other components that should be addressed for the total experience to be fine-tuned. These include mutual embodiment and shared perspectives, teleportation, gestural interaction, symmetric and asymmetric collaboration, physical and virtual co-location, inventory, and time and spatial synchronization. This paper comprises a survey of the literature, that identifies and explains the features introduced and the challenges involved with the VREs, and furthermore provides various interesting future research directions.
Explaining the decisions of AI has become vital for fostering appropriate user trust in these systems. This paper investigates explanations for a structured prediction task called ``text-to-SQL Semantic Parsing'', which translates a natural language question into a structured query language (SQL) program. In this task setting, we designed three levels of model explanation, each exposing a different amount of the model's decision-making details (called ``algorithm transparency''), and investigated how different model explanations could potentially yield different impacts on the user experience. Our study with $\sim$100 participants shows that (1) the low-/high-transparency explanations often lead to less/more user reliance on the model decisions, whereas the medium-transparency explanations strike a good balance. We also show that (2) only the medium-transparency participant group was able to engage further in the interaction and exhibit increasing performance over time, and that (3) they showed the least changes in trust before and after the study.
Despite advancements in Large Language Model (LLM) alignment, understanding the reasons behind LLM preferences remains crucial for bridging the gap between desired and actual behavior. LLMs often exhibit biases or tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. However, current methods for evaluating preference alignment often lack explainability, relying on coarse-grained comparisons. To address this, we introduce PROFILE (PRObing Factors of InfLuence for Explainability), a novel framework that uncovers and quantifies the influence of specific factors driving preferences. PROFILE's factor level analysis explains the 'why' behind human-model alignment and misalignment, offering insights into the direction of model improvement. We apply PROFILE to analyze human and LLM preferences across three tasks: summarization, helpful response generation, and document-based question-answering. Our factor level analysis reveals a substantial discrepancy between human and LLM preferences in generation tasks, whereas LLMs show strong alignment with human preferences in evaluation tasks. We demonstrate how leveraging factor level insights, including addressing misaligned factors or exploiting the generation-evaluation gap, can improve alignment with human preferences. This work underscores the importance of explainable preference analysis and highlights PROFILE's potential to provide valuable training signals, driving further improvements in human-model alignment.
Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation.
The use of machine learning (ML) in critical domains such as medicine poses risks and requires regulation. One requirement is that decisions of ML systems in high-risk applications should be human-understandable. The field of "explainable artificial intelligence" (XAI) seemingly addresses this need. However, in its current form, XAI is unfit to provide quality control for ML; it itself needs scrutiny. Popular XAI methods cannot reliably answer important questions about ML models, their training data, or a given test input. We recapitulate results demonstrating that popular XAI methods systematically attribute importance to input features that are independent of the prediction target. This limits their utility for purposes such as model and data (in)validation, model improvement, and scientific discovery. We argue that the fundamental reason for this limitation is that current XAI methods do not address well-defined problems and are not evaluated against objective criteria of explanation correctness. Researchers should formally define the problems they intend to solve first and then design methods accordingly. This will lead to notions of explanation correctness that can be theoretically verified and objective metrics of explanation performance that can be assessed using ground-truth data.
Large language models (LLMs) are becoming more advanced and widespread and have shown their applicability to various domains, including cybersecurity. Static malware analysis is one of the most important tasks in cybersecurity; however, it is time-consuming and requires a high level of expertise. Therefore, we conducted a demonstration experiment focusing on whether an LLM can be used to support static analysis. First, we evaluated the ability of the LLM to explain malware functionality. The results showed that the LLM can generate descriptions that cover functions with an accuracy of up to 90.9\%. In addition, we asked six static analysts to perform a pseudo static analysis task using LLM explanations to verify that the LLM can be used in practice. Through subsequent questionnaires and interviews with the participants, we also demonstrated the practical applicability of LLMs. Lastly, we summarized the problems and required functions when using an LLM as static analysis support, as well as recommendations for future research opportunities.
A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of such polytopes is of fundamental importance for the research and development of neural networks. So far, either theoretical or empirical studies on polytopes only stay at the level of counting their number, which is far from a complete characterization. Here, we propose to study the shapes of polytopes via the number of faces of the polytope. Then, by computing and analyzing the histogram of faces across polytopes, we find that a ReLU network has relatively simple polytopes under both initialization and gradient descent, although these polytopes can be rather diverse and complicated by a specific design. This finding can be appreciated as a kind of generalized implicit bias, subjected to the intrinsic geometric constraint in space partition of a ReLU network. Next, we perform a combinatorial analysis to explain why adding depth does not generate a more complicated polytope by bounding the average number of faces of polytopes with the dimensionality. Our results concretely reveal what kind of simple functions a network learns and what will happen when a network goes deep. Also, by characterizing the shape of polytopes, the number of faces can be a novel leverage for other problems, \textit{e.g.}, serving as a generic tool to explain the power of popular shortcut networks such as ResNet and analyzing the impact of different regularization strategies on a network's space partition.