OpenRaiser/Intern-Atlas huggingface dataset:
An Approach to Detect Abnormal Submissions for CodeWorkout Dataset
Venue: arXiv cs.CY 2024
Year: 2024

Students interactions while solving problems in learning environments (i.e. log data) are often used to support students learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments.
Human Oversight of Artificial Intelligence and Technical Standardisation
Venue: arXiv cs.CY 2024
Year: 2024

The adoption of human oversight measures makes it possible to regulate, to varying degrees and in different ways, the decision-making process of Artificial Intelligence (AI) systems, for example by placing a human being in charge of supervising the system and, upstream, by developing the AI system to enable such supervision. Within the global governance of AI, the requirement for human oversight is embodied in several regulatory formats, within a diversity of normative sources. On the one hand, it reinforces the accountability of AI systems' users (for example, by requiring them to carry out certain checks) and, on the other hand, it better protects the individuals affected by the AI-based decision (for example, by allowing them to request a review of the decision). In the European context, the AI Act imposes obligations on providers of high-risk AI systems (and to some extent also on professional users of these systems, known as deployers), including the introduction of human oversight tools throughout the life cycle of AI systems, including by design (and their implementation by deployers). The EU legislator is therefore going much further than in the past in "spelling out" the legal requirement for human oversight. But it does not intend to provide for all implementation details; it calls on standardisation to technically flesh out this requirement (and more broadly all the requirements of section 2 of chapter III) on the basis of article 40 of the AI Act. In this multi-level regulatory context, the question of the place of humans in the AI decision-making process should be given particular attention. Indeed, depending on whether it is the law or the technical standard that sets the contours of human oversight, the "regulatory governance" of AI is not the same: its nature, content and scope are different. This analysis is at the heart of the contribution made (or to be made) by legal experts to the central reflection on the most appropriate regulatory governance -- in terms of both its institutional format and its substance -- to ensure the effectiveness of human oversight and AI trustworthiness.
Untangling Cognitive Processes Underlying Knowledge Work
Venue: arXiv cs.HC 2024
Year: 2024

In a post-industrial society, the workplace is dominated primarily by Knowledge Work, which is achieved mostly through human cognitive processing, such as analysis, comprehension, evaluation, and decision-making. Many of these processes have limited support from technology in the same way that physical tasks have been enabled through a host of tools from hammers to shovels and hydraulic lifts. To develop a suite of cognitive tools, we first need to understand which processes humans use to complete work tasks. In the past century several classifications (e.g., Blooms) of cognitive processes have emerged, and we assessed their viability as the basis for designing tools that support cognitive work. This study re-used an existing data set composed of interviews of environmental scientists about their core work. While the classification uncovered many instances of cognitive process, the results showed that the existing cognitive process classifications do not provide a sufficiently comprehensive deconstruction of the human cognitive processes; the work is quite simply too abstract to be operational.
Green and Safe 6G Wireless Networks: A Hybrid Approach
Venue: arXiv cs.NI 2024
Year: 2024

With the wireless internet access being increasingly popular with services such as HD video streaming and so on, the demand for high data consuming applications is also rising. This increment in demand is coupled with a proportional rise in the power consumption. It is required that the internet traffic is offloaded to technologies that serve the users and contribute in energy consumption. There is a need to decrease the carbon footprint in the atmosphere and also make the network safe and reliable. In this article we propose a hybrid system of RF (Radio Frequency) and VLC (Visible Light Communication) for indoor communication that can provide communication along with illumination with least power consumption. The hybrid network is viable as it utilizes power with respect to the user demand and maintains the required Quality of ServiceQoS and Quality of Experience QoE for a particular application in use. This scheme aims for Green Communication and reduction in Electromagnetic EM Radiation. A comparative analysis for RF communication, Hybrid RF+ VLC and pure VLC is made and simulations are carried out using Python, Scilab and MathWorks tool. The proposal achieves high energy efficiency of about 37% low Specific Absorption Rate (SAR) lower incident and absorbed power density complexity and temperature elevation in human body tissues exposed to the radiation. It also enhances the battery lifetime of the mobile device in use by increasing the lifetime approximately by 7 hours as validated from the obtained results. Thus the overall network reliability and safety factor is enhanced with the proposed approach.
StreamTinyNet: video streaming analysis with spatial-temporal TinyML
Venue: arXiv cs.CV 2024
Year: 2024

Tiny Machine Learning (TinyML) is a branch of Machine Learning (ML) that constitutes a bridge between the ML world and the embedded system ecosystem (i.e., Internet of Things devices, embedded devices, and edge computing units), enabling the execution of ML algorithms on devices constrained in terms of memory, computational capabilities, and power consumption. Video Streaming Analysis (VSA), one of the most interesting tasks of TinyML, consists in scanning a sequence of frames in a streaming manner, with the goal of identifying interesting patterns. Given the strict constraints of these tiny devices, all the current solutions rely on performing a frame-by-frame analysis, hence not exploiting the temporal component in the stream of data. In this paper, we present StreamTinyNet, the first TinyML architecture to perform multiple-frame VSA, enabling a variety of use cases that requires spatial-temporal analysis that were previously impossible to be carried out at a TinyML level. Experimental results on public-available datasets show the effectiveness and efficiency of the proposed solution. Finally, StreamTinyNet has been ported and tested on the Arduino Nicla Vision, showing the feasibility of what proposed.
Improved symbolic drum style classification with grammar-based hierarchical representations
Venue: arXiv cs.SD 2024
Year: 2024

Deep learning models have become a critical tool for analysis and classification of musical data. These models operate either on the audio signal, e.g. waveform or spectrogram, or on a symbolic representation, such as MIDI. In the latter, musical information is often reduced to basic features, i.e. durations, pitches and velocities. Most existing works then rely on generic tokenization strategies from classical natural language processing, or matrix representations, e.g. piano roll. In this work, we evaluate how enriched representations of symbolic data can impact deep models, i.e. Transformers and RNN, for music style classification. In particular, we examine representations that explicitly incorporate musical information implicitly present in MIDI-like encodings, such as rhythmic organization, and show that they outperform generic tokenization strategies. We introduce a new tree-based representation of MIDI data built upon a context-free musical grammar. We show that this grammar representation accurately encodes high-level rhythmic information and outperforms existing encodings on the GrooveMIDI Dataset for drumming style classification, while being more compact and parameter-efficient.
Quelle {é}thique pour quelle IA ?
Venue: arXiv cs.CY 2024
Year: 2024

This study proposes an analysis of the different types of ethical approaches involved in the ethics of AI, and situates their interests and limits. First, the author introduces to the contemporary need for and meaning of ethics. He distinguishes it from other registers of normativities and underlines its inadequacy to formalization. He then presents a cartography of the landscape of ethical theories covered by moral philosophy, taking care to distinguish meta-ethics, normative ethics and applied ethics. In drawing up this overview, the author questions the relationship between ethics and artificial intelligence. The analysis focuses in particular on the main ethical currents that have imposed themselves in the ways of doing digital ethics and AI in our Western democracies. The author asks whether these practices of ethics, as they seem to crystallize today in a precise pattern, constitute a sufficient and sufficiently satisfactory response to our needs for ethics in AI. The study concludes with a reflection on the reasons why a human ethics of AI based on a pragmatic practice of contextual ethics remains necessary and irreducible to any formalization or automated treatment of the ethical questions that arise for humans.
Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
Venue: arXiv cs.LG 2024
Year: 2024

Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) have been the neural network of choice, with significant progress made in optimizing their training. Recently, Kolmogorov-Arnold Networks (KANs) were introduced as a viable alternative, with the potential of offering better interpretability and efficiency while requiring fewer parameters. In this paper, we present a fast JAX-based implementation of grid-dependent Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for solving PDEs, achieving up to 84 times faster training times than the original KAN implementation. We propose an adaptive training scheme for PIKANs, introducing an adaptive state transition technique to avoid loss function peaks between grid extensions, and a methodology for designing PIKANs with alternative basis functions. Through comparative experiments, we demonstrate that the adaptive features significantly enhance solution accuracy, decreasing the L^2 error relative to the reference solution by up to 43.02%. For the studied PDEs, our methodology approaches or surpasses the results obtained from architectures that utilize up to 8.5 times more parameters, highlighting the potential of adaptive, grid-dependent PIKANs as a superior alternative in scientific and engineering applications.
CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis
Venue: arXiv cs.CV 2024
Year: 2024

Digital Breast Tomosynthesis (DBT) is an advanced breast imaging modality that offers superior lesion detection accuracy compared to conventional mammography, albeit at the trade-off of longer reading time. Accelerating lesion detection from DBT using deep learning is hindered by limited data availability and huge annotation costs. A possible solution to this issue could be to leverage the information provided by a more widely available modality, such as mammography, to enhance DBT lesion detection. In this paper, we present a novel framework, CoMoTo, for improving lesion detection in DBT. Our framework leverages unpaired mammography data to enhance the training of a DBT model, improving practicality by eliminating the need for mammography during inference. Specifically, we propose two novel components, Lesion-specific Knowledge Distillation (LsKD) and Intra-modal Point Alignment (ImPA). LsKD selectively distills lesion features from a mammography teacher model to a DBT student model, disregarding background features. ImPA further enriches LsKD by ensuring the alignment of lesion features within the teacher before distilling knowledge to the student. Our comprehensive evaluation shows that CoMoTo is superior to traditional pretraining and image-level KD, improving performance by 7% Mean Sensitivity under low-data setting. Our code is available at https://github.com/Muhammad-Al-Barbary/CoMoTo .
PICA: A Data-driven Synthesis of Peer Instruction and Continuous Assessment
Venue: arXiv cs.CY 2024
Year: 2024

Peer Instruction (PI) and Continuous Assessment(CA) are two distinct educational techniques with extensive research demonstrating their effectiveness. The work herein combines PI and CA in a deliberate and novel manner to pair students together for a PI session in which they collaborate on a CA task. The data used to inform the pairing method is restricted to the most previous CA task students completed independently. The motivation for this data-driven collaborative learning is to improve student learning, communication, and engagement. Quantitative results from an investigation of the method show improved assessment scores on the PI CA tasks, although evidence of a positive effect on subsequent individual CA tasks was not statistically significant as anticipated. However, student perceptions were positive, engagement was high, and students interacted with a broader set of peers than is typical. These qualitative observations, together with extant research on the general benefits of improving student engagement and communication (e.g. improved sense of belonging, increased social capital, etc.), render the method worthy for further research into building and evaluating small student learning communities using student assessment data.
Hopfield Networks for Asset Allocation
Venue: arXiv cs.LG 2024
Year: 2024

We present the first application of modern Hopfield networks to the problem of portfolio optimization. We performed an extensive study based on combinatorial purged cross-validation over several datasets and compared our results to both traditional and deep-learning-based methods for portfolio selection. Compared to state-of-the-art deep-learning methods such as Long-Short Term Memory networks and Transformers, we find that the proposed approach performs on par or better, while providing faster training times and better stability. Our results show that Modern Hopfield Networks represent a promising approach to portfolio optimization, allowing for an efficient, scalable, and robust solution for asset allocation, risk management, and dynamic rebalancing.
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery
Venue: arXiv cs.CV 2024
Year: 2024

This document presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building damage assessment and spatial alignment collected from small uncrewed aerial systems (sUAS) geospatial imagery. This dataset is motivated by the increasing use of sUAS in disaster response and the lack of previous work in utilizing high-resolution geospatial sUAS imagery for machine learning and computer vision models, the lack of alignment with operational use cases, and with hopes of enabling further investigations between sUAS and satellite imagery. The CRASAR-U-DRIODs dataset consists of fifty-two (52) orthomosaics from ten (10) federally declared disasters (Hurricane Ian, Hurricane Ida, Hurricane Harvey, Hurricane Idalia, Hurricane Laura, Hurricane Michael, Musset Bayou Fire, Mayfield Tornado, Kilauea Eruption, and Champlain Towers Collapse) spanning 67.98 square kilometers (26.245 square miles), containing 21,716 building polygons and damage labels, and 7,880 adjustment annotations. The imagery was tiled and presented in conjunction with overlaid building polygons to a pool of 130 annotators who provided human judgments of damage according to the Joint Damage Scale. These annotations were then reviewed via a two-stage review process in which building polygon damage labels were first reviewed individually and then again by committee. Additionally, the building polygons have been aligned spatially to precisely overlap with the imagery to enable more performant machine learning models to be trained. It appears that CRASAR-U-DRIODs is the largest labeled dataset of sUAS orthomosaic imagery.
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biases. It remains uncertain whether these biases impact the performance of LLMs for certain tasks. In this study, we investigate the political biases of LLMs within the stance classification task, specifically examining whether these models exhibit a tendency to more accurately classify politically-charged stances. Utilizing three datasets, seven LLMs, and four distinct prompting schemes, we analyze the performance of LLMs on politically oriented statements and targets. Our findings reveal a statistically significant difference in the performance of LLMs across various politically oriented stance classification tasks. Furthermore, we observe that this difference primarily manifests at the dataset level, with models and prompting schemes showing statistically similar performances across different stance classification datasets. Lastly, we observe that when there is greater ambiguity in the target the statement is directed towards, LLMs have poorer stance classification accuracy. Code & Dataset: http://doi.org/10.5281/zenodo.12938478
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://github.com/FangHeng/SAM-MIL.
System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation
Venue: arXiv cs.NI 2024
Year: 2024

Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3GPP. This paper presents a system-level simulation framework for NB-IoT networks to evaluate their performance. The system-level simulator is structured into four parts: initialization, pre-generation, main simulation loop, and post-processing. Additionally, three essential features are investigated to enhance coverage, support massive connections, and ensure low power consumption, respectively. Simulation results demonstrate that the cumulative distribution function curves of the signal-to-interference-and-noise ratio fully comply with industrial standards. Furthermore, the throughput performance explains how NB-IoT networks realize massive connections at the cost of data rate. This work highlights its practical utility and paves the way for developing NB-IoT networks.
Enhancing Agent Learning through World Dynamics Modeling
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.