OpenRaiser/Intern-Atlas huggingface dataset:
SSD Forensic: Evidence Generation And Forensic Research On Solid State Drives Using Trim Analysis
Venue: arXiv cs.CR 2023
Year: 2023

Traditional hard drives consisting of spinning magnetic media platters are becoming things of the past as with the emergence of the latest digital technologies and electronic equipment, the demand for faster, lighter, and more reliable alternate storage solutions is imperative. To attain these requirements, flash storage technologies like Solid State Drive (SSD) has overtaken traditional hard disk drives. In a forensic analysis of flash storage devices, forensic investigators are facing severe challenges for the reason that the sovereign behavior of solid-state storage media does not look favorable compared to traditional storage media devices. Wear Leveling, a fundamental mechanism in Solid State Drive (SSD), plays a severe challenge that most often destroys forensic evidence in many cases. It makes it complicated for forensic investigators to recover the necessary evidence. Persistence of deleted data in flash storage media depends on various factors like the Garbage Collection process, TRIM command, flash media type, manufacturer, capacity, file system, type of file saved, and the Operating System, etc. In view of this, extensive experiments conducted to identify the probability of data recovery and carving. Analyzed effects of Wear Leveling and Garbage Collection processes in Solid State Drive (SSD) of different manufacturers, having the same storage capacities and with a different type of files utilized. In conclusion, experimental findings established the fact that Wear Leveling in solid-state media can obfuscate digital evidence, and a conventional assumption regarding the behavior of storage media is no more valid. Moreover, data persistency also depends on the manufacturers, time-lapse of forensic analysis after data deletion, type of files, and size of files stored in Solid State Drives (SSD).
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown
Venue: arXiv cs.CR 2023
Year: 2023

The disruptive application of ChatGPT (GPT-3.5, GPT-4) to a variety of domains has become a topic of much discussion in the scientific community and society at large. Large Language Models (LLMs), e.g., BERT, Bard, Generative Pre-trained Transformers (GPTs), LLaMA, etc., have the ability to take instructions, or prompts, from users and generate answers and solutions based on very large volumes of text-based training data. This paper assesses the impact and potential impact of ChatGPT on the field of digital forensics, specifically looking at its latest pre-trained LLM, GPT-4. A series of experiments are conducted to assess its capability across several digital forensic use cases including artefact understanding, evidence searching, code generation, anomaly detection, incident response, and education. Across these topics, its strengths and risks are outlined and a number of general conclusions are drawn. Overall this paper concludes that while there are some potential low-risk applications of ChatGPT within digital forensics, many are either unsuitable at present, since the evidence would need to be uploaded to the service, or they require sufficient knowledge of the topic being asked of the tool to identify incorrect assumptions, inaccuracies, and mistakes. However, to an appropriately knowledgeable user, it could act as a useful supporting tool in some circumstances.
Combining Vision and EMG-Based Hand Tracking for Extended Reality Musical Instruments
Venue: arXiv cs.CV 2023
Year: 2023

Hand tracking is a critical component of natural user interactions in extended reality (XR) environments, including extended reality musical instruments (XRMIs). However, self-occlusion remains a significant challenge for vision-based hand tracking systems, leading to inaccurate results and degraded user experiences. In this paper, we propose a multimodal hand tracking system that combines vision-based hand tracking with surface electromyography (sEMG) data for finger joint angle estimation. We validate the effectiveness of our system through a series of hand pose tasks designed to cover a wide range of gestures, including those prone to self-occlusion. By comparing the performance of our multimodal system to a baseline vision-based tracking method, we demonstrate that our multimodal approach significantly improves tracking accuracy for several finger joints prone to self-occlusion. These findings suggest that our system has the potential to enhance XR experiences by providing more accurate and robust hand tracking, even in the presence of self-occlusion.
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Venue: arXiv cs.CR 2023
Year: 2023

Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of prominent deep NILM baselines to adversarial attacks, which have proven to be a significant threat in domains such as computer vision and speech recognition. Adversarial attacks entail the introduction of imperceptible noise into the input data with the aim of misleading the neural network into generating erroneous outputs. We investigate the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to perturb the input sequences fed into two commonly employed CNN-based NILM baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models. Our findings provide compelling evidence for the vulnerability of these models, particularly the S2P model which exhibits an average decline of 20\% in the F1-score even with small amounts of noise. Such weakness has the potential to generate profound implications for energy management systems in residential and industrial sectors reliant on NILM models.
Pandemic, Hybrid Teaching & Stress: Examining Indian Teachers' Sociotechnical Support Practices in Low-income Schools
Venue: arXiv cs.HC 2023
Year: 2023

Support plays a vital role in the teaching profession. A good support system can empower teachers to regulate their emotions and effectively manage stress while working in isolation. The COVID-19 pandemic has ushered in a hybrid form of education, necessitating the acquisition of new skills by teachers and compelling them to adapt to remote teaching. This new development further amplifies the sense of isolation prevalent amongst the teaching community. Against this backdrop, our study investigates the availability of sociotechnical support infrastructures for teachers in low-income schools while also looking into the support practices embraced by this class of teachers following the pandemic. Through 28 qualitative interviews involving teachers, management and personnel from support organizations, we demonstrate how teachers have largely taken the initiative to establish their own informal support networks in the absence of formal support infrastructures. Smartphones have significantly augmented these support practices, serving as both a valuable source of support as well as a medium for facilitating support practices. However, in comparison to other forms of support received from these sources, the availability of emotion-focused support for teachers have proven to be inadequate, creating imbalances in their support seeking practices. Our paper provides different contextual ways to reduce these imbalances and improve the occupational well-being of teachers.
Causal Laws and Multi-Valued Fluents
Venue: arXiv cs.AI 2023
Year: 2023

This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz. The only fluents directly representable in language C+ are truth-valued fluents, which is often inconvenient. We show that both causal logic and language C can be extended to allow values from arbitrary nonempty sets. Our extension of language C, called C+, also makes it possible to describe actions in terms of their attributes, which is important from the perspective of elaboration tolerance. We describe an embedding of C+ in causal theories with multi-valued constants, relate C+ to Pednault's action language ADL, and show how multi-valued constants can be eliminated in favor of Boolean constants.
Study on the Impacts of Hazardous Behaviors on Autonomous Vehicle Collision Rates Based on Humanoid Scenario Generation in CARLA
Venue: arXiv cs.RO 2023
Year: 2023

Testing of function safety and Safety Of The Intended Functionality (SOTIF) is important for autonomous vehicles (AVs). It is hard to test the AV's hazard response in the real world because it would involve hazards to passengers and other road users. This paper studied on virtual testing of AV on the CARLA platform and proposed a Humanoid Scenario Generation (HSG) scheme to investigate the impacts of hazardous behaviors on AV collision rates. The HSG scheme breakthrough the current limitation on the rarity and reproducibility of real scenes. By accurately capturing five prominent human driver behaviors that directly contribute to vehicle collisions in the real world, the methodology significantly enhances the realism and diversity of the simulation, as evidenced by collision rate statistics across various traffic scenarios. Thus, the modular framework allows for customization, and its seamless integration within the CARLA platform ensures compatibility with existing tools. Ultimately, the comparison results demonstrate that all vehicles that exhibited hazardous behaviors followed the predefined random speed distribution and the effectiveness of the HSG was validated by the distinct characteristics displayed by these behaviors.
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Venue: arXiv cs.AI 2023
Year: 2023

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.
Automated Action Model Acquisition from Narrative Texts
Venue: arXiv cs.CL 2023
Year: 2023

Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
Venue: arXiv cs.CV 2023
Year: 2023

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.
Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges
Venue: arXiv cs.HC 2023
Year: 2023

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.
Interpretable Timbre Synthesis using Variational Autoencoders Regularized on Timbre Descriptors
Venue: arXiv cs.SD 2023
Year: 2023

Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, the interpretability of these latent spaces in terms of human perception is often limited. To address this limitation and enhance the control over timbre generation, we propose a regularized VAE-based latent space that incorporates timbre descriptors. Moreover, we suggest a more concise representation of sound by utilizing its harmonic content, in order to minimize the dimensionality of the latent space.
Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G
Venue: arXiv cs.NI 2023
Year: 2023

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardization and pre-standardization landscape.
Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A User-Study
Venue: arXiv cs.CV 2023
Year: 2023

Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights investigators, that explores the capability of Artificial Intelligence (AI)-based image filters to potentially mitigate the emotional impact of viewing such disturbing content. We tested five different filter styles, both traditional (Blurring and Partial Blurring) and AI-based (Drawing, Colored Drawing, and Painting), and measured their effectiveness in terms of conveying image information while reducing emotional distress. Our findings suggest that the AI-based Drawing style filter demonstrates the best performance, offering a promising solution for reducing negative feelings (-30.38%) while preserving the interpretability of the image (97.19%). Despite the requirement for many professionals to eventually inspect the original images, participants suggested potential strategies for integrating AI filters into their workflow, such as using AI filters as an initial, preparatory step before viewing the original image. Overall, this paper contributes to the development of a more ethically considerate and effective visual environment for professionals routinely engaging with potentially disturbing imagery.
Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
Venue: arXiv cs.SI 2023
Year: 2023

As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like profile completion, social behavior decision-making, and social content generation. However, there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like social network populated by LLMs-driven social bots and embarked on an exploratory study. Our findings indicate that: (1) LLMs-driven social bots possess enhanced individual-level camouflage while exhibiting certain collective characteristics; (2) these bots have the ability to exert influence on online communities through toxic behaviors; (3) existing detection methods are applicable to the activity environment of LLMs-driven social bots but may be subject to certain limitations in effectiveness. Moreover, we have organized the data collected in our study into the Masquerade-23 dataset, which we have publicly released, thus addressing the data void in the subfield of LLMs-driven social bots behavior datasets. Our research outcomes provide primary insights for the research and governance of LLMs-driven social bots within the research community.
NFT-Based Blockchain-Oriented Security Framework for Metaverse Applications
Venue: arXiv cs.CR 2023
Year: 2023

The Metaverse is rapidly evolving, bringing us closer to its imminent reality. However, the widespread adoption of this new automated technology poses significant research challenges in terms of authenticity, integrity, interoperability, and efficiency. These challenges originate from the core technologies underlying the Metaverse and are exacerbated by its complex nature. As a solution to these challenges, this paper presents a novel framework based on Non-Fungible Tokens (NFTs). The framework employs the Proof-of-Stake consensus algorithm, a blockchain-based technology, for data transaction, validation, and resource management. PoS efficiently consume energy and provide a streamlined validation approach instead of resource-intensive mining. This ability makes PoS an ideal candidate for Metaverse applications. By combining NFTs for user authentication and PoS for data integrity, enhanced transaction throughput, and improved scalability, the proposed blockchain mechanism demonstrates noteworthy advantages. Through security analysis, experimental and simulation results, it is established that the NFT-based approach coupled with the PoS algorithm is secure and efficient for Metaverse applications.
Estudio de la Experiencia de Usuario mediante un Sistema de Dashboards de Análisis de Aprendizaje Multimodal
Venue: arXiv cs.HC 2023
Year: 2023

In the article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in user experiences (UX) in a Learning Analytics (LA) system in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures and visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGGE tool. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the learning analytic system in order to improve the system and the user experience of the learners. -- En este artículo, presentamos M2LADS, un sistema que permite la integración y visualización de datos multimodales en forma de Dashboards Web. Estos datos provienen de sesiones de experiencia de usuario en un sistema de Learning Analytics (LA) llevadas a cabo por estudiantes de MOOCs. Los datos multimodales incluyen señales biométricas y de comportamiento monitorizados por la plataforma edBB, como electroencefalogramas (EEG) de 5 canales, frecuencia cardíaca, atención visual, videos en el espectro visible y NIR, entre otros. Además, se incluyen datos de interacción de los estudiantes con el sistema de LA a través de la herramienta LOGGE. Toda esta información proporciona una comprensión completa de la experiencia del usuario al utilizar el sistema de LA, lo que ha permitido tanto mejorar el sistema LA como la experiencia de aprendizaje de los estudiantes de MOOCs.