OpenRaiser/Intern-Atlas huggingface dataset:
Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real Data
Venue: arXiv cs.CL 2024
Year: 2024

Despite significant advances in quantum computing across various domains, research on applying quantum approaches to language compositionality - such as modeling linguistic structures and interactions - remains limited. This gap extends to the integration of quantum language data with real-world data from sources like images, video, and audio. This thesis explores how quantum computational methods can enhance the compositional modeling of language through multimodal data integration. Specifically, it advances Multimodal Quantum Natural Language Processing (MQNLP) by applying the Lambeq toolkit to conduct a comparative analysis of four compositional models and evaluate their influence on image-text classification tasks. Results indicate that syntax-based models, particularly DisCoCat and TreeReader, excel in effectively capturing grammatical structures, while bag-of-words and sequential models struggle due to limited syntactic awareness. These findings underscore the potential of quantum methods to enhance language modeling and drive breakthroughs as quantum technology evolves.
The Impact of Quantum-Safe Cryptography (QSC) on Website Response
Venue: arXiv cs.CR 2024
Year: 2024

Modern web traffic relies on 2048-bit RSA encryption to secure our data in transit. Rapid advances in Quantum Computing pose a grave challenge by allowing hackers to break this encryption in hours. In August of 2024, the National Institute of Standards and Technology published Quantum-Safe Cryptography (QSC) standards, including CRYSTALS-Kyber for general encryption and CRYSTALS-Dilithium, FALCON, and SPHINCS+ for digital signatures. Despite this proactive approach, the slow adoption of encryption protocols remains a concern, leaving a significant portion of data vulnerable to interception. In this context, this study aims to evaluate the impact of NIST's Quantum-Resistant Cryptographic Algorithms on website response times, particularly focusing on SSL handshake time and total download time under varying network conditions. By assessing the performance of these algorithms, this research seeks to provide empirical evidence and a reusable framework for validating the efficacy of QSC in real-world scenarios. It was found that the QSC algorithms outperformed the classical algorithm under normal and congested network conditions. There was also found to be an improvement in the total download time for larger file sizes, and a better performance by QSC under higher latency and packet loss conditions. Therefore, this study recommends that websites switch to QSC when the standards are ratified. These insights are crucial for accelerating the adoption of QSC and ensuring the security of data in the face of quantum computing threats.
Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic value alignment of human-sourced and LLM-sourced textual data.
Selecting Between BERT and GPT for Text Classification in Political Science Research
Venue: arXiv cs.CL 2024
Year: 2024

Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across various classification tasks, differing in the number of classes and complexity, to evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. Our findings indicate that while zero-shot and few-shot learning with GPT models provide reasonable performance and are well-suited for early-stage research exploration, they generally fall short - or, at best, match - the performance of BERT fine-tuning, particularly as the training set reaches a substantial size (e.g., 1,000 samples). We conclude by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. Our results are particularly relevant for those engaged in quantitative text analysis in low-resource settings or with limited labeled data.
FMEA Builder: Expert Guided Text Generation for Equipment Maintenance
Venue: arXiv cs.CL 2024
Year: 2024

Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment, the ways it may fail and the consequences thereof. Our system uses large language models to enable fast and expert supervised generation of new FMEA documents. Empirical analysis shows that foundation models can correctly generate over half of an FMEA's key content. Results from polling audiences of reliability professionals show a positive outlook on using generative AI to create these documents for critical assets.
MissionGPT: Mission Planner for Mobile Robot based on Robotics Transformer Model
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a novel approach to building mission planners based on neural networks with Transformer architecture and Large Language Models (LLMs). This approach demonstrates the possibility of setting a task for a mobile robot and its successful execution without the use of perception algorithms, based only on the data coming from the camera. In this work, a success rate of more than 50\% was obtained for one of the basic actions for mobile robots. The proposed approach is of practical importance in the field of warehouse logistics robots, as in the future it may allow to eliminate the use of markings, LiDARs, beacons and other tools for robot orientation in space. In conclusion, this approach can be scaled for any type of robot and for any number of robots.
Haptic Information Feedback Given to Handles in Guide Dog Training
Venue: arXiv cs.HC 2024
Year: 2024

In guide dog training, trainers use haptic information transmitted through the handle of the harness worn by the guide dog to understand the dog's state. They then apply appropriate force to the handle to train the dog to make correct judgments. This tactile experience can only be felt between the dog and the trainer, making it challenging to communicate the amount of force applied to others quantitatively. To solve this problem, this study proposes a method for real-time visualization of the force exerted on the handle and quantification of the handle movement through image processing, which can be applied to actual guide dog training.
STEM: Soft Tactile Electromagnetic Actuator for Virtual Environment Interactions
Venue: arXiv cs.HC 2024
Year: 2024

The research aims to expand tactile feedback beyond vibrations to various modes of stimuli, such as indentation, vibration, among others. By incorporating soft material into the design of a novel tactile actuator, we can achieve multi-modality and enhance the device's wearability, which encompasses compliance, safety, and portability. The proposed tactile device can elevate the presence and immersion in VR by enabling diverse haptic feedback such as, force indentation, vibration and other arbitrary force outputs. This approach enables the rendering of haptic interactions with virtual objects, such as grasping of aa 3D virtual object to feel its stiffness - action that was difficult to achieve using widely adopted vibrotactile motors.
The impact of mobility, beam sweeping and smart jammers on security vulnerabilities of 5G cells
Venue: arXiv cs.CR 2024
Year: 2024

The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm in our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.
Quality Assurance Practices in Agile Methodology
Venue: arXiv cs.SE 2024
Year: 2024

The complexity of software is increasing day by day the requirement and need for a verity of softwareproducts increases, this necessitates the provision of a strong tool that will make a balance betweenproduction and quality. The practice of applying software metrics to the development process and to asoftware product is a critical task and crucial enough that requires study and discipline and whichbrings knowledge of the status of the process and/or product of software in regards to the goals toachieve, this discipline is known as quality assurance which is the key factor behind the success ofevery software engineering project, the quality assurance activities are what result in the qualitativeproduct as well as the process in both conventional software development methodology and agilemethodology. However, agile methodology is now becoming one of the dominant method adopted bymost of the software industries because it allows developing of software with very limited requirementand supports rapid changes in the requirement, the method may produce the product very fast but wemight not guarantee the quality of the product unless we apply the SQA activities to the process. Thisresearch paper aimed to study the quality assurance activities practice in agile software developmentmethodology, investigate the common problems and key drivers of quality in agile, and propose asolution to improve the practice of SQA in agile methodology by analyzing the parameters that assurequality in agile software.
A Vibrotactile Belt for Interpersonal Synchronization of Breath
Venue: arXiv cs.HC 2024
Year: 2024

This paper introduces a vibrotactile belt for interpersonal synchronization of breath. It can synchronize the breathing tempo of two people by transferring breathing rhythm of one user to vibration signals of another belt, where the depth of breathing is represented by the intensity of vibration. This provides a novel way of emotional connect between people. Meanwhile, this breath-sharing device may also be combined with smart devices in the future to form a one-to-many, many-to-many internet of breath, which has promising application prospects in healthcare, sports breathing guidance and other scenarios.
Inclusion in Assistive Haircare Robotics: Practical and Ethical Considerations in Hair Manipulation
Venue: arXiv cs.RO 2024
Year: 2024

Robot haircare systems could provide a controlled and personalized environment that is respectful of an individual's sensitivities and may offer a comfortable experience. We argue that because of hair and hairstyles' often unique importance in defining and expressing an individual's identity, we should approach the development of assistive robot haircare systems carefully while considering various practical and ethical concerns and risks. In this work, we specifically list and discuss the consideration of hair type, expression of the individual's preferred identity, cost accessibility of the system, culturally-aware robot strategies, and the associated societal risks. Finally, we discuss the planned studies that will allow us to better understand and address the concerns and considerations we outlined in this work through interactions with both haircare experts and end-users. Through these practical and ethical considerations, this work seeks to systematically organize and provide guidance for the development of inclusive and ethical robot haircare systems.
Relaxed or Tense? Mutual Biosignal Transmission with Heartbeat Vibrations during Online Gameplay
Venue: arXiv cs.HC 2024
Year: 2024

Esports offers a platform for players to engage in competitive and cooperative gaming with others remotely via the Internet. Despite these opportunities for social interaction, many players may still experience loneliness while playing online games. This study aims to enhance the social presence of partner players during online gameplay. The demonstration system, designed for 1-on-1 online competitive games, mutually transmits the partner's biosignals, through heartbeat-like vibrotactile stimuli. The system generates vibrotactile signals that represent two-dimensional emotions, arousal and valence, based on biosignals such as heart rate and electrodermal activity.
Break Times: Virtual Reality Art Therapy
Venue: arXiv cs.HC 2024
Year: 2024

This paper presents a Virtual Reality (VR) art therapy known as "Break Times" which aims to enhance students' mental well-being and foster creative expression. The proposed "Break Times" application mimics the art therapy sessions in the VR environment design. Pilot user acceptance test with 10 participants showed a notable reduction in stress levels, with 50% reporting normal stress levels post-intervention, compared to 20% pre-intervention. Participants praised the "Break Times" therapy's functionality and engagement features and suggested improvements such as saving creations, incorporating 3D painting, and expanding the artmaking scene variety. The study highlights that VR art therapy has potential as an effective tool for stress management, emphasizing the need for continued refinement to maximize its therapeutic benefits.
Presentation of tracing sensation with different roughness by using disk with uneven surface
Venue: arXiv cs.HC 2024
Year: 2024

Tracing sensation plays an important role in discriminating friction and texture of objects. We have been studying a method of presenting the tracing sensation by contacting the center of a rotating disk with the fingertip. Although this method can reduce the size and complexity of the presentation device compared to conventional methods, it has the problem of fixed sensation. In this study, we examined a method to dynamically modulate the roughness sensation by devising the surface shape of the disk. We designed two types of disks: slit disks and milling disks. Experiments revealed that the perceived roughness of these disks can be changed by altering the rotation speed and direction.
Wearable Haptic Device to Render 360-degree Torque Feedback on the Wrist
Venue: arXiv cs.HC 2024
Year: 2024

Haptic feedback increases the realism of virtual environments. This paper proposes a wearable haptic device that renders torque feedback to the user's wrist from any angle. The device comprises a control part and a handle part. The control part consists of three DC gear motors and a microcontroller, while the handle part securely holds the Oculus Quest 2 right controller. The control part manages string tension to deliver the sensation of torque feedback during interactions with virtual tools or objects. The three points of the handle part are connected to the three motors of the control part via strings, which pull the handle part to render precise 360-degree (yaw and pitch) torque feedback to the user's wrist. Finally, to show the effectiveness of the proposed device, two VR demos were implemented- Shooting Game and Shielding Experience.
A self-healing tactile sensor using an optical waveguide
Venue: arXiv cs.HC 2024
Year: 2024

We propose an optical tactile sensor using self-healing materials. The proposed tactile sensor consists of a structure that includes a diode, a phototransistor, and an optical waveguide made from self-healing materials. This design offers the advantage of being less susceptible to electromagnetic noise compared to traditional tactile sensors based on electrical detection principles. The sensor estimates the applied force by detecting changes in the total internal reflection caused by deformation due to contact force. In this study, we first established a fabrication method for the optical waveguide-based tactile sensor using self-healing materials. Subsequently, we measured the sensor output when a static load was applied to the fabricated tactile sensor and evaluated its characteristics. The results confirmed that the sensor output decreases in response to the applied load.
Investigation of Tactile Texture Simulation on Online Shopping Experience
Venue: arXiv cs.HC 2024
Year: 2024

With safety measures towards the current Covid-19 pandemic, many retails clothing stores have restricted on-site fittings and shifted their business online. Inability to touch on product evaluations shows an apparent limitation as compared to retail shopping especially when the object's material information is crucial like clothing. Haptic technologies show potential of bridging the gap between online shops and the shoppers by providing a sense of touch, yet little research has been done especially on the effect of the simulation of tactile texture on the shopping experience. In this study, we modified a mock-up e-commerce website by adding clothing products and enabling a mid-air haptic interface with Ultrahaptics Evaluation Kit (UHEV1). We developed texture sensations using Time Point Streaming (TSP) modulation for clothing products with different texture materials and a user study was carried out to investigate the tactile texture sensation on shoppers' experience in evaluating online products. Our results show that tactile texture sensation using multipoint mid-air haptic feedback improves online shopper's satisfaction on the product browsing experience. This study contributes to the improvement of general lifestyle of the society in terms of e-commerce experience and could expand its application to impact different sectors like education and different communities including the visually impaired.
Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations
Venue: arXiv cs.LG 2024
Year: 2024

Recent progress on large language models (LLMs) has enabled dialogue agents to generate highly naturalistic and plausible text. However, current LLM language generation focuses on responding accurately to questions and requests with a single effective response. In reality, many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. Accounting for how an agent can effectively steer a conversation is a crucial ability in many dialogue tasks, from healthcare to preference elicitation. Existing methods for fine-tuning dialogue agents to accomplish such tasks would rely on curating some amount of expert data. However, doing so often requires understanding the underlying cognitive processes of the conversational partner, which is a skill neither humans nor LLMs trained on human data can reliably do. Our key insight is that while LLMs may not be adept at identifying effective strategies for steering conversations a priori, or in the middle of an ongoing conversation, they can do so post-hoc, or in hindsight, after seeing how their conversational partner responds. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. We apply our approach to two domains that require understanding human mental state, intelligent interaction, and persuasion: mental health support, and soliciting charitable donations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
STAND-Guard: A Small Task-Adaptive Content Moderation Model
Venue: arXiv cs.CL 2024
Year: 2024

Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models. Content moderation contains various tasks, each with its unique requirements tailored to specific scenarios. Therefore, it is crucial to develop a model that can be easily adapted to novel or customized content moderation tasks accurately without extensive model tuning. This paper presents STAND-GUARD, a Small Task-Adaptive coNtent moDeration model. The basic motivation is: by performing instruct tuning on various content moderation tasks, we can unleash the power of small language models (SLMs) on unseen (out-of-distribution) content moderation tasks. We also carefully study the effects of training tasks and model size on the efficacy of cross-task fine-tuning mechanism. Experiments demonstrate STAND-Guard is comparable to GPT-3.5-Turbo across over 40 public datasets, as well as proprietary datasets derived from real-world business scenarios. Remarkably, STAND-Guard achieved nearly equivalent results to GPT-4-Turbo on unseen English binary classification tasks
Anticipatory Understanding of Resilient Agriculture to Climate
Venue: arXiv cs.CV 2024
Year: 2024

With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.