OpenRaiser/Intern-Atlas huggingface dataset:
Error detection using pneumatic logic
Venue: arXiv cs.ET 2024
Year: 2024

Pneumatic systems are common in manufacturing, healthcare, transportation, robotics, and many other fields. Failures in these systems can have very serious consequences, particularly if they go undetected. In this work, we present an air-powered error detector device that can detect and respond to failures in pneumatically actuated systems. The device contains 21 monolithic membrane valves that act like transistors in a pneumatic logic "circuit" that uses vacuum to represent TRUE and atmospheric pressure as FALSE. Three pneumatic exclusive-OR (XOR) gates are used to calculate the parity bit corresponding to the values of several control bits. If the calculated value of the parity bit differs from the expected value, then an error (like a leak or a blocked air line) has been detected and the device outputs a pneumatic error signal which can in turn be used to alert a user, shut down the system, or take some other action. As a proof-of-concept, we used our pneumatic error detector to monitor the operation of a medical device, an intermittent pneumatic compression (IPC) device commonly used to prevent the formation of life-threatening blood clots in the wearer's legs. Experiments confirm that when the IPC device was damaged, the pneumatic error detector immediately recognized the error (a leak) and alerted the wearer using sound. By providing a simple and low-cost way to add fault detection to pneumatic actuation systems without using sensors, our pneumatic error detector can promote safety and reliability across the wide range of pneumatic systems.
Effect of recommending users and opinions on the network connectivity and idea generation process
Venue: arXiv cs.SI 2024
Year: 2024

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. This study investigates how recommendation systems influence the impact of personal behavioral traits on social network dynamics. It explores the interplay between homophily, users' openness to novel ideas, and recommendation-driven exposure to new opinions. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional concepts.
FPGA Technology Mapping Using Sketch-Guided Program Synthesis
Venue: arXiv cs.AR 2024
Year: 2024

FPGA technology mapping is the process of implementing a hardware design expressed in high-level HDL (hardware design language) code using the low-level, architecture-specific primitives of the target FPGA. As FPGAs become increasingly heterogeneous, achieving high performance requires hardware synthesis tools that better support mapping to complex, highly configurable primitives like digital signal processors (DSPs). Current tools support DSP mapping via handwritten special-case mapping rules, which are laborious to write, error-prone, and often overlook mapping opportunities. We introduce Lakeroad, a principled approach to technology mapping via sketch-guided program synthesis. Lakeroad leverages two techniques -- architecture-independent sketch templates and semantics extraction from HDL -- to provide extensible technology mapping with stronger correctness guarantees and higher coverage of mapping opportunities than state-of-the-art tools. Across representative microbenchmarks, Lakeroad produces 2--3.5$\times$ the number of optimal mappings compared to proprietary state-of-the-art tools and 6--44$\times$ the number of optimal mappings compared to popular open-source tools, while also providing correctness guarantees not given by any other tool.
GuReT: Distinguishing Guilt and Regret related Text
Venue: arXiv cs.CL 2024
Year: 2024

The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions subtle distinctions and interplay are often overlooked in computational models. This paper introduces a dataset tailored to dissect the relationship between guilt and regret and their unique textual markers, filling a notable gap in affective computing research. Our approach treats guilt and regret recognition as a binary classification task and employs three machine learning and six transformer-based deep learning techniques to benchmark the newly created dataset. The study further implements innovative reasoning methods like chain-of-thought and tree-of-thought to assess the models interpretive logic. The results indicate a clear performance edge for transformer-based models, achieving a 90.4% macro F1 score compared to the 85.3% scored by the best machine learning classifier, demonstrating their superior capability in distinguishing complex emotional states.
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Venue: arXiv cs.LG 2024
Year: 2024

Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point. In the era of big data, tasks involving multi-label classification (MLC) or ranking present significant and intricate challenges, capturing considerable attention in diverse domains. Inherent difficulties in MLC include dealing with high-dimensional data, addressing label correlations, and handling partial labels, for which conventional methods prove ineffective. Recent years have witnessed a notable increase in adopting deep learning (DL) techniques to address these challenges more effectively in MLC. Notably, there is a burgeoning effort to harness the robust learning capabilities of DL for improved modelling of label dependencies and other challenges in MLC. However, it is noteworthy that comprehensive studies specifically dedicated to DL for multi-label learning are limited. Thus, this survey aims to thoroughly review recent progress in DL for multi-label learning, along with a summary of open research problems in MLC. The review consolidates existing research efforts in DL for MLC,including deep neural networks, transformers, autoencoders, and convolutional and recurrent architectures. Finally, the study presents a comparative analysis of the existing methods to provide insightful observations and stimulate future research directions in this domain.
ONDA: ONline Database Architect
Venue: arXiv cs.DB 2024
Year: 2024

Database modeling is a key activity towards the fulfillment of storage requirements. Despite the availability of several database modeling tools for developers, these often come with associated costs, setup complexities, usability challenges, or dependency on specific operating systems. In this paper we present ONDA, a web-based tool developed at the University of Coimbra, that allows the creation of Entity-Relationship diagrams, visualization of physical models, and generation of SQL code for various database engines. ONDA is freely available at https://onda.dei.uc.pt and was created with the intention of supporting teaching activities at university-level database courses. At the time of writing, the tool being used by more than three hundred university students every academic year.
Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation
Venue: arXiv cs.CY 2024
Year: 2024

The pervasive spread of misinformation and disinformation poses a significant threat to society. Professional fact-checkers play a key role in addressing this threat, but the vast scale of the problem forces them to prioritize their limited resources. This prioritization may consider a range of factors, such as varying risks of harm posed to specific groups of people. In this work, we investigate potential implications of using a large language model (LLM) to facilitate such prioritization. Because fact-checking impacts a wide range of diverse segments of society, it is important that diverse views are represented in the claim prioritization process. This paper examines whether a LLM can reflect the views of various groups when assessing the harms of misinformation, focusing on gender as a primary variable. We pose two central questions: (1) To what extent do prompts with explicit gender references reflect gender differences in opinion in the United States on topics of social relevance? and (2) To what extent do gender-neutral prompts align with gendered viewpoints on those topics? To analyze these questions, we present the TopicMisinfo dataset, containing 160 fact-checked claims from diverse topics, supplemented by nearly 1600 human annotations with subjective perceptions and annotator demographics. Analyzing responses to gender-specific and neutral prompts, we find that GPT 3.5-Turbo reflects empirically observed gender differences in opinion but amplifies the extent of these differences. These findings illuminate AI's complex role in moderating online communication, with implications for fact-checkers, algorithm designers, and the use of crowd-workers as annotators. We also release the TopicMisinfo dataset to support continuing research in the community.
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces
Venue: arXiv cs.LG 2024
Year: 2024

Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic communication, typically leverages modern developments in artificial intelligence and machine learning to improve the efficiency and robustness of communication systems. However, a standard model for capturing and quantifying the details of "meaning" is lacking, with many leading approaches to semantic communication adopting a black-box framework with little understanding of what exactly the model is learning. One solution is to utilize the conceptual spaces framework, which models meaning explicitly in a geometric manner. Though prior work studying semantic communication with conceptual spaces has shown promising results, these previous attempts involve hand-crafting a conceptual space model, severely limiting the scalability and practicality of the approach. In this work, we develop a framework for learning a domain of a conceptual space model using only the raw data with high-level property labels. In experiments using the MNIST and CelebA datasets, we show that the domains learned using the framework maintain semantic similarity relations and possess interpretable dimensions.
Inter-instance Data Impacts in Business Processes: A Model-based Analysis
Venue: arXiv cs.SE 2024
Year: 2024

A business process model represents the expected behavior of a set of process instances (cases). The process instances may be executed in parallel and may affect each other through data or resources. In particular, changes in values of data shared by process instances may affect a set of process instances and require some operations in response. Such potential effects do not explicitly appear in the process model. This paper addresses possible impacts that may be affected through shared data across process instances and suggests how to analyze them at design time (when the actual process instances do not yet exist). The suggested method uses both a process model and a (relational) data model in order to identify potential inter-instance data impact sets. These sets may guide process users in tracking the impacts of data changes and supporting their handling at runtime. They can also assist process designers in exploring possible constraints over data. The applicability of the method was evaluated using three different realistic processes. Using a process expert, we further assessed the usefulness of the method, revealing some useful insights for coping with unexpected data-related changes suggested by our approach.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Venue: arXiv cs.CL 2024
Year: 2024

Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. In this paper, we propose Token-Level Prompt Decomposition (ToPro), which facilitates the prompt-based method for token-level sequence labeling tasks. The ToPro method decomposes an input sentence into single tokens and applies one prompt template to each token. Our experiments on multilingual NER and POS tagging datasets demonstrate that ToPro-based fine-tuning outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer, especially for languages that are typologically different from the source language English. Our method also attains state-of-the-art performance when employed with the mT5 model. Besides, our exploratory study in multilingual large language models shows that ToPro performs much better than the current in-context learning method. Overall, the performance improvements show that ToPro could potentially serve as a novel and simple benchmarking method for sequence labeling tasks.
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we present an exploration and assessment of employing a centralized deep Q-network (DQN) controller as a substitute for the prevalent use of PID controllers in the context of 6DOF swimming robots. Our primary focus centers on illustrating this transition with the specific case of underwater object tracking. DQN offers advantages such as data efficiency and off-policy learning, while remaining simpler to implement than other reinforcement learning methods. Given the absence of a dynamic model for our robot, we propose an RL agent to control this multi-input-multi-output (MIMO) system, where a centralized controller may offer more robust control than distinct PIDs. Our approach involves initially using classical controllers for safe exploration, then gradually shifting to DQN to take full control of the robot. We divide the underwater tracking task into vision and control modules. We use established methods for vision-based tracking and introduce a centralized DQN controller. By transmitting bounding box data from the vision module to the control module, we enable adaptation to various objects and effortless vision system replacement. Furthermore, dealing with low-dimensional data facilitates cost-effective online learning for the controller. Our experiments, conducted within a Unity-based simulator, validate the effectiveness of a centralized RL agent over separated PID controllers, showcasing the applicability of our framework for training the underwater RL agent and improved performance compared to traditional control methods. The code for both real and simulation implementations is at https://github.com/FARAZLOTFI/underwater-object-tracking.
FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War
Venue: arXiv cs.IR 2024
Year: 2024

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87\%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion. The dataset is available on Github\footnote{https://github.com/Gautamshahi/FakeClaim}
A New Approach to Harnessing Side Information in Multi-Server Private Information Retrieval
Venue: arXiv cs.IT 2024
Year: 2024

This paper presents new solutions for Private Information Retrieval (PIR) with side information. This problem is motivated by PIR settings in which a client has side information about the data held by the servers and would like to leverage this information in order to improve the download rate. The problem of PIR with side information has been the subject of several recent studies that presented achievability schemes as well as converses for both multi-server and single-server settings. However, the solutions for the multi-server settings adapted from the solutions for the single-server setting in a rather straightforward manner, relying on the concept of super-messages. Such solutions require an exponential degree of sub-packetization (in terms of the number of messages). This paper makes the following contributions. First, we revisit the PIR problem with side information and present a new approach to leverage side information in the context of PIR. The key idea of our approach is a randomized algorithm to determine the linear combinations of the sub-packets that need to be recovered from each server. In addition, our approach takes advantage of the fact that the identity of the side information messages does not need to be kept private, and, as a result, the information retrieval scheme does not need to be symmetric. Second, we present schemes for PIR with side information that achieve a higher rate than previously proposed solutions and require a significantly lower degree of sub-packetization (linear in the number of servers). Our scheme not only achieves the highest known download rate for the problem at hand but also invalidates a previously claimed converse bound on the maximum achievable download rate.
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Venue: arXiv cs.LG 2024
Year: 2024

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets requires significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. We also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.
Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication
Venue: arXiv cs.LG 2024
Year: 2024

Task-based behavioral biometric authentication of users interacting in virtual reality (VR) environments enables seamless continuous authentication by using only the motion trajectories of the person's body as a unique signature. Deep learning-based approaches for behavioral biometrics show high accuracy when using complete or near complete portions of the user trajectory, but show lower performance when using smaller segments from the start of the task. Thus, any systems designed with existing techniques are vulnerable while waiting for future segments of motion trajectories to become available. In this work, we present the first approach that predicts future user behavior using Transformer-based forecasting and using the forecasted trajectory to perform user authentication. Our work leverages the notion that given the current trajectory of a user in a task-based environment we can predict the future trajectory of the user as they are unlikely to dramatically shift their behavior since it would preclude the user from successfully completing their task goal. Using the publicly available 41-subject ball throwing dataset of Miller et al. we show improvement in user authentication when using forecasted data. When compared to no forecasting, our approach reduces the authentication equal error rate (EER) by an average of 23.85% and a maximum reduction of 36.14%.
Enabling BLV Developers with LLM-driven Code Debugging
Venue: arXiv cs.HC 2024
Year: 2024

BLVRUN is a command line shell script designed to offer developers within the BLV community a succinct and insightful overview of traceback errors. Its primary function involves parsing errors and utilizing a refined large language model to generate informative error summaries. In terms of performance, our model rivals that of well-known models like ChatGPT or AI-chatbot plug-ins tailored for specific Integrated Development Environments (IDEs). Importantly, BLV users can seamlessly integrate this tool into their existing development workflows, eliminating the need for any modifications or adaptations to facilitate debugging tasks.
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
Venue: arXiv cs.LG 2024
Year: 2024

The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models. These models represent a significant breakthrough, overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts. This study explores the evolution of these advanced artificial intelligence forecast models, and based on the identified commonalities, proposes the "Three Large Rules" to measure their development. We discuss the potential of artificial intelligence in revolutionizing numerical weather prediction, and briefly outlining the underlying reasons for its great potential. While acknowledging the high accuracy, computational efficiency, and ease of deployment of large artificial intelligence forecast models, we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models. We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models. Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts. Additionally, we illustrate how forecasters can adapt and leverage the advanced artificial intelligence model through an example by building a large artificial intelligence model for global ocean wave forecast.