OpenRaiser/Intern-Atlas huggingface dataset:
Verification and Validation of the Stakeholder Tool for Assessing Radioactive Transportation (START)
Venue: arXiv cs.CY 2023
Year: 2023

The U.S. Department of Energy (DOE) Office of Integrated Waste Management is planning for the eventual transportation, storage, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and DOE sites. The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based, geospatial decision-support tool developed for evaluating routing options and other aspects of transporting SNF and HLW, covering rail, truck, barge, and intermodal infrastructure and operations in the continental United States. The verification and validation (V&V) process is intended to independently assess START to provide confidence in the ability of START to accurately provide intended results. The V&V process checks the START tool using a variety of methods, ranging from independent hand calculations to comparison of START performance and results to those of other codes. The V&V activity was conducted independently from the START development team with opportunities to provide feedback and collaborate throughout the process. The V&V analyzed attributes of transportation routes produced by START, including route distance and both population and population density captured within buffer zones around routes. Population in the buffer zone, population density in the buffer zone, and route distance were all identified as crucial outputs of the START code and were subject to V&V tasks. Some of the improvements identified through the V&V process were standardizing the underlying population data in START, changing the projection of the population raster data, and changes to the methodology used for population density to improve its applicability for expected users. This collaboration also led to suggested improvements to some of the underlying shape file segments within START.
Friend or Foe? Exploring the Implications of Large Language Models on the Science System
Venue: arXiv cs.CY 2023
Year: 2023

The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 experts specialising in research and AI. The study focused on applications and limitations of LLMs, their effects on the science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action.
A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development
Venue: arXiv cs.NE 2023
Year: 2023

Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone. In addition, in terms of sustainability, it is essential to find methods to reduce the energy consumption of mobile devices since the extensive use of billions of smartphones worldwide significantly impacts the environment. Despite the existence of several energy-efficient programming practices in Android, the leading mobile ecosystem, machine learning-based energy prediction algorithms for mobile App development have yet to be reported. Therefore, this paper proposes a histogram-based gradient boosting classification machine (HGBC), boosted by a metaheuristic approach, for energy prediction in mobile App development. Our metaheuristic approach is responsible for two issues. First, it finds redundant and irrelevant features without any noticeable change in performance. Second, it performs a hyper-parameter tuning for the HGBC algorithm. Since our proposed metaheuristic approach is algorithm-independent, we selected 12 algorithms for the search strategy to find the optimal search algorithm. Our finding shows that a success-history-based parameter adaption for differential evolution with linear population size (L-SHADE) offers the best performance. It can improve performance and decrease the number of features effectively. Our extensive set of experiments clearly shows that our proposed approach can provide significant results for energy consumption prediction.
The Evolution theory of Learning: From Natural Selection to Reinforcement Learning
Venue: arXiv cs.NE 2023
Year: 2023

Evolution is a fundamental process that shapes the biological world we inhabit, and reinforcement learning is a powerful tool used in artificial intelligence to develop intelligent agents that learn from their environment. In recent years, researchers have explored the connections between these two seemingly distinct fields, and have found compelling evidence that they are more closely related than previously thought. This paper examines these connections and their implications, highlighting the potential for reinforcement learning principles to enhance our understanding of evolution and the role of feedback in evolutionary systems.
Rewriting the Script: Adapting Text Instructions for Voice Interaction
Venue: arXiv cs.HC 2023
Year: 2023

Voice assistants have sharply risen in popularity in recent years, but their use has been limited mostly to simple applications like music, hands-free search, or control of internet-of-things devices. What would it take for voice assistants to guide people through more complex tasks? In our work, we study the limitations of the dominant approach voice assistants take to complex task guidance: reading aloud written instructions. Using recipes as an example, we observe twelve participants cook at home with a state-of-the-art voice assistant. We learn that the current approach leads to nine challenges, including obscuring the bigger picture, overwhelming users with too much information, and failing to communicate affordances. Instructions delivered by a voice assistant are especially difficult because they cannot be skimmed as easily as written instructions. Alexa in particular did not surface crucial details to the user or answer questions well. We draw on our observations to propose eight ways in which voice assistants can ``rewrite the script'' -- summarizing, signposting, splitting, elaborating, volunteering, reordering, redistributing, and visualizing -- to transform written sources into forms that are readily communicated through spoken conversation. We conclude with a vision of how modern advancements in natural language processing can be leveraged for intelligent agents to guide users effectively through complex tasks.
Unlocking Insights into Business Trajectories with Transformer-based Spatio-temporal Data Analysis
Venue: arXiv cs.IR 2023
Year: 2023

The world of business is constantly evolving and staying ahead of the curve requires a deep understanding of market trends and performance. This article addresses this requirement by modeling business trajectories using news articles data.
Document Layout Annotation: Database and Benchmark in the Domain of Public Affairs
Venue: arXiv cs.IR 2023
Year: 2023

Every day, thousands of digital documents are generated with useful information for companies, public organizations, and citizens. Given the impossibility of processing them manually, the automatic processing of these documents is becoming increasingly necessary in certain sectors. However, this task remains challenging, since in most cases a text-only based parsing is not enough to fully understand the information presented through different components of varying significance. In this regard, Document Layout Analysis (DLA) has been an interesting research field for many years, which aims to detect and classify the basic components of a document. In this work, we used a procedure to semi-automatically annotate digital documents with different layout labels, including 4 basic layout blocks and 4 text categories. We apply this procedure to collect a novel database for DLA in the public affairs domain, using a set of 24 data sources from the Spanish Administration. The database comprises 37.9K documents with more than 441K document pages, and more than 8M labels associated to 8 layout block units. The results of our experiments validate the proposed text labeling procedure with accuracy up to 99%.
Measuring IT Carbon Footprint: What is the Current Status Actually?
Venue: arXiv cs.SE 2023
Year: 2023

Despite the new Corporate Sustainability Reporting Directive from the European Union, which presses large enterprises to be more transparent about their GHG emissions, and though large technology- or advisory firms might peddle otherwise, there are plenty of challenges ahead when it comes to measuring GHG emissions from IT activities in the first place. This paper categories those challenges into 4 categories, and explains the current status, shortcomings and potential future research directions. These categories are: measuring software energy consumption, server overhead energy consumption, Energy Mix and emissions from embodied carbon. Next to that, various non-profit and open-source initiatives are introduced as well as a mathematical framework, based on CPU consumption, that can act as a rule-of-thumb for quick and effortless assessments.
Generate to Understand for Representation
Venue: arXiv cs.CL 2023
Year: 2023

In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks. Traditionally, these models are pretrained on custom domain corpora and finetuned for specific tasks, resulting in high costs related to GPU usage and labor. Unfortunately, recent trends in language modeling have shifted towards enhancing performance through scaling, further exacerbating the associated costs. Introducing GUR: a pretraining framework that combines language modeling and contrastive learning objectives in a single training step. We select similar text pairs based on their Longest Common Substring (LCS) from raw unlabeled documents and train the model using masked language modeling and unsupervised contrastive learning. The resulting model, GUR, achieves impressive results without any labeled training data, outperforming all other pretrained baselines as a retriever at the recall benchmark in a zero-shot setting. Additionally, GUR maintains its language modeling ability, as demonstrated in our ablation experiment. Our code is available at \url{https://github.com/laohur/GUR}.
AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
Venue: arXiv cs.CE 2023
Year: 2023

Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where electricity prices become more volatile and less predictable using classical approaches. The Locational Marginal Pricing (LMP) pricing mechanism is used in many modern power markets, where the traditional approach utilizes optimal power flow (OPF) solvers. However, for large electricity grids this process becomes prohibitively time-consuming and computationally intensive. Machine learning (ML) based predictions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. This study evaluates the performance of popular machine learning and deep learning models in predicting LMP on multiple electricity grids. The accuracy and robustness of these models in predicting LMP is assessed considering multiple scenarios. The results show that ML models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6\% error rate, highlighting the potential of ML models in LMP prediction for large-scale power models with the assistance of hardware infrastructure like multi-core CPUs and GPUs in modern HPC clusters.
DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding
Venue: arXiv cs.CV 2023
Year: 2023

The human brain possesses remarkable abilities in visual processing, including image recognition and scene summarization. Efforts have been made to understand the cognitive capacities of the visual brain, but a comprehensive understanding of the underlying mechanisms still needs to be discovered. Advancements in brain decoding techniques have led to sophisticated approaches like fMRI-to-Image reconstruction, which has implications for cognitive neuroscience and medical imaging. However, challenges persist in fMRI-to-image reconstruction, such as incorporating global context and contextual information. In this article, we propose fMRI captioning, where captions are generated based on fMRI data to gain insight into the neural correlates of visual perception. This research presents DreamCatcher, a novel framework for fMRI captioning. DreamCatcher consists of the Representation Space Encoder (RSE) and the RevEmbedding Decoder, which transform fMRI vectors into a latent space and generate captions, respectively. We evaluated the framework through visualization, dataset training, and testing on subjects, demonstrating strong performance. fMRI-based captioning has diverse applications, including understanding neural mechanisms, Human-Computer Interaction, and enhancing learning and training processes.
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
Venue: arXiv cs.LG 2023
Year: 2023

Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by dividing the bill into smaller parts to deduct as much as possible. This article proposes an end-to-end approach of automatically learning the optimal deduction paths (deduction amount in order), which reduces the cost of manual path design and maximizes the amount of successful deduction. Specifically, in view of the large search space of the paths and the extreme sparsity of historical successful deduction records, we propose a deep hierarchical reinforcement learning approach which abstracts the action into a two-level hierarchical space: an upper agent that determines the number of steps of deductions each day and a lower agent that decides the amount of deduction at each step. In such a way, the action space is structured via prior knowledge and the exploration space is reduced. Moreover, the inherited information incompleteness of the business makes the environment just partially observable. To be precise, the deducted amounts indicate merely the lower bounds of the available account balance. To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business. In the world's largest electronic payment business, we have verified the effectiveness of this scheme offline and deployed it online to serve millions of users.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
Venue: arXiv cs.CL 2023
Year: 2023

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance maps visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes.
A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models
Venue: arXiv cs.LG 2023
Year: 2023

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels.
Othered, Silenced and Scapegoated: Understanding the Situated Security of Marginalised Populations in Lebanon
Venue: arXiv cs.CY 2023
Year: 2023

In this paper we explore the digital security experiences of marginalised populations in Lebanon such as LGBTQI+ identifying people, refugees and women. We situate our work in the post-conflict Lebanese context, which is shaped by sectarian divides, failing governance and economic collapse. We do so through an ethnographically informed study conducted in Beirut, Lebanon, in July 2022 and through interviews with 13 people with Lebanese digital and human rights expertise. Our research highlights how LGBTQI+ identifying people and refugees are scapegoated for the failings of the Lebanese government, while women who speak out against such failings are silenced. We show how government-supported incitements of violence aimed at transferring blame from the political leadership to these groups lead to amplified digital security risks for already at-risk populations. Positioning our work in broader sociological understandings of security, we discuss how the Lebanese context impacts identity and ontological security. We conclude by proposing to design for and with positive security in post-conflict settings.
Magnificent Minified Models
Venue: arXiv cs.LG 2023
Year: 2023

This paper concerns itself with the task of taking a large trained neural network and 'compressing' it to be smaller by deleting parameters or entire neurons, with minimal decreases in the resulting model accuracy. We compare various methods of parameter and neuron selection: dropout-based neuron damage estimation, neuron merging, absolute-value based selection, random selection, OBD (Optimal Brain Damage). We also compare a variation on the classic OBD method that slightly outperformed all other parameter and neuron selection methods in our tests with substantial pruning, which we call OBD-SD. We compare these methods against quantization of parameters. We also compare these techniques (all applied to a trained neural network), with neural networks trained from scratch (random weight initialization) on various pruned architectures. Our results are only barely consistent with the Lottery Ticket Hypothesis, in that fine-tuning a parameter-pruned model does slightly better than retraining a similarly pruned model from scratch with randomly initialized weights. For neuron-level pruning, retraining from scratch did much better in our experiments.
Catastrophic Forgetting in the Context of Model Updates
Venue: arXiv cs.LG 2023
Year: 2023

A large obstacle to deploying deep learning models in practice is the process of updating models post-deployment (ideally, frequently). Deep neural networks can cost many thousands of dollars to train. When new data comes in the pipeline, you can train a new model from scratch (randomly initialized weights) on all existing data. Instead, you can take an existing model and fine-tune (continue to train) it on new data. The former is costly and slow. The latter is cheap and fast, but catastrophic forgetting generally causes the new model to 'forget' how to classify older data well. There are a plethora of complicated techniques to keep models from forgetting their past learnings. Arguably the most basic is to mix in a small amount of past data into the new data during fine-tuning: also known as 'data rehearsal'. In this paper, we compare various methods of limiting catastrophic forgetting and conclude that if you can maintain access to a portion of your past data (or tasks), data rehearsal is ideal in terms of overall accuracy across all time periods, and performs even better when combined with methods like Elastic Weight Consolidation (EWC). Especially when the amount of past data (past 'tasks') is large compared to new data, the cost of updating an existing model is far cheaper and faster than training a new model from scratch.
ALP: Action-Aware Embodied Learning for Perception
Venue: arXiv cs.CV 2023
Year: 2023

Current methods in training and benchmarking vision models exhibit an over-reliance on passive, curated datasets. Although models trained on these datasets have shown strong performance in a wide variety of tasks such as classification, detection, and segmentation, they fundamentally are unable to generalize to an ever-evolving world due to constant out-of-distribution shifts of input data. Therefore, instead of training on fixed datasets, can we approach learning in a more human-centric and adaptive manner? In this paper, we introduce Action-Aware Embodied Learning for Perception (ALP), an embodied learning framework that incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. Our method actively explores in complex 3D environments to both learn generalizable task-agnostic visual representations as well as collect downstream training data. We show that ALP outperforms existing baselines in several downstream perception tasks. In addition, we show that by training on actively collected data more relevant to the environment and task, our method generalizes more robustly to downstream tasks compared to models pre-trained on fixed datasets such as ImageNet.