OpenRaiser/Intern-Atlas huggingface dataset:
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines
Venue: arXiv cs.LG 2023
Year: 2023

Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task independent constructs: their general composition remains the same, regardless of the data. In contrast, the structure of flexible pipelines varies depending on the input, making them finely tailored to individual tasks. However, flexible pipelines can be structurally overcomplicated and have poor explainability. We propose the EVOSA approach that compensates for the negative points of flexible pipelines by incorporating a sensitivity analysis which increases the robustness and interpretability of the flexible solutions. EVOSA quantitatively estimates positive and negative impact of an edge or a node on a pipeline graph, and feeds this information to the evolutionary AutoML optimizer. The correctness and efficiency of EVOSA was validated in tabular, multimodal and computer vision tasks, suggesting generalizability of the proposed approach across domains.
The Value of Mediation in Long Cheap Talk
Venue: arXiv cs.GT 2023
Year: 2023

In this paper, we study an extension of the classic long cheap talk equilibrium introduced by Aumann and Hart~\citeN{aumann-hart-03}, and ask how much can the players benefit from having a trusted mediator compared with the standard unmediated model. We focus on a setting where a fully informed sender without commitment power must disclose its information to influence the behavior of a self-interested receiver. We show that, in the case of binary actions, even though a mediator does not help neither the sender nor the receiver directly, it may still allow improving the payoff of an external decision-maker whose utility is affected by the realized state and the receiver's action. Moreover, we show that if there are more than two actions, there exist games in which both the sender and the receiver simultaneously benefit from mediation.
Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs) have spurred progress in text-to-SQL, the task of generating SQL queries from natural language questions based on a given database schema. Despite the declarative nature of SQL, it continues to be a complex programming language. In this paper, we investigate the potential of an alternative query language with simpler syntax and modular specification of complex queries. The purpose is to create a query language that can be learned more easily by modern neural semantic parsing architectures while also enabling non-programmers to better assess the validity of the query plans produced by an interactive query plan assistant. The proposed alternative query language is called Query Plan Language (QPL). It is designed to be modular and can be translated into a restricted form of SQL Common Table Expressions (CTEs). The aim of QPL is to make complex data retrieval accessible to non-programmers by allowing users to express their questions in natural language while also providing an easier-to-verify target language. The paper demonstrates how neural LLMs can benefit from QPL's modularity to generate complex query plans in a compositional manner. This involves a question decomposition strategy and a planning stage. We conduct experiments on a version of the Spider text-to-SQL dataset that has been converted to QPL. The hierarchical structure of QPL programs enables us to measure query complexity naturally. Based on this assessment, we identify the low accuracy of existing text-to-SQL systems on complex compositional queries. We present ways to address the challenge of complex queries in an iterative, user-controlled manner, using fine-tuned LLMs and a variety of prompting strategies in a compositional manner.
AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators
Venue: arXiv cs.PL 2023
Year: 2023

This paper addresses the need for automatic and efficient generation of host driver code for arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an important workload in various applications, including machine learning and scientific computing. While existing tools have focused on automating accelerator prototyping, little attention has been paid to the host-accelerator interaction. This paper introduces AXI4MLIR, an extension of the MLIR compiler framework designed to facilitate the automated generation of host-accelerator driver code. With new MLIR attributes and transformations, AXI4MLIR empowers users to specify accelerator features (including their instructions) and communication patterns and exploit the host memory hierarchy. We demonstrate AXI4MLIR's versatility across different types of accelerators and problems, showcasing significant CPU cache reference reductions (up to 56%) and up to a 1.65x speedup compared to manually optimized driver code implementations. AXI4MLIR implementation is open-source and available at: https://github.com/AXI4MLIR/axi4mlir.
Prototype-Guided Text-based Person Search based on Rich Chinese Descriptions
Venue: arXiv cs.CV 2023
Year: 2023

Text-based person search aims to simultaneously localize and identify the target person based on query text from uncropped scene images, which can be regarded as the unified task of person detection and text-based person retrieval task. In this work, we propose a large-scale benchmark dataset named PRW-TPS-CN based on the widely used person search dataset PRW. Our dataset contains 47,102 sentences, which means there is quite more information than existing dataset. These texts precisely describe the person images from top to bottom, which in line with the natural description order. We also provide both Chinese and English descriptions in our dataset for more comprehensive evaluation. These characteristics make our dataset more applicable. To alleviate the inconsistency between person detection and text-based person retrieval, we take advantage of the rich texts in PRW-TPS-CN dataset. We propose to aggregate multiple texts as text prototypes to maintain the prominent text features of a person, which can better reflect the whole character of a person. The overall prototypes lead to generating the image attention map to eliminate the detection misalignment causing the decrease of text-based person retrieval. Thus, the inconsistency between person detection and text-based person retrieval is largely alleviated. We conduct extensive experiments on the PRW-TPS-CN dataset. The experimental results show the PRW-TPS-CN dataset's effectiveness and the state-of-the-art performance of our approach.
YAYI 2: Multilingual Open-Source Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence. To better facilitate research on LLMs, many open-source LLMs, such as Llama 2 and Falcon, have recently been proposed and gained comparable performances to proprietary models. However, these models are primarily designed for English scenarios and exhibit poor performances in Chinese contexts. In this technical report, we propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback. Extensive experiments on multiple benchmarks, such as MMLU and CMMLU, consistently demonstrate that the proposed YAYI 2 outperforms other similar sized open-source models.
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Venue: arXiv cs.DC 2023
Year: 2023

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the `integrated' dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid.
A Cascaded Neural Network System For Rating Student Performance In Surgical Knot Tying Simulation
Venue: arXiv cs.CV 2023
Year: 2023

As part of their training all medical students and residents have to pass basic surgical tasks such as knot tying, needle-passing, and suturing. Their assessment is typically performed in the operating room by surgical faculty where mistakes and failure by the student increases the operation time and cost. This evaluation is quantitative and has a low margin of error. Simulation has emerged as a cost effective option but it lacks assessment or requires additional expensive hardware for evaluation. Apps that provide training videos on surgical knot trying are available to students but none have evaluation. We propose a cascaded neural network architecture that evaluates a student's performance just from a video of themselves simulating a surgical knot tying task. Our model converts video frame images into feature vectors with a pre-trained deep convolutional network and then models the sequence of frames with a temporal network. We obtained videos of medical students and residents from the Robert Wood Johnson Hospital performing knot tying on a standardized simulation kit. We manually annotated each video and proceeded to do a five-fold cross-validation study on them. Our model achieves a median precision, recall, and F1-score of 0.71, 0.66, and 0.65 respectively in determining the level of knot related tasks of tying and pushing the knot. Our mean precision score averaged across different probability thresholds is 0.8. Both our F1-score and mean precision score are 8% and 30% higher than that of a recently published study for the same problem. We expect the accuracy of our model to further increase as we add more training videos to the model thus making it a practical solution that students can use to evaluate themselves.
Effectful Semantics in 2-Dimensional Categories: Premonoidal and Freyd Bicategories
Venue: arXiv cs.LO 2023
Year: 2023

Premonoidal categories and Freyd categories provide an encompassing framework for the semantics of call-by-value programming languages. Premonoidal categories are a weakening of monoidal categories in which the interchange law for the tensor product may not hold, modelling the fact that effectful programs cannot generally be re-ordered. A Freyd category is a pair of categories with the same objects: a premonoidal category of general programs, and a monoidal category of 'effect-free' programs which do admit re-ordering. Certain recent innovations in semantics, however, have produced models which are not categories but bicategories. Here we develop the theory to capture such examples by introducing premonoidal and Freyd structure in a bicategorical setting. The second dimension introduces new subtleties, so we verify our definitions with several examples and a correspondence theorem (between Freyd bicategories and certain actions of monoidal bicategories) which parallels the categorical framework.
Challenges and Opportunities of Moderating Usage of Large Language Models in Education
Venue: arXiv cs.HC 2023
Year: 2023

The increased presence of large language models (LLMs) in educational settings has ignited debates concerning negative repercussions, including overreliance and inadequate task reflection. Our work advocates moderated usage of such models, designed in a way that supports students and encourages critical thinking. We developed two moderated interaction methods with ChatGPT: hint-based assistance and presenting multiple answer choices. In a study with students (N=40) answering physics questions, we compared the effects of our moderated models against two baseline settings: unmoderated ChatGPT access and internet searches. We analyzed the interaction strategies and found that the moderated versions exhibited less unreflected usage (e.g., copy \& paste) compared to the unmoderated condition. However, neither ChatGPT-supported condition could match the ratio of reflected usage present in internet searches. Our research highlights the potential benefits of moderating language models, showing a research direction toward designing effective AI-supported educational strategies.
On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?
Venue: arXiv cs.IR 2023
Year: 2023

Typical investors start off the day by going through the daily news to get an intuition about the performance of the market. The speculations based on the tone of the news ultimately shape their responses towards the market. Today, computers are being trained to compute the news sentiment so that it can be used as a variable to predict stock market movements and returns. Some researchers have even developed news-based market indices to forecast stock market returns. Majority of the research in the field of news sentiment analysis has focussed on using libraries like Vader, Loughran-McDonald (LM), Harvard IV and Pattern. However, are the popular approaches for measuring financial news sentiment really approaching the problem of sentiment analysis correctly? Our experiments suggest that measuring sentiments using these libraries, especially for financial news, fails to depict the true picture and hence may not be very reliable. Therefore, the question remains: What is the most effective and accurate approach to measure financial news sentiment? Our paper explores these questions and attempts to answer them through SENTInews: a one-of-its-kind financial news sentiment analyzer customized to the Indian context
Have Learning Analytics Dashboards Lived Up to the Hype? A Systematic Review of Impact on Students' Achievement, Motivation, Participation and Attitude
Venue: arXiv cs.HC 2023
Year: 2023

While learning analytics dashboards (LADs) are the most common form of LA intervention, there is limited evidence regarding their impact on students learning outcomes. This systematic review synthesizes the findings of 38 research studies to investigate the impact of LADs on students' learning outcomes, encompassing achievement, participation, motivation, and attitudes. As we currently stand, there is no evidence to support the conclusion that LADs have lived up to the promise of improving academic achievement. Most studies reported negligible or small effects, with limited evidence from well-powered controlled experiments. Many studies merely compared users and non-users of LADs, confounding the dashboard effect with student engagement levels. Similarly, the impact of LADs on motivation and attitudes appeared modest, with only a few exceptions demonstrating significant effects. Small sample sizes in these studies highlight the need for larger-scale investigations to validate these findings. Notably, LADs showed a relatively substantial impact on student participation. Several studies reported medium to large effect sizes, suggesting that LADs can promote engagement and interaction in online learning environments. However, methodological shortcomings, such as reliance on traditional evaluation methods, self-selection bias, the assumption that access equates to usage, and a lack of standardized assessment tools, emerged as recurring issues. To advance the research line for LADs, researchers should use rigorous assessment methods and establish clear standards for evaluating learning constructs. Such efforts will advance our understanding of the potential of LADs to enhance learning outcomes and provide valuable insights for educators and researchers alike.
Generative AI and the History of Architecture
Venue: arXiv cs.AI 2023
Year: 2023

Recent generative AI platforms are able to create texts or impressive images from simple text prompts. This makes them powerful tools for summarizing knowledge about architectural history or deriving new creative work in early design tasks like ideation, sketching and modelling. But, how good is the understanding of the generative AI models of the history of architecture? Has it learned to properly distinguish styles, or is it hallucinating information? In this chapter, we investigate this question for generative AI platforms for text and image generation for different architectural styles, to understand the capabilities and boundaries of knowledge of those tools. We also analyze how they are already being used by analyzing a data set of 101 million Midjourney queries to see if and how practitioners are already querying for specific architectural concepts.
Admission Control with Response Time Objectives for Low-latency Online Data Systems
Venue: arXiv cs.DB 2023
Year: 2023

To provide quick responses to users, Internet companies rely on online data systems able to answer queries in milliseconds. These systems employ complementary overload management techniques to ensure they provide a continued, acceptable service through-out traffic surges, where 'acceptable' partly means that serviced queries meet or track closely their response time objectives. Thus, in this paper we present Bouncer, an admission control policy aimed to keep admitted queries under or near their service level objectives (SLOs) on percentile response times. It computes inexpensive estimates of percentile response times for every incoming query and compares the estimates against the objective values to decide whether to accept or reject the query. Bouncer allows assigning separate SLOs to different classes of queries in the workload, implements early rejections to let clients react promptly and to help data systems avoid doing useless work, and complements other load shedding policies that guard systems from exceeding their capacity. Moreover, we propose two starvation avoidance strategies that supplement Bouncer's basic formulation and prevent query types from receiving no service (starving). We evaluate Bouncer and its starvation-avoiding variants against other policies in simulation and on a production-grade in-memory distributed graph database. Our results show that Bouncer and its variants allow admitted queries to meet or stay close to the SLOs when the other policies do not. They also report fewer overall rejections, a small overhead, and with the given latency SLOs, they let the system reach high utilization. In addition, we observe that the proposed strategies can stop query starvation, but at the expense of a modest increase in overall rejections and causing SLO violations for serviced requests.
Majority-based Preference Diffusion on Social Networks
Venue: arXiv cs.SI 2023
Year: 2023

We study a majority based preference diffusion model in which the members of a social network update their preferences based on those of their connections. Consider an undirected graph where each node has a strict linear order over a set of $α$ alternatives. At each round, a node randomly selects two adjacent alternatives and updates their relative order with the majority view of its neighbors. We bound the convergence time of the process in terms of the number of nodes/edges and $α$. Furthermore, we study the minimum cost to ensure that a desired alternative will ``win'' the process, where occupying each position in a preference order of a node has a cost. We prove tight bounds on the minimum cost for general graphs and graphs with strong expansion properties. Furthermore, we investigate a more light-weight process where each node chooses one of its neighbors uniformly at random and copies its order fully with some fixed probability and remains unchanged otherwise. We characterize the convergence properties of this process, namely convergence time and stable states, using Martingale and reversible Markov chain analysis. Finally, we present the outcomes of our experiments conducted on different synthetic random graph models and graph data from online social platforms. These experiments not only support our theoretical findings, but also shed some light on some other fundamental problems, such as designing powerful countermeasures.
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
Venue: arXiv cs.AI 2023
Year: 2023

Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.
The Bridge Toward 6G: 5G-Advanced Evolution in 3GPP Release 19
Venue: arXiv cs.NI 2023
Year: 2023

The 3rd generation partnership project (3GPP) initiated 5G-Advanced in Release 18, laying a solid foundation for the further evolution of 5G-Advanced. Release 19-the next wave of 5G-Advanced-will primarily focus on commercial deployment needs while serving as a bridge toward 6G. In this article, we provide an in-depth overview of the 5G-Advanced evolution in 3GPP Release 19. We not only delve into the key technology components and their corresponding use cases in 5G-Advanced evolution but also shed light on initial 3GPP studies toward 6G.