OpenRaiser/Intern-Atlas huggingface dataset:
Generating gradients in the energy landscape using rectified linear type cost functions for efficiently solving 0/1 matrix factorization in Simulated Annealing
Venue: arXiv cs.LG 2023
Year: 2023

The 0/1 matrix factorization defines matrix products using logical AND and OR as product-sum operators, revealing the factors influencing various decision processes. Instances and their characteristics are arranged in rows and columns. Formulating matrix factorization as an energy minimization problem and exploring it with Simulated Annealing (SA) theoretically enables finding a minimum solution in sufficient time. However, searching for the optimal solution in practical time becomes problematic when the energy landscape has many plateaus with flat slopes. In this work, we propose a method to facilitate the solution process by applying a gradient to the energy landscape, using a rectified linear type cost function readily available in modern annealing machines. We also propose a method to quickly obtain a solution by updating the cost function's gradient during the search process. Numerical experiments were conducted, confirming the method's effectiveness with both noise-free artificial and real data.
Intelligent Parsing: An Automated Parsing Framework for Extracting Design Semantics from E-commerce Creatives
Venue: arXiv cs.CV 2023
Year: 2023

In the industrial e-commerce landscape, creative designs such as banners and posters are ubiquitous. Extracting structured semantic information from creative e-commerce design materials (manuscripts crafted by designers) to obtain design semantics represents a core challenge in the realm of intelligent design. In this paper, we propose a comprehensive automated framework for intelligently parsing creative materials. This framework comprises material recognition, preprocess, smartname, and label layers. The material recognition layer consolidates various detection and recognition interfaces, covering business aspects including detection of auxiliary areas within creative materials and layer-level detection, alongside label identification. Algorithmically, it encompasses a variety of coarse-to-fine methods such as Cascade RCNN, GFL, and other models. The preprocess layer involves filtering creative layers and grading creative materials. The smartname layer achieves intelligent naming for creative materials, while the label layer covers multi-level tagging for creative materials, enabling tagging at different hierarchical levels. Intelligent parsing constitutes a complete parsing framework that significantly aids downstream processes such as intelligent creation, creative optimization, and material library construction. Within the practical business applications at Suning, it markedly enhances the exposure, circulation, and click-through rates of creative materials, expediting the closed-loop production of creative materials and yielding substantial benefits.
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach
Venue: arXiv cs.LG 2023
Year: 2023

Engineering system design, viewed as a decision-making process, faces challenges due to complexity and uncertainty. In this paper, we present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of engineering systems. We outline a step-by-step framework for optimizing engineering system designs. The goal is to find policies that maximize the output of a simulation model given multiple sources of uncertainties. The proposed algorithm handles linear and non-linear multi-stage stochastic problems, where decision variables are discrete, and the objective function and constraints are assessed via a Monte Carlo simulation. We demonstrate the effectiveness of our proposed framework by solving two engineering system design problems in the presence of multiple uncertainties, such as price and demand.
Effect of dimensionality change on the bias of word embeddings
Venue: arXiv cs.CL 2023
Year: 2023

Word embedding methods (WEMs) are extensively used for representing text data. The dimensionality of these embeddings varies across various tasks and implementations. The effect of dimensionality change on the accuracy of the downstream task is a well-explored question. However, how the dimensionality change affects the bias of word embeddings needs to be investigated. Using the English Wikipedia corpus, we study this effect for two static (Word2Vec and fastText) and two context-sensitive (ElMo and BERT) WEMs. We have two observations. First, there is a significant variation in the bias of word embeddings with the dimensionality change. Second, there is no uniformity in how the dimensionality change affects the bias of word embeddings. These factors should be considered while selecting the dimensionality of word embeddings.
SentinelLMs: Encrypted Input Adaptation and Fine-tuning of Language Models for Private and Secure Inference
Venue: arXiv cs.CR 2023
Year: 2023

This paper addresses the privacy and security concerns associated with deep neural language models, which serve as crucial components in various modern AI-based applications. These models are often used after being pre-trained and fine-tuned for specific tasks, with deployment on servers accessed through the internet. However, this introduces two fundamental risks: (a) the transmission of user inputs to the server via the network gives rise to interception vulnerabilities, and (b) privacy concerns emerge as organizations that deploy such models store user data with restricted context. To address this, we propose a novel method to adapt and fine-tune transformer-based language models on passkey-encrypted user-specific text. The original pre-trained language model first undergoes a quick adaptation (without any further pre-training) with a series of irreversible transformations applied to the tokenizer and token embeddings. This enables the model to perform inference on encrypted inputs while preventing reverse engineering of text from model parameters and intermediate outputs. After adaptation, models are fine-tuned on encrypted versions of existing training datasets. Experimental evaluation employing adapted versions of renowned models (e.g., BERT, RoBERTa) across established benchmark English and multilingual datasets for text classification and sequence labeling shows that encrypted models achieve performance parity with their original counterparts. This serves to safeguard performance, privacy, and security cohesively.
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
Venue: arXiv cs.CL 2023
Year: 2023

Phrase mining is a fundamental text mining task that aims to identify quality phrases from context. Nevertheless, the scarcity of extensive gold labels datasets, demanding substantial annotation efforts from experts, renders this task exceptionally challenging. Furthermore, the emerging, infrequent, and domain-specific nature of quality phrases presents further challenges in dealing with this task. In this paper, we propose LMPhrase, a novel unsupervised context-aware quality phrase mining framework built upon large pre-trained language models (LMs). Specifically, we first mine quality phrases as silver labels by employing a parameter-free probing technique called Perturbed Masking on the pre-trained language model BERT (coined as Annotator). In contrast to typical statistic-based or distantly-supervised methods, our silver labels, derived from large pre-trained language models, take into account rich contextual information contained in the LMs. As a result, they bring distinct advantages in preserving informativeness, concordance, and completeness of quality phrases. Secondly, training a discriminative span prediction model heavily relies on massive annotated data and is likely to face the risk of overfitting silver labels. Alternatively, we formalize phrase tagging task as the sequence generation problem by directly fine-tuning on the Sequence-to-Sequence pre-trained language model BART with silver labels (coined as Generator). Finally, we merge the quality phrases from both the Annotator and Generator as the final predictions, considering their complementary nature and distinct characteristics. Extensive experiments show that our LMPhrase consistently outperforms all the existing competitors across two different granularity phrase mining tasks, where each task is tested on two different domain datasets.
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNs
Venue: arXiv cs.LG 2023
Year: 2023

We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign). In addition to its generality, our Quaternionic Laplacian is the only Laplacian to completely preserve the topology of a digraph, as it can handle graphs and digraphs containing antiparallel pairs of edges (digons) of different weights without reducing them to a single (directed or undirected) edge as done with other Laplacians. Experimental results show the superior performance of QuaterGCN compared to other state-of-the-art GCNs, particularly in scenarios where the information the digons carry is crucial to successfully address the task at hand.
Seqnature: Extracting Network Fingerprints from Packet Sequences
Venue: arXiv cs.CR 2023
Year: 2023

This paper proposes a general network fingerprinting framework, Seqnature, that uses packet sequences as its basic data unit and that makes it simple to implement any fingerprinting technique that can be formulated as a problem of identifying packet exchanges that consistently occur when the fingerprinted event is triggered. We demonstrate the versatility of Seqnature by using it to implement five different fingerprinting techniques, as special cases of the framework, which broadly fall into two categories: (i) fingerprinting techniques that consider features of each individual packet in a packet sequence, e.g., size and direction; and (ii) fingerprinting techniques that only consider stream-wide features, specifically what Internet endpoints are contacted. We illustrate how Seqnature facilitates comparisons of the relative performance of different fingerprinting techniques by applying the five fingerprinting techniques to datasets from the literature. The results confirm findings in prior work, for example that endpoint information alone is insufficient to differentiate between individual events on Internet of Things devices, but also show that smart TV app fingerprints based exclusively on endpoint information are not as distinct as previously reported.
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e
Venue: arXiv cs.LG 2023
Year: 2023

We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an automated controller capable of providing real-time feedback and calibration of the Spill Regulation System (SRS) parameters on a millisecond timescale. We treat the Mu2e accelerator system as a Markov Decision Process suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and enhance training stability. A key innovation in our approach is the integration of a neuralized Proportional-Integral-Derivative (PID) controller into the policy function, resulting in a significant improvement in the Spill Duty Factor (SDF) by 13.6%, surpassing the performance of the current PID controller baseline by an additional 1.6%. This paper presents the preliminary offline results based on a differentiable simulator of the Mu2e accelerator. It paves the groundwork for real-time implementations and applications, representing a crucial step towards automated proton beam intensity control for the Mu2e experiment.
Parameter Optimization with Conscious Allocation (POCA)
Venue: arXiv cs.LG 2023
Year: 2023

The performance of modern machine learning algorithms depends upon the selection of a set of hyperparameters. Common examples of hyperparameters are learning rate and the number of layers in a dense neural network. Auto-ML is a branch of optimization that has produced important contributions in this area. Within Auto-ML, hyperband-based approaches, which eliminate poorly-performing configurations after evaluating them at low budgets, are among the most effective. However, the performance of these algorithms strongly depends on how effectively they allocate the computational budget to various hyperparameter configurations. We present the new Parameter Optimization with Conscious Allocation (POCA), a hyperband-based algorithm that adaptively allocates the inputted budget to the hyperparameter configurations it generates following a Bayesian sampling scheme. We compare POCA to its nearest competitor at optimizing the hyperparameters of an artificial toy function and a deep neural network and find that POCA finds strong configurations faster in both settings.
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, both at the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.
Reconfigurable Frequency Multipliers Based on Complementary Ferroelectric Transistors
Venue: arXiv cs.ET 2023
Year: 2023

Frequency multipliers, a class of essential electronic components, play a pivotal role in contemporary signal processing and communication systems. They serve as crucial building blocks for generating high-frequency signals by multiplying the frequency of an input signal. However, traditional frequency multipliers that rely on nonlinear devices often require energy- and area-consuming filtering and amplification circuits, and emerging designs based on an ambipolar ferroelectric transistor require costly non-trivial characteristic tuning or complex technology process. In this paper, we show that a pair of standard ferroelectric field effect transistors (FeFETs) can be used to build compact frequency multipliers without aforementioned technology issues. By leveraging the tunable parabolic shape of the 2FeFET structures' transfer characteristics, we propose four reconfigurable frequency multipliers, which can switch between signal transmission and frequency doubling. Furthermore, based on the 2FeFET structures, we propose four frequency multipliers that realize triple, quadruple frequency modes, elucidating a scalable methodology to generate more multiplication harmonics of the input frequency. Performance metrics such as maximum operating frequency, power, etc., are evaluated and compared with existing works. We also implement a practical case of frequency modulation scheme based on the proposed reconfigurable multipliers without additional devices. Our work provides a novel path of scalable and reconfigurable frequency multiplier designs based on devices that have characteristics similar to FeFETs, and show that FeFETs are a promising candidate for signal processing and communication systems in terms of maximum operating frequency and power.
Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning
Venue: arXiv cs.AI 2023
Year: 2023

Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of an AI should be culturally attuned: just as a child raised in a particular culture learns the specific values and norms of that culture, we propose that an AI agent operating in a particular human community should acquire that community's moral, ethical, and cultural codes. How AI systems might acquire such codes from human observation and interaction has remained an open question. Here, we propose using inverse reinforcement learning (IRL) as a method for AI agents to acquire a culturally-attuned value system implicitly. We test our approach using an experimental paradigm in which AI agents use IRL to learn different reward functions, which govern the agents' moral values, by observing the behavior of different cultural groups in an online virtual world requiring real-time decision making. We show that an AI agent learning from the average behavior of a particular cultural group can acquire altruistic characteristics reflective of that group's behavior, and this learned value system can generalize to new scenarios requiring altruistic judgments. Our results provide, to our knowledge, the first demonstration that AI agents could potentially be endowed with the ability to continually learn their values and norms from observing and interacting with humans, thereby becoming attuned to the culture they are operating in.
The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model
Venue: arXiv cs.SE 2023
Year: 2023

Automatic program repair (APR) techniques have the potential to reduce manual efforts in uncovering and repairing program defects during the code review (CR) process. However, the limited accuracy and considerable time costs associated with existing APR approaches hinder their adoption in industrial practice. One key factor is the under-utilization of review comments, which provide valuable insights into defects and potential fixes. Recent advancements in Large Language Models (LLMs) have enhanced their ability to comprehend natural and programming languages, enabling them to generate patches based on review comments. This paper conducts a comprehensive investigation into the effective utilization of LLMs for repairing CR defects. In this study, various prompts are designed and compared across mainstream LLMs using two distinct datasets from human reviewers and automated checkers. Experimental results demonstrate a remarkable repair rate of 72.97% with the best prompt, highlighting a substantial improvement in the effectiveness and practicality of automatic repair techniques.
Performance of Distributed File Systems on Cloud Computing Environment: An Evaluation for Small-File Problem
Venue: arXiv cs.DC 2023
Year: 2023

Various performance characteristics of distributed file systems have been well studied. However, the performance efficiency of distributed file systems on small-file problems with complex machine learning algorithms scenarios is not well addressed. In addition, demands for unified storage of big data processing and high-performance computing have been crucial. Hence, developing a solution combining high-performance computing and big data with shared storage is very important. This paper focuses on the performance efficiency of distributed file systems with small-file datasets. We propose an architecture combining both high-performance computing and big data with shared storage and perform a series of experiments to investigate the performance of these distributed file systems. The result of the experiments confirms the applicability of the proposed architecture in terms of complex machine learning algorithms.
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
Venue: arXiv cs.AR 2023
Year: 2023

Approximate Computing (AxC) techniques have become increasingly popular in trading off accuracy for performance gains in various applications. Selecting the best AxC techniques for a given application is challenging. Among proposed approaches for exploring the design space, Machine Learning approaches such as Reinforcement Learning (RL) show promising results. In this paper, we proposed an RL-based multi-objective Design Space Exploration strategy to find the approximate versions of the application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks.
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Venue: arXiv cs.CL 2023
Year: 2023

Quantities are distinct and critical components of texts that characterize the magnitude properties of entities, providing a precise perspective for the understanding of natural language, especially for reasoning tasks. In recent years, there has been a flurry of research on reasoning tasks based on large language models (LLMs), most of which solely focus on numerical values, neglecting the dimensional concept of quantities with units despite its importance. We argue that the concept of dimension is essential for precisely understanding quantities and of great significance for LLMs to perform quantitative reasoning. However, the lack of dimension knowledge and quantity-related benchmarks has resulted in low performance of LLMs. Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception. We first construct a dimensional unit knowledge base (DimUnitKB) to address the knowledge gap in this area. We propose a benchmark DimEval consisting of seven tasks of three categories to probe and enhance the dimension perception skills of LLMs. To evaluate the effectiveness of our methods, we propose a quantitative reasoning task and conduct experiments. The experimental results show that our dimension perception method dramatically improves accuracy (43.55%->50.67%) on quantitative reasoning tasks compared to GPT-4.