OpenRaiser/Intern-Atlas huggingface dataset:
Addressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs
Venue: arXiv cs.IR 2023
Year: 2023

The initial interaction of a user with a recommender system is problematic because, in such a so-called cold start situation, the recommender system has very little information about the user, if any. Moreover, in collaborative filtering, users need to share their preferences with the service provider by rating items while in content-based filtering there is no need for such information sharing. We have recently shown that a content-based model that uses hypercube graphs can determine user preferences with a very limited number of ratings while better preserving user privacy. In this paper, we confirm these findings on the basis of experiments with more than 1,000 users in the restaurant and movie domains. We show that the proposed method outperforms standard machine learning algorithms when the number of available ratings is at most 10, which often happens, and is competitive with larger training sets. In addition, training is simple and does not require large computational efforts.
A Computational Approach to Style in American Poetry
Venue: arXiv cs.CL 2023
Year: 2023

We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another. Qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. These features are used to discover comprehensive stylistic information from a poem's multi-layered latent structure, and to compute distances between poems in this space. Visualizations provide ready access to the analytical components. We demonstrate our method on several collections of poetry, showing that it better delineates poetry style than the traditional word-occurrence features that are used in typical text analysis algorithms. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
Venue: arXiv cs.CL 2023
Year: 2023

We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities. SALM comprises a frozen text LLM, a audio encoder, a modality adapter module, and LoRA layers to accommodate speech input and associated task instructions. The unified SALM not only achieves performance on par with task-specific Conformer baselines for Automatic Speech Recognition (ASR) and Speech Translation (AST), but also exhibits zero-shot in-context learning capabilities, demonstrated through keyword-boosting task for ASR and AST. Moreover, {\em speech supervised in-context training} is proposed to bridge the gap between LLM training and downstream speech tasks, which further boosts the in-context learning ability of speech-to-text models. Proposed model is open-sourced via NeMo toolkit.
Enhancing BERT-Based Visual Question Answering through Keyword-Driven Sentence Selection
Venue: arXiv cs.CL 2023
Year: 2023

The Document-based Visual Question Answering competition addresses the automatic detection of parent-child relationships between elements in multi-page documents. The goal is to identify the document elements that answer a specific question posed in natural language. This paper describes the PoliTo's approach to addressing this task, in particular, our best solution explores a text-only approach, leveraging an ad hoc sampling strategy. Specifically, our approach leverages the Masked Language Modeling technique to fine-tune a BERT model, focusing on sentences containing sensitive keywords that also occur in the questions, such as references to tables or images. Thanks to the effectiveness of this approach, we are able to achieve high performance compared to baselines, demonstrating how our solution contributes positively to this task.
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks
Venue: arXiv cs.CL 2023
Year: 2023

Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and encouraging knowledge transfer (KT). The existing literature mainly focused on overcoming CF. Some work has also been done on KT when the tasks are similar. To our knowledge, only one method has been proposed to learn a sequence of mixed tasks. However, these techniques still suffer from CF and/or limited KT. This paper proposes a new CL method to achieve both. It overcomes CF by isolating the knowledge of each task via discovering a subnetwork for it. A soft-masking mechanism is also proposed to preserve the previous knowledge and to enable the new task to leverage the past knowledge to achieve KT. Experiments using classification, generation, information extraction, and their mixture (i.e., heterogeneous tasks) show that the proposed method consistently outperforms strong baselines.
Can CNNs Accurately Classify Human Emotions? A Deep-Learning Facial Expression Recognition Study
Venue: arXiv cs.LG 2023
Year: 2023

Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the primary factor that distinguishes living humans and mechanical machines. For AIs to be classified as "emotional," they should be able to empathize with others and classify their emotions because without such abilities they cannot normally interact with humans. This study investigates the CNN model's ability to recognize and classify human facial expressions (positive, neutral, negative). The CNN model made for this study is programmed in Python and trained with preprocessed data from the Chicago Face Database. The model is intentionally designed with less complexity to further investigate its ability. We hypothesized that the model will perform better than chance (33.3%) in classifying each emotion class of input data. The model accuracy was tested with novel images. Accuracy was summarized in a percentage report, comparative plot, and confusion matrix. Results of this study supported the hypothesis as the model had 75% accuracy over 10,000 images (data), highlighting the possibility of AIs that accurately analyze human emotions and the prospect of viable Emotional AIs.
Unified High-binding Watermark for Unconditional Image Generation Models
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning techniques have implemented many unconditional image generation (UIG) models, such as GAN, Diffusion model, etc. The extremely realistic images (also known as AI-Generated Content, AIGC for short) produced by these models bring urgent needs for intellectual property protection such as data traceability and copyright certification. An attacker can steal the output images of the target model and use them as part of the training data to train a private surrogate UIG model. The implementation mechanisms of UIG models are diverse and complex, and there is no unified and effective protection and verification method at present. To address these issues, we propose a two-stage unified watermark verification mechanism with high-binding effects for such models. In the first stage, we use an encoder to invisibly write the watermark image into the output images of the original AIGC tool, and reversely extract the watermark image through the corresponding decoder. In the second stage, we design the decoder fine-tuning process, and the fine-tuned decoder can make correct judgments on whether the suspicious model steals the original AIGC tool data. Experiments demonstrate our method can complete the verification work with almost zero false positive rate under the condition of only using the model output images. Moreover, the proposed method can achieve data steal verification across different types of UIG models, which further increases the practicality of the method.
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection
Venue: arXiv cs.CV 2023
Year: 2023

Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning. However, scene complexity and large-scale variation in the real world make accurate detection more difficult. Therefore, we present a modified Yolov8 which improves head detection performance through reinforcing target perception. An Auxiliary Learning Feature Fusion (ALFF) module comprised of LSTM and convolutional blocks is used as the auxiliary task to help the model perceive targets. In addition, we introduce Noise Calibration into Distribution Focal Loss to facilitate model fitting and improve the accuracy of detection. Considering the requirements of high accuracy and speed for the head detection task, our method is adapted with two kinds of backbone, namely Yolov8n and Yolov8m. The results demonstrate the superior performance of our approach in improving detection accuracy and robustness.
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning nowadays offers expert-level and sometimes even super-expert-level performance, but achieving such performance demands massive annotated data for training (e.g., Google's proprietary CXR Foundation Model (CXR-FM) was trained on 821,544 labeled and mostly private chest X-rays (CXRs)). Numerous datasets are publicly available in medical imaging but individually small and heterogeneous in expert labels. We envision a powerful and robust foundation model that can be trained by aggregating numerous small public datasets. To realize this vision, we have developed Ark, a framework that accrues and reuses knowledge from heterogeneous expert annotations in various datasets. As a proof of concept, we have trained two Ark models on 335,484 and 704,363 CXRs, respectively, by merging several datasets including ChestX-ray14, CheXpert, MIMIC-II, and VinDr-CXR, evaluated them on a wide range of imaging tasks covering both classification and segmentation via fine-tuning, linear-probing, and gender-bias analysis, and demonstrated our Ark's superior and robust performance over the SOTA fully/self-supervised baselines and Google's proprietary CXR-FM. This enhanced performance is attributed to our simple yet powerful observation that aggregating numerous public datasets diversifies patient populations and accrues knowledge from diverse experts, yielding unprecedented performance yet saving annotation cost. With all codes and pretrained models released at GitHub.com/JLiangLab/Ark, we hope that Ark exerts an important impact on open science, as accruing and reusing knowledge from expert annotations in public datasets can potentially surpass the performance of proprietary models trained on unusually large data, inspiring many more researchers worldwide to share codes and datasets to build open foundation models, accelerate open science, and democratize deep learning for medical imaging.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Venue: arXiv cs.CL 2023
Year: 2023

Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency and growing efficacy. However, its efficiency is still bottlenecked by quadratic complexity in sequence lengths, which is prohibitive for scaling to long sequence generation and few works have been done to mitigate this problem. In this paper, we propose a novel MLP variant, \textbf{A}ttentive \textbf{M}ulti-\textbf{L}ayer \textbf{P}erceptron~(AMLP), to produce a generation model with linear time and space complexity. Different from classic MLP with static and learnable projection matrices, AMLP leverages adaptive projections computed from inputs in an attentive mode. The sample-aware adaptive projections enable communications among tokens in a sequence, and model the measurement between the query and key space. Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity. Empirical results show that such marriage architecture surpasses competitive efficient NAR models, by a significant margin on text-to-speech synthesis and machine translation. We also test AMLP's self- and cross-attention ability separately with extensive ablation experiments, and find them comparable or even superior to the other efficient models. The efficiency analysis further shows that AMLP extremely reduces the memory cost against vanilla non-autoregressive models for long sequences.
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However, leveraging LLMs for domain-specific question answering suffers from severe limitations. The generated answer tends to hallucinate due to the training data collection time (when using off-the-shelf), complex user utterance and wrong retrieval (in retrieval-augmented generation). Furthermore, due to the lack of awareness about the domain and expected output, such LLMs may generate unexpected and unsafe answers that are not tailored to the target domain. In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. Specifically, CarExpert employs LLMs to control the input, provide domain-specific documents to the extractive and generative answering components, and controls the output to ensure safe and domain-specific answers. A comprehensive empirical evaluation exhibits that CarExpert outperforms state-of-the-art LLMs in generating natural, safe and car-specific answers.
An End-to-End System for Reproducibility Assessment of Source Code Repositories via Their Readmes
Venue: arXiv cs.CL 2023
Year: 2023

Increased reproducibility of machine learning research has been a driving force for dramatic improvements in learning performances. The scientific community further fosters this effort by including reproducibility ratings in reviewer forms and considering them as a crucial factor for the overall evaluation of papers. Accompanying source code is not sufficient to make a work reproducible. The shared codes should meet the ML reproducibility checklist as well. This work aims to support reproducibility evaluations of papers with source codes. We propose an end-to-end system that operates on the Readme file of the source code repositories. The system checks the compliance of a given Readme to a template proposed by a widely used platform for sharing source codes of research. Our system generates scores based on a custom function to combine section scores. We also train a hierarchical transformer model to assign a class label to a given Readme. The experimental results show that the section similarity-based system performs better than the hierarchical transformer. Moreover, it has an advantage regarding explainability since one can directly relate the score to the sections of Readme files.
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems
Venue: arXiv cs.LG 2023
Year: 2023

Column generation (CG) is a vital method to solve large-scale problems by dynamically generating variables. It has extensive applications in common combinatorial optimization, such as vehicle routing and scheduling problems, where each iteration step requires solving an NP-hard constrained shortest path problem. Although some heuristic methods for acceleration already exist, they are not versatile enough to solve different problems. In this work, we propose a reinforcement learning-based hyper-heuristic framework, dubbed RLHH, to enhance the performance of CG. RLHH is a selection module embedded in CG to accelerate convergence and get better integer solutions. In each CG iteration, the RL agent selects a low-level heuristic to construct a reduced network only containing the edges with a greater chance of being part of the optimal solution. In addition, we specify RLHH to solve two typical combinatorial optimization problems: Vehicle Routing Problem with Time Windows (VRPTW) and Bus Driver Scheduling Problem (BDSP). The total cost can be reduced by up to 27.9\% in VRPTW and 15.4\% in BDSP compared to the best lower-level heuristic in our tested scenarios, within equivalent or even less computational time. The proposed RLHH is the first RL-based CG method that outperforms traditional approaches in terms of solution quality, which can promote the application of CG in combinatorial optimization.
Staged Depthwise Correlation and Feature Fusion for Siamese Object Tracking
Venue: arXiv cs.CV 2023
Year: 2023

In this work, we propose a novel staged depthwise correlation and feature fusion network, named DCFFNet, to further optimize the feature extraction for visual tracking. We build our deep tracker upon a siamese network architecture, which is offline trained from scratch on multiple large-scale datasets in an end-to-end manner. The model contains a core component, that is, depthwise correlation and feature fusion module (correlation-fusion module), which facilitates model to learn a set of optimal weights for a specific object by utilizing ensembles of multi-level features from lower and higher layers and multi-channel semantics on the same layer. We combine the modified ResNet-50 with the proposed correlation-fusion layer to constitute the feature extractor of our model. In training process, we find the training of model become more stable, that benifits from the correlation-fusion module. For comprehensive evaluations of performance, we implement our tracker on the popular benchmarks, including OTB100, VOT2018 and LaSOT. Extensive experiment results demonstrate that our proposed method achieves favorably competitive performance against many leading trackers in terms of accuracy and precision, while satisfying the real-time requirements of applications.
Leveraging Urban Big Data for Informed Business Location Decisions: A Case Study of Starbucks in Tianhe District, Guangzhou City
Venue: arXiv cs.HC 2023
Year: 2023

With the development of the information age, cities provide a large amount of data that can be analyzed and utilized to facilitate the decision-making process. Urban big data and analytics are particularly valuable in the analysis of business location decisions, providing insight and supporting informed choices. By examining data relating to commercial locations, it becomes possible to analyze various spatial characteristics and derive the feasibility of different locations. This analytical approach contributes to effective decision-making and the formulation of robust location strategies. To illustrate this, the study focuses on Starbucks cafes in the Tianhe District of Guangzhou City, China. Utilizing data visualization maps, the spatial distribution characteristics and influencing factors of Starbucks locations are analyzed. By examining the geographical coordinates of Starbucks, main distribution characteristics are identified. Through this analysis, it explores the factors influencing the spatial layout of commercial store locations, using Starbucks as a case study. The findings offer valuable insights into the management of industrial layout and the location strategies of commercial businesses in urban environments, opening avenues for further research and development in this field.
Exploring the Correlation between Urban Microclimate Simulation and Urban Morphology: A Case Study in Yeongdeungpo-gu, Seoul
Venue: arXiv cs.HC 2023
Year: 2023

Different social backgrounds and planning policies give rise to diverse urban morphologies. These morphologies influence urban microclimate factors and contribute to the formation of unique local microclimates, particularly in terms of outdoor temperature. In recent times, the heat island effect has gained increasing significance during the summer season. Therefore, this study aims to explore the correlation between urban microclimate simulation and urban morphology within the context of the heat island effect. Specifically, we investigate how the outside temperature varies across different types of residential buildings in Yeongdeungpo-gu, Seoul, South Korea, during the summer period. We compare temperature conditions using a multi-dimensional system of building clusters' morphological indices and employ ENVI-met software for simulation purposes. The results of the urban microclimate simulation are comprehensively analyzed, revealing a significant finding: high-rise residential buildings exhibit considerably higher outdoor temperatures compared to low-rise residential buildings. Furthermore, the presence of open spaces plays a crucial role in mitigating high neighborhood temperatures. By deriving insights from these findings, we aim to provide valuable conclusions to support city managers in making informed decisions.
AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings
Venue: arXiv cs.CV 2023
Year: 2023

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has been made recently, the adversarial noise generated by most of the existing attack methods is still too conspicuous to the human eyes and proved to be easily detected by defense mechanisms. Resulting that these malicious examples cannot contribute to exploring the vulnerabilities of existing DNNs sufficiently. Thus, to better reveal the defects of DNNs and further help enhance their robustness under noise-limited situations, a new inconspicuous adversarial examples generation method is exactly needed to be proposed. To bridge this gap, we propose a novel Normalize Flow-based end-to-end attack framework, called AFLOW, to synthesize imperceptible adversarial examples under strict constraints. Specifically, rather than the noise-adding manner, AFLOW directly perturbs the hidden representation of the corresponding image to craft the desired adversarial examples. Compared with existing methods, extensive experiments on three benchmark datasets show that the adversarial examples built by AFLOW exhibit superiority in imperceptibility, image quality and attack capability. Even on robust models, AFLOW can still achieve higher attack results than previous methods.
A Number Representation Systems Library Supporting New Representations Based on Morris Tapered Floating-point with Hidden Exponent Bit
Venue: arXiv cs.MS 2023
Year: 2023

The introduction of posit reopened the debate about the utility of IEEE754 in specific domains. In this context, we propose a high-level language (Scala) library that aims to reduce the effort of designing and testing new number representation systems (NRSs). The library's efficiency is tested with three new NRSs derived from Morris Tapered Floating-Point by adding a hidden exponent bit. We call these NRSs MorrisHEB, MorrisBiasHEB, and MorrisUnaryHEB, respectively. We show that they offer a better dynamic range, better decimal accuracy for unary operations, more exact results for addition (37.61% in the case of MorrisUnaryHEB), and better average decimal accuracy for inexact results on binary operations than posit and IEEE754. Going through existing benchmarks in the literature, and favorable/unfavorable examples for IEEE754/posit, we show that these new NRSs produce similar (less than one decimal accuracy difference) or even better results than IEEE754 and posit. Given the entire spectrum of results, there are arguments for MorrisBiasHEB to be used as a replacement for IEEE754 in general computations. MorrisUnaryHEB has a more populated ``golden zone'' (+13.6%) and a better dynamic range (149X) than posit, making it a candidate for machine learning computations.