OpenRaiser/Intern-Atlas huggingface dataset:
LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Venue: arXiv cs.AI 2023
Year: 2023

Background and objectives: Dynamic handwriting analysis, due to its non-invasive and readily accessible nature, has recently emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease. In this study, we design a compact and efficient network architecture to analyse the distinctive handwriting patterns of patients' dynamic handwriting signals, thereby providing an objective identification for the Parkinson's disease diagnosis. Methods: The proposed network is based on a hybrid deep learning approach that fully leverages the advantages of both long short-term memory (LSTM) and convolutional neural networks (CNNs). Specifically, the LSTM block is adopted to extract the time-varying features, while the CNN-based block is implemented using one-dimensional convolution for low computational cost. Moreover, the hybrid model architecture is continuously refined under ablation studies for superior performance. Finally, we evaluate the proposed method with its generalization under a five-fold cross-validation, which validates its efficiency and robustness. Results: The proposed network demonstrates its versatility by achieving impressive classification accuracies on both our new DraWritePD dataset ($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the network architecture also stands out for its excellent lightweight design, occupying a mere $0.084$M of parameters, with a total of only $0.59$M floating-point operations. It also exhibits near real-time CPU inference performance, with inference times ranging from $0.106$ to $0.220$s. Conclusions: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed hybrid neural network in extracting distinctive handwriting patterns for precise diagnosis of Parkinson's disease.
Improving Real Estate Appraisal with POI Integration and Areal Embedding
Venue: arXiv cs.AI 2023
Year: 2023

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.
Neural Graph Collaborative Filtering Using Variational Inference
Venue: arXiv cs.IR 2023
Year: 2023

The customization of recommended content to users holds significant importance in enhancing user experiences across a wide spectrum of applications such as e-commerce, music, and shopping. Graph-based methods have achieved considerable performance by capturing user-item interactions. However, these methods tend to utilize randomly constructed embeddings in the dataset used for training the recommender, which lacks any user preferences. Here, we propose the concept of variational embeddings as a means of pre-training the recommender system to improve the feature propagation through the layers of graph convolutional networks (GCNs). The graph variational embedding collaborative filtering (GVECF) is introduced as a novel framework to incorporate representations learned through a variational graph auto-encoder which are embedded into a GCN-based collaborative filtering. This approach effectively transforms latent high-order user-item interactions into more trainable vectors, ultimately resulting in better performance in terms of recall and normalized discounted cumulative gain(NDCG) metrics. The experiments conducted on benchmark datasets demonstrate that our proposed method achieves up to 13.78% improvement in the recall over the test data.
VLM-Eval: A General Evaluation on Video Large Language Models
Venue: arXiv cs.CV 2023
Year: 2023

Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent. In this paper, we introduce a unified evaluation that encompasses multiple video tasks, including captioning, question and answering, retrieval, and action recognition. In addition to conventional metrics, we showcase how GPT-based evaluation can match human-like performance in assessing response quality across multiple aspects. We propose a simple baseline: Video-LLaVA, which uses a single linear projection and outperforms existing video LLMs. Finally, we evaluate video LLMs beyond academic datasets, which show encouraging recognition and reasoning capabilities in driving scenarios with only hundreds of video-instruction pairs for fine-tuning. We hope our work can serve as a unified evaluation for video LLMs, and help expand more practical scenarios. The evaluation code will be available soon.
Mapping Pipelines and Simultaneous Localization for Petrochemical Industry Robots
Venue: arXiv cs.RO 2023
Year: 2023

Inspecting petrochemical pipelines is challenging due to hazardous materials, narrow diameters, and inaccessible locations. Mobile robots are promising for autonomous pipeline inspection and mapping. This project aimed to simulate and implement a robot capable of simultaneous localization and mapping (SLAM) in an indoor maze-like environment representing simplified pipelines. The approach involved simulating a differential drive robot in Gazebo/ROS, equipping it with sensors, implementing SLAM using mapping, and path planning with move_base. A physical robot was then built and tested by manually driving it in a constructed maze while collecting sensor data and mapping. Sensor fusion of wheel encoders, Kinect camera, and inertial measurement unit (IMU) data was explored to improve odometry and mapping accuracy without encoders. The final map had reasonable correspondence to the true maze despite lacking wheel encoders. In summary, results show the feasibility of using ROS-based SLAM for pipeline inspection if accounting for real-world complexities.
Automatic Analysis of Substantiation in Scientific Peer Reviews
Venue: arXiv cs.CL 2023
Year: 2023

With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures. In this paper, we restrict our attention to substantiation -- one popular quality aspect indicating whether the claims in a review are sufficiently supported by evidence -- and provide a solution automatizing this evaluation process. To achieve this goal, we first formulate the problem as claim-evidence pair extraction in scientific peer reviews, and collect SubstanReview, the first annotated dataset for this task. SubstanReview consists of 550 reviews from NLP conferences annotated by domain experts. On the basis of this dataset, we train an argument mining system to automatically analyze the level of substantiation in peer reviews. We also perform data analysis on the SubstanReview dataset to obtain meaningful insights on peer reviewing quality in NLP conferences over recent years.
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
Venue: arXiv cs.SE 2023
Year: 2023

Due to the ever-increasing complexity of income tax laws in the United States, the number of US taxpayers filing their taxes using tax preparation software (henceforth, tax software) continues to increase. According to the U.S. Internal Revenue Service (IRS), in FY22, nearly 50% of taxpayers filed their individual income taxes using tax software. Given the legal consequences of incorrectly filing taxes for the taxpayer, ensuring the correctness of tax software is of paramount importance. Metamorphic testing has emerged as a leading solution to test and debug legal-critical tax software due to the absence of correctness requirements and trustworthy datasets. The key idea behind metamorphic testing is to express the properties of a system in terms of the relationship between one input and its slightly metamorphosed twinned input. Extracting metamorphic properties from IRS tax publications is a tedious and time-consuming process. As a response, this paper formulates the task of generating metamorphic specifications as a translation task between properties extracted from tax documents - expressed in natural language - to a contrastive first-order logic form. We perform a systematic analysis on the potential and limitations of in-context learning with Large Language Models(LLMs) for this task, and outline a research agenda towards automating the generation of metamorphic specifications for tax preparation software.
Categorizing the Visual Environment and Analyzing the Visual Attention of Dogs
Venue: arXiv cs.CV 2023
Year: 2023

Dogs have a unique evolutionary relationship with humans and serve many important roles e.g. search and rescue, blind assistance, emotional support. However, few datasets exist to categorize visual features and objects available to dogs, as well as how dogs direct their visual attention within their environment. We collect and study a dataset with over 11,698 gazes to categorize the objects available to be gazed at by 11 dogs in everyday outdoor environments i.e. a walk around a college campus and urban area. We explore the availability of these object categories and the visual attention of dogs over these categories using a head mounted eye tracking apparatus. A small portion (approx. 600 images or < 20% of total dataset) of the collected data is used to fine tune a MaskRCNN for the novel image domain to segment objects present in the scene, enabling further statistical analysis on the visual gaze tendencies of dogs. The MaskRCNN, with eye tracking apparatus, serves as an end to end model for automatically classifying the visual fixations of dogs. The fine tuned MaskRCNN performs far better than chance. There are few individual differences between the 11 dogs and we observe greater visual fixations on buses, plants, pavement, and construction equipment. This work takes a step towards understanding visual behavior of dogs and their interaction with the physical world.
Pursing the Sparse Limitation of Spiking Deep Learning Structures
Venue: arXiv cs.NE 2023
Year: 2023

Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial neural networks (ANNs). To facilitate deployment on memory-constrained devices, numerous studies have explored SNN pruning. However, these efforts are hindered by challenges such as scalability challenges in more complex architectures and accuracy degradation. Amidst these challenges, the Lottery Ticket Hypothesis (LTH) emerges as a promising pruning strategy. It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance. To explore a more structure-sparse and energy-saving model, we investigate the unique synergy of SNNs with LTH and design two novel spiking winning tickets to push the boundaries of sparsity within SNNs. Furthermore, we introduce an innovative algorithm capable of simultaneously identifying both weight and patch-level winning tickets, enabling the achievement of sparser structures without compromising on the final model's performance. Through comprehensive experiments on both RGB-based and event-based datasets, we demonstrate that our spiking lottery ticket achieves comparable or superior performance even when the model structure is extremely sparse.
Covert Online Decision Making: From Sequential Hypothesis Testing to Stochastic Bandits
Venue: arXiv cs.IT 2023
Year: 2023

We study the problem of covert online decision-making in which an agent attempts to identify a parameter governing a system by probing the system while escaping detection from an adversary. The system is modeled as a Markov kernel whose input is controlled by the agent and whose two outputs are observed by the agent and the adversary, respectively. This problem is motivated by applications such as covert sensing or covert radar, in which one tries to perform a sensing task without arousing suspicion by an adversary monitoring the environment for the presence of sensing signals. Specifically, we consider two situations corresponding to different amounts of knowledge of the system. If the kernel is known but governed by an unknown fixed parameter, we formulate the problem as a sequential hypothesis testing problem. If the kernel determining the observations of the agent is unknown but the kernel determining those of the adversary is known, we formulate the problem as a best-arm identification problem in a bandit setting. In both situations, we characterize the exponent of the probability of identification error. As expected because of the covertness requirement, the probability of identification error decays exponentially with the square root of the blocklength.
Leveraging Closed-Access Multilingual Embedding for Automatic Sentence Alignment in Low Resource Languages
Venue: arXiv cs.CL 2023
Year: 2023

The importance of qualitative parallel data in machine translation has long been determined but it has always been very difficult to obtain such in sufficient quantity for the majority of world languages, mainly because of the associated cost and also the lack of accessibility to these languages. Despite the potential for obtaining parallel datasets from online articles using automatic approaches, forensic investigations have found a lot of quality-related issues such as misalignment, and wrong language codes. In this work, we present a simple but qualitative parallel sentence aligner that carefully leveraged the closed-access Cohere multilingual embedding, a solution that ranked second in the just concluded #CoHereAIHack 2023 Challenge (see https://ai6lagos.devpost.com). The proposed approach achieved $94.96$ and $54.83$ f1 scores on FLORES and MAFAND-MT, compared to $3.64$ and $0.64$ of LASER respectively. Our method also achieved an improvement of more than 5 BLEU scores over LASER, when the resulting datasets were used with MAFAND-MT dataset to train translation models. Our code and data are available for research purposes here (https://github.com/abumafrim/Cohere-Align).
Common (good) practices measuring trust in HRI
Venue: arXiv cs.RO 2023
Year: 2023

Trust in robots is widely believed to be imperative for the adoption of robots into people's daily lives. It is, therefore, understandable that the literature of the last few decades focuses on measuring how much people trust robots -- and more generally, any agent - to foster such trust in these technologies. Researchers have been exploring how people trust robot in different ways, such as measuring trust on human-robot interactions (HRI) based on textual descriptions or images without any physical contact, during and after interacting with the technology. Nevertheless, trust is a complex behaviour, and it is affected and depends on several factors, including those related to the interacting agents (e.g. humans, robots, pets), itself (e.g. capabilities, reliability), the context (e.g. task), and the environment (e.g. public spaces vs private spaces vs working spaces). In general, most roboticists agree that insufficient levels of trust lead to a risk of disengagement while over-trust in technology can cause over-reliance and inherit dangers, for example, in emergency situations. It is, therefore, very important that the research community has access to reliable methods to measure people's trust in robots and technology. In this position paper, we outline current methods and their strengths, identify (some) weakly covered aspects and discuss the potential for covering a more comprehensive amount of factors influencing trust in HRI.
Characterizing Browser Fingerprinting and its Mitigations
Venue: arXiv cs.CR 2023
Year: 2023

People are becoming increasingly concerned with their online privacy, especially with how advertising companies track them across websites (a practice called cross-site tracking), as reconstructing a user's browser history can reveal sensitive information. Recent legislation like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act have tried to limit the extent to which third parties perform cross-site tracking, and browsers have also made tracking more difficult by deprecating the most-common tracking mechanism: third-party cookies. However, online advertising companies continue to track users through other mechanisms that do not rely on cookies. This work explores one of these tracking techniques: browser fingerprinting. We detail how browser fingerprinting works, how prevalent it is, and what defenses can mitigate it.
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction
Venue: arXiv cs.CR 2023
Year: 2023

Adversarial patch-based attacks have shown to be a major deterrent towards the reliable use of machine learning models. These attacks involve the strategic modification of localized patches or specific image areas to deceive trained machine learning models. In this paper, we propose \textit{DefensiveDR}, a practical mechanism using a dimensionality reduction technique to thwart such patch-based attacks. Our method involves projecting the sample images onto a lower-dimensional space while retaining essential information or variability for effective machine learning tasks. We perform this using two techniques, Singular Value Decomposition and t-Distributed Stochastic Neighbor Embedding. We experimentally tune the variability to be preserved for optimal performance as a hyper-parameter. This dimension reduction substantially mitigates adversarial perturbations, thereby enhancing the robustness of the given machine learning model. Our defense is model-agnostic and operates without assumptions about access to model decisions or model architectures, making it effective in both black-box and white-box settings. Furthermore, it maintains accuracy across various models and remains robust against several unseen patch-based attacks. The proposed defensive approach improves the accuracy from 38.8\% (without defense) to 66.2\% (with defense) when performing LaVAN and GoogleAp attacks, which supersedes that of the prominent state-of-the-art like LGS (53.86\%) and Jujutsu (60\%).
Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization
Venue: arXiv cs.AI 2023
Year: 2023

As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized machine learning or deep learning techniques to implement probabilistic forecasting reconciliation and have made notable progress. However, these methods treat the reconciliation step as a fixed and hard post-processing step, leading to a trade-off between accuracy and coherency. In this paper, we propose a new approach for probabilistic forecast reconciliation. Unlike existing approaches, our proposed approach fuses the prediction step and reconciliation step into a deep learning framework, making the reconciliation step more flexible and soft by introducing the Kullback-Leibler divergence regularization term into the loss function. The approach is evaluated using three hierarchical time series datasets, which shows the advantages of our approach over other probabilistic forecast reconciliation methods.
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers
Venue: arXiv cs.CV 2023
Year: 2023

Existing 3D semantic segmentation methods rely on point-wise or voxel-wise feature descriptors to output segmentation predictions. However, these descriptors are often supervised at point or voxel level, leading to segmentation models that can behave poorly at instance-level. In this paper, we proposed a novel instance-aware approach for 3D semantic segmentation. Our method combines several geometry processing tasks supervised at instance-level to promote the consistency of the learned feature representation. Specifically, our methods use shape generators and shape classifiers to perform shape reconstruction and classification tasks for each shape instance. This enforces the feature representation to faithfully encode both structural and local shape information, with an awareness of shape instances. In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.
Noise in Relation Classification Dataset TACRED: Characterization and Reduction
Venue: arXiv cs.CL 2023
Year: 2023

The overarching objective of this paper is two-fold. First, to explore model-based approaches to characterize the primary cause of the noise. in the RE dataset TACRED Second, to identify the potentially noisy instances. Towards the first objective, we analyze predictions and performance of state-of-the-art (SOTA) models to identify the root cause of noise in the dataset. Our analysis of TACRED shows that the majority of the noise in the dataset originates from the instances labeled as no-relation which are negative examples. For the second objective, we explore two nearest-neighbor-based strategies to automatically identify potentially noisy examples for elimination and reannotation. Our first strategy, referred to as Intrinsic Strategy (IS), is based on the assumption that positive examples are clean. Thus, we have used false-negative predictions to identify noisy negative examples. Whereas, our second approach, referred to as Extrinsic Strategy, is based on using a clean subset of the dataset to identify potentially noisy negative examples. Finally, we retrained the SOTA models on the eliminated and reannotated dataset. Our empirical results based on two SOTA models trained on TACRED-E following the IS show an average 4% F1-score improvement, whereas reannotation (TACRED-R) does not improve the original results. However, following ES, SOTA models show the average F1-score improvement of 3.8% and 4.4% when trained on respective eliminated (TACRED-EN) and reannotated (TACRED-RN) datasets respectively. We further extended the ES for cleaning positive examples as well, which resulted in an average performance improvement of 5.8% and 5.6% for the eliminated (TACRED-ENP) and reannotated (TACRED-RNP) datasets respectively.
Jup2Kub: algorithms and a system to translate a Jupyter Notebook pipeline to a fault tolerant distributed Kubernetes deployment
Venue: arXiv cs.DC 2023
Year: 2023

Scientific workflows facilitate computational, data manipulation, and sometimes visualization steps for scientific data analysis. They are vital for reproducing and validating experiments, usually involving computational steps in scientific simulations and data analysis. These workflows are often developed by domain scientists using Jupyter notebooks, which are convenient yet face limitations: they struggle to scale with larger data sets, lack failure tolerance, and depend heavily on the stability of underlying tools and packages. To address these issues, Jup2Kup has been developed. This software system translates workflows from Jupyter notebooks into a distributed, high-performance Kubernetes environment, enhancing fault tolerance. It also manages software dependencies to maintain operational stability amidst changes in tools and packages.