OpenRaiser/Intern-Atlas huggingface dataset:
Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters
Venue: arXiv cs.AI 2023
Year: 2023

Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = .60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Venue: arXiv cs.CV 2023
Year: 2023

Structural re-parameterization is a general training scheme for Convolutional Neural Networks (CNNs), which achieves performance improvement without increasing inference cost. As Vision Transformers (ViTs) are gradually surpassing CNNs in various visual tasks, one may question: if a training scheme specifically for ViTs exists that can also achieve performance improvement without increasing inference cost? Recently, Mixture-of-Experts (MoE) has attracted increasing attention, as it can efficiently scale up the capacity of Transformers at a fixed cost through sparsely activated experts. Considering that MoE can also be viewed as a multi-branch structure, can we utilize MoE to implement a ViT training scheme similar to structural re-parameterization? In this paper, we affirmatively answer these questions, with a new general training strategy for ViTs. Specifically, we decouple the training and inference phases of ViTs. During training, we replace some Feed-Forward Networks (FFNs) of the ViT with specially designed, more efficient MoEs that assign tokens to experts by random uniform partition, and perform Experts Weights Averaging (EWA) on these MoEs at the end of each iteration. After training, we convert each MoE into an FFN by averaging the experts, transforming the model back into original ViT for inference. We further provide a theoretical analysis to show why and how it works. Comprehensive experiments across various 2D and 3D visual tasks, ViT architectures, and datasets validate the effectiveness and generalizability of the proposed training scheme. Besides, our training scheme can also be applied to improve performance when fine-tuning ViTs. Lastly, but equally important, the proposed EWA technique can significantly improve the effectiveness of naive MoE in various 2D visual small datasets and 3D visual tasks.
Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation
Venue: arXiv cs.CV 2023
Year: 2023

Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality. However, it is currently difficult to compare the performance of these VCE methods as the evaluation procedures largely vary and often boil down to visual inspection of individual examples and small scale user studies. In this work, we propose a framework for systematic, quantitative evaluation of the VCE methods and a minimal set of metrics to be used. We use this framework to explore the effects of certain crucial design choices in the latest diffusion-based generative models for VCEs of natural image classification (ImageNet). We conduct a battery of ablation-like experiments, generating thousands of VCEs for a suite of classifiers of various complexity, accuracy and robustness. Our findings suggest multiple directions for future advancements and improvements of VCE methods. By sharing our methodology and our approach to tackle the computational challenges of such a study on a limited hardware setup (including the complete code base), we offer a valuable guidance for researchers in the field fostering consistency and transparency in the assessment of counterfactual explanations.
Finding Long Directed Cycles Is Hard Even When DFVS Is Small Or Girth Is Large
Venue: arXiv cs.DS 2023
Year: 2023

We study the parameterized complexity of two classic problems on directed graphs: Hamiltonian Cycle and its generalization {\sc Longest Cycle}. Since 2008, it is known that Hamiltonian Cycle is W[1]-hard when parameterized by directed treewidth [Lampis et al., ISSAC'08]. By now, the question of whether it is FPT parameterized by the directed feedback vertex set (DFVS) number has become a longstanding open problem. In particular, the DFVS number is the largest natural directed width measure studied in the literature. In this paper, we provide a negative answer to the question, showing that even for the DFVS number, the problem remains W[1]-hard. As a consequence, we also obtain that Longest Cycle is W[1]-hard on directed graphs when parameterized multiplicatively above girth, in contrast to the undirected case. This resolves an open question posed by Fomin et al. [ACM ToCT'21] and Gutin and Mnich [arXiv:2207.12278]. Our hardness results apply to the path versions of the problems as well. On the positive side, we show that Longest Path parameterized multiplicatively above girth} belongs to the class XP.
Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Inter-city highway transportation is significant for citizens' modern urban life and generates heterogeneous sensory data with spatio-temporal characteristics. As a routine analysis in transportation domain, daily traffic volume estimation faces challenges for highway toll stations including lacking of exploration of correlative spatio-temporal features from a long-term perspective and effective means to deal with data imbalance which always deteriorates the predictive performance. In this paper, a deep spatio-temporal learning method is proposed to predict daily traffic volume in three phases. In feature pre-processing phase, data is normalized elaborately according to latent long-tail distribution. In spatio-temporal learning phase, a hybrid model is employed combining fully convolution network (FCN) and long short-term memory (LSTM), which considers time, space, meteorology, and calendar from heterogeneous data. In decision phase, traffic volumes on a coming day at network-wide toll stations would be achieved effectively, which is especially calibrated for vital few highway stations. Using real-world data from one Chinese provincial highway, extensive experiments show our method has distinct improvement for predictive accuracy than various traditional models, reaching 5.269 and 0.997 in MPAE and R-squre metrics, respectively.
Rethinking the Localization in Weakly Supervised Object Localization
Venue: arXiv cs.CV 2023
Year: 2023

Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the image-level supervision. Recently, dividing WSOL into two parts (class-agnostic object localization and object classification) has become the state-of-the-art pipeline for this task. However, existing solutions under this pipeline usually suffer from the following drawbacks: 1) they are not flexible since they can only localize one object for each image due to the adopted single-class regression (SCR) for localization; 2) the generated pseudo bounding boxes may be noisy, but the negative impact of such noise is not well addressed. To remedy these drawbacks, we first propose to replace SCR with a binary-class detector (BCD) for localizing multiple objects, where the detector is trained by discriminating the foreground and background. Then we design a weighted entropy (WE) loss using the unlabeled data to reduce the negative impact of noisy bounding boxes. Extensive experiments on the popular CUB-200-2011 and ImageNet-1K datasets demonstrate the effectiveness of our method.
Noise-Resilient Designs for Optical Neural Networks
Venue: arXiv cs.NE 2023
Year: 2023

All analog signal processing is fundamentally subject to noise, and this is also the case in modern implementations of Optical Neural Networks (ONNs). Therefore, to mitigate noise in ONNs, we propose two designs that are constructed from a given, possibly trained, Neural Network (NN) that one wishes to implement. Both designs have the capability that the resulting ONNs gives outputs close to the desired NN. To establish the latter, we analyze the designs mathematically. Specifically, we investigate a probabilistic framework for the first design that establishes that the design is correct, i.e., for any feed-forward NN with Lipschitz continuous activation functions, an ONN can be constructed that produces output arbitrarily close to the original. ONNs constructed with the first design thus also inherit the universal approximation property of NNs. For the second design, we restrict the analysis to NNs with linear activation functions and characterize the ONNs' output distribution using exact formulas. Finally, we report on numerical experiments with LeNet ONNs that give insight into the number of components required in these designs for certain accuracy gains. We specifically study the effect of noise as a function of the depth of an ONN. The results indicate that in practice, adding just a few components in the manner of the first or the second design can already be expected to increase the accuracy of ONNs considerably.
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
Venue: arXiv cs.CV 2023
Year: 2023

Complex emotion recognition is a cognitive task that has so far eluded the same excellent performance of other tasks that are at or above the level of human cognition. Emotion recognition through facial expressions is particularly difficult due to the complexity of emotions expressed by the human face. For a machine to approach the same level of performance in complex facial expression recognition as a human, it may need to synthesise knowledge and understand new concepts in real-time, as humans do. Humans are able to learn new concepts using only few examples by distilling important information from memories. Inspired by human cognition and learning, we propose a novel continual learning method for complex facial expression recognition that can accurately recognise new compound expression classes using few training samples, by building on and retaining its knowledge of basic expression classes. In this work, we also use GradCAM visualisations to demonstrate the relationship between basic and compound facial expressions. Our method leverages this relationship through knowledge distillation and a novel Predictive Sorting Memory Replay, to achieve the current state-of-the-art in continual learning for complex facial expression recognition, with 74.28% Overall Accuracy on new classes. We also demonstrate that using continual learning for complex facial expression recognition achieves far better performance than non-continual learning methods, improving on state-of-the-art non-continual learning methods by 13.95%. Our work is also the first to apply few-shot learning to complex facial expression recognition, achieving the state-of-the-art with 100% accuracy using only a single training sample per class.
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks
Venue: arXiv cs.RO 2023
Year: 2023

Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter. Thus, robots designed like this are fragile and fail outside of highly-controlled environments. Causal models provide a principled framework to encode formal knowledge of the causal relationships that govern the robot's interaction with its environment, in addition to probabilistic representations of noise and uncertainty typically encountered by real-world robots. Combined with causal inference, these models permit an autonomous agent to understand, reason about, and explain its environment. In this work, we focus on the problem of a robot block-stacking task due to the fundamental perception and manipulation capabilities it demonstrates, required by many applications including warehouse logistics and domestic human support robotics. We propose a novel causal probabilistic framework to embed a physics simulation capability into a structural causal model to permit robots to perceive and assess the current state of a block-stacking task, reason about the next-best action from placement candidates, and generate post-hoc counterfactual explanations. We provide exemplar next-best action selection results and outline planned experimentation in simulated and real-world robot block-stacking tasks.
A framework for smart homes for elderly people using Labview
Venue: arXiv cs.HC 2023
Year: 2023

This paper deals with home automation systems that are essential for safe and independent living of elderly people. These individuals must be able to perform their Activities of Daily Living (ADLs) without help from caretakers. They must be confident enough that help is just a few minutes with the systems in place. The focus will be on the medical and emergency systems, which are most essential for independent living. Zigbee based Wireless Sensor Networks (WSN) are used to collect the data throughout the home. Smart shirts and smart phones monitor different parameters of the individual and transmit the data to the home network. A PC running LabVIEW is used as the central control unit for the entire house. The house must have a fail proof communication framework for connecting with emergency services, doctors, helpers and relatives. The home network is connected to the Public Switched Telephone Network (PSTN) and internet. SMS, email, prerecorded voice-based telephone calls are appropriately used to report emergencies. A GUI (Graphical User Interface) was built in LabVIEW in order to control and simulate an entire home. Ambient Intelligence is built into the system to make it adaptive and dynamic as far as possible.
Street View Data Collection Design for Disaster Reconnaissance
Venue: arXiv cs.HC 2023
Year: 2023

Over the last decade, street-view type images have been used across disciplines to generate and understand various place-based metrics. However efforts to collect this data were often meant to support investigator-driven research without regard to the utility of the data for other researchers. To address this, we describe our methods for collecting and publishing longitudinal data of this type in the wake of the COVID-19 pandemic and discuss some of the challenges we encountered along the way. Our process included designing a route taking into account both broad area canvassing and community capitals transects. We also implemented procedures for uploading and publishing data from each survey. Our methods successfully generated the kind of longitudinal data that can be beneficial to a variety of research disciplines. However, there were some challenges with data collection consistency and the sheer magnitude of data produced. Overall, our approach demonstrates the feasibility of generating longitudinal street-view data in the wake of a disaster event. Based on our experience, we provide recommendations for future researchers attempting to create a similar data set.
An Integrated Visual Analytics System for Studying Clinical Carotid Artery Plaques
Venue: arXiv cs.HC 2023
Year: 2023

Carotid artery plaques can cause arterial vascular diseases such as stroke and myocardial infarction, posing a severe threat to human life. However, the current clinical examination mainly relies on a direct assessment by physicians of patients' clinical indicators and medical images, lacking an integrated visualization tool for analyzing the influencing factors and composition of carotid artery plaques. We have designed an intelligent carotid artery plaque visual analysis system for vascular surgery experts to comprehensively analyze the clinical physiological and imaging indicators of carotid artery diseases. The system mainly includes two functions: First, it displays the correlation between carotid artery plaque and various factors through a series of information visualization methods and integrates the analysis of patient physiological indicator data. Second, it enhances the interface guidance analysis of the inherent correlation between the components of carotid artery plaque through machine learning and displays the spatial distribution of the plaque on medical images. Additionally, we conducted two case studies on carotid artery plaques using real data obtained from a hospital, and the results indicate that our designed carotid analysis system can effectively provide clinical diagnosis and treatment guidance for vascular surgeons.
Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion
Venue: arXiv cs.SD 2023
Year: 2023

Voice conversion (VC) aims at altering a person's voice to make it sound similar to the voice of another person while preserving linguistic content. Existing methods suffer from a dilemma between content intelligibility and speaker similarity; i.e., methods with higher intelligibility usually have a lower speaker similarity, while methods with higher speaker similarity usually require plenty of target speaker voice data to achieve high intelligibility. In this work, we propose a novel method \textit{Phoneme Hallucinator} that achieves the best of both worlds. Phoneme Hallucinator is a one-shot VC model; it adopts a novel model to hallucinate diversified and high-fidelity target speaker phonemes based just on a short target speaker voice (e.g. 3 seconds). The hallucinated phonemes are then exploited to perform neighbor-based voice conversion. Our model is a text-free, any-to-any VC model that requires no text annotations and supports conversion to any unseen speaker. Objective and subjective evaluations show that \textit{Phoneme Hallucinator} outperforms existing VC methods for both intelligibility and speaker similarity.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Venue: arXiv cs.CV 2023
Year: 2023

Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddles, and various kinds of road patches i.e., earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset (R2S100K) -- a large-scale dataset and benchmark for training and evaluation of road segmentation in aforementioned challenging unstructured roadways. R2S100K comprises 100K images extracted from a large and diverse set of video sequences covering more than 1000 KM of roadways. Out of these 100K privacy respecting images, 14,000 images have fine pixel-labeling of road regions, with 86,000 unlabeled images that can be leveraged through semi-supervised learning methods. Alongside, we present an Efficient Data Sampling (EDS) based self-training framework to improve learning by leveraging unlabeled data. Our experimental results demonstrate that the proposed method significantly improves learning methods in generalizability and reduces the labeling cost for semantic segmentation tasks. Our benchmark will be publicly available to facilitate future research at https://r2s100k.github.io/.
M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector
Venue: arXiv cs.CV 2023
Year: 2023

Deep-learning-based object detection methods show promise for improving screening mammography, but high rates of false positives can hinder their effectiveness in clinical practice. To reduce false positives, we identify three challenges: (1) unlike natural images, a malignant mammogram typically contains only one malignant finding; (2) mammography exams contain two views of each breast, and both views ought to be considered to make a correct assessment; (3) most mammograms are negative and do not contain any findings. In this work, we tackle the three aforementioned challenges by: (1) leveraging Sparse R-CNN and showing that sparse detectors are more appropriate than dense detectors for mammography; (2) including a multi-view cross-attention module to synthesize information from different views; (3) incorporating multi-instance learning (MIL) to train with unannotated images and perform breast-level classification. The resulting model, M&M, is a Multi-view and Multi-instance learning system that can both localize malignant findings and provide breast-level predictions. We validate M&M's detection and classification performance using five mammography datasets. In addition, we demonstrate the effectiveness of each proposed component through comprehensive ablation studies.
Multiclass Learnability Does Not Imply Sample Compression
Venue: arXiv cs.LG 2023
Year: 2023

A hypothesis class admits a sample compression scheme, if for every sample labeled by a hypothesis from the class, it is possible to retain only a small subsample, using which the labels on the entire sample can be inferred. The size of the compression scheme is an upper bound on the size of the subsample produced. Every learnable binary hypothesis class (which must necessarily have finite VC dimension) admits a sample compression scheme of size only a finite function of its VC dimension, independent of the sample size. For multiclass hypothesis classes, the analog of VC dimension is the DS dimension. We show that the analogous statement pertaining to sample compression is not true for multiclass hypothesis classes: every learnable multiclass hypothesis class, which must necessarily have finite DS dimension, does not admit a sample compression scheme of size only a finite function of its DS dimension.
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams
Venue: arXiv cs.HC 2023
Year: 2023

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations.