Reviewerly/exHarmony huggingface dataset:
Unbiased Scene Graph Generation using Predicate Similarities

Scene Graphs are widely applied in computer vision as a graphical representation of relationships between objects shown in images. However, these applications have not yet reached a practical stage of development owing to biased training caused by long-tailed predicate distributions. In recent years, many studies have tackled this problem. In contrast, relatively few works have considered predicate similarities as a unique dataset feature which also leads to the biased prediction. Due to the feature, infrequent predicates (e.g., parked on, covered in) are easily misclassified as closely-related frequent predicates (e.g., on, in). Utilizing predicate similarities, we propose a new classification scheme that branches the process to several fine-grained classifiers for similar predicate groups. The classifiers aim to capture the differences among similar predicates in detail. We also introduce the idea of transfer learning to enhance the features for the predicates which lack sufficient training samples to learn the descriptive representations. The results of extensive experiments on the Visual Genome dataset show that the combination of our method and an existing debiasing approach greatly improves performance on tail predicates in challenging SGCls/SGDet tasks. Nonetheless, the overall performance of the proposed approach does not reach that of the current state of the art, so further analysis remains necessary as future work.
Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes

We consider Linear Stochastic Approximation (LSA) with a constant stepsize and Markovian data. Viewing the joint process of the data and LSA iterate as a time-homogeneous Markov chain, we prove its convergence to a unique limiting and stationary distribution in Wasserstein distance and establish non-asymptotic, geometric convergence rates. Furthermore, we show that the bias vector of this limit admits an infinite series expansion with respect to the stepsize. Consequently, the bias is proportional to the stepsize up to higher order terms. This result stands in contrast with LSA under i.i.d. data, for which the bias vanishes. In the reversible chain setting, we provide a general characterization of the relationship between the bias and the mixing time of the Markovian data, establishing that they are roughly proportional to each other. While Polyak-Ruppert tail-averaging reduces the variance of the LSA iterates, it does not affect the bias. The above characterization allows us to show that the bias can be reduced using Richardson-Romberg extrapolation with $m\ge 2$ stepsizes, which eliminates the $m-1$ leading terms in the bias expansion. This extrapolation scheme leads to an exponentially smaller bias and an improved mean squared error, both in theory and empirically. Our results immediately apply to the Temporal Difference learning algorithm with linear function approximation, Markovian data, and constant stepsizes.
Attention Distillation: self-supervised vision transformer students need more guidance

Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research. The code is here: https://github.com/wangkai930418/attndistill
Data Budgeting for Machine Learning

Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practitioners, data investment remains to be a leap of faith in practice. In this work, we study the data budgeting problem and formulate it as two sub-problems: predicting (1) what is the saturating performance if given enough data, and (2) how many data points are needed to reach near the saturating performance. Different from traditional dataset-independent methods like PowerLaw, we proposed a learning method to solve data budgeting problems. To support and systematically evaluate the learning-based method for data budgeting, we curate a large collection of 383 tabular ML datasets, along with their data vs performance curves. Our empirical evaluation shows that it is possible to perform data budgeting given a small pilot study dataset with as few as $50$ data points.
Complexity of Polyadic Boolean Modal Logics: Model Checking and Satisfiability

We study the computational complexity of model checking and satisfiability problems of polyadic modal logics extended with permutations and Boolean operators on accessibility relations. First, we show that the combined complexity of the model checking problem for the resulting logic is PTime-complete. Secondly, we show that the satisfiability problem of polyadic modal logic extended with negation on accessibility relations is ExpTime-complete. Finally, we show that the satisfiability problem of polyadic modal logic with permutations and Boolean operators on accessibility relations is ExpTime-complete, under the necessary assumption that the number of accessibility relations that can be used is bounded by a constant.
On duality and model theory for polyadic spaces

This paper is a study of first-order coherent logic from the point of view of duality and categorical logic. We prove a duality theorem between coherent hyperdoctrines and open polyadic Priestley spaces, which we subsequently apply to prove completeness, omitting types, and Craig interpolation theorems for coherent or intuitionistic logic. Our approach emphasizes the role of interpolation and openness properties, and allows for a modular, syntax-free treatment of these model-theoretic results. As further applications of the same method, we prove completeness theorems for constant domain and G\"odel-Dummett intuitionistic predicate logics.
Device-independent uncloneable encryption

Uncloneable encryption, first introduced by Broadbent and Lord (TQC 2020) is a quantum encryption scheme in which a quantum ciphertext cannot be distributed between two non-communicating parties such that, given access to the decryption key, both parties cannot learn the underlying plaintext. In this work, we introduce a variant of uncloneable encryption in which several possible decryption keys can decrypt a particular encryption, and the security requirement is that two parties who receive independently generated decryption keys cannot both learn the underlying ciphertext. We show that this variant of uncloneable encryption can be achieved device-independently, i.e., without trusting the quantum states and measurements used in the scheme, and that this variant works just as well as the original definition in constructing quantum money. Moreover, we show that a simple modification of our scheme yields a single-decryptor encryption scheme, which was a related notion introduced by Georgiou and Zhandry. In particular, the resulting single-decryptor encryption scheme achieves device-independent security with respect to a standard definition of security against random plaintexts. Finally, we derive an "extractor" result for a two-adversary scenario, which in particular yields a single-decryptor encryption scheme for single bit-messages that achieves perfect anti-piracy security without needing the quantum random oracle model.
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is restricted by the domain gap between rendered depth maps and images, as well as the diversity of depth distributions. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Dual-Path Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for few-shot learning. The dual-path structure allows the joint use of CLIP and CLIP2Point, and the simplified adapter can well fit few-shot tasks without post-search. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP and other self-supervised 3D networks, achieving state-of-the-art results on zero-shot and few-shot classification.
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision &amp; Language Models

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available at https://www.adrianbulat.com/lasp
That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation

Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual or tactile sensing, where unfortunately one fails to capture high-frequency interaction, while the other can be too delicate for large-scale data collection. In this work, we propose a data-centric approach to dynamic manipulation that uses an often ignored source of information: sound. We first collect a dataset of 25k interaction-sound pairs across five dynamic tasks using commodity contact microphones. Then, given this data, we leverage self-supervised learning to accelerate behavior prediction from sound. Our experiments indicate that this self-supervised 'pretraining' is crucial to achieving high performance, with a 34.5% lower MSE than plain supervised learning and a 54.3% lower MSE over visual training. Importantly, we find that when asked to generate desired sound profiles, online rollouts of our models on a UR10 robot can produce dynamic behavior that achieves an average of 11.5% improvement over supervised learning on audio similarity metrics.
Improve the Assessment Model of Personnel Develop Level in Higher Education Based on Machine Learning

In order to improve the ability to manage the quality constraint index parameters of higher education talent training, an evaluation model of higher education talent training quality based on improved machine learning is proposed. Combined with the characteristic analysis method of higher education talent training quality constraint index parameters, an autocorrelation feature matching model of higher education talent training quality constraint index parameters is established, and fuzzy association rule scheduling is used to realize the feature extraction and fuzzy clustering of higher education talent training quality constraint index parameters. The mutual information coupling parameter analysis of the quality constraint index parameters of higher education talent training, combined with the generalized association rules and periodic association rules, realizes the feature clustering and global candidate item set analysis of the quality constraint index parameters of higher education talent training and realizes the dynamic evaluation of the quality of higher education talent training according to the machine learning analysis results. The test results show that this method has good clustering of data characteristics, good reliability of the evaluation process, and good convergence of machine learning process, which improves the dynamic and quantitative management ability of talent training quality in higher education.
Effects of Insurgency and Sit-At-Home Syndrome on Quality Secondary School Administration in South East Nigeria

In recent times, one of the most devastating factors to Nigeria’s development, which has also posed the biggest threat to national leadership and security, is the proliferation and criminal activities of multiple insurgent groups across the various geopolitical regions of the country. More than any other factor, insurgency and other forms of terrorism have caused a colossal damage to Nigeria’s human, social, economic and infrastructural development. In the Northern parts of Nigeria, most of the schools have either been closed down or completely damaged. Hundreds of students have equally been abducted and are still in captivity. Many local government areas and communities have also been taken over and are under full control and administration of Boko Haram (which means western education is forbidden). This constitutes a threat to Nigeria’s corporate political existence as well as a great challenge and setback for educational management and administration and therefore need reform. The paper is based on the fact that education is the life wire of every nation. In the absence of quality assurance in secondary education, this important provision of the national policy on education becomes jeopardized. This paper examined some key concepts in the discourse and the negative effects of these syndromes on education. Recommendations on measures that can restore or curb deficiencies arising from these effects are made. One of the recommendations is the extension of school time table to accommodate about 450 hours of lesson period and more than 135 topics of sub-topics of the scheme of work or curriculum which are either lost or not taught every school term in the various subjects of both the junior and senior secondary school classes.
Reliability Theory Reformulated Conditional Probability of Failure: A Corollary of the VRPD Theorem

&lt;p&gt;The Conditional Probability of Failure (CPF) has been used in reliability theory for reparable units as for non-reparable units in the same way. However, they respond to different stochastic processes. This is why the Reliability Function is replaced by other distribution functions that better fit the events rate curve to obtain a better approximation of the desired estimation.&lt;/p&gt; &lt;p&gt;The Probability of Failure for non-reparable units is derived here through a proof based on a mental experiment for non-reparable units, avoiding assumptions on the distribution of the random variable, letting the experiment reveals the nature of the phenomenon under test. The proof leads to a Conditional Probability of Failure, presented after a detailed discussion where it is also proved that the hazard rate in the traditional CPF can’t be a constant. &lt;/p&gt;
Multi-Graph Convolutional Neural Network for Breast Cancer Multi-Task Classification

Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep learning (DL) approaches to breast cancer detection incur high costs and are prone to classify incorrectly. Therefore, they are not sufficiently reliable to replace existing techniques used by medical practitioners. Specifically, these DL approaches do not exploit the complex texture patterns and interactions in mammograms. These DL approaches need labelled data to enable learning, limiting the scalability of these methods because of sufficient labelled datasets. Further, these DL models lack generalisation capability to newly-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into highly correlated multi-graphs, encoding rich structural relations and high-level texture features. Then, we consider a self-supervised learning multi-graph encoder (SSL-MG) to improve the features presentation, especially when limited labelled data is available. Finally, we design a semi-supervised mammogram multi graph convolution neural network downstream model (MMGCN) to perform multi classification of mammogram segments encoded in the multi-graph nodes. We evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over several training settings. Our results reveal the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method of Hao Du et al. (2021) with 0.81 AUC accuracy.
Development of classifiers to determine factors associated with older adult’s cognitive functions and game user experience in VR using head kinematics

&lt;p&gt;Virtual reality (VR) is increasingly being used to promote exercise among older adults. The data captured through VR may be useful indicator of the game user’s experience as well as providing insight into functional ability of older adults. This paper presents classifiers to predict game user experience variables using VR data from community-dwelling older adults. Head-kinematic data of the VR headset was collected from 13 participants over a six-week period with three 20-minutes exergame sessions per week (e.g., 360 minutes per participant). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCa) and multisensory response-time (RT). Game user experience was captured through perceived-levels of cybersickness, enjoyment, and exertion after each session. Data was used as references for discrete binary and ternary classification patterns. Combinations of kinematic features were used to train different classifiers: K-nearest-neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and decision trees. Maximum classification accuracy of 70% was found for MoCa, 68% for perceived exertion, 60% for cybersickness, 59% for multisensory RT, and 53% for perceived enjoyment. Results suggest unobtrusive recording of head kinematics from VR headsets combined with machine learning classifiers could be used to predict cognition, exertion, and game user experience among older adults.&lt;/p&gt;
Traffic Sign Classification Using Deep and Quantum Neural Networks

&lt;p&gt;Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.&lt;/p&gt;
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

&lt;p&gt;3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://github.com/vision-agh/pointpillars\_backbone.&lt;/p&gt;
Multi-classification generative adversarial network for streaming data with emerging new classes: method and its application to condition monitoring

&lt;p&gt;The paper extends the application of Generative Adversarial Network (GAN) to streaming data with the emerging new classes (SENC) problem. Four challenges of the SENC problem are proposed in this work. To deal with these problems, a novel multi-classification GAN (MC-GAN) is developed and investigated. The proposed MC-GAN employs a Generator to learn the implicit information embedded in data and adopts a re-designed Discriminator for multi-classification and novelty detection. The training of the whole network is realised by a modified loss function. In contrast to previous studies, the MC-GAN integrates the procedure of novelty detection into multi-classification. Moreover, the realisation of novelty detection does not require any predefined threshold. Case studies on rolling element bearings show that the proposed MC-GAN can maintain high classification accuracy for known classes as well as emerging new classes. &lt;/p&gt;