OpenRaiser/Intern-Atlas huggingface dataset:
Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts
Venue: arXiv cs.CL 2022
Year: 2022

Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Politécnico Nacional, Centro de Investigación en Computación (CIC) team's system description paper for the CoLI-Kanglish shared task at ICON2022. In this paper, we propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.
Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image
Venue: arXiv cs.CV 2022
Year: 2022

Surgical instrument tracking is an active research area that can provide surgeons feedback about the location of their tools relative to anatomy. Recent tracking methods are mainly divided into two parts: segmentation and object detection. However, both can only predict 2D information, which is limiting for application to real-world surgery. An accurate 3D surgical instrument model is a prerequisite for precise predictions of the pose and depth of the instrument. Recent single-view 3D reconstruction methods are only used in natural object reconstruction and do not achieve satisfying reconstruction accuracy without 3D attribute-level supervision. Further, those methods are not suitable for the surgical instruments because of their elongated shapes. In this paper, we firstly propose an end-to-end surgical instrument reconstruction system -- Self-supervised Surgical Instrument Reconstruction (SSIR). With SSIR, we propose a multi-cycle-consistency strategy to help capture the texture information from a slim instrument while only requiring a binary instrument label map. Experiments demonstrate that our approach improves the reconstruction quality of surgical instruments compared to other self-supervised methods and achieves promising results.
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Venue: arXiv cs.IT 2022
Year: 2022

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.
Resampling community detection to maximize propagation in complex network
Venue: arXiv cs.SI 2022
Year: 2022

Identifying important nodes in complex networks is essential in theoretical and applied fields. A small number of such nodes have deterministic power to decide information spreading, so it is of importance to find a set of nodes that maximize the propagation in networks. Based on baseline ranking methods, various improved methods were proposed, but there does not exist one enhanced method that covers all the base methods. In this paper, we propose a penalized method called RCD-Map, which is short for resampling community detection to maximize propagation, on five baseline ranking methods(Degree centrality, Closeness centrality, Betweennees centrality, K-shell and PageRank) with nodes' local community information. We perturbed the original graph by resampling to decrease the biases and randomness brought by community detection methods-both overlapping and non-overlapping methods. To assess the performance of our identifying method, SIR(susceptible-infected-recovered) model is applied to simulate the information propagation process. The result shows that methods with penalties perform better with a vaster propagation range in general.
Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks with sinusoidal activations have been proposed as an alternative to networks with traditional activation functions. Despite their promise, particularly for learning implicit models, their training behavior is not yet fully understood, leading to a number of empirical design choices that are not well justified. In this work, we first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis. We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth. Finally, we utilize these insights to inform the sinusoidal network initialization, optimizing their performance for each of a series of tasks, including learning implicit models and solving differential equations.
Visual Place Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Visual position recognition affects the safety and accuracy of automatic driving. To accurately identify the location, this paper studies a visual place recognition algorithm based on HMM filter and HMM smoother. Firstly, we constructed the traffic situations in Canberra city. Then the mathematical models of the HMM filter and HMM smoother were performed. Finally, the vehicle position was predicted based on the algorithms. Experiment results show that HMM smoother is better than HMM filter in terms of prediction accuracy.
Target-Free Text-guided Image Manipulation
Venue: arXiv cs.CV 2022
Year: 2022

We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training. To address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN) in this paper, which is able to realize where and how to edit the image regions of interest. Specifically, the image editor in cManiGAN learns to identify and complete the input image, while cross-modal interpreter and reasoner are deployed to verify the semantic correctness of the output image based on the input instruction. While the former utilizes factual/counterfactual description learning for authenticating the image semantics, the latter predicts the "undo" instruction and provides pixel-level supervision for the training of cManiGAN. With such operational cycle-consistency, our cManiGAN can be trained in the above weakly supervised setting. We conduct extensive experiments on the datasets of CLEVR and COCO, and the effectiveness and generalizability of our proposed method can be successfully verified. Project page: https://sites.google.com/view/wancyuanfan/projects/cmanigan.
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images
Venue: arXiv cs.CV 2022
Year: 2022

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e.,macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to flter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://github.com/FDU-VTS/DRTiD.
A Remote Baby Surveillance System with RFID and GPS Tracking
Venue: arXiv cs.CY 2022
Year: 2022

In the 21st century, sending babies or children to daycare centres has become more and more common among young guardians. The balance between full-time work and child care is increasingly challenging nowadays. In Malaysia, thousands of child abuse cases have been reported from babysitting centres every year, which indeed triggers the anxiety and stress of the guardians. Hence, this paper proposes to construct a remote baby surveillance system with radio-frequency identification (RFID) and global positioning system (GPS) tracking. With the incorporation of the Internet of Things (IoT), a sensor-based microcontroller is used to detect the conditions of the baby as well as the surrounding environment and then display the real-time data as well as notifications to alert the guardians via a mobile application. These conditions include the crying and waking of the baby, as well as temperature, the mattress's wetness, and moving objects around the baby. In addition, RFID and GPS location tracking are implemented to ensure the safety of the baby, while white noise is used to increase the comfort of the baby. In the end, a prototype has been successfully developed for functionality and reliability testing. Several experiments have been conducted to measure the efficiency of the mattress's wetness detection, the RFID transmission range, the frequency spectrum of white noise, and also the output power of the solar panel. The proposed system is expected to assist guardians in ensuring the safety and comfort of their babies remotely, as well as prevent any occurrence of child abuse.
An Efficient Black-Box Support of Advanced Coverage Criteria for Klee
Venue: arXiv cs.SE 2022
Year: 2022

Dynamic symbolic execution (DSE) is a powerful test generation approach based on an exploration of the path space of the program under test. Well-adapted for path coverage, this approach is however less efficient for conditions, decisions, advanced coverage criteria (such as multiple conditions, weak mutations, boundary testing) or user-provided test objectives. While theoretical solutions to adapt DSE to a large set of criteria have been proposed, they have never been integrated into publicly available testing tools. This paper presents a first integration of an optimized test generation strategy for advanced coverage criteria into a popular open-source testing tool based on DSE, namely, Klee. The integration is performed in a fully black-box manner, and can therefore inspire an easy integration into other similar tools. The resulting version of the tool, named Klee4labels, is publicly available. We present the design of the proposed technique and evaluate it on several benchmarks. Our results confirm the benefits of the proposed tool for advanced coverage criteria.
Mixture of Decision Trees for Interpretable Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a single decision tree, but which can alternatively be divided into subproblems. Each subproblem can then be learned well from a single decision tree. Therefore, MoDT can be considered as a method that improves performance while maintaining interpretability by making each of its decisions understandable and traceable to humans. Our work is accompanied by a Python implementation, which uses an interpretable gating function, a fast learning algorithm, and a direct interface to fine-tuned interpretable visualization methods. The experiments confirm that the implementation works and, more importantly, show the superiority of our approach compared to single decision trees and random forests of similar complexity.
A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads
Venue: arXiv cs.DC 2022
Year: 2022

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6% over the existing approaches.
How Crucial is Transformer in Decision Transformer?
Venue: arXiv cs.LG 2022
Year: 2022

Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem and uses a Transformer model to predict the next action in a sequence of states, actions, and rewards. In this paper, we analyze how crucial the Transformer model is in the complete DT architecture on continuous control tasks. Namely, we replace the Transformer by an LSTM model while keeping the other parts unchanged to obtain what we call a Decision LSTM model. We compare it to DT on continuous control tasks, including pendulum swing-up and stabilization, in simulation and on physical hardware. Our experiments show that DT struggles with continuous control problems, such as inverted pendulum and Furuta pendulum stabilization. On the other hand, the proposed Decision LSTM is able to achieve expert-level performance on these tasks, in addition to learning a swing-up controller on the real system. These results suggest that the strength of the Decision Transformer for continuous control tasks may lie in the overall sequential modeling architecture and not in the Transformer per se.
Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions
Venue: arXiv cs.LG 2022
Year: 2022

We present a technique for neural network verification using mixed-integer programming (MIP) formulations. We derive a \emph{strong formulation} for each neuron in a network using piecewise linear activation functions. Additionally, as in general, these formulations may require an exponential number of inequalities, we also derive a separation procedure that runs in super-linear time in the input dimension. We first introduce and develop our technique on the class of \emph{staircase} functions, which generalizes the ReLU, binarized, and quantized activation functions. We then use results for staircase activation functions to obtain a separation method for general piecewise linear activation functions. Empirically, using our strong formulation and separation technique, we can reduce the computational time in exact verification settings based on MIP and improve the false negative rate for inexact verifiers relying on the relaxation of the MIP formulation.
BALF: Simple and Efficient Blur Aware Local Feature Detector
Venue: arXiv cs.CV 2022
Year: 2022

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images.
Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks
Venue: arXiv cs.NI 2022
Year: 2022

The automotive and railway industries are rapidly transforming with a strong drive towards automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications, and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this paper, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.
Quantifying spatial, temporal, angular and spectral structure of effective daylight in perceptually meaningful ways
Venue: arXiv cs.GR 2022
Year: 2022

We present a method to capture the 7-dimensional light field structure, and translate it into perceptually-relevant information. Our spectral cubic illumination method quantifies objective correlates of perceptually relevant diffuse and directed light components, including their variations over time, space, in color and direction, and the environment's response to sky and sunlight. We applied it 'in the wild', capturing how light on a sunny day differs between light and shadow, and how light varies over sunny and cloudy days. We discuss the added value of our method for capturing nuanced lighting effects on scene and object appearance, such as chromatic gradients.
Dense Text Retrieval based on Pretrained Language Models: A Survey
Venue: arXiv cs.IR 2022
Year: 2022

Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language. From classic retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn the text representation and model the relevance matching. The recent success of pretrained language models (PLMs) sheds light on developing more capable text retrieval approaches by leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling. Such a retrieval approach is referred to as dense retrieval, since it employs dense vectors (a.k.a., embeddings) to represent the texts. Considering the rapid progress on dense retrieval, in this survey, we systematically review the recent advances on PLM-based dense retrieval. Different from previous surveys on dense retrieval, we take a new perspective to organize the related work by four major aspects, including architecture, training, indexing and integration, and summarize the mainstream techniques for each aspect. We thoroughly survey the literature, and include 300+ related reference papers on dense retrieval. To support our survey, we create a website for providing useful resources, and release a code repertory and toolkit for implementing dense retrieval models. This survey aims to provide a comprehensive, practical reference focused on the major progress for dense text retrieval.
Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations
Venue: arXiv cs.IR 2022
Year: 2022

Embedding based product recommendations have gained popularity in recent years due to its ability to easily integrate to large-scale systems and allowing nearest neighbor searches in real-time. The bulk of studies in this area has predominantly been focused on similar item recommendations. Research on complementary item recommendations, on the other hand, still remains considerably under-explored. We define similar items as items that are interchangeable in terms of their utility and complementary items as items that serve different purposes, yet are compatible when used with one another. In this paper, we apply a novel approach to finding complementary items by leveraging dual embedding representations for products. We demonstrate that the notion of relatedness discovered in NLP for skip-gram negative sampling (SGNS) models translates effectively to the concept of complementarity when training item representations using co-purchase data. Since sparsity of purchase data is a major challenge in real-world scenarios, we further augment the model using synthetic samples to extend coverage. This allows the model to provide complementary recommendations for items that do not share co-purchase data by leveraging other abundantly available data modalities such as images, text, clicks etc. We establish the effectiveness of our approach in improving both coverage and quality of recommendations on real world data for a major online retail company. We further show the importance of task specific hyperparameter tuning in training SGNS. Our model is effective yet simple to implement, making it a great candidate for generating complementary item recommendations at any e-commerce website.