OpenRaiser/Intern-Atlas huggingface dataset:
GES Model :Combining Pearson Correlation Coefficient Analysis with Multilayer Perceptron
Venue: arXiv cs.CE 2022
Year: 2022

With the development of technological progress, mining on asteroids is becoming a reality. This paper focuses on how to distribute asteroid mineral resources in a reasonable way to ensure global equity. To distribute asteroid resources fairly, 7 primary indicators and 20 secondary indicators are introduced to build a mathematical model to evaluate global equity and the weights are given by Analytic Hierarchy Process (AHP). Then Global Equity Score(GES) Model based on 12 primary indicators and 40 secondary indicators is built and TOPSIS method is applied to rank all countries. A t-distribution probability density function is applied to simulate the rate of asteroid mining. The Backward Algorithm is applied to quantitatively measure the impact of changing indicators on global equity. Then Pearson correlation coefficient analysis is conducted for each indicator, and t-test is performed lastly. The results demonstrate that asteroid mining promotes global equity that poor countries can be allocated slightly more mineral resources, and a schedule of the implementation of each measure is given. To gain more insight, sensitivity analysis is conducted and the results demonstrate that scores vary less than 7%. It can be concluded that our GES model have great potential as its robustness, accuracy and strengths.
Analysis of Quantization on MLP-based Vision Models
Venue: arXiv cs.CV 2022
Year: 2022

Quantization is wildly taken as a model compression technique, which obtains efficient models by converting floating-point weights and activations in the neural network into lower-bit integers. Quantization has been proven to work well on convolutional neural networks and transformer-based models. Despite the decency of these models, recent works have shown that MLP-based models are able to achieve comparable results on various tasks ranging from computer vision, NLP to 3D point cloud, while achieving higher throughput due to the parallelism and network simplicity. However, as we show in the paper, directly applying quantization to MLP-based models will lead to significant accuracy degradation. Based on our analysis, two major issues account for the accuracy gap: 1) the range of activations in MLP-based models can be too large to quantize, and 2) specific components in the MLP-based models are sensitive to quantization. Consequently, we propose to 1) apply LayerNorm to control the quantization range of activations, 2) utilize bounded activation functions, 3) apply percentile quantization on activations, 4) use our improved module named multiple token-mixing MLPs, and 5) apply linear asymmetric quantizer for sensitive operations. Equipped with the abovementioned techniques, our Q-MLP models can achieve 79.68% accuracy on ImageNet with 8-bit uniform quantization (model size 30 MB) and 78.47% with 4-bit quantization (15 MB).
Open Source Horizontal IoT Platforms: A Comparative Study on Functional Requirements
Venue: arXiv cs.NI 2022
Year: 2022

The growth in the deployment of Internet of Things (IoT) devices in various industries required the use of IoT platforms to manage, automate and control devices. This introduced different commercial and open source IoT platforms for developers and researchers to deploy. As a result, selecting one of these platforms for a specific application and use case became a challenge. In this study, a guideline for selecting an open source platform is presented. The process starts by identifying a list of functional requirements that would reflect the requirements of an IoT system in general. This list of requirements is used to compare between four major open source platforms: 1) OM2M (OneM2M standard), 2) IoTivity (OCF standard), LwM2M (OMA SpecWorks LwM2M standard), and 4) FIWARE (FIWARE standard). The purpose of this comparison is to indicate the capability and limitations of the different platforms and how they satisfy each requirement. Afterwards, two examples are presented to demonstrate how this guideline is used to select the most suitable platform for an e-health and a smart city use case. This includes how to define each use case and all the required information that could affect the process of selecting the most suitable platform for the development of the IoT platform.
Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking
Venue: arXiv cs.CV 2022
Year: 2022

Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization. In this paper, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuition that spatially aligned corresponding points (via 3D registration) tend to achieve consistent feature representations. Specifically, our method consists of two modules, including a tracking-specific nonlocal registration module and a registration-aided Sinkhorn template-feature aggregation module. The registration module targets at the precise spatial alignment between the template and search area. The tracking-specific spatial distance constraint is proposed to refine the cross-attention weights in the nonlocal module for discriminative feature learning. Then, we use the weighted SVD to compute the rigid transformation between the template and search area, and align them to achieve the desired spatially aligned corresponding points. For the feature aggregation model, we formulate the feature matching between the transformed template and search area as an optimal transport problem and utilize the Sinkhorn optimization to search for the outlier-robust matching solution. Also, a registration-aided spatial distance map is built to improve the matching robustness in indistinguishable regions (e.g., smooth surface). Finally, guided by the obtained feature matching map, we aggregate the target information from the template into the search area to construct the target-specific feature, which is then fed into a CenterPoint-like detection head for object localization. Extensive experiments on KITTI, NuScenes and Waymo datasets verify the effectiveness of our proposed method.
Considering Image Information and Self-similarity: A Compositional Denoising Network
Venue: arXiv cs.CV 2022
Year: 2022

Recently, convolutional neural networks (CNNs) have been widely used in image denoising. Existing methods benefited from residual learning and achieved high performance. Much research has been paid attention to optimizing the network architecture of CNN but ignored the limitations of residual learning. This paper suggests two limitations of it. One is that residual learning focuses on estimating noise, thus overlooking the image information. The other is that the image self-similarity is not effectively considered. This paper proposes a compositional denoising network (CDN), whose image information path (IIP) and noise estimation path (NEP) will solve the two problems, respectively. IIP is trained by an image-to-image way to extract image information. For NEP, it utilizes the image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output a similar estimated noise distribution for different image patches with a specific kind of noise. Finally, image information and noise distribution information will be comprehensively considered for image denoising. Experiments show that CDN achieves state-of-the-art results in synthetic and real-world image denoising. Our code will be released on https://github.com/JiaHongZ/CDN.
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
Venue: arXiv cs.CL 2022
Year: 2022

As pre-trained language models become more resource-demanding, the inequality between resource-rich languages such as English and resource-scarce languages is worsening. This can be attributed to the fact that the amount of available training data in each language follows the power-law distribution, and most of the languages belong to the long tail of the distribution. Some research areas attempt to mitigate this problem. For example, in cross-lingual transfer learning and multilingual training, the goal is to benefit long-tail languages via the knowledge acquired from resource-rich languages. Although being successful, existing work has mainly focused on experimenting on as many languages as possible. As a result, targeted in-depth analysis is mostly absent. In this study, we focus on a single low-resource language and perform extensive evaluation and probing experiments using cross-lingual post-training (XPT). To make the transfer scenario challenging, we choose Korean as the target language, as it is a language isolate and thus shares almost no typology with English. Results show that XPT not only outperforms or performs on par with monolingual models trained with orders of magnitudes more data but also is highly efficient in the transfer process.
ConvNeXt Based Neural Network for Audio Anti-Spoofing
Venue: arXiv cs.SD 2022
Year: 2022

With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed a number of anti-spoofing methods based on hand-crafted features. However, using hand-crafted features rather than raw waveform will lose implicit information for anti-spoofing. Inspired by the promising performance of ConvNeXt in image classification tasks, we revise the ConvNeXt network architecture and propose a lightweight end-to-end anti-spoofing model. By integrating with the channel attention block and using the focal loss function, the proposed model can focus on the most informative sub-bands of speech representations and the difficult samples that are hard to classify. Experiments show that our proposed system could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the ASVSpoof 2019 LA evaluation dataset, which outperforms the state-of-the-art systems.
Exploiting dynamic sparse matrices for performance portable linear algebra operations
Venue: arXiv cs.DC 2022
Year: 2022

Sparse matrices and linear algebra are at the heart of scientific simulations. More than 70 sparse matrix storage formats have been developed over the years, targeting a wide range of hardware architectures and matrix types. Each format is developed to exploit the particular strengths of an architecture, or the specific sparsity patterns of matrices, and the choice of the right format can be crucial in order to achieve optimal performance. The adoption of dynamic sparse matrices that can change the underlying data-structure to match the computation at runtime without introducing prohibitive overheads has the potential of optimizing performance through dynamic format selection. In this paper, we introduce Morpheus, a library that provides an efficient abstraction for dynamic sparse matrices. The adoption of dynamic matrices aims to improve the productivity of developers and end-users who do not need to know and understand the implementation specifics of the different formats available, but still want to take advantage of the optimization opportunity to improve the performance of their applications. We demonstrate that by porting HPCG to use Morpheus, and without further code changes, 1) HPCG can now target heterogeneous environments and 2) the performance of the SpMV kernel is improved up to 2.5x and 7x on CPUs and GPUs respectively, through runtime selection of the best format on each MPI process.
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Venue: arXiv cs.SD 2022
Year: 2022

Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to reproduce similar high quality when a whole paragraph is considered in TTS, where a large amount of contextual information needs to be considered in building a paragraph-based TTS model. To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training. Three sub-modules, including linguistics-aware, prosody-aware and sentence-position networks, are trained together with a modified Tacotron2. Specifically, to learn the information embedded in a paragraph and the relations among the corresponding component sentences, we utilize linguistics-aware and prosody-aware networks. The information in a paragraph is captured by encoders and the inter-sentence information in a paragraph is learned with multi-head attention mechanisms. The relative sentence position in a paragraph is explicitly exploited by a sentence-position network. Trained on a storytelling audio-book corpus (4.08 hours), recorded by a female Mandarin Chinese speaker, the proposed TTS model demonstrates that it can produce rather natural and good-quality speech paragraph-wise. The cross-sentence contextual information, such as break and prosodic variations between consecutive sentences, can be better predicted and rendered than the sentence-based model. Tested on paragraph texts, of which the lengths are similar to, longer than, or much longer than the typical paragraph length of the training data, the TTS speech produced by the new model is consistently preferred over the sentence-based model in subjective tests and confirmed in objective measures.
Learning to Evaluate Performance of Multi-modal Semantic Localization
Venue: arXiv cs.CV 2022
Year: 2022

Semantic localization (SeLo) refers to the task of obtaining the most relevant locations in large-scale remote sensing (RS) images using semantic information such as text. As an emerging task based on cross-modal retrieval, SeLo achieves semantic-level retrieval with only caption-level annotation, which demonstrates its great potential in unifying downstream tasks. Although SeLo has been carried out successively, but there is currently no work has systematically explores and analyzes this urgent direction. In this paper, we thoroughly study this field and provide a complete benchmark in terms of metrics and testdata to advance the SeLo task. Firstly, based on the characteristics of this task, we propose multiple discriminative evaluation metrics to quantify the performance of the SeLo task. The devised significant area proportion, attention shift distance, and discrete attention distance are utilized to evaluate the generated SeLo map from pixel-level and region-level. Next, to provide standard evaluation data for the SeLo task, we contribute a diverse, multi-semantic, multi-objective Semantic Localization Testset (AIR-SLT). AIR-SLT consists of 22 large-scale RS images and 59 test cases with different semantics, which aims to provide a comprehensive evaluations for retrieval models. Finally, we analyze the SeLo performance of RS cross-modal retrieval models in detail, explore the impact of different variables on this task, and provide a complete benchmark for the SeLo task. We have also established a new paradigm for RS referring expression comprehension, and demonstrated the great advantage of SeLo in semantics through combining it with tasks such as detection and road extraction. The proposed evaluation metrics, semantic localization testsets, and corresponding scripts have been open to access at github.com/xiaoyuan1996/SemanticLocalizationMetrics .
FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion
Venue: arXiv cs.CV 2022
Year: 2022

We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs. The second part of our network learns highly accurate similarity functions between this learned features. It consists of densely-connected convolution layers with a deformable convolution block at the end to further improve the accuracy of the results. After this step an initial disparity map is created and the left-right consistency check is performed in order to remove inconsistent points. The last part of the network then uses this input together with the corresponding left RGB image in order to train a network that fills in the missing measurements. Consistent depth estimations are gathered around invalid points and are parsed together with the RGB points into a shallow CNN network structure in order to recover the missing values. We evaluate our method on challenging real world indoor and outdoor scenes, in particular Middlebury, KITTI and ETH3D were it produces competitive results. We furthermore show that this method generalizes well and is well suited for many applications without the need of further training. The code of our full framework is available at: https://github.com/thedodo/FCDSN-DC
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Venue: arXiv cs.LG 2022
Year: 2022

Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system's predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.
A Generic Privacy-Preserving Protocol For Keystroke Dynamics-Based Continuous Authentication
Venue: arXiv cs.CR 2022
Year: 2022

Continuous authentication utilizes automatic recognition of certain user features for seamless and passive authentication without requiring user attention. Such features can be divided into categories of physiological biometrics and behavioral biometrics. Keystroke dynamics is proposed for behavioral biometrics-oriented authentication by recognizing users by means of their typing patterns. However, it has been pointed out that continuous authentication using physiological biometrics and behavior biometrics incur privacy risks, revealing personal characteristics and activities. In this paper, we consider a previously proposed keystroke dynamics-based authentication scheme that has no privacy-preserving properties. In this regard, we propose a generic privacy-preserving version of this authentication scheme in which all user features are encrypted -- preventing disclosure of those to the authentication server. Our scheme is generic in the sense that it assumes homomorphic cryptographic primitives. Authentication is conducted on the basis of encrypted data due to the homomorphic cryptographic properties of our protocol.
The Impact of Model Transformation Language Features on Quality Properties of MTLs: A Study Protocol
Venue: arXiv cs.SE 2022
Year: 2022

Background: Dedicated model transformation languages are claimed to provide many benefits over the use of general purpose languages for developing model transformations. However, the actual advantages and disadvantages associated with the use of model transformation languages are poorly understood empirically. There is little knowledge over what advantages and disadvantages hold in which cases and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors surrounding model transformation languages as well as a number of moderating factors that moderate the influence. Objective: We aim to quantitatively asses the interview results to confirm or reject the influences and moderation effects posed by different factors and to gain insights into how valuable different factors are to the discussion. Method: We gather data on the factors and quality attributes using an online survey. To analyse the data and examine the hypothesised influences and moderations we use universal structure modelling based on a structural equation model. Universal structure modelling will produce significance values and path coefficients for each hypothesised and modelled interdependence between factors and quality attributes that can be used to confirm or reject correlation and to weigh the strength of influence present. Limitations: Due to the complexity and abstractness of the concepts under investigation, a measurement via reflective or formative indicators is not possible. Instead participants are queried about their assessment of concepts through a single item question. We further assume that positive and negative effects of a feature are more prominent if the feature is used more frequently.
Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry
Venue: arXiv cs.RO 2022
Year: 2022

We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46 % trajectory estimation with respect to an individual-agent approach, while reducing up to 89 % the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.