OpenRaiser/Intern-Atlas huggingface dataset:
Do altmetric scores reflect article quality? Evidence from the UK Research Excellence Framework 2021
Venue: arXiv cs.DL 2022
Year: 2022

Altmetrics are web-based quantitative impact or attention indicators for academic articles that have been proposed to supplement citation counts. This article reports the first assessment of the extent to which mature altmetrics from Altmetric.com and Mendeley associate with journal article quality. It exploits expert norm-referenced peer review scores from the UK Research Excellence Framework 2021 for 67,030+ journal articles in all fields 2014-17/18, split into 34 Units of Assessment (UoAs). The results show that altmetrics are better indicators of research quality than previously thought, although not as good as raw and field normalised Scopus citation counts. Surprisingly, field normalising citation counts can reduce their strength as a quality indicator for articles in a single field. For most UoAs, Mendeley reader counts are the best, tweet counts are also a relatively strong indicator in many fields, and Facebook, blogs and news citations are moderately strong indicators in some UoAs, at least in the UK. In general, altmetrics are the strongest indicators of research quality in the health and physical sciences and weakest in the arts and humanities. The Altmetric Attention Score, although hybrid, is almost as good as Mendeley reader counts as a quality indicator and reflects more non-scholarly impacts.
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.
Hierarchical Codebook Design for Near-Field MmWave MIMO Communications Systems
Venue: arXiv cs.IT 2022
Year: 2022

Communications system with analog or hybrid analog/digital architectures usually relies on a pre-defined codebook to perform beamforming. With the increase in the size of the antenna array, the characteristics of the spherical wavefront in the near-field situation are not negligible. Therefore, it is necessary to design a codebook that is adaptive to near-field scenarios. In this letter, we investigate the hierarchical codebook design method in the near-field situation. We develop a steering beam gain calculation method and design the lower-layer codebook to satisfy the coverage of the Fresnel region. For the upper-layer codebook, we propose beam rotation and beam relocation methods to place an arbitrary beam pattern at target locations. The simulation results show the superiority of the proposed near-field hierarchical codebook design.
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
Venue: arXiv cs.AI 2022
Year: 2022

Structural Health Monitoring (SHM) describes a process for inferring quantifiable metrics of structural condition, which can serve as input to support decisions on the operation and maintenance of infrastructure assets. Given the long lifespan of critical structures, this problem can be cast as a sequential decision making problem over prescribed horizons. Partially Observable Markov Decision Processes (POMDPs) offer a formal framework to solve the underlying optimal planning task. However, two issues can undermine the POMDP solutions. Firstly, the need for a model that can adequately describe the evolution of the structural condition under deterioration or corrective actions and, secondly, the non-trivial task of recovery of the observation process parameters from available monitoring data. Despite these potential challenges, the adopted POMDP models do not typically account for uncertainty on model parameters, leading to solutions which can be unrealistically confident. In this work, we address both key issues. We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions. The MCMC inference estimates distributions of the involved model parameters. We then form and solve the POMDP problem by exploiting the inferred distributions, to derive solutions that are robust to model uncertainty. We successfully apply our approach on maintenance planning for railway track assets on the basis of a "fractal value" indicator, which is computed from actual railway monitoring data.
iCardo: A Machine Learning Based Smart Healthcare Framework for Cardiovascular Disease Prediction
Venue: arXiv cs.CY 2022
Year: 2022

The point of care services and medication have become simpler with efficient consumer electronics devices in a smart healthcare system. Cardiovascular disease is a critical illness which causes heart failure, and early and prompt identification can lessen damage and prevent premature mortality. Machine learning has been used to predict cardiovascular disease (CVD) in the literature. The article explains choosing the best classifier model for the selected feature sets and the distinct feature sets selected using four feature selection models. The paper compares seven classifiers using each of the sixteen feature sets. Originally, the data had 56 attributes and 303 occurrences, of which 87 were in good health, and the remainder had cardiovascular disease (CVD). Demographic data with several features make up the four groups of overall features. Lasso, Tree-based algorithms, Chi-Square and RFE have all been used to choose the four distinct feature sets, each containing five, ten, fifteen, and twenty features, respectively. Seven distinct classifiers have been trained and evaluated for each of the sixteen feature sets. To determine the most effective blend of feature set and model, a total of 112 models have been trained, tested, and their performance has been compared. SVM classifier with fifteen chosen features is shown to be the best in terms of overall accuracy. The healthcare data has been maintained in the cloud and would be accessible to patients, caretakers, and healthcare providers through integration with the Internet of Medical Things (IoMT) enabled smart healthcare. Subsequently, the feature selection model chooses the most appropriate feature for CVD prediction to calibrate the system, and the proposed framework can be utilised to anticipate CVD.
Why Should and How Can Quantum Technologies Be Leveraged at National Levels?
Venue: arXiv cs.CY 2022
Year: 2022

Quantum technologies (QT) promise to change the landscape of technologies disruptively in diverse industries. For this reason, many nations around the globe are investing to emerge within the global quantum ecosystem through initiating national programs and international partnerships. Nonetheless, some other countries are still running behind and yet their governments need to take series actions to help their private and public sectors adapt to the looming changes, considering the new regulations required and the huge influence that QT will present in the near future. In this opinion piece, we provide, for the best of our knowledge, the first generally applicable, yet comprehensive and brief, framework for leveraging the emerging quantum technologies to facilitate the establishment of national initiatives properly. The insights presented in this article were driven based on investigating various approaches, initiatives, and roadmaps adopted globally and meeting with local and regional leaders, professionals, and governmental officials. Furthermore, taken into account socioeconomic and institutional dimensions of the Libyan society, we project the framework for the Libyan nation. This opinion piece is intended to inspire researchers, technical industrial experts, stakeholders, and governmental bodies to find roles they need to play to bring QT forward.
Essentials of Parallel Graph Analytics
Venue: arXiv cs.DC 2022
Year: 2022

We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulk-synchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.
Azimuth: Systematic Error Analysis for Text Classification
Venue: arXiv cs.LG 2022
Year: 2022

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.
SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its Retrieval
Venue: arXiv cs.SE 2022
Year: 2022

Pre-trained giant code models (PCMs) start coming into the developers' daily practices. Understanding what types of and how much software knowledge is packed into PCMs is the foundation for incorporating PCMs into software engineering (SE) tasks and fully releasing their potential. In this work, we conduct the first systematic study on the SE factual knowledge in the state-of-the-art PCM CoPilot, focusing on APIs' Fully Qualified Names (FQNs), the fundamental knowledge for effective code analysis, search and reuse. Driven by FQNs' data distribution properties, we design a novel lightweight in-context learning on Copilot for FQN inference, which does not require code compilation as traditional methods or gradient update by recent FQN prompt-tuning. We systematically experiment with five in-context-learning design factors to identify the best in-context learning configuration that developers can adopt in practice. With this best configuration, we investigate the effects of amount of example prompts and FQN data properties on Copilot's FQN inference capability. Our results confirm that Copilot stores diverse FQN knowledge and can be applied for the FQN inference due to its high inference accuracy and non-reliance on code analysis. Based on our experience interacting with Copilot, we discuss various opportunities to improve human-CoPilot interaction in the FQN inference task.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval
Venue: arXiv cs.CV 2022
Year: 2022

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities. In recent years, researchers have made great progress in exploring the accurate alignment between image and text. However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information. Actually, integrating the local fine-grained information and global context background information can provide more semantic clues for retrieval. In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment. To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30K datasets. Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.
Robust Learning Protocol for Federated Tumor Segmentation Challenge
Venue: arXiv cs.LG 2022
Year: 2022

In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.
Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation
Venue: arXiv cs.SD 2022
Year: 2022

Recently, frequency domain all-neural beamforming methods have achieved remarkable progress for multichannel speech separation. In parallel, the integration of time domain network structure and beamforming also gains significant attention. This study proposes a novel all-neural beamforming method in time domain and makes an attempt to unify the all-neural beamforming pipelines for time domain and frequency domain multichannel speech separation. The proposed model consists of two modules: separation and beamforming. Both modules perform temporal-spectral-spatial modeling and are trained from end-to-end using a joint loss function. The novelty of this study lies in two folds. Firstly, a time domain directional feature conditioned on the direction of the target speaker is proposed, which can be jointly optimized within the time domain architecture to enhance target signal estimation. Secondly, an all-neural beamforming network in time domain is designed to refine the pre-separated results. This module features with parametric time-variant beamforming coefficient estimation, without explicitly following the derivation of optimal filters that may lead to an upper bound. The proposed method is evaluated on simulated reverberant overlapped speech data derived from the AISHELL-1 corpus. Experimental results demonstrate significant performance improvements over frequency domain state-of-the-arts, ideal magnitude masks and existing time domain neural beamforming methods.
Fast-moving object counting with an event camera
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.
Search Budget in Multi-Objective Refactoring Optimization: a Model-Based Empirical Study
Venue: arXiv cs.SE 2022
Year: 2022

Software model optimization is the task of automatically generate design alternatives, usually to improve quality aspects of software that are quantifiable, like performance and reliability. In this context, multi-objective optimization techniques have been applied to help the designer find suitable trade-offs among several non-functional properties. In this process, design alternatives can be generated through automated model refactoring, and evaluated on non-functional models. Due to their complexity, this type of optimization tasks require considerable time and resources, often limiting their application in software engineering processes. In this paper, we investigate the effects of using a search budget, specifically a time limit, to the search for new solutions. We performed experiments to quantify the impact that a change in the search budget may have on the quality of solutions. Furthermore, we analyzed how different genetic algorithms (i.e., NSGA-II, SPEA2, and PESA2) perform when imposing different budgets. We experimented on two case studies of different size, complexity, and domain. We observed that imposing a search budget considerably deteriorates the quality of the generated solutions, but the specific algorithm we choose seems to play a crucial role. From our experiments, NSGA-II is the fastest algorithm, while PESA2 generates solutions with the highest quality. Differently, SPEA2 is the slowest algorithm, and produces the solutions with the lowest quality.
From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we present a modified Xception architecture, the NEXcepTion network. Our network has significantly better performance than the original Xception, achieving top-1 accuracy of 81.5% on the ImageNet validation dataset (an improvement of 2.5%) as well as a 28% higher throughput. Another variant of our model, NEXcepTion-TP, reaches 81.8% top-1 accuracy, similar to ConvNeXt (82.1%), while having a 27% higher throughput. Our model is the result of applying improved training procedures and new design decisions combined with an application of Neural Architecture Search (NAS) on a smaller dataset. These findings call for revisiting older architectures and reassessing their potential when combined with the latest enhancements.
Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing
Venue: arXiv cs.CL 2022
Year: 2022

Most recent studies on neural constituency parsing focus on encoder structures, while few developments are devoted to decoders. Previous research has demonstrated that probabilistic statistical methods based on syntactic rules are particularly effective in constituency parsing, whereas syntactic rules are not used during the training of neural models in prior work probably due to their enormous computation requirements. In this paper, we first implement a fast CKY decoding procedure harnessing GPU acceleration, based on which we further derive a syntactic rule-based (rule-constrained) CKY decoding. In the experiments, our method obtains 95.89 and 92.52 F1 on the datasets of PTB and CTB respectively, which shows significant improvements compared with previous approaches. Besides, our parser achieves strong and competitive cross-domain performance in zero-shot settings.
Robust Explanation Constraints for Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular explanations methods have been found to be fragile to minor perturbations of input features or model parameters. Relying on constraint relaxation techniques from non-convex optimization, we develop a method that upper-bounds the largest change an adversary can make to a gradient-based explanation via bounded manipulation of either the input features or model parameters. By propagating a compact input or parameter set as symbolic intervals through the forwards and backwards computations of the neural network we can formally certify the robustness of gradient-based explanations. Our bounds are differentiable, hence we can incorporate provable explanation robustness into neural network training. Empirically, our method surpasses the robustness provided by previous heuristic approaches. We find that our training method is the only method able to learn neural networks with certificates of explanation robustness across all six datasets tested.
Resource-Interaction Graph: Efficient Graph Representation for Anomaly Detection
Venue: arXiv cs.CR 2022
Year: 2022

Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond what is necessary for many security tasks and are not feasible for resource constrained environments, such as edge devices. To address this problem, we present the \textit{resource-interaction graph} that is built directly from the audit log. We show that the resource-interaction graph's storage requirements are significantly lower than provenance graphs using an open-source data set with two container escape attacks captured from an edge device. We use a graph autoencoder and graph clustering technique to evaluate the representation for an anomaly detection task. Both approaches are unsupervised and are thus suitable for detecting zero-day attacks. The approaches can achieve f1 scores typically over 80\% and in some cases over 90\% for the selected data set and attacks.