OpenRaiser/Intern-Atlas huggingface dataset:
FRUGAL: Unlocking SSL for Software Analytics
Venue: arXiv cs.SE 2021
Year: 2021

Standard software analytics often involves having a large amount of data with labels in order to commission models with acceptable performance. However, prior work has shown that such requirements can be expensive, taking several weeks to label thousands of commits, and not always available when traversing new research problems and domains. Unsupervised Learning is a promising direction to learn hidden patterns within unlabelled data, which has only been extensively studied in defect prediction. Nevertheless, unsupervised learning can be ineffective by itself and has not been explored in other domains (e.g., static analysis and issue close time). Motivated by this literature gap and technical limitations, we present FRUGAL, a tuned semi-supervised method that builds on a simple optimization scheme that does not require sophisticated (e.g., deep learners) and expensive (e.g., 100% manually labelled data) methods. FRUGAL optimizes the unsupervised learner's configurations (via a simple grid search) while validating our design decision of labelling just 2.5% of the data before prediction. As shown by the experiments of this paper FRUGAL outperforms the state-of-the-art adoptable static code warning recognizer and issue closed time predictor, while reducing the cost of labelling by a factor of 40 (from 100% to 2.5%). Hence we assert that FRUGAL can save considerable effort in data labelling especially in validating prior work or researching new problems. Based on this work, we suggest that proponents of complex and expensive methods should always baseline such methods against simpler and cheaper alternatives. For instance, a semi-supervised learner like FRUGAL can serve as a baseline to the state-of-the-art software analytics.
Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network
Venue: arXiv cs.CV 2021
Year: 2021

This research features a deep-learning based framework to address the problem of matching a given face sketch image against a face photo database. The problem of photo-sketch matching is challenging because 1) there is large modality gap between photo and sketch, and 2) the number of paired training samples is insufficient to train deep learning based networks. To circumvent the problem of large modality gap, our approach is to use an intermediate latent space between the two modalities. We effectively align the distributions of the two modalities in this latent space by employing a bidirectional (photo -> sketch and sketch -> photo) collaborative synthesis network. A StyleGAN-like architecture is utilized to make the intermediate latent space be equipped with rich representation power. To resolve the problem of insufficient training samples, we introduce a three-step training scheme. Extensive evaluation on public composite face sketch database confirms superior performance of our method compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.
Federated Learning Meets Fairness and Differential Privacy
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.
Information Sources and Anxiety among Refugees in Kenya during COVID-19
Venue: arXiv cs.CY 2021
Year: 2021

In the COVID-19 pandemic, refugees' access to information has become increasingly important given the rapid change in the scientific and public health knowledge-base. However, this access is complicated by social distancing requirements that disrupt traditional in-person communication. Many refugees must then rely on alternative information sources to stay informed. Differences in media types and information sources in turn may be related to anxieties arising from the virus and perceptions of others' adherence to recommended protective behaviors. We examine these relationships with survey data from 1,000 refugees living in both camps and non-camp settings in Kenya. Using logit models, we test relationships between information source and anxiety and the effect of these variables on refugees' expected behaviors of community members. Our primary contributions include the finding that information sources consistently exacerbate (e.g., Facebook) or ameliorate (e.g., news from the internet) different anxieties, or can have mixed effects (e.g., radio). We also find that anxiety and information have significant impacts on refugees' expectations of compliance by others and that, whether between camps or between camps and non-camp locales, findings vary by location. Our results have implications for refugee media and infectious disease anxiety scholarship as well as for managing infectious disease response.
Students' Engagement in Anonymous Peer Review: Using the Open-Source Sakai Platform
Venue: arXiv cs.CY 2021
Year: 2021

There is a need to provide quality education without discrimination or prejudice to all students. However, there are challenges in implementing quality education in large classes, especially during remote learning. Literature indicates that providing lecturer feedback can become a tedious task, especially in large classes. Literature states that involving students in the peer review process can improve the quality of their submissions. This research used a case study and thematic analysis. Qualitative data were collected from 179 third-year Information Systems students who used the Opensource Sakai Platform. Students reviewed another student's report, without knowing their identity. The research used self-determination theory as a theoretical basis. The achievement of perceived autonomy is supported as an anonymous peer review helped students to empower themselves. Perceived competence was also achieved as the anonymous peer review improved the quality of work submitted and the development of workplace skills. Perceived relatedness is supported as students indicated that the anonymous peer review allowed them to learn from their peers. It also improved their understanding and the ability to see errors in their work. Despite the negative aspects identified using the Sakai platform, it may provide a viable alternative for providing feedback remotely, especially during the Covid-19 pandemic.
Q&A MAESTRO: Q&A Post Recommendation for Fixing Java Runtime Exceptions
Venue: arXiv cs.SE 2021
Year: 2021

Programmers often use Q&A sites (e.g., Stack Overflow) to understand a root cause of program bugs. Runtime exceptions is one of such important class of bugs that is actively discussed on Stack Overflow. However, it may be difficult for beginner programmers to come up with appropriate keywords for search. Moreover, they need to switch their attentions between IDE and browser, and it is time-consuming. To overcome these difficulties, we proposed a method, ``Q&A MAESTRO'', to find suitable Q&A posts automatically for Java runtime exception by utilizing structure information of codes described in programming Q&A website. In this paper, we describe a usage scenario of IDE-plugin, the architecture and user interface of the implementation, and results of user studies. A video is available at https://youtu.be/4X24jJrMUVw. A demo software is available at https://github.com/FujitsuLaboratories/Q-A-MAESTRO.
Discovering Spatial Relationships by Transformers for Domain Generalization
Venue: arXiv cs.CV 2021
Year: 2021

Due to the rapid increase in the diversity of image data, the problem of domain generalization has received increased attention recently. While domain generalization is a challenging problem, it has achieved great development thanks to the fast development of AI techniques in computer vision. Most of these advanced algorithms are proposed with deep architectures based on convolution neural nets (CNN). However, though CNNs have a strong ability to find the discriminative features, they do a poor job of modeling the relations between different locations in the image due to the response to CNN filters are mostly local. Since these local and global spatial relationships are characterized to distinguish an object under consideration, they play a critical role in improving the generalization ability against the domain gap. In order to get the object parts relationships to gain better domain generalization, this work proposes to use the self attention model. However, the attention models are proposed for sequence, which are not expert in discriminate feature extraction for 2D images. Considering this, we proposed a hybrid architecture to discover the spatial relationships between these local features, and derive a composite representation that encodes both the discriminative features and their relationships to improve the domain generalization. Evaluation on three well-known benchmarks demonstrates the benefits of modeling relationships between the features of an image using the proposed method and achieves state-of-the-art domain generalization performance. More specifically, the proposed algorithm outperforms the state-of-the-art by 2.2% and 3.4% on PACS and Office-Home databases, respectively.
CS-Based CSIT Estimation for Downlink Pilot Decontamination in Multi-Cell FDD Massive MIMO
Venue: arXiv cs.IT 2021
Year: 2021

Efficient channel state information at transmitter (CSIT) for frequency division duplex (FDD) massive MIMO can facilitate its backward compatibility with existing FDD cellular networks. To date, several CSIT estimation schemes have been proposed for FDD single-cell massive MIMO systems, but they fail to consider inter-cell-interference (ICI) and suffer from downlink pilot contamination in multi-cell scenario. To solve this problem, this paper proposes a compressive sensing (CS)-based CSIT estimation scheme to combat ICI in FDD multi-cell massive MIMO systems. Specifically, angle-domain massive MIMO channels exhibit the common sparsity over different subcarriers, and such sparsity is partially shared by adjacent users. By exploiting these sparsity properties, we design the pilot signal and the associated channel estimation algorithm under the framework of CS theory, where the channels associated with multiple adjacent BSs can be reliably estimated with low training overhead for downlink pilot decontamination. Simulation results verify the good downlink pilot decontamination performance of the proposed solution compared to its conventional counterparts in multi-cell FDD massive MIMO.
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, inductive graph embedding models, \emph{viz.}, graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. The performance of such networks depends strongly on the input node features, which vary across networks and applications. Selecting appropriate node features remains application-dependent and generally an open question. Moreover, owing to privacy and ethical issues, use of personalized node features is often restricted. In fact, many publicly available data from online social network do not contain any node features (e.g., demography). In this work, we provide a comprehensive experimental analysis which shows that harnessing a transductive technique (e.g., Node2Vec) for obtaining initial node representations, after which an inductive node embedding technique takes over, leads to substantial improvements in link prediction accuracy. We demonstrate that, for a wide variety of GNN variants, node representation vectors obtained from Node2Vec serve as high quality input features to GNNs, thereby improving LP performance.
A study on Machine Learning Approaches for Player Performance and Match Results Prediction
Venue: arXiv cs.AI 2021
Year: 2021

Cricket is unarguably one of the most popular sports in the world. Predicting the outcome of a cricket match has become a fundamental problem as we are advancing in the field of machine learning. Multiple researchers have tried to predict the outcome of a cricket match or a tournament, or to predict the performance of players during a match, or to predict the players who should be selected as per their current performance, form, morale, etc. using machine learning and artificial intelligence techniques keeping in mind extensive detailing, features, and parameters. We discuss some of these techniques along with a brief comparison among these techniques.
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees
Venue: arXiv cs.DB 2021
Year: 2021

Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significant progress, a majority of today's commercial solutions are highly manual, requiring offline invocation by database administrators (DBAs) who are expected to identify and supply representative training workloads. Even the latest advancements like query stores provide only limited support for dynamic environments. This status quo is untenable: identifying representative static workloads is no longer realistic; and physical design tools remain susceptible to the query optimiser's cost misestimates. Furthermore, modern application environments such as hybrid transactional and analytical processing (HTAP) systems render analytical modelling next to impossible. We propose a self-driving approach to online index selection that eschews the DBA and query optimiser, and instead learns the benefits of viable structures through strategic exploration and direct performance observation. We view the problem as one of sequential decision making under uncertainty, specifically within the bandit learning setting. Multi-armed bandits balance exploration and exploitation to provably guarantee average performance that converges to policies that are optimal with perfect hindsight. Our comprehensive empirical evaluation against a state-of-the-art commercial tuning tool demonstrates up to 75% speed-up on shifting and ad-hoc workloads and up to 28% speed-up on static workloads in analytical processing environments. In HTAP environments, our solution provides up to 59% speed-up on shifting and 51% speed-up on static workloads. Furthermore, our bandit framework outperforms deep reinforcement learning (RL) in terms of convergence speed and performance volatility (providing up to 58% speed-up).
Improving Accuracy of Permutation DAG Search using Best Order Score Search
Venue: arXiv cs.AI 2021
Year: 2021

The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretically. A compromise can be given, the Best Order Score Search, which gives results as accurate as SP but for much larger and denser graphs. BOSS (Best Order Score Search) is more accurate for two reason: (a) It assumes the "brute faithfuness" assumption, which is weaker than faithfulness, and (b) it uses a different traversal of permutations than the depth first traversal used by GSP, obtained by taking each variable in turn and moving it to the position in the permutation that optimizes the model score. Results are given comparing BOSS to several related papers in the literature in terms of performance, for linear, Gaussian data. In all cases, with the proper parameter settings, accuracy of BOSS is lifted considerably with respect to competing approaches. In configurations tested, models with 60 variables are feasible with large samples out to about an average degree of 12 in reasonable time, with near-perfect accuracy, and sparse models with an average degree of 4 are feasible out to about 300 variables on a laptop, again with near-perfect accuracy. Mixed continuous discrete and all-discrete datasets were also tested. The mixed data analysis showed advantage for BOSS over GES more apparent at higher depths with the same score; the discrete data analysis showed a very small advantage for BOSS over GES with the same score, perhaps not enough to prefer it.
Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consists of a number of clients and a centralized server. Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer. Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed. We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches. This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels.
Fusion of evidential CNN classifiers for image classification
Venue: arXiv cs.CV 2021
Year: 2021

We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extract features from input images and convert them into mass functions on different frames of discernment. A fusion module then aggregates these mass functions using Dempster's rule. An end-to-end learning procedure allows us to fine-tune the overall architecture using a learning set with soft labels, which further improves the classification performance. The effectiveness of this approach is demonstrated experimentally using three benchmark databases.
A User Study on Hybrid Graph Visualizations
Venue: arXiv cs.HC 2021
Year: 2021

Hybrid visualizations mix different metaphors in a single layout of a network. In particular, the popular NodeTrix model, introduced by Henry, Fekete, and McGuffin in 2007, combines node-link diagrams and matrix-based representations to support the analysis of real-world networks that are globally sparse but locally dense. That idea inspired a series of works, proposing variants or alternatives to NodeTrix. We present a user study that compares the classical node-link model and three hybrid visualization models designed to work on the same types of networks. The results of our study provide interesting indications about advantages/drawbacks of the considered models on performing classical tasks of analysis. At the same time, our experiment has some limitations and opens up to further research on the subject.
ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with red-green-blue (RGB) inputs, thus mitigating the effects of artefacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
Knowledge-based XAI through CBR: There is more to explanations than models can tell
Venue: arXiv cs.AI 2021
Year: 2021

The underlying hypothesis of knowledge-based explainable artificial intelligence is the data required for data-centric artificial intelligence agents (e.g., neural networks) are less diverse in contents than the data required to explain the decisions of such agents to humans. The idea is that a classifier can attain high accuracy using data that express a phenomenon from one perspective whereas the audience of explanations can entail multiple stakeholders and span diverse perspectives. We hence propose to use domain knowledge to complement the data used by agents. We formulate knowledge-based explainable artificial intelligence as a supervised data classification problem aligned with the CBR methodology. In this formulation, the inputs are case problems composed of both the inputs and outputs of the data-centric agent and case solutions, the outputs, are explanation categories obtained from domain knowledge and subject matter experts. This formulation does not typically lead to an accurate classification, preventing the selection of the correct explanation category. Knowledge-based explainable artificial intelligence extends the data in this formulation by adding features aligned with domain knowledge that can increase accuracy when selecting explanation categories.
Robust Risk-Aware Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

We present a reinforcement learning (RL) approach for robust optimisation of risk-aware performance criteria. To allow agents to express a wide variety of risk-reward profiles, we assess the value of a policy using rank dependent expected utility (RDEU). RDEU allows the agent to seek gains, while simultaneously protecting themselves against downside risk. To robustify optimal policies against model uncertainty, we assess a policy not by its distribution, but rather, by the worst possible distribution that lies within a Wasserstein ball around it. Thus, our problem formulation may be viewed as an actor/agent choosing a policy (the outer problem), and the adversary then acting to worsen the performance of that strategy (the inner problem). We develop explicit policy gradient formulae for the inner and outer problems, and show its efficacy on three prototypical financial problems: robust portfolio allocation, optimising a benchmark, and statistical arbitrage.