OpenRaiser/Intern-Atlas huggingface dataset:
Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image
Venue: arXiv cs.CV 2021
Year: 2021

Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on estimating depth from a single image. However, traditional CNNs suffer from the lower resolution and information loss caused by the pooling layers. And oversized parameters generated from fully connected layers often lead to a exploded memory usage problem. In this paper, we present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. It also reduces the amount of parameters significantly by replacing the fully connected layers with the fully convolutional layers. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.
A Review on Semi-Supervised Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Relation extraction (RE) plays an important role in extracting knowledge from unstructured text but requires a large amount of labeled corpus. To reduce the expensive annotation efforts, semisupervised learning aims to leverage both labeled and unlabeled data. In this paper, we review and compare three typical methods in semi-supervised RE with deep learning or meta-learning: self-ensembling, which forces consistent under perturbations but may confront insufficient supervision; self-training, which iteratively generates pseudo labels and retrain itself with the enlarged labeled set; dual learning, which leverages a primal task and a dual task to give mutual feedback. Mean-teacher (Tarvainen and Valpola, 2017), LST (Li et al., 2019), and DualRE (Lin et al., 2019) are elaborated as the representatives to alleviate the weakness of these three methods, respectively.
SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features
Venue: arXiv cs.LG 2021
Year: 2021

Real world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these under-represented instances. To solve this problem, many variations of synthetic minority over-sampling methods (SMOTE) have been proposed to balance the dataset which deals with continuous features. However, for datasets with both nominal and continuous features, SMOTE-NC is the only SMOTE-based over-sampling technique to balance the data. In this paper, we present a novel minority over-sampling method, SMOTE-ENC (SMOTE - Encoded Nominal and Continuous), in which, nominal features are encoded as numeric values and the difference between two such numeric value reflects the amount of change of association with minority class. Our experiments show that the classification model using SMOTE-ENC method offers better prediction than model using SMOTE-NC when the dataset has a substantial number of nominal features and also when there is some association between the categorical features and the target class. Additionally, our proposed method addressed one of the major limitations of SMOTE-NC algorithm. SMOTE-NC can be applied only on mixed datasets that have features consisting of both continuous and nominal features and cannot function if all the features of the dataset are nominal. Our novel method has been generalized to be applied on both mixed datasets and on nominal only datasets. The code is available from mkhushi.github.io
AIR4Children: Artificial Intelligence and Robotics for Children
Venue: arXiv cs.RO 2021
Year: 2021

We introduce AIR4Children, Artificial Intelligence for Children, as a way to (a) tackle aspects for inclusion, accessibility, transparency, equity, fairness and participation and (b) to create affordable child-centred materials in AI and Robotics (AIR). We present current challenges and opportunities for a child-centred approaches for AIR. Similarly, we touch on open-sourced software and hardware technologies to make a more inclusive, affordable and fair participation of children in areas of AIR. Then, we describe the avenues that AIR4Children can take with the development of open-sourced software and hardware based on our initial pilots and experiences. Similarly, we propose to follow the philosophy of Montessori education to help children to not only develop computational thinking but also to internalise new concepts and learning skills through activities of movement and repetition. Finally, we conclude with the opportunities of our work and mainly we pose the future work of putting in practice what is proposed here to evaluate the potential impact on AIR to children, instructors, parents and their community.
Lightweight Selective Disclosure for Verifiable Documents on Blockchain
Venue: arXiv cs.CR 2021
Year: 2021

To achieve lightweight selective disclosure for protecting privacy of document holders, we propose an XML format for documents that can hide arbitrary elements using a cryptographic hash function and salts, which allows to be partially digitally signed and efficiently verified, as well as a JSON format that can be converted to such XML. The documents can be efficiently proven to exist by representing multiple such structures as a Merkle tree and storing its root in blockchain. We show that our proposal has advantages over known methods that represent the document itself as a Merkle tree and partially hide it.
Optimal Embedding Calibration for Symbolic Music Similarity
Venue: arXiv cs.SD 2021
Year: 2021

In natural language processing (NLP), the semantic similarity task requires large-scale, high-quality human-annotated labels for fine-tuning or evaluation. By contrast, in cases of music similarity, such labels are expensive to collect and largely dependent on the annotator's artistic preferences. Recent research has demonstrated that embedding calibration technique can greatly increase semantic similarity performance of the pre-trained language model without fine-tuning. However, it is yet unknown which calibration method is the best and how much performance improvement can be achieved. To address these issues, we propose using composer information to construct labels for automatically evaluating music similarity. Under this paradigm, we discover the optimal combination of embedding calibration which achieves superior metrics than the baseline methods.
Performance Analysis of Multipath BGP
Venue: arXiv cs.NI 2021
Year: 2021

Multipath BGP (M-BGP) allows a BGP router to install multiple 'equally-good' paths, via parallel inter-domain border links, to a destination prefix. M-BGP differs from the multipath routing techniques in many ways, e.g. M-BGP is only implemented at border routers of Autonomous Systems (ASes); and while it shares traffic to different IP addresses in a destination prefix via different border links, any traffic to a given destination IP always follows the same border link. Recently we studied Looking Glass data and reported the wide deployment of M-BGP in the Internet; in particular, Hurricane Electric (AS6939) has implemented over 1,000 cases of M-BGP to hundreds of its peering ASes. In this paper, we analyzed the performance of M-BGP. We used RIPE Atlas to send traceroute probes to a series of destination prefixes through Hurricane Electric's border routers implemented with M-BGP. We examined the distribution of Round Trip Time to each probed IP address in a destination prefix and their variation during the measurement. We observed that the deployment of M-BGP can guarantee stable routing between ASes and enhance a network's resilience to traffic changes. Our work provides insights into the unique characteristics of M-BGP as an effective technique for load balancing.
Simeon -- Secure Federated Machine Learning Through Iterative Filtering
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware. A global model is distributed to clients, who perform training, and submit their newly-trained model to be aggregated into a superior model. However, federated learning systems are vulnerable to interference from malicious learning agents who may desire to prevent training or induce targeted misclassification in the resulting global model. A class of Byzantine-tolerant aggregation algorithms has emerged, offering varying degrees of robustness against these attacks, often with the caveat that the number of attackers is bounded by some quantity known prior to training. This paper presents Simeon: a novel approach to aggregation that applies a reputation-based iterative filtering technique to achieve robustness even in the presence of attackers who can exhibit arbitrary behaviour. We compare Simeon to state-of-the-art aggregation techniques and find that Simeon achieves comparable or superior robustness to a variety of attacks. Notably, we show that Simeon is tolerant to sybil attacks, where other algorithms are not, presenting a key advantage of our approach.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlations and inter-series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at https://github.com/microsoft/StemGNN/
Image Classifiers for Network Intrusions
Venue: arXiv cs.CR 2021
Year: 2021

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2's convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important source-destination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling
Venue: arXiv cs.CL 2021
Year: 2021

Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). In the context of zero-shot learning, this task is typically approached by either using representations from pre-trained multilingual transformers such as mBERT, or by machine translating the source data into the known target language and then fine-tuning. Our work focuses on a particular scenario where the target language is unknown during training. To this goal, we propose a novel method to augment the monolingual source data using multilingual code-switching via random translations to enhance a transformer's language neutrality when fine-tuning it for a downstream task. This method also helps discover novel insights on how code-switching with different language families around the world impact the performance on the target language. Experiments on the benchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in accuracy for intent task and +1.8% in F1 for slot task using our method over the state-of-the-art across 8 different languages. Furthermore, we present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole, collected during Haiti earthquake disaster.
Hybrid computer approach to train a machine learning system
Venue: arXiv cs.LG 2021
Year: 2021

This book chapter describes a novel approach to training machine learning systems by means of a hybrid computer setup i.e. a digital computer tightly coupled with an analog computer. As an example a reinforcement learning system is trained to balance an inverted pendulum which is simulated on an analog computer, thus demonstrating a solution to the major challenge of adequately simulating the environment for reinforcement learning.
R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Venue: arXiv cs.AI 2021
Year: 2021

Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need meta-path, which is much improved compared to the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves the state-of-the-art performance on the ogbn-mag large scale heterogeneous graph dataset.
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
Venue: arXiv cs.AI 2021
Year: 2021

The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspired by recent advances in reinforcement learning (RL), this paper aims at assessing the potential of RL in building climate control problems with occupant interaction. We apply a recent RL approach, called DDPG (deep deterministic policy gradient), for the continuous building control tasks and assess its performance with simulation studies in terms of its ability to handle (a) the partial state observability due to sensor limitations; (b) complex stochastic system with high-dimensional state-spaces, which are jointly continuous and discrete; (c) uncertainties due to ambient weather conditions, occupant's behavior, and comfort feelings. Especially, the partial observability and uncertainty due to the occupant interaction significantly complicate the control problem. Through simulation studies, the policy learned by DDPG demonstrates reasonable performance and computational tractability.
Software Architecture for ML-based Systems: What Exists and What Lies Ahead
Venue: arXiv cs.SE 2021
Year: 2021

The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has resulted in two broad research areas: i) software architecture for ML-based systems, which focuses on developing architectural techniques for better developing ML-based software systems, and ii) ML for software architectures, which focuses on developing ML techniques to better architect traditional software systems. In this work, we focus on the former side of the spectrum with a goal to highlight the different architecting practices that exist in the current scenario for architecting ML-based software systems. We identify four key areas of software architecture that need the attention of both the ML and software practitioners to better define a standard set of practices for architecting ML-based software systems. We base these areas in light of our experience in architecting an ML-based software system for solving queuing challenges in one of the largest museums in Italy.
Selfish Mining Attacks Exacerbated by Elastic Hash Supply
Venue: arXiv cs.CR 2021
Year: 2021

Several attacks have been proposed against Proof-of-Work blockchains, which may increase the attacker's share of mining rewards (e.g., selfish mining, block withholding). A further impact of such attacks, which has not been considered in prior work, is that decreasing the profitability of mining for honest nodes incentivizes them to stop mining or to leave the attacked chain for a more profitable one. The departure of honest nodes exacerbates the attack and may further decrease profitability and incentivize more honest nodes to leave. In this paper, we first present an empirical analysis showing that there is a statistically significant correlation between the profitability of mining and the total hash rate, confirming that miners indeed respond to changing profitability. Second, we present a theoretical analysis showing that selfish mining under such elastic hash supply leads either to the collapse of a chain, i.e., all honest nodes leaving, or to a stable equilibrium depending on the attacker's initial share.
Tomlinson-Harashima Precoding with Stream Combiners for MU-MIMO with Rate-Splitting
Venue: arXiv cs.IT 2021
Year: 2021

This paper introduces multiuser multiple-input multiple-output (MU-MIMO) architectures based on non-linear precoding and stream combining techniques using rate-splitting (RS), where the transmitter often has only partial knowledge of the channel state information (CSI). In contrast to existing works, we consider deployments where the receivers may be equipped with multiple antennas. This allows us to employ linear combining techniques based on the Min-Max, the maximum ratio and the minimum mean-square error criteria along with Tomlinson-Harashima precoders (THP) for RS-based MU-MIMO systems to enhance the sum-rate performance. Moreover, we incorporate the Multi-Branch (MB) concept into the RS architecture to further improve the sum-rate performance. Closed-form expressions for the signal-to-interference-plus-noise ratio and the sum-rate at the receiver end are devised through statistical analysis. Simulation results show that the proposed RS-THP schemes achieve better performance than conventional linear and THP precoders.
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
Venue: arXiv cs.LG 2021
Year: 2021

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks
Venue: arXiv cs.LG 2021
Year: 2021

Deploying convolutional neural networks (CNNs) for embedded applications presents many challenges in balancing resource-efficiency and task-related accuracy. These two aspects have been well-researched in the field of CNN compression. In real-world applications, a third important aspect comes into play, namely the robustness of the CNN. In this paper, we thoroughly study the robustness of uncompressed, distilled, pruned and binarized neural networks against white-box and black-box adversarial attacks (FGSM, PGD, C&W, DeepFool, LocalSearch and GenAttack). These new insights facilitate defensive training schemes or reactive filtering methods, where the attack is detected and the input is discarded and/or cleaned. Experimental results are shown for distilled CNNs, agent-based state-of-the-art pruned models, and binarized neural networks (BNNs) such as XNOR-Net and ABC-Net, trained on CIFAR-10 and ImageNet datasets. We present evaluation methods to simplify the comparison between CNNs under different attack schemes using loss/accuracy levels, stress-strain graphs, box-plots and class activation mapping (CAM). Our analysis reveals susceptible behavior of uncompressed and pruned CNNs against all kinds of attacks. The distilled models exhibit their strength against all white box attacks with an exception of C&W. Furthermore, binary neural networks exhibit resilient behavior compared to their baselines and other compressed variants.
Faster Algorithms for Largest Empty Rectangles and Boxes
Venue: arXiv cs.CG 2021
Year: 2021

We revisit a classical problem in computational geometry: finding the largest-volume axis-aligned empty box (inside a given bounding box) amidst $n$ given points in $d$ dimensions. Previously, the best algorithms known have running time $O(n\log^2n)$ for $d=2$ (by Aggarwal and Suri [SoCG'87]) and near $n^d$ for $d\ge 3$. We describe faster algorithms with running time (i) $O(n2^{O(\log^*n)}\log n)$ for $d=2$, (ii) $O(n^{2.5+o(1)})$ time for $d=3$, and (iii) $\widetilde{O}(n^{(5d+2)/6})$ time for any constant $d\ge 4$. To obtain the higher-dimensional result, we adapt and extend previous techniques for Klee's measure problem to optimize certain objective functions over the complement of a union of orthants.
On Planar Visibility Counting Problem
Venue: arXiv cs.CG 2021
Year: 2021

For a set $S$ of $n$ disjoint line segments in $\mathbb{R}^{2}$, the visibility counting problem is to preprocess $S$ such that the number of visible segments in $S$ from any query point $p$ can be computed quickly. There have been approximation algorithms for this problem with trade off between space and query time. We propose a new randomized algorithm to compute the exact answer of the problem. For any $0<α<1$, the space, preprocessing time and query time are $O_ε(n^{4-4α})$, $O_ε(n^{4-2α})$ and $O_ε(n^{2α})$, respectively. Where $O_ε(f(n)) = O(f(n)n^ε)$ and $ε>0$ is an arbitrary constant number.