OpenRaiser/Intern-Atlas huggingface dataset:
Feasibility of Proof of Authority as a Consensus Protocol Model
Venue: arXiv cs.DC 2021
Year: 2021

Blockchain is a type of decentralized distributed network which acts as an immutable digital ledger. Despite the absence of any central governing authority to validate the blocks in the ledger, it is considered secure and immutable due to the consensus protocol among various nodes of the network. A consensus algorithm is a mechanism that guarantees the reliability of the blockchain and helps all connected nodes or peers to reach common ground regarding the present state of the blockchain network thus an ideal consensus algorithm must be secure, reliable, and fast. There are several different algorithms to reach a consensus among the nodes thus this article seeks to test the practicality of Proof of Authority in the blockchain network as a consensus algorithm and its comparison with current mainstream consensus algorithms.
Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients
Venue: arXiv cs.LG 2021
Year: 2021

As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale.
Generation of Synthetic Electronic Health Records Using a Federated GAN
Venue: arXiv cs.LG 2021
Year: 2021

Sensitive medical data is often subject to strict usage constraints. In this paper, we trained a generative adversarial network (GAN) on real-world electronic health records (EHR). It was then used to create a data-set of "fake" patients through synthetic data generation (SDG) to circumvent usage constraints. This real-world data was tabular, binary, intensive care unit (ICU) patient diagnosis data. The entire data-set was split into separate data silos to mimic real-world scenarios where multiple ICU units across different hospitals may have similarly structured data-sets within their own organisations but do not have access to each other's data-sets. We implemented federated learning (FL) to train separate GANs locally at each organisation, using their unique data silo and then combining the GANs into a single central GAN, without any siloed data ever being exposed. This global, central GAN was then used to generate the synthetic patients data-set. We performed an evaluation of these synthetic patients with statistical measures and through a structured review by a group of medical professionals. It was shown that there was no significant reduction in the quality of the synthetic EHR when we moved between training a single central model and training on separate data silos with individual models before combining them into a central model. This was true for both the statistical evaluation (Root Mean Square Error (RMSE) of 0.0154 for single-source vs. RMSE of 0.0169 for dual-source federated) and also for the medical professionals' evaluation (no quality difference between EHR generated from a single source and EHR generated from multiple sources).
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT
Venue: arXiv cs.CR 2021
Year: 2021

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber-attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.
Guiding Global Placement With Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Recent advances in GPU accelerated global and detail placement have reduced the time to solution by an order of magnitude. This advancement allows us to leverage data driven optimization (such as Reinforcement Learning) in an effort to improve the final quality of placement results. In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL). We propose novel control schemes with either global or localized control of the placement process. We then train reinforcement learning agents to use these controls to guide placement to improved solutions. In both cases, the augmented optimizer finds improved placement solutions. Our trained agents achieve an average 1% improvement in final detail place HPWL across a range of academic benchmarks and more than 1% in global place HPWL on real industry designs.
An Evaluation-Focused Framework for Visualization Recommendation Algorithms
Venue: arXiv cs.HC 2021
Year: 2021

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images
Venue: arXiv cs.CV 2021
Year: 2021

Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to meet with the high yield and high quality crop production. Classification and recognition in Unmanned Aerial Vehicles (UAV) images are important phases for crop monitoring. Advances in deep learning models relying on Convolutional Neural Network (CNN) have achieved high performances in image classification in the agricultural domain. Despite the success of this architecture, CNN still faces many challenges such as high computation cost, the need of large labelled datasets, ... Natural language processing's transformer architecture can be an alternative approach to deal with CNN's limitations. Making use of the self-attention paradigm, Vision Transformer (ViT) models can achieve competitive or better results without applying any convolution operations. In this paper, we adopt the self-attention mechanism via the ViT models for plant classification of weeds and crops: red beet, off-type beet (green leaves), parsley and spinach. Our experiments show that with small set of labelled training data, ViT models perform better compared to state-of-the-art CNN-based models EfficientNet and ResNet, with a top accuracy of 99.8\% achieved by the ViT model.
Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots
Venue: arXiv cs.LG 2021
Year: 2021

As machine learning systems become more ubiquitous, methods for understanding and interpreting these models become increasingly important. In particular, practitioners are often interested both in what features the model relies on and how the model relies on them--the feature's impact on model predictions. Prior work on feature impact including partial dependence plots (PDPs) and Individual Conditional Expectation (ICE) plots has focused on a visual interpretation of feature impact. We propose a natural extension to ICE plots with ICE feature impact, a model-agnostic, performance-agnostic feature impact metric drawn out from ICE plots that can be interpreted as a close analogy to linear regression coefficients. Additionally, we introduce an in-distribution variant of ICE feature impact to vary the influence of out-of-distribution points as well as heterogeneity and non-linearity measures to characterize feature impact. Lastly, we demonstrate ICE feature impact's utility in several tasks using real-world data.
An Intelligent Material with Chemical Pathway Networks
Venue: arXiv cs.ET 2021
Year: 2021

A new type of material with embedded intelligence, namely 'intelligent plasma', is introduced. Such new material exhibits programmable chemical pathway networks resembling artificial neural networks. As a Markov process of chemistry, the chemical pathway network can be customized and thus the intelligent plasmas can be programmed to make their own decisions to react to the dynamic external and internal conditions. It finally can accomplish complex missions without any external controls from the humans while relying on its preprogrammed chemical network topology before the mission. To that end, only basic data input and readings are required without any external controls during the mission. The approach to 'if' conditions and 'while' loops of the programmable intelligent plasmas are also discussed with examples of applications including automatic workflows, and signal processing.
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond
Venue: arXiv cs.LG 2021
Year: 2021

Training deep neural networks may be challenging in real world data. Using models as black-boxes, even with transfer learning, can result in poor generalization or inconclusive results when it comes to small datasets or specific applications. This tutorial covers the basic steps as well as more recent options to improve models, in particular, but not restricted to, supervised learning. It can be particularly useful in datasets that are not as well-prepared as those in challenges, and also under scarce annotation and/or small data. We describe basic procedures: as data preparation, optimization and transfer learning, but also recent architectural choices such as use of transformer modules, alternative convolutional layers, activation functions, wide and deep networks, as well as training procedures including as curriculum, contrastive and self-supervised learning.
Lightweight, Multi-Stage, Compiler-Assisted Application Specialization
Venue: arXiv cs.SE 2021
Year: 2021

Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. These approaches are either based on unsound strategies or demanding techniques, leading to unsafe results or a high overhead debloating process. In this paper, we address these limitations by applying partial-evaluation principles to generate specialized applications. Our approach relies on a simple observation that an application typically consists of configuration logic, followed by the main logic of the program. The configuration logic specifies what functionality in the main logic should be executed. LMCAS performs partial interpretation to capture a precise program state of the configuration logic based on the supplied inputs. LMCAS then applies partial-evaluation optimizations to generate a specialized program by propagating the constants in the captured partial state, eliminating unwanted code, and preserving the desired functionalities. Our evaluation of LMCAS on commonly used benchmarks and real-world applications shows that it successfully removes unwanted features while preserving the functionality and robustness of the deblated programs, runs faster than prior tools, and reduces the attack surface of specialized programs. LMCAS runs 1500x, 4.6x, and 1.2x faster than the state-of-the-art debloating tools CHISEL, RAZOR, and OCCAM, respectively; achieves 25% reduction in the binary size; reduces the attack surface of code-reuse attacks by removing 51.7% of the total gadgets and eliminating 83% of known CVE vulnerabilities
Trojan Signatures in DNN Weights
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks have been shown to be vulnerable to backdoor, or trojan, attacks where an adversary has embedded a trigger in the network at training time such that the model correctly classifies all standard inputs, but generates a targeted, incorrect classification on any input which contains the trigger. In this paper, we present the first ultra light-weight and highly effective trojan detection method that does not require access to the training/test data, does not involve any expensive computations, and makes no assumptions on the nature of the trojan trigger. Our approach focuses on analysis of the weights of the final, linear layer of the network. We empirically demonstrate several characteristics of these weights that occur frequently in trojaned networks, but not in benign networks. In particular, we show that the distribution of the weights associated with the trojan target class is clearly distinguishable from the weights associated with other classes. Using this, we demonstrate the effectiveness of our proposed detection method against state-of-the-art attacks across a variety of architectures, datasets, and trigger types.
Integrating Regular Expressions with Neural Networks via DFA
Venue: arXiv cs.CL 2021
Year: 2021

Human-designed rules are widely used to build industry applications. However, it is infeasible to maintain thousands of such hand-crafted rules. So it is very important to integrate the rule knowledge into neural networks to build a hybrid model that achieves better performance. Specifically, the human-designed rules are formulated as Regular Expressions (REs), from which the equivalent Minimal Deterministic Finite Automatons (MDFAs) are constructed. We propose to use the MDFA as an intermediate model to capture the matched RE patterns as rule-based features for each input sentence and introduce these additional features into neural networks. We evaluate the proposed method on the ATIS intent classification task. The experiment results show that the proposed method achieves the best performance compared to neural networks and four other methods that combine REs and neural networks when the training dataset is relatively small.
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
Venue: arXiv cs.LG 2021
Year: 2021

Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.
Learning to Combine the Modalities of Language and Video for Temporal Moment Localization
Venue: arXiv cs.CV 2021
Year: 2021

Temporal moment localization aims to retrieve the best video segment matching a moment specified by a query. The existing methods generate the visual and semantic embeddings independently and fuse them without full consideration of the long-term temporal relationship between them. To address these shortcomings, we introduce a novel recurrent unit, cross-modal long short-term memory (CM-LSTM), by mimicking the human cognitive process of localizing temporal moments that focuses on the part of a video segment related to the part of a query, and accumulates the contextual information across the entire video recurrently. In addition, we devise a two-stream attention mechanism for both attended and unattended video features by the input query to prevent necessary visual information from being neglected. To obtain more precise boundaries, we propose a two-stream attentive cross-modal interaction network (TACI) that generates two 2D proposal maps obtained globally from the integrated contextual features, which are generated by using CM-LSTM, and locally from boundary score sequences and then combines them into a final 2D map in an end-to-end manner. On the TML benchmark dataset, ActivityNet-Captions, the TACI outperform state-of-the-art TML methods with R@1 of 45.50% and 27.23% for IoU@0.5 and IoU@0.7, respectively. In addition, we show that the revised state-of-the-arts methods by replacing the original LSTM with our CM-LSTM achieve performance gains.
Grassmannian Graph-attentional Landmark Selection for Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

Domain adaptation aims to leverage information from the source domain to improve the classification performance in the target domain. It mainly utilizes two schemes: sample reweighting and feature matching. While the first scheme allocates different weights to individual samples, the second scheme matches the feature of two domains using global structural statistics. The two schemes are complementary with each other, which are expected to jointly work for robust domain adaptation. Several methods combine the two schemes, but the underlying relationship of samples is insufficiently analyzed due to the neglect of the hierarchy of samples and the geometric properties between samples. To better combine the advantages of the two schemes, we propose a Grassmannian graph-attentional landmark selection (GGLS) framework for domain adaptation. GGLS presents a landmark selection scheme using attention-induced neighbors of the graphical structure of samples and performs distribution adaptation and knowledge adaptation over Grassmann manifold. the former treats the landmarks of each sample differently, and the latter avoids feature distortion and achieves better geometric properties. Experimental results on different real-world cross-domain visual recognition tasks demonstrate that GGLS provides better classification accuracies compared with state-of-the-art domain adaptation methods.
Sequential Attention Module for Natural Language Processing
Venue: arXiv cs.AI 2021
Year: 2021

Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on the language models. We, therefore, propose a simple yet effective plug-and-play module, Sequential Attention Module (SAM), on the token embeddings learned from a pre-trained language model. Our proposed SAM consists of two main attention modules deployed sequentially: Feature-wise Attention Module (FAM) and Token-wise Attention Module (TAM). More specifically, FAM can effectively identify the importance of features at each dimension and promote the effect via dot-product on the original token embeddings for downstream NLP applications. Meanwhile, TAM can further re-weight the features at the token-wise level. Moreover, we propose an adaptive filter on FAM to prevent noise impact and increase information absorption. Finally, we conduct extensive experiments to demonstrate the advantages and properties of our proposed SAM. We first show how SAM plays a primary role in the champion solution of two subtasks of SemEval'21 Task 7. After that, we apply SAM on sentiment analysis and three popular NLP tasks and demonstrate that SAM consistently outperforms the state-of-the-art baselines.
A Novel Multimodal Biometric Authentication System using Machine Learning and Blockchain
Venue: arXiv cs.CR 2021
Year: 2021

Traditional authentication systems that rely on simple passwords, PIN numbers or tokens have many security issues, like easily guessed passwords, PIN numbers written on the back of cards, etc. Thus, biometric authentication methods that rely on physical and behavioural characteristics have been proposed as an alternative for those systems. In real-world applications, authentication systems that involve a single biometric faced many issues, especially lack of accuracy and noisy data, which boost the research community to create multibiometric systems that involve a variety of biometrics. Those systems provide better performance and higher accuracy compared to other authentication methods. However, most of them are inconvenient and requires complex interactions from the user. Thus, in this paper, we introduce a novel multimodal authentication system that relies on machine learning and blockchain, with the aim of providing a more secure, transparent, and convenient authentication mechanism. The proposed system combines four important biometrics, fingerprint, face, age, and gender. The supervised learning algorithm Decision Tree has been used to combine the results of the biometrics verification process and produce a confidence level related to the user. The initial experimental results show the efficiency and robustness of the proposed multimodal systems.