OpenRaiser/Intern-Atlas huggingface dataset:
Domino: A Tailored Network-on-Chip Architecture to Enable Highly Localized Inter- and Intra-Memory DNN Computing
Venue: arXiv cs.AR 2021
Year: 2021

The ever-increasing computation complexity of fast-growing Deep Neural Networks (DNNs) has requested new computing paradigms to overcome the memory wall in conventional Von Neumann computing architectures. The emerging Computing-In-Memory (CIM) architecture has been a promising candidate to accelerate neural network computing. However, the data movement between CIM arrays may still dominate the total power consumption in conventional designs. This paper proposes a flexible CIM processor architecture named Domino to enable stream computing and local data access to significantly reduce the data movement energy. Meanwhile, Domino employs tailored distributed instruction scheduling within Network-on-Chip (NoC) to implement inter-memory-computing and attain mapping flexibility. The evaluation with prevailing CNN models shows that Domino achieves 1.15-to-9.49$\times$ power efficiency over several state-of-the-art CIM accelerators and improves the throughput by 1.57-to-12.96$\times$.
On the Interplay of Smells Large Class, Complex Class and Duplicate Code
Venue: arXiv cs.SE 2021
Year: 2021

Bad smells have been defined to describe potential problems in code, possibly pointing out refactoring opportunities. Several empirical studies have highlighted that smells have a negative impact on comprehension and maintainability. Consequently, several approaches have been proposed to detect and restructure them. However, studies on the inter-relationship of occurrence of different types of smells in source code are still lacking, especially those focused on the quantification of this inter-relationship. In this work, we aim at understand and quantify the possible the inter-relation of smells Large Class - LC, Complex Class - CC and Duplicate Code - DC. In particular, we investigate patterns of LC and CC regarding the presence or absence of duplicate code. We conduct a quantitative study on five open source projects, and also a qualitative analysis to measure and understand the association of specific smells. As one of the main results, we highlight that there are "occurrence patterns" among these smells, for example: either in Complex Class or in the co-occurrence of Large Class and Complex Class, clones tend to be more prevalent in highly complex classes than less complex classes. The found patterns could be used to improve the performance of detection tools or even help in refactoring tasks.
Semantic Reasoning with Differentiable Graph Transformations
Venue: arXiv cs.AI 2021
Year: 2021

This paper introduces a differentiable semantic reasoner, where rules are presented as a relevant set of graph transformations. These rules can be written manually or inferred by a set of facts and goals presented as a training set. While the internal representation uses embeddings in a latent space, each rule can be expressed as a set of predicates conforming to a subset of Description Logic.
More Parameters? No Thanks!
Venue: arXiv cs.CL 2021
Year: 2021

This work studies the long-standing problems of model capacity and negative interference in multilingual neural machine translation MNMT. We use network pruning techniques and observe that pruning 50-70% of the parameters from a trained MNMT model results only in a 0.29-1.98 drop in the BLEU score. Suggesting that there exist large redundancies even in MNMT models. These observations motivate us to use the redundant parameters and counter the interference problem efficiently. We propose a novel adaptation strategy, where we iteratively prune and retrain the redundant parameters of an MNMT to improve bilingual representations while retaining the multilinguality. Negative interference severely affects high resource languages, and our method alleviates it without any additional adapter modules. Hence, we call it parameter-free adaptation strategy, paving way for the efficient adaptation of MNMT. We demonstrate the effectiveness of our method on a 9 language MNMT trained on TED talks, and report an average improvement of +1.36 BLEU on high resource pairs. Code will be released here.
Machine Learning Approaches to Automated Flow Cytometry Diagnosis of Chronic Lymphocytic Leukemia
Venue: arXiv cs.LG 2021
Year: 2021

Flow cytometry is a technique that measures multiple fluorescence and light scatter-associated parameters from individual cells as they flow a single file through an excitation light source. These cells are labeled with antibodies to detect various antigens and the fluorescence signals reflect antigen expression. Interpretation of the multiparameter flow cytometry data is laborious, time-consuming, and expensive. It involves manual interpretation of cell distribution and pattern recognition on two-dimensional plots by highly trained medical technologists and pathologists. Using various machine learning algorithms, we attempted to develop an automated analysis for clinical flow cytometry cases that would automatically classify normal and chronic lymphocytic leukemia cases. We achieved the best success with the Gradient Boosting. The XGBoost classifier achieved a specificity of 1.00 and a sensitivity of 0.67, a negative predictive value of 0.75, a positive predictive value of 1.00, and an overall accuracy of 0.83 in prospectively classifying cases with malignancies.
Single-Shot Compression for Hypothesis Testing
Venue: arXiv cs.IT 2021
Year: 2021

Enhanced processing power in the cloud allows constrained devices to offload costly computations: for instance, complex data analytics tasks can be computed by remote servers. Remote execution calls for a new compression paradigm that optimizes performance on the analytics task within a rate constraint, instead of the traditional rate-distortion framework which focuses on source reconstruction. This paper considers a simple binary hypothesis testing scenario where the resource constrained client (transmitter) performs fixed-length single-shot compression on data sampled from one of two distributions; the server (receiver) performs a hypothesis test on multiple received samples to determine the correct source distribution. To this end, the task-aware compression problem is formulated as finding the optimal source coder that maximizes the asymptotic error performance of the hypothesis test on the server side under a rate constraint. A new source coding strategy based on a greedy optimization procedure is proposed and it is shown that that the proposed compression scheme outperforms universal fixed-length single-shot coding scheme for a range of rate constraints.
Limits of Detecting Extraterrestrial Civilizations
Venue: arXiv cs.IT 2021
Year: 2021

The search for extraterrestrial intelligence (SETI) is a scientific endeavor which struggles with unique issues -- a strong indeterminacy in what data to look for and when to do so. This has led to attempts at finding both fundamental limits of the communication between extraterrestrial intelligence and human civilizations, as well as benchmarks so as to predict what kinds of signals we might most expect. Previous work has been formulated in terms of the information-theoretic task of communication, but we instead argue it should be viewed as a detection problem, specifically one-shot (asymmetric) hypothesis testing. With this new interpretation, we develop fundamental limits as well as provide simple examples of how to use this framework to analyze and benchmark different possible signals from extraterrestrial civilizations. We show that electromagnetic signaling for detection requires much less power than for communication, that detection as a function of power can be non-linear, and that much of the analysis in this framework may be addressed using computationally efficient optimization problems, thereby demonstrating tools for further inquiry.
Optimizing the order of actions in contact tracing
Venue: arXiv cs.DS 2021
Year: 2021

Contact tracing is a key tool for managing epidemic diseases like HIV, tuberculosis, and COVID-19. Manual investigations by human contact tracers remain a dominant way in which this is carried out. This process is limited by the number of contact tracers available, who are often overburdened during an outbreak or epidemic. As a result, a crucial decision in any contact tracing strategy is, given a set of contacts, which person should a tracer trace next? In this work, we develop a formal model that articulates these questions and provides a framework for comparing contact tracing strategies. Through analyzing our model, we give provably optimal prioritization policies via a clean connection to a tool from operations research called a "branching bandit". Examining these policies gives qualitative insight into trade-offs in contact tracing applications.
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
Venue: arXiv cs.CL 2021
Year: 2021

Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that our approach improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al., 2020).
Firmware Re-hosting Through Static Binary-level Porting
Venue: arXiv cs.CR 2021
Year: 2021

The rapid growth of the Industrial Internet of Things (IIoT) has brought embedded systems into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, greatly limiting the ability of security analysis. In this work, we explore the problem of firmware re-hosting related to the real-time operating system (RTOS). Specifically, developers create a Board Support Package (BSP) and develop device drivers to make that RTOS run on their platform. By providing high-level replacements for BSP routines and device drivers, we can make the minimal modification of the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach capable of offering the ability to execute firmware at scale through patching firmware in an automated manner without modifying the existing emulators. Our approach, called static binary-level porting, first identifies the BSP and device drivers in target firmware, then patches the firmware with pre-built BSP routines and drivers that can be adapted to the existing emulators. Finally, we demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The result shows that the approach is flexible enough to emulate firmware for vulnerability assessment and exploits development.
ECG Heartbeat Classification Using Multimodal Fusion
Venue: arXiv cs.LG 2021
Year: 2021

Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the stateof-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNets MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively.
On the Generalized Covering Radii of Reed-Muller Codes
Venue: arXiv cs.IT 2021
Year: 2021

We study generalized covering radii, a fundamental property of linear codes that characterizes the trade-off between storage, latency, and access in linear data-query protocols such as PIR. We prove lower and upper bounds on the generalized covering radii of Reed-Muller codes, as well as finding their exact value in certain extreme cases. With the application to linear data-query protocols in mind, we also construct a covering algorithm that gets as input a set of points in space, and find a corresponding set of codewords from the Reed-Muller code that are jointly not farther away from the input than the upper bound on the generalized covering radius of the code. We prove that the algorithm runs in time that is polynomial in the code parameters.
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
Venue: arXiv cs.CV 2021
Year: 2021

In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit. The text router and image router in Dual-Router accept text modal information and image modal information, and use MWF-Layer to determine the importance of modal information. Based on the result of the determination, MWF-Layer generates fused weights for the fusion of experts. Experts are model backbones that match the current task. DRDF has high performance and high generality, and we have tested 12 backbones such as Visual BERT on multimodal dataset Hateful memes, unimodal dataset CIFAR10, CIFAR100, and TinyImagenet. Our DRDF outperforms all the baselines. We also verified the components of DRDF in detail by ablations, compared and discussed the reasons and ideas of DRDF design.
Online structural kernel selection for mobile health
Venue: arXiv cs.LG 2021
Year: 2021

Motivated by the need for efficient and personalized learning in mobile health, we investigate the problem of online kernel selection for Gaussian Process regression in the multi-task setting. We propose a novel generative process on the kernel composition for this purpose. Our method demonstrates that trajectories of kernel evolutions can be transferred between users to improve learning and that the kernels themselves are meaningful for an mHealth prediction goal.
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Venue: arXiv cs.CL 2021
Year: 2021

[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74 % in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.
Comparison of Czech Transformers on Text Classification Tasks
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we present our progress in pre-training monolingual Transformers for Czech and contribute to the research community by releasing our models for public. The need for such models emerged from our effort to employ Transformers in our language-specific tasks, but we found the performance of the published multilingual models to be very limited. Since the multilingual models are usually pre-trained from 100+ languages, most of low-resourced languages (including Czech) are under-represented in these models. At the same time, there is a huge amount of monolingual training data available in web archives like Common Crawl. We have pre-trained and publicly released two monolingual Czech Transformers and compared them with relevant public models, trained (at least partially) for Czech. The paper presents the Transformers pre-training procedure as well as a comparison of pre-trained models on text classification task from various domains.
Performance landscape of resource-constrained platforms targeting DNNs
Venue: arXiv cs.PF 2021
Year: 2021

Over the recent years, a significant number of complex, deep neural networks have been developed for a variety of applications including speech and face recognition, computer vision in the areas of health-care, automatic translation, image classification, etc. Moreover, there is an increasing demand in deploying these networks in resource-constrained edge devices. As the computational demands of these models keep increasing, pushing to their limits the targeted devices, the constant development of new hardware systems tailored to those workloads has been observed. Since programmability of these diverse and complex platforms -- compounded by the rapid development of new DNN models -- is a major challenge, platform vendors have developed Machine Learning tailored SDKs to maximize the platform's performance. This work investigates the performance achieved on a number of modern commodity embedded platforms coupled with the vendors' provided software support when state-of-the-art DNN models from image classification, object detection and image segmentation are targeted. The work quantifies the relative latency gains of the particular embedded platforms and provides insights on the relationship between the required minimum batch size for achieving maximum throughput, concluding that modern embedded systems reach their maximum performance even for modest batch sizes when a modern state of the art DNN model is targeted. Overall, the presented results provide a guide for the expected performance for a number of state-of-the-art DNNs on popular embedded platforms across the image classification, detection and segmentation domains.
You Better Look Twice: a new perspective for designing accurate detectors with reduced computations
Venue: arXiv cs.CV 2021
Year: 2021

General object detectors use powerful backbones that uniformly extract features from images for enabling detection of a vast amount of object types. However, utilization of such backbones in object detection applications developed for specific object types can unnecessarily over-process an extensive amount of background. In addition, they are agnostic to object scales, thus redundantly process all image regions at the same resolution. In this work we introduce BLT-net, a new low-computation two-stage object detection architecture designed to process images with a significant amount of background and objects of variate scales. BLT-net reduces computations by separating objects from background using a very lite first-stage. BLT-net then efficiently merges obtained proposals to further decrease processed background and then dynamically reduces their resolution to minimize computations. Resulting image proposals are then processed in the second-stage by a highly accurate model. We demonstrate our architecture on the pedestrian detection problem, where objects are of different sizes, images are of high resolution and object detection is required to run in real-time. We show that our design reduces computations by a factor of x4-x7 on the Citypersons and Caltech datasets with respect to leading pedestrian detectors, on account of a small accuracy degradation. This method can be applied on other object detection applications in scenes with a considerable amount of background and variate object sizes to reduce computations.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Venue: arXiv cs.CV 2021
Year: 2021

Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces. This work fills this gap. For the first time, we deeply study numerous strategies to perform video forecasting in multi-prediction spaces and fuse their results together to boost performance. The prediction in the pixel space usually lacks the ability to preserve the semantic and structure content of the video however the prediction in the high-level feature space is prone to generate errors in the reduction and recovering process. Therefore, we build a recurrent connection between different feature spaces and incorporate their generations in the upsampling process. Rather surprisingly, this simple idea yields a much more significant performance boost than PhyDNet (performance improved by 32.1% MAE on MNIST-2 dataset, and 21.4% MAE on KTH dataset). Both qualitative and quantitative evaluations on four datasets demonstrate the generalization ability and effectiveness of our approach. We show that our model significantly reduces the troublesome distortions and blurry artifacts and brings remarkable improvements to the accuracy in long term video prediction. The code will be released soon.