OpenRaiser/Intern-Atlas huggingface dataset:
Q-ViT: Fully Differentiable Quantization for Vision Transformer
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose a fully differentiable quantization method for vision transformer (ViT) named as Q-ViT, in which both of the quantization scales and bit-widths are learnable parameters. Specifically, based on our observation that heads in ViT display different quantization robustness, we leverage head-wise bit-width to squeeze the size of Q-ViT while preserving performance. In addition, we propose a novel technique named switchable scale to resolve the convergence problem in the joint training of quantization scales and bit-widths. In this way, Q-ViT pushes the limits of ViT quantization to 3-bit without heavy performance drop. Moreover, we analyze the quantization robustness of every architecture component of ViT and show that the Multi-head Self-Attention (MSA) and the Gaussian Error Linear Units (GELU) are the key aspects for ViT quantization. This study provides some insights for further research about ViT quantization. Extensive experiments on different ViT models, such as DeiT and Swin Transformer show the effectiveness of our quantization method. In particular, our method outperforms the state-of-the-art uniform quantization method by 1.5% on DeiT-Tiny.
On Multi-domain Network Slicing Orchestration Architecture & Federated Resource Control
Venue: arXiv cs.NI 2022
Year: 2022

A sophisticated and efficient network slicing architecture is needed to support the orchestration of network slices across multiple administrative domains. Such multi-domain architecture shall be agnostic of the underlying virtualization and network infrastructure technologies. Its objective is to extend the traditional orchestration, management and control capabilities by means of models and constructs in order to form a well-stitched composition of network slices. To facilitate such composition of networking and compute/storage resources, this paper introduces a management and orchestration architecture that incorporates Software Defined Networking (SDN) and Network Function Virtualization (NFV) components to the basic 3GPP network slice management. The proposed architecture is broadly divided into four major strata, namely Multi-domain Service Conductor Stratum, Domain-specific Fully-Fledged Orchestration Stratum, Sub-Domain Management and Orchestration (MANO) and Connectivity Stratum, and Logical Multidomain Slice Instance stratum. Each of these strata is described in detail providing also the fundamental operational specifics for instantiating and managing the resulting federated network slices.
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
Venue: arXiv cs.HC 2022
Year: 2022

One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to the intelligibility of their logic-based expressions. However, decision trees can grow too deep and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more flexible. However, the unstructured visual representation of rules makes it hard to make inferences across rules. To address these issues, we present a workflow that includes novel algorithmic and interactive solutions. First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations. Particularly, we present a novel feature-aligned tree to overcome the shortcomings of existing rule visualizations. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees and find that it scales reasonably well and outperforms decision trees in many respects. We also evaluate the visualization and the VA system by a usability study with 24 volunteers and an observational study with 7 domain experts. Our investigation shows that the participants can use feature-aligned trees to perform non-trivial tasks with very high accuracy. We also discuss many interesting observations that can be useful for future research on designing effective rule-based VA systems.
Data-to-Value: An Evaluation-First Methodology for Natural Language Projects
Venue: arXiv cs.CL 2022
Year: 2022

Big data, i.e. collecting, storing and processing of data at scale, has recently been possible due to the arrival of clusters of commodity computers powered by application-level distributed parallel operating systems like HDFS/Hadoop/Spark, and such infrastructures have revolutionized data mining at scale. For data mining project to succeed more consistently, some methodologies were developed (e.g. CRISP-DM, SEMMA, KDD), but these do not account for (1) very large scales of processing, (2) dealing with textual (unstructured) data (i.e. Natural Language Processing (NLP, "text analytics"), and (3) non-technical considerations (e.g. legal, ethical, project managerial aspects). To address these shortcomings, a new methodology, called "Data to Value" (D2V), is introduced, which is guided by a detailed catalog of questions in order to avoid a disconnect of big data text analytics project team with the topic when facing rather abstract box-and-arrow diagrams commonly associated with methodologies.
"Learn the Facts About COVID-19": Analyzing the Use of Warning Labels on TikTok Videos
Venue: arXiv cs.SI 2022
Year: 2022

During the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society. To mitigate this adverse effect, mainstream social media platforms employed soft moderation interventions (i.e., warning labels) on potentially harmful posts. Despite the recent popularity of these moderation interventions, we lack empirical analyses aiming to uncover how these warning labels are used in the wild, particularly during challenging times like the COVID-19 pandemic. In this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos. First, we construct a set of 26 COVID-19 related hashtags, then we collect 41K videos that include those hashtags in their description. Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok. Then, we perform an in-depth qualitative study, using thematic analysis, on 222 COVID-19 related videos to assess the content and the connection between the content and the warning labels. Our analysis shows that TikTok broadly applies warning labels on TikTok videos, likely based on hashtags included in the description. More worrying is the addition of COVID-19 warning labels on videos where their actual content is not related to COVID-19 (23% of the cases in a sample of 143 English videos that are not related to COVID-19). Finally, our qualitative analysis on a sample of 222 videos shows that 7.7% of the videos share misinformation/harmful content and do not include warning labels, 37.3% share benign information and include warning labels, and that 35% of the videos that share misinformation/harmful content (and need a warning label) are made for fun. Our study demonstrates the need to develop more accurate and precise soft moderation systems, especially on a platform like TikTok that is extremely popular among people of younger age.
Towards holistic scene understanding: Semantic segmentation and beyond
Venue: arXiv cs.CV 2022
Year: 2022

This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various datasets. In Chapter 2 we design a framework of hierarchical classifiers over a single convolutional backbone, and train it end-to-end on a combination of pixel-labeled datasets, improving generalizability and the number of recognizable semantic concepts. Chapter 3 focuses on enriching semantic segmentation with weak supervision and proposes a weakly-supervised algorithm for training with bounding box-level and image-level supervision instead of only with per-pixel supervision. The memory and computational load challenges that arise from simultaneous training on multiple datasets are addressed in Chapter 4. We propose two methodologies for selecting informative and diverse samples from datasets with weak supervision to reduce our networks' ecological footprint without sacrificing performance. Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework. This framework achieves consistent increases in performance metrics and semantic knowledgeability by exploiting various scene understanding datasets. Chapter 6 introduces the novel task of part-aware panoptic segmentation, which extends our reasoning towards holistic scene understanding. This task combines scene and parts-level semantics with instance-level object detection. In conclusion, our contributions span over convolutional network architectures, weakly-supervised learning, part and panoptic segmentation, paving the way towards a holistic, rich, and sustainable visual scene understanding.
Detection of Correlated Alarms Using Graph Embedding
Venue: arXiv cs.LG 2022
Year: 2022

Industrial alarm systems have recently progressed considerably in terms of network complexity and the number of alarms. The increase in complexity and number of alarms presents challenges in these systems that decrease system efficiency and cause distrust of the operator, which might result in widespread damages. One contributing factor in alarm inefficiency is the correlated alarms. These alarms do not contain new information and only confuse the operator. This paper tries to present a novel method for detecting correlated alarms based on artificial intelligence methods to help the operator. The proposed method is based on graph embedding and alarm clustering, resulting in the detection of correlated alarms. To evaluate the proposed method, a case study is conducted on the well-known Tennessee-Eastman process.
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. To demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed, which we will make available as open-source library of all codes included in this framework.
Towards a General Deep Feature Extractor for Facial Expression Recognition
Venue: arXiv cs.CV 2022
Year: 2022

The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this task. However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset. DeepFEVER outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's extracted features also generalise extremely well to other datasets -- even those unseen during training -- namely, the Real-World Affective Faces (RAF) dataset.
Rate Splitting for General Multicast
Venue: arXiv cs.IT 2022
Year: 2022

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates the optimization of general rate splitting with linear beamforming for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. Linear beamforming is adopted at the BS, and joint decoding is adopted at each user. We consider the maximization of the weighted sum rate, which is a challenging nonconvex problem. Then, we propose an iterative algorithm for the problem to obtain a KKT point using the concave-convex procedure (CCCP). The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes and reveal the design insights of general rate splitting for general multicast.
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
Venue: arXiv cs.LG 2022
Year: 2022

We devise a performance model for GPU training of Deep Learning Recommendation Models (DLRM), whose GPU utilization is low compared to other well-optimized CV and NLP models. We show that both the device active time (the sum of kernel runtimes) but also the device idle time are important components of the overall device time. We therefore tackle them separately by (1) flexibly adopting heuristic-based and ML-based kernel performance models for operators that dominate the device active time, and (2) categorizing operator overheads into five types to determine quantitatively their contribution to the device active time. Combining these two parts, we propose a critical-path-based algorithm to predict the per-batch training time of DLRM by traversing its execution graph. We achieve less than 10% geometric mean average error (GMAE) in all kernel performance modeling, and 4.61% and 7.96% geomean errors for GPU active time and overall E2E per-batch training time prediction with overheads from individual workloads, respectively. A slight increase of 2.19% incurred in E2E prediction error with shared overheads across workloads suggests the feasibility of using shared overheads in large-scale prediction. We show that our general performance model not only achieves low prediction error on DLRM, which has highly customized configurations and is dominated by multiple factors but also yields comparable accuracy on other compute-bound ML models targeted by most previous methods. Using this performance model and graph-level data and task dependency analysis, we show our system can provide more general model-system co-design than previous methods.
An Automated Robotic Arm: A Machine Learning Approach
Venue: arXiv cs.RO 2022
Year: 2022

The term robot generally refers to a machine that looks and works in a way similar to a human. The modern industry is rapidly shifting from manual control of systems to automation, in order to increase productivity and to deliver quality products. Computer-based systems, though feasible for improving quality and productivity, are inflexible to work with, and the cost of such systems is significantly high. This led to the swift adoption of automated systems to perform industrial tasks. One such task of industrial significance is of picking and placing objects from one place to another. The implementation of automation in pick and place tasks helps to improve efficiency of system and also the performance. In this paper, we propose to demonstrate the designing and working of an automated robotic arm with the Machine Learning approach. The work uses Machine Learning approach for object identification detection and traversal, which is adopted with Tensor flow package for better and accurate results.
Sensing Method for Two-Target Detection in Time-Constrained Vector Poisson Channel
Venue: arXiv cs.IT 2022
Year: 2022

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.
Shades of Finality and Layer 2 Scaling
Venue: arXiv cs.CR 2022
Year: 2022

Blockchains combine a distributed append-only log with a virtual machine that defines how log entries are interpreted. By viewing transactions as state transformation functions for the virtual machine, we separate the naming of a state from the computation of its value and reaching consensus on that value. This distinction allows us to separate the notion of transaction order finality from state value finality. Further consideration of how blockchain governance handles catastrophic failures such as zero day exploits leads us to the notion of checkpoint finality. Consensus on the transaction order determines the ground truth. Everything else -- computing the value of a state or handling catastrophic failures such as bugs / zero-day based attacks -- are just optimizations.
Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods
Venue: arXiv cs.CV 2022
Year: 2022

Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as the domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments. A comparison is made between traditional image processing methods and some state-of-the-art deep learning models. It is found that the best approach is a deep learning architecture known as UNet, along with its two improvements, Attention UNet and UNet++. The models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between Attention UNet and UNet++. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.
A Low-Complexity Location-Based Hybrid Multiple Access and Relay Selection in V2X Multicast Communications
Venue: arXiv cs.IT 2022
Year: 2022

This study investigated relay-assisted mode 1 sidelink (SL) multicast transmission, which encounters interference from mode 2 SL transmission, for use in low-latency vehicle-to-everything communications. To accommodate mode 1--mode 2 SL traffic, we use the hybrid multiple access (MA) approach, which combines orthogonal MA (OMA) and nonorthogonal MA (NOMA) schemes. We introduce a low-complexity location-based hybrid MA algorithm and its associated relay selection that can be used when SL channel state information is unavailable.
APIRO: A Framework for Automated Security Tools API Recommendation
Venue: arXiv cs.CR 2022
Year: 2022

Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team's ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API Recommendation for security Orchestration, automation, and response, APIRO. To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top 3 relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using 3 security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy.