OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Venue: arXiv cs.LG 2022
Year: 2022

Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling, self-driving assistance, and source code understanding. With rapid exploration, more and more complex DNN architectures have been proposed along with huge pre-trained model parameters. The common way to use such DNN models in user-friendly devices (e.g., mobile phones) is to perform model compression before deployment. However, recent research has demonstrated that model compression, e.g., model quantization, yields accuracy degradation as well as outputs disagreements when tested on unseen data. Since the unseen data always include distribution shifts and often appear in the wild, the quality and reliability of quantized models are not ensured. In this paper, we conduct a comprehensive study to characterize and help users understand the behaviors of quantized models. Our study considers 4 datasets spanning from image to text, 8 DNN architectures including feed-forward neural networks and recurrent neural networks, and 42 shifted sets with both synthetic and natural distribution shifts. The results reveal that 1) data with distribution shifts happen more disagreements than without. 2) Quantization-aware training can produce more stable models than standard, adversarial, and Mixup training. 3) Disagreements often have closer top-1 and top-2 output probabilities, and $Margin$ is a better indicator than the other uncertainty metrics to distinguish disagreements. 4) Retraining with disagreements has limited efficiency in removing disagreements. We opensource our code and models as a new benchmark for further studying the quantized models.
Vision-Based American Sign Language Classification Approach via Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model that aims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.
Evaluating the Adversarial Robustness for Fourier Neural Operators
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains. Among them, the Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution and superior accuracy, which significantly improves the speed when compared to traditional partial differential equation (PDE) solvers. To inspect the trustworthiness, we provide the first study on the adversarial robustness of scientific discovery models by generating adversarial examples for FNO, based on norm-bounded data input perturbations. Evaluated on the mean squared error between the FNO model's output and the PDE solver's output, our results show that the model's robustness degrades rapidly with increasing perturbation levels, particularly in non-simplistic cases like the 2D Darcy and the Navier cases. Our research provides a sensitivity analysis tool and evaluation principles for assessing the adversarial robustness of ML-based scientific discovery models.
Matrix Syncer -- A Multi-chain Data Aggregator For Supporting Blockchain-based Metaverses
Venue: arXiv cs.MM 2022
Year: 2022

Due to the rising complexity of the metaverse's business logic and the low-latency nature of the metaverse, developers typically encounter the challenge of effectively reading, writing, and retrieving historical on-chain data in order to facilitate their functional implementations at scale. While it is true that accessing blockchain states is simple, more advanced real-world operations such as search, aggregation, and conditional filtering are not available when interacting directly with blockchain networks, particularly when dealing with requirements for on-chain event reflection. We offer Matrix Syncer, the ultimate middleware that bridges the data access gap between blockchains and end-user applications. Matrix Syncer is designed to facilitate the consolidation of on-chain information into a distributed data warehouse while also enabling customized on-chain state transformation for a scalable storage, access, and retrieval. It offers a unified layer for both on- and off-chain state, as well as a fast and flexible atomic query. Matrix Syncer is easily incorporated into any infrastructure to aggregate data from various blockchains concurrently, such as Ethereum and Flow. The system has been deployed to support several metaverse projects with a total value of more than $15 million USD.
Solving $X^{2^{3n}+2^{2n}+2^{n}-1}+(X+1)^{2^{3n}+2^{2n}+2^{n}-1}=b$ in $GF{2^{4n}}$
Venue: arXiv cs.IT 2022
Year: 2022

This article determines all the solutions in the finite field $GF{2^{4n}}$ of the equation $x^{2^{3n}+2^{2n}+2^{n}-1}+(x+1)^{2^{3n}+2^{2n}+2^{n}-1}=b$. Specifically, we explicitly determine the set of $b$'s for which the equation has $i$ solutions for any positive integer $i$. Such sets, which depend on the number of solutions $i$, are given explicitly and expressed nicely, employing the absolute trace function over $GF{2^{n}}$, the norm function over $GF{2^{4n}}$ relatively to $GF{2^{n}}$ and the set of $2^n+1$st roots of unity in $GF{2^{4n}}$. The equation considered in this paper comes from an article by Budaghyan et al. \cite{BCCDK20}. As an immediate consequence of our results, we prove that the above equation has $2^{2n}$ solutions for one value of $b$, $2^{2n}-2^n$ solutions for $2^n$ values of $b$ in $GF{2^{4n}}$ and has at most two solutions for all remaining points $b$, leading to complete proof of the conjecture raised by Budaghyan et al. We highlight that the recent work of Li et al., in \cite{Li-et-al-2020} gives the complete differential spectrum of $F$ and also gives an affirmative answer to the conjecture of Budaghyan et al. However, we emphasize that our approach is interesting and promising by being different from Li et al. Indeed, on the opposite to their article, our technique allows determine ultimately the set of $b$'s for which the considered equation has solutions as well as the solutions of the equation for any $b$ in $GF{2^{4n}}$.
Performance portable ice-sheet modeling with MALI
Venue: arXiv cs.CE 2022
Year: 2022

High resolution simulations of polar ice-sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth-system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution. The latest exascale machines poised to come online contain a diverse set of computing architectures. In an effort to avoid architecture specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MALI uses high level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this paper, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26-1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We also perform a weak scalability study and show that simulations on GPU-based machines perform 1.24-1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.9% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of two years of development.
A Siren Song of Open Source Reproducibility
Venue: arXiv cs.LG 2022
Year: 2022

As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is part of a larger trend of taking action based on assumed ideals, without studying if those actions will yield the desired outcome. Our argument is that this focus on code for replication is misguided if we want to improve the state of reproducible research. This focus can be harmful -- we should not force code to be submitted. There is a lack of evidence for effective actions taken by conferences to encourage and reward reproducibility. We argue that venues must take more action to advance reproducible machine learning research today.
A dataset of ant colonies motion trajectories in indoor and outdoor scenes for social cluster behavior study
Venue: arXiv cs.CV 2022
Year: 2022

Motion and interaction of social insects (such as ants) have been studied by many researchers to understand the clustering mechanism. Most studies in the field of ant behavior have only focused on indoor environments, while outdoor environments are still underexplored. In this paper, we collect 10 videos of ant colonies from different indoor and outdoor scenes. And we develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of ants in the video. In all 5354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114112 annotations. Moreover, we provide visual analysis tools to assess and validate the technical quality and reproducibility of our data. It is hoped that this dataset will contribute to a deeper exploration on the behavior of the ant colony.
Harmonic Centralization of Some Graph Families
Venue: arXiv cs.SI 2022
Year: 2022

Centrality describes the importance of nodes in a graph and is modeled by various measures. Its global analogue, called centralization, is a general formula for calculating a graph-level centrality score based on the node-level centrality measure. The latter enables us to compare graphs based on the extent to which the connections of a given network are concentrated on a single vertex or group of vertices. One of the measures of centrality in social network analysis is harmonic centrality. It sums the inverse of the geodesic distances of each node to other nodes where it is 0 if there is no path from one node to another, with the sum normalized by dividing it by $m-1$, where $m$ is the number of nodes of the graph. In this paper, we present some results regarding the harmonic centralization of some important families of graphs with the hope that formulas generated herein will be of use when one determines the harmonic centralization of more complex graphs.
Federated Unsupervised Domain Adaptation for Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions are different. However, existing works are inapplicable to face recognition under privacy constraints because they require sharing of sensitive face images between domains. To address this problem, we propose federated unsupervised domain adaptation for face recognition, FedFR. FedFR jointly optimizes clustering-based domain adaptation and federated learning to elevate performance on the target domain. Specifically, for unlabeled data in the target domain, we enhance a clustering algorithm with distance constrain to improve the quality of predicted pseudo labels. Besides, we propose a new domain constraint loss (DCL) to regularize source domain training in federated learning. Extensive experiments on a newly constructed benchmark demonstrate that FedFR outperforms the baseline and classic methods on the target domain by 3% to 14% on different evaluation metrics.
Leaderless Swarm Formation Control: From Global Specifications to Local Control Laws
Venue: arXiv cs.RO 2022
Year: 2022

This paper introduces a distributed leaderless swarm formation control framework to address the problem of collectively driving a swarm of robots to track a time-varying formation. The swarm's formation is captured by the trajectory of an abstract shape that circumscribes the convex hull of robots' positions and is independent of the number of robots and their ordering in the swarm. For each robot in the swarm, given global specifications in terms of the trajectory of the abstract shape parameters, the proposed framework synthesizes a control law that steers the swarm to track the desired formation using the information available at the robot's local neighbors. For this purpose, we generate a suitable local reference trajectory that the robot controller tracks by solving the input-output linearization problem. Here, we select the swarm output to be the parameters of the abstract shape. For this purpose, we design a dynamic average consensus estimator to estimate the abstract shape parameters. The abstract shape parameters are used as the swarm state feedback to generate a suitable robot trajectory. We demonstrate the effectiveness and robustness of the proposed control framework by providing the simulation of coordinated collective navigation of a group of car-like robots in the presence of robots and communication link failures.
A BAT-based Exact-Solution Algorithm for the Series-Parallel Redundancy Allocation Problem with Mixed Components
Venue: arXiv cs.DM 2022
Year: 2022

The series-parallel (active) redundancy allocation problem with mixed components (RAP) involves setting reliable objectives for components or subsystems to meet the resource consumption constraint, e.g., the total cost. RAP has been an active research area for the past four decades. The NP-hard difficulties confronted by RAP are maintaining feasibility with respect to two constraints: cost and weight. A novel algorithm called the bound-rule-BAT (BRB) based on the binary-addition-tree algorithm (BAT), the dominance rule, and dynamic bounds are proposed to solve the exact solutions of the most famous RAP benchmark problems called the (33-variation) Fyffe RAP. From the experiments, the proposed BRB can solve the Fyffe RAP correctly under the assumption that the maximal number of components of each subsystem is eight, and this is the first exact-solution algorithm that can solve the Fyffe RAP within 8 seconds and 60 seconds if no reliability lower bound is used.
FoundationLayerNorm: Scaling BERT and GPT to 1,000 Layers
Venue: arXiv cs.CL 2022
Year: 2022

The mainstream BERT/GPT model contains only 10 to 20 layers, and there is little literature to discuss the training of deep BERT/GPT. This paper proposes a simple yet effective method to stabilize BERT and GPT training. We successfully scale up BERT and GPT to 1,000 layers, which is an order of magnitude deeper than previous BERT and GPT. The proposed method FoundationLayerNormalization enables efficient training of deep neural networks and is validated at the 1000-layer scale.
FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks
Venue: arXiv cs.LG 2022
Year: 2022

Despite their effective use in various fields, many aspects of neural networks are poorly understood. One important way to investigate the characteristics of neural networks is to explore the loss landscape. However, most models produce a high-dimensional non-convex landscape which is difficult to visualize. We discuss and extend existing visualization methods based on 1D- and 2D slicing with a novel method that approximates the actual loss landscape geometry by using charts with interpretable axes. Based on the assumption that observations on small neural networks can generalize to more complex systems and provide us with helpful insights, we focus on small models in the range of a few dozen weights, which enables computationally cheap experiments and the use of an interactive dashboard. We observe symmetries around the zero vector, the influence of different layers on the global landscape, the different weight sensitivities around a minimizer, and how gradient descent navigates high-loss obstacles. The user study resulted in an average SUS (System Usability Scale) score with suggestions for improvement and opened up a number of possible application scenarios, such as autoencoders and ensemble networks.
On the Exploitation of Deepfake Model Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Despite recent advances in Generative Adversarial Networks (GANs), with special focus to the Deepfake phenomenon there is no a clear understanding neither in terms of explainability nor of recognition of the involved models. In particular, the recognition of a specific GAN model that generated the deepfake image compared to many other possible models created by the same generative architecture (e.g. StyleGAN) is a task not yet completely addressed in the state-of-the-art. In this work, a robust processing pipeline to evaluate the possibility to point-out analytic fingerprints for Deepfake model recognition is presented. After exploiting the latent space of 50 slightly different models through an in-depth analysis on the generated images, a proper encoder was trained to discriminate among these models obtaining a classification accuracy of over 96%. Once demonstrated the possibility to discriminate extremely similar images, a dedicated metric exploiting the insights discovered in the latent space was introduced. By achieving a final accuracy of more than 94% for the Model Recognition task on images generated by models not employed in the training phase, this study takes an important step in countering the Deepfake phenomenon introducing a sort of signature in some sense similar to those employed in the multimedia forensics field (e.g. for camera source identification task, image ballistics task, etc).
Adaptable Plug and Play Security Operations Center Leveraging a Novel Programmable Plugin-based Intrusion Detection and Prevention System
Venue: arXiv cs.CR 2022
Year: 2022

The number of cyber-attacks have substantially increased over the past decade resulting in huge organizational financial losses. Indeed, it is no longer a matter of "if" but "when" a security incident will take place. A Security Operations Center(SOC) adoption will help in the detection, identification, prevention, and resolution of issues before they end up causing extensive cyber-related damage. In this paper, our proposed framework is brought about to address the problem that current open-source SOC implementations are plagued with. These include lack of ability to be strengthened on the fly, slow development processes, and their ineptness for continuous timely updates. We, herein, propose a framework that would offer a fully automated open-source SOC deployment; otherwise dubbed, a "plug-and-play framework"; full horizontal scalability incorporating a modular architecture. These underpinning features are meant to mitigate underlying SOC challenges, which often emerge as a result of many pre-determined and repeated processes, bolstering their ability for expansion with new tools. This is on top of enhancing their ability to handle more servers in the clusters as a single logical unit. We also introduce a new system of its kind called a Programmable Plugin-based Intrusion Detection and Prevention System (PPIDPS). This system will extend a SOC's ability to add any tool to the monitored devices while collecting logs that can trigger alerts whenever a suspicious behavior is detected.
An improved method for constructing linear codes with small hulls
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we give a method for constructing linear codes with small hulls by generalizing the method in \cite{LCD-T-matric}. As a result, we obtain many optimal Euclidean LCD codes and Hermitian LCD codes, which improve the previously known lower bound on the largest minimum distance. We also obtain many optimal codes with one-dimension hull. Furthermore, we give three tables about formally self-dual LCD codes.
A Palm Calculus Approach to the Distribution of the Age of Information
Venue: arXiv cs.PF 2022
Year: 2022

A key metric to express the timeliness of status updates in latency-sensitive networked systems is the age of information (AoI), i.e., the time elapsed since the generation of the last received informative status message. This metric allows studying a number of applications including updates of sensory and control information in cyber-physical systems and vehicular networks as well as, job and resource allocation in cloud clusters. State-of-the-art approaches to analyzing the AoI rely on queueing models that are composed of one or many queuing systems endowed with service order, e.g., FIFO, LIFO, or last-generated-first-out order. A major difficulty arising in these analysis methods is capturing the AoI under message reordering when the delivery is non-preemptive and non-FIFO, i.e., when messages can overtake each other and the reception of informative messages may obsolete some messages that are underway. In this paper, we derive an exact formulation for the distribution of AoI in non-preemptive, non-FIFO systems where the main ingredients of our analysis are Palm calculus and time inversion. Owing to the rationality of the Laplace-Stieltjes transforms that are used in our approach, we obtain computable exact expressions for the distribution of AoI.
Amalgamation of Indirect Gamification into Development and Operations (DEVOPS) Course Teaching
Venue: arXiv cs.SE 2022
Year: 2022

In DevOps Course, the Installation and configuration of the various software tools are useful in the context of application development. To teach DevOps Tools precisely to achieve the learning outcome is a skilled task for a trainer. From the perspective of the learner, the learning environment has to be encouraging and exciting. The author used the third-party Competition as an indirect Gamification technique to achieve the learning outcomes of the Course. The author encouraged the students to participate in the Hacktoberfest Competition to use the practical skills learned in the Course. The Under Graduate and Post Graduate Students have gone through the regular sessions of DevOps. For the participation purpose, both groups of students communicated on one platform. The students who succeeded faster during the GitHub Pull Request submission shared their experiences with other participants. The PG students participated 68.75% higher than UG students. The active participation of the small number of UG students became a motivational factor for the PG students. The Gaussian distribution on the marks obtained by the experimental group shows the absence of outliers. The research shows that the effectiveness of indirect Gamification depends on the age group, level, course content, and learning environment. The participation of a faculty member in the Competition during the learning activity boosts the desire of the Student to complete the task. The experimental group of 15 Students has outperformed in terms of the marks obtained compared to the control group of 52 students.