OpenRaiser/Intern-Atlas huggingface dataset:
Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using Objective and Subjective Quality Assessment
Venue: arXiv cs.MM 2022
Year: 2022

With the increasing advancements in video compression efficiency achieved by newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies, as well as improved bandwidth availability,there has been a proliferation and acceptance of newer services such as Netflix, Twitch, etc. However, such higher compression efficiencies are achieved at the cost of higher complexity and encoding delay, while many applications are delay sensitive. Hence, there is a requirement for faster, more efficient codecs to achieve higher encoding efficiency without significant trade-off in terms of both complexity and speed. We present in this work an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for live gaming video streaming applications. The results are presented in terms of bitrate savings using both subjective and objective quality measures as well as a comparison of the encoding speeds. Our results indicate that, for the encoding settings used in this work, LCEVC outperforms both x264 and x265 codecs in terms of bitrate savings using VMAF by approximately 42\% and 38\%. Using subjective results, it is found that LCEVC outperforms the respective base codecs, especially for low bitrates. This effect is more evident for x264 than for x265, i.e., for the latter the absolute improvement of quality scores is smaller. The objective and subjective results as well as sample video sequences are made available as part of an open dataset, LCEVC-LiveGaming at https://github.com/NabajeetBarman/LCEVC-LiveGaming.
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
Venue: arXiv cs.LG 2022
Year: 2022

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset. However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a subset of the dataset via supervised learning. Therefore, it seems natural to ask: when can an offline RL method outperform BC with an equal amount of expert data, even when BC is a natural choice? To answer this question, we characterize the properties of environments that allow offline RL methods to perform better than BC methods, even when only provided with expert data. Additionally, we show that policies trained on sufficiently noisy suboptimal data can attain better performance than even BC algorithms with expert data, especially on long-horizon problems. We validate our theoretical results via extensive experiments on both diagnostic and high-dimensional domains including robotic manipulation, maze navigation, and Atari games, with a variety of data distributions. We observe that, under specific but common conditions such as sparse rewards or noisy data sources, modern offline RL methods can significantly outperform BC.
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback. However, this is challenging when we want to use discrete messages to reduce the message size since gradients cannot flow through a discrete communication channel. Previous work proposed methods to deal with this problem. However, these methods are tested in different communication learning architectures and environments, making it hard to compare them. In this paper, we compare several state-of-the-art discretization methods as well as two methods that have not been used for communication learning before. We do this comparison in the context of communication learning using gradients from other agents and perform tests on several environments. Our results show that none of the methods is best in all environments. The best choice in discretization method greatly depends on the environment. However, the discretize regularize unit (DRU), straight through DRU and the straight through gumbel softmax show the most consistent results across all the tested environments. Therefore, these methods prove to be the best choice for general use while the straight through estimator and the gumbel softmax may provide better results in specific environments but fail completely in others.
A Generative Approach for Financial Causality Extraction
Venue: arXiv cs.CL 2022
Year: 2022

Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports. Each financial causality contains a cause span and an effect span. Previous works proposed sequence labeling approaches to solve this task. But sequence labeling models find it difficult to extract multiple causalities and overlapping causalities from the text segments. In this paper, we explore a generative approach for causality extraction using the encoder-decoder framework and pointer networks. We use a causality dataset from the financial domain, \textit{FinCausal}, for our experiments and our proposed framework achieves very competitive performance on this dataset.
Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris
Venue: arXiv cs.CV 2022
Year: 2022

The lack of resolution has a negative impact on the performance of image-based biometrics. Many applications which are becoming ubiquitous in mobile devices do not operate in a controlled environment, and their performance significantly drops due to the lack of pixel resolution. While many generic super-resolution techniques have been studied to restore low-resolution images for biometrics, the results obtained are not always as desired. Those generic methods are usually aimed to enhance the visual appearance of the scene. However, producing an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Such techniques are designed to restore generic images and therefore do not exploit the specific structure found in biometric images (e.g. iris or faces), which causes the solution to be sub-optimal. For this reason, super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. In recent years, there has been an increased interest in the application of super-resolution to different biometric modalities, such as face iris, gait or fingerprint. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images, which are the two prevalent modalities in selfie biometrics. We also provide experimental results using several state-of-the-art reconstruction algorithms, demonstrating the benefits of using super-resolution to improve the quality of face and iris images prior to classification. In the reported experiments, we study the application of super-resolution to face and iris images captured in the visible range, using experimental setups that represent well the selfie biometrics scenario.
Self-Supervised Losses for One-Class Textual Anomaly Detection
Venue: arXiv cs.CL 2022
Year: 2022

Current deep learning methods for anomaly detection in text rely on supervisory signals in inliers that may be unobtainable or bespoke architectures that are difficult to tune. We study a simpler alternative: fine-tuning Transformers on the inlier data with self-supervised objectives and using the losses as an anomaly score. Overall, the self-supervision approach outperforms other methods under various anomaly detection scenarios, improving the AUROC score on semantic anomalies by 11.6% and on syntactic anomalies by 22.8% on average. Additionally, the optimal objective and resultant learnt representation depend on the type of downstream anomaly. The separability of anomalies and inliers signals that a representation is more effective for detecting semantic anomalies, whilst the presence of narrow feature directions signals a representation that is effective for detecting syntactic anomalies.
Medusa: Universal Feature Learning via Attentional Multitasking
Venue: arXiv cs.LG 2022
Year: 2022

Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a generic representation. Meanwhile, a novel Multi-Scale Attention head allows the network to better combine per-task features from different scales when making the final prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement), while maintaining MTL performance and being 25% more efficient than previous approaches.
Computing Without Borders: The Way Towards Liquid Computing
Venue: arXiv cs.DC 2022
Year: 2022

Despite the de-facto technological uniformity fostered by the cloud and edge computing paradigms, resource fragmentation across isolated clusters hinders the dynamism in application placement, leading to suboptimal performance and operational complexity. Building upon and extending these paradigms, we propose a novel approach envisioning a transparent continuum of resources and services on top of the underlying fragmented infrastructure, called liquid computing. Fully decentralized, multi-ownership-oriented and intent-driven, it enables an overarching abstraction for improved applications execution, while at the same time opening up for new scenarios, including resource sharing and brokering. Following the above vision, we present liqo, an open-source project that materializes this approach through the creation of dynamic and seamless Kubernetes multi-cluster topologies. Extensive experimental evaluations have shown its effectiveness in different contexts, both in terms of Kubernetes overhead and compared to other open-source alternatives.
Distributed Transmission and Spatially Coupled Forward Error Correction in Regenerative Multipoint-to-Point Networks
Venue: arXiv cs.IT 2022
Year: 2022

We investigate the performance of coded modulation for multi-hop regenerative optical networks. We analyze options for computing decoder input LLRs, show reach increases by optimized regenerator placement and experimentally compare strategies and guidelines for distributed FEC.
LifeLonger: A Benchmark for Continual Disease Classification
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark will be made publicly available.
BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis
Venue: arXiv cs.CR 2022
Year: 2022

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, where any individual can access the record of a transaction record on the public ledger. In this paper, we build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data, which is the largest labeled Bitcoin address behavior dataset publicly available to our knowledge. We then use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost. The results show that the accuracy rates of these machine learning models for the multi-classification task on our proposed dataset are between 93.24% and 97.13%. We also analyze the proposed features and their relationships from the experiments, and propose a k-hop subgraph generation algorithm to extract a k-hop subgraph from the entire Bitcoin transaction graph constructed by the directed heterogeneous multigraph starting from a specific Bitcoin address node (e.g., a known transaction associated with a criminal investigation). Besides, we initially analyze the behavior patterns of different types of Bitcoin addresses according to the extracted features.
CyNER: A Python Library for Cybersecurity Named Entity Recognition
Venue: arXiv cs.CR 2022
Year: 2022

Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python library for cybersecurity named entity recognition (NER). CyNER combines transformer-based models for extracting cybersecurity-related entities, heuristics for extracting different indicators of compromise, and publicly available NER models for generic entity types. We provide models trained on a diverse corpus that users can readily use. Events are described as classes in previous research - MALOnt2.0 (Christian et al., 2021) and MALOnt (Rastogi et al., 2020) and together extract a wide range of malware attack details from a threat intelligence corpus. The user can combine predictions from multiple different approaches to suit their needs. The library is made publicly available.
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense
Venue: arXiv cs.CR 2022
Year: 2022

Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if the hidden backdoor is activated by the attacker defined trigger. This threat could happen when the training process is not fully controlled, such as training on third-party data-sets or adopting third-party models. There has been a lot of research and different methods to defend such type of backdoor attacks, one being robustness-aware perturbation-based defense method. This method mainly exploits big gap of robustness between poisoned and clean samples. In our work, we break this defense by controlling the robustness gap between poisoned and clean samples using adversarial training step.
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System
Venue: arXiv cs.LG 2022
Year: 2022

The stochastic multi-armed bandit has provided a framework for studying decision-making in unknown environments. We propose a variant of the stochastic multi-armed bandit where the rewards are sampled from a stochastic linear dynamical system. The proposed strategy for this stochastic multi-armed bandit variant is to learn a model of the dynamical system while choosing the optimal action based on the learned model. Motivated by mathematical finance areas such as Intertemporal Capital Asset Pricing Model proposed by Merton and Stochastic Portfolio Theory proposed by Fernholz that both model asset returns with stochastic differential equations, this strategy is applied to quantitative finance as a high-frequency trading strategy, where the goal is to maximize returns within a time period.
Hard Problems are Easier for Success-based Parameter Control
Venue: arXiv cs.NE 2022
Year: 2022

Recent works showed that simple success-based rules for self-adjusting parameters in evolutionary algorithms (EAs) can match or outperform the best fixed parameters on discrete problems. Non-elitism in a (1,$λ$) EA combined with a self-adjusting offspring population size $λ$ outperforms common EAs on the multimodal Cliff problem. However, it was shown that this only holds if the success rate $s$ that governs self-adjustment is small enough. Otherwise, even on OneMax, the self-adjusting (1,$λ$) EA stagnates on an easy slope, where frequent successes drive down the offspring population size. We show that self-adjustment works as intended in the absence of easy slopes. We define everywhere hard functions, for which successes are never easy to find and show that the self-adjusting (1,$λ$) EA is robust with respect to the choice of success rates $s$. We give a general fitness-level upper bound on the number of evaluations and show that the expected number of generations is at most $O(d + \log(1/p_{\min}))$ where $d$ is the number of non-optimal fitness values and $p_{\min}$ is the smallest probability of finding an improvement from a non-optimal search point. We discuss implications for the everywhere hard function LeadingOnes and a new class OneMaxBlocks of everywhere hard functions with tunable difficulty.
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
Venue: arXiv cs.CL 2022
Year: 2022

Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ significantly, and there has been limited systematic comparison of these factors. In this work, we present a large-scale evaluation of modeling choices and their impact on zero-shot generalization. In particular, we focus on text-to-text models and experiment with three model architectures (causal/non-causal decoder-only and encoder-decoder), trained with two different pretraining objectives (autoregressive and masked language modeling), and evaluated with and without multitask prompted finetuning. We train models with over 5 billion parameters for more than 170 billion tokens, thereby increasing the likelihood that our conclusions will transfer to even larger scales. Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely unsupervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform the best among our experiments. We therefore consider the adaptation of pretrained models across architectures and objectives. We find that pretrained non-causal decoder models can be adapted into performant generative causal decoder models, using autoregressive language modeling as a downstream task. Furthermore, we find that pretrained causal decoder models can be efficiently adapted into non-causal decoder models, ultimately achieving competitive performance after multitask finetuning. Code and checkpoints are available at https://github.com/bigscience-workshop/architecture-objective.
The MIT Supercloud Workload Classification Challenge
Venue: arXiv cs.DC 2022
Year: 2022

High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides detailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website : https://dcc.mit.edu.
A Decentralized Resource Management System Proposal For Disasters: NGO-RMSD (STK-AKYS)
Venue: arXiv cs.CY 2022
Year: 2022

Disaster and emergency management are under the responsibility of many organizations and there are serious coordination problems in post-disaster crisis management. This paper proposes a decentralized non-governmental organization resource management system for disasters (NGO-RMSD / STK-AKYS). This system is based on blockchain technology and it will enable the non-governmental organizations (NGO) and public institutions to manage and coordinate the resources in a trusted environment in the case of disasters. A proof of concept implementation is developed by using the Quorum blockchain framework which is more energy-efficient than crypto currency-based blockchain solutions. Smart contracts are developed for the autonomous working of the system. These smart contacts are used for the verification of the needs of the one who is in need, delivering resources to the right people, and identifying the urgent needs. The system aims to reach more disaster victims in a more timely manner. NGO-RMSD is designed according to the needs of the NGOs in the field. The application is shared with the free software license and further development with the community is aimed.