OpenRaiser/Intern-Atlas huggingface dataset:
AraLegal-BERT: A pretrained language model for Arabic Legal text
Venue: arXiv cs.CL 2022
Year: 2022

The effectiveness of the BERT model on multiple linguistic tasks has been well documented. On the other hand, its potentials for narrow and specific domains such as Legal, have not been fully explored. In this paper, we examine how BERT can be used in the Arabic legal domain and try customizing this language model for several downstream tasks using several different domain-relevant training and testing datasets to train BERT from scratch. We introduce the AraLegal-BERT, a bidirectional encoder Transformer-based model that have been thoroughly tested and carefully optimized with the goal to amplify the impact of NLP-driven solution concerning jurisprudence, legal documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it against three BERT variations for Arabic language in three natural languages understanding (NLU) tasks. The results show that the base version of AraLegal-BERT achieve better accuracy than the general and original BERT over the Legal text.
Linear Scalarization for Byzantine-robust learning on non-IID data
Venue: arXiv cs.LG 2022
Year: 2022

In this work we study the problem of Byzantine-robust learning when data among clients is heterogeneous. We focus on poisoning attacks targeting the convergence of SGD. Although this problem has received great attention; the main Byzantine defenses rely on the IID assumption causing them to fail when data distribution is non-IID even with no attack. We propose the use of Linear Scalarization (LS) as an enhancing method to enable current defenses to circumvent Byzantine attacks in the non-IID setting. The LS method is based on the incorporation of a trade-off vector that penalizes the suspected malicious clients. Empirical analysis corroborates that the proposed LS variants are viable in the IID setting. For mild to strong non-IID data splits, LS is either comparable or outperforming current approaches under state-of-the-art Byzantine attack scenarios.
Transformer-based dimensionality reduction
Venue: arXiv cs.CV 2022
Year: 2022

Recently, Transformer is much popular and plays an important role in the fields of Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV), etc. In this paper, based on the Vision Transformer (ViT) model, a new dimensionality reduction (DR) model is proposed, named Transformer-DR. From data visualization, image reconstruction and face recognition, the representation ability of Transformer-DR after dimensionality reduction is studied, and it is compared with some representative DR methods to understand the difference between Transformer-DR and existing DR methods. The experimental results show that Transformer-DR is an effective dimensionality reduction method.
MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue Operations
Venue: arXiv cs.LG 2022
Year: 2022

Efficient and quick remote communication in search and rescue operations can be life-saving for the first responders. However, while operating on the field means of communication based on text, image and audio are not suitable for several disaster scenarios. In this paper, we present a smartwatch-based application, which utilizes a Deep Learning (DL) model, to recognize a set of predefined arm gestures, maps them into Morse code via vibrations enabling remote communication amongst first responders. The model performance was evaluated by training it using 4,200 gestures performed by 7 subjects (cross-validation) wearing a smartwatch on their dominant arm. Our DL model relies on convolutional pooling and surpasses the performance of existing DL approaches and common machine learning classifiers, obtaining gesture recognition accuracy above 95%. We conclude by discussing the results and providing future directions.
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection
Venue: arXiv cs.IR 2022
Year: 2022

Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.
Decoupling Deep Learning for Interpretable Image Recognition
Venue: arXiv cs.CV 2022
Year: 2022

The interpretability of neural networks has recently received extensive attention. Previous prototype-based explainable networks involved prototype activation in both reasoning and interpretation processes, requiring specific explainable structures for the prototype, thus making the network less accurate as it gains interpretability. Therefore, the decoupling prototypical network (DProtoNet) was proposed to avoid this problem. This new model contains encoder, inference, and interpretation modules. As regards the encoder module, unrestricted feature masks were presented to generate expressive features and prototypes. Regarding the inference module, a multi-image prototype learning method was introduced to update prototypes so that the network can learn generalized prototypes. Finally, concerning the interpretation module, a multiple dynamic masks (MDM) decoder was suggested to explain the neural network, which generates heatmaps using the consistent activation of the original image and mask image at the detection nodes of the network. It decouples the inference and interpretation modules of a prototype-based network by avoiding the use of prototype activation to explain the network's decisions in order to simultaneously improve the accuracy and interpretability of the neural network. The multiple public general and medical datasets were tested, and the results confirmed that our method could achieve a 5% improvement in accuracy and state-of-the-art interpretability compared with previous methods.
Semantic Video Moments Retrieval at Scale: A New Task and a Baseline
Venue: arXiv cs.CV 2022
Year: 2022

Motivated by the increasing need of saving search effort by obtaining relevant video clips instead of whole videos, we propose a new task, named Semantic Video Moments Retrieval at scale (SVMR), which aims at finding relevant videos coupled with re-localizing the video clips in them. Instead of a simple combination of video retrieval and video re-localization, our task is more challenging because of several essential aspects. In the 1st stage, our SVMR should take into account the fact that: 1) a positive candidate long video can contain plenty of irrelevant clips which are also semantically meaningful. 2) a long video can be positive to two totally different query clips if it contains clips relevant to two queries. The 2nd re-localization stage also exhibits different assumptions from existing video re-localization tasks, which hold an assumption that the reference video must contain semantically similar segments corresponding to the query clip. Instead, in our scenario, the retrieved long video can be a false positive one due to the inaccuracy of the first stage. To address these challenges, we propose our two-stage baseline solution of candidate videos retrieval followed by a novel attention-based query-reference semantically alignment framework to re-localize target clips from candidate videos. Furthermore, we build two more appropriate benchmark datasets from the off-the-shelf ActivityNet-1.3 and HACS for a thorough evaluation of SVMR models. Extensive experiments are carried out to show that our solution outperforms several reference solutions.
A Multilevel Reinforcement Learning Framework for PDE-based Control
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement learning (RL) is a promising method to solve control problems. However, model-free RL algorithms are sample inefficient and require thousands if not millions of samples to learn optimal control policies. A major source of computational cost in RL corresponds to the transition function, which is dictated by the model dynamics. This is especially problematic when model dynamics is represented with coupled PDEs. In such cases, the transition function often involves solving a large-scale discretization of the said PDEs. We propose a multilevel RL framework in order to ease this cost by exploiting sublevel models that correspond to coarser scale discretization (i.e. multilevel models). This is done by formulating an approximate multilevel Monte Carlo estimate of the objective function of the policy and / or value network instead of Monte Carlo estimates, as done in the classical framework. As a demonstration of this framework, we present a multilevel version of the proximal policy optimization (PPO) algorithm. Here, the level refers to the grid fidelity of the chosen simulation-based environment. We provide two examples of simulation-based environments that employ stochastic PDEs that are solved using finite-volume discretization. For the case studies presented, we observed substantial computational savings using multilevel PPO compared to its classical counterpart.
Using Virtual Reality to Simulate Human-Robot Emergency Evacuation Scenarios
Venue: arXiv cs.RO 2022
Year: 2022

This paper describes our recent effort to use virtual reality to simulate threatening emergency evacuation scenarios in which a robot guides a person to an exit. Our prior work has demonstrated that people will follow a robot's guidance, even when the robot is faulty, during an emergency evacuation. Yet, because physical in-person emergency evacuation experiments are difficult and costly to conduct and because we would like to evaluate many different factors, we are motivated to develop a system that immerses people in the simulation environment to encourage genuine subject reactions. We are working to complete experiments verifying the validity of our approach.
Learning Probabilities of Causation from Finite Population Data
Venue: arXiv cs.LG 2022
Year: 2022

This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data. The tight bounds of three basic probabilities of causation, the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN), were derived by Tian and Pearl. However, obtaining the bounds for each subpopulation requires experimental and observational distributions of each subpopulation, which is usually impractical to estimate given finite population data. We propose a machine learning model that helps to learn the bounds of the probabilities of causation for subpopulations given finite population data. We further show by a simulated study that the machine learning model is able to learn the bounds of PNS for 32768 subpopulations with only knowing roughly 500 of them from the finite population data.
Two classes of narrow-sense BCH codes and their duals
Venue: arXiv cs.IT 2022
Year: 2022

BCH codes and their dual codes are two special subclasses of cyclic codes and are the best linear codes in many cases. A lot of progress on the study of BCH cyclic codes has been made, but little is known about the minimum distances of the duals of BCH codes. Recently, a new concept called dually-BCH code was introduced to investigate the duals of BCH codes and the lower bounds on their minimum distances in \cite{GDL21}. For a prime power $q$ and an integer $m \ge 4$, let $n=\frac{q^m-1}{q+1}$ \ ($m$ even), or $n=\frac{q^m-1}{q-1}$ \ ($q>2$). In this paper, some sufficient and necessary conditions in terms of the designed distance will be given to ensure that the narrow-sense BCH codes of length $n$ are dually-BCH codes, which extended the results in \cite{GDL21}. Lower bounds on the minimum distances of their dual codes are developed for $n=\frac{q^m-1}{q+1}$ \ ($m$ even). As byproducts, we present the largest coset leader $δ_1$ modulo $n$ being of two types, which proves a conjecture in \cite{WLP19} and partially solves an open problem in \cite{Li2017}. We also investigate the parameters of the narrow-sense BCH codes of length $n$ with design distance $δ_1$. The BCH codes presented in this paper have good parameters in general.
HQNAS: Auto CNN deployment framework for joint quantization and architecture search
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems. In order to achieve higher energy efficiency with the limited resource budget, neural networks(NNs) must be carefully designed in two steps, the architecture design and the quantization policy choice. Neural Architecture Search(NAS) and Quantization have been proposed separately when deploying NNs onto embedded devices. However, taking the two steps individually is time-consuming and leads to a sub-optimal final deployment. To this end, we propose a novel neural network design framework called Hardware-aware Quantized Neural Architecture Search(HQNAS) framework which combines the NAS and Quantization together in a very efficient manner using weight-sharing and bit-sharing. It takes only 4 GPU hours to discover an outstanding NN policy on CIFAR10. It also takes only %10 GPU time to generate a comparable model on Imagenet compared to the traditional NAS method with 1.8x decrease of latency and a negligible accuracy loss of only 0.7%. Besides, our method can be adapted in a lifelong situation where the neural network needs to evolve occasionally due to changes of local data, environment and user preference.
On the necessity of separating MAC protocols into data and control planes
Venue: arXiv cs.NI 2022
Year: 2022

The network protocol architecture not only can be designed from the traditional view of layers, but also can be designed from the view of planes, i.e., the data, control and management planes. Media access control (MAC) is a function of the data link layer, and thus the MAC protocols involve of both the data and control planes. However, although the international wireless local area network (WLAN) standard, IEEE 802.11 or Wi-Fi, has developed over 20 years, the control plane of the MAC protocols is not explicitly described yet. Thus, does it need to separate the MAC protocols into data and control planes? If not, are there some problems in existing hybrid architecture? To answer above questions, we analyse the possible problems of the current MAC protocols in IEEE 802.11, particularly in std 802.11-2020. These problems can be seen as new starts for the next study of the WLAN for the next generation (WNG).
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
Venue: arXiv cs.CL 2022
Year: 2022

The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients - providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.
A Policy-based Approach to the SpecAugment Method for Low Resource E2E ASR
Venue: arXiv cs.SD 2022
Year: 2022

SpecAugment is a very effective data augmentation method for both HMM and E2E-based automatic speech recognition (ASR) systems. Especially, it also works in low-resource scenarios. However, SpecAugment masks the spectrum of time or the frequency domain in a fixed augmentation policy, which may bring relatively less data diversity to the low-resource ASR. In this paper, we propose a policy-based SpecAugment (Policy-SpecAugment) method to alleviate the above problem. The idea is to use the augmentation-select policy and the augmentation-parameter changing policy to solve the fixed way. These policies are learned based on the loss of validation set, which is applied to the corresponding augmentation policies. It aims to encourage the model to learn more diverse data, which the model relatively requires. In experiments, we evaluate the effectiveness of our approach in low-resource scenarios, i.e., the 100 hours librispeech task. According to the results and analysis, we can see that the above issue can be obviously alleviated using our proposal. In addition, the experimental results show that, compared with the state-of-the-art SpecAugment, the proposed Policy-SpecAugment has a relative WER reduction of more than 10% on the Test/Dev-clean set, more than 5% on the Test/Dev-other set, and an absolute WER reduction of more than 1% on all test sets.
Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World
Venue: arXiv cs.CL 2022
Year: 2022

Linguistic disparity in the NLP world is a problem that has been widely acknowledged recently. However, different facets of this problem, or the reasons behind this disparity are seldom discussed within the NLP community. This paper provides a comprehensive analysis of the disparity that exists within the languages of the world. We show that simply categorising languages considering data availability may not be always correct. Using an existing language categorisation based on speaker population and vitality, we analyse the distribution of language data resources, amount of NLP/CL research, inclusion in multilingual web-based platforms and the inclusion in pre-trained multilingual models. We show that many languages do not get covered in these resources or platforms, and even within the languages belonging to the same language group, there is wide disparity. We analyse the impact of family, geographical location, GDP and the speaker population of languages and provide possible reasons for this disparity, along with some suggestions to overcome the same.
Acute Triangulation of Constant Curvature Polygonal Complexes
Venue: arXiv cs.CG 2022
Year: 2022

We prove that every 2-dimensional polygonal complex, where each polygon is given a constant curvature metric and belongs to one of finitely many isometry classes can be triangulated using only acute simplices. There is no requirement on the complex to be finite or even locally finite.
Towards Dynamic Fault Tolerance for Hardware-Implemented Artificial Neural Networks: A Deep Learning Approach
Venue: arXiv cs.LG 2022
Year: 2022

The functionality of electronic circuits can be seriously impaired by the occurrence of dynamic hardware faults. Particularly, for digital ultra low-power systems, a reduced safety margin can increase the probability of dynamic failures. This work investigates a deep learning approach to mitigate dynamic fault impact for artificial neural networks. As a theoretic use case, image compression by means of a deep autoencoder is considered. The evaluation shows a linear dependency of the test loss to the fault injection rate during testing. If the number of training epochs is sufficiently large, our approach shows more than 2% reduction of the test loss compared to a baseline network without the need of additional hardware. At the absence of faults during testing, our approach also decreases the test loss compared to reference networks.
Evaluating Guiding Spaces for Motion Planning
Venue: arXiv cs.RO 2022
Year: 2022

Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the \emph{motion planning guiding space}, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.
AttTrack: Online Deep Attention Transfer for Multi-object Tracking
Venue: arXiv cs.CV 2022
Year: 2022

Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving. The time and storage complexity required to execute deep learning models for visual object tracking hinder their adoption on embedded devices with limited computing power. In this paper, we aim to accelerate MOT by transferring the knowledge from high-level features of a complex network (teacher) to a lightweight network (student) at both training and inference times. The proposed AttTrack framework has three key components: 1) cross-model feature learning to align intermediate representations from the teacher and student models, 2) interleaving the execution of the two models at inference time, and 3) incorporating the updated predictions from the teacher model as prior knowledge to assist the student model. Experiments on pedestrian tracking tasks are conducted on the MOT17 and MOT15 datasets using two different object detection backbones YOLOv5 and DLA34 show that AttTrack can significantly improve student model tracking performance while sacrificing only minor degradation of tracking speed.
Scaling up Trustless DNN Inference with Zero-Knowledge Proofs
Venue: arXiv cs.CR 2022
Year: 2022

As ML models have increased in capabilities and accuracy, so has the complexity of their deployments. Increasingly, ML model consumers are turning to service providers to serve the ML models in the ML-as-a-service (MLaaS) paradigm. As MLaaS proliferates, a critical requirement emerges: how can model consumers verify that the correct predictions were served, in the face of malicious, lazy, or buggy service providers? In this work, we present the first practical ImageNet-scale method to verify ML model inference non-interactively, i.e., after the inference has been done. To do so, we leverage recent developments in ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), a form of zero-knowledge proofs. ZK-SNARKs allows us to verify ML model execution non-interactively and with only standard cryptographic hardness assumptions. In particular, we provide the first ZK-SNARK proof of valid inference for a full resolution ImageNet model, achieving 79\% top-5 accuracy. We further use these ZK-SNARKs to design protocols to verify ML model execution in a variety of scenarios, including for verifying MLaaS predictions, verifying MLaaS model accuracy, and using ML models for trustless retrieval. Together, our results show that ZK-SNARKs have the promise to make verified ML model inference practical.