OpenRaiser/Intern-Atlas huggingface dataset:
Example-Based Machine Translation from Text to a Hierarchical Representation of Sign Language
Venue: arXiv cs.CL 2022
Year: 2022

This article presents an original method for Text-to-Sign Translation. It compensates data scarcity using a domain-specific parallel corpus of alignments between text and hierarchical formal descriptions of Sign Language videos in AZee. Based on the detection of similarities present in the source text, the proposed algorithm recursively exploits matches and substitutions of aligned segments to build multiple candidate translations for a novel statement. This helps preserving Sign Language structures as much as possible before falling back on literal translations too quickly, in a generative way. The resulting translations are in the form of AZee expressions, designed to be used as input to avatar synthesis systems. We present a test set tailored to showcase its potential for expressiveness and generation of idiomatic target language, and observed limitations. This work finally opens prospects on how to evaluate translation and linguistic aspects, such as accuracy and grammatical fluency.
Living Innovation Lab: A Human Centric Computing toward Healthy Living
Venue: arXiv cs.HC 2022
Year: 2022

Living Lab is an umbrella term used for referring to a methodology of user-centric innovation in real-life environments within a wider network of relevant stake holders. Real-life environment refers to living houses and hospitals inter wined and connected together in a way which promotes direct usability of research by the end users. It primarily consists of three stages, Design thinking to actual Conceptualisation, Evaluation and Prototyping and Final product prototyping to commercialisation. The increasing demand of cutting age healthcare system is in itself a challenge and requires user involvement to mobilise knowledge to build a patient centered and knowledge-based economy. Innovations are constantly needed to reduce the problematic barriers to efficient knowledge exchange and improve collaborative problem solving. Living Innovation Lab, as open knowledge system, have immense potential to address these gaps that are underexplored in the healthcare system.
Minimizing Age of Information under Arbitrary Arrival Model with Arbitrary Packet Size
Venue: arXiv cs.IT 2022
Year: 2022

We consider a single source-destination pair, where information updates arrive at the source at arbitrary time instants. For each update, its size, i.e. the service time required for complete transmission to the destination, is also arbitrary. At any time, age of information (AoI) is equal to the difference between the current time, and the arrival time of the latest update (at the source) that has been completely transmitted (to the destination). AoI quantifies the staleness of the update (information) at the destination. The goal is to find a causal scheduling policy that minimizes the time average of AoI, where the possible decisions at any time are i) whether to preempt the update under transmission upon arrival of a new update, and ii) if no update is under transmission, then choose which update to transmit (among the available updates). In this paper, we propose a causal policy called SRPT$^+$ that at each time, i) preempts the update under transmission if a new update arrives with a smaller size, and ii) if no update is under transmission, then begins to transmit the update for which the ratio of the reduction in AoI upon complete transmission (if not preempted in future) and the remaining size, is maximum. We characterize the performance of SRPT$^+$ using the metric called the competitive ratio, i.e. the ratio of the average AoI of causal policy and the average AoI of an optimal offline policy (that knows the entire input in advance), maximized over all possible inputs. We show that the competitive ratio of SRPT$^+$ is at most $4$. Further, we propose a simpler policy called SRPT$^L$, that i) preempts the update under transmission if a new update arrives with a smaller size, and ii) if no update is under transmission, then begins to transmit the update with the latest arrival time. We show that the competitive ratio of SRPT$^L$ is at most $29$.
A Data Cartography based MixUp for Pre-trained Language Models
Venue: arXiv cs.CL 2022
Year: 2022

MixUp is a data augmentation strategy where additional samples are generated during training by combining random pairs of training samples and their labels. However, selecting random pairs is not potentially an optimal choice. In this work, we propose TDMixUp, a novel MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. Our proposed TDMixUp first measures confidence, variability, (Swayamdipta et al., 2020), and Area Under the Margin (AUM) (Pleiss et al., 2020) to identify the characteristics of training samples (e.g., as easy-to-learn or ambiguous samples), and then interpolates these characterized samples. We empirically validate that our method not only achieves competitive performance using a smaller subset of the training data compared with strong baselines, but also yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings in a wide range of NLP tasks. We publicly release our code.
Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images
Venue: arXiv cs.CV 2022
Year: 2022

Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-and-pepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images.
Hitting time for Markov decision process
Venue: arXiv cs.LG 2022
Year: 2022

We define the hitting time for a Markov decision process (MDP). We do not use the hitting time of the Markov process induced by the MDP because the induced chain may not have a stationary distribution. Even it has a stationary distribution, the stationary distribution may not coincide with the (normalized) occupancy measure of the MDP. We observe a relationship between the MDP and the PageRank. Using this observation, we construct an MP whose stationary distribution coincides with the normalized occupancy measure of the MDP and we define the hitting time of the MDP as the hitting time of the associated MP.
Lattices, Homomorphic Encryption, and CKKS
Venue: arXiv cs.CR 2022
Year: 2022

This is a survey on some topics in Lattice based cryptography and Homomorphic Encryption. In particular, we define some lattice problems, LWE and RLWE, and state the reductions given by Regev and Peikert. We also give a full treatment of the recent CKKS homomorphic encryption scheme and give some worked out examples.
Training Enhancement of Deep Learning Models for Massive MIMO CSI Feedback with Small Datasets
Venue: arXiv cs.IT 2022
Year: 2022

Accurate downlink channel state information (CSI) is vital to achieving high spectrum efficiency in massive MIMO systems. Existing works on the deep learning (DL) model for CSI feedback have shown efficient compression and recovery in frequency division duplex (FDD) systems. However, practical DL networks require sizeable wireless CSI datasets during training to achieve high model accuracy. To address this labor-intensive problem, this work develops an efficient training enhancement solution of DL-based feedback architecture based on a modest dataset by exploiting the complex CSI features, and augmenting CSI dataset based on domain knowledge. We first propose a spherical CSI feedback network, SPTM2-ISTANet+, which employs the spherical normalization framework to mitigate the effect of path loss variation. We exploit the trainable measurement matrix and residual recovery structure to improve the encoding efficiency and recovery accuracy. For limited CSI measurements, we propose a model-driven lightweight and universal augmentation strategy based on decoupling CSI magnitude and phase information, applying the circular shift in angular-delay domain, and randomizing the CSI phase to approximate phase distribution. Test results demonstrate the efficacy and efficiency of the proposed training strategy and feedback architecture for accurate CSI feedback under limited measurements.
Anomaly Detection in Intra-Vehicle Networks
Venue: arXiv cs.CR 2022
Year: 2022

The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-vehicle networks and external networks. However, a primary challenge in the automotive industry is to make the vehicle safe and reliable; particularly with the loopholes in the existing traditional protocols, cyber-attacks on the vehicle network are rising drastically. Practically every vehicle uses the universal Controller Area Network (CAN) bus protocol for the communication between electronic control units to transmit key vehicle functionality and messages related to driver safety. The CAN bus system, although its critical significance, lacks the key feature of any protocol authentication and authorization. Resulting in compromises of CAN bus security leads to serious issues to both car and driver safety. This paper discusses the security issues of the CAN bus protocol and proposes an Intrusion Detection System (IDS) that detects known attacks on in-vehicle networks. Multiple Artificial Intelligence (AI) algorithms are employed to provide recognition of known potential cyber-attacks based on messages, timestamps, and data packets traveling through the CAN. The main objective of this paper is to accurately detect cyberattacks by considering time-series features and attack frequency. The majority of the evaluated AI algorithms, when considering attack frequency, correctly identify known attacks with remarkable accuracy of more than 99%. However, these models achieve approximately 92% to 97% accuracy when timestamps are not taken into account. Long Short Term Memory (LSTM), Xgboost, and SVC have proved to the well-performing classifiers.
Can We Run in Parallel? Automating Loop Parallelization for TornadoVM
Venue: arXiv cs.PL 2022
Year: 2022

With the advent of multi-core systems, GPUs and FPGAs, loop parallelization has become a promising way to speed-up program execution. In order to stay up with time, various performance-oriented programming languages provide a multitude of constructs to allow programmers to write parallelizable loops. Correspondingly, researchers have developed techniques to automatically parallelize loops that do not carry dependences across iterations, and/or call pure functions. However, in managed languages with platform-independent runtimes such as Java, it is practically infeasible to perform complex dependence analysis during JIT compilation. In this paper, we propose AutoTornado, a first of its kind static+JIT loop parallelizer for Java programs that parallelizes loops for heterogeneous architectures using TornadoVM (a Graal-based VM that supports insertion of @Parallel constructs for loop parallelization). AutoTornado performs sophisticated dependence and purity analysis of Java programs statically, in the Soot framework, to generate constraints encoding conditions under which a given loop can be parallelized. The generated constraints are then fed to the Z3 theorem prover (which we have integrated with Soot) to annotate canonical for loops that can be parallelized using the @Parallel construct. We have also added runtime support in TornadoVM to use static analysis results for loop parallelization. Our evaluation over several standard parallelization kernels shows that AutoTornado correctly parallelizes 61.3% of manually parallelizable loops, with an efficient static analysis and a near-zero runtime overhead. To the best of our knowledge, AutoTornado is not only the first tool that performs program-analysis based parallelization for a real-world JVM, but also the first to integrate Z3 with Soot for loop parallelization.
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks are prevailing in deep learning tasks. However, they suffer from massive cost issues when working on mobile devices. Network pruning is an effective method of model compression to handle such problems. This paper presents a novel structured network pruning method with auxiliary gating structures which assigns importance marks to blocks in backbone network as a criterion when pruning. Block-wise pruning is then realized by proposed voting strategy, which is different from prevailing methods who prune a model in small granularity like channel-wise. We further develop a three-stage training scheduling for the proposed architecture incorporating knowledge distillation for better performance. Our experiments demonstrate that our method can achieve state-of-the-arts compression performance for the classification tasks. In addition, our approach can integrate synergistically with other pruning methods by providing pretrained models, thus achieving a better performance than the unpruned model with over 93\% FLOPs reduced.
PRESTO: Predicting System-level Disruptions through Parametric Model Checking
Venue: arXiv cs.SE 2022
Year: 2022

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system requirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the pre diction of system-level disruptions (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (Monitor-Analyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to identify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and performance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain.
Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices
Venue: arXiv cs.CV 2022
Year: 2022

Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendlyF to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices. Targeting the MIT-BIH arrhythmia database, the classifier based on this network reached an accuracy of 95.67% in the five-class test. Compared with the proposed baseline full-precision network with an accuracy of 96.45%, it is only 0.78% lower. Importantly, it achieves 12.65 times the computing speedup, 24.8 times the storage compression ratio, and only requires a quarter of the memory overhead.
GenISP: Neural ISP for Low-Light Machine Cognition
Venue: arXiv cs.CV 2022
Year: 2022

Object detection in low-light conditions remains a challenging but important problem with many practical implications. Some recent works show that, in low-light conditions, object detectors using raw image data are more robust than detectors using image data processed by a traditional ISP pipeline. To improve detection performance in low-light conditions, one can fine-tune the detector to use raw image data or use a dedicated low-light neural pipeline trained with paired low- and normal-light data to restore and enhance the image. However, different camera sensors have different spectral sensitivity and learning-based models using raw images process data in the sensor-specific color space. Thus, once trained, they do not guarantee generalization to other camera sensors. We propose to improve generalization to unseen camera sensors by implementing a minimal neural ISP pipeline for machine cognition, named GenISP, that explicitly incorporates Color Space Transformation to a device-independent color space. We also propose a two-stage color processing implemented by two image-to-parameter modules that take down-sized image as input and regress global color correction parameters. Moreover, we propose to train our proposed GenISP under the guidance of a pre-trained object detector and avoid making assumptions about perceptual quality of the image, but rather optimize the image representation for machine cognition. At the inference stage, GenISP can be paired with any object detector. We perform extensive experiments to compare our method to other low-light image restoration and enhancement methods in an extrinsic task-based evaluation and validate that GenISP can generalize to unseen sensors and object detectors. Finally, we contribute a low-light dataset of 7K raw images annotated with 46K bounding boxes for task-based benchmarking of future low-light image restoration and object detection.
Almost 3-Approximate Correlation Clustering in Constant Rounds
Venue: arXiv cs.DS 2022
Year: 2022

We study parallel algorithms for correlation clustering. Each pair among $n$ objects is labeled as either "similar" or "dissimilar". The goal is to partition the objects into arbitrarily many clusters while minimizing the number of disagreements with the labels. Our main result is an algorithm that for any $ε> 0$ obtains a $(3+ε)$-approximation in $O(1/ε)$ rounds (of models such as massively parallel computation, local, and semi-streaming). This is a culminating point for the rich literature on parallel correlation clustering. On the one hand, the approximation (almost) matches a natural barrier of 3 for combinatorial algorithms. On the other hand, the algorithm's round-complexity is essentially constant. To achieve this result, we introduce a simple $O(1/ε)$-round parallel algorithm. Our main result is to provide an analysis of this algorithm, showing that it achieves a $(3+ε)$-approximation. Our analysis draws on new connections to sublinear-time algorithms. Specifically, it builds on the work of Yoshida, Yamamoto, and Ito [STOC'09] on bounding the "query complexity" of greedy maximal independent set. To our knowledge, this is the first application of this method in analyzing the approximation ratio of any algorithm.
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
Venue: arXiv cs.LG 2022
Year: 2022

The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for graph have focused on using network predictions to generate soft pseudo-labels or instructing message propagation, which inevitably contains the incorrect prediction due to the over-confident in the predictions. Our proposed Dual-Channel Consistency based Graph Convolutional Networks (DCC-GCN) uses dual-channel to extract embeddings from node features and topological structures, and then achieves reliable low-confidence and high-confidence samples selection based on dual-channel consistency. We further confirmed that the low-confidence samples obtained based on dual-channel consistency were low in accuracy, constraining the model's performance. Unlike previous studies ignoring low-confidence samples, we calibrate the feature embeddings of the low-confidence samples by using the neighborhood's high-confidence samples. Our experiments have shown that the DCC-GCN can more accurately distinguish between low-confidence and high-confidence samples, and can also significantly improve the accuracy of low-confidence samples. We conducted extensive experiments on the benchmark datasets and demonstrated that DCC-GCN is significantly better than state-of-the-art baselines at different label rates.
Cover time of graphs with bounded genus
Venue: arXiv cs.DM 2022
Year: 2022

The cover time of a finite connected graph is the expected number of steps needed for a simple random walk on the graph to visit all vertices of the graph. It is known that the cover time of any finite connected $n$-vertex graph is at least $(1 + o(1)) n \log n$ and at most $(1 + o(1)) \frac{4}{27} n^3$. By Jonasson and Schramm, the cover time of any bounded-degree finite connected $n$-vertex planar graph is at least $c n(\log n)^2$ and at most $6n^2$, where $c$ is a positive constant depending only on the maximal degree of the graph. In particular, the lower bound is established via the use of circle packing of planar graphs on the Riemann sphere. In this paper, we show that the cover time of any finite $n$-vertex graph $G$ with maximum degree $Δ$ on the compact Riemann surface $S$ of given genus $g$ is at least $c n(\log n)^2/ Δ(g + 1)$ and at most $(6 + o(1))n^2$, where $c$ is an absolute constant, if $n$ is sufficiently large and three sufficient conditions for $S$ and a circle packing of $G$ filling $S$.