OpenRaiser/Intern-Atlas huggingface dataset:
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Venue: arXiv cs.CV 2021
Year: 2021

Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice. Popular techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) provide a coarse map of salient features in an image, which rarely tells the whole story of what a convolutional neural network (CNN) learned. Using COVID-19 chest X-rays, we present a method for interpreting what a CNN has learned by utilizing Generative Adversarial Networks (GANs). Our GAN framework disentangles lung structure from COVID-19 features. Using this GAN, we can visualize the transition of a pair of COVID negative lungs in a chest radiograph to a COVID positive pair by interpolating in the latent space of the GAN, which provides fine-grained visualization of how the CNN responds to varying features within the lungs.
Learning Representations for Zero-Shot Retrieval over Structured Data
Venue: arXiv cs.IR 2021
Year: 2021

Large Scale Question-Answering systems today are widely used in downstream applications such as chatbots and conversational dialogue agents. Typically, such systems consist of an Answer Passage retrieval layer coupled with Machine Comprehension models trained on natural language query-passage pairs. Recent studies have explored Question Answering over structured data sources such as web-tables and relational databases. However, architectures such as Seq2SQL assume the correct table a priori which is input to the model along with the free text question. Our proposed method, analogues to a passage retrieval model in traditional Question-Answering systems, describes an architecture to discern the correct table pertaining to a given query from amongst a large pool of candidate tables.
Predicting Atlantic Multidecadal Variability
Venue: arXiv cs.LG 2021
Year: 2021

Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System Model 1 Large Ensemble Project, a state-of-the-art climate model with 3,440 years of data. Our results demonstrate that all of the models we use outperform the traditional persistence forecast baseline. Predicting the AMV is important for identifying future extreme temperatures and precipitation, as well as hurricane activity, in Europe and North America up to 25 years in advance.
Characterization of the Imbalance Problem on Complete Bipartite Graphs
Venue: arXiv cs.DM 2021
Year: 2021

We study the imbalance problem on complete bipartite graphs. The imbalance problem is a graph layout problem and is known to be NP-complete. Graph layout problems find their applications in the optimization of networks for parallel computer architectures, VLSI circuit design, information retrieval, numerical analysis, computational biology, graph theory, scheduling and archaeology. In this paper, we give characterizations for the optimal solutions of the imbalance problem on complete bipartite graphs. Using the characterizations, we can solve the imbalance problem in $\mathcal{O}(\log(|V|) \cdot \log(\log(|V|)))$ time, when given the cardinalities of the parts of the graph, and verify whether a given solution is optimal in $O(|V|)$ time on complete bipartite graphs. We also introduce a restricted form of proper interval bipartite graphs on which the imbalance problem is solvable in $\mathcal{O}(c \cdot \log(|V|) \cdot \log(\log(|V|)))$ time, where $c = \mathcal{O}(|V|)$, by using the aforementioned characterizations.
Automatic Knowledge Augmentation for Generative Commonsense Reasoning
Venue: arXiv cs.CL 2021
Year: 2021

Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative language models still struggle to provide outputs, and the training set does not contain patterns that are sufficient for generative commonsense reasoning. In this paper, we propose a data-centric method that uses automatic knowledge augmentation to extend commonsense knowledge using a machine knowledge generator. This method can generate semi-golden sentences that improve the generative commonsense reasoning of a language model without architecture modifications. Furthermore, this approach is a model-agnostic method and does not require human effort for data construction.
Backdoor Pre-trained Models Can Transfer to All
Venue: arXiv cs.CL 2021
Year: 2021

Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.
Imitating Arbitrary Talking Style for Realistic Audio-DrivenTalking Face Synthesis
Venue: arXiv cs.CV 2021
Year: 2021

People talk with diversified styles. For one piece of speech, different talking styles exhibit significant differences in the facial and head pose movements. For example, the "excited" style usually talks with the mouth wide open, while the "solemn" style is more standardized and seldomly exhibits exaggerated motions. Due to such huge differences between different styles, it is necessary to incorporate the talking style into audio-driven talking face synthesis framework. In this paper, we propose to inject style into the talking face synthesis framework through imitating arbitrary talking style of the particular reference video. Specifically, we systematically investigate talking styles with our collected \textit{Ted-HD} dataset and construct style codes as several statistics of 3D morphable model~(3DMM) parameters. Afterwards, we devise a latent-style-fusion~(LSF) model to synthesize stylized talking faces by imitating talking styles from the style codes. We emphasize the following novel characteristics of our framework: (1) It doesn't require any annotation of the style, the talking style is learned in an unsupervised manner from talking videos in the wild. (2) It can imitate arbitrary styles from arbitrary videos, and the style codes can also be interpolated to generate new styles. Extensive experiments demonstrate that the proposed framework has the ability to synthesize more natural and expressive talking styles compared with baseline methods.
Optimizing Binary Symptom Checkers via Approximate Message Passing
Venue: arXiv cs.LG 2021
Year: 2021

Symptom checkers have been widely adopted as an intelligent e-healthcare application during the ongoing pandemic crisis. Their performance have been limited by the fine-grained quality of the collected medical knowledge between symptom and diseases. While the binarization of the relationships between symptoms and diseases simplifies the data collection process, it also leads to non-convex optimization problems during the inference step. In this paper, we formulate the symptom checking problem as an underdertermined non-convex optimization problem, thereby justifying the use of the compressive sensing framework to solve it. We show that the generalized vector approximate message passing (G-VAMP) algorithm provides the best performance for binary symptom checkers.
TargetUM: Targeted High-Utility Itemset Querying
Venue: arXiv cs.DB 2021
Year: 2021

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of \textbf{TargetUM} is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.
On multiple IoT data streams processing using LoRaWAN
Venue: arXiv cs.NI 2021
Year: 2021

LoraWAN has turned out to be one of the most successful frameworks in IoT devices. Real world scenarios demand the use of such networks along with a robust stream processing application layer. To maintain the exactly once processing semantics one must ensure that we have proper ways to proactively detect message drops and handle the same. An important use case where stream processing plays a crucial role is joining various data streams that are transmitted via gateways connected to edge devices which are related to each other as part of some common business requirement. LoraWAN supports connectivity to multiple gateways for edge devices and by virtue of its different device classes, the network can send and receive messages in an effective way that conserves battery power as well as network bandwidth. Rather than relying on explicit acknowledgements for the transmitted messages we take the advantage of these characteristics of the devices to detect , handle missing messages and finally process them.
Interpreting Deep Knowledge Tracing Model on EdNet Dataset
Venue: arXiv cs.AI 2021
Year: 2021

With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention. Our previous study(Lu et al. 2020) on building and interpreting the KT model mainly adopts the ASSISTment dataset(Feng, Heffernan, and Koedinger 2009),, whose size is relatively small. In this work, we perform the similar tasks but on a large and newly available dataset, called EdNet(Choi et al. 2020). The preliminary experiment results show the effectiveness of the interpreting techniques, while more questions and tasks are worthy to be further explored and accomplished.
DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents
Venue: arXiv cs.CL 2021
Year: 2021

Causality detection draws plenty of attention in the field of Natural Language Processing and linguistics research. It has essential applications in information retrieval, event prediction, question answering, financial analysis, and market research. In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers. For this purpose, we propose an approach that combines POS tagging with the BIO scheme, which can be integrated with modern transformer models to address this challenge of identifying causality in a given text. Our best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of 0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal 2021 Workshop.
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation
Venue: arXiv cs.AI 2021
Year: 2021

While Out-of-distribution (OOD) detection has been well explored in computer vision, there have been relatively few prior attempts in OOD detection for NLP classification. In this paper we argue that these prior attempts do not fully address the OOD problem and may suffer from data leakage and poor calibration of the resulting models. We present PnPOOD, a data augmentation technique to perform OOD detection via out-of-domain sample generation using the recently proposed Plug and Play Language Model (Dathathri et al., 2020). Our method generates high quality discriminative samples close to the class boundaries, resulting in accurate OOD detection at test time. We demonstrate that our model outperforms prior models on OOD sample detection, and exhibits lower calibration error on the 20 newsgroup text and Stanford Sentiment Treebank dataset (Lang, 1995; Socheret al., 2013). We further highlight an important data leakage issue with datasets used in prior attempts at OOD detection, and share results on a new dataset for OOD detection that does not suffer from the same problem.
Optimal Targeted Advertising Strategy For Secure Wireless Edge Metaverse
Venue: arXiv cs.IT 2021
Year: 2021

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seamlessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Metaverse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users' privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of "covert", targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user's experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising.
Optimization of a Millimeter-Wave UAV-to-Ground Network in Urban Deployments
Venue: arXiv cs.IT 2021
Year: 2021

An urban tactical wireless network is considered wherein the base stations are situated on unmanned aerial vehicles (UAVs) that provide connectivity to ground assets such as vehicles located on city streets. The UAVs are assumed to be randomly deployed at a fixed height according to a two-dimensional point process. Millimeter-wave (mmWave) frequencies are used to avail of large available bandwidths and spatial isolation due to beamforming. In urban environments, mmWave signals are prone to blocking of the line-of-sight (LoS) by buildings. While reflections are possible, the desire for consistent connectivity places a strong preference on the existence of an unblocked LoS path. As such, the key performance metric considered in this paper is the connectivity probability, which is the probability of an unblocked LoS path to at least one UAV within some maximum transmission distance. By leveraging tools from stochastic geometry, the connectivity probability is characterized as a function of the city type (e.g., urban, dense urban, suburban), density of UAVs (average number of UAVs per square km), and height of the UAVs. The city streets are modeled as a Manhattan Poisson Line Process (MPLP) and the building heights are randomly distributed. The analysis first finds the connectivity probability conditioned on a particular network realization (location of the UAVs) and then removes the conditioning to uncover the distribution of the connectivity; i.e., the fraction of network realizations that will fail to meet an outage threshold. While related work has applied an MPLP to networks with a single UAV, the contributions of this paper are that it (1) considers networks of multiple UAVs, (2) characterizes the performance by a connectivity distribution, and (3) identifies the optimal altitude for the UAVs.
PIE: Pseudo-Invertible Encoder
Venue: arXiv cs.LG 2021
Year: 2021

We consider the problem of information compression from high dimensional data. Where many studies consider the problem of compression by non-invertible transformations, we emphasize the importance of invertible compression. We introduce new class of likelihood-based autoencoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders. We provide the theoretical explanation of their principles. We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperforms WAE and VAE in sharpness of the generated images.
Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding
Venue: arXiv cs.MM 2021
Year: 2021

Comprehensive understanding of key players and actions in multiplayer sports broadcast videos is a challenging problem. Unlike in news or finance videos, sports videos have limited text. While both action recognition for multiplayer sports and detection of players has seen robust research, understanding contextual text in video frames still remains one of the most impactful avenues of sports video understanding. In this work we study extremely accurate semantic text detection and recognition in sports clocks, and challenges therein. We observe unique properties of sports clocks, which makes it hard to utilize general-purpose pre-trained detectors and recognizers, so that text can be accurately understood to the degree of being used to align to external knowledge. We propose a novel distant supervision technique to automatically build sports clock datasets. Along with suitable data augmentations, combined with any state-of-the-art text detection and recognition model architectures, we extract extremely accurate semantic text. Finally, we share our computational architecture pipeline to scale this system in industrial setting and proposed a robust dataset for the same to validate our results.
VSEC: Transformer-based Model for Vietnamese Spelling Correction
Venue: arXiv cs.CL 2021
Year: 2021

Spelling error correction is one of topics which have a long history in natural language processing. Although previous studies have achieved remarkable results, challenges still exist. In the Vietnamese language, a state-of-the-art method for the task infers a syllable's context from its adjacent syllables. The method's accuracy can be unsatisfactory, however, because the model may lose the context if two (or more) spelling mistakes stand near each other. In this paper, we propose a novel method to correct Vietnamese spelling errors. We tackle the problems of mistyped errors and misspelled errors by using a deep learning model. The embedding layer, in particular, is powered by the byte pair encoding technique. The sequence to sequence model based on the Transformer architecture makes our approach different from the previous works on the same problem. In the experiment, we train the model with a large synthetic dataset, which is randomly introduced spelling errors. We test the performance of the proposed method using a realistic dataset. This dataset contains 11,202 human-made misspellings in 9,341 different Vietnamese sentences. The experimental results show that our method achieves encouraging performance with 86.8% errors detected and 81.5% errors corrected, which improves the state-of-the-art approach 5.6% and 2.2%, respectively.
Unsupervised Domain Adaptation with Adapter
Venue: arXiv cs.CL 2021
Year: 2021

Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from various domains. However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to deployment a whole fine-tuned PrLM for each domain. This paper explores an adapter-based fine-tuning approach for unsupervised domain adaptation. Specifically, several trainable adapter modules are inserted in a PrLM, and the embedded generic knowledge is preserved by fixing the parameters of the original PrLM at fine-tuning. A domain-fusion scheme is introduced to train these adapters using a mix-domain corpus to better capture transferable features. Elaborated experiments on two benchmark datasets are carried out, and the results demonstrate that our approach is effective with different tasks, dataset sizes, and domain similarities.
B-DAC: A Decentralized Access Control Framework on Northbound Interface for Securing SDN Using Blockchain
Venue: arXiv cs.CR 2021
Year: 2021

Software-Defined Network (SDN) is a new arising terminology of network architecture with outstanding features of orchestration by decoupling the control plane and the data plane in each network element. Even though it brings several benefits, SDN is vulnerable to a diversity of attacks. Abusing the single point of failure in the SDN controller component, hackers can shut down all network operations. More specifics, a malicious OpenFlow application can access to SDN controller to carry out harmful actions without any limitation owing to the lack of the access control mechanism as a standard in the Northbound. The sensitive information about the whole network such as network topology, flow information, and statistics can be gathered and leaked out. Even worse, the entire network can be taken over by the compromised controller. Hence, it is vital to build a scheme of access control for SDN's Northbound. Furthermore, it must also protect the data integrity and availability during data exchange between application and controller. To address such limitations, we introduce B-DAC, a blockchain-based framework for decentralized authentication and fine-grained access control for the Northbound interface to assist administrators in managing and protecting critical resources. With strict policy enforcement, B-DAC can perform decentralized access control for each request to keep network applications under surveillance for preventing over-privileged activities or security policy conflicts. To demonstrate the feasibility of our approach, we also implement a prototype of this framework to evaluate the security impact, effectiveness, and performance through typical use cases.