OpenRaiser/Intern-Atlas huggingface dataset:
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches
Venue: arXiv cs.CL 2022
Year: 2022

Emotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces. Detecting emotion can help us in different fields, including opinion mining. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms is informal, making the emotion detection task difficult. EmoPars and ArmanEmo are two new human-labeled emotion datasets for the Persian language. These datasets, especially EmoPars, are suffering from inequality between several samples between two classes. In this paper, we evaluate EmoPars and compare them with ArmanEmo. Throughout this analysis, we use data augmentation techniques, data re-sampling, and class-weights with Transformer-based Pretrained Language Models(PLMs) to handle the imbalance problem of these datasets. Moreover, feature selection is used to enhance the models' performance by emphasizing the text's specific features. In addition, we provide a new policy for selecting data from EmoPars, which selects the high-confidence samples; as a result, the model does not see samples that do not have specific emotion during training. Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and EmoPars, respectively, which are new state-of-the-art results in these benchmarks.
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
Venue: arXiv cs.CL 2022
Year: 2022

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a {\it dual-teaching} manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER. Code is available at https://github.com/lemon0830/dualNER.
Prediction Accuracy and Autonomy
Venue: arXiv cs.IR 2022
Year: 2022

The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives. In this article we try to assess the objectives of recommender system research and offer a nuanced discussion of how these objectives can align with users' goals. This discussion employs a qualitative literature survey connecting the dots between relevant research within the fields of psychology, design ethics, interaction design and recommender systems. Finally, we focus on the specific use-case of YouTube's recommender system and propose design changes that will better align with individuals' autonomy. Based on our analysis we offer directions for future research that will help secure rights to digital autonomy in the attention economy.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Venue: arXiv cs.AI 2022
Year: 2022

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities. Compared to KGs, TKGs require temporal reasoning techniques for modeling, which naturally increases the difficulty in dealing with novel, yet unseen entities. In this work, we focus on the inductive learning of unseen entities' representations on TKGs. We propose a few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the missing entities from the links concerning unseen entities by employing a meta-learning framework and utilizing the meta-information provided by only few edges associated with each unseen entity. We construct three new datasets for TKG few-shot OOG link prediction, and we propose a model that mines the concept-aware information among entities. Experimental results show that our model achieves superior performance on all three datasets and our concept-aware modeling component demonstrates a strong effect.
A Comparative Study of Question Answering over Knowledge Bases
Venue: arXiv cs.CL 2022
Year: 2022

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at \url{https://github.com/tamlhp/kbqa}.
Differentially Private Sampling from Distributions
Venue: arXiv cs.LG 2022
Year: 2022

We initiate an investigation of private sampling from distributions. Given a dataset with $n$ independent observations from an unknown distribution $P$, a sampling algorithm must output a single observation from a distribution that is close in total variation distance to $P$ while satisfying differential privacy. Sampling abstracts the goal of generating small amounts of realistic-looking data. We provide tight upper and lower bounds for the dataset size needed for this task for three natural families of distributions: arbitrary distributions on $\{1,\ldots ,k\}$, arbitrary product distributions on $\{0,1\}^d$, and product distributions on $\{0,1\}^d$ with bias in each coordinate bounded away from 0 and 1. We demonstrate that, in some parameter regimes, private sampling requires asymptotically fewer observations than learning a description of $P$ nonprivately; in other regimes, however, private sampling proves to be as difficult as private learning. Notably, for some classes of distributions, the overhead in the number of observations needed for private learning compared to non-private learning is completely captured by the number of observations needed for private sampling.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Venue: arXiv cs.CL 2022
Year: 2022

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.
3D Cascade RCNN: High Quality Object Detection in Point Clouds
Venue: arXiv cs.CV 2022
Year: 2022

Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection. However, there has not been evidence in support of building such cascade structures for 3D object detection, a challenging detection scenario with highly sparse LiDAR point clouds. In this work, we present a simple yet effective cascade architecture, named 3D Cascade RCNN, that allocates multiple detectors based on the voxelized point clouds in a cascade paradigm, pursuing higher quality 3D object detector progressively. Furthermore, we quantitatively define the sparsity level of the points within 3D bounding box of each object as the point completeness score, which is exploited as the task weight for each proposal to guide the learning of each stage detector. The spirit behind is to assign higher weights for high-quality proposals with relatively complete point distribution, while down-weight the proposals with extremely sparse points that often incur noise during training. This design of completeness-aware re-weighting elegantly upgrades the cascade paradigm to be better applicable for the sparse input data, without increasing any FLOP budgets. Through extensive experiments on both the KITTI dataset and Waymo Open Dataset, we validate the superiority of our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object detection techniques. The source code is publicly available at \url{https://github.com/caiqi/Cascasde-3D}.
The RED-BLUE SEPARATION problem on graphs
Venue: arXiv cs.DM 2022
Year: 2022

We introduce the Red-Blue Separation problem on graphs, where we are given a graph $G=(V,E)$ whose vertices are colored either red or blue, and we want to select a (small) subset $S \subseteq V$, called red-blue separating set, such that for every red-blue pair of vertices, there is a vertex $s \in S$ whose closed neighborhood contains exactly one of the two vertices of the pair. We study the computational complexity of Red-Blue Separation, in which one asks whether a given red-blue colored graph has a red-blue separating set of size at most a given integer. We prove that the problem is NP-complete even for restricted graph classes. We also show that it is always approximable in polynomial time within a factor of $2\ln n$, where $n$ is the input graph's order. In contrast, for triangle-free graphs and for graphs of bounded maximum degree, we show that Red-Blue Separation is solvable in polynomial time when the size of the smaller color class is bounded by a constant. However, on general graphs, we show that the problem is $W[2]$-hard even when parameterized by the solution size plus the size of the smaller color class. We also consider the problem Max Red-Blue Separation where the coloring is not part of the input. Here, given an input graph $G$, we want to determine the smallest integer $k$ such that, for every possible red-blue coloring of $G$, there is a red-blue separating set of size at most $k$. We derive tight bounds on the cardinality of an optimal solution of Max Red-Blue Separation, showing that it can range from logarithmic in the graph order, up to the order minus one. We also give bounds with respect to related parameters. For trees however we prove an upper bound of two-thirds the order. We then show that Max Red-Blue Separation is NP-hard, even for graphs of bounded maximum degree, but can be approximated in polynomial time within a factor of $O(\ln^2 n)$.
Mobile-Aware Scheduling for Low Latency Backhaul over DOCSIS
Venue: arXiv cs.NI 2022
Year: 2022

In this paper, we discuss latency reduction techniques for mobile backhaul over Data Over Cable Service Interface Specifications (DOCSIS) networks. When the latencies from both the wireless and the DOCSIS networks are added together, it can result in noticeable end-to-end system latency, particularly under network congestion. Previously, we proposed a method to improve upstream user-to-mobile core latency by coordinating the LTE and DOCSIS scheduling. The method reduces the impact on system latency from the DOCSIS network's request-grant-data loop, which is the main contributor of backhaul upstream latency. Since the method reduces latency on the DOCSIS data path, it will therefore improve performance of latency sensitive applications, particularly if TCP is used as the transport protocol, especially when the link is congested. In this paper, we investigate the effect of HARQ failure on system performance. Through simulation, we show that despite the uncertainty introduced by the LTE protocol, coordinated scheduling improves overall system latency.
Low Latency Techniques for Mobile Backhaul over DOCSIS
Venue: arXiv cs.NI 2022
Year: 2022

The mobile network operators (MNOs) are looking into economically viable backhaul solutions as alternatives to fiber, specifically the hybrid fiber coaxial networks (HFC). When the latencies from both the wireless and the HFC networks are added together, the result is a noticeable end-to-end system latency, particularly under network congestion. In order to decrease total system latency, we proposed a method to improve upstream user- to-mobile core latency by coordinating the LTE and HFC scheduling in previous papers. In this paper, we implement and optimize the proposed method on a custom LTE and DOCSIS end-to-end system testbed. The testbed uses the OpenAirInterface (OAI) platform for the LTE network, along with Cisco's broadband router cBR-8 that is currently deployed in the HFC networks around the world. Our results show a backhaul latency improvement under all traffic load conditions.
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Venue: arXiv cs.RO 2022
Year: 2022

Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions suggested by the current policy as the most probable ones. This makes them vulnerable to domain shift and inefficient in the number of required demonstrations. We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis. PARTNR employs an adaptive, sensitivity-based, gating function that decides if additional user demonstrations are required. User demonstrations are aggregated to the dataset and used for subsequent training. In this way, the policy can adapt promptly to domain shift and it can minimize the number of required demonstrations for a well-trained policy. The adaptive threshold enables to achieve the user-acceptable level of ambiguity to execute the policy autonomously and in turn, increase the trustworthiness of our system. We demonstrate the performance of PARTNR in a table-top pick and place task.
Covariance-Based Hybrid Beamforming for Spectrally Efficient Joint Radar-Communications
Venue: arXiv cs.IT 2022
Year: 2022

Joint radar-communications (JRC) is considered to be a vital technology in deploying the next generation systems, since its useful in decongestion of the radio frequency (RF) spectrum and utilising the same hardware resources for dual functions. Using JRC systems for dual function generates interference between both the operations which needs to be addressed in future standardization. Furthermore, JRC systems can be advanced by deploying hybrid beamforming which implements fewer number of RF chains than the number of transmit antennas. This paper designs a robust hybrid beamformer for minimizing the interference of a JRC transmitter via RF chain selection resulting into mutual information maximization. We consider a weighted mutual information for the dual function JRC system and implement a common analog beamformer for both the operations. The mutual information maximization problem is formulated which is non-convex and difficult to solve. The problem is simplified to convex form and solved using Dinkelbach approximation abased fractional programming. The performance of the optimal RF selection based proposed approach is evaluated, compared with baselines and its effectiveness is inferred via numerical results.
Probabilistic Deep Metric Learning for Hyperspectral Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes a probabilistic deep metric learning (PDML) framework for hyperspectral image classification, which aims to predict the category of each pixel for an image captured by hyperspectral sensors. The core problem for hyperspectral image classification is the spectral variability between intraclass materials and the spectral similarity between interclass materials, motivating the further incorporation of spatial information to differentiate a pixel based on its surrounding patch. However, different pixels and even the same pixel in one patch might not encode the same material due to the low spatial resolution of most hyperspectral sensors, leading to an inconsistent judgment of a specific pixel. To address this issue, we propose a probabilistic deep metric learning framework to model the categorical uncertainty of the spectral distribution of an observed pixel. We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions. We treat each pixel in a patch as a training sample, enabling us to exploit more information from the patch compared with conventional methods. Our framework can be readily applied to existing hyperspectral image classification methods with various network architectures and loss functions. Extensive experiments on four widely used datasets including IN, UP, KSC, and Houston 2013 datasets demonstrate that our framework improves the performance of existing methods and further achieves the state of the art. Code is available at: https://github.com/wzzheng/PDML.
Structured Exploration Through Instruction Enhancement for Object Navigation
Venue: arXiv cs.AI 2022
Year: 2022

Finding an object of a specific class in an unseen environment remains an unsolved navigation problem. Hence, we propose a hierarchical learning-based method for object navigation. The top-level is capable of high-level planning, and building a memory on a floorplan-level (e.g., which room makes the most sense for the agent to visit next, where has the agent already been?). While the lower-level is tasked with efficiently navigating between rooms and looking for objects in them. Instructions can be provided to the agent using a simple synthetic language. The top-level intelligently enhances the instructions in order to make the overall task more tractable. Language grounding, mapping instructions to visual observations, is performed by utilizing an additional separate supervised trained goal assessment module. We demonstrate the effectiveness of our method on a dynamic configurable domestic environment.
LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process
Venue: arXiv cs.RO 2022
Year: 2022

Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and homogeneous error assumption. We propose a heterogeneous loss function based on concentrated Gaussian distribution with the Lie group to overcome these difficulties. Following our experiment, the proposed method allows us to speed up the training process significantly (from 300 to 10 epochs) with acceptable error values.
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, neural network based image denoising approaches have revolutionized the analysis of biomedical microscopy data. Self-supervised methods, such as Noise2Void (N2V), are applicable to virtually all noisy datasets, even without dedicated training data being available. Arguably, this facilitated the fast and widespread adoption of N2V throughout the life sciences. Unfortunately, the blind-spot training underlying N2V can lead to rather visible checkerboard artifacts, thereby reducing the quality of final predictions considerably. In this work, we present two modifications to the vanilla N2V setup that both help to reduce the unwanted artifacts considerably. Firstly, we propose a modified network architecture, i.e., using BlurPool instead of MaxPool layers throughout the used U-Net, rolling back the residual U-Net to a non-residual U-Net, and eliminating the skip connections at the uppermost U-Net level. Additionally, we propose new replacement strategies to determine the pixel intensity values that fill in the elected blind-spot pixels. We validate our modifications on a range of microscopy and natural image data. Based on added synthetic noise from multiple noise types and at varying amplitudes, we show that both proposed modifications push the current state-of-the-art for fully self-supervised image denoising.
A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilities
Venue: arXiv cs.CR 2022
Year: 2022

Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks. Source code static analysis has been used extensively to detect the unintentional defects, i.e. vulnerabilities, introduced into the source codes by software developers. In this paper, we propose a deep learning approach to detect vulnerabilities from their LLVM IR representations based on the techniques that have been used in natural language processing. The proposed approach uses a hierarchical process to first identify source codes with vulnerabilities, and then it identifies the lines of codes that contribute to the vulnerability within the detected source codes. This proposed two-step approach reduces the false alarm of detecting vulnerable lines. Our extensive experiment on real-world and synthetic codes collected in NVD and SARD shows high accuracy (about 98\%) in detecting source code vulnerabilities.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
Venue: arXiv cs.LG 2022
Year: 2022

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, for learning a low-dimensional representation of the data, with a density-estimation model based on random Fourier features and density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. The method predicts a degree of normality for new samples based on the estimated density. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method performs on par with or outperforms other state-of-the-art methods.