OpenRaiser/Intern-Atlas huggingface dataset:
TPRM: A Topic-based Personalized Ranking Model for Web Search
Venue: arXiv cs.IR 2021
Year: 2021

Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents. In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking list. Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly.
Piecewise Linear De-skewing for LiDAR Inertial Odometry
Venue: arXiv cs.RO 2021
Year: 2021

Light detection and ranging (LiDAR) on a moving agent could suffer from motion distortion due to simultaneous rotation of the LiDAR and fast movement of the agent. An accurate piecewise linear de skewing algorithm is proposed to correct the motion distortions for LiDAR inertial odometry (LIO) using high frequency motion information provided by an Inertial Measurement Unit (IMU). Experimental results show that the proposed algorithm can be adopted to improve the performance of existing LIO algorithms especially in cases of fast movement.
Scaling Up Hardware Accelerator Verification using A-QED with Functional Decomposition
Venue: arXiv cs.LO 2021
Year: 2021

Hardware accelerators (HAs) are essential building blocks for fast and energy-efficient computing systems. Accelerator Quick Error Detection (A-QED) is a recent formal technique which uses Bounded Model Checking for pre-silicon verification of HAs. A-QED checks an HA for self-consistency, i.e., whether identical inputs within a sequence of operations always produce the same output. Under modest assumptions, A-QED is both sound and complete. However, as is well-known, large design sizes significantly limit the scalability of formal verification, including A-QED. We overcome this scalability challenge through a new decomposition technique for A-QED, called A-QED with Decomposition (A-QED$^2$). A-QED$^2$ systematically decomposes an HA into smaller, functional sub-modules, called sub-accelerators, which are then verified independently using A-QED. We prove completeness of A-QED$^2$; in particular, if the full HA under verification contains a bug, then A-QED$^2$ ensures detection of that bug during A-QED verification of the corresponding sub-accelerators. Results on over 100 (buggy) versions of a wide variety of HAs with millions of logic gates demonstrate the effectiveness and practicality of A-QED$^2$.
A VCSEL Array Transmission System with Novel Beam Activation Mechanisms
Venue: arXiv cs.IT 2021
Year: 2021

Optical wireless communication (OWC) is considered to be a promising technology which will alleviate traffic burden caused by the increasing number of mobile devices. In this study, a novel vertical-cavity surface-emitting laser (VCSEL) array is proposed for indoor OWC systems. To activate the best beam for a mobile user, two beam activation methods are proposed for the system. The method based on a corner-cube retroreflector (CCR) provides very low latency and allows real-time activation for high-speed users. The other method uses the omnidirectional transmitter (ODTx). The ODTx can serve the purpose of uplink transmission and beam activation simultaneously. Moreover, systems with ODTx are very robust to the random orientation of a user equipment (UE). System level analyses are carried out for the proposed VCSEL array system. For a single user scenario, the probability density function (PDF) of the signal-to-noise ratio (SNR) for the central beam of the VCSEL array system can be approximated as a uniform distribution. In addition, the average data rate of the central beam and its upper bound are given analytically and verified by Monte-Carlo simulations. For a multi-user scenario, an analytical upper bound for the average data rate is given. The effects of the cell size and the full width at half maximum (FWHM) angle on the system performance are studied. The results show that the system with a FWHM angle of $4^\circ$ outperforms the others.
W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
Venue: arXiv cs.LG 2021
Year: 2021

Motivated by the success of masked language modeling~(MLM) in pre-training natural language processing models, we propose w2v-BERT that explores MLM for self-supervised speech representation learning. w2v-BERT is a framework that combines contrastive learning and MLM, where the former trains the model to discretize input continuous speech signals into a finite set of discriminative speech tokens, and the latter trains the model to learn contextualized speech representations via solving a masked prediction task consuming the discretized tokens. In contrast to existing MLM-based speech pre-training frameworks such as HuBERT, which relies on an iterative re-clustering and re-training process, or vq-wav2vec, which concatenates two separately trained modules, w2v-BERT can be optimized in an end-to-end fashion by solving the two self-supervised tasks~(the contrastive task and MLM) simultaneously. Our experiments show that w2v-BERT achieves competitive results compared to current state-of-the-art pre-trained models on the LibriSpeech benchmarks when using the Libri-Light~60k corpus as the unsupervised data. In particular, when compared to published models such as conformer-based wav2vec~2.0 and HuBERT, our model shows~5\% to~10\% relative WER reduction on the test-clean and test-other subsets. When applied to the Google's Voice Search traffic dataset, w2v-BERT outperforms our internal conformer-based wav2vec~2.0 by more than~30\% relatively.
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.
Code Perfumes: Reporting Good Code to Encourage Learners
Venue: arXiv cs.SE 2021
Year: 2021

Block-based programming languages like Scratch enable children to be creative while learning to program. Even though the block-based approach simplifies the creation of programs, learning to program can nevertheless be challenging. Automated tools such as linters therefore support learners by providing feedback about potential bugs or code smells in their programs. Even when this feedback is elaborate and constructive, it still represents purely negative criticism and by construction ignores what learners have done correctly in their programs. In this paper we introduce an orthogonal approach to linting: We complement the criticism produced by a linter with positive feedback. We introduce the concept of code perfumes as the counterpart to code smells, indicating the correct application of programming practices considered to be good. By analysing not only what learners did wrong but also what they did right we hope to encourage learners, to provide teachers and students a better understanding of learners' progress, and to support the adoption of automated feedback tools. Using a catalogue of 25 code perfumes for Scratch, we empirically demonstrate that these represent frequent practices in Scratch, and we find that better programs indeed contain more code perfumes.
Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases
Venue: arXiv cs.CL 2021
Year: 2021

We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i.e., anti-communism) and racist (i.e., antisemitism) one. For this, we use a new corpus of German parliamentary proceedings, DeuPARL, spanning the period 1867--2020. We complement this analysis of historical biases in diachronic word embeddings with a novel measure of bias on the basis of term co-occurrences and graph-based label propagation. The results of our bias measurements align with commonly perceived historical trends of antisemitic and anti-communist biases in German politics in different time periods, thus indicating the viability of analyzing historical bias trends using semantic spaces induced from historical corpora.
Global Tweet Mentions of COVID-19
Venue: arXiv cs.CY 2021
Year: 2021

Background. After a year and half and over 4 million deaths, the COVID-19 pandemic continues to be widespread, and its related topics continue to dominate the global media. Although COVID-19 diagnoses have been well monitored, neither the impacts of the disease on human behavior and social dynamics nor the effectiveness of policy interventions aimed at its containment are fully understood. Monitoring the spatial and temporal patterns of behavior, social dynamics and policy - and then their interrelations - can provide critical information for preparatory action and effective response. Methods. Here we present an open-source dataset of 1.92 million keyword-selected Twitter posts, updated weekly from January 2020 to present, along with a dynamic dashboard showing totals at national and subnational administrative divisions. Results. The dashboard presents 100% of the geotagged tweets that contain keywords or hashtags related COVID-19. We validated our inclusion criteria using a machine learning-based text classifier and found that 88% of the selected tweets were correctly labeled as related to COVID-19. With this information we tested the correlation between tweets and covid diagnosis from January 1, 2020 through December 31, 2020 and see a decreasing correlation across time. Conclusions. With emerging COVID variants but ongoing vaccine hesitancy and resistance, this dataset could be used by researchers to study numerous aspects of COVID-19 and provide valuable insights for preparing future pandemics.
The Sharpe predictor for fairness in machine learning
Venue: arXiv cs.LG 2021
Year: 2021

In machine learning (ML) applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a constraint or a penalization term in the optimization of a ML model, which does not lead to the discovery of the complete landscape of the trade-offs among learning accuracy and fairness metrics, and does not integrate fairness in a meaningful way. Recently, we have introduced a new paradigm for FML based on Stochastic Multi-Objective Optimization (SMOO), where accuracy and fairness metrics stand as conflicting objectives to be optimized simultaneously. The entire trade-offs range is defined as the Pareto front of the SMOO problem, which can then be efficiently computed using stochastic-gradient type algorithms. SMOO also allows defining and computing new meaningful predictors for FML, a novel one being the Sharpe predictor that we introduce and explore in this paper, and which gives the highest ratio of accuracy-to-unfairness. Inspired from SMOO in finance, the Sharpe predictor for FML provides the highest prediction return (accuracy) per unit of prediction risk (unfairness).
Probability Distributions for Elliptic Curves in the CGL Hash Function
Venue: arXiv cs.CR 2021
Year: 2021

Hash functions map data of arbitrary length to data of predetermined length. Good hash functions are hard to predict, making them useful in cryptography. We are interested in the elliptic curve CGL hash function, which maps a bitstring to an elliptic curve by traversing an input-determined path through an isogeny graph. The nodes of an isogeny graph are elliptic curves, and the edges are special maps betwixt elliptic curves called isogenies. Knowing which hash values are most likely informs us of potential security weaknesses in the hash function. We use stochastic matrices to compute the expected probability distributions of the hash values. We generalize our experimental data into a theorem that completely describes all possible probability distributions of the CGL hash function. We use this theorem to evaluate the collision resistance of the CGL hash function and compare this to the collision resistance of an "ideal" hash function.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Venue: arXiv cs.IR 2021
Year: 2021

Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues. Contrastive SSL constructs augmentations from unlabelled instances, where agreements among positive pairs are maximized. It is challenging to devise a contrastive SSL framework for a sequential recommendation, due to its discrete nature, correlations among items, and skewness of length distributions. To this end, we propose a novel framework, Contrastive Self-supervised Learning for sequential Recommendation (CoSeRec). We introduce two informative augmentation operators leveraging item correlations to create high-quality views for contrastive learning. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method on improving model performance and the robustness against sparse and noisy data. Our implementation is available online at \url{https://github.com/YChen1993/CoSeRec}
Refractive Geometry for Underwater Domes
Venue: arXiv cs.CV 2021
Year: 2021

Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere's center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications.
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Venue: arXiv cs.CL 2021
Year: 2021

We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. The study of the selection strategy can help us to (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.
Investigating the Relationship Between Dropout Regularization and Model Complexity in Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Dropout Regularization, serving to reduce variance, is nearly ubiquitous in Deep Learning models. We explore the relationship between the dropout rate and model complexity by training 2,000 neural networks configured with random combinations of the dropout rate and the number of hidden units in each dense layer, on each of the three data sets we selected. The generated figures, with binary cross entropy loss and binary accuracy on the z-axis, question the common assumption that adding depth to a dense layer while increasing the dropout rate will certainly enhance performance. We also discover a complex correlation between the two hyperparameters that we proceed to quantify by building additional machine learning and Deep Learning models which predict the optimal dropout rate given some hidden units in each dense layer. Linear regression and polynomial logistic regression require the use of arbitrary thresholds to select the cost data points included in the regression and to assign the cost data points a binary classification, respectively. These machine learning models have mediocre performance because their naive nature prevented the modeling of complex decision boundaries. Turning to Deep Learning models, we build neural networks that predict the optimal dropout rate given the number of hidden units in each dense layer, the desired cost, and the desired accuracy of the model. Though, this attempt encounters a mathematical error that can be attributed to the failure of the vertical line test. The ultimate Deep Learning model is a neural network whose decision boundary represents the 2,000 previously generated data points. This final model leads us to devise a promising method for tuning hyperparameters to minimize computational expense yet maximize performance. The strategy can be applied to any model hyperparameters, with the prospect of more efficient tuning in industrial models.
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
Venue: arXiv cs.CV 2021
Year: 2021

Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited. Therefore, we propose the few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions: the inability to capture subtle action details and the inadequacy in learning from data with low inter-class variance. To tackle the first issue, a human vision inspired bidirectional attention module (BAM) is proposed. Combining top-down task-driven signals with bottom-up salient stimuli, BAM captures subtle action details by accurately highlighting informative spatio-temporal regions. To address the second issue, we introduce contrastive meta-learning (CML). Compared with the widely adopted ProtoNet-based method, CML generates more discriminative video representations for low inter-class variance data, since it makes full use of potential contrastive pairs in each training episode. Furthermore, to fairly compare different models, we establish specific benchmark protocols on two large-scale fine-grained action recognition datasets. Extensive experiments show that our method consistently achieves state-of-the-art performance across evaluated tasks.
Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling
Venue: arXiv cs.CV 2021
Year: 2021

3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially for outdoor scenes, the problem becomes more severe due to the sparseness of the point cloud and the complexity of urban scenes. Semi-supervised learning is a promising technique to mitigate the data annotation issue. Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks. We design the Adaptive Class Confidence Selection module (ACCS) to generate high-quality pseudo-labels. Besides, we propose Holistic Point Cloud Augmentation (HPCA) for unlabeled data to improve robustness. Experiments on the KITTI benchmark demonstrate the effectiveness of our method.
Mithril: Cooperative Row Hammer Protection on Commodity DRAM Leveraging Managed Refresh
Venue: arXiv cs.CR 2021
Year: 2021

Since its public introduction in the mid-2010s, the Row Hammer (RH) phenomenon has drawn significant attention from the research community due to its security implications. Although many RH-protection schemes have been proposed by processor vendors, DRAM manufacturers, and academia, they still have shortcomings. Solutions implemented in the memory controller (MC) incur increasingly higher costs due to their conservative design for the worst case in terms of the number of DRAM banks and RH threshold to support. Meanwhile, DRAM-side implementation either has a limited time margin for RH-protection measures or requires extensive modifications to the standard DRAM interface. Recently, a new command for RH-protection has been introduced in the DDR5/LPDDR5 standards, referred to as refresh management (RFM). RFM enables the separation of the tasks for RHprotection to both MC and DRAM by having the former generate an RFM command at a specific activation frequency and the latter take proper RH-protection measures within a given time window. Although promising, no existing study presents and analyzes RFM-based solutions for RH-protection. In this paper, we propose Mithril, the first RFM interfacecompatible, DRAM-MC cooperative RH-protection scheme providing deterministic protection guarantees. Mithril has minimal energy overheads for common use cases without adversarial memory access patterns. We also introduce Mithril+, an optional extension to provide minimal performance overheads at the expense of a tiny modification to the MC, while utilizing existing DRAM commands.