OpenRaiser/Intern-Atlas huggingface dataset:
Semi-Deterministic Subspace Selection for Sparse Recursive Projection-Aggregation Decoding of Reed-Muller Codes
Venue: arXiv cs.IT 2022
Year: 2022

Recursive projection aggregation (RPA) decoding as introduced in [1] is a novel decoding algorithm which performs close to the maximum likelihood decoder for short-length Reed-Muller codes. Recently, an extension to RPA decoding, called sparse multi-decoder RPA (SRPA), has been proposed [2]. The SRPA approach makes use of multiple pruned RPA decoders to lower the amount of computations while keeping the performance loss small compared to RPA decoding. However, the use of multiple sparse decoders again increases the computational burden. Therefore, the focus is on the optimization of sparse single-decoder RPA decoding to keep the complexity small. In this paper, a novel method is proposed, to select subsets of subspaces used in the projection and aggregation step of SRPA decoding in order to decrease the decoding error probability on AWGN channels. The proposed method replaces the random selection of subspace subsets with a semi-deterministic selection method based on a figure of merit that evaluates the performance of each subspace. Our simulation results show that the semi-deterministic subspace selection improves the decoding performance up to $0.2\,\text{dB}$ compared to SRPA. At the same time, the complexity of SRPA decoding for RM codes of order $r\geq 3$ is reduced by up to 81% compared to SRPA.
FiFo: Fishbone Forwarding in Massive IoT Networks
Venue: arXiv cs.IT 2022
Year: 2022

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighted pair group method with the arithmetic average; and 2) it creates the main axis and sub axes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world dataset. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation
Venue: arXiv cs.IR 2022
Year: 2022

Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. However, there has been little research into what set of practices should serve as a starting point during experimentation. In this paper, we examine four larger issues in recommender system research regarding uncertainty estimation, generalization, hyperparameter optimization and dataset pre-processing in more detail to arrive at a set of guidelines. We present a TrainRec, a lightweight and flexible toolkit for offline training and evaluation of recommender systems that implements these guidelines. Different from other frameworks, TrainRec is a toolkit that focuses on experimentation alone, offering flexible modules that can be can be used together or in isolation. Finally, we demonstrate TrainRec's usefulness by evaluating a diverse set of twelve baselines across ten datasets. Our results show that (i) many results on smaller datasets are likely not statistically significant, (ii) there are at least three baselines that perform well on most datasets and should be considered in future experiments, and (iii) improved uncertainty quantification (via nested CV and statistical testing) rules out some reported differences between linear and neural methods. Given these results, we advocate that future research should standardize evaluation using our suggested guidelines.
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
Venue: arXiv cs.SD 2022
Year: 2022

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions.
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement Learning (RL)-based control system has received considerable attention in recent decades. However, in many real-world problems, such as Batch Process Control, the environment is uncertain, which requires expensive interaction to acquire the state and reward values. In this paper, we present a cost-efficient framework, such that the RL model can evolve for itself in a Virtual Space using the predictive models with only historical data. The proposed framework enables a step-by-step RL model to predict the future state and select optimal actions for long-sight decisions. The main focuses are summarized as: 1) how to balance the long-sight and short-sight rewards with an optimal strategy; 2) how to make the virtual model interacting with real environment to converge to a final learning policy. Under the experimental settings of Fed-Batch Process, our method consistently outperforms the existing state-of-the-art methods.
Multi-Vector Retrieval as Sparse Alignment
Venue: arXiv cs.CL 2022
Year: 2022

Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alignment between query and document tokens. We propose AligneR, a novel multi-vector retrieval model that learns sparsified pairwise alignments between query and document tokens (e.g. `dog' vs. `puppy') and per-token unary saliences reflecting their relative importance for retrieval. We show that controlling the sparsity of pairwise token alignments often brings significant performance gains. While most factoid questions focusing on a specific part of a document require a smaller number of alignments, others requiring a broader understanding of a document favor a larger number of alignments. Unary saliences, on the other hand, decide whether a token ever needs to be aligned with others for retrieval (e.g. `kind' from `kind of currency is used in new zealand}'). With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval. We learn the sparse unary saliences with entropy-regularized linear programming, which outperforms other methods to achieve sparsity. In a zero-shot setting, AligneR scores 51.1 points nDCG@10, achieving a new retriever-only state-of-the-art on 13 tasks in the BEIR benchmark. In addition, adapting pairwise alignments with a few examples (<= 8) further improves the performance up to 15.7 points nDCG@10 for argument retrieval tasks. The unary saliences of AligneR helps us to keep only 20% of the document token representations with minimal performance loss. We further show that our model often produces interpretable alignments and significantly improves its performance when initialized from larger language models.
A Health Focused Text Classification Tool (HFTCT)
Venue: arXiv cs.IR 2022
Year: 2022

Due to the high number of users on social media and the massive amounts of queries requested every second to share a new video, picture, or message, social platforms struggle to manage this humungous amount of data that is endlessly coming in. HFTCT relies on wordlists to classify opinions. It can carry out its tasks reasonably well; however, sometimes, the wordlists themselves fail to be reliable as they are a limited source of positive and negative words.
Item-based Variational Auto-encoder for Fair Music Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

We present our solution for the EvalRS DataChallenge. The EvalRS DataChallenge aims to build a more realistic recommender system considering accuracy, fairness, and diversity in evaluation. Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the bias in popularity, we use an item-based VAE for each popularity group with an additional fairness regularization. To make a reasonable recommendation even the predictions are inaccurate, we combine the recommended list of BPRMF and that of item-based VAE. Through the experiments, we demonstrate that the item-based VAE with fairness regularization significantly reduces popularity bias compared to the user-based VAE. The ensemble between the item-based VAE and BPRMF makes the top-1 item similar to the ground truth even the predictions are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as a novel evaluation metric based on our reflections from the extensive experiments.
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
Venue: arXiv cs.CV 2022
Year: 2022

Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and effective human activity recognition (HAR) models. Systems like IMUTube were originally designed to cover activities that are based on substantial body (part) movements. Yet, life is complex, and a range of activities of daily living is based on only rather subtle movements, which bears the question to what extent systems like IMUTube are of value also for fine-grained HAR, i.e., When does IMUTube break? In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest--the motion subtlety index (MSI)--which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy. We then perform a "stress-test" on IMUTube and explore for which activities with underlying subtle movements a cross-modality transfer approach works, and for which not. As such, the work presented in this paper allows us to map out the landscape for IMUTube applications in practical scenarios.
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Venue: arXiv cs.CL 2022
Year: 2022

Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but also such data may have lots of annotation errors even after the annotation job is completed, resulting in a very poor model performance. In this paper, we present an active learning framework that leverages human in the loop learning to identify data samples from the annotated dataset for re-annotation that are more likely to contain annotation errors. In this way, we largely reduce the need for data re-annotation for the whole dataset. We conduct extensive experiments with our proposed approach for Named Entity Recognition and observe that by re-annotating only about 6% training instances out of the whole dataset, the F1 score for a certain entity type can be significantly improved by about 25%.
Speeding up NAS with Adaptive Subset Selection
Venue: arXiv cs.LG 2022
Year: 2022

A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance. Towards this goal, several low-fidelity and performance prediction methods have been considered, including those that train only on subsets of the training data. In this work, we present an adaptive subset selection approach to NAS and present it as complementary to state-of-the-art NAS approaches. We uncover a natural connection between one-shot NAS algorithms and adaptive subset selection and devise an algorithm that makes use of state-of-the-art techniques from both areas. We use these techniques to substantially reduce the runtime of DARTS-PT (a leading one-shot NAS algorithm), as well as BOHB and DEHB (leading multifidelity optimization algorithms), without sacrificing accuracy. Our results are consistent across multiple datasets, and towards full reproducibility, we release our code at https: //anonymous.4open.science/r/SubsetSelection NAS-B132.
User or Labor: An Interaction Framework for Human-Machine Relationships in NLP
Venue: arXiv cs.HC 2022
Year: 2022

The bridging research between Human-Computer Interaction and Natural Language Processing is developing quickly these years. However, there is still a lack of formative guidelines to understand the human-machine interaction in the NLP loop. When researchers crossing the two fields talk about humans, they may imply a user or labor. Regarding a human as a user, the human is in control, and the machine is used as a tool to achieve the human's goals. Considering a human as a laborer, the machine is in control, and the human is used as a resource to achieve the machine's goals. Through a systematic literature review and thematic analysis, we present an interaction framework for understanding human-machine relationships in NLP. In the framework, we propose four types of human-machine interactions: Human-Teacher and Machine-Learner, Machine-Leading, Human-Leading, and Human-Machine Collaborators. Our analysis shows that the type of interaction is not fixed but can change across tasks as the relationship between the human and the machine develops. We also discuss the implications of this framework for the future of NLP and human-machine relationships.
NaRPA: Navigation and Rendering Pipeline for Astronautics
Venue: arXiv cs.GR 2022
Year: 2022

This paper presents Navigation and Rendering Pipeline for Astronautics (NaRPA) - a novel ray-tracing-based computer graphics engine to model and simulate light transport for space-borne imaging. NaRPA incorporates lighting models with attention to atmospheric and shading effects for the synthesis of space-to-space and ground-to-space virtual observations. In addition to image rendering, the engine also possesses point cloud, depth, and contour map generation capabilities to simulate passive and active vision-based sensors and to facilitate the designing, testing, or verification of visual navigation algorithms. Physically based rendering capabilities of NaRPA and the efficacy of the proposed rendering algorithm are demonstrated using applications in representative space-based environments. A key demonstration includes NaRPA as a tool for generating stereo imagery and application in 3D coordinate estimation using triangulation. Another prominent application of NaRPA includes a novel differentiable rendering approach for image-based attitude estimation is proposed to highlight the efficacy of the NaRPA engine for simulating vision-based navigation and guidance operations.
Computing a many-to-many matching with demands and capacities between two sets using the Hungarian algorithm
Venue: arXiv cs.DS 2022
Year: 2022

Given two sets A={a_1,a_2,...,a_s} and {b_1,b_2,...,b_t}, a many-to-many matching with demands and capacities (MMDC) between A and B matches each element a_i in A to at least α_i and at most α'_i elements in B, and each element b_j in B to at least β_j and at most β'_j elements in A for all 1=<i<=s and 1=<j<=t. In this paper, we present an algorithm for finding a minimum-cost MMDC between A and B using the well-known Hungarian algorithm.
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction
Venue: arXiv cs.CL 2022
Year: 2022

Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. Specifically, we propose a novel zero-shot approach for spelling error correction, which is simple but effective, obtaining a high precision to avoid error accumulation of the pipeline structure. To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence. Our proposed framework achieves a 42.11 F0.5 score on CGEC dataset without using any synthetic data or data augmentation methods, which outperforms the previous state-of-the-art by a wide margin of 1.30 points. Moreover, our model produces meaningful POS representations that capture different POS words and convey reasonable POS transition rules.
Image-based Early Detection System for Wildfires
Venue: arXiv cs.CV 2022
Year: 2022

Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.
$\mathcal{X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images. The method builds on a novel formulation of the $N$-dimensional joint intensity distribution by representing the common anatomy as latent variables and estimating the appearance model with nonparametric estimators. Through connection to maximum likelihood and the expectation-maximization algorithm, an information\hyp{}theoretic metric called $\mathcal{X}$-metric and a co-registration algorithm named $\mathcal{X}$-CoReg are induced, allowing groupwise registration of the $N$ observed images with computational complexity of $\mathcal{O}(N)$. Moreover, the method naturally extends for a weakly-supervised scenario where anatomical labels of certain images are provided. This leads to a combined\hyp{}computing framework implemented with deep learning, which performs registration and segmentation simultaneously and collaboratively in an end-to-end fashion. Extensive experiments were conducted to demonstrate the versatility and applicability of our model, including multimodal groupwise registration, motion correction for dynamic contrast enhanced magnetic resonance images, and deep combined computing for multimodal medical images. Results show the superiority of our method in various applications in terms of both accuracy and efficiency, highlighting the advantage of the proposed representation of the imaging process.
Repeatable Random Permutation Set
Venue: arXiv cs.AI 2022
Year: 2022

Random permutation set (RPS), as a recently proposed theory, enables powerful information representation by traversing all possible permutations. However, the repetition of items is not allowed in RPS while it is quite common in real life. To address this issue, we propose repeatable random permutation set ($\rm R^2PS$) which takes the repetition of items into consideration. The right and left junctional sum combination rules are proposed and their properties including consistency, pseudo-Matthew effect and associativity are researched. Based on these properties, a decision support system application is simulated to show the effectiveness of $\rm R^2PS$.
Data-efficient End-to-end Information Extraction for Statistical Legal Analysis
Venue: arXiv cs.CL 2022
Year: 2022

Legal practitioners often face a vast amount of documents. Lawyers, for instance, search for appropriate precedents favorable to their clients, while the number of legal precedents is ever-growing. Although legal search engines can assist finding individual target documents and narrowing down the number of candidates, retrieved information is often presented as unstructured text and users have to examine each document thoroughly which could lead to information overloading. This also makes their statistical analysis challenging. Here, we present an end-to-end information extraction (IE) system for legal documents. By formulating IE as a generation task, our system can be easily applied to various tasks without domain-specific engineering effort. The experimental results of four IE tasks on Korean precedents shows that our IE system can achieve competent scores (-2.3 on average) compared to the rule-based baseline with as few as 50 training examples per task and higher score (+5.4 on average) with 200 examples. Finally, our statistical analysis on two case categories--drunk driving and fraud--with 35k precedents reveals the resulting structured information from our IE system faithfully reflects the macroscopic features of Korean legal system.
Methodology for Simulation-based Comparison of Algorithms for Distributed Mutual Exclusion
Venue: arXiv cs.DC 2022
Year: 2022

In this paper, we show how different types of distributed mutual algorithms can be compared in terms of performance through simulations. A simulation-based approach is presented, together with an overview of the relevant evaluation metrics and approach for statistical processing of the results. The presented simulations can be used to learn master students of a course on distributed software the basics of algorithms for distributed mutual exclusion, together with how to properly conduct a detailed comparison study. Finally, a related work section is provided with relevant use cases where distributed mutual exclusion algorithm can be beneficial.