OpenRaiser/Intern-Atlas huggingface dataset:
Cognitive Visual-learning Environment for PostgreSQL
Venue: arXiv cs.LG 2022
Year: 2022

PostgreSQL is an object-relational database (ORDBMS) that was introduced into the database community and has been avidly used for a variety of information extraction use cases. It is also known to be an advanced SQL-compliant open source Object RDBMS. However, users have not yet resolved to PostgreSQL due to the fact that it is still under the layers and the complexity of its persistent textual environment for an amateur user. Hence, there is a dire need to provide an easy environment for users to comprehend the procedure and standards with which databases are created, tables and the relationships among them, manipulating queries and their flow based on conditions in PostgreSQL. As such, this project identifies the dominant features offered by Postgresql, analyzes the constraints that exist in the database user community in migrating to PostgreSQL and based on the scope and constraints identified, develop a system that will serve as a query generation platform as well as a learning tool that will provide an interactive environment to cognitively learn PostgreSQL query building. This is achieved using a visual editor incorporating a textual editor for a well-versed user. By providing visually-draggable query components to work with, this research aims to offer a cognitive, visual and tactile environment where users can interactively learn PostgreSQL query generation.
Shadow-Aware Dynamic Convolution for Shadow Removal
Venue: arXiv cs.CV 2022
Year: 2022

With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC.
Fast Obstacle Avoidance Based on Real-Time Sensing
Venue: arXiv cs.RO 2022
Year: 2022

Humans are remarkable at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance robust to partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance. The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. We can ensure that the agent does not get stuck when a feasible path exists between obstacles. The algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in a shared control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller on the onboard computer took 1 millisecond to evaluate over 30000 data points.
Fast Performance Evaluation of Linear Block Codes over Memoryless Continuous Channels
Venue: arXiv cs.IT 2022
Year: 2022

There are rising scenarios in communication systems, where the noises exhibit impulsive behavior and are not adequate to be modeled as the Gaussian distribution. The generalized Gaussian distribution instead is an effective model to describe real-world systems with impulsive noises. In this paper, the problem of efficiently evaluating the error performance of linear block codes over an additive white generalized Gaussian noise (AWGGN) channel is considered. The Monte Carlo (MC) simulation is a widely used but inefficient performance evaluation method, especially in the low error probability regime. As a variance-reduction technique, importance sampling (IS) can significantly reduce the sample size needed for reliable estimation based on a well-designed IS distribution. By deriving the optimal IS distribution on the one-dimensional space mapped from the observation space, we present a general framework to designing IS estimators for memoryless continuous channels. Specifically, for the AWGGN channel, we propose an $L_p$-norm-based minimum-variance IS estimator. As an efficiency measure, the asymptotic IS gain of the proposed estimator is derived in a multiple integral form as the signal-to-noise ratio tends to infinity. Specifically, for the Laplace and Gaussian noises, the gains can be derived in a one-dimensional integral form, which makes the numerical calculation affordable. In addition, by limiting the use of the union bound to an optimized $L_1$-norm sphere, we derive the sphere bound for the additive white Laplace noise channel. Simulation results verify the accuracy of the derived IS gain in predicting the efficiency of the proposed IS estimator.
Robust Optimization for Local Differential Privacy
Venue: arXiv cs.IT 2022
Year: 2022

We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimization problem. By deriving closed-form expressions for the duals of the constraints involved we obtain a convex optimization problem. We compare the performance of four possible optimization problems depending on whether or not we require robustness in i) utility and ii) privacy.
Accelerating the Training of Video Super-Resolution Models
Venue: arXiv cs.CV 2022
Year: 2022

Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for VSR? In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, i.e., in an easy-to-hard manner. In particular, the whole training is divided into several stages and the earlier stage has smaller training spatial shape. Inside each stage, the temporal size also varies from short to long while the spatial size remains unchanged. Training is accelerated by such a multigrid training strategy, as most of computation is performed on smaller spatial and shorter temporal shapes. For further acceleration with GPU parallelization, we also investigate the large minibatch training without the loss in accuracy. Extensive experiments demonstrate that our method is capable of largely speeding up training (up to $6.2\times$ speedup in wall-clock training time) without performance drop for various VSR models. The code is available at https://github.com/TencentARC/Efficient-VSR-Training.
Towards Climate Awareness in NLP Research
Venue: arXiv cs.CL 2022
Year: 2022

The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.
Bayesian Prior Learning via Neural Networks for Next-item Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Next-item prediction is a a popular problem in the recommender systems domain. As the name suggests, the task is to recommend subsequent items that a user would be interested in given contextual information and historical interaction data. In our paper, we model a general notion of context via a sequence of item interactions. We model the next item prediction problem using the Bayesian framework and capture the probability of appearance of a sequence through the posterior mean of the Beta distribution. We train two neural networks to accurately predict the alpha & beta parameter values of the Beta distribution. Our novel approach of combining black-box style neural networks, known to be suitable for function approximation with Bayesian estimation methods have resulted in an innovative method that outperforms various state-of-the-art baselines. We demonstrate the effectiveness of our method in two real world datasets. Our framework is an important step towards the goal of building privacy preserving recommender systems.
Developing cooperative policies for multi-stage reinforcement learning tasks
Venue: arXiv cs.LG 2022
Year: 2022

Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider the value of using skills that are cooperative instead of independent. This paper proposes the Cooperative Consecutive Policies (CCP) method of enabling consecutive agents to cooperatively solve long time horizon multi-stage tasks. This method is achieved by modifying the policy of each agent to maximise both the current and next agent's critic. Cooperatively maximising critics allows each agent to take actions that are beneficial for its task as well as subsequent tasks. Using this method in a multi-room maze domain and a peg in hole manipulation domain, the cooperative policies were able to outperform a set of naive policies, a single agent trained across the entire domain, as well as another sequential HRL algorithm.
User Guide for KOTE: Korean Online Comments Emotions Dataset
Venue: arXiv cs.CL 2022
Year: 2022

Sentiment analysis that classifies data into positive or negative has been dominantly used to recognize emotional aspects of texts, despite the deficit of thorough examination of emotional meanings. Recently, corpora labeled with more than just valence are built to exceed this limit. However, most Korean emotion corpora are small in the number of instances and cover a limited range of emotions. We introduce KOTE dataset. KOTE contains 50k (250k cases) Korean online comments, each of which is manually labeled for 43 emotion labels or one special label (NO EMOTION) by crowdsourcing (Ps = 3,048). The emotion taxonomy of the 43 emotions is systematically established by cluster analysis of Korean emotion concepts expressed on word embedding space. After explaining how KOTE is developed, we also discuss the results of finetuning and analysis for social discrimination in the corpus.
The Conflict Between Explainable and Accountable Decision-Making Algorithms
Venue: arXiv cs.CY 2022
Year: 2022

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility.
Open Problems in Fuzzing RESTful APIs: A Comparison of Tools
Venue: arXiv cs.SE 2022
Year: 2022

RESTful APIs are a type of web services that are widely used in industry. In the last few years, a lot of effort in the research community has been spent in designing novel techniques to automatically fuzz those APIs to find faults in them. Many real faults were automatically found in a large variety of RESTful APIs. However, usually the analyzed fuzzers treat the APIs as black-box, and no analysis of what is actually covered in these systems is done. Therefore, although these fuzzers are clearly useful for practitioners, we do not know what are their current limitations and actual effectiveness. Solving this is a necessary step to be able to design better, more efficient and effective techniques. To address this issue, in this paper we compare seven state-of-the-art fuzzers on 18 open-source and one industrial RESTful APIs. We then analyzed the source code of which parts of these APIs the fuzzers fail to generate tests for. This analysis points to clear limitations of these current fuzzers, listing concrete challenges for the research community to follow up on.
Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy
Venue: arXiv cs.LG 2022
Year: 2022

Sensors from smart consumer devices have demonstrated high potential to serve as digital biomarkers in the identification of movement disorders in recent years. With the usage of broadly available smartwatches we have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD). In total, 504 participants, including PD patients, differential diagnoses (DD) and healthy controls (HC), were captured with a comprehensive system utilizing two smartwatches and two smartphones. To the best of our knowledge, this study provided the largest PD sample size of two-hand synchronous smartwatch measurements. To establish a future easy-to use home-based assessment system in PD screening, we systematically evaluated the performance of the system based on a significantly reduced set of assessments with only one-sided measures and assessed, whether we can maintain classification accuracy.
AutoKE: An automatic knowledge embedding framework for scientific machine learning
Venue: arXiv cs.LG 2022
Year: 2022

Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, governing equations often have complex forms, including complex partial derivatives or stochastic physical fields, which results in significant inconveniences from the perspective of implementation. In this paper, a scientific machine learning framework, called AutoKE, is proposed, and a reservoir flow problem is taken as an instance to demonstrate that this framework can effectively automate the process of embedding physical knowledge. In AutoKE, an emulator comprised of deep neural networks (DNNs) is built for predicting the physical variables of interest. An arbitrarily complex equation can be parsed and automatically converted into a computational graph through the equation parser module, and the fitness of the emulator to the governing equation is evaluated via automatic differentiation. Furthermore, the fixed weights in the loss function are substituted with adaptive weights by incorporating the Lagrangian dual method. Neural architecture search (NAS) is also introduced into the AutoKE to select an optimal network architecture of the emulator according to the specific problem. Finally, we apply transfer learning to enhance the scalability of the emulator. In experiments, the framework is verified by a series of physical problems in which it can automatically embed physical knowledge into an emulator without heavy hand-coding. The results demonstrate that the emulator can not only make accurate predictions, but also be applied to similar problems with high efficiency via transfer learning.
Recommending Research Papers to Chemists: A Specialized Interface for Chemical Entity Exploration
Venue: arXiv cs.IR 2022
Year: 2022

Researchers and scientists increasingly rely on specialized information retrieval (IR) or recommendation systems (RS) to support them in their daily research tasks. Paper recommender systems are one such tool scientists use to stay on top of the ever-increasing number of academic publications in their field. Improving research paper recommender systems is an active research field. However, less research has focused on how the interfaces of research paper recommender systems can be tailored to suit the needs of different research domains. For example, in the field of biomedicine and chemistry, researchers are not only interested in textual relevance but may also want to discover or compare the contained chemical entity information found in a paper's full text. Existing recommender systems for academic literature do not support the discovery of this non-textual, but semantically valuable, chemical entity data. We present the first implementation of a specialized chemistry paper recommender system capable of visualizing the contained chemical structures, chemical formulae, and synonyms for chemical compounds within the document's full text. We review existing tools and related research in this field before describing the implementation of our ChemVis system. With the help of chemists, we are expanding the functionality of ChemVis, and will perform an evaluation of recommendation performance and usability in future work.
Experiments on Bipolar Transmission with Direct Detection
Venue: arXiv cs.IT 2022
Year: 2022

Achievable information rates of bipolar 4- and 8-ary constellations are experimentally compared to those of intensity modulation (IM) when using an oversampled direct detection receiver. The bipolar constellations gain up to 1.8 dB over their IM counterparts.
Raw Filtering of JSON Data on FPGAs
Venue: arXiv cs.DB 2022
Year: 2022

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of such formats is that an application may spend a significant amount of processing time just on unselectively parsing all data. To relax this issue, the concept of raw filtering is proposed with the idea to remove data from a stream prior to the costly parsing stage. However, as accurate filtering of raw data is often only possible after the data has been parsed, raw filters are designed to be approximate in the sense of allowing false-positives in order to be implemented efficiently. Contrary to previously proposed CPU-based raw filtering techniques that are restricted to string matching, we present FPGA-based primitives for filtering strings, numbers and also number ranges. In addition, a primitive respecting the basic structure of JSON data is proposed that can be used to further increase the accuracy of introduced raw filters. The proposed raw filter primitives are designed to allow for their composition according to a given filter expression of a query. Thus, complex raw filters can be created for FPGAs which enable a drastical decrease in the amount of generated false-positives, particularly for IoT workload. As there exists a trade-off between accuracy and resource consumption, we evaluate primitives as well as composed raw filters using different queries from the RiotBench benchmark. Our results show that up to 94.3% of the raw data can be filtered without producing any observed false-positives using only a few hundred LUTs.
Utilizing coarse-grained data in low-data settings for event extraction
Venue: arXiv cs.CL 2022
Year: 2022

Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtain, instead of annotating more documents. We utilize a multi-task model with two auxiliary tasks, document and sentence binary classification, in addition to the main task of token classification. We perform a series of experiments with varying data regimes for the aforementioned integration. Results show that while introducing extra coarse-grained data offers greater improvement and robustness, a gain is still possible with only the addition of negative documents that have no information on any event.
Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review
Venue: arXiv cs.CV 2022
Year: 2022

The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases like sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor-intensive, time-consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.