Reviewerly/exHarmony huggingface dataset:
Fast-Replanning Motion Control for Non-Holonomic Vehicles with Aborting A*

Autonomously driving vehicles must be able to navigate in dynamic and unpredictable environments in a collision-free manner. So far, this has only been partially achieved in driverless cars and warehouse installations where marked structures such as roads, lanes, and traffic signs simplify the motion planning and collision avoidance problem. We are presenting a new control approach for car-like vehicles that is based on an unprecedentedly fast-paced A* implementation that allows the control cycle to run at a frequency of 30 Hz. This frequency enables us to place our A* algorithm as a low-level replanning controller that is well suited for navigation and collision avoidance in virtually any dynamic environment. Due to an efficient heuristic consisting of rotate-translate-rotate motions laid out along the shortest path to the target, our Short-Term Aborting A* (STAA*) converges fast and can be aborted early in order to guarantee a high and steady control rate. While our STAA* expands states along the shortest path, it takes care of collision checking with the environment including predicted states of moving obstacles, and returns the best solution found when the computation time runs out. Despite the bounded computation time, our STAA* does not get trapped in corners due to the following of the shortest path. In simulated and real-robot experiments, we demonstrate that our control approach eliminates collisions almost entirely and is superior to an improved version of the Dynamic Window Approach with predictive collision avoidance capabilities [1].
A Study on Long Short Term Memory Algorithm in Sentiment Analysis with VietNamese Language

The increase in the number and content of electronic information sites such as Facebook, Twitter, Instagram$,\dots$ is not only a place to provide information in the form of events but also a place where users express their feelings and exchange information, feelings, experiences about life issues. Research in content mining comments to observe user reactions to make adjustments and improvements in organizations is being studied. However, how to build a good prediction algorithm on the Vietnamese data set is not simple because of Vietnamese polysemy and polymorphism. In this paper, the authors have developed the Long Short Term Memory algorithm - an extension of RNN to learn sequential data and long-term connections more accurately than RNNs standard. Using a dataset of Vietnamese comments from social networks collected by the research team together with a preprocessing, standardization, and labeling solution. The experimental results indicate the proposed model achieves an accuracy of 98.52%. From this experimental result, it is possible to further develop the research on the emotional analysis of Vietnamese sentences and can be applied in practice.
Learning-based Identification of Coding Best Practices from Software Documentation

Automatic identification of coding best practices can scale the development of code and application analyzers. We present Doc2BP, a deep learning tool to identify coding best practices in software documentation. Natural language descriptions are mapped to an informative embedding space, optimized under the dual objectives of binary and few shot classification. The binary objective powers general classification into known best practice categories using a deep learning classifier. The few shot objective facilitates example-based classification into novel categories by matching embeddings with user-provided examples at run-time, without having to retrain the underlying model. We analyze the effects of manually and synthetically labeled examples, context, and cross-domain information.We have applied Doc2BP to Java, Python, AWS Java SDK, and AWS CloudFormation documentations. With respect to prior works that primarily leverage keyword heuristics and our own parts of speech pattern baselines, we obtain 3-5% F1 score improvement for Java and Python, and 15-20% for AWS Java SDK and AWS CloudFormation. Experiments with four few shot use-cases show promising results (5-shot accuracy of 99%+ for Java NullPointerException and AWS Java metrics, 65% for AWS CloudFormation numerics, and 35% for Python best practices).Doc2BP has contributed new rules and improved specifications in Amazon's code and application analyzers: (a) 500+ new checks in cfn-lint, an open-source AWS CloudFormation linter, (b) over 97% automated coverage of metrics APIs and related practices in Amazon DevOps Guru, (c) support for nullable AWS APIs in Amazon CodeGuru's Java NullPointerException (NPE) detector, (d) 200+ new best practices for Java, Python, and respective AWS SDKs in Amazon CodeGuru, and (e) 2% reduction in false positives in Amazon CodeGuru's Java resource leak detector.
Development of a Lightweight Software Engineering Laboratory Management System Based on Scrum

The research is based on agile development framework of Scrum, and taking a laboratory management system with the lightweight design architecture of Spring Boot as an example. This study is devoted to the rapid development of this laboratory management system with the technology of ZigBee, and describes the feasible process of development and realization on the first Sprint for Scrum, which is combined with local environment. The feedback on application of experimental software engineering laboratory is positively. Finally, further work of development of a sub-system for experiment login and allocation that can be integrated through the third-party access control system and adding to elements of intelligent management which are reflected in laboratory management system are put forward in this paper.
SelfD: Self-Learning Large-Scale Driving Policies From the Web

Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on the internet can advance generalized intelligent systems, i.e., to robustly scale across perspectives, platforms, environmental conditions, scenarios, and geographical locations. However, it is difficult to directly leverage such large amounts of unlabeled and highly diverse datafor complex 3D reasoning and planning tasks. Consequently, researchers have primarily focused on its use for various auxiliary pixel- and image-level computer vision tasks that do not consider an ultimate navigational objective. In this work, we introduce SelfD, a framework for learning scalable driving by utilizing large amounts of online monocular images. Our key idea is to leverage iterative semi-supervised training when learning imitative agents from unlabeled data. To handle unconstrained viewpoints, scenes, and camera parameters, we train an image-based model that directly learns to plan in the Bird's Eye View (BEV) space. Next, we use unla-beled data to augment the decision-making knowledge and robustness of an initially trained model via self-training. In particular, we propose a pseudo-labeling step which enables making full use of highly diverse demonstration data through “hypothetical” planning-based data augmentation. We employ a large dataset of publicly available YouTube videos to train SelfD and comprehensively analyze its generalization benefits across challenging navigation scenarios. Without requiring any additional data collection or annotation efforts, SelfD demonstrates consistent improvements (by up to 24%) in driving performance evaluation on nuScenes, Argoverse, Waymo, and CARLA.
Open Source System Integration Towards Natural Interaction with Robots

Speech is an intuitive way to interact with social robots: spoken language dialogues can help users to express their intents in a natural and flexible manner. In recent years, there has been remarkable progress in artificial intelligence related to spoken dialogue technology, including speech recognition and natural language processing. In this paper, we present the integration of the open source speech recognition, natural language processing, and dialogue management components into a robot software platform, and also report on a preliminary experiment of the integrated system using real users. Gesturing of the robot, which is also important in human-robot interaction, is combined with the spoken content of the robot utterance and included in the dialogue management component. As the dialogue domain we chose mealtime discussions on food and recipes, since spoken communication with a companion robot in such scenarios is considered natural and useful.
End-to-End Visual Editing with a Generatively Pre-trained Artist

We consider the targeted image editing problem, namely blending a region in a source image with a driveg that specifiesthe desired change. Differently from prior works, we solve this problem by learning a conditional probability distribution of the edits, end-to-end in code space. Training such a model requires addressing the lack of example edits for training. To this end, we propose a self-supervised approach that simulates edits by augmenting off-the-shelf images in a target domain. The benefits are remarkable: implemented as a state-of-the-art auto-regressive transformer, our approach is simple, sidesteps difficulties with previous methods based on GAN-like priors, obtains significantly better edits, and is efficient. Furthermore, we show that different blending effects can be learned by an intuitive control of the augmentation process, with no other changes required to the model architecture. We demonstrate the superiority of this approach across several datasets in extensive quantitative and qualitative experiments, including human studies, significantly outperforming prior work.
On Mitigating Hard Clusters for Face Clustering

Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the heterogeneity, i.elet@tokeneonedot, high variations in size and sparsity, of the clusters. Consequently, the conventional way of using a uniform threshold (to identify clusters) often leads to a terrible misclassification for the samples that should belong to hard clusters. We tackle this problem by leveraging the neighborhood information of samples and inferring the cluster memberships (of samples) in a probabilistic way. We introduce two novel modules, Neighborhood-Diffusion-based Density (NDDe) and Transition-Probability-based Distance (TPDi), based on which we can simply apply the standard Density Peak Clustering algorithm with a uniform threshold. Our experiments on multiple benchmarks show that each module contributes to the final performance of our method, and by incorporating them into other advanced face clustering methods, these two modules can boost the performance of these methods to a new state-of-the-art. Code is available at: https://github.com/echoanran/On-Mitigating-Hard-Clusters .
AI-backed OCR in Healthcare

As revolutionary technology, scanning technology - like OCR - knows an attracting increasing interest in the medical system. In fact, scanning technology, driven by big data and machine learning, helps to drive successful change processes in healthcare. Nowadays, the OCR systems based on the promise of artificial intelligence can contribute to a greater understanding of entire processes by automating the medical transcription and in general mining information (largely handwritten) clinical notes. The project aims to create an OCR application for offline character recognition using deep learning (a combination of RNN and CNN) that can be used in the medical sector. Concerning problems of medical care, large data such as dispersed dataset, weak consistency, low electronic degree, and low visualization degree, a set of innovative image recognition, dataset, arrangement, and storage projects were put forward. The proposed pilot method achieves results comparable to current SoTA.
Ordinal Classification Using Single-Model Evidential Extreme Learning Machine

The extreme learning machine model for ordinal classification is extended to the uncertain case. Dealing with epistemic uncertainty by Dempster-Shafer theory, in this paper, the single-model multi-output extreme learning machine is learned from evidential training data. Taking both the uncertainty and the ordering relation of labels into consideration, given mass functions of training labels, different evidential encoding schemes for model output are proposed. On that basis, adopting the structure of a single extreme learning machine model with multiple output nodes, the construction procedure of evidential ordinal classification model is designed. According to the encoding mechanism and learning details, when there is no epistemic uncertainty in training labels, the proposed evidential ordinal method can be reduced to the traditional ordinal one. Experiments on artificial and UCI datasets illustrate the practical implementation and effectiveness of proposed evidential extreme learning machine for ordinal classification.
BI-CARU Feature Extraction for Semantic Analysis

Semantic analysis is an important part of the NLP task that enables computers to understand humans. In this paper, we present the feature extraction method of our results by applying a bidirectional RNN network with CARU units and filter the feature for each domain aspect word with major information extraction. We also developed a weighted layer to represent the combination of hidden states generated by two CARU layers. Finally, the output states are concatenated to an advanced self-weighting layer to obtain more accuracy. The experimental results show that the proposed method can slightly improve the accuracy and has great potential for improvement and analysis for any neural network using RNN architecture.
A Survey of Automatic Text Classification Based on Thai Social Media Data

In the digital age, the information on social media, such as Facebook, Twitter, and Instagram, is increasing rapidly. Therefore, it has led to studies and researches on social media analytics to extract useful models or knowledge from the data. One of the most interesting topics in social media analytics is text classification on social media data. However, since social media data has a diverse and complex data structure, text analysis and classification are considered a challenging issue that requires a specific technique to implement. The objective of this review paper is to collect and review research related to the automatic classification of Thai text on social media by presenting and explaining the process of text classification on various issues. These include data collection and data sources, amount of data and data preparation for research, feature extraction methods, text classification automated modeling methods, efficacy evaluation and measurement methods, the results of text classification, and summary of the overall trend of research on the topic.
A Real Time Super Resolution Accelerator with Tilted Layer Fusion

Deep learning based superresolution achieves high-quality results, but its heavy computational workload, large buffer, and high external memory bandwidth inhibit its usage in mobile devices. To solve the above issues, this paper proposes a real-time hardware accelerator with the tilted layer fusion method that reduces the external DRAM bandwidth by 92% and just needs 102KB on-chip memory. The design implemented with a 40nm CMOS process achieves 1920xl080@60fps throughput with 544. 3K gate count when running at 600MHz; it has higher throughput and lower area cost than previous designs.
Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation

In recent years, the deep learning method based on fully convolution networks has proven to be an effective method for the semantic segmentation of remote sensing images (RSIs). However, the rich information and complex content of RSIs make networks training for segmentation more challenging. Specifically, the observing distance between the space-borne cameras and the ground objects is extraordinarily far, resulting in that some smaller objects only occupy a few pixels in the image. However, due to the rapid degeneration of tiny objects during the training process, most algorithms cannot properly handle these common small objects in RSIs with satisfactory results. In this article, we propose a novel feature guide network with a context aggregation pyramid (CAP) for RSIs segmentation to conquer these issues. An innovative edge-guide feature transform module is designed to take advantage of the edge and body information of objects to strengthen edge contours and the internal consistency in homogeneous regions, which can explicitly enhance the representation of tiny objects and relieve the degradation of small objects. Furthermore, we design a CAP pooling strategy to adaptively capture optimal feature characterization that can assemble multiscale features according to the significance of different contexts. Extensive experiments on three large-scale remote sensing datasets demonstrate that our method not only can outperform the state-of-the-art methods for objects of different scales but can also achieve robust segmentation results, especially for tiny objects.
Which bugs are missed in code reviews

In pull-based development systems, code reviews and pull request comments play important roles in improving code quality. In such systems, reviewers attempt to carefully check a piece of code by different unit tests. Unfortunately, sometimes they miss bugs in their review of pull requests, which lead to quality degradations of the systems. In other words, disastrous consequences occur when bugs are observed after merging the pull requests. The lack of a concrete understanding of these bugs led us to investigate and categorize them. In this research, we try to identify missed bugs in pull requests of SmartSHARK dataset projects. Our contribution is twofold. First, we hypothesized merged pull requests that have code reviews, code review comments,or pull request comments after merging, may have missed bugs after the code review. We considered these merged pull requests as candidate pull requests having missed bugs. Based on our assumption, we obtained 3,261 candidate pull requests from 77 open-source GitHub projects. After two rounds of restrictive manual analysis, we found 187 bugs missed in 173 pull requests. In the first step, we found 224 buggy pull requests containing missed bugs after merging the pull requests. Secondly, we defined and finalized a taxonomy that is appropriate for the bugs that we found and then found the distribution of bug categories after analysing those pull requests all over again. The categories of missed bugs in pull requests and their distributions are: semantic (51.34%), build (15.5%), analysis checks (9.09%), compatibility (7.49%), concurrency (4.28%), configuration (4.28%), GUI (2.14%), API (2.14%), security (2.14%), and memory (1.6%).
Impact of API Rate Limit on Reliability of Microservices-Based Architectures

Many API patterns and best practices have been developed around microservices-based architectures, such as Rate Limiting and Circuit Breaking, to increase quality properties such as reliability, availability, scalability, and performance. Even though estimates on such properties would be beneficial, especially during the early design of such architectures, the real impact of the patterns on these properties has not been rigorously studied yet. This paper focuses on API Rate Limit and its impact on reliability properties from the perspective of API clients. We present an analytical model that considers specific workload configurations and predefined rate limits and then accurately predicts the success and failure rates of the back-end services. The model also presents a method for adaptively fine-tuning rate limits. We performed two extensive data experiments to validate the model and measured Rate Limiting impacts, firstly on a private cloud to minimize latency and other biases, and secondly on the Google Cloud Platform to test our model in a realistic cloud environment. In both experiments, we observed a low percentage of prediction errors. Thus, we conclude that our model can provide distributed system engineers and architects with insights into an acceptable value for the rate limits to choose for a given workload. Very few works empirically studied the impact of Rate Limit or similar API-related patterns on reliability.
Multi-granularity Distillation Scheme Towards Lightweight Semi-supervised Semantic Segmentation

Albeit with varying degrees of progress in the field of Semi-Supervised Semantic Segmentation, most of its recent successes are involved in unwieldy models and the lightweight solution is still not yet explored. We find that existing knowledge distillation techniques pay more attention to pixel-level concepts from labeled data, which fails to take more informative cues within unlabeled data into account. Consequently, we offer the first attempt to provide lightweight SSSS models via a novel multi-granularity distillation (MGD) scheme, where multi-granularity is captured from three aspects: i) complementary teacher structure; ii) labeled-unlabeled data cooperative distillation; iii) hierarchical and multi-levels loss setting. Specifically, MGD is formulated as a labeled-unlabeled data cooperative distillation scheme, which helps to take full advantage of diverse data characteristics that are essential in the semi-supervised setting. Image-level semantic-sensitive loss, region-level content-aware loss, and pixel-level consistency loss are set up to enrich hierarchical distillation abstraction via structurally complementary teachers. Experimental results on PASCAL VOC2012 and Cityscapes reveal that MGD can outperform the competitive approaches by a large margin under diverse partition protocols. For example, the performance of ResNet-18 and MobileNet-v2 backbone is boosted by 11.5% and 4.6% respectively under 1/16 partition protocol on Cityscapes. Although the FLOPs of the model backbone is compressed by 3.4–5.3 $$\times $$ (ResNet-18) and 38.7–59.6 $$\times $$ (MobileNetv2), the model manages to achieve satisfactory segmentation results.
The most efficient indifferentiable hashing to elliptic curves of<i>j</i>-invariant 1728

Abstract This article makes an important contribution to solving the long-standing problem of whether all elliptic curves can be equipped with a hash function (indifferentiable from a random oracle) whose running time amounts to one exponentiation in the basic finite field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi mathvariant="double-struck">F</m:mi></m:mrow><m:mrow><m:mi>q</m:mi></m:mrow></m:msub></m:math> {{\mathbb{F}}}_{q} . More precisely, we construct a new indifferentiable hash function to any ordinary elliptic <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi mathvariant="double-struck">F</m:mi></m:mrow><m:mrow><m:mi>q</m:mi></m:mrow></m:msub></m:math> {{\mathbb{F}}}_{q} -curve <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi>E</m:mi></m:mrow><m:mrow><m:mi>a</m:mi></m:mrow></m:msub></m:math> {E}_{a} of j -invariant 1728 with the cost of extracting one quartic root in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi mathvariant="double-struck">F</m:mi></m:mrow><m:mrow><m:mi>q</m:mi></m:mrow></m:msub></m:math> {{\mathbb{F}}}_{q} . As is known, the latter operation is equivalent to one exponentiation in finite fields with which we deal in practice. In comparison, the previous fastest random oracles to <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi>E</m:mi></m:mrow><m:mrow><m:mi>a</m:mi></m:mrow></m:msub></m:math> {E}_{a} require to perform two exponentiations in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:msub><m:mrow><m:mi mathvariant="double-struck">F</m:mi></m:mrow><m:mrow><m:mi>q</m:mi></m:mrow></m:msub></m:math> {{\mathbb{F}}}_{q} . Since it is highly unlikely that there is a hash function to an elliptic curve without any exponentiations at all (even if it is supersingular), the new result seems to be unimprovable.