Reviewerly/exHarmony huggingface dataset:
Model Free Adaptive Control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine (ReOSELM) is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.
A New Compression Method for Deep Neural Networks with Accuracy Improvement

Deep neural networks have achieved great success in many fields, but deploying deep neural networks on mobile devices with low memory resources or in applications with strict latency requirements is difficult. The networks are both computationally and memory-intensive, which hinders deployment in mobile computing environments. To address this problem, a new compression method for deep neural networks is proposed in this paper. First, the standard convolution is replaced with depthwise separable convolution to simplify the calculation and decrease the number of parameters of the convolution layers. Then, the deep networks are pruned by removing unimportant connections. Finally, weight quantization is performed to compress the network further based on the weight sharing applied using the shared weights obtained from kernel k-means clustering. Several experiments are completed to verify the viability and effectiveness of the proposed method. The experimental results show that deep neural networks can be compressed effectively with improvement in accuracy. On the MNIST and ImageNet datasets, our method reduces the storage required by the LeNet, AlexNet and VGG-16 networks by 33× to 45×. Reducing the storage requirement makes the deployment of deep neural networks possible on mobile systems where application size and download bandwidth are constrained.
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis

Documents are central to many business systems, and include forms, reports, contracts, invoices or purchase orders. The information in documents is typically in natural language, but can be organized in various layouts and formats. There have been recent spurt of interest in understanding document content with novel deep learning architectures. However, document understanding tasks need dense information annotations, which are costly to scale and generalize. Several active learning techniques have been proposed to reduce the overall budget of annotation while maintaining the performance of the underlying deep learning model. In this paper, we propose OPAD, a novel framework using reinforcement policy for active learning in content detection tasks for documents. The proposed framework learns the acquisition function to decide the samples to be selected while optimizing performance metrics that the tasks typically have. Furthermore, we extend to weak labelling scenarios to further reduce the cost of annotation significantly. We propose novel rewards to account for class imbalance and user feedback in the annotation interface, to improve the active learning method. We show superior performance of the proposed OPAD framework for active learning for various tasks related to document understanding like layout parsing, object detection and named entity recognition. Ablation studies for human feedback and class imbalance rewards are presented, along with a comparison of annotation times for different approaches.
Transactional Business Application Based on Microservice Architecture

This paper discusses a transactional business application based on microservice architecture. The main advantages and disadvantages of monolithic and microservice architecture are analyzed. A mathematical model of integration of data of information systems with inhomogeneous ontological specifications is developed.A software system for data integration of information systems has been developed, which implements the proposed models, methods and algorithms. The architecture is built, which allows to work optimally with several data sources. The practical application of the obtained business application is demonstrated on the example of a company that provides centralized gas supply services.
Deep Learning Empowered Automatic Bone Age Assessment

Skeletal bone age assessment (BAA) is a commonly employed clinical practice used for the diagnosis of endocrine and metabolic illness in child growth. BAA approach generally starts with the acquisition of the X ray image of the left hand from the wrist to fingertip. The bones in the X ray image undergo comparison with the radiological images that exist in the standard atlas of bone development. Since manual methods are time consuming and erroneous, the recently developed deep learning (DL) models find useful in the design of automated BAA using X ray images. In this view, this paper presents a new DL empowered automated BAA (DL-ABAA) model using X ray images. The proposed DL-ABAA model performs initial preprocessing to improve the image quality. Followed by Fast region convolutional neural network (Fast-RCNN) with VGG-19 model-based feature extractor is involved for deriving the features from the input X ray images. At the same time, shuffled frog leaf optimization (SFLO) algorithm is utilized as a hyperparameter optimizer of the VGG-19 model. In addition, softmax (SM) based age prediction and extreme gradient boosting (XGBoost) based stage classification processes are applied to predict the age and determine the class labels. A detailed experimental results analysis stated the improved performance of the BAA technique over the recent approaches with the higher accuracy of 96.53%.
A hierarchical residual network with compact triplet-center loss for sketch recognition

With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognition task becomes more significant than before. To accomplish this task, it is necessary to solve the critical issue of improving the distinction of the sketch features. To this end, we have made efforts in three aspects. First, a novel multi-scale residual block is designed. Compared with the conventional basic residual block, it can better perceive multi-scale information and reduce the number of parameters during training. Second, a hierarchical residual structure is built by stacking multi-scale residual blocks in a specific way. In contrast with the single-level residual structure, the learned features from this structure are more sufficient. Last but not least, the compact triplet-center loss is proposed specifically for the sketch recognition task. It can solve the problem that the triplet-center loss does not fully consider too large intra-class space and too small inter-class space in sketch field. By studying the above modules, a hierarchical residual network as a whole is proposed for sketch recognition and evaluated on Tu-Berlin benchmark thoroughly. The experimental results show that the proposed network outperforms most of baseline methods and it is excellent among non-sequential models at present.
Generative strategy based backdoor attacks to 3D point clouds

3D deep learning has been applied in safety-critical scenarios, e.g., autonomous driving. Several works have raised the security problems of 3D deep learnings mainly from the perspective of adversarial attacks. In this paper, we propose a novel backdoor attack method to threaten 3D deep learning without the original training data. Several neurons are selected and made sensitive to backdoor triggers. The backdoor triggers are generated by reversing neural network, and the shape of which is constrained to map the objects in the physical world. Sufficient training data can be also generated by reverse engineering. Finally, retraining with the generated 3D trigger and training data is applied to inject backdoors, which is in no need of accessing the original training process and data.
Relation Prediction for Answering Natural Language Questions over knowledge Graphs

Large-scale knowledge graphs (KGs) play a critical role in question answering over KGs (KGs-QA). Despite of large scale, KGs suffer from incompleteness which results in the low-precision of KGs-QA. Since existing researches of relation prediction process each triple independently, the hidden relations which are inherently present in the neighborhood can not be captured. Complementarily, to capture both entity and relation features in a given entity's neighborhood, an entity importance estimation network of attention-based graph embedding is proposed, which consists of the attention-based graph embedding module and the entity importance estimation module. The new embedding of an entity from its n-hop neighbor is learned by an attention-based graph embedding module, and then the learned embedding is integrated into the entity importance estimation module to find entities of high importance in n-hop neighbors of the central entity. Multi-hop relations are encapsulated and an auxiliary edge of n-hop neighbors is introduced, which realizes the relation prediction task. In addition, we are the first to realize KGs-QA while realizing relation prediction, which alleviates the phenomenon of missing relations and the low-precision problem of KGs-QA. As expected, KGs-QA's F1 increases significantly when relation prediction is invoked. In 80% missing relation, we observe a F1 increase of 21, 17, 18 and 16 points over the no-relation prediction configuration for SQ, WQ, GQ and QALD-5, respectively.
Robust Collision Warning System based on Multi Objects Distance Estimation

The annual number of road deaths is still increasing, especially in less developed and developing countries. Road accidents are the 5th cause of death and the leading reason for death among young people between 5 and 29 years of age in 2030. In this study, a robust solution is implemented by integrating object recognition with distance estimation to maximize driving safety. The proposed system will be able to detect common objects within the region of interest on the road and estimate how far these objects are from the camera position. The system will trigger an alarm to attract the driver’s attention in real time when the distance to one of the detected objects is less than a predefined threshold value. In this work YOLO (You Only Look Once) approach is used to detect the objects in real time and the properties of the depth map based on deep learning is applied to estimate the distance at a given point.
Robust Facial Manipulation Detection via Domain Generalization

Face generation and forgery algorithms are available on the Internet, which promotes facial manipulation detection to be an important topic. Recently, many methods have been presented to detect facial manipulation images and videos. Most of which focus on specific datasets and achieve promising results on them. However, it is hard for them to detect the facial images manipulated by unknown face synthesis algorithms. In this paper, we present a method to improve the generalization ability of the detection models using one class domain generalization. Unlike the methods using datasets to train deep neural networks directly, we propose to shape the problem to domain generalization. The images manipulated by different algorithms are regarded as different domains. To obtain domain-invariant features, we take the fake facial images from multiple domains into the domain discriminator for domain adversarial training. The models can discriminate between the real and fake facial images from different domains, even the fake images generated by unknown algorithms. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding performance and improves the robustness of the detection models.
Prototype for Integration of colorization and super resolution model using machine learning

Colorization is the process of converting black and white images to color digitally. On the other hand, super resolution is the process of enhancing the quality of low-resolution pictures by converting them into high resolution, while maintaining the original details of the picture. The idea behind our implementation is to combine these two machine learning models. The combined model will take in old, blurry, hazed images as input and produce colorized, detailed and high-resolution image as an output. This research work has utilized various machine learning and computer vision techniques to accomplish this task. For colorization, classification based deep learning model was utilized. For super resolution, Post-up-sampling convolutional neural networks have been used.
An Empirical Framework for Bangla Word Sense Disambiguation Using Statistical Approach

Recently, word sense disambiguation has gained increased attention by NLP practitioner due to its various potential applications in language technology. This paper proposes a Naïve Bayes classifier for resolving lexical ambiguities of Bangla words with the help of a Bangla sense annotated corpus. At the initial stage, a Bangla sense annotated corpus is generated from a raw text corpus for serving as a training dataset. For a given input Bangla sentence, ambiguous words detection is done first and then Bayes probability theorem is applied to calculate the posterior probability that an ambiguous word belongs to a particular sense class. The values of posterior probability of several senses of the detected ambiguous word finally train the Naïve Bayes classifier to classify a closest sense of the ambiguous word. Experimental outcome reveals that the proposed method outdoes existing techniques by achieving the highest F1-score of \(90\%\) on the test data.
Identification of best discrimination surface by mixed-integer semi-definite programming for support vector machine

This paper proposes two improvements to the support vector machine (SVM): (i) extension to a semi-positive definite quadratic surface, which improves the discrimination accuracy; (ii) addition of a variable selection constraint. However, this model is formulated as a mixed-integer semi-definite programming (MISDP) problem, and it cannot be solved easily. Therefore, we propose a heuristic algorithm for solving the MISDP problem efficiently and show its effectiveness by using corporate credit rating data.
SCPL: A Markup Language for Source Code Patterns Localization

Context: Static analysis tools (SATs) have become commonly used to locate defects in source code. Many SATs allow for development of custom static analysis rules, which intend to help users to find application-specific defects. However, custom static analysis rules are not well adopted in practice. One possible reason for the low adoption is the difficulty to develop custom rules. Aims: In this paper, we present Source Code Pattern Language (SCPL), a pattern-finding language which uses markups in code examples, instead of more complex abstractions, to facilitate development of custom static analysis rules. Method: SCPL uses markups in the source code and tree isomorphism methods to locate the patterns. In order to perform a proof-of-concept, we developed custom static analysis rules to automatically check four GoF design patterns. Result: We spent approximately 30 man-hours to develop 33 custom static analysis rules that automatically check the four design patterns. Conclusion: SCPL provide a rich feature set and potentially facilitates the programming of custom static analysis rules by using markups directly in source code. Video: https://youtu.be/B-Ovi3zurnM
A Monocular Reflection-Free Head-Mounted 3D Eye Tracking System

Head-mounted eye tracking has significant potential for gaze baesd application such as consumer attention monitoring, human-computer interaction, or virtual reality (VR). Existing methods, however, either use pupil center-corneal reflection (PCCR) vectors as gaze directions or require complex hardware setups and use average physiological parameters of the eye to obtain gaze directions. In view of this situation, we propose a novel method which uses only a single camera to obtain gaze direction by fitting a 3D eye model based on the motion trajectory of pupil contour. Then a 3D to 2D mapping model is proposed based on the fitting model, so the complex structure of hardware and the use of average parameters for the eyes are avoided. The experimental results show that the method can improve the gaze accuracy and simplify the hardware structure.
Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games

Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic games or dynamic games). Most of the existing literature on MARL concentrates on zero-sum Markov games but is not applicable to general-sum Markov games. It is known that the best response dynamics in general-sum Markov games are not a contraction. Therefore, different equilibria in general-sum Markov games can have different values. Moreover, the Q-function is not sufficient to completely characterize the equilibrium. Given these challenges, model-based learning is an attractive approach for MARL in general-sum Markov games. In this paper, we investigate the fundamental question of sample complexity for model-based MARL algorithms in general-sum Markov games. We show two results. We first use Hoeffding inequality-based bounds to show that $$\tilde{{\mathcal {O}}}( (1-\gamma )^{-4} \alpha ^{-2})$$ samples per state–action pair are sufficient to obtain a $$\alpha $$ -approximate Markov perfect equilibrium with high probability, where $$\gamma $$ is the discount factor, and the $$\tilde{{\mathcal {O}}}(\cdot )$$ notation hides logarithmic terms. We then use Bernstein inequality-based bounds to show that $$\tilde{{\mathcal {O}}}( (1-\gamma )^{-1} \alpha ^{-2} )$$ samples are sufficient. To obtain these results, we study the robustness of Markov perfect equilibrium to model approximations. We show that the Markov perfect equilibrium of an approximate (or perturbed) game is always an approximate Markov perfect equilibrium of the original game and provide explicit bounds on the approximation error. We illustrate the results via a numerical example.
Chinese short text classification method based on word embedding and Long Short-Term Memory Neural Network

The value of Internet short texts is increasingly prominent, and traditional classification methods cannot be applied to short texts with weak feature expression. In this regard, this paper proposes a Chinese Short Text Classification method based on Word Embedding and LSTM with feature enhancement (hereinafter called CSTCFE-WE-LSTM). This method uses word embedding learned from Wikipedia corpus as initial features for the model, and then uses category factors and TF-IDF to generate weights to enhance features. Finally, it uses a 6-layer neural network for classification, which includes a word embedding layer, two LSTM layers, a Dropout layer, and two fully connected layers. In order to verify the method CSTCFE-WE-LSTM, we collected short text sets on 3 topics, and reached following conclusions: 1. In the best model, P, F and other indicators are better than the classifier of using Wikipedia word embedding and KNN. 2. For texts with sentences average length less than 10 words, the effect of two-layer LSTM is better than that of single-layer LSTM, and the effect is better when the number of single-layer nodes is 50. 3. The effect of feature enhancement is better for the health category than the commercial category.
Action Recognition Algorithm based on 2D Human Pose Estimation Method

This paper presents an action recognition method based on 2D human body node data in video. This method uses the pose estimation algorithm to detect the human body node data in each frame of video information. We get the two-dimensional coordinates and confidence data of the nodes, and optimize the arrangement of these data into a 3D array form similar to images. Finally, we use the classical two-dimensional convolutional neural network to carry out classification training. The test on UCF-101 data set shows that this method can indeed improve the accuracy of action recognition based on RGB information to a certain extent, and reduce the training cost.
Gaffer: Cloud Computing based Serverless Orchestration Framework for Unprecedented Workflow

Modern serverless orchestrations are based on either simple step chaining or function invocations. This raises the challenges of implementing high-performance systems. In the context of this, the paper explores how a system can be structured to scale across cross-platform infrastructures by design. This research work has proposed a framework that can be used under circumstances where heavy computation with data consistent results are demanded from the serverless networks, in which the instances depend upon multiple nested servers and the flow of the data is unprecedented without worrying about concurrency and function invocation limitation resulting in zero throttling of requests on the service. This means that the function that needs to be triggered next will be decided by the present function which can demand multiple arguments to run that are to be provided by some other functions. The algorithm is based on the Gaffer Framework which is designed to take care of resource allocation, maintaining the function(servers) level order dependency also sub-level dependency, and resource integration using event-driven semaphore signals. The framework proposed in this paper demonstrates high coordination-free consistency along with the performance that exceeds the current orchestrations. Gaffer Framework run-time system also takes care of distributed data partitioning and efficient handling of machine failures. This makes it simple for programmers with little or no expertise with serverless and distributed systems to create and maintain large interconnected serverless containers.
Research on Data Integrity Verification Technology Based on Blockchain

With the rapid development of Internet of Things technology, it has changed People's Daily production and life to a great extent, but the large number of applications of Internet of Things devices affect the security level of data to a certain extent. In order to ensure data integrity in the Internet of Things environment, it is necessary to conduct in-depth research on blockchain-based data integrity verification technology, analyze the authenticity structure based on data sources, and carry out data integrity verification exploration according to data source identification structure. The experimental system for data integrity verification is proposed. In this way, the effectiveness of blockchain-based data integrity verification technology can be guaranteed, and the integrity of data can be guaranteed under the condition of decentralization, while the scattered storage of data and data privacy can be protected.