Reviewerly/exHarmony huggingface dataset:
Dynamic Scheduling Algorithm Based on Random Distribution for Mimic Defense System

DHR architecture could address threats from unknown vulnerabilities and backdoors effectively without prior knowledge. Compared to traditional defense techniques, the security of DHR is based on its dynamic and heterogeneous nature. In this paper, we discuss refining the scheduling algorithm to improve the dynamic and heterogeneous nature of the DHR architecture and propose the RDWC algorithm based on random distribution and weighting coefficients. Simulation shows that the algorithm can significantly reduce the average execution time of variants containing vulnerabilities.
Linguistic functionality of Uzbek Electron Corpus: uzbekcorpus.uz

The paper is devoted to the structure and its linguistic annotation for building Uzbek Corpus. Linguistic annotation, metadata and corpus manager as formal-functional model of the corpus are important for usage for many purposes. The fact that the platform allows users to address language and literature issues, use it online. The Uzbek corpus based on structural and sub corpus models, which partially represented in this paper, is going on process to develop Uzbek language technology.
Net Traffic Classification Based on GRU Network Using Sequential Features

Net traffic classification is the basis for providing various network services such as network security, network monitoring and Quality of Service (QoS) etc. Therefore, this field has always been a hot spot in academic and industrial research. Through proper data processing, the researcher said that it is possible to classify network flows through machine learning. Therefore, it is necessary to explore suitable processing methods and model structures. To our best knowledge, classification methods based on sequential feature learning are rarely discussed, so this paper proposes a model based on sequential features of net traffic. Different from the previous classification methods based on machine learning, the classification method based on GRU network focuses on exploring the sequential feature information of network traffic. This classification model based on deep learning is very suitable for big data processing. In terms of evaluation, we used the USTC-TFC2016 data set, compared with the basic model and previous methods, the experimental results show that: (1) the effectiveness of the sequential model for net traffic classification. (2) The sequential model has good performance in both accuracy and stability.
Evaluation of Quantization Techniques for Deep Neural Networks

Over the last few years, different quantization techniques were proposed for Deep Neural Networks (DNNs). This paper makes an overview of some popular and efficient methods. The main purpose of quantization is to help DNNs’ deployment on embedded system, and we divide techniques into two categories: Post-training Quantization and Quantization-aware training. In addition, we also compare the results of different methods on two representative networks in DNNs: Resnet and Mobilenet, and analyse some ablation study. Further more, the remaining questions and future direction are summarized to boost the development of quantization method.
Simulation of the electroerosion treatment process using neural networks

The paper provides formalization and construction of a model of the process of electrical discharge machining. When describing the process, a T-shaped equivalent circuit containing an RLC circuit was used. Determine the transfer function of the proposed substitution scheme. Also, a task is formulated and an algorithm for neural network parametric identification of a T-shaped equivalent circuit is proposed. The problem is posed and an algorithm is developed for neural network parametric identification of the equivalent circuit with a computational experiment, the formation of training samples on its basis, and the subsequent training of dynamic and static neural networks used in the identification problem. The process was simulated in Simulink, Matlab package. Acceptable coincidence of the calculated data with the experimental ones showed that the proposed model of electrical discharge machining reflects real electromagnetic processes occurring in the interelectrode gap.
Multi-Scale Graph Convolutional Network and Dynamic Iterative Class Loss for Ship Segmentation in Remote Sensing Images

The accuracy of the semantic segmentation results of ships is of great significance to coastline navigation, resource management, and territorial protection. Although the ship semantic segmentation method based on deep learning has made great progress, there is still the problem of not exploring the correlation between the targets. In order to avoid the above problems, this paper designed a multi-scale graph convolutional network and dynamic iterative class loss for ship segmentation in remote sensing images to generate more accurate segmentation results. Based on DeepLabv3+, our network uses deep convolutional networks and atrous convolutions for multi-scale feature extraction. In particular, for multi-scale semantic features, we propose to construct a Multi-Scale Graph Convolution Network (MSGCN) to introduce semantic correlation information for pixel feature learning by GCN, which enhances the segmentation result of ship objects. In addition, we propose a Dynamic Iterative Class Loss (DICL) based on iterative batch-wise class rectification instead of pre-computing the fixed weights over the whole dataset, which solves the problem of imbalance between positive and negative samples. We compared the proposed algorithm with the most advanced deep learning target detection methods and ship detection methods and proved the superiority of our method. On a High-Resolution SAR Images Dataset [1], ship detection and instance segmentation can be implemented well.
Entropy, Chaos, and Language

Natural language is a rich source of information, with a complex structure and a kaleidoscope of contents. This arises from the flexibility that living languages exhibit in order to reflect the speaker's intention, and also due to communication needs. From computational linguistics, multiple strategies have been developed that allow detecting and interpreting textual contents, but there is an uncovered margin, an interpretive range that remains outside the scope of automatic processing, and that requires rethinking the scope and perspectives of these tools. This chapter aims to show this gap and its implications, exploring dialectical and technical reasons. It also proposes a new perspective of interpretation and scope of textual processing, a sort of thermodynamics of productions that involve the communication of certain types. As part of the scope, there is a bibliographic analysis and a statistical and heuristic exploration of the proposal applied to 20Q game.
How Shrinking Gradient Noise Helps the Performance of Neural Networks

In the big data era, neural networks are the workhorse behind many applications. As an increasing size of training data are available, training complex architectures such as neural networks, is possible. Adding gradient noise has been widely used in training models with gradient descent, for example, in non-convex optimization and robust training. Previous works have experimentally shown that adding suitable step-wise shrinking noise will promote the test performance of highly over-parameterized neural networks. However, it is not well understood as to why adding suitable step-wise shrinking noise can help the test performance. In this paper, we study the role of gradient noise towards understanding such phenomenon. Motivated by the observation that there is an inherent tension between training accuracy and generalization ability due to the size and shrinkage rate of the added noise, we study the training dynamics and generalization gaps of noisy gradient descent of shallow and wide neural networks. By introducing a time dependent kernel and theoretically characterizing such inherent tension, our analysis sheds light on why gradient Langevin dynamics cannot reach good test accuracy, when compared with training without noise, while suitably shrinking gradient noise can yield good and even better test accuracy.
Robots Learn Visual Pouring Task Using Deep Reinforcement Learning with Minimal Human Effort

In this paper, the entire pipeline for teaching a robot to perform the visual pouring task is addressed with a minimal human supervision. In this task the robot’s arm that carrying a glass, and the target container are initialized in an arbitrary position. The reinforcement learning approach is employed without the use of reward engineering where the reward function is approximated by a Convolutional Neural Network. This network, which is trained on a little set of goal images (positive samples), takes a raw RGB image as input. In data gathering step, only goal conditions are established by an expert, and positive samples are collected in a self-supervised manner. The major contribution of this paper consists in using only the RGB image, without 3D analysis which is not possible by classical approaches, and self-supervised positive sample gathering which has not been addressed in recent RL studies. Moreover, the deep metric learning approach is utilized to train the reward network in order to obtain increased robustness against unseen negative samples. The foregoing is applied since this approach classifies the negative samples as ignore class and prevents the network from being fooled by the RL algorithm. Soft Actor-Critic is the RL algorithm employed in this study. A fixed monocular camera is the single sensor used for perception and RGB image is used as the representation of the state for action selection, value estimation, and input of reward network. The action-space is defined such a way that reduces convergence time, and in this approach, the policy network generates the desired 3D position in each step. This method of defining the action-space resulted in a reduction in training steps from 100k-1M to 1k. The provided method is compared to joint-based action-space and sparse-reward to validate the good effect of this combination of action-space and reward , i.e., position-based action-space, and continuous reward. Some experiments are designed as case studies in which a NAO H25 robot interacts with the environment and performs the visual pouring task. A Coppeliasim simulation environment is used for both training and evaluation and the proposed method achieved the succsess rate 93.7 after 1k episodes for the approaching phase of visual pouring task.
Boosting the Training Time of Weakly Coordinated Distributed Machine Learning

In this paper, we propose a novel communication-efficient algorithm for distributed matrix factorisation. Our goal is to find a good trade-off between the communication overhead and the overall model training time. In our setting, the training data is distributed across multiple servers that aim to learn a joint machine learning model. In contrast to standard distributed computation, due to privacy concerns, the participating servers are not allowed to share raw data, however, sharing of the non-personal model parameters is allowed. We investigate the draw-backs of traditional strongly coordinated distributed techniques and compare them to weakly coordinated gossip approaches. The advantage of strongly coordinated approaches is that the learning process closely mimics that of a centralised algorithm and hence this approach can keep the overall training time at a minimum. However, this is at the expense of a large communication footprint of the algorithm. On the other hand, the weakly coordinated gossip approach offers a communication efficient solution that can take a large amount of training time to reach a good accuracy. As a solution, we develop a hybrid approach combining the above two approaches. We apply the hybrid approach on a latent factor model solving a top-N recommendation problem and we show that the hybrid approach achieves good accuracy in relatively short training time with minimal communication overhead particularly on very sparse data.
Listening to the Crowd for the Change File Localization of Mobile Apps

In order to allow developers to implement operable code-level modification tasks based on user feedback directly, so as to achieve rapid and continuous app updates and releases. We propose an efficient automated approach named LCFCR, which leverages natural language processing and clustering algorithms to group user reviews. Then, it enriches the semantic information of each group. Further, by combining the textual information from both commit messages and source code, it automatically localizes potential change files. We have evaluated the LCFCR on 10 open source mobile apps. The experiments demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.
Evaluation of the volleyball coaches’ experience in a 3-dimensions environment

The new technologies in sport can help to reproduce a real game context. Therefore, we aim to characterize the experience of volleyball coaches immerged in a 3D volleyball game to see if the device could be a credible tool in a coaching training program. Seventeen volleyball coaches were immerged into three sequences of a 3D volleyball game with Oculus Rift headset before completing a perceptions’ questionnaire composed by 33 items concerning users’ experience factors and 3 open-ended questions. The results show positive scores for the immersion ( M = 6.49, SD = 2.84), positive emotions ( M = 5.24, SD = 2.69), judgment ( M = 7.70, SD = 1.82) and flow ( M = 5.83, SD = 2.75) even if this last result has to be taken carefully. The answers to the open-ended questions confirm those data. Despite some limitations, the link between the results and some important notions of the behaviour change as the intrinsic motivation, the self-determination theory and the learning outcomes seems to emphasize the benefits of an implementation of the device into a coaching training program. Nevertheless, this use has to be limited to the training of observations of the environment rather than the intervention training because of the lack of control on the environment and interaction allows by the 3D device.
An Improved Canonical Correlation Analysis Method with Adaptive Graph Learning

Graph learning describes the local structure information hidden in samples by adjacent matrices and the key step is determining the nearest samples where the local and sparse preserving methods are widely adopted. Inspired by the adaptive neighbor searching strategy in clustering tasks, an improved canonical correlation analysis method is proposed to extract more discriminative features from multiple datasets through faithful neighbor sample selection and reliable pseudo label indicator prediction. Experimental results on two handwritten numeral datasets show that our method is better than traditional approaches.
Object Detection in Omnidirectional Images Based on Spherical CNN

Omnidirectional cameras are gaining popularity in VR/AR applications and autonomous driving due to their wide field of view. However, the images produced by the cameras have geometric distortions especially in the polar regions. This distortion poses a great challenge to computer vision tasks such as object detection. In this paper, we propose a CNN architecture called spherical CNN which is designed for omnidirectional images. According to the mapping relationship between the sphere and plane, our spherical CNN changes the size of convolution kernel and the locations of sampling points at different latitudes to adapt the image distortion. In order to verify the effectiveness of spherical CNN for the omnidirectional image object detection task, it is applied to detection network SSD(Single Shot MultiBox Detector). In our experiments, we achieve a 2% improvement on the mAP75 which represents the accuracy of detection. The experimental results verify that spherical CNN can improve the detection performance for omnidirectional images.
Blockchain as a method of resolving hold-up problem

The hold-up problem is a form of opportunistic behavior of contractual partners. It occurs when the optimal volume and structure of transactions cannot be defined with ex ante certainty. The consequence of the hold-up problem is that, once a contractual relationship has been established, one of the parties seeks to modify the distribution of benefits in such a way that it has a higher level of profit from the contract than is justified by the contractual investments it has made. The paper examines the potential of the Blockchain concept to, applied as a framework of "smart" contracts, contribute to the elimination or reduce opportunities for the emergence of a hold-up situation. The Blockchain concept with its characteristics (transparency, protection of data integrity, shareability) deploys the foregoing potential in three ways: by witnessing the transaction via the Blockchain; ensuring the execution of a (by Blockchain certified) transaction; by verifying transactions through a decentralized system that replaces verification by third parties (courts or arbitration). Consequently, the Blockchain concept for storing and managing information substitutes the role played by the institute of trust in the classical ("analog") legal relationship.
Research on Fault Prediction Based on Elevator Life Cycle Big Data

Fault prediction and accident prevention are the main objectives of elevator safety. Applying the big data method to the mass data generated in the whole life cycle of elevator can realize elevator fault prediction in a broad sense. Based on data collection and preprocessing of manufacturing, installation, use, maintenance, inspection and other links during the lifecycle of elevator, the elevator database was built, and the light GBM (light gradient boosting machine) decision tree algorithm was used for feature extraction, data connection, training and prediction model setting up, which could realize the elevator historical fault query of a region and the statistics of regional fault distribution, fault type, system and component, thus the elevator fault prediction could be realized.
DAH: Domain Adapted Deep Image Hashing

With abundant labeled data, deep hashing methods have shown great success in image retrieval. However, these methods are often less powerful when applied to novel datasets. In this paper, we apply unsupervised domain adaptation techniques to improve a state-of-the-art deep hashing method, used in a cross-domain scenario where the model is trained with labeled source data and is evaluated with target data. Experiments show that the generalization capability of a supervised hashing method can be improved by the applied domain adaptation techniques.
Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations

Nowadays, the inexpensive memory space promotes an accelerating growth of stored image data. To exploit the data using supervised Machine or Deep Learning, it needs to be labeled. Manually labeling the vast amount of data is time-consuming and expensive, especially if human experts with specific domain knowledge are indispensable. Active learning addresses this shortcoming by querying the user the labels of the most informative images first. One way to obtain the ‘informativeness’ is by using uncertainty sampling as a query strategy, where the system queries those images it is most uncertain about how to classify. In this paper, we present a web-based active learning framework that helps to accelerate the labeling process. After manually labeling some images, the user gets recommendations of further candidates that could potentially be labeled equally (bulk image folder shift). We aim to explore the most efficient ‘uncertainty’ measure to improve the quality of the recommendations such that all images are sorted with a minimum number of user interactions (clicks). We conducted experiments using a manually labeled reference dataset to evaluate different combinations of classifiers and uncertainty measures. The results clearly show the effectiveness of an uncertainty sampling with bulk image shift recommendations (our novel method), which can reduce the number of required clicks to only around 20% compared to manual labeling.
Bayesian Causal Network for Discrete Variables

Abstract Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.
A Study of Counterfactual Inference Based on Instrumental Variables and Machine Learning

Causal inference is based on the inference of cause to effect and is part of causal analysis. It is an important method in data analysis and data science, and its application is very widespread in many fields. Counterfactual inference is a very important part of causal inference, which is the activity of thinking in which facts that have occurred in the past are negated and re-represented in order to construct a hypothesis of possibility and this paper specifically investigates two ideas for solving the counterfactual inference problem, the first approach is to use instrumental variables and the second approach is to use machine learning. In addition, based on the previous work which introduced Balancing Neural Network (BNN), this paper illustrates two ideas of modifying the architecture of BNN using shortcut connection. The assessment of performance of these two ideas will be done in future works.
An Analytical Approach for the Correction of Optical Readable Answer Sheets Using NLP

We have concentrated more on the use of how to correct the answer sheets using a machine. In these days, correcting the mark sheets manually have become a burden and time-consuming methodology. In this era, we have come across identifying handwriting and converting them to text concept. So, we decided to dedicate to make up an improved version and accurate of this system which will be more helpful in evaluating the mark sheets, assignments, and notes of the students. We have gathered a lot of technology currently being used in today s’ world. And thereby, taking an initiative to implement this in our educational system will be a great advantage and helpful in future. In this, we will be focusing on OCR, EPR method, applications of image processing, NLP, Quillionz platform, etc. We have managed to add more additional data into the work for an improved version of the system.