Reviewerly/exHarmony huggingface dataset:
Privacy Preservation Approaches for Social Network Data Publishing

Recently, Online Social Networks have become much popular because of its useful features. People can freely access such platforms and use it to share the thought, get the information and to connect with others. Each day social network generates a huge amount data posted by the users. The data extracted from such platforms are analysed in various fields to bring out useful information. But, as it also contains a user’s personal information, the hidden (sensitive) data could be misused to breach one’s privacy. In order to resolve this issue, the data should be modified before it becomes publicly available. The aim of this chapter is to highlight the various privacy preserving techniques that modifies the original social dataset to provide user privacy while keeping the usefulness of the published dataset. The privacy preservation techniques covers: (1) Proposed anonymization methods for degree and mutual friend attack. (2) Differential privacy technique for node degree publishing. This chapter includes detailed discussion of these privacy preservation techniques. The proposed clustering approach is based on Machine Learning concepts. The graph topological properties are considered as performance parameters to present the experimental results of anonymization approaches. Both anonymization approaches consider the efficient edge insertion operation to obtain significant improvement in data utility. Our proposed “graph projection approach” for node differential privacy r retains more information by accessing the node according to the ascending order of degree. The proposed approaches provide more data utility compared to the existing approaches while providing same level of privacy.
DE-CO: A Two-Step Spelling Correction Model for Combating Adversarial Typos

The robustness of text classifier based on the deep neural networks can be improved by correcting adversarial spelling mistakes. The main challenge brought by these mistakes is that it is difficult for the text classifier to recognize the words in the text correctly. Existing works to deal with these mistakes can be divided into two types: optimization of training data and reconstruction of text classification model. However, these works are not suitable for the text classification model that has been deployed, as retraining or reconstruction is always involved. To address the above problem, we propose a two-step spelling correction model, which consists of a misspelled word detector and a misspelled word correcter, referred to as DE-CO. Specifically, we use a detector to recognize misspelled words in the text, use a corrector to correct the misspelled words, and then feed the correction results into the downstream classifier. In this way, without reconstruction or retraining, the normal recognition of words by the text classifier can be guaranteed. We evaluate DE-CO on adversarial examples generated from the Stanford Sentiment Treebank (SST). The classification accuracy of the downstream classifier is improved in a variety of attack scenarios, which demonstrates that DE-CO improves the robustness of the text classifier.
Deep Embedding Features for Action Recognition on Raw Depth Maps

In this paper we present an approach for embedding features for action recognition on raw depth maps. Our approach demonstrates high potential when amount of training data is small. A convolutional autoencoder is trained to learn embedded features, encapsulating the content of single depth maps. Afterwards, multichannel 1D CNN features are extracted on multivariate time-series of such embedded features to represent actions on depth map sequences. In the second stream the dynamic time warping is used to extract action features on multivariate streams of statistical features from single depth maps. The output of the third stream are class-specific action features extracted by TimeDistributed and LSTM layers. The action recognition is achieved by voting in an ensemble of one-vs-all weak classifiers. We demonstrate experimentally that the proposed algorithm achieves competitive results on UTD-MHAD dataset and outperforms by a large margin the best algorithms on 3D Human-Object Interaction Set (SYSU 3DHOI).
VC dimension and distribution-free sample-based testing

We consider the problem of determining which classes of functions can be tested more efficiently than they can be learned, in the distribution-free sample-based model that corresponds to the standard PAC learning setting. Our main result shows that while VC dimension by itself does not always provide tight bounds on the number of samples required to test a class of functions in this model, it can be combined with a closely-related variant that we call “lower VC” (or LVC) dimension to obtain strong lower bounds on this sample complexity.
Towards Interpretable Attention Networks for Cervical Cancer Analysis

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals, including analysis of cervical cancer. Many previous works focus on the analysis of isolated cervical cells, or do not offer explainable methods to explore and understand how the proposed models reach their classification decisions on multi-cell images which contain multiple cells. Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks to classify multiple cervical cells. Our aim is to provide interpretable deep learning models by comparing their explainability through the gradients visualization. We demonstrate the importance of using images that contain multiple cells over using isolated single-cell images. We show the effectiveness of the residual channel attention model for extracting important features from a group of cells, and demonstrate this model's efficiency for multiple cervical cells classification. This work highlights the benefits of attention networks to exploit relations and distributions within multi-cell images for cervical cancer analysis. Such an approach can assist clinicians in understanding a model's prediction by providing interpretable results.
Exploring the Error-Runtime Trade-off in Decentralized Optimization

Decentralized stochastic gradient descent (SGD) has recently become one of the most promising methods to use data parallelism in order to train a machine learning model on a network of arbitrarily connected nodes/edge devices. Although the error convergence of decentralized SGD has been well studied in the last decade, most of the previous works do not explicitly consider how the network topology influences the overall convergence time. Communicating over all available links in the network may give faster error convergence, however, it will also incur higher communication overhead. The MATCHA algorithm proposed in [1] achieves a win-win in this error-runtime trade-off by judiciously sampling the communication graph. In this paper, we propose several variants of the MATCHA algorithm and show that MATCHA can work with many other activation schemes and decentralized computation tasks. It is a flexible framework to reduce the communication delay for free in decentralized environments.
Capsule Network Versus Convolutional Neural Network in Image Classification

Many concepts behind Capsule Networks cannot be proved due to limited research, performed so far. In the paper, we compare the CapsNet architecture with the most common implementations of convolutional networks (CNNs) for image classification. We also introduced Convolutional CapsNet - a network that mimics the original CapsNet architecture but remains a pure CNN - and compare it against CapsNet. The networks are tested using popular benchmark image data sets and additional test sets, specifically generated for the task. We show that for a group of data sets, usage of CapsNet-specific elements influences the network performance. Moreover, we indicate that the use of Capsule Network and CNN may be highly dependent on the particular data set in image classification.
Design Automation and Optimization Methodology for Electric Multicopter Unmanned Aerial Robots

The traditional multicopter design method usually requires a long iterative process to find the optimal design based on given performance requirements. The method is uneconomical and inefficient. In this article, a practical method is proposed to automatically calculate the optimal multicopter design according to the given design requirements including flight time, altitude, payload capacity, and maneuverability. The proposed method contains two algorithms, including an off-line algorithm and an online algorithm. The off-line algorithm finds the optimal components (propeller and electronic speed controller) for each motor to establish its component combination, and subsequently, these component combinations and their key performance parameters are stored in a combination database. The online algorithm obtains the multicopter design results that satisfy the given requirements by searching through the component combinations in the database and calculating the optimal parameters for the battery and airframe. Subsequently, these requirement-satisfied multicopter design results are obtained and sorted according to an objective function that contains evaluation indexes, including size, weight, performance, and practicability. The proposed method has the advantages of high precision and quick calculating speed because parameter calibrations and time-consuming calculations are completed offline. Experiments are performed to validate the effectiveness and practicality of the proposed method. Comparisons with the brutal search method and other design methods demonstrate the efficiency of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The proposed method is fast and practical to obtain an optimal solution by only using a low-performance web server, and the algorithm has been published online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.flyeval.com/recalc.html</uri> to provide an online optimization design service for users. To make it convenient to apply the proposed method to multicopter designs, the propulsion system combination database obtained by our off-line algorithm is released along with the article. This database includes more than 1500 experimentally calibrated propulsion combinations, which are adequate for readers to use the proposed optimization algorithms to design multicopters with weights (sizes) ranging from 0.2 to 50 kg.
A Multi-instance Multi-label Weakly Supervised Approach for Dealing with Emerging MeSH Descriptors

The constant evolution of Medical Subject Headings (MeSH) vocabulary and specifically the changes in its descriptors brings forth a number of issues that need automation. The main one being that changed descriptors often lack proper ground truth articles. Therefore, the learning models which demand strong supervision are not directly applicable, settling the predictions on such changes not a straightforward task. The importance of this problem is also enforced by its multi-label nature and the fine-grained character of the examined class-descriptors, factors that demand a lot of human resources. In this work, we alleviate these issues through retrieving insights from a source of information about those descriptors present in MeSH in order to create a weakly-labeled train set. Furthermore, we exploit short-text information per article, implementing an averaging transformation on the corresponding sentence embeddings, applying a similarity mechanism for assigning weak-labels to our formatted data set, thus we named our approach WeakMeSH. The benefits of applying the proposed end-to-end approach are examined on a large-scale subset of the BioASQ 2018 data set consisting of 900 thousand instances, investigating two separate groups of MeSH changes: brand new and complex changes. Our performance tested on BioASQ 2020 data set against several other approaches that can either distill weak information on their own or apply alternative transformations against the proposed one was proven highly competitive.
Optimized Deep Neural Model for Cancer Detection and Classification Over ResNet

Deep learning models can assist dermatologist in classification of cancer into benign or malignant with high success rate. So, we proposed a new convolutional neural network model for classification of skin cancer, brain tumor, breast cancer, and colon cancer into benign or malignant. The proposed model and ResNet-50 model are trained on training set and compared by evaluating them on testing set. The insights of the results show that proposed model performs better than ResNet-50 in every category. The proposed model achieved 95% accuracy for brain tumor classification; the ResNet-50 model obtained 82% accuracy for brain tumor classification. The proposed model clearly outperformed ResNet-50 model. The average time taken per each epoch for proposed model is 4 s and for ResNet-50 model is 9 s.
Research on the evolution of movement difficulty of competitive aerobics based on digital image processing

Competitive aerobics is a sports project which combines music, aerobics, difficulty and performance as the whole, and takes health, strength and beauty as its movement characteristics. Competitive aerobics is one of the sports that is dominated by skills, and the technical requirements are higher. Through the analysis of the existing literature, the current research on the difficulty of aerobics is only in the general qualitative description and experience summary. So far, there is no comprehensive analysis of the evolution of the competitive gymnastics articles, in the theoretical aspects of the study which is more general, limited. In this work, the method of digital image processing is used to focus on the evolution of the difficulty of aerobics. Combining the rules of the international competitive aerobics competition in each cycle, this work analyzes the statistical data of the movement difficulty of the aerobics championships, discusses the difficulty of aerobics development and evolution process, from summing up the characteristics of the development of difficult movements,the development trend of difficult movements of competitive aerobics is scientifically predicted.
A Correlation and Slope-Based Neighbor Selection Model for Recommender Systems

The recommendation system is used to render personal suggestions to its clients while choosing a product from a list of items. Collaborative filtering is the most commonly used method for recommendation systems. In a collaborative filtering algorithm, the similarity factor employed in discovering the users with the same actions with respect to the chosen products is one of the key components of recommendations without considering any other information related to the recommender system entity. In this paper, we present a new correlation and slope-based neighbor selection model which concentrates on measuring the importance effecting of entities on each other. The proposed model picks the similarity in the importance of the correlated entities and takes it into account. Additionally, we suggest a new rating prediction model based on the first available rating from the nearest neighbors. We tested other new models in addition to our proposed model. The experiments are performed on three real datasets to test our model accuracy and compare with many state-of-the-art similarity measures accuracy. Experimental outcome confirms that the proposed model surpasses the recommendations accuracy of the previous methods. The results authenticate the effect of using the recommendation system entities information like the degree of importance to identify suitable neighborhoods and obtain a fitted recommendation system. Therefore, we can consider that our proposal is an effective method to select the nearest neighbors successfully and support the recommendation process carefully.
AdversarialStyle: GAN Based Style Guided Verification Framework for Deep Learning Systems

Verification and validation of deep learning algorithms is an important and challenging topic of artificial intelligence. Without approving by reliable and rigorous verification methods, deep learning algorithms, for instance, the convolutional neural networks, are not qualified to be used in real-world applications, especially in safety-critical areas. The gap between deep learning systems and the requirements in safety-critical application areas, such as autonomous robotics and self-driving vehicles, is the lack of Black-box V&V techniques that can test and evaluate the performance and the robustness of deep learning systems. To bridge this gap, we proposed a GAN based Black-box verification framework called AdversarialStyle for generating and searching adversarial examples in both targeted and non-targeted way from different styles or domains of interest. The AdversarialStyle can not only evaluate deep learning models but also can discover the robustness level of every instance in the test set. Therefore, this framework can support deep learning model designers to understand and to explore their algorithms and improve the trustworthiness of AI techniques.
SWOT Analysis of Mobile Applications in the High Education E-Learning of the Chinese Language

The research aims at studying of mobile applications’ use for teaching Chinese to university students using TrainChinese and LaoshiDict applications as examples. The feature is the analysis of the capabilities through the SWOT analysis method used in management consulting for situational analysis and determining an action plan. Questionnaire was conducted among students learning Chinese of Russian State Social University. The results showed so as the teaching of the Chinese language in universities is expanding every year, the need for mobile applications that reflect the program, goals and objectives of studying the Chinese language in universities is growing. To optimize and increase the efficiency of using these mobile applications, it is necessary to continue to develop their educational functions; make changes to educational programs for easier integration with them.
Effective grounding for hybrid planning problems represented in PDDL+

Abstract Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and discrete state variables, along with changes that can be either instantaneous or durative. PDDL+ (Planning Domain Definition Language +) models support the encoding of such representations, but the resulting planning problems are notoriously difficult for AI planners to cope with due to non-linear dependencies arising from the variables and infinite search spaces. This difficulty is exacerbated by the potentially huge fully ground representations used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle. This paper investigates two grounding techniques for PDDL+ problems, both aimed at reducing the size of the full ground representation by reasoning over the lifted, more abstract problem structure. The first method extends the simple mechanism of invariant analysis to limit the groundings of operators upfront. The second method proposes to tackle the grounding process through a PDDL+ to classical planning abstraction; this allows us to leverage the amount of research done in the classical planning area. Our empirical analysis studies the effect of these novel approaches over both real-world hybrid applications and synthetic PDDL+ problems took from standard benchmarks of the planning community; our results reveal that not only the techniques improve the running time of previous grounding mechanisms but also let the planner extend the reach to problems that were not solvable before.
Recognition of character information for automation of production processes

Purpose of research. Nowadays optical character recognition systems have a high level of dependence on the specific type of marking that is to be recognized, and therefore, the creation of a universal solution is an important and difficult task. The paper considers the issue of creating a system for recognizing symbolic information that can be used at various stages of production to automate processes in control systems, in particular, to analyze the labeling of circuit breakers. Methods. Binarization, filtering, and boundary detection are digital image processing techniques. Line search method, baseline search method, word splitting algorithms, image enhancement methods by segmentation, damaged characters recognition method, an algorithm for increasing the final recognition quality are character recognition methods. Results. The analysis of algorithms used for preprocessing and subsequent recognition of images containing marking of circuit breakers is carried out. The mathematical model of image processing for subsequent recognition has been created. We have described methods used to define marking symbols. Illustrative examples of the operation of the algorithms on which the system is built are given. The obtained solution was tested. The ways of system development are described here, they can lead to improved results, for particular use cases. Conclusion. It is proposed a solution that recognizes symbolic information on the labeling of circuit breakers, which can be the basis for the development and description of systems serving for the automation of production, by transferring information read from the product during the production process. This system, by its example, describes the components of character recognition systems, and for direct use, it needs to be refined in accordance with the technical requirements and the specifics of the conditions in which it will be used.
Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.