Reviewerly/exHarmony huggingface dataset:
Soft-Computing-Based Real-Time Control of Two Wheel Mobile Robot (TWMR)

This chapter considers various soft-computing techniques for control of self-balancing two wheel mobile robot (TWMR). Initially, a mathematical model of the system was developed using Newton's second law. Thereafter, a simulink of the proposed system was developed in Matlab Simulink environment. Two different controllers, namely fuzzy logic controller and ANFIS controller, were used for control of proposed system. Finally, a real-time model of TWMR was designed which was controlled using Arduino Uno microcontroller, and its results were used for training of ANFIS controller.
Nonlinear System's Identification using Neuro-Fuzzy model tuned by Crow Search Algorithm

We propose in this work a new algorithm of optimization named Crow Search Algorithm CSA to elicit neuro-fuzzy model such TS type. In the proposed study, a particle is formed by two tasks: its structure and its parameters. The CSA algorithm was compared with others: GA and PSO through a modeling of nonlinear system. The results prove that CSA method gives optimal mean of MSE and optimal of standard deviation of MSE compared to GA and PSO.
Simplifying security: how to navigate the integration challenge

We deploy a lot of security technology, but breaches continue. Either we’re bad at this or the game is stacked against us. It turns out it's the latter – there are structural reasons why security remains so elusive. We’re short staffed, up against a heavily automated group of adversaries, and our IT infrastructure is fragmenting. Mike Lloyd at RedSeal suggests that we need to find a better way to target investments in security to where they are most needed, and then integrate a single view of our ever-widening attack surface. When it comes to cyber security, the home team is losing. We spend a lot of money and deploy a lot of technology, but failures and breaches continue. It seems that either we’re pretty bad at this, or the game must be stacked against us.
Secure Fog -assisted Sharing for Multimedia Health Records in the Cloud

In recent years, multimedia has been widely used in the medical and health fields and plays a pivotal role in personal health records (PHR). In the PHR system, people can easily access to their PHR data, which is mainly composed of X-ray, ECG and other multimedia data, by mobile devices. Owing to the need for efficiently accessing to the PHR data, the hybrid cloud-fog system is very suitable for the storage of PHR data because the computing capacity in this system is extended to the edge of the network to achieve low delay. PHR data generally contains a lot of sensitive information that may be directly used for medical diagnosis, so it demands more security capacity to ensure the fine-grained access control, confidentiality and validity of data, while protecting the privacy of data owners. Moreover, PHR systems should allow data owners to share their PHR data with specific institutions or individuals. For resolving the above problems, we propose a secure fog-assisted sharing scheme for multimedia PHR in the cloud based on multi-authority attribute-based signcryption, called SFSP. In the scheme, outsourcing computing is involved to transfer some computation to fog nodes to reduce the computation overhead on the user side in the signcryption and decryption stages because of the limited computing power of normal users' mobile devices. Performance analysis shows that SFSP is more suitable for the sharing of PHR in the cloud due to its higher efficiency than the existing schemes.
Unscented Bayes Methods for Hierarchical Gaussian Processes

In this paper, we propose an unscented Bayes method for hierarchical Gaussian processes. The hierarchical Gaussian process consists of multiple layers of Gaussian process, which leads to intractable marginal likelihood and posterior distributions. Instead of resorting to the traditional sampling approach, we use the unscented transform to compute the intractable quantities in a hierarchical model, which allows us to optimize the hyperparameters using a gradient based approach and to obtain the predictive distributions. We develop the proposed approach to different application scenarios. The performance of the proposed method is validated in two experiments with comparison to the state-of-art methods.
The Intensional Structure of Epistemic Convictions

We discuss an axiomatic setup as an appropriate account to the intensional structure of epistemic convictions. This includes a resolution of the problem of logical omniscience as well as the individual rendering of knowledge by different persons.
Online training of specialists in the energy industry

Abstract We considered the psychological and pedagogical problems arising from online learning, especially in a pandemic. Since in these conditions the lecturer’s emotional impact on the audience is depleted, the task is to use the achievements of information technologies to find ways to “bring” the audience closer to the lecturer. A computer program has been developed that uses the image recognition function so that in the online learning process the lecturer receives information about the emotional state of the audience directly during classes. The developed program can be used for the practical implementation of the proposed approach.
A Comprehensive Study and Detection of Anomalies for Autonomous Video Surveillance Using Neuromorphic Computing and Self Learning Algorithm

Video Analytics is widely applied in the field of surveillance. Recently, with the advent in technology deep learning network has been incorporated in the video action detection. Traditional CNN is employed to extract 2D spatial features of image but for video it is required to exploit CNN for temporal information. In this work we propose to do instance segmentation in video bytes and predicting the actions with the help of deep learning. And, we aim to present an implementation of an algorithm that can depict anomalies in real time video feed.
ACP: A Deep Learning Approach for Aspect-category Sentiment Polarity Detection

In recent years, sentiment analysis has gained popularity due to its wide range of application domains. With the fundamental aim of identifying the polarity of sentiment hidden in a piece of text, the ways to conduct sentiment analysis have improved with time too. Aspect-based sentiment analysis (ABSA) is one such approach. The main objective of ABSA is the extraction of sentiment polarity with respect to certain ‘aspects’ of the text. Semantically, an aspect itself is not a single entity but rather a representation of an aspect-category. Therefore, it makes more sense to find sentiment over an aspect-category. As the aspect-category concept is quite subjective, determining sentiment over aspect-category becomes challenging. In this paper, we developed a deep learning model that aims to perform ABSA by explicitly incorporating the aspect information into the model itself by assigning sentiment to every aspect-category. We evaluate our model on a benchmark SemEval’14 dataset and explore the state-of-the-art text-representations techniques. This paper tries to bring forward performances of these representations on the taken aspect-based model.
Fast Test Method of Intelligent Image Recognition System Based on Antagonistic Samples

With the development of science and technology in the field of computer hardware and algorithm, deep neural network is widely used in the field of computer vision. However, intelligent image recognition systems have also encountered difficulties in reliability and safety. Especially when facing the counterexample, these systems cannot perform well. In this paper, the handwriting digital image recognition test is taken as an example with establishing a neural network of image recognition system. The difference of recognition performance between multi-layer network and single-layer network based on convolutional network is compared, and the confrontation sample and the test sample pair are used respectively. The network conducted tests to analyze the differences between them and concluded that tests based on countermeasures were more effective than tests based solely on test samples. And it is more efficient to find the reliability of intelligent image recognition system in recognition.
Interpretable Next Basket Prediction Boosted with Representative Recipes

Food is an essential element of our lives, cultures, and a crucial part of human experience. The study of food purchases can drive the design of practical services such as next basket predictor and shopping list reminder. Current approaches aimed at realizing these services do not exploit a contextual dimension involving food, i.e., recipes. To this aim, we design a next basket predictor based on representative recipes able to exploit the interest of customers towards certain ingredients when making the recommendation. The proposed method first identifies the representative recipes of a customer by analyzing her purchases and then estimates the rating of the items for the prediction. The ratings are based on both the purchases and the ingredients of the representative recipes. In addition, through our method, it is easy to justify why a specific set of items is predicted while such explanations are often not easily available in many other effective but opaque recommenders. Experimentation on a real-world dataset shows that the usage of recipes leverages the performance of existing next basket predictors.
The Implementation of E-Learning in an Italian University as a Consequence of the Emergency Challenges

This research aims to analyze the implementation process of new knowledge management tools (KMT) in an Italian university as a result of the emergency challenges (COVID-19). The processes of academic knowledge transfer (AKT) and knowledge management tools (KMT) are undergoing rapid changes due to technological innovation and new user requirements. Universities are obliged to offer educational and training courses through distance learning technologies and e-learning platforms. The research adopts a research-action approach to analyze the case study. The results of this research show that the extensive use of KMT and e-learning requires new approaches for the creation of educational content on the part of professors; on the other hand, the implementation of these new technologies requires the redesign of the knowledge transfer processes to meet the requirements of web connection and the quality of teaching materials which are shared with many students at the same time.
FENet: Fast Real-time Semantic Edge Detection Network

Semantic edge is a geometric-aware semantic feature that can be leveraged in robotic perception systems for increased situational awareness and high-level environment understanding. This sparse representation nicely encapsulates the semantic information (object categories) within the geometric object boundaries. State-of-the-art semantic edge detection approaches require significant computation power and fail to approach real-time performance on embedded devices for robotic applications. In this paper, we present FENet (Fast Realtime Semantic Edge Detection Network), a semantic edge detection approach for robots with Size, Weight, and Power (SWaP) constraints. Specifically, we adopt MobileNetV2 as a lightweight backbone network, and we utilize joint pyramid upsampling to improve the system performance. We further reduce the model complexity and latency through network pruning and multiple upsampling strategies to adapt the model on embedded devices such as NVIDIA Jetson TX2. The proposed method is evaluated on Cityscapes with accuracy performances close to the state-of-the-art methods, but with substantially reduced computational complexity that speeds up the network by a factor of 10. To the best of our knowledge, FENet is the first real-time semantic edge detection network for robotic platforms.
SCIENTIFIC PUBLICATIONS OF NOVOSIBIRSK UNIVERSITIES IN SPSTL SB RAS’ COLLECTIONS

The legal deposit copy, obliging each recipient to provide its eternal preservation, has always guaranteed the completeness of joint library resources, formed by native libraries. In course of time, a new approach proved itself: if it were undesirable and, moreover, impossible in the conditions of stack crisis to preserve constantly everything published, the depository libraries should be given the right to choose materials having permanent information, cultural and historical value. It determined the necessity of partnership between libraries of different types and kinds, coordination in document resources formation, other activities aspects. However, a number of legal juristic acts and financial documents, adopted in the previous decade, changed the paradigm of interrelations between institutions of different financial forms. Profiling their resources, libraries of higher educational institutions consciously limit subjects and types of editions as well as depth of their retrospection with due regard for possibilities of satisfying their users’ wide information needs by the stocks of other – basicly large universal scientific – libraries. For all that, practice of stocks formation even in SPSTL SB RAS indicates of the short supply – because of small circulation of this literature, lack of proper information and non- receipt of legal copies, etc. – of scientific editions of this segment. Statistic data formed on the base of a number of Novosibirsk State Universities publishing repertory as well the results of the analysis of this segment of SPSTL SB RAS stocks could serve as an obvious case. We suppose that it is necessary to cumulate statistical data on other basic universities of the Siberian Federal Region (SFR). That will permit, from our viewpoint, not only to determine with a high degree of certainty modern completeness and guality of SPSTL SB RAS’ stock formation as the receiver of the legal deposit copy, but also to formulate perspectives of interaction between all the actors of the territorial scientific-aducational complex.
Weakly supervised object detection based on deep metric learning

Abstract Weakly supervised object detection is a hot issue in the computer vision field, which aims to train a high performance detection model with low cost annotation data. The existing methods of weakly supervised object detection only summarize the object category but don’t consider the similarity between the objects in the optimization process. For solving this problem to improve detection accuracy, this paper proposes a weakly supervised object detection model based on deep metric learning. In the initial training phase, an initial metric has been learned in advance to measure the similarity between these objects; in the correction phase, we propose an adjacent instance mining method based on proxy samples, this approach expands the model’s recognition view, and prevents premature locking the wrong object position. We design a series of experiments on the PASCAL VOC2007 dataset to prove the effectiveness of this method.
Answer set programming encoding users opinions merging in social networks

The present paper describes briefly a project idea in progress about the evolvement of individuals' opinions, beliefs and perceptions on social networks (such as Facebook, Twitter, Instagram, youtube...) which is a thorny subject that has whetted nowadays the curiosity of a hulk of researchers from various disciplines. For this purpose, differently from a lot of works in the literature, we rely on logical knowledge representation tools in order to investigate the belief merging operation of Artificial Intelligence (AI). The major objective of this project is to provide efficient operator for merging heterogeneous, inconsistent and uncertain multiple sources information in the context of social networks taking into account the fact that opinion can be formed and developed through the concept of social influence with its two forms (informational social influence and normative social influence) and the concept of social trust. We intend thus through this research work presenting an adaptative version to our context of an approach [7] expressed thanks to Answer Set Programming (ASP) paradigm with stable model semantics. It is worth to say that our approach profits from the impressive volume data produced by users in social networks about a particular topic by learning from opinions, beliefs and perceptions that their freinds/neighbors share and therefore allows to use this kind of data to extract initial opinions, and to validate the proposed opinions merging process allowing even the prediction of users' behaviors.
Post-processing algorithms for the formation of online handwritten Gurmukhi character/akshara

Formation of Gurmukhi character/akshara from the recognized strokes in online handwriting recognition systems is a challenging task. In this paper, the task of character and akshara formation in an unconstrained environment have been addressed. After the recognition of online handwritten strokes the Gurmukhi akshara is formed using a hybrid approach. Two classifiers, namely, Support Vector Machine (SVM) and Recurrent Neural Network (RNN) have been experimented in this study. The classifier, yielded the maximum cross-validation accuracy has been utilized for stroke recognition. A total of 52,500 word samples have been collected from 175 writers in order to train the classifiers. Three post processing algorithms have been proposed in this article for improving the character and akshara recognition accuracy. The proposed methodology when tested on a dataset of 21,500 aksharas, written by 50 new writers, achieved average the accuracy rate of 97.1% and 87.1% for base character and akshara recognition, respectively.
Hybrid Cryptosystem Using Quasi Group and Chaotic Maps Applied on 1D Data

In this paper cryptosystem algorithm is proposed, this cryptosystem has a symmetric key, designed using both chaotic maps and quasigroup algorithms. The designed cryptosystem destroys any existing patterns in the input data and maximizes entropy to reach 10. The autocorrelation of all the cases variation is very similar to nearly steady values in spit the alteration of the input data, The Experimental results show an excellent outcome in terms of diffusion and confusion concerning the plaintext and the key, the obtained voice cryptosystem is immune to the statistics attacks.
MSNet: A Multi-scale Segmentation Network for Documents Layout Analysis

Layout analysis is often a crucial step in document image analysis and understanding. In this paper, we propose a deep learning-based layout analysis approach to identify and categorize the regions of interests in the scanned image of text document. Although semantic segmentation has been applied at pixel-level of document image for geometric layout analysis with much progress, many challenges remain with complex and heterogeneous documents which often have a sparse structure without closed boundaries and fine typologies with variable scales. We propose a multi-scale segmentation network, called MSNet, for high-resolution document image. The model is characterized by the enlarged receptive field size and multi-scale feature extraction. Experiments are conducted on a Chinese document dataset with satisfying performance.
Design and application on Adaptive Particle Filtering for Integrated Train Positioning

Aiming at the problem of system model error and non-Gaussian noise interference caused by the complex train environment, a new adaptive filter is proposed for integrated train positioning, which is based on particle filter. It uses the IGG method to construct an equivalent weight function and introduces an adaptive factor to optimize the importance density function. The sensitivity of the algorithm to measuring environmental interference is reduced. The proposed AUPF algorithm is applied to the train integrated positioning system for simulation. Simulation and experimental results demonstrate that the performance of the proposed algorithm is superior to the standard UKF and UPF under the interference of non-Gaussian noise, leading to reduced positioning error.
An Overview of Communication System Model Using Deep Learning Techniques

Despite huge walks regarding hypothetical advancement, assessing and looking at GANs remains a daunting task. The measure of information required was huge henceforth reinforcement learning has been implemented. The proposed model utilizes channel modelling alongside deep learning techniques to start and finish correspondence framework, transmitter and receiver bind the architecture of the module in order to keep up with the quality and smoothness in the system. The AWGN channel sustains simple guided waves to framework that accomplishes higher efficiency and accuracy for the communication module developed. All the information retrieved from the channel has been utilized for training the transmitter and receiver module for a given dataset that reduces the interference present in module by determining the loss, in turn, capacity of the channel and opens gate for reliable and robust communication between the systems. From the simulation, it has been determined that the system is more efficient to reduce interference as there was a substantial increase in SNR (Signal-to-Noise Ratio) due to reduction in BER (Bit Error Rate).
Discovering conditional properties of recursive functions in a proof assistant

Theory exploration is a bottom-up approach to automatically discovering a collection of theorems in a given mathematical theory. It is especially important for the verification of programs, since the correctness property are often proved by induction, and such inductive proof essentially requires plenty of lemmas. In theory exploration, it is hard to find syntactically complex properties. Especially, there have been not so much investigation on exploration of conditional properties. In this paper, we study how the top-down approach based on generalization can be used in theory exploration to find complex conditional properties through an example.
The weight analysis model of user experience elements and technical indicators in DEA method

The Quality of Experience (QoE) plays an important role in 3d scenes and is of great significance to the future development of technology. Technical parameters are still a key factor influencing the Quality of Experience (QoE). In order to better explain the relationship between technical parameters and Quality of Experience (QoE). In this paper, Quality of Experience (QoE) is divided into three elements and implements a weight analysis model between user experience elements and technical indicators. In view of the above model, we put forward the overall efficiency analysis method based on DEA, and calculated the weights of all aspects. In this way, when determining the technical scheme of 3d video, by balancing important indicators and non-important indicators, one/several technical aspects can be improved pertinently and better video effect can be achieved with less cost.
Mechanical smart cleaning table

Abstract Smart home is the inevitable development trend of future home furnishings. As an integral part of the home system, the dining table is bound to face a qualitative leap. As people yearn for a better, more comfortable and convenient life, the development of the dining table will become more intelligent and humanized. At present, people have a great demand for a variable dining area, and hope to avoid the tedious cleaning of the table after eating, and desire to be free from housework. Based on the above background, the team members designed a new type of self-cleaning dining table-mechanical intelligent cleaning dining table. The dining table has the characteristics of ingenious structure, diverse functions and stable performance, including three functional modules: folding and extending, table cleaning, and garbage collection. The ingenious cooperation between the various institutions can realize the folding and extension of the dining table, table cleaning and garbage collection functions. The mechanical intelligent cleaning table saves people’s time, facilitates people’s lives, and has high practical value and promotion value.