Reviewerly/exHarmony huggingface dataset:
Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions as Interpretable Neural Networks

Equivalence between Neural Networks with ReLU activation and Takagi-Sugeno fuzzy systems with triangular membership functions is studied. We prove an equivalence relation between the above mentioned systems under relaxed conditions. As the above proofs are constructive, this method allows us to transform between the considered Neural Networks and Takagi-Sugeno systems. The interpretability of the proposed system is discussed and future research directions are explored.
A Hybrid Recommendation Algorithm Based on Latent Factor Model and Collaborative Filtering

The basic recommendation algorithms include item-based CF algorithms and recommendation algorithms based on machine learning. The following article focuses on the system theory and implementation of these two basic algorithms. Based on their core ideas, a new method is proposed, which is a hybrid recommendation algorithm based on the CF algorithm and LFM algorithm. The algorithm uses a latent factor model to generate user preference matrix and item hidden feature matrix, and learns to update the matrix through gradient descent, and then calculates the similarity between items according to the calculation method in collaborative filtering, and finally, after weighting, obtains Top-N recommendation results. The algorithm has been verified, which improves the accuracy of recommendation, improves the shortcomings of collaborative filtering algorithm that only analyzes and displays features, and also alleviates the influence of sparse matrix on recommendation results.
Fault-tolerant neural network training framework based on client-server

In order to realize low power consumption and real-time inference, AIoT devices have been applied in many fields of deep learning in recent years. However, some manufacturing processes cause some soft errors on AIOT devices in inference. For a neural network accelerator with a large amount of computation, it may lead to a large amount of computing error and a huge loss of prediction accuracy, which is intolerable for precision-sensitive applications such as autonomous drones. However, conventional fault tolerance techniques such as triple modular redundancy can incur considerable power consumption and performance penalty. In this paper, a client-server collaborative fault-tolerant neural network training framework is proposed. In the training, an AIoT processor with soft errors is used as the client, and the server learns the on-site computing errors with the application data of AIoT processor. Several representative neural network models were selected in the experiment. Compared with the off-line training model, the model trained by this method increases the top5 accuracy of the neural network by an average of 2.8%.
Advances in Machine Learning and Deep Learning Algorithms and Their Applications

In this work, machine learning and deep learning algorithms are explained with examples. The relationship between machine learning, deep learning and artificial intelligence is also detailed out. Applications of machine learning and deep learning are listed. Difference between machine learning and deep learning is explained in various dimensions of size of data, hardware, applications, type of prediction, number of layers, etc. Machine learning algorithms are classified into three main groups, namely supervised learning methods, unsupervised learning methods, and reinforcement leaning methods. Some of the supervised learning techniques like linear regression, logistic regression, naïve Bayes, and support vector machines are explained in brief. Unsupervised learning techniques like principal component analysis and linear discriminant analysis are also explained. Deep learning algorithms have been detailed out, and examples are given.
Exploring the Role of Agent and Multi-Agent in Data Fusion Systems

Data fusion methods reduce the effort of data processing systems and facilitate achieving optimum results for complex problems. Recently, various complex and distributed systems such as Internet of Things (IoT) systems are implemented where many sensors and data sources are allocated to collect heterogeneous data. These systems demand innovative and advanced data fusion methods. This paper presents an overview of agent and multi-agent roles in data fusion methods. Basically, agents provide features that enable complex and distributed systems to achieve deep data fusion in a collaborative multi-agent environment. Interaction among agents allows disseminating global overviews to a localized set of data fusion modules. There are different architectures to establish communication and authority among agents, such as (1) centralized, (2) decentralized, and (3) hybrid. Each of these approaches affords essential attributes of multi-agent systems (MAS). As a result, agents have the ability to improve communication and decision-making among data fusion modules and ensure producing better features or values during the fusion processes.
Deep Learning and Knowledge Generalization

This work concerns the new studies related to the deep learning of machines. In particular, it tries to see what lies behind the behavior of deep neural networks, which have been very successful in various fields, such as image recognition, interpretation of natural language and much more. The work analyzes the deep networks and the behavior of the backpropagation algorithm. From this it seems that the success of these networks, which amazed the authors of the algorithms themselves, seems to lie in the processing of information and in the ability of the algorithms to extract relevant knowledge, discarding that which is not relevant for the purposes of the learning target. The bottleneck principle (Tishby & Zaslavsky, 2015 IEEE Information Theory Workshop (ITW), Jerusalem, pp. 1--5, 2015), in particular, appears to be a promising vision for the design of deep artificial neural networks, based on a general principle related to the processing of knowledge.
Enhancing the confidentiality of text embedding using image steganography in spatial domain

Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components.
Optimizing a Tableau Reasoner and Its Implementation in Prolog

One of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism. The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILL and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavours. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners. In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations.
Artificial Neural Network in Classification of Human Blood Cells Using Faster R-CNN

Blood cells are an element found in the human body that has a function in the body's working mechanism. In the medical world, blood can be used as a source of diagnosis of a source of disease, this is because there is a lot of important information contained by blood cells. In conducting the analysis of blood cells, it includes a series of laboratory tests, one of which is a test to determine the morphology of the blood cells. In laboratory tests carried out by doctors and medical personnel, it is usually still done manually, which has a low level of accuracy and precision. This can be caused by the knowledge, physical condition, and also the concentration of the doctor and also the laboratory staff, which makes it possible to get different analysis results. In manual laboratory tests, this can be overcome by creating an automated system that can classify human blood cells using artificial intelligence. In this research, the Faster R-CNN algorithm used 364 images of human blood cells. From the experiment above, the data is divided into 328 training data and also 36 testing data, it can be seen that Faster R-CNN has an accuracy of 94.92% in classifying human red blood cells and white blood cells.
The Application of AI Technology in English Teaching Under the Background of Big Data

Through the role of AI technology in teaching, the relationship between the technology and college English teaching is analyzed, and the importance of AI technology application is discussed in depth by enumerating common problems in daily teaching. At the same time, by enumerating SAIETS and blue ink cloud technology, make a practical interpretation of the actual teaching application of AI technology. The example of the map more specifically explains the relevant application principles of the technology.
LOVER - Platform for Segmentation and Activation of Media Users based on Big Data and Artificial Intelligence Techniques

Nowadays, the media publishers, traditionally focused on their editorial work, are being forced to innovate in the technological area to respond to the new demands of their users. Publishers are focusing their efforts on optimizing the results of online newspapers due to the significant decline in profits from advertising in print newspapers. This article presents LOVER, an intelligent personalization platform that will allow the segmentation and activation of users. This approach leverages advanced Big Data and artificial intelligence algorithms that will allow, on the one hand, a deep personalization of contents and messages, and on the other hand, the automatic sharing of contents and users among a set of websites of local and vertical publishers. The segmentation allows enable a series of real-time services aimed at improving business performance. Experimental results in real environments are promising, and they support the interest of the proposal.
Formation of Psychological Readiness of the Teacher to Implement Information and Communication Technologies in Professional Activities

The relevance of discussing the components of the psychological strategy for teachers’ use of Information and Communications Technology (ICT) at General High School Educational Institution (GHSEI) involves the categories of cooperation, reflexive, motivational and informational parts of the strategy have been developed and characterized as components of teacher readiness for professional activity in a multidisciplinary environment. The aim of the research is to develop a pedagogical strategy for psychological readiness of teachers to use ICT to eliminate internal and external barriers that interfere with the intensive exchange of information among and behind the educational team. Methodology. Sociological survey (number of participants 1200) on the platform “Online Test Pad” among teachers of GHSEI (Ukraine). Results. The list of components in the structure of psychological readiness of students to implementation of educational innovations, including emotional-motivational, cognitive-motivational, orientational, activity-operational, moral, communicative, volitional, mobilizing, evaluative-resulting. The article outlines the stages of solving the main psychological barriers to innovation in the use of information and communication technologies. Presented is the assessment of the components of psychological readiness to the implementation of ICT through the respondents’ free responses. The article revealed the criteria of psychological actualization of personal achievements in the use of ICT in professional activity. The conditions of psychological readiness for the use of ICTs in pedagogical activity as well as the professional tasks of ICT use were determined. The practical value of the study was the presentation of the recommended strategy for implementation of ICT-warehouses in professional activities with the use of information on the psychological readiness of the teacher.
Sentiment Classification on Twitter Media: A Novel Approach Using the Desired Information from User

All around the globe brimming with the IT companies, each company has different data that needs to be analyzed. It is impractical to build models for different data available. Hence, the work carried out serves for this need to build a model that works for different data efficiently. An application will be created, wherein a search box is included that takes an input (a word or any phrase) and gives the related tweets. This application solves the problem in two sections. Primarily, the training section is where we train the given dataset with Naive Bayes, KNN, and random forest algorithms, and in the second section, this trained model is used to test the dynamically extracted tweets from the Twitter media. The results are visualized with the help of a confusion matrix and the pie chart.
Forced Independent Optimized Implementation of 4-Bit S-Box

Searching the optimal circuit implementation of a Boolean function is still an open problem. This work proposes a new optimizing scheme, which could find circuit expressions with optimal gate equivalent complexity (GEC) using SAT solvers under a depth-L framework. To obtain a better GEC performance in the optimizing scheme, we first propose the ternary and area profile models for SAT problems. The former introduces multiple efficient 3-input logic gates, and the latter takes the different weights of various gates into account in solving. To demonstrate the validity and usefulness, we use our optimizing methodology to search optimized implementation of a given 4-bit S-box with the forced independent property. For an S-box hardware implementation, its forced independent property can ensure that no gate is shared between every two component-circuits, which is beneficial to prevent Differential Fault Analysis (DFA). Finally, we evaluate the implementation performances of two models (i.e., ternary and binary models) and two implementation approaches (i.e., Table-based and Boolean expression methods) by case studies covering several know S-boxes. The experimental results show that our models and approach have better area performance for the S-boxes with forced independence property in most instances.
A Novel Adaptation of Information Extraction Algorithm to Process Natural Text Descriptions of Pedestrian Encounters

Complete understanding of pedestrian behavior still poses challenges to current automated driving technologies. This study introduces a knowledge extraction framework to better understand the human perception of pedestrian crossing behavior from the ego-vehicle perspective. Using manually annotated explanatory texts, we train an information extraction algorithm to map the unstructured descriptive text into structured data. Evaluation of the algorithm confirms a good performance in most of the key feature categories. This model can identify and extract vital cues from pedestrian encounter descriptions, which can assist in analyzing human decision-making processes to estimate the pedestrians&#x0027; situational crossing intent. The knowledge extracted from such structured text data can help to holistically study interconnection among the human-used factors which is crucial for modeling pedestrian behavior.
Forward-Secure Group Encryptions from Lattices

Group encryption (GE) is a fundamental anonymity primitive analogue of group signature, which guarantees the decryption ability of recipients to specific ciphertexts while hiding these users within a crowd. Since its first birth by Kiayias et al., numerous constructions have been proposed, among which there is only one lattice-based scheme is post-quantum secure. However, the security of all these schemes will be damaged once an unexpected key-exposure attack occurs (which is extremely unavoidable in the real world). To solve this problem, we first consider a forward-secure group encryption primitive and provide a concrete instantiation over lattices, which efficiently mitigates the threats from both key exposure and quantum computation. The key idea is to introduce an appropriate periodical key-updating mechanism into the group encryptions to restrain any key-exposure adversary from breaking ciphertexts generated in prior time periods. Concretely, we modify the Agrawal-Boneh-Boyen HIBEs into the binary tree encryptions (BTE). Then, combining with other cryptographic techniques, we construct a lattice-based GE scheme that features short ciphertexts and achieves the forward-secure message secrecy and anonymity. Finally, we prove that our construction is forward secure in the standard model under the Short Integer Solution (SIS) and Learning With Errors (LWE) assumptions.
Study on the operation time of FFT permutation algorithm

The input data need to be sorted before the in-situ operation of FFT(Fast Fourier Transform). The main methods include calculating the reversed data serial number according to the positive data serial number, calculating the reversed data serial number based on the previous reversed data serial number, and table look-up. Through the simulation on IAR and KEIL platforms, this paper studies the operation time of various algorithms. The experimental results are highly consistent on two platforms. The results show that the most direct and simplest table look-up method is obviously better than the other two methods in terms of the operation time.
Features of Modern Marketing in Innovation for Socio-Economic Development

The modern stage of the innovation policy development has been marked by the major breakthrough and the principal changes of the Russian economy resulting in increasing demand for the practical implementation of marketing concepts as a driver of the performance and competitive growth of the domestic production. The special aspects of implementing the modern marketing concepts of innovations is the necessity of simultaneous application of its main tools, i.e. products, prices, distribution, promotion and positioning. At present, if an organization sets an objective to maximize returns, minimize costs and create a positive image of the company, it needs to implement the integrated marketing techniques in practice. The integrated marketing is a high-performance strategy of market interaction that permeates all organization divisions for the successful market penetration, market share gain, strengthening the corporate influence in the target market segment and business financial stability.
An Approach for Audio/Text Summary Generation from Webinars/Online Meetings

Due to the coronavirus disease (COVID-19) pandemic, most of the public work is carrying out online. Universities all around the globe moved to online education, job interviews are mainly conducting online, many first-level health consultations are happening online, and companies hold periodic meetings entirely online. Google Meet, Microsoft Team, and other online meeting software applications are widely accessible on the market. In this work, we are addressing a topic that has a lot of practical applications. In this paper, we present a method that takes a recorded video as an input and generates a written and/or audio summary of the same as an output. The suggested method can also be used to generate lecture notes from lecture videos, meeting minutes, subtitles, or storyline production from entertainment videos, among several other things. The suggested system takes the video's audio track, which is then transformed to text. In addition, we created the text summary utilising text summarising algorithms. The system's users have the option of using the text summary or creating an audio output that matches the text summary. The proposed method is implemented in Python, and the proposed scheme is evaluated using short videos acquired from YouTube. Since there is no benchmark measure for evaluating the efficiency and there is no specific dataset available for the relevant study, the proposed method is manually validated on the downloaded video set.
Пунктуация: деловая и… цифровая: о знаках препинания в современной бизнес-коммуникации

The article deals with punctuation in modern business communication. The authors compare the traditional setting of punctuation marks with the one used in chats, social networks, e-mails, etc. With the transition of business correspondence to chats and messengers, punctuation ceases to obey the general language norm, and develops according to the laws of the Internet: traditional punctuation is replaced by emoticons or rethought emotionally. The authors analyze in detail the functions of the exclamation mark, which serves to distinguish sentences and to express a subjective attitude. Considering the use of the exclamation mark in correspondence, the authors note its frequency in online texts. The exclamation makes the statement relevant to the recipient, and the exclamation mark becomes a conative (addressed to the recipient) component of business communication. Particular attention is paid to another conative sign, the ellipsis as omission/suspension points. The use of punctuation marks today depends on the transition of traditionally verbal forms of communication to writing, on following in business communication not a language norm, but a corporate substandard, on digital writing technology and the actions of the writer.
Real Time Distraction Detection by Facial Attributes Recognition

The deficit of attention on any critical activity has been a principal source of accidents leading to injuries and fatalities. Therefore the fast detection of it has to be a priority in order to achieve the safe completion of any task and also to ensure the display the maximum capabilities of the user when achieving the respective activity. While multiple methods has been developed, a new trend of non-intrusive vision based methodologies has been strongly picked by both the research and industrial communities as one with the most potential effectiveness and usability on real life scenarios.