Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Existing and a Novel Approach to Topic Detection on Conversational Dialogue Data

Topic detection in dialogue datasets has become a significant challenge for unsupervised and unlabeled data to develop a cohesive and engaging dialogue system. In this paper, we proposed unsupervised and semi-supervised techniques for topic detection in the conversational dialogue dataset and compared them with existing topic detection techniques. The paper proposes a novel approach for topic detection, which takes preprocessed data as an input and performs similarity analysis with the TF-IDF scores bag of words technique (BOW) to identify higher frequency words from dialogue utterances. It then refines the higher frequency words by integrating the clustering and elbow methods and using the Parallel Latent Dirichlet Allocation (PLDA) model to detect the topics. The paper comprised a comparative analysis of the proposed approach on the Switchboard, Personachat and MultiWOZ dataset. The experimental results show that the proposed topic detection approach performs significantly better using a semi-supervised dialogue dataset. We also performed topic quantification to check how accurate extracted topics are to compare with manually annotated data. For example, extracted topics from Switchboard are 92.72%, Peronachat 87.31% and MultiWOZ 93.15% accurate with manually annotated data.
Deep Learning Approach for Event Monitoring System

With an increasing number of extreme events and complexity, more alarms are being used to monitor control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It is important to have a rigid surveillance that should guarantee protection from any sought of hazard. Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO algorithm, it divides an image from the video into grid system and each grid detects an object within itself.
EDMONDO DE AMICIS’ BOOKS IN THE COLLECTION OF THE HERZEN STATE PEDAGOGICAL UNIVERSITY’S FUNDAMENTAL LIBRARY

The article provides an overview of the works of E. De Amicis published in Russian and stored in the collections of the Fundamental Library of the Herzen State Pedagogical University. Most of the publications contain certain book signs, by which you can find out the history of the existence of each copy and trace the path of their entry into the library. The books of E. De Amicis were very popular, therefore they were present in almost every library oriented to the children’s readership: libraries of state institutions (for example, the library of employees in the Ministry of Finance, the library for employees of the State Bank) and various educational institutions (schools, colleges, institutes), public children’s libraries and personal collections. Recommendations for the purchase of books by E. De Amicis for reading to children came from both official departments of the Ministry of Public Education and specialists in children’s literature as part of various indexes and on the pages of periodicals. The main attention is paid to translations and editions of the most popular story “Cuore”, as well as editions of individual stories extracted from it. Their number was difficult to name already at the beginning of the 20 th century.
ANALYTICAL ROLE OF TRUST IN MICROFINANCE SUCCESS IN EAST AFRICAN COUNTRIES

East Africa is an emerging market that has the potential to position the sub-Saharan Africa in the global context through the diverse and robust resources.One of the core drivers of economic feasibility and market metrics in the region is the effective development of microfinance across such countries as Uganda, Kenya, and Tanzania.The region's economic environment is characterized with increased value-addition approaches to its agricultural sector and having the significant approaches to instill the relevant success connotations to diversify the industries including the informal sectors.The microfinance borrowers and users have the definite and expanded market in east Africa that conform to the needs of the people and align to the policies in the context of sustainable outcomes.Trust is a crucial virtue in understanding the effectiveness of microfinance in east African countries, including the critical assessment of the challenges and solutions.A publication on the subject requires that the roles of trust among the microfinance institutions and the stakeholders such as consumers and the government agencies are crucial part of the sustainable management practices (Odera, 2013).The focus of the discussion is on the roles of trust in ascertaining the success of microfinance in east African countries.
FPGA implementation of hybrid asymmetric key-based digital signature and Diffie-Hellman key exchange algorithm for IoT application

A new RSA-based Diffie-Hellman key exchange with a digital signature algorithm has been proposed and implemented through FPGAs. In the proposed algorithm, the problem of man-in-the-middle attack is solved. The proposed algorithm is more useful in the case of the client and server-based interaction like in the case of wireless mobile internet (4G/LTE) usage by an individual and in the case of bank transactions that are made by the individuals. Security, resistance to side attacks and collisions, larger key size, and non-factorisation of the prime numbers are the advantages of the RSA which is used in the proposed algorithm. Synthesis and implementation of the encrypted block have been compared and analysed on Spartan-3, Spartan-6e, Virtex-4 and Spartan-6 FPGA boards. Complete security is achieved using digital signature and previous problems of the traditional algorithm.
Improving Energy Efficiency in Cloud Computing Data centres Using Intelligent Mobile Agents

Cloud computing has become a profound name and also a solid bedrock for new emerging technology. Cloud technology fully supports the dynamic provisioning of computing resources as a utility service on a pay-as-you-go approach. Its numerous benefits (such as rapid elasticity, flexibility, network resource pooling) empower small, medium, and large enterprises to use other technologies through the Internet's flexibility. Despite the benefits cloud technology offers, the challenge of the high power consumption rate it incurs as it leverages its promised attributes remains a major concern. Academic research has shown that a typical 500 square meter data centre consumes about 27,048 kilowatts per hour of power per day regardless of whether it is active or not. Over the years, the most dominant energy efficient techniques for managing data centre has been Dynamic voltage frequency scaling and virtual machine consolidation, which has had a significant setback due to its inability to manage systems on overload state. Therefore, a novel paradigm based on an intelligent mobile agent approach has been proposed. This proposed approach is highly intelligent can easily detect underutilised and overloaded components of the data centre due to its unique feature. Agent technique has successfully shown it can prevent and manage overloading issues due to change in workloads and achieve a more efficient load balancing with a low power consumption rate. The mobile agent was embedded into servers and switches to regulate their activities and then shut down underutilised components. Mobile agent (Java agent) is the first of its kind used in a cloud environment. This research proposal saves a significant amount of energy and improves the entire system performance. In this thesis, the intelligent-based Agent approach is used to address energy efficiency and costaware related problems. Agent approach helps facilitate resource management, allocation of cloud data centre components with a significant reduction in energy usage rate with a more efficient system performance while maintaining a highly reliable system as promised by service providers.
Student Feedback Analysis with Recommendations

When modern institutions use their student data, they can better understand their students' educational experiences. Teachers are better able to educate their kids as a result of this. Big Data is also being used to transform the educational system in order to provide a well-rounded education to pupils. Analyzing how well the teaching has been effective for the students is an important criterion in teaching. Students' feedback is an important component that should be encouraged in order to improve the learning experience. Each faculty member is rated on a scale of 1 to 5, with 5 being the highest, based on student feedback. To capture and process emotions from feedback, Naive Bayes classifiers and simple text mining algorithms were applied. Rating scores are clustered using the K-means clustering technique.We use sentiment analysis techniques to analyse student feedback in this work.
A New Approach towards Quantum Resistant Cryptography

Quantum computing is another developing innovation which is going to impact whole computing process and especially cryptography. Most of the cryptosystems will fail when quantum computing becomes a reality. In such situation cryptography through lattice and mersenne primes is a ray of hope. In this paper, we investigate the impact of quantum computing on cryptography and possible solution to provide a secure system.
Applications of Cryptography in Blockchain

Blockchain is an innovative application model that integrates distributed data storage, peer-to-peer transmission, consensus mechanisms, digital encryption technology and other computer technologies. It is decentralized, secure, and Information disclosure. In blockchain, digital encryption technology has a core position. The security of user information and transaction data is a necessary condition for the promotion of blockchain. The development of cryptography and related technology promotes and restricts the further development of blockchain. This paper outlines the infrastructure of blockchain, including the data layer, network layer, consensus layer, contract layer and application layer. The principles of encryption technology are introduced briefly, such as hash function, asymmetric cryptosystem, and digital signature. The application of cryptography in all levels of blockchain is analyzed, including data layer, network layer, consensus layer, etc. It shows that cryptography runs through the whole blockchain system. The existing security problems of blockchain are analyzed, and the future research direction is expected.
A Note on Public Key Cryptosystems

Public key or asymmetric cryptosystems use public-private key pair for the secure transmission of data. RSA and ECC (Elliptic Curve Cryptography/Cryptosystems) are widely used cryptosystems in this category. Public key cryptosystems rely on mathematical problems known as hard problems. The security of these cryptosystems is based on these hard problems. Public key cryptosystems solve the key transportation problem of symmetric key cryptosystems and able to provides digital signatures also.
A Note on Symmetric Key Cryptosystems

In symmetric key cryptosystems, a common secret key is shared between transmitter and receiver. Symmetric key methods usually categorized as stream ciphers or block ciphers. DES (Data Encryption Standard) was used earlier which is replaced by AES (Advanced Encryption Standard) and it is most widely used symmetric key method now days. Here in this paper, we review some characteristics, advantages and disadvantages of symmetric key cryptosystems.
Advanced Unified Encrypting Methodology to Enhance The Security of Health Information in Cloud Storage Employing Blockchain

Security of health information has been a major issue in the health care industry. Health information exchange have been help doctors, patients, and other healthcare providers to securely access and share the medical information. Cloud computing led to easier and safer access and sharing of medical records. To provide additional security to cloud computing employ encryption techniques and blockchain. In this method develop the mobile application to enhance the security of health information in cloud storage using various encryption techniques and blockchain.Base64 encoding and AES algorithm are used for providing more security to the medical records. Blockchain transactions occur within a peer-to-peer network of patient records. SHA-256 algorithm is used for implementing the blockchain. This system protects the confidentiality of health data stored on cloud storage.
Offline Handwritten Mathematical Expression Recognition using CNN and Xception

Mathematical expressions generally play a requisite role in scientific communications. They are not just used for numerical calculations, on the other hand, are also employed for fetching scientific information with less ambiguity, and facilitate researchers to exactly outline and formalize target problems. It takes far longer to manually enter mathematical formulas into a computer than it does to write them down on paper using a pen. Recently, we proposed deep learning methods that can identify images of trigonometric expressions from 2dimensional layouts to 1dimensional strings in order to solve this issue. As densely connected convolutional neural networks (CNN) can boost accuracy, we utilize CNN to improve the results in this study. In order to compare performance, the Transfer Learning framework Exception is employed, which obtains 90% accuracy when recognizing handwritten mathematical expressions. CNN provides 98% accuracy in this regard. Therefore, the CNN model that we created has a higher accuracy rating than the Transfer Learning model Xception
Content-Based Image Retrieval Using Deep Learning

The most prevalent and well-used method for obtaining images from huge, unlabelled image datasets is content-based image retrieval. Convolutional Neural Networks are pre-trained deep neural networks which can generate and extract accurate features from image databases. These CNN models have been trained using large databases with thousands of classes that include a huge number of images, making it simple to use their information. Based on characteristics retrieved using the pre-trained CNN models, we created CBIR systems in the work. These pre-trained CNN models VGG16, and MobileNet have been employed in this instance to extract sets of features that are afterward saved independently and used for image retrieval.
An Intense Study of Machine Learning Research Approach to Identify Toxic Comments

A large number of online public domain comments are usually constructive, but a significant proportion is toxic. The comments include several errors that allow the machine-learning algorithm to train the data set by processing dataset with numerous variety of tasks, in the method of conversion of raw comments previously feeding it to Classification models using a ML method. In this study, we have proposed classification of toxic comments using a ML approach on a multilinguistic toxic comment dataset. The logistic regression method is applied to classify processed dataset, which will distinguish toxic comments from non-toxic comments. The multi-headed model comprises toxicity (obscene, insult, severe toxic, threat, &amp; identity-hate) or Nontoxicity Estimation. We have implemented four models (LSTM, GRU RNN, and BiLSTM) and detected the toxic comments. In Python 3, all models have a simple structure that can adapt to the resolution of other tasks. The classification problem resolution findings are presented with the aid of the proposed models. It has been concluded that all models solve the challenge effectively, but the BiLSTM is the most effective to ensure the best practicable accuracy.
A Study on The Role of Machine Learning in Natural Language Processing

Natural Language Processing (NLP) is the part of Artificial Intelligence that helps computers communicate with humans in their own language and scales other language-related tasks. NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important. Combined with machine learning algorithms, NLP generates systems that learn to perform tasks on their own and get better through experience. NLP-powered tools can help you to classify social media posts by sentiment, or extract named entities from business emails, among many other things. The role of machine learning in the NLP is clearly defined in this paper with its application areas.
Text Summarization Using Machine Learning Algorithm

In the age of technology, data is critical. The data on the internet is formless and poorly organized. The concept of text summarization is introduced in order to convert data summaries. Text summarization is the process of extracting useful information from raw data without diluting the main theme of the data. Today’s readers must contend with task of reading comments, reviews, news articles, blogs and other forms of informal and noisy communication. It is difficult to retrieve the correct gist of the gist, which is required by all readers. To achieve the benefits of both extractive and abstractive summarization, the proposed approach combines TF-TDF-TR(Term Frequency – Inverse Document Frequency – Text Rank) as an unsupervised learning algorithm and the seq2seq (Sequence to Sequence) model as a supervised learning algorithm. In terms of ROUGE score, the proposed TFRSP approach outperforms existing text summarization methods, resulting in high summary accuracy.
DESIGNING A WORK PERMIT REPORTING INFORMATION SYSTEM ON HIBA GROUP

In 1949 the HIBA Group was here to meet the transportation needs of the community. The company's commitment is to provide increased customer satisfaction through professional management and operational team support to the demands of the changing times. HIBA Group offers complete, professional, modern, and integrated transportation services. In addition to providing comprehensive, professional, current, and integrated transportation services, the HIBA Group also provides work comfort to employees, especially for maintenance, construction, and bus bodybuilding. In the construction or maintenance process, each activity must report a work permit or Permit to Work (PTW), whose function is to monitor and analyze the work to be carried out. The analysis is carried out from the workers' health, difficulty, the environment, and work tools (PPE). The reporting process is still manual form or conventional system to slow down the work being done if the work is essential for a tender. The PTW must also be analyzed and reported on the related circumstances so that PTW work tends to be slow. An online-based PTW information system Website design was made to simplify and speed up the PTW reporting to solve this problem. The method used for developing information systems uses the Prototype Model with an object-oriented system approach modeled using the Unified Modeling Language (UML). This research is the design of the PTW information system Website used in the HIBA Group.
APPLICATION OF CLASSIFICATION ALGORITHM FOR SALES PREDICTION

Increasing sales results is a desired target for all companies both at home and abroad. The company has a wide variety of products to offer. This paper (to fulfill a Business Intelligence course assignment) is the result of an experiment from data (keaggle) about consumer demand for products during the 2013-2015 period, then based on this data we try to predict to classify product sales, in order to make it easier for companies to classification for sales predictions. To find out the sales of the best-selling products, data mining classification techniques are used, namely XGBoost, Decision Tree, Random Forest, Linear Regression, and Nave Bayes. Based on the test results of the five classification techniques, the XGBoost model is the best with the data training value producing an RMSE value of 0.68% and data testing of 0.79%. This method is also better than the results of previous studies.
META-REFERENTIAL REALITY IN THE ABYSS OF MEDIA IMAGES AND THEORIES

McLuhan’s statement “the medium is the message”, made at the time of the expansion of television in the 1960s, was confirmed by the advent of the Internet, social media platforms and the digital transformation of modern society. The media form encircles the recipients, becoming an environment more real than reality, much like Baudrillard’s simulacrum. “Immersed” in a media kaleidoscope in which “pictures within a picture” are endlessly multiplied, users find it increasingly difficult to distinguish facts from interpretations, facts from factoids, lost like “Alice in Wonderland” in the virtual space of never-ending stories. Technological progress has preserved all the old media, while producing new ones, in a way similar to the Russian “matryoshka dolls”, but has pushed the truth and moral dilemmas into the background. Entertainment has become the main media content, information is delivered in the “infotainment” form, with the rapid succession of images as a popularized “montage of attractions”. Marx’s fascination with machines and productive forces as grounds of a “social existence that determines men’s consciousness” has undergone an unusual inversion: the media production represents and creates a worldview in a much more sophisticated way than heavy industry, which as a relic of the past becomes an ecological threat to humanity and the planet. “Reality”, the one word that, at the insistence of writer Nabokov, should always appear in quotation marks, is an astonishing artifact of the media and their powerful masters, and the Earth itself has been utilized as Duchamp’s “readymade”, thus becoming an artwork subjected to relentless exploitation for profit. Mastering Manovich’s “language of new media” is necessary for understanding modern information and visual culture, in order to make communication between people possible at all, because the contemporary culture is meta-referential and self-centered. A critical understanding of the world, which requires media literacy and continuous media education, is the only way to avoid complicity in the collapse of reality under the weight of media images that erase the boundaries between reality and fiction, facts and theoretical interpretations.
Physical Encryption and Pilot Direction Reversal With Channel Modulation Techniques

Encryption for channel modulation techniques on a physical basis is considered. A channel modulation scheme is proposed in which the selected channel depends on both transmitter and receiver configuration. The planned selections of the receiver become the encryption key. The communication system is prototyped in an anechoic chamber, where channels are distinguished based on phase differences. It is explored that, as the data is transmitted solely through the selection of the channel, the receiver could transmit the pilot signals and the transmitter could remain passive, i.e. the transmitter does not transmit electromagnetic waves.
Entanglement and non-locality of four-qubit connected hypergraph states

We study entanglement and non-locality of connected four-qubit hypergraph states. One obtains the SLOCC classification from the known LU-orbits. We then consider Mermin's polynomials and show that all four-qubit hypergraph states exhibit non-local behavior. Finally, we implement some of the corresponding inequalities on the IBM Quantum Experience.